docs/tutorial.ipynb

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    "# Tutorial\n",
    "\n",
    "This repository contains several utility functions that enable easier analysis across\n",
    "CMIP6 model data.\n",
    "\n",
    "It offers solutions to the following problems:\n",
    "\n",
    "1. [Inconsistent naming of dimensions and coordinates](#rename)\n",
    "2. [Inconsistent values,shape and dataset location of coordinates](#coords)\n",
    "3. [Inconsistent longitude conventions](#lon)\n",
    "4. [Inconsistent units](#units)\n",
    "5. [Inconsistent longitude/latitude bounds](#bounds)\n",
    "<!-- 5. [Missing grid metrics](#metrics) -->\n",
    "<!-- 6. [Variables on staggered grids](#grids) -->\n",
    "5. [TL;DR How to put it all together](#combo)"
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    "import matplotlib.pyplot as plt\n",
    "import intake\n",
    "import dask\n",
    "%matplotlib inline"
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    "url = \"https://storage.googleapis.com/cmip6/pangeo-cmip6.json\"\n",
    "col = intake.open_esm_datastore(url)"
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    "<a id=\"rename\"></a>\n",
    "## Inconsistent naming of dimensions and coordinates\n",
    "All cmip6 models (except for the unstructured grid of the AWI model) have in principal the same datastructure and **should** have a consistent naming, such that the user can test an analysis on one model and then seamlessly apply it on another. In practice some models have alternate naming for e.g. the logical (x,y,z) dimensions. `xmip.preprocessing.rename_cmip6` accesses an internal dictionary to rename all models consistently to the following scheme:\n",
    "\n",
    "- `x`, `y`,`lev` for the logical grid index in the x,y,z direction\n",
    "- `lon`, `lat` for geographical position coordinates\n",
    "- `bnds`, `vertex` for cell bounds or vertex indicies\n",
    "- `time_bounds`, `lev_bounds`, `lon_bounds`, `lat_bounds` for cell bounding values"
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      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n"
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    "# load a few models to illustrate the problem\n",
    "query = dict(experiment_id=['piControl'], table_id='Oyr', \n",
    "             variable_id='o2', grid_label=['gn', 'gr'],\n",
    "             source_id=['IPSL-CM6A-LR', 'CanESM5', 'GFDL-ESM4']\n",
    "            )\n",
    "cat = col.search(**query)\n",
    "\n",
    "cat.df['source_id'].unique()\n",
    "z_kwargs = {'consolidated': True, 'decode_times':False}\n",
    "with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n",
    "    dset_dict = cat.to_dataset_dict(zarr_kwargs=z_kwargs)#"
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      "CMIP.IPSL.IPSL-CM6A-LR.piControl.Oyr.gn\n",
      "['axis_nbounds', 'member_id', 'nvertex', 'olevel', 'time', 'x', 'y']\n",
      "CMIP.NOAA-GFDL.GFDL-ESM4.piControl.Oyr.gr\n",
      "['bnds', 'lat', 'lev', 'lon', 'member_id', 'time']\n",
      "CMIP.CCCma.CanESM5.piControl.Oyr.gn\n",
      "['bnds', 'i', 'j', 'lev', 'member_id', 'time', 'vertices']\n"
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    "# show coordinates\n",
    "for k, ds in dset_dict.items():\n",
    "    print(k)\n",
    "    print(list(ds.dims))"
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    "You can see that e.g. the x dimension is not consistently labelled. E.g. in one model it is called `i` in the other `x`. We can fix this by passing `rename_cmip6` as `preprocess` argument to `to_dataset_dict`:"
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      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n"
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      "CMIP.IPSL.IPSL-CM6A-LR.piControl.Oyr.gn\n",
      "['bnds', 'lev', 'member_id', 'time', 'vertex', 'x', 'y']\n",
      "CMIP.NOAA-GFDL.GFDL-ESM4.piControl.Oyr.gr\n",
      "['bnds', 'lev', 'member_id', 'time', 'x', 'y']\n",
      "CMIP.CCCma.CanESM5.piControl.Oyr.gn\n",
      "['bnds', 'lev', 'member_id', 'time', 'vertex', 'x', 'y']\n"
