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"## 3. Use-cases and preparations for the petabyte scale\n",
"\n",
"**Requirement:** First run #1"
]
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"#From #2\n",
"import xarray as xr\n",
"port=9010\n",
"hostname=!echo $HOSTNAME\n",
"hosturl=\"https://\"+hostname[0]+\":\"+str(port)\n",
"dsname=\"example\"\n",
"zarr_url='/'.join([hosturl,\"datasets\",dsname,\"zarr\"])\n",
"storage_options=dict(verify_ssl=False)\n",
"ds=xr.open_zarr(\n",
" zarr_url,\n",
" consolidated=True,\n",
" storage_options=storage_options,\n",
")"
]
},
{
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"</style><pre class='xr-text-repr-fallback'><xarray.Dataset> Size: 314MB\n",
"Dimensions: (lat: 192, time: 1032, bnds: 2, lon: 384)\n",
"Coordinates:\n",
" height float64 8B ...\n",
" * lat (lat) float64 2kB -89.28 -88.36 -87.42 ... 87.42 88.36 89.28\n",
" * lon (lon) float64 3kB 0.0 0.9375 1.875 2.812 ... 357.2 358.1 359.1\n",
" * time (time) datetime64[ns] 8kB 2015-01-16T12:00:00 ... 2100-12-16T1...\n",
"Dimensions without coordinates: bnds\n",
"Data variables:\n",
" lat_bnds (time, lat, bnds) float64 3MB dask.array<chunksize=(60, 192, 2), meta=np.ndarray>\n",
" lon_bnds (time, lon, bnds) float64 6MB dask.array<chunksize=(60, 384, 2), meta=np.ndarray>\n",
" tas (time, lat, lon) float32 304MB dask.array<chunksize=(1, 192, 384), meta=np.ndarray>\n",
" time_bnds (time, bnds) datetime64[ns] 17kB dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
"Attributes: (12/47)\n",
" Conventions: CF-1.7 CMIP-6.2\n",
" activity_id: ScenarioMIP AerChemMIP\n",
" branch_method: standard\n",
" branch_time_in_child: 60265.0\n",
" branch_time_in_parent: 60265.0\n",
" contact: cmip6-mpi-esm@dkrz.de\n",
" ... ...\n",
" title: MPI-ESM1-2-HR output prepared for CMIP6\n",
" variable_id: tas\n",
" variant_label: r1i1p1f1\n",
" license: CMIP6 model data produced by DKRZ is licensed und...\n",
" cmor_version: 3.4.0\n",
" tracking_id: hdl:21.14100/d6f0f6b2-b4c2-460b-bc5e-f1c72f932399</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-68bf8c03-8018-40c8-9f64-1626451b733f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-68bf8c03-8018-40c8-9f64-1626451b733f' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>time</span>: 1032</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lon</span>: 384</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d75405c3-6362-4216-b11c-4f9c0020c681' class='xr-section-summary-in' type='checkbox' checked><label for='section-d75405c3-6362-4216-b11c-4f9c0020c681' 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>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0c6d923a-e34e-4312-9999-47fc36ed71d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c6d923a-e34e-4312-9999-47fc36ed71d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85f61a68-5ecc-4e18-828c-9b7042b6b4fb' class='xr-var-data-in' type='checkbox'><label for='data-85f61a68-5ecc-4e18-828c-9b7042b6b4fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><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'>-89.28 -88.36 ... 88.36 89.28</div><input id='attrs-66aa3074-9ec2-4923-9132-2ffffe5fecd9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66aa3074-9ec2-4923-9132-2ffffe5fecd9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d59115f3-0c24-47d3-ac7a-3dfb92002d17' class='xr-var-data-in' type='checkbox'><label for='data-d59115f3-0c24-47d3-ac7a-3dfb92002d17' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-89.284228, -88.357004, -87.424304, -86.490367, -85.55596 , -84.621327,\n",
" -83.686567, -82.751728, -81.816839, -80.881913, -79.946962, -79.011992,\n",
" -78.077007, -77.142011, -76.207005, -75.271992, -74.336973, -73.401949,\n",
" -72.466921, -71.531889, -70.596854, -69.661816, -68.726776, -67.791734,\n",
" -66.856691, -65.921645, -64.986599, -64.051551, -63.116502, -62.181452,\n",
" -61.246401, -60.311349, -59.376297, -58.441244, -57.50619 , -56.571136,\n",
" -55.636081, -54.701026, -53.76597 , -52.830914, -51.895857, -50.960801,\n",
" -50.025743, -49.090686, -48.155628, -47.22057 , -46.285512, -45.350454,\n",
" -44.415395, -43.480336, -42.545277, -41.610218, -40.675159, -39.740099,\n",
" -38.805039, -37.86998 , -36.93492 , -35.99986 , -35.064799, -34.129739,\n",
" -33.194679, -32.259618, -31.324558, -30.389497, -29.454436, -28.519375,\n",
" -27.584315, -26.649254, -25.714193, -24.779132, -23.84407 , -22.909009,\n",
" -21.973948, -21.038887, -20.103825, -19.168764, -18.233703, -17.298641,\n",
" -16.36358 , -15.428518, -14.493457, -13.558395, -12.623333, -11.688272,\n",
" -10.75321 , -9.818148, -8.883087, -7.948025, -7.012963, -6.077901,\n",
" -5.14284 , -4.207778, -3.272716, -2.337654, -1.402593, -0.467531,\n",
" 0.467531, 1.402593, 2.337654, 3.272716, 4.207778, 5.14284 ,\n",
" 6.077901, 7.012963, 7.948025, 8.883087, 9.818148, 10.75321 ,\n",
" 11.688272, 12.623333, 13.558395, 14.493457, 15.428518, 16.36358 ,\n",
" 17.298641, 18.233703, 19.168764, 20.103825, 21.038887, 21.973948,\n",
" 22.909009, 23.84407 , 24.779132, 25.714193, 26.649254, 27.584315,\n",
" 28.519375, 29.454436, 30.389497, 31.324558, 32.259618, 33.194679,\n",
" 34.129739, 35.064799, 35.99986 , 36.93492 , 37.86998 , 38.805039,\n",
" 39.740099, 40.675159, 41.610218, 42.545277, 43.480336, 44.415395,\n",
" 45.350454, 46.285512, 47.22057 , 48.155628, 49.090686, 50.025743,\n",
" 50.960801, 51.895857, 52.830914, 53.76597 , 54.701026, 55.636081,\n",
" 56.571136, 57.50619 , 58.441244, 59.376297, 60.311349, 61.246401,\n",
" 62.181452, 63.116502, 64.051551, 64.986599, 65.921645, 66.856691,\n",
" 67.791734, 68.726776, 69.661816, 70.596854, 71.531889, 72.466921,\n",
" 73.401949, 74.336973, 75.271992, 76.207005, 77.142011, 78.077007,\n",
" 79.011992, 79.946962, 80.881913, 81.816839, 82.751728, 83.686567,\n",
" 84.621327, 85.55596 , 86.490367, 87.424304, 88.357004, 89.284228])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.9375 1.875 ... 358.1 359.1</div><input id='attrs-8d91d640-17cd-45b5-a088-0d51d252f0ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d91d640-17cd-45b5-a088-0d51d252f0ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a7ba789-60f4-4be9-9d74-bb9a137415b7' class='xr-var-data-in' type='checkbox'><label for='data-5a7ba789-60f4-4be9-9d74-bb9a137415b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([ 0. , 0.9375, 1.875 , ..., 357.1875, 358.125 , 359.0625])</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'>2015-01-16T12:00:00 ... 2100-12-...</div><input id='attrs-87945860-5e78-4617-ad2e-59b04f3d45ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87945860-5e78-4617-ad2e-59b04f3d45ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a61b1641-69a3-4960-ba44-355ca501c880' class='xr-var-data-in' type='checkbox'><label for='data-a61b1641-69a3-4960-ba44-355ca501c880' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(['2015-01-16T12:00:00.000000000', '2015-02-15T00:00:00.000000000',\n",
" '2015-03-16T12:00:00.000000000', ..., '2100-10-16T12:00:00.000000000',\n",
" '2100-11-16T00:00:00.000000000', '2100-12-16T12:00:00.000000000'],\n",
" dtype='datetime64[ns]')</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8515a4be-ad4a-4f60-9059-5bf954d45a0a' class='xr-section-summary-in' type='checkbox' checked><label for='section-8515a4be-ad4a-4f60-9059-5bf954d45a0a' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(time, lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(60, 192, 2), meta=np.ndarray></div><input id='attrs-beafa294-a872-4df7-9882-949bf40e9289' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-beafa294-a872-4df7-9882-949bf40e9289' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c7864127-1ae7-4655-b156-209260ca09c3' class='xr-var-data-in' type='checkbox'><label for='data-c7864127-1ae7-4655-b156-209260ca09c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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" <td> (1032, 192, 2) </td>\n",
" <td> (60, 192, 2) </td>\n",
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" <td colspan=\"2\"> 18 chunks in 2 graph layers </td>\n",
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" <tr>\n",
" <th> Data type </th>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(time, lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(60, 384, 2), meta=np.ndarray></div><input id='attrs-cce19196-fe53-4834-94cc-55ba0ce280a0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cce19196-fe53-4834-94cc-55ba0ce280a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eca9c4df-b8a8-4dad-af41-cb7b1541dafa' class='xr-var-data-in' type='checkbox'><label for='data-eca9c4df-b8a8-4dad-af41-cb7b1541dafa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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" <td> 6.05 MiB </td>\n",
" <td> 360.00 kiB </td>\n",
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" <td> (60, 384, 2) </td>\n",
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" <td colspan=\"2\"> 18 chunks in 2 graph layers </td>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1, 192, 384), meta=np.ndarray></div><input id='attrs-7f9aa570-4715-451d-bb5a-1546f2c03c82' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f9aa570-4715-451d-bb5a-1546f2c03c82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de4bf0d7-79af-43db-9112-0198fe0fd2f5' class='xr-var-data-in' type='checkbox'><label for='data-de4bf0d7-79af-43db-9112-0198fe0fd2f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>comment :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2019-07-20T13:41:51Z altered by CMOR: Treated scalar dimension: 'height'. 2019-07-20T13:41:51Z altered by CMOR: replaced missing value flag (-9e+33) with standard missing value (1e+20). 2019-07-20T13:41:51Z altered by CMOR: Converted type from 'd' to 'f'. 2019-07-20T13:41:52Z altered by CMOR: Inverted axis: lat.</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
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" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 290.25 MiB </td>\n",
" <td> 288.00 kiB </td>\n",
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" \n",
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" <td> (1032, 192, 384) </td>\n",
" <td> (1, 192, 384) </td>\n",
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" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 1032 chunks in 2 graph layers </td>\n",
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" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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" </tbody>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1, 2), meta=np.ndarray></div><input id='attrs-5eb54471-4b22-475a-aa47-fccaf000a4a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5eb54471-4b22-475a-aa47-fccaf000a4a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d191cfdb-a29f-4edc-ad3c-d348a979abfb' class='xr-var-data-in' type='checkbox'><label for='data-d191cfdb-a29f-4edc-ad3c-d348a979abfb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
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" <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> 16.12 kiB </td>\n",
" <td> 16 B </td>\n",
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" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (1032, 2) </td>\n",
" <td> (1, 2) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 1032 chunks in 2 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
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"\n",
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"\n",
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"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-8443f37d-9571-4e36-a7bd-6916df55d000' class='xr-section-summary-in' type='checkbox' ><label for='section-8443f37d-9571-4e36-a7bd-6916df55d000' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0e03616f-60d4-4bd8-9e89-0e91a2c14a37' class='xr-index-data-in' type='checkbox'/><label for='index-0e03616f-60d4-4bd8-9e89-0e91a2c14a37' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-89.28422753251364, -88.35700351866494, -87.42430374606988,\n",
" -86.49036676628116, -85.55596048489265, -84.6213271076488,\n",
" -83.68656681656385, -82.75172847343066, -81.81683872860319,\n",
" -80.8819133467975,\n",
" ...\n",
" 80.8819133467975, 81.81683872860319, 82.75172847343066,\n",
" 83.68656681656385, 84.6213271076488, 85.55596048489265,\n",
" 86.49036676628116, 87.42430374606988, 88.35700351866494,\n",
" 89.28422753251364],\n",
" dtype='float64', name='lat', length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8ce805fa-1988-44cd-8c66-ebf15937a3c6' class='xr-index-data-in' type='checkbox'/><label for='index-8ce805fa-1988-44cd-8c66-ebf15937a3c6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0.0, 0.9375, 1.875, 2.8125, 3.75, 4.6875, 5.625,\n",
" 6.5625, 7.5, 8.4375,\n",
" ...\n",
" 350.625, 351.5625, 352.5, 353.4375, 354.375, 355.3125, 356.25,\n",
" 357.1875, 358.125, 359.0625],\n",
" dtype='float64', name='lon', length=384))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bc514063-c4b1-47f8-88e8-a99cb54825d9' class='xr-index-data-in' type='checkbox'/><label for='index-bc514063-c4b1-47f8-88e8-a99cb54825d9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex(['2015-01-16 12:00:00', '2015-02-15 00:00:00',\n",
" '2015-03-16 12:00:00', '2015-04-16 00:00:00',\n",
" '2015-05-16 12:00:00', '2015-06-16 00:00:00',\n",
" '2015-07-16 12:00:00', '2015-08-16 12:00:00',\n",
" '2015-09-16 00:00:00', '2015-10-16 12:00:00',\n",
" ...\n",
" '2100-03-16 12:00:00', '2100-04-16 00:00:00',\n",
" '2100-05-16 12:00:00', '2100-06-16 00:00:00',\n",
" '2100-07-16 12:00:00', '2100-08-16 12:00:00',\n",
" '2100-09-16 00:00:00', '2100-10-16 12:00:00',\n",
" '2100-11-16 00:00:00', '2100-12-16 12:00:00'],\n",
" dtype='datetime64[ns]', name='time', length=1032, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-521f5d99-3cba-4f95-b4f7-17b823220587' class='xr-section-summary-in' type='checkbox' ><label for='section-521f5d99-3cba-4f95-b4f7-17b823220587' class='xr-section-summary' >Attributes: <span>(47)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>ScenarioMIP AerChemMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>60265.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>60265.0</dd><dt><span>contact :</span></dt><dd>cmip6-mpi-esm@dkrz.de</dd><dt><span>creation_date :</span></dt><dd>2019-07-20T13:41:51Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.30</dd><dt><span>experiment :</span></dt><dd>gap-filling scenario reaching 7.0 based on SSP3</dd><dt><span>experiment_id :</span></dt><dd>ssp370</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.DKRZ.MPI-ESM1-2-HR.ssp370.none.r1i1p1f1</dd><dt><span>grid :</span></dt><dd>gn</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>2019-07-20T13:41:51Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>Deutsches Klimarechenzentrum, Hamburg 20146, Germany</dd><dt><span>institution_id :</span></dt><dd>DKRZ</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>100 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>historical</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>MPI-ESM1-2-HR</dd><dt><span>parent_time_units :</span></dt><dd>days since 1850-1-1 00:00:00</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>project_id :</span></dt><dd>CMIP6</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>references :</span></dt><dd>MPI-ESM: Mauritsen, T. et al. (2019), Developments in the MPI‐M Earth System Model version 1.2 (MPI‐ESM1.2) and Its Response to Increasing CO2, J. Adv. Model. Earth Syst.,11, 998-1038, doi:10.1029/2018MS001400,\n",
"Mueller, W.A. et al. (2018): A high‐resolution version of the Max Planck Institute Earth System Model MPI‐ESM1.2‐HR. J. Adv. Model. EarthSyst.,10,1383–1413, doi:10.1029/2017MS001217</dd><dt><span>source :</span></dt><dd>MPI-ESM1.2-HR (2017): \n",
"aerosol: none, prescribed MACv2-SP\n",
"atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa)\n",
"atmosChem: none\n",
"land: JSBACH3.20\n",
"landIce: none/prescribed\n",
"ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m)\n",
"ocnBgchem: HAMOCC6\n",
"seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model)</dd><dt><span>source_id :</span></dt><dd>MPI-ESM1-2-HR</dd><dt><span>source_type :</span></dt><dd>AOGCM</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Amon</dd><dt><span>table_info :</span></dt><dd>Creation Date:(09 May 2019) MD5:e6ef8ececc8f338646ebfb3aeed36bfc</dd><dt><span>title :</span></dt><dd>MPI-ESM1-2-HR output prepared for CMIP6</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by DKRZ is licensed under a Creative Commons Attribution ShareAlike 4.0 International License (https://creativecommons.org/licenses). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file) and. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>cmor_version :</span></dt><dd>3.4.0</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/d6f0f6b2-b4c2-460b-bc5e-f1c72f932399</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset> Size: 314MB\n",
"Dimensions: (lat: 192, time: 1032, bnds: 2, lon: 384)\n",
"Coordinates:\n",
" height float64 8B ...\n",
" * lat (lat) float64 2kB -89.28 -88.36 -87.42 ... 87.42 88.36 89.28\n",
" * lon (lon) float64 3kB 0.0 0.9375 1.875 2.812 ... 357.2 358.1 359.1\n",
" * time (time) datetime64[ns] 8kB 2015-01-16T12:00:00 ... 2100-12-16T1...\n",
"Dimensions without coordinates: bnds\n",
"Data variables:\n",
" lat_bnds (time, lat, bnds) float64 3MB dask.array<chunksize=(60, 192, 2), meta=np.ndarray>\n",
" lon_bnds (time, lon, bnds) float64 6MB dask.array<chunksize=(60, 384, 2), meta=np.ndarray>\n",
" tas (time, lat, lon) float32 304MB dask.array<chunksize=(1, 192, 384), meta=np.ndarray>\n",
" time_bnds (time, bnds) datetime64[ns] 17kB dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
"Attributes: (12/47)\n",
" Conventions: CF-1.7 CMIP-6.2\n",
" activity_id: ScenarioMIP AerChemMIP\n",
" branch_method: standard\n",
" branch_time_in_child: 60265.0\n",
" branch_time_in_parent: 60265.0\n",
" contact: cmip6-mpi-esm@dkrz.de\n",
" ... ...\n",
" title: MPI-ESM1-2-HR output prepared for CMIP6\n",
" variable_id: tas\n",
" variant_label: r1i1p1f1\n",
" license: CMIP6 model data produced by DKRZ is licensed und...\n",
" cmor_version: 3.4.0\n",
" tracking_id: hdl:21.14100/d6f0f6b2-b4c2-460b-bc5e-f1c72f932399"
]
},
"execution_count": 2,
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"### Server-side processing: Animated interactive geoplots\n",
"\n",
"We build an API based on the pyviz stack that creates large figures of animated interactive geoplots for any selection of dataset, variable and coordinates of the hosted datasets. Additionally, you can set *kind* and *colorbar*. \n",
"\n",
"**Note**: On long term, plot creation should move to client-side. All that can be done in Jupyterlite.\n",
"\n",
"In the following example, we write the plot into \"test.html\" which you can open in jupyterhub afterwards or download to your local computer."
