EN422g10_Lee.ipynb

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    "import matplotlib.pyplot as plt  # plotting library\n",
    "import numpy as np  # numerical library\n",
    "import xarray as xr  # netCDF library\n",
    "import cartopy  # Map projections libary\n",
    "import cartopy.crs as ccrs  # Projections list\n",
    "import cftime\n",
    "# Some defaults:\n",
    "plt.rcParams['figure.figsize'] = (12, 5)  # Default plot size"
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    "data_dir='/Users/pvb/Dropbox/Oceanografia/Data/Climatologias/EN.4.2.2.g10/NC/'"
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    "files = []\n",
    "for iy in range(1950,2024):\n",
    "    for im in range(1,13):\n",
    "        files.append(data_dir+\"EN.4.2.2.f.analysis.g10.%04d%02d.nc\"%(iy,im))"
   ]
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    "data = xr.open_mfdataset(files)"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 93GB\n",
       "Dimensions:                          (depth: 42, lat: 173, lon: 360, time: 888,\n",
       "                                      bnds: 2)\n",
       "Coordinates:\n",
       "  * depth                            (depth) float32 168B 5.022 ... 5.35e+03\n",
       "  * lat                              (lat) float32 692B -83.0 -82.0 ... 89.0\n",
       "  * lon                              (lon) float32 1kB 1.0 2.0 ... 359.0 360.0\n",
       "  * time                             (time) datetime64[ns] 7kB 1950-01-16T12:...\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    temperature                      (time, depth, lat, lon) float64 19GB dask.array&lt;chunksize=(1, 21, 87, 180), meta=np.ndarray&gt;\n",
       "    salinity                         (time, depth, lat, lon) float64 19GB dask.array&lt;chunksize=(1, 21, 87, 180), meta=np.ndarray&gt;\n",
       "    temperature_uncertainty          (time, depth, lat, lon) float64 19GB dask.array&lt;chunksize=(1, 21, 87, 180), meta=np.ndarray&gt;\n",
       "    salinity_uncertainty             (time, depth, lat, lon) float64 19GB dask.array&lt;chunksize=(1, 21, 87, 180), meta=np.ndarray&gt;\n",
       "    temperature_observation_weights  (time, depth, lat, lon) float32 9GB dask.array&lt;chunksize=(1, 21, 87, 180), meta=np.ndarray&gt;\n",
       "    salinity_observation_weights     (time, depth, lat, lon) float32 9GB dask.array&lt;chunksize=(1, 21, 87, 180), meta=np.ndarray&gt;\n",
       "    time_bnds                        (time, bnds) datetime64[ns] 14kB dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\n",
       "    depth_bnds                       (time, depth, bnds) float32 298kB dask.array&lt;chunksize=(1, 42, 2), meta=np.ndarray&gt;\n",
       "Attributes: (12/22)\n",
       "    Conventions:            CF-1.0\n",
       "    title:                  Temperature and salinity analysis\n",
       "    DSD_entry_id:           UKMO-L4UHFnd-GLOB-v01\n",
       "    references:             Website and paper: https://www.metoffice.gov.uk/h...\n",
       "    institution:            UK Met Office\n",
       "    contact:                Rachel Killick - rachel.killick@metoffice.gov.uk\n",
       "    ...                     ...\n",
       "    southernmost_latitude:  -90.5\n",
       "    northernmost_latitude:  89.5\n",
       "    westernmost_longitude:  0.5\n",
       "    easternmost_longitude:  362.5\n",
       "    file_quality_index:     0\n",
       "    licence:                EN4 is distributed under the Non Commercial Gover...</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-c21dbdf2-40be-4acd-a0d4-bf1acec47b1b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c21dbdf2-40be-4acd-a0d4-bf1acec47b1b' 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'>depth</span>: 42</li><li><span class='xr-has-index'>lat</span>: 173</li><li><span class='xr-has-index'>lon</span>: 360</li><li><span class='xr-has-index'>time</span>: 888</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7ec19795-f36f-4559-84a0-9fc38f05a186' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7ec19795-f36f-4559-84a0-9fc38f05a186' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth</span></div><div class='xr-var-dims'>(depth)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.022 15.08 ... 5.051e+03 5.35e+03</div><input id='attrs-9d879485-e85b-4c35-b982-cbc1d78c880b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d879485-e85b-4c35-b982-cbc1d78c880b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4325d5f5-0d7b-44d7-b970-64c318e05fc7' class='xr-var-data-in' type='checkbox'><label for='data-4325d5f5-0d7b-44d7-b970-64c318e05fc7' 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>depth</dd><dt><span>units :</span></dt><dd>metres</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>bounds :</span></dt><dd>depth_bnds</dd></dl></div><div class='xr-var-data'><pre>array([5.021590e+00, 1.507854e+01, 2.516046e+01, 3.527829e+01, 4.544776e+01,\n",
       "       5.569149e+01, 6.604198e+01, 7.654591e+01, 8.727029e+01, 9.831118e+01,\n",
       "       1.098062e+02, 1.219519e+02, 1.350285e+02, 1.494337e+02, 1.657285e+02,\n",
       "       1.846975e+02, 2.074254e+02, 2.353862e+02, 2.705341e+02, 3.153741e+02,\n",
       "       3.729655e+02, 4.468009e+02, 5.405022e+02, 6.573229e+02, 7.995496e+02,\n",
       "       9.679958e+02, 1.161806e+03, 1.378661e+03, 1.615291e+03, 1.868071e+03,\n",
       "       2.133517e+03, 2.408583e+03, 2.690780e+03, 2.978166e+03, 3.269278e+03,\n",
       "       3.563041e+03, 3.858676e+03, 4.155628e+03, 4.453502e+03, 4.752021e+03,\n",
       "       5.050990e+03, 5.350272e+03], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>-83.0 -82.0 -81.0 ... 88.0 89.0</div><input id='attrs-547dffcc-d8f3-43f2-97f7-415894e3e517' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-547dffcc-d8f3-43f2-97f7-415894e3e517' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15e63e7d-c1a8-4d6b-9fc4-4798e86fcd02' class='xr-var-data-in' type='checkbox'><label for='data-15e63e7d-c1a8-4d6b-9fc4-4798e86fcd02' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-83., -82., -81., -80., -79., -78., -77., -76., -75., -74., -73., -72.,\n",
       "       -71., -70., -69., -68., -67., -66., -65., -64., -63., -62., -61., -60.,\n",
       "       -59., -58., -57., -56., -55., -54., -53., -52., -51., -50., -49., -48.,\n",
       "       -47., -46., -45., -44., -43., -42., -41., -40., -39., -38., -37., -36.,\n",
       "       -35., -34., -33., -32., -31., -30., -29., -28., -27., -26., -25., -24.,\n",