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    "from xmip.preprocessing import rename_cmip6\n",
    "\n",
    "# load a few models to illustrate the problem\n",
    "cat = col.search(**query)\n",
    "cat.df['source_id'].unique()\n",
    "\n",
    "\n",
    "# pass the preprocessing directly\n",
    "with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n",
    "    dset_dict_renamed = cat.to_dataset_dict(zarr_kwargs=z_kwargs, preprocess=rename_cmip6)\n",
    "\n",
    "for k, ds in dset_dict_renamed.items():\n",
    "    print(k)\n",
    "    print(list(ds.dims))"
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    "Beautiful! They have exactly the same dimensions!\n",
    "\n",
    "![](https://media.giphy.com/media/142UITjG5GjIRi/giphy.gif)"
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    "> You can also always apply the utility functions after loading the data, but be aware that some models have even inconsistent namings between timesteps and ensemble members. This can cause problems with the concatenation that `intake_esm` does. Passing the function will apply it before concatenation, which works nicely above. Here is an example of how it causes problems when applied afterwards:"
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       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (bnds: 2, lev: 75, member_id: 1, time: 250, vertex: 4, x: 362, y: 332)\n",
       "Coordinates:\n",
       "    lat_bounds   (y, x, vertex) float32 dask.array&lt;chunksize=(332, 362, 4), meta=np.ndarray&gt;\n",
       "    lon_bounds   (y, x, vertex) float32 dask.array&lt;chunksize=(332, 362, 4), meta=np.ndarray&gt;\n",
       "    lat          (y, x) float32 dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;\n",
       "    lon          (y, x) float32 dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;\n",
       "  * lev          (lev) float32 0.5058 1.556 2.668 ... 5.698e+03 5.902e+03\n",
       "    lev_bounds   (lev, bnds) float32 dask.array&lt;chunksize=(75, 2), meta=np.ndarray&gt;\n",
       "  * time         (time) int64 0 8760 17532 26304 ... 2165184 2173944 2182704\n",
       "    time_bounds  (time, bnds) float64 dask.array&lt;chunksize=(250, 2), meta=np.ndarray&gt;\n",
       "  * member_id    (member_id) &lt;U8 &#x27;r1i2p1f1&#x27;\n",
       "Dimensions without coordinates: bnds, vertex, x, y\n",
       "Data variables:\n",
       "    area         (y, x) float32 dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;\n",
       "    o2           (member_id, time, lev, y, x) float32 dask.array&lt;chunksize=(1, 9, 75, 332, 362), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    CMIP6_CV_version:        cv=6.2.15.1\n",
       "    Conventions:             CF-1.7 CMIP-6.2\n",
       "    EXPID:                   piControl\n",
       "    activity_id:             CMIP\n",
       "    branch_method:           standard\n",
       "    branch_time_in_child:    0.0\n",
       "    branch_time_in_parent:   36524.0\n",
       "    contact:                 ipsl-cmip6@listes.ipsl.fr\n",
       "    creation_date:           2019-02-11T12:01:20Z\n",
       "    data_specs_version:      01.00.28\n",
       "    description:             DECK: control\n",
       "    dr2xml_md5sum:           c2dce418e78ca835be1e2ff817c2c403\n",
       "    dr2xml_version:          1.16\n",
       "    experiment:              pre-industrial control\n",
       "    experiment_id:           piControl\n",
       "    external_variables:      areacello volcello\n",
       "    forcing_index:           1\n",
       "    frequency:               yr\n",
       "    further_info_url:        https://furtherinfo.es-doc.org/CMIP6.IPSL.IPSL-C...\n",
       "    grid:                    native ocean tri-polar grid with 105 k ocean cells\n",
       "    grid_label:              gn\n",
       "    history:                 none\n",
       "    initialization_index:    2\n",
       "    institution:             Institut Pierre Simon Laplace, Paris 75252, France\n",
       "    institution_id:          IPSL\n",