]
},
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"cell_type": "code",
"execution_count": 2,
"id": "2746baa9-b56b-49ab-8e0c-32f6e9ca5fdb",
"metadata": {
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"outputs": [
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"name": "stdout",
"output_type": "stream",
"text": [
"https://l40038.lvt.dkrz.de:9010/datasets/example/plot/tas/image/coolwarm/time/-12_-1\n",
" % Total % Received % Xferd Average Speed Time Time Time Current\n",
" Dload Upload Total Spent Left Speed\n",
"100 5925k 0 5925k 0 0 318k 0 --:--:-- 0:00:18 --:--:-- 1345k\n"
]
}
],
"source": [
"varname=\"tas\"\n",
"kind=\"image\"\n",
"colorbar=\"coolwarm\"\n",
"selection=\"time/-12_-1\"\n",
"plotfile=\"test.html\"\n",
"plot_url='/'.join([hosturl,\"datasets\",dsname,\"plot\",varname,kind,colorbar,selection])\n",
"print(plot_url)\n",
"!curl -k {plot_url} >{plotfile}"
]
},
{
"cell_type": "markdown",
"id": "84450da7-12cc-4d66-af1d-9c9d0e504868",
"metadata": {},
"source": [
"### Server-side processing: Compression\n",
"\n",
"In case bandwidth is the bottle-neck for users, we can help them with providing some server-side computing resources to e.g. further lossy compress our data. In the following, we firstly store the first 5 years on disk as provided. Secondly, we restart the app with enabled lossy compression and do the same. Afterwards, we compare speed and sizes."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "ec89f56c-eb1e-4b31-a8e4-becd07d99019",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import numpy as np\n",
"import time\n",
"\n",
"def prepare_for_storage(ds_to_store):\n",
" bnds=[\"lat_bnds\",\"lon_bnds\"]\n",
" for a in bnds:\n",
" ds_to_store[a]=ds_to_store[a].isel(time=0).squeeze()\n",
" ds_to_store=ds_to_store.set_coords(bnds)\n",
" ds_to_store[\"time_bnds\"].load()\n",
"\n",
" return ds_to_store\n",
"\n",
"def store_ds(ds_to_store,fn):\n",
" !rm {fn}\n",
" s=time.time()\n",
" ds_to_store.to_netcdf(\n",
" fn,\n",
" unlimited_dims=[\"time\"],\n",
" encoding=dict(tas=dict(compression=\"zlib\"))\n",
" )\n",
" e=time.time()\n",
" print(e-s, \" seconds\")\n",
" !ls -lrths {fn}"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "2cce4ce3-fa18-42d4-8c2f-2b9a276a683a",
"metadata": {
"tags": []
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"outputs": [],
"source": [
"ds5=ds.where(ds.time.dt.year.isin(range(2015,2020)),drop=True)\n",
"ds5=prepare_for_storage(ds5)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "9c7b068e-db80-4985-9731-48b1a9c11e14",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.4420695304870605 seconds\n",
"512 -rw-r--r-- 1 k204210 bm0021 6.6M Jan 8 10:42 compressed.nc\n"
]
}
],
"source": [
"store_ds(ds5,\"compressed.nc\")"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e4d2fda6-7fe8-4cbb-aa79-95980e0b502d",
"metadata": {
"tags": []
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"outputs": [
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"</style><pre class='xr-text-repr-fallback'><xarray.Dataset> Size: 18MB\n",
"Dimensions: (lat: 192, bnds: 2, lon: 384, time: 60)\n",
"Coordinates:\n",
" lat_bnds (lat, bnds) float64 3kB -89.75 -88.82 -88.82 ... 88.82 89.75\n",
" lon_bnds (lon, bnds) float64 6kB -0.4688 0.4688 0.4688 ... 358.6 359.5\n",
" height float64 8B 2.0\n",
" * lat (lat) float64 2kB -89.28 -88.36 -87.42 ... 87.42 88.36 89.28\n",
" * lon (lon) float64 3kB 0.0 0.9375 1.875 2.812 ... 357.2 358.1 359.1\n",
" * time (time) datetime64[ns] 480B 2015-01-16T12:00:00 ... 2019-12-16T...\n",
"Dimensions without coordinates: bnds\n",
"Data variables:\n",
" tas (time, lat, lon) float32 18MB 243.3 243.3 243.3 ... 251.1 251.1\n",
" time_bnds (time, bnds) datetime64[ns] 960B 2015-01-01 ... 2020-01-01\n",
"Attributes: (12/47)\n",
" Conventions: CF-1.7 CMIP-6.2\n",
" activity_id: ScenarioMIP AerChemMIP\n",
" branch_method: standard\n",
" branch_time_in_child: 60265.0\n",
" branch_time_in_parent: 60265.0\n",
" contact: cmip6-mpi-esm@dkrz.de\n",
" ... ...\n",
" title: MPI-ESM1-2-HR output prepared for CMIP6\n",
" variable_id: tas\n",
" variant_label: r1i1p1f1\n",
" license: CMIP6 model data produced by DKRZ is licensed und...\n",
" cmor_version: 3.4.0\n",
" tracking_id: hdl:21.14100/d6f0f6b2-b4c2-460b-bc5e-f1c72f932399</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-36f0e552-8c96-46fa-936a-b246962af0ca' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-36f0e552-8c96-46fa-936a-b246962af0ca' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 192</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lon</span>: 384</li><li><span class='xr-has-index'>time</span>: 60</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e83ba46c-ff05-4359-83bf-f187866addf2' class='xr-section-summary-in' type='checkbox' checked><label for='section-e83ba46c-ff05-4359-83bf-f187866addf2' class='xr-section-summary' >Coordinates: <span>(6)</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>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.75 -88.82 ... 88.82 89.75</div><input id='attrs-4ae25e84-8a3c-41cc-8c56-34a5c0b578f0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4ae25e84-8a3c-41cc-8c56-34a5c0b578f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45a230ae-d3d3-4a30-a899-407f4e020aaf' class='xr-var-data-in' type='checkbox'><label for='data-45a230ae-d3d3-4a30-a899-407f4e020aaf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-89.74783954, -88.82061553],\n",
" [-88.82061553, -87.89065363],\n",
" [-87.89065363, -86.95733526],\n",
" [-86.95733526, -86.02316363],\n",
" [-86.02316363, -85.0886438 ],\n",
" [-85.0886438 , -84.15394696],\n",
" [-84.15394696, -83.21914764],\n",
" [-83.21914764, -82.2842836 ],\n",
" [-82.2842836 , -81.34937604],\n",
" [-81.34937604, -80.4144378 ],\n",
" [-80.4144378 , -79.47947712],\n",
" [-79.47947712, -78.54449952],\n",
" [-78.54449952, -77.60950886],\n",
" [-77.60950886, -76.67450789],\n",
" [-76.67450789, -75.73949865],\n",
" [-75.73949865, -74.80448268],\n",
" [-74.80448268, -73.86946114],\n",
" [-73.86946114, -72.93443496],\n",
" [-72.93443496, -71.99940486],\n",
" [-71.99940486, -71.06437142],\n",
"...\n",
" [ 71.06437142, 71.99940486],\n",
" [ 71.99940486, 72.93443496],\n",
" [ 72.93443496, 73.86946114],\n",
" [ 73.86946114, 74.80448268],\n",
" [ 74.80448268, 75.73949865],\n",
" [ 75.73949865, 76.67450789],\n",
" [ 76.67450789, 77.60950886],\n",
" [ 77.60950886, 78.54449952],\n",
" [ 78.54449952, 79.47947712],\n",
" [ 79.47947712, 80.4144378 ],\n",
" [ 80.4144378 , 81.34937604],\n",
" [ 81.34937604, 82.2842836 ],\n",
" [ 82.2842836 , 83.21914764],\n",
" [ 83.21914764, 84.15394696],\n",
" [ 84.15394696, 85.0886438 ],\n",
" [ 85.0886438 , 86.02316363],\n",
" [ 86.02316363, 86.95733526],\n",
" [ 86.95733526, 87.89065363],\n",
" [ 87.89065363, 88.82061553],\n",
" [ 88.82061553, 89.74783954]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.4688 0.4688 ... 358.6 359.5</div><input id='attrs-e0f2798e-49e2-4d0d-8062-641f0051cafa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e0f2798e-49e2-4d0d-8062-641f0051cafa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32928353-1e33-4298-95eb-31df48e9ab27' class='xr-var-data-in' type='checkbox'><label for='data-32928353-1e33-4298-95eb-31df48e9ab27' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ -0.46875, 0.46875],\n",
" [ 0.46875, 1.40625],\n",
" [ 1.40625, 2.34375],\n",
" [ 2.34375, 3.28125],\n",
" [ 3.28125, 4.21875],\n",
" [ 4.21875, 5.15625],\n",
" [ 5.15625, 6.09375],\n",
" [ 6.09375, 7.03125],\n",
" [ 7.03125, 7.96875],\n",
" [ 7.96875, 8.90625],\n",
" [ 8.90625, 9.84375],\n",
" [ 9.84375, 10.78125],\n",
" [ 10.78125, 11.71875],\n",
" [ 11.71875, 12.65625],\n",
" [ 12.65625, 13.59375],\n",
" [ 13.59375, 14.53125],\n",
" [ 14.53125, 15.46875],\n",
" [ 15.46875, 16.40625],\n",
" [ 16.40625, 17.34375],\n",
" [ 17.34375, 18.28125],\n",
"...\n",
" [340.78125, 341.71875],\n",
" [341.71875, 342.65625],\n",
" [342.65625, 343.59375],\n",
" [343.59375, 344.53125],\n",
" [344.53125, 345.46875],\n",
" [345.46875, 346.40625],\n",
" [346.40625, 347.34375],\n",
" [347.34375, 348.28125],\n",
" [348.28125, 349.21875],\n",
" [349.21875, 350.15625],\n",
" [350.15625, 351.09375],\n",
" [351.09375, 352.03125],\n",
" [352.03125, 352.96875],\n",
" [352.96875, 353.90625],\n",
" [353.90625, 354.84375],\n",
" [354.84375, 355.78125],\n",
" [355.78125, 356.71875],\n",
" [356.71875, 357.65625],\n",
" [357.65625, 358.59375],\n",
" [358.59375, 359.53125]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-11aa0dc0-d1e0-4e14-8163-7e3b095bf102' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11aa0dc0-d1e0-4e14-8163-7e3b095bf102' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-567f0e61-176c-4015-a9f1-facc256a4c09' class='xr-var-data-in' type='checkbox'><label for='data-567f0e61-176c-4015-a9f1-facc256a4c09' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array(2.)</pre></div></li><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'>-89.28 -88.36 ... 88.36 89.28</div><input id='attrs-6ce7b562-f4cb-4e30-8dff-d65926f06cf6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ce7b562-f4cb-4e30-8dff-d65926f06cf6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4065b0c-19a2-4da0-a91d-9ab5b1d7fa99' class='xr-var-data-in' type='checkbox'><label for='data-f4065b0c-19a2-4da0-a91d-9ab5b1d7fa99' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-89.284228, -88.357004, -87.424304, -86.490367, -85.55596 , -84.621327,\n",
" -83.686567, -82.751728, -81.816839, -80.881913, -79.946962, -79.011992,\n",
" -78.077007, -77.142011, -76.207005, -75.271992, -74.336973, -73.401949,\n",
" -72.466921, -71.531889, -70.596854, -69.661816, -68.726776, -67.791734,\n",
" -66.856691, -65.921645, -64.986599, -64.051551, -63.116502, -62.181452,\n",
" -61.246401, -60.311349, -59.376297, -58.441244, -57.50619 , -56.571136,\n",
" -55.636081, -54.701026, -53.76597 , -52.830914, -51.895857, -50.960801,\n",
" -50.025743, -49.090686, -48.155628, -47.22057 , -46.285512, -45.350454,\n",
" -44.415395, -43.480336, -42.545277, -41.610218, -40.675159, -39.740099,\n",
" -38.805039, -37.86998 , -36.93492 , -35.99986 , -35.064799, -34.129739,\n",
" -33.194679, -32.259618, -31.324558, -30.389497, -29.454436, -28.519375,\n",
" -27.584315, -26.649254, -25.714193, -24.779132, -23.84407 , -22.909009,\n",
" -21.973948, -21.038887, -20.103825, -19.168764, -18.233703, -17.298641,\n",
" -16.36358 , -15.428518, -14.493457, -13.558395, -12.623333, -11.688272,\n",
" -10.75321 , -9.818148, -8.883087, -7.948025, -7.012963, -6.077901,\n",
" -5.14284 , -4.207778, -3.272716, -2.337654, -1.402593, -0.467531,\n",
" 0.467531, 1.402593, 2.337654, 3.272716, 4.207778, 5.14284 ,\n",
" 6.077901, 7.012963, 7.948025, 8.883087, 9.818148, 10.75321 ,\n",
" 11.688272, 12.623333, 13.558395, 14.493457, 15.428518, 16.36358 ,\n",
" 17.298641, 18.233703, 19.168764, 20.103825, 21.038887, 21.973948,\n",
" 22.909009, 23.84407 , 24.779132, 25.714193, 26.649254, 27.584315,\n",
" 28.519375, 29.454436, 30.389497, 31.324558, 32.259618, 33.194679,\n",
" 34.129739, 35.064799, 35.99986 , 36.93492 , 37.86998 , 38.805039,\n",
" 39.740099, 40.675159, 41.610218, 42.545277, 43.480336, 44.415395,\n",
" 45.350454, 46.285512, 47.22057 , 48.155628, 49.090686, 50.025743,\n",
" 50.960801, 51.895857, 52.830914, 53.76597 , 54.701026, 55.636081,\n",
" 56.571136, 57.50619 , 58.441244, 59.376297, 60.311349, 61.246401,\n",
" 62.181452, 63.116502, 64.051551, 64.986599, 65.921645, 66.856691,\n",
" 67.791734, 68.726776, 69.661816, 70.596854, 71.531889, 72.466921,\n",
" 73.401949, 74.336973, 75.271992, 76.207005, 77.142011, 78.077007,\n",
" 79.011992, 79.946962, 80.881913, 81.816839, 82.751728, 83.686567,\n",
" 84.621327, 85.55596 , 86.490367, 87.424304, 88.357004, 89.284228])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.9375 1.875 ... 358.1 359.1</div><input id='attrs-d5b077d8-e796-4c24-b50f-31d8a5e6c7c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5b077d8-e796-4c24-b50f-31d8a5e6c7c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ce77505-60ab-45c4-8b40-b49a7ec7f41e' class='xr-var-data-in' type='checkbox'><label for='data-7ce77505-60ab-45c4-8b40-b49a7ec7f41e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([ 0. , 0.9375, 1.875 , ..., 357.1875, 358.125 , 359.0625])</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'>2015-01-16T12:00:00 ... 2019-12-...</div><input id='attrs-5b68fe5a-ac3b-4492-a382-1623cf3b3f6c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b68fe5a-ac3b-4492-a382-1623cf3b3f6c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a142c583-fa70-4c76-bcf1-3f6b1e433c79' class='xr-var-data-in' type='checkbox'><label for='data-a142c583-fa70-4c76-bcf1-3f6b1e433c79' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(['2015-01-16T12:00:00.000000000', '2015-02-15T00:00:00.000000000',\n",
" '2015-03-16T12:00:00.000000000', '2015-04-16T00:00:00.000000000',\n",
" '2015-05-16T12:00:00.000000000', '2015-06-16T00:00:00.000000000',\n",
" '2015-07-16T12:00:00.000000000', '2015-08-16T12:00:00.000000000',\n",
" '2015-09-16T00:00:00.000000000', '2015-10-16T12:00:00.000000000',\n",
" '2015-11-16T00:00:00.000000000', '2015-12-16T12:00:00.000000000',\n",
" '2016-01-16T12:00:00.000000000', '2016-02-15T12:00:00.000000000',\n",
" '2016-03-16T12:00:00.000000000', '2016-04-16T00:00:00.000000000',\n",
" '2016-05-16T12:00:00.000000000', '2016-06-16T00:00:00.000000000',\n",
" '2016-07-16T12:00:00.000000000', '2016-08-16T12:00:00.000000000',\n",
" '2016-09-16T00:00:00.000000000', '2016-10-16T12:00:00.000000000',\n",
" '2016-11-16T00:00:00.000000000', '2016-12-16T12:00:00.000000000',\n",
" '2017-01-16T12:00:00.000000000', '2017-02-15T00:00:00.000000000',\n",
" '2017-03-16T12:00:00.000000000', '2017-04-16T00:00:00.000000000',\n",
" '2017-05-16T12:00:00.000000000', '2017-06-16T00:00:00.000000000',\n",
" '2017-07-16T12:00:00.000000000', '2017-08-16T12:00:00.000000000',\n",
" '2017-09-16T00:00:00.000000000', '2017-10-16T12:00:00.000000000',\n",
" '2017-11-16T00:00:00.000000000', '2017-12-16T12:00:00.000000000',\n",
" '2018-01-16T12:00:00.000000000', '2018-02-15T00:00:00.000000000',\n",
" '2018-03-16T12:00:00.000000000', '2018-04-16T00:00:00.000000000',\n",
" '2018-05-16T12:00:00.000000000', '2018-06-16T00:00:00.000000000',\n",
" '2018-07-16T12:00:00.000000000', '2018-08-16T12:00:00.000000000',\n",
" '2018-09-16T00:00:00.000000000', '2018-10-16T12:00:00.000000000',\n",
" '2018-11-16T00:00:00.000000000', '2018-12-16T12:00:00.000000000',\n",
" '2019-01-16T12:00:00.000000000', '2019-02-15T00:00:00.000000000',\n",
" '2019-03-16T12:00:00.000000000', '2019-04-16T00:00:00.000000000',\n",
" '2019-05-16T12:00:00.000000000', '2019-06-16T00:00:00.000000000',\n",
" '2019-07-16T12:00:00.000000000', '2019-08-16T12:00:00.000000000',\n",
" '2019-09-16T00:00:00.000000000', '2019-10-16T12:00:00.000000000',\n",
" '2019-11-16T00:00:00.000000000', '2019-12-16T12:00:00.000000000'],\n",
" dtype='datetime64[ns]')</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ce7de4b0-b47f-42c4-a357-b21aff29d690' class='xr-section-summary-in' type='checkbox' checked><label for='section-ce7de4b0-b47f-42c4-a357-b21aff29d690' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>243.3 243.3 243.3 ... 251.1 251.1</div><input id='attrs-f8e095f4-9e0b-4159-8b01-e43fcc743979' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8e095f4-9e0b-4159-8b01-e43fcc743979' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cdff2f42-2658-4639-8288-95c8a1ebd2ba' class='xr-var-data-in' type='checkbox'><label for='data-cdff2f42-2658-4639-8288-95c8a1ebd2ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>comment :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2019-07-20T13:41:51Z altered by CMOR: Treated scalar dimension: 'height'. 2019-07-20T13:41:51Z altered by CMOR: replaced missing value flag (-9e+33) with standard missing value (1e+20). 2019-07-20T13:41:51Z altered by CMOR: Converted type from 'd' to 'f'. 2019-07-20T13:41:52Z altered by CMOR: Inverted axis: lat.</dd></dl></div><div class='xr-var-data'><pre>array([[[243.30435, 243.29459, 243.28091, ..., 243.29654, 243.29459,\n",
" 243.19107],\n",
" [244.38638, 244.34927, 244.28873, ..., 244.486 , 244.45865,\n",
" 244.42935],\n",
" [244.8649 , 244.78677, 244.64224, ..., 245.03287, 244.98795,\n",
" 244.9235 ],\n",
" ...,\n",
" [253.50748, 253.49966, 253.52896, ..., 253.38248, 253.42545,\n",
" 253.43912],\n",
" [252.84732, 252.85904, 252.861 , ..., 252.63638, 252.75748,\n",
" 252.80826],\n",
" [252.24771, 252.23404, 252.28677, ..., 252.23795, 252.23795,\n",
" 252.2399 ]],\n",
"\n",
" [[235.70682, 235.68729, 235.70096, ..., 235.71658, 235.70877,\n",
" 235.69315],\n",
" [236.74393, 236.70096, 236.6287 , ..., 236.90408, 236.9158 ,\n",
" 236.84549],\n",
" [237.98221, 237.96854, 237.81229, ..., 238.19315, 238.11111,\n",
" 238.0662 ],\n",
"...\n",
" [256.05878, 256.15448, 256.21698, ..., 255.79707, 255.90254,\n",
" 255.97871],\n",
" [255.88496, 255.91426, 255.96309, ..., 255.77168, 255.8127 ,\n",
" 255.84981],\n",
" [255.592 , 255.57832, 255.62715, ..., 255.54317, 255.58028,\n",
" 255.61543]],\n",
"\n",
" [[242.98508, 243.05539, 243.08664, ..., 243.00656, 243.01047,\n",
" 242.98508],\n",
" [245.07687, 245.10812, 245.0007 , ..., 245.0261 , 245.03586,\n",
" 245.11594],\n",
" [245.00656, 245.13547, 245.10226, ..., 245.2468 , 245.15695,\n",
" 245.0593 ],\n",
" ...,\n",
" [253.30539, 253.31516, 253.29562, ..., 253.29367, 253.32297,\n",
" 253.31906],\n",
" [252.05539, 252.05539, 252.08078, ..., 252.00266, 252.00266,\n",
" 252.00266],\n",
" [251.07687, 251.06516, 251.1257 , ..., 251.0632 , 251.0925 ,\n",
" 251.08273]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2015-01-01 ... 2020-01-01</div><input id='attrs-38e459a5-5852-4a7a-a7b4-21e93ed6be8b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-38e459a5-5852-4a7a-a7b4-21e93ed6be8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27da1fff-193c-4220-a3e1-4ee0b62d856a' class='xr-var-data-in' type='checkbox'><label for='data-27da1fff-193c-4220-a3e1-4ee0b62d856a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([['2015-01-01T00:00:00.000000000', '2015-02-01T00:00:00.000000000'],\n",
" ['2015-02-01T00:00:00.000000000', '2015-03-01T00:00:00.000000000'],\n",
" ['2015-03-01T00:00:00.000000000', '2015-04-01T00:00:00.000000000'],\n",
" ['2015-04-01T00:00:00.000000000', '2015-05-01T00:00:00.000000000'],\n",
" ['2015-05-01T00:00:00.000000000', '2015-06-01T00:00:00.000000000'],\n",
" ['2015-06-01T00:00:00.000000000', '2015-07-01T00:00:00.000000000'],\n",
" ['2015-07-01T00:00:00.000000000', '2015-08-01T00:00:00.000000000'],\n",
" ['2015-08-01T00:00:00.000000000', '2015-09-01T00:00:00.000000000'],\n",
" ['2015-09-01T00:00:00.000000000', '2015-10-01T00:00:00.000000000'],\n",
" ['2015-10-01T00:00:00.000000000', '2015-11-01T00:00:00.000000000'],\n",
" ['2015-11-01T00:00:00.000000000', '2015-12-01T00:00:00.000000000'],\n",
" ['2015-12-01T00:00:00.000000000', '2016-01-01T00:00:00.000000000'],\n",
" ['2016-01-01T00:00:00.000000000', '2016-02-01T00:00:00.000000000'],\n",
" ['2016-02-01T00:00:00.000000000', '2016-03-01T00:00:00.000000000'],\n",
" ['2016-03-01T00:00:00.000000000', '2016-04-01T00:00:00.000000000'],\n",
" ['2016-04-01T00:00:00.000000000', '2016-05-01T00:00:00.000000000'],\n",
" ['2016-05-01T00:00:00.000000000', '2016-06-01T00:00:00.000000000'],\n",
" ['2016-06-01T00:00:00.000000000', '2016-07-01T00:00:00.000000000'],\n",
" ['2016-07-01T00:00:00.000000000', '2016-08-01T00:00:00.000000000'],\n",
" ['2016-08-01T00:00:00.000000000', '2016-09-01T00:00:00.000000000'],\n",
"...\n",
" ['2018-06-01T00:00:00.000000000', '2018-07-01T00:00:00.000000000'],\n",
" ['2018-07-01T00:00:00.000000000', '2018-08-01T00:00:00.000000000'],\n",
" ['2018-08-01T00:00:00.000000000', '2018-09-01T00:00:00.000000000'],\n",
" ['2018-09-01T00:00:00.000000000', '2018-10-01T00:00:00.000000000'],\n",
" ['2018-10-01T00:00:00.000000000', '2018-11-01T00:00:00.000000000'],\n",