       "       -23., -22., -21., -20., -19., -18., -17., -16., -15., -14., -13., -12.,\n",
       "       -11., -10.,  -9.,  -8.,  -7.,  -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   0.,\n",
       "         1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.,  12.,\n",
       "        13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,  22.,  23.,  24.,\n",
       "        25.,  26.,  27.,  28.,  29.,  30.,  31.,  32.,  33.,  34.,  35.,  36.,\n",
       "        37.,  38.,  39.,  40.,  41.,  42.,  43.,  44.,  45.,  46.,  47.,  48.,\n",
       "        49.,  50.,  51.,  52.,  53.,  54.,  55.,  56.,  57.,  58.,  59.,  60.,\n",
       "        61.,  62.,  63.,  64.,  65.,  66.,  67.,  68.,  69.,  70.,  71.,  72.,\n",
       "        73.,  74.,  75.,  76.,  77.,  78.,  79.,  80.,  81.,  82.,  83.,  84.,\n",
       "        85.,  86.,  87.,  88.,  89.], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>1.0 2.0 3.0 ... 358.0 359.0 360.0</div><input id='attrs-952465db-f738-464d-9d05-4516085cae07' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-952465db-f738-464d-9d05-4516085cae07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b767246c-3eee-4691-949e-d53676963fe2' class='xr-var-data-in' type='checkbox'><label for='data-b767246c-3eee-4691-949e-d53676963fe2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  1.,   2.,   3., ..., 358., 359., 360.], dtype=float32)</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'>1950-01-16T12:00:00 ... 2023-12-...</div><input id='attrs-afa19908-434a-480a-a2e6-656133bf3791' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-afa19908-434a-480a-a2e6-656133bf3791' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7bc7729-1cc8-4e0f-815b-2123f83fe1ad' class='xr-var-data-in' type='checkbox'><label for='data-e7bc7729-1cc8-4e0f-815b-2123f83fe1ad' 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>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1950-01-16T12:00:00.000000000&#x27;, &#x27;1950-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1950-03-16T12:00:00.000000000&#x27;, ..., &#x27;2023-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2023-11-16T00:00:00.000000000&#x27;, &#x27;2023-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f68140ae-987c-4d96-ab68-e16bbed2d8fb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f68140ae-987c-4d96-ab68-e16bbed2d8fb' class='xr-section-summary' >Data variables: <span>(8)</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>temperature</span></div><div class='xr-var-dims'>(time, depth, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 21, 87, 180), meta=np.ndarray&gt;</div><input id='attrs-4a147655-9f1a-4b1d-8596-f34573133494' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a147655-9f1a-4b1d-8596-f34573133494' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d708a6d-6084-4147-bb2f-125d27658ec3' class='xr-var-data-in' type='checkbox'><label for='data-3d708a6d-6084-4147-bb2f-125d27658ec3' 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>temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_min :</span></dt><dd>-5.0</dd><dt><span>valid_max :</span></dt><dd>45.0</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> 17.31 GiB </td>\n",
       "                        <td> 2.51 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (888, 42, 173, 360) </td>\n",
       "                        <td> (1, 21, 87, 180) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 7104 chunks in 1813 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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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salinity</span></div><div class='xr-var-dims'>(time, depth, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 21, 87, 180), meta=np.ndarray&gt;</div><input id='attrs-cf331676-5cfb-4598-8584-ee515c116ab5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf331676-5cfb-4598-8584-ee515c116ab5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90032b38-7b30-4e09-a51c-371de68a9cd7' class='xr-var-data-in' type='checkbox'><label for='data-90032b38-7b30-4e09-a51c-371de68a9cd7' 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>salinity</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd><dt><span>valid_min :</span></dt><dd>-5.0</dd><dt><span>valid_max :</span></dt><dd>48.0</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                <thead>\n",
       "                    <tr>\n",
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       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 17.31 GiB </td>\n",
       "                        <td> 2.51 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (888, 42, 173, 360) </td>\n",
       "                        <td> (1, 21, 87, 180) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                        <th> Data type </th>\n",
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       "                        <td> 8.65 GiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 8.65 GiB </td>\n",
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       "  <text x=\"190.047221\" y=\"71.945465\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,190.047221,71.945465)\">42</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</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&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-935edae2-ecd7-4811-ac82-341c671dff30' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-935edae2-ecd7-4811-ac82-341c671dff30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d7249e8-d1ce-4e5f-83aa-f088cfca06b5' class='xr-var-data-in' type='checkbox'><label for='data-2d7249e8-d1ce-4e5f-83aa-f088cfca06b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 13.88 kiB </td>\n",
       "                        <td> 16 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (888, 2) </td>\n",
       "                        <td> (1, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 888 chunks in 1777 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">888</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth_bnds</span></div><div class='xr-var-dims'>(time, depth, bnds)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 42, 2), meta=np.ndarray&gt;</div><input id='attrs-66f1ea07-5238-402b-ae8e-2f5a6d554693' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-66f1ea07-5238-402b-ae8e-2f5a6d554693' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-089b03fe-54c9-4b1e-b4c5-ab2775314e85' class='xr-var-data-in' type='checkbox'><label for='data-089b03fe-54c9-4b1e-b4c5-ab2775314e85' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 291.38 kiB </td>\n",