       "    license:                 CMIP6 model data produced by IPSL is licensed un...\n",
       "    mip_era:                 CMIP6\n",
       "    model_version:           6.1.8\n",
       "    name:                    /ccc/work/cont003/gencmip6/lebasn/IGCM_OUT/IPSLC...\n",
       "    nominal_resolution:      100 km\n",
       "    parent_activity_id:      CMIP\n",
       "    parent_experiment_id:    piControl-spinup\n",
       "    parent_mip_era:          CMIP6\n",
       "    parent_source_id:        IPSL-CM6A-LR\n",
       "    parent_time_units:       days since 1750-01-01 00:00:00\n",
       "    parent_variant_label:    r1i2p1f1\n",
       "    physics_index:           1\n",
       "    product:                 model-output\n",
       "    realization_index:       1\n",
       "    realm:                   ocnBgchem\n",
       "    source:                  IPSL-CM6A-LR (2017):  atmos: LMDZ (NPv6, N96; 14...\n",
       "    source_id:               IPSL-CM6A-LR\n",
       "    source_type:             AOGCM BGC\n",
       "    sub_experiment:          none\n",
       "    sub_experiment_id:       none\n",
       "    table_id:                Oyr\n",
       "    title:                   IPSL-CM6A-LR model output prepared for CMIP6 / C...\n",
       "    tracking_id:             hdl:21.14100/5fb53c12-afe1-499d-9039-590b6e6b150...\n",
       "    variable_id:             o2\n",
       "    variant_info:            Equivalent to r1i1p1f1 but started on a differen...\n",
       "    variant_label:           r1i2p1f1\n",
       "    status:                  2019-10-25;created;by nhn2@columbia.edu\n",
       "    netcdf_tracking_ids:     hdl:21.14100/5fb53c12-afe1-499d-9039-590b6e6b150...\n",
       "    version_id:              v20190319\n",
       "    intake_esm_varname:      [&#x27;o2&#x27;]\n",
       "    intake_esm_dataset_key:  CMIP.IPSL.IPSL-CM6A-LR.piControl.Oyr.gn</pre><div class='xr-wrap' hidden><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-d58926e6-0dcc-495f-b948-363e04446ea5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d58926e6-0dcc-495f-b948-363e04446ea5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lev</span>: 75</li><li><span class='xr-has-index'>member_id</span>: 1</li><li><span class='xr-has-index'>time</span>: 250</li><li><span>vertex</span>: 4</li><li><span>x</span>: 362</li><li><span>y</span>: 332</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ff78675a-3f71-4896-aaf8-c6f4877decf3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ff78675a-3f71-4896-aaf8-c6f4877decf3' class='xr-section-summary' >Coordinates: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat_bounds</span></div><div class='xr-var-dims'>(y, x, vertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(332, 362, 4), meta=np.ndarray&gt;</div><input id='attrs-458c28db-3fd0-4d8b-b9ab-aacbd7e323af' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-458c28db-3fd0-4d8b-b9ab-aacbd7e323af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b993a408-df85-4d70-86ff-15fcdb71c588' class='xr-var-data-in' type='checkbox'><label for='data-b993a408-df85-4d70-86ff-15fcdb71c588' 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>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 1.92 MB </td> <td> 1.92 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (332, 362, 4) </td> <td> (332, 362, 4) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bounds</span></div><div class='xr-var-dims'>(y, x, vertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(332, 362, 4), meta=np.ndarray&gt;</div><input id='attrs-57a5d2dd-a0b7-42a6-963e-afc6ea67b539' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-57a5d2dd-a0b7-42a6-963e-afc6ea67b539' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56759f98-f023-4dbd-b105-6f634e85caf8' class='xr-var-data-in' type='checkbox'><label for='data-56759f98-f023-4dbd-b105-6f634e85caf8' 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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       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 1.92 MB </td> <td> 1.92 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (332, 362, 4) </td> <td> (332, 