" ['2018-11-01T00:00:00.000000000', '2018-12-01T00:00:00.000000000'],\n",
" ['2018-12-01T00:00:00.000000000', '2019-01-01T00:00:00.000000000'],\n",
" ['2019-01-01T00:00:00.000000000', '2019-02-01T00:00:00.000000000'],\n",
" ['2019-02-01T00:00:00.000000000', '2019-03-01T00:00:00.000000000'],\n",
" ['2019-03-01T00:00:00.000000000', '2019-04-01T00:00:00.000000000'],\n",
" ['2019-04-01T00:00:00.000000000', '2019-05-01T00:00:00.000000000'],\n",
" ['2019-05-01T00:00:00.000000000', '2019-06-01T00:00:00.000000000'],\n",
" ['2019-06-01T00:00:00.000000000', '2019-07-01T00:00:00.000000000'],\n",
" ['2019-07-01T00:00:00.000000000', '2019-08-01T00:00:00.000000000'],\n",
" ['2019-08-01T00:00:00.000000000', '2019-09-01T00:00:00.000000000'],\n",
" ['2019-09-01T00:00:00.000000000', '2019-10-01T00:00:00.000000000'],\n",
" ['2019-10-01T00:00:00.000000000', '2019-11-01T00:00:00.000000000'],\n",
" ['2019-11-01T00:00:00.000000000', '2019-12-01T00:00:00.000000000'],\n",
" ['2019-12-01T00:00:00.000000000', '2020-01-01T00:00:00.000000000']],\n",
" dtype='datetime64[ns]')</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ba8cafab-5e5e-4832-a273-667f30c17278' class='xr-section-summary-in' type='checkbox' ><label for='section-ba8cafab-5e5e-4832-a273-667f30c17278' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8a1e5631-2b16-4301-a48a-cdfc424536ef' class='xr-index-data-in' type='checkbox'/><label for='index-8a1e5631-2b16-4301-a48a-cdfc424536ef' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-89.28422753251364, -88.35700351866494, -87.42430374606988,\n",
" -86.49036676628116, -85.55596048489265, -84.6213271076488,\n",
" -83.68656681656385, -82.75172847343066, -81.81683872860319,\n",
" -80.8819133467975,\n",
" ...\n",
" 80.8819133467975, 81.81683872860319, 82.75172847343066,\n",
" 83.68656681656385, 84.6213271076488, 85.55596048489265,\n",
" 86.49036676628116, 87.42430374606988, 88.35700351866494,\n",
" 89.28422753251364],\n",
" dtype='float64', name='lat', length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3e7c2792-08fb-4ddf-95ef-577ae0a647b1' class='xr-index-data-in' type='checkbox'/><label for='index-3e7c2792-08fb-4ddf-95ef-577ae0a647b1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0.0, 0.9375, 1.875, 2.8125, 3.75, 4.6875, 5.625,\n",
" 6.5625, 7.5, 8.4375,\n",
" ...\n",
" 350.625, 351.5625, 352.5, 353.4375, 354.375, 355.3125, 356.25,\n",
" 357.1875, 358.125, 359.0625],\n",
" dtype='float64', name='lon', length=384))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c3f153c0-787b-4291-88d6-eee07fa53285' class='xr-index-data-in' type='checkbox'/><label for='index-c3f153c0-787b-4291-88d6-eee07fa53285' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex(['2015-01-16 12:00:00', '2015-02-15 00:00:00',\n",
" '2015-03-16 12:00:00', '2015-04-16 00:00:00',\n",
" '2015-05-16 12:00:00', '2015-06-16 00:00:00',\n",
" '2015-07-16 12:00:00', '2015-08-16 12:00:00',\n",
" '2015-09-16 00:00:00', '2015-10-16 12:00:00',\n",
" '2015-11-16 00:00:00', '2015-12-16 12:00:00',\n",
" '2016-01-16 12:00:00', '2016-02-15 12:00:00',\n",
" '2016-03-16 12:00:00', '2016-04-16 00:00:00',\n",
" '2016-05-16 12:00:00', '2016-06-16 00:00:00',\n",
" '2016-07-16 12:00:00', '2016-08-16 12:00:00',\n",
" '2016-09-16 00:00:00', '2016-10-16 12:00:00',\n",
" '2016-11-16 00:00:00', '2016-12-16 12:00:00',\n",
" '2017-01-16 12:00:00', '2017-02-15 00:00:00',\n",
" '2017-03-16 12:00:00', '2017-04-16 00:00:00',\n",
" '2017-05-16 12:00:00', '2017-06-16 00:00:00',\n",
" '2017-07-16 12:00:00', '2017-08-16 12:00:00',\n",
" '2017-09-16 00:00:00', '2017-10-16 12:00:00',\n",
" '2017-11-16 00:00:00', '2017-12-16 12:00:00',\n",
" '2018-01-16 12:00:00', '2018-02-15 00:00:00',\n",
" '2018-03-16 12:00:00', '2018-04-16 00:00:00',\n",
" '2018-05-16 12:00:00', '2018-06-16 00:00:00',\n",
" '2018-07-16 12:00:00', '2018-08-16 12:00:00',\n",
" '2018-09-16 00:00:00', '2018-10-16 12:00:00',\n",
" '2018-11-16 00:00:00', '2018-12-16 12:00:00',\n",
" '2019-01-16 12:00:00', '2019-02-15 00:00:00',\n",
" '2019-03-16 12:00:00', '2019-04-16 00:00:00',\n",
" '2019-05-16 12:00:00', '2019-06-16 00:00:00',\n",
" '2019-07-16 12:00:00', '2019-08-16 12:00:00',\n",
" '2019-09-16 00:00:00', '2019-10-16 12:00:00',\n",
" '2019-11-16 00:00:00', '2019-12-16 12:00:00'],\n",
" dtype='datetime64[ns]', name='time', freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ec585823-d184-4bff-ac95-5be2eb951e4e' class='xr-section-summary-in' type='checkbox' ><label for='section-ec585823-d184-4bff-ac95-5be2eb951e4e' class='xr-section-summary' >Attributes: <span>(47)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>ScenarioMIP AerChemMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>60265.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>60265.0</dd><dt><span>contact :</span></dt><dd>cmip6-mpi-esm@dkrz.de</dd><dt><span>creation_date :</span></dt><dd>2019-07-20T13:41:51Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.30</dd><dt><span>experiment :</span></dt><dd>gap-filling scenario reaching 7.0 based on SSP3</dd><dt><span>experiment_id :</span></dt><dd>ssp370</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.DKRZ.MPI-ESM1-2-HR.ssp370.none.r1i1p1f1</dd><dt><span>grid :</span></dt><dd>gn</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>2019-07-20T13:41:51Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>Deutsches Klimarechenzentrum, Hamburg 20146, Germany</dd><dt><span>institution_id :</span></dt><dd>DKRZ</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>100 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>historical</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>MPI-ESM1-2-HR</dd><dt><span>parent_time_units :</span></dt><dd>days since 1850-1-1 00:00:00</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>project_id :</span></dt><dd>CMIP6</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>references :</span></dt><dd>MPI-ESM: Mauritsen, T. et al. (2019), Developments in the MPI‐M Earth System Model version 1.2 (MPI‐ESM1.2) and Its Response to Increasing CO2, J. Adv. Model. Earth Syst.,11, 998-1038, doi:10.1029/2018MS001400,\n",
"Mueller, W.A. et al. (2018): A high‐resolution version of the Max Planck Institute Earth System Model MPI‐ESM1.2‐HR. J. Adv. Model. EarthSyst.,10,1383–1413, doi:10.1029/2017MS001217</dd><dt><span>source :</span></dt><dd>MPI-ESM1.2-HR (2017): \n",
"aerosol: none, prescribed MACv2-SP\n",
"atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa)\n",
"atmosChem: none\n",
"land: JSBACH3.20\n",
"landIce: none/prescribed\n",
"ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m)\n",
"ocnBgchem: HAMOCC6\n",
"seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model)</dd><dt><span>source_id :</span></dt><dd>MPI-ESM1-2-HR</dd><dt><span>source_type :</span></dt><dd>AOGCM</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Amon</dd><dt><span>table_info :</span></dt><dd>Creation Date:(09 May 2019) MD5:e6ef8ececc8f338646ebfb3aeed36bfc</dd><dt><span>title :</span></dt><dd>MPI-ESM1-2-HR output prepared for CMIP6</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by DKRZ is licensed under a Creative Commons Attribution ShareAlike 4.0 International License (https://creativecommons.org/licenses). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file) and. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>cmor_version :</span></dt><dd>3.4.0</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/d6f0f6b2-b4c2-460b-bc5e-f1c72f932399</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset> Size: 18MB\n",
"Dimensions: (lat: 192, bnds: 2, lon: 384, time: 60)\n",
"Coordinates:\n",
" lat_bnds (lat, bnds) float64 3kB -89.75 -88.82 -88.82 ... 88.82 89.75\n",
" lon_bnds (lon, bnds) float64 6kB -0.4688 0.4688 0.4688 ... 358.6 359.5\n",
" height float64 8B 2.0\n",
" * lat (lat) float64 2kB -89.28 -88.36 -87.42 ... 87.42 88.36 89.28\n",
" * lon (lon) float64 3kB 0.0 0.9375 1.875 2.812 ... 357.2 358.1 359.1\n",
" * time (time) datetime64[ns] 480B 2015-01-16T12:00:00 ... 2019-12-16T...\n",
"Dimensions without coordinates: bnds\n",
"Data variables:\n",
" tas (time, lat, lon) float32 18MB 243.3 243.3 243.3 ... 251.1 251.1\n",
" time_bnds (time, bnds) datetime64[ns] 960B 2015-01-01 ... 2020-01-01\n",
"Attributes: (12/47)\n",
" Conventions: CF-1.7 CMIP-6.2\n",
" activity_id: ScenarioMIP AerChemMIP\n",
" branch_method: standard\n",
" branch_time_in_child: 60265.0\n",
" branch_time_in_parent: 60265.0\n",
" contact: cmip6-mpi-esm@dkrz.de\n",
" ... ...\n",
" title: MPI-ESM1-2-HR output prepared for CMIP6\n",
" variable_id: tas\n",
" variant_label: r1i1p1f1\n",
" license: CMIP6 model data produced by DKRZ is licensed und...\n",
" cmor_version: 3.4.0\n",
" tracking_id: hdl:21.14100/d6f0f6b2-b4c2-460b-bc5e-f1c72f932399"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import xarray as xr\n",
"xr.open_dataset(\"compressed.nc\").load()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "8dd8feff-9e4b-421b-a453-83f81e20da1a",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"k204210 52510 4121939 0 10:12 ? 00:00:01 /work/bm0021/conda-envs/cloudify/bin/python -Xfrozen_modules=off -m ipykernel_launcher -f /home/k/k204210/.local/share/jupyter/runtime/kernel-c6562e74-6d50-4cf3-92d0-27e4f4cae6f8.json\n",
"k204210 54077 52595 0 10:13 ? 00:00:00 /work/bm0021/conda-envs/cloudify/bin/python -c from multiprocessing.resource_tracker import main;main(27)\n",
"k204210 54079 52595 1 10:13 ? 00:00:27 /work/bm0021/conda-envs/cloudify/bin/python -c from multiprocessing.spawn import spawn_main; spawn_main(tracker_fd=28, pipe_handle=38) --multiprocessing-fork\n",
"k204210 54082 52595 1 10:13 ? 00:00:26 /work/bm0021/conda-envs/cloudify/bin/python -c from multiprocessing.spawn import spawn_main; spawn_main(tracker_fd=28, pipe_handle=38) --multiprocessing-fork\n",
"k204210 56270 4121939 0 10:18 ? 00:00:07 /work/bm0021/conda-envs/cloudify/bin/python -Xfrozen_modules=off -m ipykernel_launcher -f /home/k/k204210/.local/share/jupyter/runtime/kernel-e22a5303-3b28-41cb-b092-ed92a8ff6221.json\n",
"k204210 67839 4121939 16 10:42 ? 00:00:06 /work/bm0021/conda-envs/cloudify/bin/python -Xfrozen_modules=off -m ipykernel_launcher -f /home/k/k204210/.local/share/jupyter/runtime/kernel-33489dce-534b-42ed-bbe7-6d125e3f6167.json\n",
"k204210 68481 67839 0 10:42 pts/20 00:00:00 /bin/bash -c ps -ef | grep cloudify\n",
"k204210 68483 68481 0 10:42 pts/20 00:00:00 grep cloudify\n",
"k204210 4125444 4121939 0 08:57 ? 00:00:01 /work/bm0021/conda-envs/cloudify/bin/python -Xfrozen_modules=off -m ipykernel_launcher -f /home/k/k204210/.local/share/jupyter/runtime/kernel-f2ebf347-166b-4e0f-89ee-e7e663b08a4c.json\n",
"k204210 4125453 4121939 0 08:57 ? 00:00:01 /work/bm0021/conda-envs/cloudify/bin/python -Xfrozen_modules=off -m ipykernel_launcher -f /home/k/k204210/.local/share/jupyter/runtime/kernel-16ae079b-b9fb-4460-bc7c-808797637e88.json\n"
]
}
],
"source": [
"!ps -ef | grep cloudify"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "d8e827fd-5501-47ef-b2e8-8e00a0dba7cd",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"!kill 52595"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "42c31b41-7cd7-43f0-b65e-6223ca38c9a8",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import os\n",
"os.environ[\"L_LOSSY\"]=\"1\""
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "13936d6b-d9af-4b0b-88ce-abc921aed6e0",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"%%bash --bg\n",
"\n",
"source activate /work/bm0021/conda-envs/cloudify\n",
"python xpublish_example.py \\\n",
" example \\\n",
" /work/ik1017/CMIP6/data/CMIP6/ScenarioMIP/DKRZ/MPI-ESM1-2-HR/ssp370/r1i1p1f1/Amon/tas/gn/v20190710/*.nc"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "d4ac3289-05b0-4d26-8d35-c9ce55fbb91c",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"dslossy=xr.open_zarr(\n",
" zarr_url,\n",
" consolidated=True,\n",
" storage_options=storage_options,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "1e726b61-baf3-4145-864a-72ec4e925e7c",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"ds5lossy=dslossy.where(ds.time.dt.year.isin(range(2015,2020)),drop=True)\n",
"ds5lossy=prepare_for_storage(ds5lossy)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "4bc9468c-ce8c-4e8c-a786-90cee9e9e8fd",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1.9779608249664307 seconds\n",
"512 -rw-r--r-- 1 k204210 bm0021 3.5M Jan 8 10:50 lossy_compressed.nc\n"
]
}
],
"source": [
"store_ds(ds5,\"lossy_compressed.nc\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "29f8dd36-7af2-4e0b-92b2-85a4e0cb8dae",
"metadata": {
"tags": []
},
"outputs": [
{
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"</style><pre class='xr-text-repr-fallback'><xarray.Dataset> Size: 18MB\n",
"Dimensions: (lat: 192, bnds: 2, lon: 384, time: 60)\n",
"Coordinates:\n",
" lat_bnds (lat, bnds) float64 3kB -89.75 -88.82 -88.82 ... 88.82 89.75\n",
" lon_bnds (lon, bnds) float64 6kB -0.4688 0.4688 0.4688 ... 358.6 359.5\n",
" height float64 8B 2.0\n",
" * lat (lat) float64 2kB -89.28 -88.36 -87.42 ... 87.42 88.36 89.28\n",
" * lon (lon) float64 3kB 0.0 0.9375 1.875 2.812 ... 357.2 358.1 359.1\n",
" * time (time) datetime64[ns] 480B 2015-01-16T12:00:00 ... 2019-12-16T...\n",
"Dimensions without coordinates: bnds\n",
"Data variables:\n",
" tas (time, lat, lon) float32 18MB 243.3 243.3 243.3 ... 251.1 251.1\n",
" time_bnds (time, bnds) datetime64[ns] 960B 2015-01-01 ... 2020-01-01\n",
"Attributes: (12/47)\n",
" Conventions: CF-1.7 CMIP-6.2\n",
" activity_id: ScenarioMIP AerChemMIP\n",
" branch_method: standard\n",
" branch_time_in_child: 60265.0\n",
" branch_time_in_parent: 60265.0\n",
" contact: cmip6-mpi-esm@dkrz.de\n",
" ... ...\n",
" title: MPI-ESM1-2-HR output prepared for CMIP6\n",
" variable_id: tas\n",
" variant_label: r1i1p1f1\n",
" license: CMIP6 model data produced by DKRZ is licensed und...\n",
" cmor_version: 3.4.0\n",
" tracking_id: hdl:21.14100/d6f0f6b2-b4c2-460b-bc5e-f1c72f932399</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-47379a13-ae7c-4405-88d1-1be0418dbdee' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-47379a13-ae7c-4405-88d1-1be0418dbdee' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 192</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lon</span>: 384</li><li><span class='xr-has-index'>time</span>: 60</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0534f870-ff7f-4592-a1a9-7f5453fd5e5b' class='xr-section-summary-in' type='checkbox' checked><label for='section-0534f870-ff7f-4592-a1a9-7f5453fd5e5b' class='xr-section-summary' >Coordinates: <span>(6)</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>lat_bnds</span></div><div class='xr-var-dims'>(lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.75 -88.82 ... 88.82 89.75</div><input id='attrs-294e59c2-dbd5-4c92-9a83-d9507e79ec19' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-294e59c2-dbd5-4c92-9a83-d9507e79ec19' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-576eca74-1468-45a8-aa68-4c07dca69642' class='xr-var-data-in' type='checkbox'><label for='data-576eca74-1468-45a8-aa68-4c07dca69642' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[-89.74783954, -88.82061553],\n",
" [-88.82061553, -87.89065363],\n",
" [-87.89065363, -86.95733526],\n",
" [-86.95733526, -86.02316363],\n",
" [-86.02316363, -85.0886438 ],\n",
" [-85.0886438 , -84.15394696],\n",
" [-84.15394696, -83.21914764],\n",
" [-83.21914764, -82.2842836 ],\n",
" [-82.2842836 , -81.34937604],\n",
" [-81.34937604, -80.4144378 ],\n",
" [-80.4144378 , -79.47947712],\n",
" [-79.47947712, -78.54449952],\n",
" [-78.54449952, -77.60950886],\n",
" [-77.60950886, -76.67450789],\n",
" [-76.67450789, -75.73949865],\n",
" [-75.73949865, -74.80448268],\n",
" [-74.80448268, -73.86946114],\n",
" [-73.86946114, -72.93443496],\n",
" [-72.93443496, -71.99940486],\n",
" [-71.99940486, -71.06437142],\n",
"...\n",
" [ 71.06437142, 71.99940486],\n",
" [ 71.99940486, 72.93443496],\n",
" [ 72.93443496, 73.86946114],\n",
" [ 73.86946114, 74.80448268],\n",
" [ 74.80448268, 75.73949865],\n",
" [ 75.73949865, 76.67450789],\n",
" [ 76.67450789, 77.60950886],\n",
" [ 77.60950886, 78.54449952],\n",
" [ 78.54449952, 79.47947712],\n",
" [ 79.47947712, 80.4144378 ],\n",
" [ 80.4144378 , 81.34937604],\n",
" [ 81.34937604, 82.2842836 ],\n",
" [ 82.2842836 , 83.21914764],\n",
" [ 83.21914764, 84.15394696],\n",
" [ 84.15394696, 85.0886438 ],\n",
" [ 85.0886438 , 86.02316363],\n",
" [ 86.02316363, 86.95733526],\n",
" [ 86.95733526, 87.89065363],\n",
" [ 87.89065363, 88.82061553],\n",
" [ 88.82061553, 89.74783954]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.4688 0.4688 ... 358.6 359.5</div><input id='attrs-9ffed295-81de-462c-8077-22200827b212' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9ffed295-81de-462c-8077-22200827b212' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e325e714-c89a-45b8-9266-0852991421a9' class='xr-var-data-in' type='checkbox'><label for='data-e325e714-c89a-45b8-9266-0852991421a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ -0.46875, 0.46875],\n",
" [ 0.46875, 1.40625],\n",
" [ 1.40625, 2.34375],\n",
" [ 2.34375, 3.28125],\n",
" [ 3.28125, 4.21875],\n",
" [ 4.21875, 5.15625],\n",
" [ 5.15625, 6.09375],\n",
" [ 6.09375, 7.03125],\n",
" [ 7.03125, 7.96875],\n",
" [ 7.96875, 8.90625],\n",
" [ 8.90625, 9.84375],\n",
" [ 9.84375, 10.78125],\n",
" [ 10.78125, 11.71875],\n",
" [ 11.71875, 12.65625],\n",
" [ 12.65625, 13.59375],\n",
" [ 13.59375, 14.53125],\n",
" [ 14.53125, 15.46875],\n",
" [ 15.46875, 16.40625],\n",
" [ 16.40625, 17.34375],\n",
" [ 17.34375, 18.28125],\n",
"...\n",
" [340.78125, 341.71875],\n",
" [341.71875, 342.65625],\n",
" [342.65625, 343.59375],\n",
" [343.59375, 344.53125],\n",
" [344.53125, 345.46875],\n",
" [345.46875, 346.40625],\n",
" [346.40625, 347.34375],\n",
" [347.34375, 348.28125],\n",
" [348.28125, 349.21875],\n",
" [349.21875, 350.15625],\n",
" [350.15625, 351.09375],\n",
" [351.09375, 352.03125],\n",
" [352.03125, 352.96875],\n",
" [352.96875, 353.90625],\n",
" [353.90625, 354.84375],\n",
" [354.84375, 355.78125],\n",
" [355.78125, 356.71875],\n",
" [356.71875, 357.65625],\n",
" [357.65625, 358.59375],\n",
" [358.59375, 359.53125]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-b35fcf93-418d-4f77-8c0e-2b454a4d9e95' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b35fcf93-418d-4f77-8c0e-2b454a4d9e95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7bcff009-fc48-4a4c-ba36-34d48c1ad5e9' class='xr-var-data-in' type='checkbox'><label for='data-7bcff009-fc48-4a4c-ba36-34d48c1ad5e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array(2.)</pre></div></li><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'>-89.28 -88.36 ... 88.36 89.28</div><input id='attrs-ddbd2ba7-dfc8-4f37-9b6c-5ff407371943' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ddbd2ba7-dfc8-4f37-9b6c-5ff407371943' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10d002b3-a5f4-4759-b1bf-6bb9bca8ef3e' class='xr-var-data-in' type='checkbox'><label for='data-10d002b3-a5f4-4759-b1bf-6bb9bca8ef3e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-89.284228, -88.357004, -87.424304, -86.490367, -85.55596 , -84.621327,\n",
" -83.686567, -82.751728, -81.816839, -80.881913, -79.946962, -79.011992,\n",
" -78.077007, -77.142011, -76.207005, -75.271992, -74.336973, -73.401949,\n",
" -72.466921, -71.531889, -70.596854, -69.661816, -68.726776, -67.791734,\n",
" -66.856691, -65.921645, -64.986599, -64.051551, -63.116502, -62.181452,\n",
" -61.246401, -60.311349, -59.376297, -58.441244, -57.50619 , -56.571136,\n",
" -55.636081, -54.701026, -53.76597 , -52.830914, -51.895857, -50.960801,\n",
" -50.025743, -49.090686, -48.155628, -47.22057 , -46.285512, -45.350454,\n",
" -44.415395, -43.480336, -42.545277, -41.610218, -40.675159, -39.740099,\n",
" -38.805039, -37.86998 , -36.93492 , -35.99986 , -35.064799, -34.129739,\n",