       "                        <td> 336 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (888, 42, 2) </td>\n",
       "                        <td> (1, 42, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 888 chunks in 2665 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "  <text x=\"35.294118\" y=\"89.454668\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,89.454668)\">888</text>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-0510457b-986f-4ec3-a4a8-e08b4eaa722d' class='xr-section-summary-in' type='checkbox'  ><label for='section-0510457b-986f-4ec3-a4a8-e08b4eaa722d' class='xr-section-summary' >Indexes: <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-index-name'><div>depth</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b96b9b86-d280-4e0d-9bb1-dee04bea3a46' class='xr-index-data-in' type='checkbox'/><label for='index-b96b9b86-d280-4e0d-9bb1-dee04bea3a46' 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([ 5.021589756011963, 15.078539848327637, 25.160459518432617,\n",
       "       35.278289794921875,  45.44776153564453,  55.69149398803711,\n",
       "        66.04198455810547,  76.54591369628906,   87.2702865600586,\n",
       "         98.3111801147461,  109.8061752319336, 121.95189666748047,\n",
       "       135.02854919433594, 149.43373107910156, 165.72845458984375,\n",
       "        184.6974639892578, 207.42544555664062, 235.38616943359375,\n",
       "       270.53411865234375, 315.37408447265625,  372.9654541015625,\n",
       "        446.8009338378906,   540.502197265625,  657.3229370117188,\n",
       "        799.5496215820312,   967.995849609375,  1161.805908203125,\n",
       "       1378.6610107421875,   1615.29052734375, 1868.0706787109375,\n",
       "         2133.51708984375,   2408.58349609375,    2690.7802734375,\n",
       "           2978.166015625,  3269.278076171875,  3563.040771484375,\n",
       "         3858.67626953125,    4155.6279296875,     4453.501953125,\n",
       "         4752.02099609375,   5050.98974609375,   5350.27197265625],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;depth&#x27;))</pre></div></li><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-a8559885-339e-4a0d-8679-fd52ef0abac8' class='xr-index-data-in' type='checkbox'/><label for='index-a8559885-339e-4a0d-8679-fd52ef0abac8' 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([-83.0, -82.0, -81.0, -80.0, -79.0, -78.0, -77.0, -76.0, -75.0, -74.0,\n",
       "       ...\n",
       "        80.0,  81.0,  82.0,  83.0,  84.0,  85.0,  86.0,  87.0,  88.0,  89.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=173))</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-e1866698-8bfd-48ac-b949-597d561fafa0' class='xr-index-data-in' type='checkbox'/><label for='index-e1866698-8bfd-48ac-b949-597d561fafa0' 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([  1.0,   2.0,   3.0,   4.0,   5.0,   6.0,   7.0,   8.0,   9.0,  10.0,\n",
       "       ...\n",
       "       351.0, 352.0, 353.0, 354.0, 355.0, 356.0, 357.0, 358.0, 359.0, 360.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=360))</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-31c030e6-a3dc-40fd-b346-794d72fab387' class='xr-index-data-in' type='checkbox'/><label for='index-31c030e6-a3dc-40fd-b346-794d72fab387' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1950-01-16 12:00:00&#x27;, &#x27;1950-02-15 00:00:00&#x27;,\n",
       "               &#x27;1950-03-16 12:00:00&#x27;, &#x27;1950-04-16 00:00:00&#x27;,\n",
       "               &#x27;1950-05-16 12:00:00&#x27;, &#x27;1950-06-16 00:00:00&#x27;,\n",
       "               &#x27;1950-07-16 12:00:00&#x27;, &#x27;1950-08-16 12:00:00&#x27;,\n",
       "               &#x27;1950-09-16 00:00:00&#x27;, &#x27;1950-10-16 12:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2023-03-16 12:00:00&#x27;, &#x27;2023-04-16 00:00:00&#x27;,\n",
       "               &#x27;2023-05-16 12:00:00&#x27;, &#x27;2023-06-16 00:00:00&#x27;,\n",
       "               &#x27;2023-07-16 12:00:00&#x27;, &#x27;2023-08-16 12:00:00&#x27;,\n",
       "               &#x27;2023-09-16 00:00:00&#x27;, &#x27;2023-10-16 12:00:00&#x27;,\n",
       "               &#x27;2023-11-16 00:00:00&#x27;, &#x27;2023-12-16 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=888, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-30f58ad8-51a9-44fd-b408-fd9c296bf770' class='xr-section-summary-in' type='checkbox'  ><label for='section-30f58ad8-51a9-44fd-b408-fd9c296bf770' class='xr-section-summary' >Attributes: <span>(22)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>title :</span></dt><dd>Temperature and salinity analysis</dd><dt><span>DSD_entry_id :</span></dt><dd>UKMO-L4UHFnd-GLOB-v01</dd><dt><span>references :</span></dt><dd>Website and paper: https://www.metoffice.gov.uk/hadobs/en4/; Good, S. A., M. J. Martin and N. A. Rayner, 2013. EN4: quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates, Journal of Geophysical Research: Oceans, 118, 6704-6716, doi:10.1002/2013JC009067</dd><dt><span>institution :</span></dt><dd>UK Met Office</dd><dt><span>contact :</span></dt><dd>Rachel Killick - rachel.killick@metoffice.gov.uk</dd><dt><span>GDS_version_id :</span></dt><dd>v1.7</dd><dt><span>netcdf_version_id :</span></dt><dd>3.5</dd><dt><span>creation_date :</span></dt><dd>2021-03-16 15:08:37.447 -00:00</dd><dt><span>product_version :</span></dt><dd>1.0</dd><dt><span>history :</span></dt><dd></dd><dt><span>grid_resolution :</span></dt><dd>   1.00000 degree</dd><dt><span>start_date :</span></dt><dd>2001-01-01 UTC</dd><dt><span>start_time :</span></dt><dd>00:00:00 UTC</dd><dt><span>stop_date :</span></dt><dd>2001-01-01 UTC</dd><dt><span>stop_time :</span></dt><dd>00:00:00 UTC</dd><dt><span>southernmost_latitude :</span></dt><dd>-90.5</dd><dt><span>northernmost_latitude :</span></dt><dd>89.5</dd><dt><span>westernmost_longitude :</span></dt><dd>0.5</dd><dt><span>easternmost_longitude :</span></dt><dd>362.5</dd><dt><span>file_quality_index :</span></dt><dd>0</dd><dt><span>licence :</span></dt><dd>EN4 is distributed under the Non Commercial Government Licence: http://www.nationalarchives.gov.uk/doc/non-commercial-government-licence/version/2/. The data are available for non-commercial use with attribution to the data providers, please see the product website (see global attribute: references) for terms and conditions.</dd></dl></div></li></ul></div></div>"