362, 4) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;</div><input id='attrs-a24521e1-ec13-4f9b-a683-8272ff4f63d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a24521e1-ec13-4f9b-a683-8272ff4f63d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96f31743-4d6d-4c04-810b-def459587138' class='xr-var-data-in' type='checkbox'><label for='data-96f31743-4d6d-4c04-810b-def459587138' 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>bounds_nav_lat</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;</div><input id='attrs-811b8f98-7aa0-4d4a-be57-21b4dc691d9f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-811b8f98-7aa0-4d4a-be57-21b4dc691d9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebc984ee-ddc4-462e-b2b5-11f949b12eca' class='xr-var-data-in' type='checkbox'><label for='data-ebc984ee-ddc4-462e-b2b5-11f949b12eca' 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>bounds_nav_lon</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></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'>float32</div><div class='xr-var-preview xr-preview'>0.5058 1.556 ... 5.902e+03</div><input id='attrs-03c5a2cb-210e-40ee-b792-c3637e4dd7fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03c5a2cb-210e-40ee-b792-c3637e4dd7fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9209f74-2317-498e-b68d-8bc24771389a' class='xr-var-data-in' type='checkbox'><label for='data-f9209f74-2317-498e-b68d-8bc24771389a' 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>olevel_bounds</dd><dt><span>long_name :</span></dt><dd>Vertical T levels</dd><dt><span>name :</span></dt><dd>olevel</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([5.057600e-01, 1.555855e+00, 2.667682e+00, 3.856280e+00, 5.140361e+00,\n",
       "       6.543034e+00, 8.092519e+00, 9.822750e+00, 1.177368e+01, 1.399104e+01,\n",
       "       1.652532e+01, 1.942980e+01, 2.275762e+01, 2.655830e+01, 3.087456e+01,\n",
       "       3.574020e+01, 4.118002e+01, 4.721189e+01, 5.385064e+01, 6.111284e+01,\n",
       "       6.902168e+01, 7.761116e+01, 8.692943e+01, 9.704131e+01, 1.080303e+02,\n",
       "       1.200000e+02, 1.330758e+02, 1.474062e+02, 1.631645e+02, 1.805499e+02,\n",
       "       1.997900e+02, 2.211412e+02, 2.448906e+02, 2.713564e+02, 3.008875e+02,\n",
       "       3.338628e+02, 3.706885e+02, 4.117939e+02, 4.576256e+02, 5.086399e+02,\n",
       "       5.652923e+02, 6.280260e+02, 6.972587e+02, 7.733683e+02, 8.566790e+02,\n",
       "       9.474479e+02, 1.045854e+03, 1.151991e+03, 1.265861e+03, 1.387377e+03,\n",
       "       1.516364e+03, 1.652568e+03, 1.795671e+03, 1.945296e+03, 2.101027e+03,\n",
       "       2.262422e+03, 2.429025e+03, 2.600380e+03, 2.776039e+03, 2.955570e+03,\n",
       "       3.138565e+03, 3.324641e+03, 3.513446e+03, 3.704657e+03, 3.897982e+03,\n",
       "       4.093159e+03, 4.289953e+03, 4.488155e+03, 4.687581e+03, 4.888070e+03,\n",
       "       5.089479e+03, 5.291683e+03, 5.494575e+03, 5.698061e+03, 5.902058e+03],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lev_bounds</span></div><div class='xr-var-dims'>(lev, bnds)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(75, 2), meta=np.ndarray&gt;</div><input id='attrs-d334a994-b016-4862-a18c-7fcc79d36e9a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d334a994-b016-4862-a18c-7fcc79d36e9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ccdbad88-c6ec-4c09-8637-f77bede12f7f' class='xr-var-data-in' type='checkbox'><label for='data-ccdbad88-c6ec-4c09-8637-f77bede12f7f' 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></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 600 B </td> <td> 600 B </td></tr>\n",