" -33.194679, -32.259618, -31.324558, -30.389497, -29.454436, -28.519375,\n",
" -27.584315, -26.649254, -25.714193, -24.779132, -23.84407 , -22.909009,\n",
" -21.973948, -21.038887, -20.103825, -19.168764, -18.233703, -17.298641,\n",
" -16.36358 , -15.428518, -14.493457, -13.558395, -12.623333, -11.688272,\n",
" -10.75321 , -9.818148, -8.883087, -7.948025, -7.012963, -6.077901,\n",
" -5.14284 , -4.207778, -3.272716, -2.337654, -1.402593, -0.467531,\n",
" 0.467531, 1.402593, 2.337654, 3.272716, 4.207778, 5.14284 ,\n",
" 6.077901, 7.012963, 7.948025, 8.883087, 9.818148, 10.75321 ,\n",
" 11.688272, 12.623333, 13.558395, 14.493457, 15.428518, 16.36358 ,\n",
" 17.298641, 18.233703, 19.168764, 20.103825, 21.038887, 21.973948,\n",
" 22.909009, 23.84407 , 24.779132, 25.714193, 26.649254, 27.584315,\n",
" 28.519375, 29.454436, 30.389497, 31.324558, 32.259618, 33.194679,\n",
" 34.129739, 35.064799, 35.99986 , 36.93492 , 37.86998 , 38.805039,\n",
" 39.740099, 40.675159, 41.610218, 42.545277, 43.480336, 44.415395,\n",
" 45.350454, 46.285512, 47.22057 , 48.155628, 49.090686, 50.025743,\n",
" 50.960801, 51.895857, 52.830914, 53.76597 , 54.701026, 55.636081,\n",
" 56.571136, 57.50619 , 58.441244, 59.376297, 60.311349, 61.246401,\n",
" 62.181452, 63.116502, 64.051551, 64.986599, 65.921645, 66.856691,\n",
" 67.791734, 68.726776, 69.661816, 70.596854, 71.531889, 72.466921,\n",
" 73.401949, 74.336973, 75.271992, 76.207005, 77.142011, 78.077007,\n",
" 79.011992, 79.946962, 80.881913, 81.816839, 82.751728, 83.686567,\n",
" 84.621327, 85.55596 , 86.490367, 87.424304, 88.357004, 89.284228])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.9375 1.875 ... 358.1 359.1</div><input id='attrs-9721fd8a-19ea-4da3-817b-80146b20e0c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9721fd8a-19ea-4da3-817b-80146b20e0c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be1ed8b4-72da-40c8-aa35-e4c424cdd544' class='xr-var-data-in' type='checkbox'><label for='data-be1ed8b4-72da-40c8-aa35-e4c424cdd544' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([ 0. , 0.9375, 1.875 , ..., 357.1875, 358.125 , 359.0625])</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'>2015-01-16T12:00:00 ... 2019-12-...</div><input id='attrs-67370813-228a-45b4-9f6d-919864cffdea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67370813-228a-45b4-9f6d-919864cffdea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ddea2049-ba4c-445d-8f25-37d48af70e43' class='xr-var-data-in' type='checkbox'><label for='data-ddea2049-ba4c-445d-8f25-37d48af70e43' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(['2015-01-16T12:00:00.000000000', '2015-02-15T00:00:00.000000000',\n",
" '2015-03-16T12:00:00.000000000', '2015-04-16T00:00:00.000000000',\n",
" '2015-05-16T12:00:00.000000000', '2015-06-16T00:00:00.000000000',\n",
" '2015-07-16T12:00:00.000000000', '2015-08-16T12:00:00.000000000',\n",
" '2015-09-16T00:00:00.000000000', '2015-10-16T12:00:00.000000000',\n",
" '2015-11-16T00:00:00.000000000', '2015-12-16T12:00:00.000000000',\n",
" '2016-01-16T12:00:00.000000000', '2016-02-15T12:00:00.000000000',\n",
" '2016-03-16T12:00:00.000000000', '2016-04-16T00:00:00.000000000',\n",
" '2016-05-16T12:00:00.000000000', '2016-06-16T00:00:00.000000000',\n",
" '2016-07-16T12:00:00.000000000', '2016-08-16T12:00:00.000000000',\n",
" '2016-09-16T00:00:00.000000000', '2016-10-16T12:00:00.000000000',\n",
" '2016-11-16T00:00:00.000000000', '2016-12-16T12:00:00.000000000',\n",
" '2017-01-16T12:00:00.000000000', '2017-02-15T00:00:00.000000000',\n",
" '2017-03-16T12:00:00.000000000', '2017-04-16T00:00:00.000000000',\n",
" '2017-05-16T12:00:00.000000000', '2017-06-16T00:00:00.000000000',\n",
" '2017-07-16T12:00:00.000000000', '2017-08-16T12:00:00.000000000',\n",
" '2017-09-16T00:00:00.000000000', '2017-10-16T12:00:00.000000000',\n",
" '2017-11-16T00:00:00.000000000', '2017-12-16T12:00:00.000000000',\n",
" '2018-01-16T12:00:00.000000000', '2018-02-15T00:00:00.000000000',\n",
" '2018-03-16T12:00:00.000000000', '2018-04-16T00:00:00.000000000',\n",
" '2018-05-16T12:00:00.000000000', '2018-06-16T00:00:00.000000000',\n",
" '2018-07-16T12:00:00.000000000', '2018-08-16T12:00:00.000000000',\n",
" '2018-09-16T00:00:00.000000000', '2018-10-16T12:00:00.000000000',\n",
" '2018-11-16T00:00:00.000000000', '2018-12-16T12:00:00.000000000',\n",
" '2019-01-16T12:00:00.000000000', '2019-02-15T00:00:00.000000000',\n",
" '2019-03-16T12:00:00.000000000', '2019-04-16T00:00:00.000000000',\n",
" '2019-05-16T12:00:00.000000000', '2019-06-16T00:00:00.000000000',\n",
" '2019-07-16T12:00:00.000000000', '2019-08-16T12:00:00.000000000',\n",
" '2019-09-16T00:00:00.000000000', '2019-10-16T12:00:00.000000000',\n",
" '2019-11-16T00:00:00.000000000', '2019-12-16T12:00:00.000000000'],\n",
" dtype='datetime64[ns]')</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f57064ac-97c8-4a2f-bd8f-26cd4fd8f777' class='xr-section-summary-in' type='checkbox' checked><label for='section-f57064ac-97c8-4a2f-bd8f-26cd4fd8f777' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>243.3 243.3 243.3 ... 251.1 251.1</div><input id='attrs-adbdfd91-370a-40bc-9176-87250ccfd2fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-adbdfd91-370a-40bc-9176-87250ccfd2fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-570c56e5-f722-401a-bbcd-88894ed4dc0a' class='xr-var-data-in' type='checkbox'><label for='data-570c56e5-f722-401a-bbcd-88894ed4dc0a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>comment :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2019-07-20T13:41:51Z altered by CMOR: Treated scalar dimension: 'height'. 2019-07-20T13:41:51Z altered by CMOR: replaced missing value flag (-9e+33) with standard missing value (1e+20). 2019-07-20T13:41:51Z altered by CMOR: Converted type from 'd' to 'f'. 2019-07-20T13:41:52Z altered by CMOR: Inverted axis: lat.</dd></dl></div><div class='xr-var-data'><pre>array([[[243.3125 , 243.28125, 243.28125, ..., 243.28125, 243.28125,\n",
" 243.1875 ],\n",
" [244.375 , 244.34375, 244.28125, ..., 244.5 , 244.46875,\n",
" 244.4375 ],\n",
" [244.875 , 244.78125, 244.65625, ..., 245.03125, 245. ,\n",
" 244.9375 ],\n",
" ...,\n",
" [253.5 , 253.5 , 253.53125, ..., 253.375 , 253.4375 ,\n",
" 253.4375 ],\n",
" [252.84375, 252.84375, 252.875 , ..., 252.625 , 252.75 ,\n",
" 252.8125 ],\n",
" [252.25 , 252.21875, 252.28125, ..., 252.25 , 252.25 ,\n",
" 252.25 ]],\n",
"\n",
" [[235.71875, 235.6875 , 235.6875 , ..., 235.71875, 235.71875,\n",
" 235.6875 ],\n",
" [236.75 , 236.6875 , 236.625 , ..., 236.90625, 236.90625,\n",
" 236.84375],\n",
" [237.96875, 237.96875, 237.8125 , ..., 238.1875 , 238.125 ,\n",
" 238.0625 ],\n",
"...\n",
" [256.0625 , 256.125 , 256.1875 , ..., 255.8125 , 255.90625,\n",
" 255.96875],\n",
" [255.875 , 255.90625, 255.96875, ..., 255.78125, 255.8125 ,\n",
" 255.84375],\n",
" [255.59375, 255.59375, 255.625 , ..., 255.53125, 255.59375,\n",
" 255.625 ]],\n",
"\n",
" [[243. , 243.0625 , 243.09375, ..., 243. , 243. ,\n",
" 243. ],\n",
" [245.0625 , 245.09375, 245. , ..., 245.03125, 245.03125,\n",
" 245.125 ],\n",
" [245. , 245.125 , 245.09375, ..., 245.25 , 245.15625,\n",
" 245.0625 ],\n",
" ...,\n",
" [253.3125 , 253.3125 , 253.28125, ..., 253.28125, 253.3125 ,\n",
" 253.3125 ],\n",
" [252.0625 , 252.0625 , 252.09375, ..., 252. , 252. ,\n",
" 252. ],\n",
" [251.0625 , 251.0625 , 251.125 , ..., 251.0625 , 251.09375,\n",
" 251.09375]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2015-01-01 ... 2020-01-01</div><input id='attrs-0edc4fba-c0e7-43b2-b9d5-2c3a9eb36934' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0edc4fba-c0e7-43b2-b9d5-2c3a9eb36934' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc44fa67-c61f-44d0-8ae5-b13c8e7f4a71' class='xr-var-data-in' type='checkbox'><label for='data-fc44fa67-c61f-44d0-8ae5-b13c8e7f4a71' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([['2015-01-01T00:00:00.000000000', '2015-02-01T00:00:00.000000000'],\n",
" ['2015-02-01T00:00:00.000000000', '2015-03-01T00:00:00.000000000'],\n",
" ['2015-03-01T00:00:00.000000000', '2015-04-01T00:00:00.000000000'],\n",
" ['2015-04-01T00:00:00.000000000', '2015-05-01T00:00:00.000000000'],\n",
" ['2015-05-01T00:00:00.000000000', '2015-06-01T00:00:00.000000000'],\n",
" ['2015-06-01T00:00:00.000000000', '2015-07-01T00:00:00.000000000'],\n",
" ['2015-07-01T00:00:00.000000000', '2015-08-01T00:00:00.000000000'],\n",
" ['2015-08-01T00:00:00.000000000', '2015-09-01T00:00:00.000000000'],\n",
" ['2015-09-01T00:00:00.000000000', '2015-10-01T00:00:00.000000000'],\n",
" ['2015-10-01T00:00:00.000000000', '2015-11-01T00:00:00.000000000'],\n",
" ['2015-11-01T00:00:00.000000000', '2015-12-01T00:00:00.000000000'],\n",
" ['2015-12-01T00:00:00.000000000', '2016-01-01T00:00:00.000000000'],\n",
" ['2016-01-01T00:00:00.000000000', '2016-02-01T00:00:00.000000000'],\n",
" ['2016-02-01T00:00:00.000000000', '2016-03-01T00:00:00.000000000'],\n",
" ['2016-03-01T00:00:00.000000000', '2016-04-01T00:00:00.000000000'],\n",
" ['2016-04-01T00:00:00.000000000', '2016-05-01T00:00:00.000000000'],\n",
" ['2016-05-01T00:00:00.000000000', '2016-06-01T00:00:00.000000000'],\n",
" ['2016-06-01T00:00:00.000000000', '2016-07-01T00:00:00.000000000'],\n",
" ['2016-07-01T00:00:00.000000000', '2016-08-01T00:00:00.000000000'],\n",
" ['2016-08-01T00:00:00.000000000', '2016-09-01T00:00:00.000000000'],\n",
"...\n",
" ['2018-06-01T00:00:00.000000000', '2018-07-01T00:00:00.000000000'],\n",
" ['2018-07-01T00:00:00.000000000', '2018-08-01T00:00:00.000000000'],\n",
" ['2018-08-01T00:00:00.000000000', '2018-09-01T00:00:00.000000000'],\n",
" ['2018-09-01T00:00:00.000000000', '2018-10-01T00:00:00.000000000'],\n",
" ['2018-10-01T00:00:00.000000000', '2018-11-01T00:00:00.000000000'],\n",
" ['2018-11-01T00:00:00.000000000', '2018-12-01T00:00:00.000000000'],\n",
" ['2018-12-01T00:00:00.000000000', '2019-01-01T00:00:00.000000000'],\n",
" ['2019-01-01T00:00:00.000000000', '2019-02-01T00:00:00.000000000'],\n",
" ['2019-02-01T00:00:00.000000000', '2019-03-01T00:00:00.000000000'],\n",
" ['2019-03-01T00:00:00.000000000', '2019-04-01T00:00:00.000000000'],\n",
" ['2019-04-01T00:00:00.000000000', '2019-05-01T00:00:00.000000000'],\n",
" ['2019-05-01T00:00:00.000000000', '2019-06-01T00:00:00.000000000'],\n",
" ['2019-06-01T00:00:00.000000000', '2019-07-01T00:00:00.000000000'],\n",
" ['2019-07-01T00:00:00.000000000', '2019-08-01T00:00:00.000000000'],\n",
" ['2019-08-01T00:00:00.000000000', '2019-09-01T00:00:00.000000000'],\n",
" ['2019-09-01T00:00:00.000000000', '2019-10-01T00:00:00.000000000'],\n",
" ['2019-10-01T00:00:00.000000000', '2019-11-01T00:00:00.000000000'],\n",
" ['2019-11-01T00:00:00.000000000', '2019-12-01T00:00:00.000000000'],\n",
" ['2019-12-01T00:00:00.000000000', '2020-01-01T00:00:00.000000000']],\n",
" dtype='datetime64[ns]')</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f0a3aaa4-57bf-4ace-9b9d-52e79e4b4de1' class='xr-section-summary-in' type='checkbox' ><label for='section-f0a3aaa4-57bf-4ace-9b9d-52e79e4b4de1' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1bfb1c10-c68f-4332-896c-ae02baff70e0' class='xr-index-data-in' type='checkbox'/><label for='index-1bfb1c10-c68f-4332-896c-ae02baff70e0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-89.28422753251364, -88.35700351866494, -87.42430374606988,\n",
" -86.49036676628116, -85.55596048489265, -84.6213271076488,\n",
" -83.68656681656385, -82.75172847343066, -81.81683872860319,\n",
" -80.8819133467975,\n",
" ...\n",
" 80.8819133467975, 81.81683872860319, 82.75172847343066,\n",
" 83.68656681656385, 84.6213271076488, 85.55596048489265,\n",
" 86.49036676628116, 87.42430374606988, 88.35700351866494,\n",
" 89.28422753251364],\n",
" dtype='float64', name='lat', length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fed4bbce-4a0d-479d-9929-4ee137bb27dc' class='xr-index-data-in' type='checkbox'/><label for='index-fed4bbce-4a0d-479d-9929-4ee137bb27dc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0.0, 0.9375, 1.875, 2.8125, 3.75, 4.6875, 5.625,\n",
" 6.5625, 7.5, 8.4375,\n",
" ...\n",
" 350.625, 351.5625, 352.5, 353.4375, 354.375, 355.3125, 356.25,\n",
" 357.1875, 358.125, 359.0625],\n",
" dtype='float64', name='lon', length=384))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b3449b4b-ebc9-466f-b515-20f8ea2ac014' class='xr-index-data-in' type='checkbox'/><label for='index-b3449b4b-ebc9-466f-b515-20f8ea2ac014' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex(['2015-01-16 12:00:00', '2015-02-15 00:00:00',\n",
" '2015-03-16 12:00:00', '2015-04-16 00:00:00',\n",
" '2015-05-16 12:00:00', '2015-06-16 00:00:00',\n",
" '2015-07-16 12:00:00', '2015-08-16 12:00:00',\n",
" '2015-09-16 00:00:00', '2015-10-16 12:00:00',\n",
" '2015-11-16 00:00:00', '2015-12-16 12:00:00',\n",
" '2016-01-16 12:00:00', '2016-02-15 12:00:00',\n",
" '2016-03-16 12:00:00', '2016-04-16 00:00:00',\n",
" '2016-05-16 12:00:00', '2016-06-16 00:00:00',\n",
" '2016-07-16 12:00:00', '2016-08-16 12:00:00',\n",
" '2016-09-16 00:00:00', '2016-10-16 12:00:00',\n",
" '2016-11-16 00:00:00', '2016-12-16 12:00:00',\n",
" '2017-01-16 12:00:00', '2017-02-15 00:00:00',\n",
" '2017-03-16 12:00:00', '2017-04-16 00:00:00',\n",
" '2017-05-16 12:00:00', '2017-06-16 00:00:00',\n",
" '2017-07-16 12:00:00', '2017-08-16 12:00:00',\n",
" '2017-09-16 00:00:00', '2017-10-16 12:00:00',\n",
" '2017-11-16 00:00:00', '2017-12-16 12:00:00',\n",
" '2018-01-16 12:00:00', '2018-02-15 00:00:00',\n",
" '2018-03-16 12:00:00', '2018-04-16 00:00:00',\n",
" '2018-05-16 12:00:00', '2018-06-16 00:00:00',\n",
" '2018-07-16 12:00:00', '2018-08-16 12:00:00',\n",
" '2018-09-16 00:00:00', '2018-10-16 12:00:00',\n",
" '2018-11-16 00:00:00', '2018-12-16 12:00:00',\n",
" '2019-01-16 12:00:00', '2019-02-15 00:00:00',\n",
" '2019-03-16 12:00:00', '2019-04-16 00:00:00',\n",
" '2019-05-16 12:00:00', '2019-06-16 00:00:00',\n",
" '2019-07-16 12:00:00', '2019-08-16 12:00:00',\n",
" '2019-09-16 00:00:00', '2019-10-16 12:00:00',\n",
" '2019-11-16 00:00:00', '2019-12-16 12:00:00'],\n",
" dtype='datetime64[ns]', name='time', freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-61d18298-80e3-4356-bdb2-30cf2225cd1c' class='xr-section-summary-in' type='checkbox' ><label for='section-61d18298-80e3-4356-bdb2-30cf2225cd1c' class='xr-section-summary' >Attributes: <span>(47)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>ScenarioMIP AerChemMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>60265.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>60265.0</dd><dt><span>contact :</span></dt><dd>cmip6-mpi-esm@dkrz.de</dd><dt><span>creation_date :</span></dt><dd>2019-07-20T13:41:51Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.30</dd><dt><span>experiment :</span></dt><dd>gap-filling scenario reaching 7.0 based on SSP3</dd><dt><span>experiment_id :</span></dt><dd>ssp370</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.DKRZ.MPI-ESM1-2-HR.ssp370.none.r1i1p1f1</dd><dt><span>grid :</span></dt><dd>gn</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>2019-07-20T13:41:51Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>Deutsches Klimarechenzentrum, Hamburg 20146, Germany</dd><dt><span>institution_id :</span></dt><dd>DKRZ</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>100 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>historical</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>MPI-ESM1-2-HR</dd><dt><span>parent_time_units :</span></dt><dd>days since 1850-1-1 00:00:00</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>project_id :</span></dt><dd>CMIP6</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>references :</span></dt><dd>MPI-ESM: Mauritsen, T. et al. (2019), Developments in the MPI‐M Earth System Model version 1.2 (MPI‐ESM1.2) and Its Response to Increasing CO2, J. Adv. Model. Earth Syst.,11, 998-1038, doi:10.1029/2018MS001400,\n",
"Mueller, W.A. et al. (2018): A high‐resolution version of the Max Planck Institute Earth System Model MPI‐ESM1.2‐HR. J. Adv. Model. EarthSyst.,10,1383–1413, doi:10.1029/2017MS001217</dd><dt><span>source :</span></dt><dd>MPI-ESM1.2-HR (2017): \n",
"aerosol: none, prescribed MACv2-SP\n",
"atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa)\n",
"atmosChem: none\n",
"land: JSBACH3.20\n",
"landIce: none/prescribed\n",
"ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m)\n",
"ocnBgchem: HAMOCC6\n",
"seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model)</dd><dt><span>source_id :</span></dt><dd>MPI-ESM1-2-HR</dd><dt><span>source_type :</span></dt><dd>AOGCM</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Amon</dd><dt><span>table_info :</span></dt><dd>Creation Date:(09 May 2019) MD5:e6ef8ececc8f338646ebfb3aeed36bfc</dd><dt><span>title :</span></dt><dd>MPI-ESM1-2-HR output prepared for CMIP6</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by DKRZ is licensed under a Creative Commons Attribution ShareAlike 4.0 International License (https://creativecommons.org/licenses). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file) and. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>cmor_version :</span></dt><dd>3.4.0</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/d6f0f6b2-b4c2-460b-bc5e-f1c72f932399</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset> Size: 18MB\n",
"Dimensions: (lat: 192, bnds: 2, lon: 384, time: 60)\n",
"Coordinates:\n",
" lat_bnds (lat, bnds) float64 3kB -89.75 -88.82 -88.82 ... 88.82 89.75\n",
" lon_bnds (lon, bnds) float64 6kB -0.4688 0.4688 0.4688 ... 358.6 359.5\n",
" height float64 8B 2.0\n",
" * lat (lat) float64 2kB -89.28 -88.36 -87.42 ... 87.42 88.36 89.28\n",
" * lon (lon) float64 3kB 0.0 0.9375 1.875 2.812 ... 357.2 358.1 359.1\n",
" * time (time) datetime64[ns] 480B 2015-01-16T12:00:00 ... 2019-12-16T...\n",
"Dimensions without coordinates: bnds\n",
"Data variables:\n",
" tas (time, lat, lon) float32 18MB 243.3 243.3 243.3 ... 251.1 251.1\n",
" time_bnds (time, bnds) datetime64[ns] 960B 2015-01-01 ... 2020-01-01\n",
"Attributes: (12/47)\n",
" Conventions: CF-1.7 CMIP-6.2\n",
" activity_id: ScenarioMIP AerChemMIP\n",
" branch_method: standard\n",
" branch_time_in_child: 60265.0\n",
" branch_time_in_parent: 60265.0\n",
" contact: cmip6-mpi-esm@dkrz.de\n",
" ... ...\n",
" title: MPI-ESM1-2-HR output prepared for CMIP6\n",
" variable_id: tas\n",
" variant_label: r1i1p1f1\n",
" license: CMIP6 model data produced by DKRZ is licensed und...\n",
" cmor_version: 3.4.0\n",
" tracking_id: hdl:21.14100/d6f0f6b2-b4c2-460b-bc5e-f1c72f932399"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import xarray as xr\n",
"xr.open_dataset(\"lossy_compressed.nc\").load()"
]
},
{
"cell_type": "markdown",
"id": "49a2b8a1-6533-421c-bfdd-fc814559e4d7",
"metadata": {},
"source": [
"We can see that\n",
"\n",
"- uncompressed data is 16MB, compressed is 6MB, lossy compressed is 4MB.\n",
"- the accuray is on the second decimal: 243.30435 becomes 243.3125"
]
},
{
"cell_type": "markdown",
"id": "040bc746-f6ca-4a42-a867-2f4182f36fa9",
"metadata": {},
"source": [
"### Interest in a region of global ESM output stored in records\n",
"\n",
"A \"record\" refers to the GRIB-record where the entire global field is stored as one binary chunk. A chunk is the most fine-grained level of data access possible.\n",
"\n",
"Especially for high-resolution data, clients may not want to retrieve the full entity of a storage chunk, e.g. an entire global field or an entire month on hourly data, because this results in large data volumes. In such cases, where we know the data access *pattern*, we can adapt the chunk setting of our provided zarr data set to the use case. The cloudify service allows to provide smaller chunk sizes to clients than the orignal storage chunk sizes by providing some server-side computing resources for rechunking and acting as an intermediate layer between data and client. Although the splitting of storage chunks reduces performance on the server, the benefit of bandwidth reduction can be more important."