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       "<xarray.Dataset> Size: 93GB\n",
       "Dimensions:                          (depth: 42, lat: 173, lon: 360, time: 888,\n",
       "                                      bnds: 2)\n",
       "Coordinates:\n",
       "  * depth                            (depth) float32 168B 5.022 ... 5.35e+03\n",
       "  * lat                              (lat) float32 692B -83.0 -82.0 ... 89.0\n",
       "  * lon                              (lon) float32 1kB 1.0 2.0 ... 359.0 360.0\n",
       "  * time                             (time) datetime64[ns] 7kB 1950-01-16T12:...\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    temperature                      (time, depth, lat, lon) float64 19GB dask.array<chunksize=(1, 21, 87, 180), meta=np.ndarray>\n",
       "    salinity                         (time, depth, lat, lon) float64 19GB dask.array<chunksize=(1, 21, 87, 180), meta=np.ndarray>\n",
       "    temperature_uncertainty          (time, depth, lat, lon) float64 19GB dask.array<chunksize=(1, 21, 87, 180), meta=np.ndarray>\n",
       "    salinity_uncertainty             (time, depth, lat, lon) float64 19GB dask.array<chunksize=(1, 21, 87, 180), meta=np.ndarray>\n",
       "    temperature_observation_weights  (time, depth, lat, lon) float32 9GB dask.array<chunksize=(1, 21, 87, 180), meta=np.ndarray>\n",
       "    salinity_observation_weights     (time, depth, lat, lon) float32 9GB dask.array<chunksize=(1, 21, 87, 180), meta=np.ndarray>\n",
       "    time_bnds                        (time, bnds) datetime64[ns] 14kB dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "    depth_bnds                       (time, depth, bnds) float32 298kB dask.array<chunksize=(1, 42, 2), meta=np.ndarray>\n",
       "Attributes: (12/22)\n",
       "    Conventions:            CF-1.0\n",
       "    title:                  Temperature and salinity analysis\n",
       "    DSD_entry_id:           UKMO-L4UHFnd-GLOB-v01\n",
       "    references:             Website and paper: https://www.metoffice.gov.uk/h...\n",
       "    institution:            UK Met Office\n",
       "    contact:                Rachel Killick - rachel.killick@metoffice.gov.uk\n",
       "    ...                     ...\n",
       "    southernmost_latitude:  -90.5\n",
       "    northernmost_latitude:  89.5\n",
       "    westernmost_longitude:  0.5\n",
       "    easternmost_longitude:  362.5\n",
       "    file_quality_index:     0\n",
       "    licence:                EN4 is distributed under the Non Commercial Gover..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a3a7c54c-f42b-42c1-afbe-4acd4387b213",
   "metadata": {},
   "outputs": [],
   "source": [
    "EN = data.drop_vars(\"temperature_uncertainty\").drop_vars(\"salinity_uncertainty\")\n",
    "EN = EN.drop_vars(\"time_bnds\").drop_vars(\"depth_bnds\")\n",
    "EN = EN.drop_vars(\"temperature_observation_weights\").drop_vars(\"salinity_observation_weights\")"
   ]
  },
  {
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   "execution_count": 7,
   "id": "f8ed626f-6a35-4ce0-9e5b-67f2621951d3",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 37GB\n",
       "Dimensions:      (depth: 42, lat: 173, lon: 360, time: 888)\n",
       "Coordinates:\n",
       "  * depth        (depth) float32 168B 5.022 15.08 25.16 ... 5.051e+03 5.35e+03\n",
       "  * lat          (lat) float32 692B -83.0 -82.0 -81.0 -80.0 ... 87.0 88.0 89.0\n",
       "  * lon          (lon) float32 1kB 1.0 2.0 3.0 4.0 ... 357.0 358.0 359.0 360.0\n",
       "  * time         (time) datetime64[ns] 7kB 1950-01-16T12:00:00 ... 2023-12-16...\n",
       "Data variables:\n",
       "    temperature  (time, depth, lat, lon) float64 19GB dask.array&lt;chunksize=(1, 21, 87, 180), meta=np.ndarray&gt;\n",
       "    salinity     (time, depth, lat, lon) float64 19GB dask.array&lt;chunksize=(1, 21, 87, 180), meta=np.ndarray&gt;\n",
       "Attributes: (12/22)\n",
       "    Conventions:            CF-1.0\n",
       "    title:                  Temperature and salinity analysis\n",
       "    DSD_entry_id:           UKMO-L4UHFnd-GLOB-v01\n",
       "    references:             Website and paper: https://www.metoffice.gov.uk/h...\n",
       "    institution:            UK Met Office\n",
       "    contact:                Rachel Killick - rachel.killick@metoffice.gov.uk\n",
       "    ...                     ...\n",
       "    southernmost_latitude:  -90.5\n",
       "    northernmost_latitude:  89.5\n",
       "    westernmost_longitude:  0.5\n",
       "    easternmost_longitude:  362.5\n",
       "    file_quality_index:     0\n",
       "    licence:                EN4 is distributed under the Non Commercial Gover...</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-611c1cce-288c-4757-b30b-9025b47ab076' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-611c1cce-288c-4757-b30b-9025b47ab076' 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'>depth</span>: 42</li><li><span class='xr-has-index'>lat</span>: 173</li><li><span class='xr-has-index'>lon</span>: 360</li><li><span class='xr-has-index'>time</span>: 888</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7e4f23a3-1c00-46f5-9006-cf88b24aeae6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7e4f23a3-1c00-46f5-9006-cf88b24aeae6' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth</span></div><div class='xr-var-dims'>(depth)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.022 15.08 ... 5.051e+03 5.35e+03</div><input id='attrs-2aa31f5f-ce1a-40ca-bd2f-af647b4f1e1f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2aa31f5f-ce1a-40ca-bd2f-af647b4f1e1f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b602d32f-5c8a-4774-bede-3a9745ac9d90' class='xr-var-data-in' type='checkbox'><label for='data-b602d32f-5c8a-4774-bede-3a9745ac9d90' 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>depth</dd><dt><span>units :</span></dt><dd>metres</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>bounds :</span></dt><dd>depth_bnds</dd></dl></div><div class='xr-var-data'><pre>array([5.021590e+00, 1.507854e+01, 2.516046e+01, 3.527829e+01, 4.544776e+01,\n",
       "       5.569149e+01, 6.604198e+01, 7.654591e+01, 8.727029e+01, 9.831118e+01,\n",
       "       1.098062e+02, 1.219519e+02, 1.350285e+02, 1.494337e+02, 1.657285e+02,\n",