       "    <tr><th> Shape </th><td> (75, 2) </td> <td> (75, 2) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></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'>int64</div><div class='xr-var-preview xr-preview'>0 8760 17532 ... 2173944 2182704</div><input id='attrs-2921de97-95d0-494a-b250-445f6098fe1a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2921de97-95d0-494a-b250-445f6098fe1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c3fdadb-95b7-4f18-b426-5a3631bcfba0' class='xr-var-data-in' type='checkbox'><label for='data-5c3fdadb-95b7-4f18-b426-5a3631bcfba0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd><dt><span>calendar :</span></dt><dd>gregorian</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>1850-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>hours since 1850-07-02 12:00:00.000000</dd></dl></div><div class='xr-var-data'><pre>array([      0,    8760,   17532, ..., 2165184, 2173944, 2182704])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bounds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(250, 2), meta=np.ndarray&gt;</div><input id='attrs-e44aa555-a193-41b3-ba9b-34b0b6681428' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e44aa555-a193-41b3-ba9b-34b0b6681428' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28d2bc7d-5cfb-468a-9d96-0f0fc5997b31' class='xr-var-data-in' type='checkbox'><label for='data-28d2bc7d-5cfb-468a-9d96-0f0fc5997b31' 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>calendar :</span></dt><dd>gregorian</dd><dt><span>units :</span></dt><dd>days since 1850-01-01</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
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       "  <thead>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 4.00 kB </td> <td> 4.00 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (250, 2) </td> <td> (250, 2) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;r1i2p1f1&#x27;</div><input id='attrs-c8dd72e7-b8a9-40bb-8c61-342ae88b3e0f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c8dd72e7-b8a9-40bb-8c61-342ae88b3e0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7543e47b-cfea-4274-a8f7-780961e250de' class='xr-var-data-in' type='checkbox'><label for='data-7543e47b-cfea-4274-a8f7-780961e250de' 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([&#x27;r1i2p1f1&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f1203a19-075a-4e23-a7cd-9555b6251f73' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f1203a19-075a-4e23-a7cd-9555b6251f73' 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>area</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;</div><input id='attrs-8f59dc1e-7c1b-4665-a14f-da3d7330f160' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8f59dc1e-7c1b-4665-a14f-da3d7330f160' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2b151fc-b75c-4468-856d-a47fbfcee580' class='xr-var-data-in' type='checkbox'><label for='data-b2b151fc-b75c-4468-856d-a47fbfcee580' 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>cell_area</dd><dt><span>units :</span></dt><dd>m2</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 480.74 kB </td> <td> 480.74 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (332, 362) </td> <td> (332, 362) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>o2</span></div><div class='xr-var-dims'>(member_id, time, lev, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 9, 75, 332, 362), meta=np.ndarray&gt;</div><input id='attrs-4e479427-bfd2-4b1c-b367-8d31be7b9a6e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e479427-bfd2-4b1c-b367-8d31be7b9a6e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2db7731-eb22-449a-abb4-9c86074825a6' class='xr-var-data-in' type='checkbox'><label for='data-a2db7731-eb22-449a-abb4-9c86074825a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_measures :</span></dt><dd>area: areacello volume: volcello</dd><dt><span>cell_methods :</span></dt><dd>area: mean where sea time: mean</dd><dt><span>description :</span></dt><dd>&#x27;Mole concentration&#x27; means number of moles per unit volume, also called&quot;molarity&quot;, and is used in the construction mole_concentration_of_X_in_Y, whereX is a material constituent of Y.  A chemical or biological species denoted by X may be described by a single term such as &#x27;nitrogen&#x27; or a phrase such as &#x27;nox_expressed_as_nitrogen&#x27;.</dd><dt><span>history :</span></dt><dd>none</dd><dt><span>interval_operation :</span></dt><dd>2700 s</dd><dt><span>interval_write :</span></dt><dd>1 yr</dd><dt><span>long_name :</span></dt><dd>Dissolved Oxygen Concentration</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>standard_name :</span></dt><dd>mole_concentration_of_dissolved_molecular_oxygen_in_sea_water</dd><dt><span>units :</span></dt><dd>mol m-3</dd></dl></div><div class='xr-var-data'><table>\n",