]
},
{
"cell_type": "markdown",
"id": "0ab1274e-433c-49e8-825f-6600888ad3e6",
"metadata": {},
"source": [
"**Chunk setting**\n",
"\n",
"Best practice is to set the chunks when opening the data. For our test zarr dataset, we split the chunks in both spatial dimensions into half of the original. In each dimension, a chunk needs to cover at least 1 increment i.e. has to be an integer > 0. \n",
"\n",
"With the `chunks` keyword in the `open_mfdataset` command, it is controlled how the dataset is chunked. These chunks are mapped to *zarr* chunks of the *zarr* API. \n",
"\n",
"In our example script, we control that chunk setting through specific environment variables:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "84fbf2ff-f626-488a-87ee-fc62492274ea",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"192 96\n"
]
}
],
"source": [
"new_lon_chunk_size=int(len(ds[\"lon\"])/2)\n",
"new_lat_chunk_size=int(len(ds[\"lat\"])/2)\n",
"print(new_lon_chunk_size, new_lat_chunk_size)"
]
},
{
"cell_type": "markdown",
"id": "a7ed2bfb-3254-4a13-ae99-3c3caccb0f6a",
"metadata": {},
"source": [
"We set these as environment variables and restart our app which finds these."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "b702311b-bde0-4587-a823-dc61d0c65d51",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"os.environ[\"XPUBLISH_LON_CHUNK_SIZE\"]=str(new_lon_chunk_size)\n",
"os.environ[\"XPUBLISH_LAT_CHUNK_SIZE\"]=str(new_lat_chunk_size)\n",
"#to get back storage chunks:\n",
"#del os.environ[\"XPUBLISH_LON_CHUNK_SIZE\"]\n",
"#del os.environ[\"XPUBLISH_LAT_CHUNK_SIZE\"]\n",
"\n",
"#kill the existing process"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "a74dc51c-f9ce-439c-8217-1724ae32009c",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"k204210 52510 4121939 0 10:12 ? 00:00:01 /work/bm0021/conda-envs/cloudify/bin/python -Xfrozen_modules=off -m ipykernel_launcher -f /home/k/k204210/.local/share/jupyter/runtime/kernel-c6562e74-6d50-4cf3-92d0-27e4f4cae6f8.json\n",
"k204210 56270 4121939 0 10:18 ? 00:00:07 /work/bm0021/conda-envs/cloudify/bin/python -Xfrozen_modules=off -m ipykernel_launcher -f /home/k/k204210/.local/share/jupyter/runtime/kernel-e22a5303-3b28-41cb-b092-ed92a8ff6221.json\n",
"k204210 67839 4121939 0 10:42 ? 00:00:13 /work/bm0021/conda-envs/cloudify/bin/python -Xfrozen_modules=off -m ipykernel_launcher -f /home/k/k204210/.local/share/jupyter/runtime/kernel-33489dce-534b-42ed-bbe7-6d125e3f6167.json\n",
"k204210 178115 177492 0 11:23 ? 00:00:00 /work/bm0021/conda-envs/cloudify/bin/python -c from multiprocessing.resource_tracker import main;main(27)\n",
"k204210 178117 177492 4 11:23 ? 00:00:05 /work/bm0021/conda-envs/cloudify/bin/python -c from multiprocessing.spawn import spawn_main; spawn_main(tracker_fd=28, pipe_handle=38) --multiprocessing-fork\n",
"k204210 178119 177492 4 11:23 ? 00:00:05 /work/bm0021/conda-envs/cloudify/bin/python -c from multiprocessing.spawn import spawn_main; spawn_main(tracker_fd=28, pipe_handle=44) --multiprocessing-fork\n",
"k204210 179053 67839 0 11:25 pts/2 00:00:00 /bin/bash -c ps -ef | grep cloudify\n",
"k204210 179055 179053 0 11:25 pts/2 00:00:00 grep cloudify\n",
"k204210 4125444 4121939 0 08:57 ? 00:00:01 /work/bm0021/conda-envs/cloudify/bin/python -Xfrozen_modules=off -m ipykernel_launcher -f /home/k/k204210/.local/share/jupyter/runtime/kernel-f2ebf347-166b-4e0f-89ee-e7e663b08a4c.json\n",
"k204210 4125453 4121939 0 08:57 ? 00:00:01 /work/bm0021/conda-envs/cloudify/bin/python -Xfrozen_modules=off -m ipykernel_launcher -f /home/k/k204210/.local/share/jupyter/runtime/kernel-16ae079b-b9fb-4460-bc7c-808797637e88.json\n"
]
}
],
"source": [
"!ps -ef | grep cloudify"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "7cb479a6-366b-4a5c-908b-a649a6c0f057",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"!kill 177492"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "ba0e00cc-e6a3-42ac-bd50-a068d93166d2",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"%%bash --bg\n",
"source activate /work/bm0021/conda-envs/cloudify\n",
"python xpublish_example.py \\\n",
" example \\\n",
" /work/ik1017/CMIP6/data/CMIP6/ScenarioMIP/DKRZ/MPI-ESM1-2-HR/ssp370/r1i1p1f1/Amon/tas/gn/v20190710/*.nc"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "35592ae1-4873-4d08-bdae-b28130e924b6",
"metadata": {
"tags": []
},
"outputs": [
{
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" --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
" --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
" --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
" --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
" --xr-background-color: var(--jp-layout-color0, white);\n",
" --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
" --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
"}\n",
"\n",
"html[theme=dark],\n",
"html[data-theme=dark],\n",
"body[data-theme=dark],\n",
"body.vscode-dark {\n",
" --xr-font-color0: rgba(255, 255, 255, 1);\n",
" --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
" --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
" --xr-border-color: #1F1F1F;\n",
" --xr-disabled-color: #515151;\n",
" --xr-background-color: #111111;\n",
" --xr-background-color-row-even: #111111;\n",
" --xr-background-color-row-odd: #313131;\n",
"}\n",
"\n",
".xr-wrap {\n",
" display: block !important;\n",
" min-width: 300px;\n",
" max-width: 700px;\n",
"}\n",
"\n",
".xr-text-repr-fallback {\n",
" /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
" display: none;\n",
"}\n",
"\n",
".xr-header {\n",
" padding-top: 6px;\n",
" padding-bottom: 6px;\n",
" margin-bottom: 4px;\n",
" border-bottom: solid 1px var(--xr-border-color);\n",
"}\n",
"\n",
".xr-header > div,\n",
".xr-header > ul {\n",
" display: inline;\n",
" 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",
"}\n",
"\n",
".xr-var-name {\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-var-dims {\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-var-dtype {\n",
" grid-column: 3;\n",
" text-align: right;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-preview {\n",
" grid-column: 4;\n",
"}\n",
"\n",
".xr-index-preview {\n",
" grid-column: 2 / 5;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-name,\n",
".xr-var-dims,\n",
".xr-var-dtype,\n",
".xr-preview,\n",
".xr-attrs dt {\n",
" white-space: nowrap;\n",
" overflow: hidden;\n",
" text-overflow: ellipsis;\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-var-name:hover,\n",
".xr-var-dims:hover,\n",
".xr-var-dtype:hover,\n",
".xr-attrs dt:hover {\n",
" overflow: visible;\n",
" width: auto;\n",
" z-index: 1;\n",
"}\n",
"\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
" padding-bottom: 5px !important;\n",
"}\n",
"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data,\n",
".xr-index-data-in:checked ~ .xr-index-data {\n",
" display: block;\n",
"}\n",
"\n",
".xr-var-data > table {\n",
" float: right;\n",
"}\n",
"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-index-name div,\n",
".xr-index-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
".xr-attrs dt,\n",
".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2,\n",
".xr-no-icon {\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'><xarray.Dataset> Size: 314MB\n",
"Dimensions: (lat: 192, time: 1032, bnds: 2, lon: 384)\n",
"Coordinates:\n",
" height float64 8B ...\n",
" * lat (lat) float64 2kB -89.28 -88.36 -87.42 ... 87.42 88.36 89.28\n",
" * lon (lon) float64 3kB 0.0 0.9375 1.875 2.812 ... 357.2 358.1 359.1\n",
" * time (time) datetime64[ns] 8kB 2015-01-16T12:00:00 ... 2100-12-16T1...\n",
"Dimensions without coordinates: bnds\n",
"Data variables:\n",
" lat_bnds (time, lat, bnds) float64 3MB dask.array<chunksize=(60, 96, 2), meta=np.ndarray>\n",
" lon_bnds (time, lon, bnds) float64 6MB dask.array<chunksize=(60, 192, 2), meta=np.ndarray>\n",
" tas (time, lat, lon) float32 304MB dask.array<chunksize=(1, 96, 192), meta=np.ndarray>\n",
" time_bnds (time, bnds) datetime64[ns] 17kB dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
"Attributes: (12/47)\n",
" Conventions: CF-1.7 CMIP-6.2\n",
" activity_id: ScenarioMIP AerChemMIP\n",
" branch_method: standard\n",
" branch_time_in_child: 60265.0\n",
" branch_time_in_parent: 60265.0\n",
" contact: cmip6-mpi-esm@dkrz.de\n",
" ... ...\n",
" title: MPI-ESM1-2-HR output prepared for CMIP6\n",
" variable_id: tas\n",
" variant_label: r1i1p1f1\n",
" license: CMIP6 model data produced by DKRZ is licensed und...\n",
" cmor_version: 3.4.0\n",
" tracking_id: hdl:21.14100/d6f0f6b2-b4c2-460b-bc5e-f1c72f932399</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-3a6e6e10-8dc2-4c75-9a5c-47305944f570' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3a6e6e10-8dc2-4c75-9a5c-47305944f570' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>time</span>: 1032</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lon</span>: 384</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d08b7d97-5f08-49c1-8dd1-45d77cfcec4f' class='xr-section-summary-in' type='checkbox' checked><label for='section-d08b7d97-5f08-49c1-8dd1-45d77cfcec4f' 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>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-91909b37-0942-432c-bae4-b10cd2fa0b0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-91909b37-0942-432c-bae4-b10cd2fa0b0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83e94d8d-37c0-40f6-8420-9f07a8ba8257' class='xr-var-data-in' type='checkbox'><label for='data-83e94d8d-37c0-40f6-8420-9f07a8ba8257' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><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'>-89.28 -88.36 ... 88.36 89.28</div><input id='attrs-00419d93-1d85-4f5b-9a67-b52c148541e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00419d93-1d85-4f5b-9a67-b52c148541e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cfc88aef-5d38-4e94-aaa4-331516c06154' class='xr-var-data-in' type='checkbox'><label for='data-cfc88aef-5d38-4e94-aaa4-331516c06154' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-89.284228, -88.357004, -87.424304, -86.490367, -85.55596 , -84.621327,\n",
" -83.686567, -82.751728, -81.816839, -80.881913, -79.946962, -79.011992,\n",
" -78.077007, -77.142011, -76.207005, -75.271992, -74.336973, -73.401949,\n",
" -72.466921, -71.531889, -70.596854, -69.661816, -68.726776, -67.791734,\n",
" -66.856691, -65.921645, -64.986599, -64.051551, -63.116502, -62.181452,\n",
" -61.246401, -60.311349, -59.376297, -58.441244, -57.50619 , -56.571136,\n",
" -55.636081, -54.701026, -53.76597 , -52.830914, -51.895857, -50.960801,\n",
" -50.025743, -49.090686, -48.155628, -47.22057 , -46.285512, -45.350454,\n",
" -44.415395, -43.480336, -42.545277, -41.610218, -40.675159, -39.740099,\n",
" -38.805039, -37.86998 , -36.93492 , -35.99986 , -35.064799, -34.129739,\n",
" -33.194679, -32.259618, -31.324558, -30.389497, -29.454436, -28.519375,\n",
" -27.584315, -26.649254, -25.714193, -24.779132, -23.84407 , -22.909009,\n",
" -21.973948, -21.038887, -20.103825, -19.168764, -18.233703, -17.298641,\n",
" -16.36358 , -15.428518, -14.493457, -13.558395, -12.623333, -11.688272,\n",
" -10.75321 , -9.818148, -8.883087, -7.948025, -7.012963, -6.077901,\n",
" -5.14284 , -4.207778, -3.272716, -2.337654, -1.402593, -0.467531,\n",
" 0.467531, 1.402593, 2.337654, 3.272716, 4.207778, 5.14284 ,\n",
" 6.077901, 7.012963, 7.948025, 8.883087, 9.818148, 10.75321 ,\n",
" 11.688272, 12.623333, 13.558395, 14.493457, 15.428518, 16.36358 ,\n",
" 17.298641, 18.233703, 19.168764, 20.103825, 21.038887, 21.973948,\n",
" 22.909009, 23.84407 , 24.779132, 25.714193, 26.649254, 27.584315,\n",
" 28.519375, 29.454436, 30.389497, 31.324558, 32.259618, 33.194679,\n",
" 34.129739, 35.064799, 35.99986 , 36.93492 , 37.86998 , 38.805039,\n",
" 39.740099, 40.675159, 41.610218, 42.545277, 43.480336, 44.415395,\n",
" 45.350454, 46.285512, 47.22057 , 48.155628, 49.090686, 50.025743,\n",
" 50.960801, 51.895857, 52.830914, 53.76597 , 54.701026, 55.636081,\n",
" 56.571136, 57.50619 , 58.441244, 59.376297, 60.311349, 61.246401,\n",
" 62.181452, 63.116502, 64.051551, 64.986599, 65.921645, 66.856691,\n",
" 67.791734, 68.726776, 69.661816, 70.596854, 71.531889, 72.466921,\n",
" 73.401949, 74.336973, 75.271992, 76.207005, 77.142011, 78.077007,\n",
" 79.011992, 79.946962, 80.881913, 81.816839, 82.751728, 83.686567,\n",
" 84.621327, 85.55596 , 86.490367, 87.424304, 88.357004, 89.284228])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.9375 1.875 ... 358.1 359.1</div><input id='attrs-66096e25-939f-4aaf-99b6-82ce94d61053' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66096e25-939f-4aaf-99b6-82ce94d61053' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59cb0e9c-2b4a-4685-9d9b-b9df4611b4d5' class='xr-var-data-in' type='checkbox'><label for='data-59cb0e9c-2b4a-4685-9d9b-b9df4611b4d5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([ 0. , 0.9375, 1.875 , ..., 357.1875, 358.125 , 359.0625])</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'>2015-01-16T12:00:00 ... 2100-12-...</div><input id='attrs-a92386a4-795c-4d3d-8b22-0e4909e69c03' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a92386a4-795c-4d3d-8b22-0e4909e69c03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d0e964b-458e-4973-947c-7d2a2ef69139' class='xr-var-data-in' type='checkbox'><label for='data-8d0e964b-458e-4973-947c-7d2a2ef69139' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(['2015-01-16T12:00:00.000000000', '2015-02-15T00:00:00.000000000',\n",
" '2015-03-16T12:00:00.000000000', ..., '2100-10-16T12:00:00.000000000',\n",
" '2100-11-16T00:00:00.000000000', '2100-12-16T12:00:00.000000000'],\n",
" dtype='datetime64[ns]')</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-54c21586-9046-4874-86d8-e28ce18ed563' class='xr-section-summary-in' type='checkbox' checked><label for='section-54c21586-9046-4874-86d8-e28ce18ed563' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(time, lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(60, 96, 2), meta=np.ndarray></div><input id='attrs-06d83fde-c3cb-4a76-b40b-35e97daee2e1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-06d83fde-c3cb-4a76-b40b-35e97daee2e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b87a7ef-ade6-477f-8a65-e2452a4bf391' class='xr-var-data-in' type='checkbox'><label for='data-1b87a7ef-ade6-477f-8a65-e2452a4bf391' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bnds</span></div><div class='xr-var-dims'>(time, lon, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(60, 192, 2), meta=np.ndarray></div><input id='attrs-3a179d0b-84ea-47b1-9803-564bf4e9f65d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3a179d0b-84ea-47b1-9803-564bf4e9f65d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb9c8bcd-a654-43ba-b3d1-5166fd55427b' class='xr-var-data-in' type='checkbox'><label for='data-bb9c8bcd-a654-43ba-b3d1-5166fd55427b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tas</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1, 96, 192), meta=np.ndarray></div><input id='attrs-4b5da8f2-8ced-4ea9-8228-8aaeb3690964' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b5da8f2-8ced-4ea9-8228-8aaeb3690964' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc06b985-37ad-42e3-bfac-f8e9db2ad3a1' class='xr-var-data-in' type='checkbox'><label for='data-dc06b985-37ad-42e3-bfac-f8e9db2ad3a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>comment :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2019-07-20T13:41:51Z altered by CMOR: Treated scalar dimension: 'height'. 2019-07-20T13:41:51Z altered by CMOR: replaced missing value flag (-9e+33) with standard missing value (1e+20). 2019-07-20T13:41:51Z altered by CMOR: Converted type from 'd' to 'f'. 2019-07-20T13:41:52Z altered by CMOR: Inverted axis: lat.</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
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" <tr>\n",
" <th> Bytes </th>\n",
" <td> 290.25 MiB </td>\n",
" <td> 72.00 kiB </td>\n",
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" <td colspan=\"2\"> 4128 chunks in 2 graph layers </td>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1, 2), meta=np.ndarray></div><input id='attrs-4aa76449-35d0-43d9-92be-81414d598163' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4aa76449-35d0-43d9-92be-81414d598163' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93d569c2-e77e-4596-84f8-5a7ac15d208d' class='xr-var-data-in' type='checkbox'><label for='data-93d569c2-e77e-4596-84f8-5a7ac15d208d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
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" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 16.12 kiB </td>\n",
" <td> 16 B </td>\n",
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" \n",
" <tr>\n",
" <th> Shape </th>\n",
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" <td> (1, 2) </td>\n",
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" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 1032 chunks in 2 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
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" </tbody>\n",
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"\n",
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"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-f9310394-cffc-4e7c-8454-6e2999a00dff' class='xr-section-summary-in' type='checkbox' ><label for='section-f9310394-cffc-4e7c-8454-6e2999a00dff' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-efbeb39d-84ef-4af6-b3b2-a33d8e32986c' class='xr-index-data-in' type='checkbox'/><label for='index-efbeb39d-84ef-4af6-b3b2-a33d8e32986c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-89.28422753251364, -88.35700351866494, -87.42430374606988,\n",
" -86.49036676628116, -85.55596048489265, -84.6213271076488,\n",
" -83.68656681656385, -82.75172847343066, -81.81683872860319,\n",
" -80.8819133467975,\n",
" ...\n",
" 80.8819133467975, 81.81683872860319, 82.75172847343066,\n",
" 83.68656681656385, 84.6213271076488, 85.55596048489265,\n",
" 86.49036676628116, 87.42430374606988, 88.35700351866494,\n",
" 89.28422753251364],\n",
" dtype='float64', name='lat', length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-40c4506b-5073-4e31-b9c3-45bc7bf3e821' class='xr-index-data-in' type='checkbox'/><label for='index-40c4506b-5073-4e31-b9c3-45bc7bf3e821' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0.0, 0.9375, 1.875, 2.8125, 3.75, 4.6875, 5.625,\n",
" 6.5625, 7.5, 8.4375,\n",
" ...\n",
" 350.625, 351.5625, 352.5, 353.4375, 354.375, 355.3125, 356.25,\n",
" 357.1875, 358.125, 359.0625],\n",
" dtype='float64', name='lon', length=384))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7e703175-8bbf-4278-a48f-c5975eb5db8f' class='xr-index-data-in' type='checkbox'/><label for='index-7e703175-8bbf-4278-a48f-c5975eb5db8f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex(['2015-01-16 12:00:00', '2015-02-15 00:00:00',\n",