       "       1.846975e+02, 2.074254e+02, 2.353862e+02, 2.705341e+02, 3.153741e+02,\n",
       "       3.729655e+02, 4.468009e+02, 5.405022e+02, 6.573229e+02, 7.995496e+02,\n",
       "       9.679958e+02, 1.161806e+03, 1.378661e+03, 1.615291e+03, 1.868071e+03,\n",
       "       2.133517e+03, 2.408583e+03, 2.690780e+03, 2.978166e+03, 3.269278e+03,\n",
       "       3.563041e+03, 3.858676e+03, 4.155628e+03, 4.453502e+03, 4.752021e+03,\n",
       "       5.050990e+03, 5.350272e+03], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>-83.0 -82.0 -81.0 ... 88.0 89.0</div><input id='attrs-ef8bb64e-497e-48e4-9e92-fbd31393a2aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef8bb64e-497e-48e4-9e92-fbd31393a2aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02e29754-4df8-4c56-b44a-d029bceae76a' class='xr-var-data-in' type='checkbox'><label for='data-02e29754-4df8-4c56-b44a-d029bceae76a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-83., -82., -81., -80., -79., -78., -77., -76., -75., -74., -73., -72.,\n",
       "       -71., -70., -69., -68., -67., -66., -65., -64., -63., -62., -61., -60.,\n",
       "       -59., -58., -57., -56., -55., -54., -53., -52., -51., -50., -49., -48.,\n",
       "       -47., -46., -45., -44., -43., -42., -41., -40., -39., -38., -37., -36.,\n",
       "       -35., -34., -33., -32., -31., -30., -29., -28., -27., -26., -25., -24.,\n",
       "       -23., -22., -21., -20., -19., -18., -17., -16., -15., -14., -13., -12.,\n",
       "       -11., -10.,  -9.,  -8.,  -7.,  -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   0.,\n",
       "         1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.,  12.,\n",
       "        13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,  22.,  23.,  24.,\n",
       "        25.,  26.,  27.,  28.,  29.,  30.,  31.,  32.,  33.,  34.,  35.,  36.,\n",
       "        37.,  38.,  39.,  40.,  41.,  42.,  43.,  44.,  45.,  46.,  47.,  48.,\n",
       "        49.,  50.,  51.,  52.,  53.,  54.,  55.,  56.,  57.,  58.,  59.,  60.,\n",
       "        61.,  62.,  63.,  64.,  65.,  66.,  67.,  68.,  69.,  70.,  71.,  72.,\n",
       "        73.,  74.,  75.,  76.,  77.,  78.,  79.,  80.,  81.,  82.,  83.,  84.,\n",
       "        85.,  86.,  87.,  88.,  89.], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>1.0 2.0 3.0 ... 358.0 359.0 360.0</div><input id='attrs-afd6a0a5-ab2f-4bec-84f1-677bb749e65d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-afd6a0a5-ab2f-4bec-84f1-677bb749e65d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ec26697-31c6-4b0f-b220-cae84b4877ce' class='xr-var-data-in' type='checkbox'><label for='data-4ec26697-31c6-4b0f-b220-cae84b4877ce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  1.,   2.,   3., ..., 358., 359., 360.], dtype=float32)</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'>1950-01-16T12:00:00 ... 2023-12-...</div><input id='attrs-78aff90c-4120-4326-830d-2340c00e3ec7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78aff90c-4120-4326-830d-2340c00e3ec7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8fe4b90a-28c5-4ce6-9eac-9a80a1301fc0' class='xr-var-data-in' type='checkbox'><label for='data-8fe4b90a-28c5-4ce6-9eac-9a80a1301fc0' 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>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1950-01-16T12:00:00.000000000&#x27;, &#x27;1950-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1950-03-16T12:00:00.000000000&#x27;, ..., &#x27;2023-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2023-11-16T00:00:00.000000000&#x27;, &#x27;2023-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-be54fe8c-985e-42f2-bc78-5dbd8160dd44' class='xr-section-summary-in' type='checkbox'  checked><label for='section-be54fe8c-985e-42f2-bc78-5dbd8160dd44' 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>temperature</span></div><div class='xr-var-dims'>(time, depth, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 21, 87, 180), meta=np.ndarray&gt;</div><input id='attrs-34e5406d-1ab2-4627-9212-837270953d0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34e5406d-1ab2-4627-9212-837270953d0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d9d3bf9-e8bc-47da-b8a1-7628b9d18285' class='xr-var-data-in' type='checkbox'><label for='data-2d9d3bf9-e8bc-47da-b8a1-7628b9d18285' 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>temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_min :</span></dt><dd>-5.0</dd><dt><span>valid_max :</span></dt><dd>45.0</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> 17.31 GiB </td>\n",
       "                        <td> 2.51 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (888, 42, 173, 360) </td>\n",
       "                        <td> (1, 21, 87, 180) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 7104 chunks in 1813 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",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salinity</span></div><div class='xr-var-dims'>(time, depth, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 21, 87, 180), meta=np.ndarray&gt;</div><input id='attrs-dad69237-80f8-4f29-9f7b-711a4b487e0c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dad69237-80f8-4f29-9f7b-711a4b487e0c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d73ad65a-16b8-4f2e-8160-a40903f4366e' class='xr-var-data-in' type='checkbox'><label for='data-d73ad65a-16b8-4f2e-8160-a40903f4366e' 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>salinity</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd><dt><span>valid_min :</span></dt><dd>-5.0</dd><dt><span>valid_max :</span></dt><dd>48.0</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> 17.31 GiB </td>\n",
       "                        <td> 2.51 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (888, 42, 173, 360) </td>\n",
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       "       35.278289794921875,  45.44776153564453,  55.69149398803711,\n",
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       "        446.8009338378906,   540.502197265625,  657.3229370117188,\n",
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       "       1378.6610107421875,   1615.29052734375, 1868.0706787109375,\n",
       "         2133.51708984375,   2408.58349609375,    2690.7802734375,\n",
       "           2978.166015625,  3269.278076171875,  3563.040771484375,\n",
       "         3858.67626953125,    4155.6279296875,     4453.501953125,\n",