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       "    <tr><th> Count </th><td> 57 Tasks </td><td> 28 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "hdl:21.14100/0825a99b-e57e-4796-9f3b-0d260c960c30</dd><dt><span>variable_id :</span></dt><dd>o2</dd><dt><span>variant_info :</span></dt><dd>Equivalent to r1i1p1f1 but started on a different machine. Information provided by this attribute may in some cases be flawed. Users can find more comprehensive and up-to-date documentation via the further_info_url global attribute.</dd><dt><span>variant_label :</span></dt><dd>r1i2p1f1</dd><dt><span>status :</span></dt><dd>2019-10-25;created;by nhn2@columbia.edu</dd><dt><span>netcdf_tracking_ids :</span></dt><dd>hdl:21.14100/5fb53c12-afe1-499d-9039-590b6e6b1501\n",
       "hdl:21.14100/0825a99b-e57e-4796-9f3b-0d260c960c30</dd><dt><span>version_id :</span></dt><dd>v20190319</dd><dt><span>intake_esm_varname :</span></dt><dd>[&#x27;o2&#x27;]</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>CMIP.IPSL.IPSL-CM6A-LR.piControl.Oyr.gn</dd></dl></div></li></ul></div></div>"
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       "<xarray.Dataset>\n",
       "Dimensions:      (bnds: 2, lev: 75, member_id: 1, time: 250, vertex: 4, x: 362, y: 332)\n",
       "Coordinates:\n",
       "    lat_bounds   (y, x, vertex) float32 dask.array<chunksize=(332, 362, 4), meta=np.ndarray>\n",
       "    lon_bounds   (y, x, vertex) float32 dask.array<chunksize=(332, 362, 4), meta=np.ndarray>\n",
       "    lat          (y, x) float32 dask.array<chunksize=(332, 362), meta=np.ndarray>\n",
       "    lon          (y, x) float32 dask.array<chunksize=(332, 362), meta=np.ndarray>\n",
       "  * lev          (lev) float32 0.5058 1.556 2.668 ... 5.698e+03 5.902e+03\n",
       "    lev_bounds   (lev, bnds) float32 dask.array<chunksize=(75, 2), meta=np.ndarray>\n",
       "  * time         (time) int64 0 8760 17532 26304 ... 2165184 2173944 2182704\n",
       "    time_bounds  (time, bnds) float64 dask.array<chunksize=(250, 2), meta=np.ndarray>\n",
       "  * member_id    (member_id) <U8 'r1i2p1f1'\n",
       "Dimensions without coordinates: bnds, vertex, x, y\n",
       "Data variables:\n",
       "    area         (y, x) float32 dask.array<chunksize=(332, 362), meta=np.ndarray>\n",
       "    o2           (member_id, time, lev, y, x) float32 dask.array<chunksize=(1, 9, 75, 332, 362), meta=np.ndarray>\n",
       "Attributes:\n",
       "    CMIP6_CV_version:        cv=6.2.15.1\n",
       "    Conventions:             CF-1.7 CMIP-6.2\n",
       "    EXPID:                   piControl\n",
       "    activity_id:             CMIP\n",
       "    branch_method:           standard\n",
       "    branch_time_in_child:    0.0\n",
       "    branch_time_in_parent:   36524.0\n",
       "    contact:                 ipsl-cmip6@listes.ipsl.fr\n",
       "    creation_date:           2019-02-11T12:01:20Z\n",
       "    data_specs_version:      01.00.28\n",
       "    description:             DECK: control\n",
       "    dr2xml_md5sum:           c2dce418e78ca835be1e2ff817c2c403\n",
       "    dr2xml_version:          1.16\n",
       "    experiment:              pre-industrial control\n",
       "    experiment_id:           piControl\n",
       "    external_variables:      areacello volcello\n",
       "    forcing_index:           1\n",
       "    frequency:               yr\n",
       "    further_info_url:        https://furtherinfo.es-doc.org/CMIP6.IPSL.IPSL-C...\n",
       "    grid:                    native ocean tri-polar grid with 105 k ocean cells\n",
       "    grid_label:              gn\n",
       "    history:                 none\n",
       "    initialization_index:    2\n",
       "    institution:             Institut Pierre Simon Laplace, Paris 75252, France\n",
       "    institution_id:          IPSL\n",