" '2015-03-16 12:00:00', '2015-04-16 00:00:00',\n",
" '2015-05-16 12:00:00', '2015-06-16 00:00:00',\n",
" '2015-07-16 12:00:00', '2015-08-16 12:00:00',\n",
" '2015-09-16 00:00:00', '2015-10-16 12:00:00',\n",
" ...\n",
" '2100-03-16 12:00:00', '2100-04-16 00:00:00',\n",
" '2100-05-16 12:00:00', '2100-06-16 00:00:00',\n",
" '2100-07-16 12:00:00', '2100-08-16 12:00:00',\n",
" '2100-09-16 00:00:00', '2100-10-16 12:00:00',\n",
" '2100-11-16 00:00:00', '2100-12-16 12:00:00'],\n",
" dtype='datetime64[ns]', name='time', length=1032, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8e5a92c9-2ff6-49d5-bb35-efacc97ef7ea' class='xr-section-summary-in' type='checkbox' ><label for='section-8e5a92c9-2ff6-49d5-bb35-efacc97ef7ea' class='xr-section-summary' >Attributes: <span>(47)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>ScenarioMIP AerChemMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>60265.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>60265.0</dd><dt><span>contact :</span></dt><dd>cmip6-mpi-esm@dkrz.de</dd><dt><span>creation_date :</span></dt><dd>2019-07-20T13:41:51Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.30</dd><dt><span>experiment :</span></dt><dd>gap-filling scenario reaching 7.0 based on SSP3</dd><dt><span>experiment_id :</span></dt><dd>ssp370</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.DKRZ.MPI-ESM1-2-HR.ssp370.none.r1i1p1f1</dd><dt><span>grid :</span></dt><dd>gn</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>2019-07-20T13:41:51Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>Deutsches Klimarechenzentrum, Hamburg 20146, Germany</dd><dt><span>institution_id :</span></dt><dd>DKRZ</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>100 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>historical</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>MPI-ESM1-2-HR</dd><dt><span>parent_time_units :</span></dt><dd>days since 1850-1-1 00:00:00</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>project_id :</span></dt><dd>CMIP6</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>references :</span></dt><dd>MPI-ESM: Mauritsen, T. et al. (2019), Developments in the MPI‐M Earth System Model version 1.2 (MPI‐ESM1.2) and Its Response to Increasing CO2, J. Adv. Model. Earth Syst.,11, 998-1038, doi:10.1029/2018MS001400,\n",
"Mueller, W.A. et al. (2018): A high‐resolution version of the Max Planck Institute Earth System Model MPI‐ESM1.2‐HR. J. Adv. Model. EarthSyst.,10,1383–1413, doi:10.1029/2017MS001217</dd><dt><span>source :</span></dt><dd>MPI-ESM1.2-HR (2017): \n",
"aerosol: none, prescribed MACv2-SP\n",
"atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa)\n",
"atmosChem: none\n",
"land: JSBACH3.20\n",
"landIce: none/prescribed\n",
"ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m)\n",
"ocnBgchem: HAMOCC6\n",
"seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model)</dd><dt><span>source_id :</span></dt><dd>MPI-ESM1-2-HR</dd><dt><span>source_type :</span></dt><dd>AOGCM</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Amon</dd><dt><span>table_info :</span></dt><dd>Creation Date:(09 May 2019) MD5:e6ef8ececc8f338646ebfb3aeed36bfc</dd><dt><span>title :</span></dt><dd>MPI-ESM1-2-HR output prepared for CMIP6</dd><dt><span>variable_id :</span></dt><dd>tas</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by DKRZ is licensed under a Creative Commons Attribution ShareAlike 4.0 International License (https://creativecommons.org/licenses). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file) and. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>cmor_version :</span></dt><dd>3.4.0</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/d6f0f6b2-b4c2-460b-bc5e-f1c72f932399</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset> Size: 314MB\n",
"Dimensions: (lat: 192, time: 1032, bnds: 2, lon: 384)\n",
"Coordinates:\n",
" height float64 8B ...\n",
" * lat (lat) float64 2kB -89.28 -88.36 -87.42 ... 87.42 88.36 89.28\n",
" * lon (lon) float64 3kB 0.0 0.9375 1.875 2.812 ... 357.2 358.1 359.1\n",
" * time (time) datetime64[ns] 8kB 2015-01-16T12:00:00 ... 2100-12-16T1...\n",
"Dimensions without coordinates: bnds\n",
"Data variables:\n",
" lat_bnds (time, lat, bnds) float64 3MB dask.array<chunksize=(60, 96, 2), meta=np.ndarray>\n",
" lon_bnds (time, lon, bnds) float64 6MB dask.array<chunksize=(60, 192, 2), meta=np.ndarray>\n",
" tas (time, lat, lon) float32 304MB dask.array<chunksize=(1, 96, 192), meta=np.ndarray>\n",
" time_bnds (time, bnds) datetime64[ns] 17kB dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
"Attributes: (12/47)\n",
" Conventions: CF-1.7 CMIP-6.2\n",
" activity_id: ScenarioMIP AerChemMIP\n",
" branch_method: standard\n",
" branch_time_in_child: 60265.0\n",
" branch_time_in_parent: 60265.0\n",
" contact: cmip6-mpi-esm@dkrz.de\n",
" ... ...\n",
" title: MPI-ESM1-2-HR output prepared for CMIP6\n",
" variable_id: tas\n",
" variant_label: r1i1p1f1\n",
" license: CMIP6 model data produced by DKRZ is licensed und...\n",
" cmor_version: 3.4.0\n",
" tracking_id: hdl:21.14100/d6f0f6b2-b4c2-460b-bc5e-f1c72f932399"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds2=xr.open_zarr(\n",
" zarr_url,\n",
" consolidated=True,\n",
" storage_options=storage_options,\n",
" chunks={}\n",
")\n",
"ds2"
]
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"source": [
"### Towards virtually, highly aggregated datasets that include multiple variables of the same kind\n",
"\n",
"Based on experience, large aggregations are beneficial for data analysis as users can skip finding and merging data sources. E.g. to train an AI model, it helps to simplify the random access within a complete experiment. With cloudify, we can realize a virtual, highly aggregated, dataset that covers the full time series of all variables that share dimensions.\n",
"\n",
"In our example, we can try to concat and merge *all* monthly atmospheric variables more variables. For that, we use a wildcard for variables in the DRS path."
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "806f61be-1f47-4aa5-a029-b2d28e9bac55",
"metadata": {
"tags": []
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"k204210 52510 4121939 0 10:12 ? 00:00:01 /work/bm0021/conda-envs/cloudify/bin/python -Xfrozen_modules=off -m ipykernel_launcher -f /home/k/k204210/.local/share/jupyter/runtime/kernel-c6562e74-6d50-4cf3-92d0-27e4f4cae6f8.json\n",
"k204210 56270 4121939 0 10:18 ? 00:00:07 /work/bm0021/conda-envs/cloudify/bin/python -Xfrozen_modules=off -m ipykernel_launcher -f /home/k/k204210/.local/share/jupyter/runtime/kernel-e22a5303-3b28-41cb-b092-ed92a8ff6221.json\n",
"k204210 67839 4121939 0 10:42 ? 00:00:12 /work/bm0021/conda-envs/cloudify/bin/python -Xfrozen_modules=off -m ipykernel_launcher -f /home/k/k204210/.local/share/jupyter/runtime/kernel-33489dce-534b-42ed-bbe7-6d125e3f6167.json\n",
"k204210 178115 177492 0 11:23 ? 00:00:00 /work/bm0021/conda-envs/cloudify/bin/python -c from multiprocessing.resource_tracker import main;main(27)\n",
"k204210 178117 177492 19 11:23 ? 00:00:03 /work/bm0021/conda-envs/cloudify/bin/python -c from multiprocessing.spawn import spawn_main; spawn_main(tracker_fd=28, pipe_handle=38) --multiprocessing-fork\n",
"k204210 178119 177492 19 11:23 ? 00:00:03 /work/bm0021/conda-envs/cloudify/bin/python -c from multiprocessing.spawn import spawn_main; spawn_main(tracker_fd=28, pipe_handle=44) --multiprocessing-fork\n",
"k204210 178190 67839 0 11:23 pts/2 00:00:00 /bin/bash -c ps -ef | grep cloudify\n",
"k204210 178192 178190 0 11:23 pts/2 00:00:00 grep cloudify\n",
"k204210 4125444 4121939 0 08:57 ? 00:00:01 /work/bm0021/conda-envs/cloudify/bin/python -Xfrozen_modules=off -m ipykernel_launcher -f /home/k/k204210/.local/share/jupyter/runtime/kernel-f2ebf347-166b-4e0f-89ee-e7e663b08a4c.json\n",
"k204210 4125453 4121939 0 08:57 ? 00:00:01 /work/bm0021/conda-envs/cloudify/bin/python -Xfrozen_modules=off -m ipykernel_launcher -f /home/k/k204210/.local/share/jupyter/runtime/kernel-16ae079b-b9fb-4460-bc7c-808797637e88.json\n"
]
}
],
"source": [
"!ps -ef | grep cloudify"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "a34f6b2f-70f5-4c6d-8c27-4f5bb66fe446",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"!kill 142303"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "47ce9108-5ec3-48f4-88aa-a5991b8dbe18",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"#to get back storage chunks:\n",
"del os.environ[\"XPUBLISH_LON_CHUNK_SIZE\"]\n",
"del os.environ[\"XPUBLISH_LAT_CHUNK_SIZE\"]\n",
"del os.environ[\"L_LOSSY\"]"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "892786b9-ca5b-40aa-8937-40be7e5c56a8",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"%%bash --bg\n",
"source activate /work/bm0021/conda-envs/cloudify\n",
"python xpublish_example.py \\\n",
" example \\\n",
" /work/ik1017/CMIP6/data/CMIP6/ScenarioMIP/DKRZ/MPI-ESM1-2-HR/ssp370/r1i1p1f1/Amon/*/gn/v20190710/*.nc"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "8979a320-71d3-4c80-a7d8-f130b9de2e71",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"ds=xr.open_zarr(\n",
" zarr_url,\n",
" consolidated=True,\n",
" storage_options=storage_options\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "a624fa43-e9e9-4c56-b697-7c8820592482",
"metadata": {},
"outputs": [
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"</style><pre class='xr-text-repr-fallback'><xarray.Dataset> Size: 144GB\n",
"Dimensions: (time: 1032, lev: 95, bnds: 2, lat: 192, lon: 384, plev: 19)\n",
"Coordinates:\n",
" height float64 8B ...\n",
" * lat (lat) float64 2kB -89.28 -88.36 -87.42 ... 87.42 88.36 89.28\n",
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"Dimensions without coordinates: bnds\n",
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" cct (time, lat, lon) float32 304MB dask.array<chunksize=(1, 192, 384), meta=np.ndarray>\n",
" cl (time, lev, lat, lon) float32 29GB dask.array<chunksize=(1, 48, 96, 192), meta=np.ndarray>\n",
" ... ...\n",
" ua (time, plev, lat, lon) float32 6GB dask.array<chunksize=(1, 10, 96, 192), meta=np.ndarray>\n",
" uas (time, lat, lon) float32 304MB dask.array<chunksize=(1, 192, 384), meta=np.ndarray>\n",
" va (time, plev, lat, lon) float32 6GB dask.array<chunksize=(1, 10, 96, 192), meta=np.ndarray>\n",
" vas (time, lat, lon) float32 304MB dask.array<chunksize=(1, 192, 384), meta=np.ndarray>\n",
" wap (time, plev, lat, lon) float32 6GB dask.array<chunksize=(1, 10, 96, 192), meta=np.ndarray>\n",
" zg (time, plev, lat, lon) float32 6GB dask.array<chunksize=(1, 10, 96, 192), meta=np.ndarray>\n",
"Attributes: (12/47)\n",
" Conventions: CF-1.7 CMIP-6.2\n",
" activity_id: ScenarioMIP AerChemMIP\n",
" branch_method: standard\n",
" branch_time_in_child: 60265.0\n",
" branch_time_in_parent: 60265.0\n",
" contact: cmip6-mpi-esm@dkrz.de\n",
" ... ...\n",
" title: MPI-ESM1-2-HR output prepared for CMIP6\n",
" variable_id: cl\n",
" variant_label: r1i1p1f1\n",
" license: CMIP6 model data produced by DKRZ is licensed und...\n",
" cmor_version: 3.4.0\n",
" tracking_id: hdl:21.14100/e496e673-c31f-4e03-a4fe-7f09115fed79</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-32e4b97a-9d1a-4991-ab5b-787b18f50201' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-32e4b97a-9d1a-4991-ab5b-787b18f50201' 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>: 1032</li><li><span class='xr-has-index'>lev</span>: 95</li><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 384</li><li><span class='xr-has-index'>plev</span>: 19</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2accbae7-6971-4fbf-a641-bff1022cd21e' class='xr-section-summary-in' type='checkbox' checked><label for='section-2accbae7-6971-4fbf-a641-bff1022cd21e' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2e1c0519-c768-4c73-b12d-9df04f3c06b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e1c0519-c768-4c73-b12d-9df04f3c06b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a807c550-ce4d-4d74-acbf-817b13329905' class='xr-var-data-in' type='checkbox'><label for='data-a807c550-ce4d-4d74-acbf-817b13329905' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><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'>-89.28 -88.36 ... 88.36 89.28</div><input id='attrs-1053b4b3-079b-4eb4-b378-4bd54cb7096f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1053b4b3-079b-4eb4-b378-4bd54cb7096f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-947f95d8-defb-4305-b872-3196d393cd87' class='xr-var-data-in' type='checkbox'><label for='data-947f95d8-defb-4305-b872-3196d393cd87' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-89.284228, -88.357004, -87.424304, -86.490367, -85.55596 , -84.621327,\n",
" -83.686567, -82.751728, -81.816839, -80.881913, -79.946962, -79.011992,\n",
" -78.077007, -77.142011, -76.207005, -75.271992, -74.336973, -73.401949,\n",
" -72.466921, -71.531889, -70.596854, -69.661816, -68.726776, -67.791734,\n",
" -66.856691, -65.921645, -64.986599, -64.051551, -63.116502, -62.181452,\n",
" -61.246401, -60.311349, -59.376297, -58.441244, -57.50619 , -56.571136,\n",
" -55.636081, -54.701026, -53.76597 , -52.830914, -51.895857, -50.960801,\n",
" -50.025743, -49.090686, -48.155628, -47.22057 , -46.285512, -45.350454,\n",
" -44.415395, -43.480336, -42.545277, -41.610218, -40.675159, -39.740099,\n",
" -38.805039, -37.86998 , -36.93492 , -35.99986 , -35.064799, -34.129739,\n",
" -33.194679, -32.259618, -31.324558, -30.389497, -29.454436, -28.519375,\n",
" -27.584315, -26.649254, -25.714193, -24.779132, -23.84407 , -22.909009,\n",
" -21.973948, -21.038887, -20.103825, -19.168764, -18.233703, -17.298641,\n",
" -16.36358 , -15.428518, -14.493457, -13.558395, -12.623333, -11.688272,\n",
" -10.75321 , -9.818148, -8.883087, -7.948025, -7.012963, -6.077901,\n",
" -5.14284 , -4.207778, -3.272716, -2.337654, -1.402593, -0.467531,\n",
" 0.467531, 1.402593, 2.337654, 3.272716, 4.207778, 5.14284 ,\n",
" 6.077901, 7.012963, 7.948025, 8.883087, 9.818148, 10.75321 ,\n",
" 11.688272, 12.623333, 13.558395, 14.493457, 15.428518, 16.36358 ,\n",
" 17.298641, 18.233703, 19.168764, 20.103825, 21.038887, 21.973948,\n",
" 22.909009, 23.84407 , 24.779132, 25.714193, 26.649254, 27.584315,\n",
" 28.519375, 29.454436, 30.389497, 31.324558, 32.259618, 33.194679,\n",
" 34.129739, 35.064799, 35.99986 , 36.93492 , 37.86998 , 38.805039,\n",
" 39.740099, 40.675159, 41.610218, 42.545277, 43.480336, 44.415395,\n",
" 45.350454, 46.285512, 47.22057 , 48.155628, 49.090686, 50.025743,\n",
" 50.960801, 51.895857, 52.830914, 53.76597 , 54.701026, 55.636081,\n",
" 56.571136, 57.50619 , 58.441244, 59.376297, 60.311349, 61.246401,\n",
" 62.181452, 63.116502, 64.051551, 64.986599, 65.921645, 66.856691,\n",
" 67.791734, 68.726776, 69.661816, 70.596854, 71.531889, 72.466921,\n",
" 73.401949, 74.336973, 75.271992, 76.207005, 77.142011, 78.077007,\n",
" 79.011992, 79.946962, 80.881913, 81.816839, 82.751728, 83.686567,\n",
" 84.621327, 85.55596 , 86.490367, 87.424304, 88.357004, 89.284228])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9961 0.9826 ... 9.816e-06</div><input id='attrs-40d49754-cf63-410f-97ae-350e75cd7509' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40d49754-cf63-410f-97ae-350e75cd7509' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ff5cb11-d73e-454b-a0ee-4cc12a7edf22' class='xr-var-data-in' type='checkbox'><label for='data-9ff5cb11-d73e-454b-a0ee-4cc12a7edf22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lev_bnds</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>long_name :</span></dt><dd>hybrid sigma pressure coordinate</dd><dt><span>standard_name :</span></dt><dd>atmosphere_hybrid_sigma_pressure_coordinate</dd><dt><span>formula :</span></dt><dd>p = ap + b*ps</dd><dt><span>formula_terms :</span></dt><dd>ap: ap b: b ps: ps</dd></dl></div><div class='xr-var-data'><pre>array([9.961500e-01, 9.826500e-01, 9.589556e-01, 9.276460e-01, 8.908394e-01,\n",
" 8.501503e-01, 8.068475e-01, 7.620137e-01, 7.164562e-01, 6.707188e-01,\n",
" 6.252446e-01, 5.803910e-01, 5.366074e-01, 4.942692e-01, 4.537201e-01,\n",
" 4.154341e-01, 3.797069e-01, 3.466272e-01, 3.161519e-01, 2.881811e-01,\n",
" 2.625506e-01, 2.391084e-01, 2.177225e-01, 1.982337e-01, 1.804680e-01,\n",
" 1.642867e-01, 1.495502e-01, 1.361203e-01, 1.238958e-01, 1.127819e-01,\n",
" 1.026525e-01, 9.340927e-02, 8.499143e-02, 7.732509e-02, 7.033860e-02,\n",
" 6.396105e-02, 5.814545e-02, 5.284837e-02, 4.802120e-02, 4.362085e-02,\n",
" 3.960960e-02, 3.595304e-02, 3.261980e-02, 2.958129e-02, 2.681146e-02,\n",
" 2.428654e-02, 2.198489e-02, 1.988675e-02, 1.797414e-02, 1.623065e-02,\n",
" 1.464207e-02, 1.319605e-02, 1.188111e-02, 1.068652e-02, 9.602347e-03,\n",
" 8.619337e-03, 7.728926e-03, 6.923179e-03, 6.194759e-03, 5.536888e-03,\n",
" 4.943310e-03, 4.408264e-03, 3.926449e-03, 3.492988e-03, 3.103407e-03,\n",
" 2.753606e-03, 2.439827e-03, 2.158631e-03, 1.906879e-03, 1.681707e-03,\n",
" 1.480501e-03, 1.300885e-03, 1.140697e-03, 9.979719e-04, 8.709289e-04,\n",
" 7.579535e-04, 6.575843e-04, 5.685002e-04, 4.895081e-04, 4.195318e-04,\n",
" 3.577273e-04, 3.034278e-04, 2.559719e-04, 2.147130e-04, 1.790279e-04,\n",
" 1.483228e-04, 1.220382e-04, 9.965253e-05, 8.068427e-05, 6.469293e-05,\n",
" 5.127906e-05, 4.008352e-05, 3.078596e-05, 2.310285e-05, 9.815852e-06])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.9375 1.875 ... 358.1 359.1</div><input id='attrs-ce1b376e-93e7-4786-a999-73b791cf4a7f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce1b376e-93e7-4786-a999-73b791cf4a7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-498da8f4-00d0-4cc1-99e0-e657acc1bde6' class='xr-var-data-in' type='checkbox'><label for='data-498da8f4-00d0-4cc1-99e0-e657acc1bde6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([ 0. , 0.9375, 1.875 , ..., 357.1875, 358.125 , 359.0625])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>plev</span></div><div class='xr-var-dims'>(plev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+05 9.25e+04 ... 500.0 100.0</div><input id='attrs-d03dc8d5-09ef-457d-926d-93a39864153e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d03dc8d5-09ef-457d-926d-93a39864153e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af1092c0-6229-44c1-9ba2-b56115d77291' class='xr-var-data-in' type='checkbox'><label for='data-af1092c0-6229-44c1-9ba2-b56115d77291' 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>Pa</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd></dl></div><div class='xr-var-data'><pre>array([100000., 92500., 85000., 70000., 60000., 50000., 40000., 30000.,\n",
" 25000., 20000., 15000., 10000., 7000., 5000., 3000., 2000.,\n",