       "         4752.02099609375,   5050.98974609375,   5350.27197265625],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;depth&#x27;))</pre></div></li><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-6ce2ecc7-1819-4ce4-a2ab-44360c3f83dd' class='xr-index-data-in' type='checkbox'/><label for='index-6ce2ecc7-1819-4ce4-a2ab-44360c3f83dd' 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([-83.0, -82.0, -81.0, -80.0, -79.0, -78.0, -77.0, -76.0, -75.0, -74.0,\n",
       "       ...\n",
       "        80.0,  81.0,  82.0,  83.0,  84.0,  85.0,  86.0,  87.0,  88.0,  89.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=173))</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-eede4939-09e0-4d7f-bb40-ae2e9f675709' class='xr-index-data-in' type='checkbox'/><label for='index-eede4939-09e0-4d7f-bb40-ae2e9f675709' 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([  1.0,   2.0,   3.0,   4.0,   5.0,   6.0,   7.0,   8.0,   9.0,  10.0,\n",
       "       ...\n",
       "       351.0, 352.0, 353.0, 354.0, 355.0, 356.0, 357.0, 358.0, 359.0, 360.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=360))</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-ff00e66d-23db-48fb-9658-afc1266c3d3d' class='xr-index-data-in' type='checkbox'/><label for='index-ff00e66d-23db-48fb-9658-afc1266c3d3d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1950-01-16 12:00:00&#x27;, &#x27;1950-02-15 00:00:00&#x27;,\n",
       "               &#x27;1950-03-16 12:00:00&#x27;, &#x27;1950-04-16 00:00:00&#x27;,\n",
       "               &#x27;1950-05-16 12:00:00&#x27;, &#x27;1950-06-16 00:00:00&#x27;,\n",
       "               &#x27;1950-07-16 12:00:00&#x27;, &#x27;1950-08-16 12:00:00&#x27;,\n",
       "               &#x27;1950-09-16 00:00:00&#x27;, &#x27;1950-10-16 12:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2023-03-16 12:00:00&#x27;, &#x27;2023-04-16 00:00:00&#x27;,\n",
       "               &#x27;2023-05-16 12:00:00&#x27;, &#x27;2023-06-16 00:00:00&#x27;,\n",
       "               &#x27;2023-07-16 12:00:00&#x27;, &#x27;2023-08-16 12:00:00&#x27;,\n",
       "               &#x27;2023-09-16 00:00:00&#x27;, &#x27;2023-10-16 12:00:00&#x27;,\n",
       "               &#x27;2023-11-16 00:00:00&#x27;, &#x27;2023-12-16 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=888, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3f1e48bb-a443-42cb-85f2-621ee120de02' class='xr-section-summary-in' type='checkbox'  ><label for='section-3f1e48bb-a443-42cb-85f2-621ee120de02' class='xr-section-summary' >Attributes: <span>(22)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>title :</span></dt><dd>Temperature and salinity analysis</dd><dt><span>DSD_entry_id :</span></dt><dd>UKMO-L4UHFnd-GLOB-v01</dd><dt><span>references :</span></dt><dd>Website and paper: https://www.metoffice.gov.uk/hadobs/en4/; Good, S. A., M. J. Martin and N. A. Rayner, 2013. EN4: quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates, Journal of Geophysical Research: Oceans, 118, 6704-6716, doi:10.1002/2013JC009067</dd><dt><span>institution :</span></dt><dd>UK Met Office</dd><dt><span>contact :</span></dt><dd>Rachel Killick - rachel.killick@metoffice.gov.uk</dd><dt><span>GDS_version_id :</span></dt><dd>v1.7</dd><dt><span>netcdf_version_id :</span></dt><dd>3.5</dd><dt><span>creation_date :</span></dt><dd>2021-03-16 15:08:37.447 -00:00</dd><dt><span>product_version :</span></dt><dd>1.0</dd><dt><span>history :</span></dt><dd></dd><dt><span>grid_resolution :</span></dt><dd>   1.00000 degree</dd><dt><span>start_date :</span></dt><dd>2001-01-01 UTC</dd><dt><span>start_time :</span></dt><dd>00:00:00 UTC</dd><dt><span>stop_date :</span></dt><dd>2001-01-01 UTC</dd><dt><span>stop_time :</span></dt><dd>00:00:00 UTC</dd><dt><span>southernmost_latitude :</span></dt><dd>-90.5</dd><dt><span>northernmost_latitude :</span></dt><dd>89.5</dd><dt><span>westernmost_longitude :</span></dt><dd>0.5</dd><dt><span>easternmost_longitude :</span></dt><dd>362.5</dd><dt><span>file_quality_index :</span></dt><dd>0</dd><dt><span>licence :</span></dt><dd>EN4 is distributed under the Non Commercial Government Licence: http://www.nationalarchives.gov.uk/doc/non-commercial-government-licence/version/2/. The data are available for non-commercial use with attribution to the data providers, please see the product website (see global attribute: references) for terms and conditions.</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 37GB\n",
       "Dimensions:      (depth: 42, lat: 173, lon: 360, time: 888)\n",
       "Coordinates:\n",
       "  * depth        (depth) float32 168B 5.022 15.08 25.16 ... 5.051e+03 5.35e+03\n",
       "  * lat          (lat) float32 692B -83.0 -82.0 -81.0 -80.0 ... 87.0 88.0 89.0\n",
       "  * lon          (lon) float32 1kB 1.0 2.0 3.0 4.0 ... 357.0 358.0 359.0 360.0\n",
       "  * time         (time) datetime64[ns] 7kB 1950-01-16T12:00:00 ... 2023-12-16...\n",
       "Data variables:\n",
       "    temperature  (time, depth, lat, lon) float64 19GB dask.array<chunksize=(1, 21, 87, 180), meta=np.ndarray>\n",
       "    salinity     (time, depth, lat, lon) float64 19GB dask.array<chunksize=(1, 21, 87, 180), meta=np.ndarray>\n",
       "Attributes: (12/22)\n",
       "    Conventions:            CF-1.0\n",
       "    title:                  Temperature and salinity analysis\n",
       "    DSD_entry_id:           UKMO-L4UHFnd-GLOB-v01\n",
       "    references:             Website and paper: https://www.metoffice.gov.uk/h...\n",
       "    institution:            UK Met Office\n",
       "    contact:                Rachel Killick - rachel.killick@metoffice.gov.uk\n",
       "    ...                     ...\n",
       "    southernmost_latitude:  -90.5\n",
       "    northernmost_latitude:  89.5\n",
       "    westernmost_longitude:  0.5\n",
       "    easternmost_longitude:  362.5\n",
       "    file_quality_index:     0\n",
       "    licence:                EN4 is distributed under the Non Commercial Gover..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cb61952d-c3e6-4db2-8441-ae597c9dcd16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1 , 1 , figsize = (14,8))\n",
    "ax.plot(EN.time,\n",
    "        EN.temperature.sel(lat=slice( -60, 60)).mean(dim='lat').mean(dim='lon').sel(depth=200,method=\"nearest\"),\n",
    "        label = '200')\n",