       "    license:                 CMIP6 model data produced by IPSL is licensed un...\n",
       "    mip_era:                 CMIP6\n",
       "    model_version:           6.1.8\n",
       "    name:                    /ccc/work/cont003/gencmip6/lebasn/IGCM_OUT/IPSLC...\n",
       "    nominal_resolution:      100 km\n",
       "    parent_activity_id:      CMIP\n",
       "    parent_experiment_id:    piControl-spinup\n",
       "    parent_mip_era:          CMIP6\n",
       "    parent_source_id:        IPSL-CM6A-LR\n",
       "    parent_time_units:       days since 1750-01-01 00:00:00\n",
       "    parent_variant_label:    r1i2p1f1\n",
       "    physics_index:           1\n",
       "    product:                 model-output\n",
       "    realization_index:       1\n",
       "    realm:                   ocnBgchem\n",
       "    source:                  IPSL-CM6A-LR (2017):  atmos: LMDZ (NPv6, N96; 14...\n",
       "    source_id:               IPSL-CM6A-LR\n",
       "    source_type:             AOGCM BGC\n",
       "    sub_experiment:          none\n",
       "    sub_experiment_id:       none\n",
       "    table_id:                Oyr\n",
       "    title:                   IPSL-CM6A-LR model output prepared for CMIP6 / C...\n",
       "    tracking_id:             hdl:21.14100/5fb53c12-afe1-499d-9039-590b6e6b150...\n",
       "    variable_id:             o2\n",
       "    variant_info:            Equivalent to r1i1p1f1 but started on a differen...\n",
       "    variant_label:           r1i2p1f1\n",
       "    status:                  2019-10-25;created;by nhn2@columbia.edu\n",
       "    netcdf_tracking_ids:     hdl:21.14100/5fb53c12-afe1-499d-9039-590b6e6b150...\n",
       "    version_id:              v20190319\n",
       "    intake_esm_varname:      ['o2']\n",
       "    intake_esm_dataset_key:  CMIP.IPSL.IPSL-CM6A-LR.piControl.Oyr.gn"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# IPSL data is a bit of a mess\n",
    "ds = dset_dict['CMIP.IPSL.IPSL-CM6A-LR.piControl.Oyr.gn']\n",
    "ds = rename_cmip6(ds)\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "~See how the data_variable `o2` has several depth variables `o2(member_id, time, olevel, y, x, lev)`~\n",
    "> This has recently been fixed in the pangeo google store, but does still apply if you are e.g. working with a local copy of the CMIP6 netdcf file.\n",
    "\n",
    "**I strongly recommend applying all preprocessing using the `preprocess` keyword, but it is crucial to do so with the initial renaming step**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"coords\"></a>\n",
    "## Inconsistent values,shape and dataset location of coordinates\n",
    "The naming of the dimensions/coordinates is only the beginning: Some datasets use only index values for the x,y dimensions, while others use nominal longitudes, latitudes (usefull for rough region selection) or the longitudes and latitudes are only 1d arrays (e.g. for regridded outputs). Our goal is to work with all datasets int the same way, and hence we convert all datasets in this form:\n",
    "\n",
    "- `x`, `y` area given as 1D 'nominal' longitudes and latitudes. This means the `x` is the zonal average latitude (can become difficult near the Arctic, but is otherwise very useful) and `y` is the longitude at the equator.\n",
    "- `lon` and `lat` are 2-dimensional coordinate arrays with the 'true' position of grid cells (if the values were initially given as 1d arrays, they are broadcasted appropriately)\n",
    "\n",
    "We achieve this by applying `promote_empty_dims` (give empty dimensions values), `broadcast_lonlat` (convert 1d lon and lat arrays to 2d arrays) and `replace_x_y_nominal_lat_lon` (calculate nominal lon and lat and replace `x` and `y` with them)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "  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",
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       ".xr-var-item > .xr-var-name span {\n",
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       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