" 1000., 500., 100.])</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'>2015-01-16T12:00:00 ... 2100-12-...</div><input id='attrs-54c2dd44-a7a6-4b2d-ae1c-227035a0c7f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54c2dd44-a7a6-4b2d-ae1c-227035a0c7f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f712fa6d-1a94-4bc4-8946-1f51c071f2ca' class='xr-var-data-in' type='checkbox'><label for='data-f712fa6d-1a94-4bc4-8946-1f51c071f2ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(['2015-01-16T12:00:00.000000000', '2015-02-15T00:00:00.000000000',\n",
" '2015-03-16T12:00:00.000000000', ..., '2100-10-16T12:00:00.000000000',\n",
" '2100-11-16T00:00:00.000000000', '2100-12-16T12:00:00.000000000'],\n",
" dtype='datetime64[ns]')</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a0cb9e81-b790-4c22-a72e-3083a813483c' class='xr-section-summary-in' type='checkbox' ><label for='section-a0cb9e81-b790-4c22-a72e-3083a813483c' class='xr-section-summary' >Data variables: <span>(55)</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>ap</span></div><div class='xr-var-dims'>(time, lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(60, 95), meta=np.ndarray></div><input id='attrs-3924dd00-6ef6-4f15-bfe0-fdc625e54223' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3924dd00-6ef6-4f15-bfe0-fdc625e54223' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d7aee82-84bd-46c4-a0c5-74acbfc04295' class='xr-var-data-in' type='checkbox'><label for='data-1d7aee82-84bd-46c4-a0c5-74acbfc04295' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>vertical coordinate formula term: ap(k)</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 765.94 kiB </td>\n",
" <td> 44.53 kiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (1032, 95) </td>\n",
" <td> (60, 95) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 18 chunks in 2 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"88\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ap_bnds</span></div><div class='xr-var-dims'>(time, lev, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(60, 95, 2), meta=np.ndarray></div><input id='attrs-921ea4bd-f453-4b93-8d76-829abca660b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-921ea4bd-f453-4b93-8d76-829abca660b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ec264e5-efe3-4559-94ec-5ac222bf6e84' class='xr-var-data-in' type='checkbox'><label for='data-9ec264e5-efe3-4559-94ec-5ac222bf6e84' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>vertical coordinate formula term: ap(k+1/2)</dd><dt><span>units :</span></dt><dd>Pa</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
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" <th> Chunk </th>\n",
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" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
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" <td> 89.06 kiB </td>\n",
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" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (1032, 95, 2) </td>\n",
" <td> (60, 95, 2) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 18 chunks in 2 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>b</span></div><div class='xr-var-dims'>(time, lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(60, 95), meta=np.ndarray></div><input id='attrs-42a5ea10-d3fe-4aa5-b924-e6dbfbc2e930' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42a5ea10-d3fe-4aa5-b924-e6dbfbc2e930' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38c9dc1d-a534-48c1-93eb-10ab91c9861c' class='xr-var-data-in' type='checkbox'><label for='data-38c9dc1d-a534-48c1-93eb-10ab91c9861c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>vertical coordinate formula term: b(k)</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
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" <thead>\n",
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" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 765.94 kiB </td>\n",
" <td> 44.53 kiB </td>\n",
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" \n",
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" <td> (1032, 95) </td>\n",
" <td> (60, 95) </td>\n",
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" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 18 chunks in 2 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>b_bnds</span></div><div class='xr-var-dims'>(time, lev, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(60, 95, 2), meta=np.ndarray></div><input id='attrs-0634db90-e337-4816-b215-70f9b6d55560' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0634db90-e337-4816-b215-70f9b6d55560' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63227b98-0404-447c-9189-6ce633da5f15' class='xr-var-data-in' type='checkbox'><label for='data-63227b98-0404-447c-9189-6ce633da5f15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>vertical coordinate formula term: b(k+1/2)</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
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" <th> Array </th>\n",
" <th> Chunk </th>\n",
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" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 1.50 MiB </td>\n",
" <td> 89.06 kiB </td>\n",
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" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (1032, 95, 2) </td>\n",
" <td> (60, 95, 2) </td>\n",
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" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 18 chunks in 2 graph layers </td>\n",
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" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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" <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<chunksize=(1, 192, 384), meta=np.ndarray></div><input id='attrs-124179d7-6034-4d59-8af4-09974754f1d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-124179d7-6034-4d59-8af4-09974754f1d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e40ed98d-a66f-4591-ba64-75adfc52e783' class='xr-var-data-in' type='checkbox'><label for='data-e40ed98d-a66f-4591-ba64-75adfc52e783' 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>specific_humidity</dd><dt><span>long_name :</span></dt><dd>Near-Surface Specific Humidity</dd><dt><span>comment :</span></dt><dd>Near-surface (usually, 2 meter) specific humidity.</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2019-07-20T13:41:52Z altered by CMOR: Treated scalar dimension: 'height'. 2019-07-20T13:41:52Z altered by CMOR: replaced missing value flag (-9e+33) with standard missing value (1e+20). 2019-07-20T13:41:52Z altered by CMOR: Converted type from 'd' to 'f'. 2019-07-20T13:41:52Z altered by CMOR: Inverted axis: lat.</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>lev_bnds</span></div><div class='xr-var-dims'>(time, lev, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(60, 95, 2), meta=np.ndarray></div><input id='attrs-e889a926-2d90-4765-9cb2-2b78318e2caa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e889a926-2d90-4765-9cb2-2b78318e2caa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b75f5af9-3cf3-42d4-b8cf-ab597739073a' class='xr-var-data-in' type='checkbox'><label for='data-b75f5af9-3cf3-42d4-b8cf-ab597739073a' 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>formula :</span></dt><dd>p = ap + b*ps</dd><dt><span>standard_name :</span></dt><dd>atmosphere_hybrid_sigma_pressure_coordinate</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>formula_terms :</span></dt><dd>ap: ap_bnds b: b_bnds ps: ps</dd></dl></div><div class='xr-var-data'><table>\n",
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" <td> (60, 95, 2) </td>\n",
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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>o3</span></div><div class='xr-var-dims'>(time, plev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1, 10, 96, 192), meta=np.ndarray></div><input id='attrs-8ab0f7a1-4a55-4c6e-a8ac-6bb459814c14' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ab0f7a1-4a55-4c6e-a8ac-6bb459814c14' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57d6e04d-0e98-49c0-bd66-5bb932c91fb9' class='xr-var-data-in' type='checkbox'><label for='data-57d6e04d-0e98-49c0-bd66-5bb932c91fb9' 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>mole_fraction_of_ozone_in_air</dd><dt><span>long_name :</span></dt><dd>Mole Fraction of O3</dd><dt><span>comment :</span></dt><dd>Mole fraction is used in the construction mole_fraction_of_X_in_Y, where X is a material constituent of Y.</dd><dt><span>units :</span></dt><dd>mol mol-1</dd><dt><span>original_name :</span></dt><dd>o3</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2019-07-20T13:41:50Z altered by CMOR: Reordered dimensions, original order: time lat lon plev. 2019-07-20T13:41:50Z altered by CMOR: replaced missing value flag (-9e+33) with standard missing value (1e+20). 2019-07-20T13:41:50Z altered by CMOR: Inverted axis: lat.</dd></dl></div><div class='xr-var-data'><table>\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>ua</span></div><div class='xr-var-dims'>(time, plev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array<chunksize=(1, 10, 96, 192), meta=np.ndarray></div><input id='attrs-49d83544-e107-484c-8cec-bf0c47898d4e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49d83544-e107-484c-8cec-bf0c47898d4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e21a62c-4ac2-44ce-8340-36d8c403e84f' class='xr-var-data-in' type='checkbox'><label for='data-2e21a62c-4ac2-44ce-8340-36d8c403e84f' 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>eastward_wind</dd><dt><span>long_name :</span></dt><dd>Eastward Wind</dd><dt><span>comment :</span></dt><dd>Zonal wind (positive in a eastward direction).</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>cell_methods :</span></dt><dd>time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2019-07-20T13:41:50Z altered by CMOR: Reordered dimensions, original order: time lat lon plev. 2019-07-20T13:41:50Z altered by CMOR: replaced missing value flag (-9e+33) with standard missing value (1e+20). 2019-07-20T13:41:50Z altered by CMOR: Converted type from 'd' to 'f'. 2019-07-20T13:41:51Z altered by CMOR: Inverted axis: lat.</dd></dl></div><div class='xr-var-data'><table>\n",
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" <line x1=\"190\" y1=\"41\" x2=\"206\" y2=\"58\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"41\" style=\"stroke-width:2\" />\n",
" <line x1=\"198\" y1=\"8\" x2=\"198\" y2=\"50\" />\n",
" <line x1=\"206\" y1=\"16\" x2=\"206\" y2=\"58\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Colored Rectangle -->\n",
" <polygon points=\"190.0,0.0 206.80495557337377,16.804955573373785 206.80495557337377,58.50357705647646 190.0,41.69862148310268\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"190\" y1=\"0\" x2=\"234\" y2=\"0\" style=\"stroke-width:2\" />\n",
" <line x1=\"198\" y1=\"8\" x2=\"243\" y2=\"8\" />\n",
" <line x1=\"206\" y1=\"16\" x2=\"251\" y2=\"16\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"190\" y1=\"0\" x2=\"206\" y2=\"16\" style=\"stroke-width:2\" />\n",
" <line x1=\"212\" y1=\"0\" x2=\"229\" y2=\"16\" />\n",
" <line x1=\"234\" y1=\"0\" x2=\"251\" y2=\"16\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Colored Rectangle -->\n",
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"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"206\" y1=\"16\" x2=\"251\" y2=\"16\" style=\"stroke-width:2\" />\n",
" <line x1=\"206\" y1=\"37\" x2=\"251\" y2=\"37\" />\n",
" <line x1=\"206\" y1=\"58\" x2=\"251\" y2=\"58\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"206\" y1=\"16\" x2=\"206\" y2=\"58\" style=\"stroke-width:2\" />\n",
" <line x1=\"229\" y1=\"16\" x2=\"229\" y2=\"58\" />\n",
" <line x1=\"251\" y1=\"16\" x2=\"251\" y2=\"58\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Colored Rectangle -->\n",
" <polygon points=\"206.80495557337377,16.804955573373785 251.45611836407144,16.804955573373785 251.45611836407144,58.50357705647646 206.80495557337377,58.50357705647646\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
"\n",
" <!-- Text -->\n",
" <text x=\"229.130537\" y=\"78.503577\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >384</text>\n",
" <text x=\"271.456118\" y=\"37.654266\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,271.456118,37.654266)\">192</text>\n",
" <text x=\"188.402478\" y=\"70.101099\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,188.402478,70.101099)\">19</text>\n",
"</svg>\n",
" </td>\n",
" </tr>\n",
"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-a057b9de-05cc-441e-bc4f-a20865114a77' class='xr-section-summary-in' type='checkbox' ><label for='section-a057b9de-05cc-441e-bc4f-a20865114a77' class='xr-section-summary' >Indexes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2402ee71-1a2a-4afd-805a-0e2c637893b2' class='xr-index-data-in' type='checkbox'/><label for='index-2402ee71-1a2a-4afd-805a-0e2c637893b2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-89.28422753251364, -88.35700351866494, -87.42430374606988,\n",
" -86.49036676628116, -85.55596048489265, -84.6213271076488,\n",
" -83.68656681656385, -82.75172847343066, -81.81683872860319,\n",
" -80.8819133467975,\n",
" ...\n",
" 80.8819133467975, 81.81683872860319, 82.75172847343066,\n",
" 83.68656681656385, 84.6213271076488, 85.55596048489265,\n",
" 86.49036676628116, 87.42430374606988, 88.35700351866494,\n",
" 89.28422753251364],\n",
" dtype='float64', name='lat', length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lev</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-044a080a-6e8e-48f2-b5a2-a9c35373a1ab' class='xr-index-data-in' type='checkbox'/><label for='index-044a080a-6e8e-48f2-b5a2-a9c35373a1ab' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0.9961499869823456, 0.9826499819755554, 0.9589555908567136,\n",
" 0.9276459693502798, 0.8908393983675643, 0.8501502852051046,\n",
" 0.8068474689019394, 0.7620136942249918, 0.7164562136862798,\n",
" 0.6707187556893979, 0.6252446440590949, 0.5803910498630256,\n",
" 0.5366073683506467, 0.4942692315672581, 0.4537200746596845,\n",
" 0.4154341220199812, 0.3797068559311656, 0.3466272163038162,\n",
" 0.31615186284801977, 0.2881811466818235, 0.2625505749313972,\n",
" 0.2391083761673929, 0.2177224959069833, 0.19823367227649222,\n",
" 0.18046804676613204, 0.1642866912611011, 0.1495502438573978,\n",
" 0.1361203084601691, 0.12389576315665891, 0.1127819063778702,\n",
" 0.10265254219114787, 0.09340926514608601, 0.08499143437525487,\n",
" 0.07732509098198866, 0.07033859949420182, 0.06396105122748581,\n",
" 0.05814545167160128, 0.052848372347643724, 0.04802119957438934,\n",
" 0.043620851838144584, 0.03960959667221811, 0.035953036331112756,\n",
" 0.03261979576278374, 0.02958129009375771, 0.026811458381522945,\n",
" 0.024286543147051566, 0.02198488918316679, 0.01988675441031335,\n",
" 0.01797414241765359, 0.016230649461047374, 0.014642066833209968,\n",
" 0.013196053174346163, 0.011881106828660868, 0.010686522473707747,\n",
" 0.009602346544997533, 0.008619337479567604, 0.007728925959551567,\n",
" 0.006923178769969776, 0.006194759042298915, 0.00553688770278189,\n",
" 0.004943309739698989, 0.00440826408485998, 0.003926448676134962,\n",
" 0.0034929879294658276, 0.0031034074393196396, 0.002753606269661362,\n",
" 0.002439826760149696, 0.002158630883108384, 0.0019068793864594898,\n",
" 0.0016817067200655764, 0.0014805012328107652, 0.0013008852943855245,\n",
" 0.0011406967724043146, 0.0009979718648560865, 0.0008709289114062885,\n",
" 0.0007579534847354583, 0.0006575843101367814, 0.0005685002392614267,\n",
" 0.0004895081274191186, 0.00041953176502648504, 0.0003577273067885671,\n",
" 0.00030342777834802155, 0.00025597185256773534, 0.00021471303567573402,\n",
" 0.0001790279498548529, 0.00014832281342795124, 0.00012203819864095655,\n",
" 9.965253293940734e-05, 8.068427314824185e-05, 6.469292667745632e-05,\n",
" 5.127906093413877e-05, 4.0083522123670446e-05, 3.078596226644904e-05,\n",
" 2.3102853258774366e-05, 9.815852317932415e-06],\n",
" dtype='float64', name='lev'))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-63e72fdf-2d03-46f4-8b0d-33b34d94ceb1' class='xr-index-data-in' type='checkbox'/><label for='index-63e72fdf-2d03-46f4-8b0d-33b34d94ceb1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0.0, 0.9375, 1.875, 2.8125, 3.75, 4.6875, 5.625,\n",
" 6.5625, 7.5, 8.4375,\n",
" ...\n",
" 350.625, 351.5625, 352.5, 353.4375, 354.375, 355.3125, 356.25,\n",
" 357.1875, 358.125, 359.0625],\n",
" dtype='float64', name='lon', length=384))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>plev</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c4e4974b-dffe-4ca4-aa9c-aea5c99632e1' class='xr-index-data-in' type='checkbox'/><label for='index-c4e4974b-dffe-4ca4-aa9c-aea5c99632e1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([100000.0, 92500.0, 85000.0, 70000.0, 60000.0, 50000.0, 40000.0,\n",
" 30000.0, 25000.0, 20000.0, 15000.0, 10000.0, 7000.0, 5000.0,\n",
" 3000.0, 2000.0, 1000.0, 500.0, 100.0],\n",
" dtype='float64', name='plev'))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a30587c8-0a62-44cb-9495-6447b8c00c4e' class='xr-index-data-in' type='checkbox'/><label for='index-a30587c8-0a62-44cb-9495-6447b8c00c4e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex(['2015-01-16 12:00:00', '2015-02-15 00:00:00',\n",
" '2015-03-16 12:00:00', '2015-04-16 00:00:00',\n",
" '2015-05-16 12:00:00', '2015-06-16 00:00:00',\n",
" '2015-07-16 12:00:00', '2015-08-16 12:00:00',\n",
" '2015-09-16 00:00:00', '2015-10-16 12:00:00',\n",
" ...\n",
" '2100-03-16 12:00:00', '2100-04-16 00:00:00',\n",
" '2100-05-16 12:00:00', '2100-06-16 00:00:00',\n",
" '2100-07-16 12:00:00', '2100-08-16 12:00:00',\n",
" '2100-09-16 00:00:00', '2100-10-16 12:00:00',\n",
" '2100-11-16 00:00:00', '2100-12-16 12:00:00'],\n",