    "ax.grid(linestyle='-', linewidth=.9);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cb98ffa5-2a44-49a0-a32d-f76201d8205e",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mEN\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_netcdf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mEN_Global_Jan1950_Dic2023.nc\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/ocean/lib/python3.11/site-packages/xarray/core/dataset.py:2298\u001b[0m, in \u001b[0;36mDataset.to_netcdf\u001b[0;34m(self, path, mode, format, group, engine, encoding, unlimited_dims, compute, invalid_netcdf)\u001b[0m\n\u001b[1;32m   2295\u001b[0m     encoding \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m   2296\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mxarray\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m to_netcdf\n\u001b[0;32m-> 2298\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mto_netcdf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore  # mypy cannot resolve the overloads:(\u001b[39;49;00m\n\u001b[1;32m   2299\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2300\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2301\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2302\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2303\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2304\u001b[0m \u001b[43m    \u001b[49m\u001b[43mengine\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2305\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2306\u001b[0m \u001b[43m    \u001b[49m\u001b[43munlimited_dims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43munlimited_dims\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2307\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompute\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompute\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2308\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmultifile\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   2309\u001b[0m \u001b[43m    \u001b[49m\u001b[43minvalid_netcdf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minvalid_netcdf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2310\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/ocean/lib/python3.11/site-packages/xarray/backends/api.py:1348\u001b[0m, in \u001b[0;36mto_netcdf\u001b[0;34m(dataset, path_or_file, mode, format, group, engine, encoding, unlimited_dims, compute, multifile, invalid_netcdf)\u001b[0m\n\u001b[1;32m   1345\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m multifile:\n\u001b[1;32m   1346\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m writer, store\n\u001b[0;32m-> 1348\u001b[0m writes \u001b[38;5;241m=\u001b[39m \u001b[43mwriter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msync\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcompute\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompute\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1350\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(target, BytesIO):\n\u001b[1;32m   1351\u001b[0m     store\u001b[38;5;241m.\u001b[39msync()\n",
      "File \u001b[0;32m~/miniconda3/envs/ocean/lib/python3.11/site-packages/xarray/backends/common.py:297\u001b[0m, in \u001b[0;36mArrayWriter.sync\u001b[0;34m(self, compute, chunkmanager_store_kwargs)\u001b[0m\n\u001b[1;32m    294\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunkmanager_store_kwargs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    295\u001b[0m     chunkmanager_store_kwargs \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m--> 297\u001b[0m delayed_store \u001b[38;5;241m=\u001b[39m \u001b[43mchunkmanager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstore\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    298\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msources\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    299\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtargets\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    300\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlock\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    301\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompute\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompute\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    302\u001b[0m \u001b[43m    \u001b[49m\u001b[43mflush\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    303\u001b[0m \u001b[43m    \u001b[49m\u001b[43mregions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mregions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    304\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mchunkmanager_store_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    305\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    306\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msources \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m    307\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtargets \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m~/miniconda3/envs/ocean/lib/python3.11/site-packages/xarray/namedarray/daskmanager.py:249\u001b[0m, in \u001b[0;36mDaskManager.store\u001b[0;34m(self, sources, targets, **kwargs)\u001b[0m\n\u001b[1;32m    241\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mstore\u001b[39m(\n\u001b[1;32m    242\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    243\u001b[0m     sources: Any \u001b[38;5;241m|\u001b[39m Sequence[Any],\n\u001b[1;32m    244\u001b[0m     targets: Any,\n\u001b[1;32m    245\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: Any,\n\u001b[1;32m    246\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[1;32m    247\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdask\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01marray\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m store\n\u001b[0;32m--> 249\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mstore\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    250\u001b[0m \u001b[43m        \u001b[49m\u001b[43msources\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msources\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    251\u001b[0m \u001b[43m        \u001b[49m\u001b[43mtargets\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtargets\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    252\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    253\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/ocean/lib/python3.11/site-packages/dask/array/core.py:1236\u001b[0m, in \u001b[0;36mstore\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m   1234\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m compute:\n\u001b[1;32m   1235\u001b[0m     store_dsk \u001b[38;5;241m=\u001b[39m HighLevelGraph(layers, dependencies)\n\u001b[0;32m-> 1236\u001b[0m     \u001b[43mcompute_as_if_collection\u001b[49m\u001b[43m(\u001b[49m\u001b[43mArray\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstore_dsk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmap_keys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1237\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m   1239\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[0;32m~/miniconda3/envs/ocean/lib/python3.11/site-packages/dask/base.py:402\u001b[0m, in \u001b[0;36mcompute_as_if_collection\u001b[0;34m(cls, dsk, keys, scheduler, get, **kwargs)\u001b[0m\n\u001b[1;32m    400\u001b[0m schedule \u001b[38;5;241m=\u001b[39m get_scheduler(scheduler\u001b[38;5;241m=\u001b[39mscheduler, \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mcls\u001b[39m, get\u001b[38;5;241m=\u001b[39mget)\n\u001b[1;32m    401\u001b[0m dsk2 \u001b[38;5;241m=\u001b[39m optimization_function(\u001b[38;5;28mcls\u001b[39m)(dsk, keys, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m--> 402\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mschedule\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdsk2\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/ocean/lib/python3.11/site-packages/dask/threaded.py:90\u001b[0m, in \u001b[0;36mget\u001b[0;34m(dsk, keys, cache, num_workers, pool, **kwargs)\u001b[0m\n\u001b[1;32m     87\u001b[0m     \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(pool, multiprocessing\u001b[38;5;241m.\u001b[39mpool\u001b[38;5;241m.\u001b[39mPool):\n\u001b[1;32m     88\u001b[0m         pool \u001b[38;5;241m=\u001b[39m MultiprocessingPoolExecutor(pool)\n\u001b[0;32m---> 90\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mget_async\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     91\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubmit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     92\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_max_workers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     93\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdsk\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     94\u001b[0m \u001b[43m    \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     95\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     96\u001b[0m \u001b[43m    \u001b[49m\u001b[43mget_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_thread_get_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     97\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpack_exception\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpack_exception\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     98\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     99\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    101\u001b[0m \u001b[38;5;66;03m# Cleanup pools associated to dead threads\u001b[39;00m\n\u001b[1;32m    102\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m pools_lock:\n",
      "File \u001b[0;32m~/miniconda3/envs/ocean/lib/python3.11/site-packages/dask/local.py:501\u001b[0m, in \u001b[0;36mget_async\u001b[0;34m(submit, num_workers, dsk, result, cache, get_id, rerun_exceptions_locally, pack_exception, raise_exception, callbacks, dumps, loads, chunksize, **kwargs)\u001b[0m\n\u001b[1;32m    499\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m state[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwaiting\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;129;01mor\u001b[39;00m state[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mready\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;129;01mor\u001b[39;00m state[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrunning\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m    500\u001b[0m     fire_tasks(chunksize)\n\u001b[0;32m--> 501\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m key, res_info, failed \u001b[38;5;129;01min\u001b[39;00m \u001b[43mqueue_get\u001b[49m\u001b[43m(\u001b[49m\u001b[43mqueue\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mresult():\n\u001b[1;32m    502\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m failed:\n\u001b[1;32m    503\u001b[0m             exc, tb \u001b[38;5;241m=\u001b[39m loads(res_info)\n",
      "File \u001b[0;32m~/miniconda3/envs/ocean/lib/python3.11/site-packages/dask/local.py:138\u001b[0m, in \u001b[0;36mqueue_get\u001b[0;34m(q)\u001b[0m\n\u001b[1;32m    137\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mqueue_get\u001b[39m(q):\n\u001b[0;32m--> 138\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mq\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/miniconda3/envs/ocean/lib/python3.11/queue.py:171\u001b[0m, in \u001b[0;36mQueue.get\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m    169\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    170\u001b[0m     \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_qsize():\n\u001b[0;32m--> 171\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnot_empty\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    172\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m timeout \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    173\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m must be a non-negative number\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m~/miniconda3/envs/ocean/lib/python3.11/threading.py:327\u001b[0m, in \u001b[0;36mCondition.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    325\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:    \u001b[38;5;66;03m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[39;00m\n\u001b[1;32m    326\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 327\u001b[0m         \u001b[43mwaiter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43macquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    328\u001b[0m         gotit \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m    329\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "EN.to_netcdf('EN_Global_Jan1950_Dic2023.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38183c7e-2e8b-4f00-b81b-428646711294",
   "metadata": {},
   "outputs": [],
   "source": [
    "EN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4594277d-f81f-4162-bd64-36a871472b5d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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