" dtype='datetime64[ns]', name='time', length=1032, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c9035d82-5265-48d4-8e37-09d37442c9b7' class='xr-section-summary-in' type='checkbox' ><label for='section-c9035d82-5265-48d4-8e37-09d37442c9b7' class='xr-section-summary' >Attributes: <span>(47)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>activity_id :</span></dt><dd>ScenarioMIP AerChemMIP</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>branch_time_in_child :</span></dt><dd>60265.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>60265.0</dd><dt><span>contact :</span></dt><dd>cmip6-mpi-esm@dkrz.de</dd><dt><span>creation_date :</span></dt><dd>2019-07-20T13:41:52Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.30</dd><dt><span>experiment :</span></dt><dd>gap-filling scenario reaching 7.0 based on SSP3</dd><dt><span>experiment_id :</span></dt><dd>ssp370</dd><dt><span>external_variables :</span></dt><dd>areacella</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.DKRZ.MPI-ESM1-2-HR.ssp370.none.r1i1p1f1</dd><dt><span>grid :</span></dt><dd>gn</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>history :</span></dt><dd>2019-07-20T13:41:52Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>Deutsches Klimarechenzentrum, Hamburg 20146, Germany</dd><dt><span>institution_id :</span></dt><dd>DKRZ</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>100 km</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>historical</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>MPI-ESM1-2-HR</dd><dt><span>parent_time_units :</span></dt><dd>days since 1850-1-1 00:00:00</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>project_id :</span></dt><dd>CMIP6</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>atmos</dd><dt><span>references :</span></dt><dd>MPI-ESM: Mauritsen, T. et al. (2019), Developments in the MPI‐M Earth System Model version 1.2 (MPI‐ESM1.2) and Its Response to Increasing CO2, J. Adv. Model. Earth Syst.,11, 998-1038, doi:10.1029/2018MS001400,\n",
"Mueller, W.A. et al. (2018): A high‐resolution version of the Max Planck Institute Earth System Model MPI‐ESM1.2‐HR. J. Adv. Model. EarthSyst.,10,1383–1413, doi:10.1029/2017MS001217</dd><dt><span>source :</span></dt><dd>MPI-ESM1.2-HR (2017): \n",
"aerosol: none, prescribed MACv2-SP\n",
"atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa)\n",
"atmosChem: none\n",
"land: JSBACH3.20\n",
"landIce: none/prescribed\n",
"ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m)\n",
"ocnBgchem: HAMOCC6\n",
"seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model)</dd><dt><span>source_id :</span></dt><dd>MPI-ESM1-2-HR</dd><dt><span>source_type :</span></dt><dd>AOGCM</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>table_id :</span></dt><dd>Amon</dd><dt><span>table_info :</span></dt><dd>Creation Date:(09 May 2019) MD5:e6ef8ececc8f338646ebfb3aeed36bfc</dd><dt><span>title :</span></dt><dd>MPI-ESM1-2-HR output prepared for CMIP6</dd><dt><span>variable_id :</span></dt><dd>cl</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by DKRZ is licensed under a Creative Commons Attribution ShareAlike 4.0 International License (https://creativecommons.org/licenses). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file) and. The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>cmor_version :</span></dt><dd>3.4.0</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/e496e673-c31f-4e03-a4fe-7f09115fed79</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset> Size: 144GB\n",
"Dimensions: (time: 1032, lev: 95, bnds: 2, lat: 192, lon: 384, plev: 19)\n",
"Coordinates:\n",
" height float64 8B ...\n",
" * lat (lat) float64 2kB -89.28 -88.36 -87.42 ... 87.42 88.36 89.28\n",
" * lev (lev) float64 760B 0.9961 0.9826 0.959 ... 2.31e-05 9.816e-06\n",
" * lon (lon) float64 3kB 0.0 0.9375 1.875 2.812 ... 357.2 358.1 359.1\n",
" * plev (plev) float64 152B 1e+05 9.25e+04 8.5e+04 ... 1e+03 500.0 100.0\n",
" * time (time) datetime64[ns] 8kB 2015-01-16T12:00:00 ... 2100-12-16T1...\n",
"Dimensions without coordinates: bnds\n",
"Data variables: (12/55)\n",
" ap (time, lev) float64 784kB dask.array<chunksize=(60, 95), meta=np.ndarray>\n",
" ap_bnds (time, lev, bnds) float64 2MB dask.array<chunksize=(60, 95, 2), meta=np.ndarray>\n",
" b (time, lev) float64 784kB dask.array<chunksize=(60, 95), meta=np.ndarray>\n",
" b_bnds (time, lev, bnds) float64 2MB dask.array<chunksize=(60, 95, 2), meta=np.ndarray>\n",
" cct (time, lat, lon) float32 304MB dask.array<chunksize=(1, 192, 384), meta=np.ndarray>\n",
" cl (time, lev, lat, lon) float32 29GB dask.array<chunksize=(1, 48, 96, 192), meta=np.ndarray>\n",
" ... ...\n",
" ua (time, plev, lat, lon) float32 6GB dask.array<chunksize=(1, 10, 96, 192), meta=np.ndarray>\n",
" uas (time, lat, lon) float32 304MB dask.array<chunksize=(1, 192, 384), meta=np.ndarray>\n",
" va (time, plev, lat, lon) float32 6GB dask.array<chunksize=(1, 10, 96, 192), meta=np.ndarray>\n",
" vas (time, lat, lon) float32 304MB dask.array<chunksize=(1, 192, 384), meta=np.ndarray>\n",
" wap (time, plev, lat, lon) float32 6GB dask.array<chunksize=(1, 10, 96, 192), meta=np.ndarray>\n",
" zg (time, plev, lat, lon) float32 6GB dask.array<chunksize=(1, 10, 96, 192), meta=np.ndarray>\n",
"Attributes: (12/47)\n",
" Conventions: CF-1.7 CMIP-6.2\n",
" activity_id: ScenarioMIP AerChemMIP\n",
" branch_method: standard\n",
" branch_time_in_child: 60265.0\n",
" branch_time_in_parent: 60265.0\n",
" contact: cmip6-mpi-esm@dkrz.de\n",
" ... ...\n",
" title: MPI-ESM1-2-HR output prepared for CMIP6\n",
" variable_id: cl\n",
" variant_label: r1i1p1f1\n",
" license: CMIP6 model data produced by DKRZ is licensed und...\n",
" cmor_version: 3.4.0\n",
" tracking_id: hdl:21.14100/e496e673-c31f-4e03-a4fe-7f09115fed79"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "fb6bc1e9-b4e6-4614-9e57-590ddcd4cdca",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"133.80184280127287"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.nbytes/1024**3"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "feacaed0-df8d-4e52-af8c-acd094cac6f4",
"metadata": {
"tags": []
},
"outputs": [
{
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"</style><pre class='xr-text-repr-fallback'><xarray.DataArray 'tas' (lat: 192, lon: 384)> Size: 295kB\n",
"array([[243.30435, 243.29459, 243.28091, ..., 243.29654, 243.29459,\n",
" 243.19107],\n",
" [244.38638, 244.34927, 244.28873, ..., 244.486 , 244.45865,\n",
" 244.42935],\n",
" [244.8649 , 244.78677, 244.64224, ..., 245.03287, 244.98795,\n",
" 244.9235 ],\n",
" ...,\n",
" [253.50748, 253.49966, 253.52896, ..., 253.38248, 253.42545,\n",
" 253.43912],\n",
" [252.84732, 252.85904, 252.861 , ..., 252.63638, 252.75748,\n",
" 252.80826],\n",
" [252.24771, 252.23404, 252.28677, ..., 252.23795, 252.23795,\n",
" 252.2399 ]], dtype=float32)\n",
"Coordinates:\n",
" height float64 8B 2.0\n",
" * lat (lat) float64 2kB -89.28 -88.36 -87.42 -86.49 ... 87.42 88.36 89.28\n",
" lev float64 8B 0.9961\n",
" * lon (lon) float64 3kB 0.0 0.9375 1.875 2.812 ... 357.2 358.1 359.1\n",
" plev float64 8B 1e+05\n",
" time datetime64[ns] 8B 2015-01-16T12:00:00\n",
"Attributes:\n",
" standard_name: air_temperature\n",
" long_name: Near-Surface Air Temperature\n",
" comment: near-surface (usually, 2 meter) air temperature\n",
" units: K\n",
" cell_methods: area: time: mean\n",
" cell_measures: area: areacella\n",
" history: 2019-07-20T13:41:51Z altered by CMOR: Treated scalar dime...</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'>'tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 384</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-618d27ef-de51-4428-b788-4b740075614c' class='xr-array-in' type='checkbox' checked><label for='section-618d27ef-de51-4428-b788-4b740075614c' 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>243.3 243.3 243.3 243.3 243.3 243.3 ... 252.2 252.2 252.2 252.2 252.2</span></div><div class='xr-array-data'><pre>array([[243.30435, 243.29459, 243.28091, ..., 243.29654, 243.29459,\n",
" 243.19107],\n",
" [244.38638, 244.34927, 244.28873, ..., 244.486 , 244.45865,\n",
" 244.42935],\n",
" [244.8649 , 244.78677, 244.64224, ..., 245.03287, 244.98795,\n",
" 244.9235 ],\n",
" ...,\n",
" [253.50748, 253.49966, 253.52896, ..., 253.38248, 253.42545,\n",
" 253.43912],\n",
" [252.84732, 252.85904, 252.861 , ..., 252.63638, 252.75748,\n",
" 252.80826],\n",
" [252.24771, 252.23404, 252.28677, ..., 252.23795, 252.23795,\n",
" 252.2399 ]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-9eb78401-43cd-40d1-a831-c98700f09145' class='xr-section-summary-in' type='checkbox' checked><label for='section-9eb78401-43cd-40d1-a831-c98700f09145' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>height</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.0</div><input id='attrs-a5bc54ce-a5e7-4a3c-ac68-7e7a32b17d64' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a5bc54ce-a5e7-4a3c-ac68-7e7a32b17d64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99a93d0e-6cbd-40c2-aded-4c134df516f7' class='xr-var-data-in' type='checkbox'><label for='data-99a93d0e-6cbd-40c2-aded-4c134df516f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>long_name :</span></dt><dd>height</dd><dt><span>standard_name :</span></dt><dd>height</dd></dl></div><div class='xr-var-data'><pre>array(2.)</pre></div></li><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'>-89.28 -88.36 ... 88.36 89.28</div><input id='attrs-ded1e1c3-589e-4079-9a8f-73ce38383acc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ded1e1c3-589e-4079-9a8f-73ce38383acc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a4efd2b-9147-451b-b68f-d79bec4f461a' class='xr-var-data-in' type='checkbox'><label for='data-6a4efd2b-9147-451b-b68f-d79bec4f461a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-89.284228, -88.357004, -87.424304, -86.490367, -85.55596 , -84.621327,\n",
" -83.686567, -82.751728, -81.816839, -80.881913, -79.946962, -79.011992,\n",
" -78.077007, -77.142011, -76.207005, -75.271992, -74.336973, -73.401949,\n",
" -72.466921, -71.531889, -70.596854, -69.661816, -68.726776, -67.791734,\n",
" -66.856691, -65.921645, -64.986599, -64.051551, -63.116502, -62.181452,\n",
" -61.246401, -60.311349, -59.376297, -58.441244, -57.50619 , -56.571136,\n",
" -55.636081, -54.701026, -53.76597 , -52.830914, -51.895857, -50.960801,\n",
" -50.025743, -49.090686, -48.155628, -47.22057 , -46.285512, -45.350454,\n",
" -44.415395, -43.480336, -42.545277, -41.610218, -40.675159, -39.740099,\n",
" -38.805039, -37.86998 , -36.93492 , -35.99986 , -35.064799, -34.129739,\n",
" -33.194679, -32.259618, -31.324558, -30.389497, -29.454436, -28.519375,\n",
" -27.584315, -26.649254, -25.714193, -24.779132, -23.84407 , -22.909009,\n",
" -21.973948, -21.038887, -20.103825, -19.168764, -18.233703, -17.298641,\n",
" -16.36358 , -15.428518, -14.493457, -13.558395, -12.623333, -11.688272,\n",
" -10.75321 , -9.818148, -8.883087, -7.948025, -7.012963, -6.077901,\n",
" -5.14284 , -4.207778, -3.272716, -2.337654, -1.402593, -0.467531,\n",
" 0.467531, 1.402593, 2.337654, 3.272716, 4.207778, 5.14284 ,\n",
" 6.077901, 7.012963, 7.948025, 8.883087, 9.818148, 10.75321 ,\n",
" 11.688272, 12.623333, 13.558395, 14.493457, 15.428518, 16.36358 ,\n",
" 17.298641, 18.233703, 19.168764, 20.103825, 21.038887, 21.973948,\n",
" 22.909009, 23.84407 , 24.779132, 25.714193, 26.649254, 27.584315,\n",
" 28.519375, 29.454436, 30.389497, 31.324558, 32.259618, 33.194679,\n",
" 34.129739, 35.064799, 35.99986 , 36.93492 , 37.86998 , 38.805039,\n",
" 39.740099, 40.675159, 41.610218, 42.545277, 43.480336, 44.415395,\n",
" 45.350454, 46.285512, 47.22057 , 48.155628, 49.090686, 50.025743,\n",
" 50.960801, 51.895857, 52.830914, 53.76597 , 54.701026, 55.636081,\n",
" 56.571136, 57.50619 , 58.441244, 59.376297, 60.311349, 61.246401,\n",
" 62.181452, 63.116502, 64.051551, 64.986599, 65.921645, 66.856691,\n",
" 67.791734, 68.726776, 69.661816, 70.596854, 71.531889, 72.466921,\n",
" 73.401949, 74.336973, 75.271992, 76.207005, 77.142011, 78.077007,\n",
" 79.011992, 79.946962, 80.881913, 81.816839, 82.751728, 83.686567,\n",
" 84.621327, 85.55596 , 86.490367, 87.424304, 88.357004, 89.284228])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lev</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.9961</div><input id='attrs-04926c1c-d7c9-40fd-b993-53b8e5595d17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04926c1c-d7c9-40fd-b993-53b8e5595d17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e17c01d-eb12-4ac7-8368-b5a91fcfc5f6' class='xr-var-data-in' type='checkbox'><label for='data-9e17c01d-eb12-4ac7-8368-b5a91fcfc5f6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lev_bnds</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>long_name :</span></dt><dd>hybrid sigma pressure coordinate</dd><dt><span>standard_name :</span></dt><dd>atmosphere_hybrid_sigma_pressure_coordinate</dd><dt><span>formula :</span></dt><dd>p = ap + b*ps</dd><dt><span>formula_terms :</span></dt><dd>ap: ap b: b ps: ps</dd></dl></div><div class='xr-var-data'><pre>array(0.99614999)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.9375 1.875 ... 358.1 359.1</div><input id='attrs-db1284e4-d733-4f07-952a-1d16b7f8b392' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db1284e4-d733-4f07-952a-1d16b7f8b392' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49f842a0-3717-491a-b93f-a8a64a95e739' class='xr-var-data-in' type='checkbox'><label for='data-49f842a0-3717-491a-b93f-a8a64a95e739' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([ 0. , 0.9375, 1.875 , ..., 357.1875, 358.125 , 359.0625])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>plev</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+05</div><input id='attrs-02135e1b-f79e-4b54-a91a-a44f65310223' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-02135e1b-f79e-4b54-a91a-a44f65310223' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b884c94-6804-4309-a641-cccf19391ce4' class='xr-var-data-in' type='checkbox'><label for='data-4b884c94-6804-4309-a641-cccf19391ce4' 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>Pa</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd></dl></div><div class='xr-var-data'><pre>array(100000.)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2015-01-16T12:00:00</div><input id='attrs-fddcb8ee-9e43-47d4-a4a7-44d8eab02b4c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fddcb8ee-9e43-47d4-a4a7-44d8eab02b4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-841370df-0332-4e17-a996-53aadbf9616f' class='xr-var-data-in' type='checkbox'><label for='data-841370df-0332-4e17-a996-53aadbf9616f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array('2015-01-16T12:00:00.000000000', dtype='datetime64[ns]')</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-95f5cd75-f71d-4221-9062-844e32bf0dbb' class='xr-section-summary-in' type='checkbox' ><label for='section-95f5cd75-f71d-4221-9062-844e32bf0dbb' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-db20cb77-234c-4974-9a2b-3003971ea1a2' class='xr-index-data-in' type='checkbox'/><label for='index-db20cb77-234c-4974-9a2b-3003971ea1a2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-89.28422753251364, -88.35700351866494, -87.42430374606988,\n",
" -86.49036676628116, -85.55596048489265, -84.6213271076488,\n",
" -83.68656681656385, -82.75172847343066, -81.81683872860319,\n",
" -80.8819133467975,\n",
" ...\n",
" 80.8819133467975, 81.81683872860319, 82.75172847343066,\n",
" 83.68656681656385, 84.6213271076488, 85.55596048489265,\n",
" 86.49036676628116, 87.42430374606988, 88.35700351866494,\n",
" 89.28422753251364],\n",
" dtype='float64', name='lat', length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-56f8368d-6fd3-4c2e-bc9c-151542d8f378' class='xr-index-data-in' type='checkbox'/><label for='index-56f8368d-6fd3-4c2e-bc9c-151542d8f378' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0.0, 0.9375, 1.875, 2.8125, 3.75, 4.6875, 5.625,\n",
" 6.5625, 7.5, 8.4375,\n",
" ...\n",
" 350.625, 351.5625, 352.5, 353.4375, 354.375, 355.3125, 356.25,\n",
" 357.1875, 358.125, 359.0625],\n",
" dtype='float64', name='lon', length=384))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d43b84ed-caa8-4beb-b9ef-da73c7bbe25f' class='xr-section-summary-in' type='checkbox' checked><label for='section-d43b84ed-caa8-4beb-b9ef-da73c7bbe25f' class='xr-section-summary' >Attributes: <span>(7)</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>air_temperature</dd><dt><span>long_name :</span></dt><dd>Near-Surface Air Temperature</dd><dt><span>comment :</span></dt><dd>near-surface (usually, 2 meter) air temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>area: time: mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2019-07-20T13:41:51Z altered by CMOR: Treated scalar dimension: 'height'. 2019-07-20T13:41:51Z altered by CMOR: replaced missing value flag (-9e+33) with standard missing value (1e+20). 2019-07-20T13:41:51Z altered by CMOR: Converted type from 'd' to 'f'. 2019-07-20T13:41:52Z altered by CMOR: Inverted axis: lat.</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'tas' (lat: 192, lon: 384)> Size: 295kB\n",
"array([[243.30435, 243.29459, 243.28091, ..., 243.29654, 243.29459,\n",
" 243.19107],\n",
" [244.38638, 244.34927, 244.28873, ..., 244.486 , 244.45865,\n",
" 244.42935],\n",
" [244.8649 , 244.78677, 244.64224, ..., 245.03287, 244.98795,\n",
" 244.9235 ],\n",
" ...,\n",
" [253.50748, 253.49966, 253.52896, ..., 253.38248, 253.42545,\n",
" 253.43912],\n",
" [252.84732, 252.85904, 252.861 , ..., 252.63638, 252.75748,\n",
" 252.80826],\n",
" [252.24771, 252.23404, 252.28677, ..., 252.23795, 252.23795,\n",
" 252.2399 ]], dtype=float32)\n",
"Coordinates:\n",
" height float64 8B 2.0\n",
" * lat (lat) float64 2kB -89.28 -88.36 -87.42 -86.49 ... 87.42 88.36 89.28\n",
" lev float64 8B 0.9961\n",
" * lon (lon) float64 3kB 0.0 0.9375 1.875 2.812 ... 357.2 358.1 359.1\n",
" plev float64 8B 1e+05\n",
" time datetime64[ns] 8B 2015-01-16T12:00:00\n",
"Attributes:\n",
" standard_name: air_temperature\n",
" long_name: Near-Surface Air Temperature\n",
" comment: near-surface (usually, 2 meter) air temperature\n",
" units: K\n",
" cell_methods: area: time: mean\n",
" cell_measures: area: areacella\n",
" history: 2019-07-20T13:41:51Z altered by CMOR: Treated scalar dime..."
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.isel(time=0,plev=0,lev=0)[\"tas\"].load()#.drop([\"height\",\"ap\",\"ap_bnds\",\"b\",\"b_bnds\",\"lev_bnds\"]).load()"
]
},
{
"cell_type": "markdown",
"id": "7ce5826a-80a3-4aea-86c4-4b6de8b229e2",
"metadata": {},
"source": [
"We see that\n",
"\n",
"- we host O(100GB) of data \"just like that\" because of lazy data access.\n",
"- the memory usage increases with the number of dask chunks to keep in memory. This is the bottle neck for our data server.\n",
"- the more data we aggregate into one dataset, the less endpoints we provide. Thus, the catalog of endpoints becomes small and we use the zarr datasets as the *real* catalogs.\n",
" - this simplifies random access which can be crucial for training of AI models"
]
},
{
"cell_type": "markdown",
"id": "0d0f68f0-135c-47c0-9059-e9870fb85a49",
"metadata": {},
"source": [
"In the next episode, we use *kerchunks* to avoid repeatedly merging these files when opening and instead use a once-*prepared* virtual dataset representation of this aggrgeation."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5655cceb-80c1-42d7-926a-2926f2d3feba",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "cloudify",
"language": "python",
"name": "cloudify"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.0"
}
},
"nbformat": 4,
"nbformat_minor": 5
}