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       "}\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",
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       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
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       "\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",
       "  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",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
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       "\n",
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       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
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       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (bnds: 2, lev: 75, member_id: 1, time: 250, vertex: 4, x: 362, y: 332)\n",
       "Coordinates:\n",
       "    lat_bounds   (y, x, vertex) float32 dask.array&lt;chunksize=(332, 362, 4), meta=np.ndarray&gt;\n",
       "    lon_bounds   (y, x, vertex) float32 dask.array&lt;chunksize=(332, 362, 4), meta=np.ndarray&gt;\n",
       "    lat          (y, x) float32 dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;\n",
       "    lon          (y, x) float32 dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;\n",
       "  * lev          (lev) float32 0.5058 1.556 2.668 ... 5.698e+03 5.902e+03\n",
       "    lev_bounds   (lev, bnds) float32 dask.array&lt;chunksize=(75, 2), meta=np.ndarray&gt;\n",
       "  * time         (time) int64 0 8760 17532 26304 ... 2165184 2173944 2182704\n",
       "    time_bounds  (time, bnds) float64 dask.array&lt;chunksize=(250, 2), meta=np.ndarray&gt;\n",
       "  * member_id    (member_id) &lt;U8 &#x27;r1i2p1f1&#x27;\n",
       "Dimensions without coordinates: bnds, vertex, x, y\n",
       "Data variables:\n",
       "    area         (y, x) float32 dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;\n",
       "    o2           (member_id, time, lev, y, x) float32 dask.array&lt;chunksize=(1, 9, 75, 332, 362), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    CMIP6_CV_version:        cv=6.2.15.1\n",
       "    Conventions:             CF-1.7 CMIP-6.2\n",
       "    EXPID:                   piControl\n",
       "    activity_id:             CMIP\n",
       "    branch_method:           standard\n",
       "    branch_time_in_child:    0.0\n",
       "    branch_time_in_parent:   36524.0\n",
       "    contact:                 ipsl-cmip6@listes.ipsl.fr\n",
       "    creation_date:           2019-02-11T12:01:20Z\n",
       "    data_specs_version:      01.00.28\n",
       "    description:             DECK: control\n",
       "    dr2xml_md5sum:           c2dce418e78ca835be1e2ff817c2c403\n",
       "    dr2xml_version:          1.16\n",
       "    experiment:              pre-industrial control\n",
       "    experiment_id:           piControl\n",
       "    external_variables:      areacello volcello\n",
       "    forcing_index:           1\n",
       "    frequency:               yr\n",
       "    further_info_url:        https://furtherinfo.es-doc.org/CMIP6.IPSL.IPSL-C...\n",
       "    grid:                    native ocean tri-polar grid with 105 k ocean cells\n",
       "    grid_label:              gn\n",
       "    history:                 none\n",
       "    initialization_index:    2\n",
       "    institution:             Institut Pierre Simon Laplace, Paris 75252, France\n",
       "    institution_id:          IPSL\n",
       "    license:                 CMIP6 model data produced by IPSL is licensed un...\n",
       "    mip_era:                 CMIP6\n",
       "    model_version:           6.1.8\n",
       "    name:                    /ccc/work/cont003/gencmip6/lebasn/IGCM_OUT/IPSLC...\n",
       "    nominal_resolution:      100 km\n",
       "    parent_activity_id:      CMIP\n",
       "    parent_experiment_id:    piControl-spinup\n",
       "    parent_mip_era:          CMIP6\n",
       "    parent_source_id:        IPSL-CM6A-LR\n",
       "    parent_time_units:       days since 1750-01-01 00:00:00\n",
       "    parent_variant_label:    r1i2p1f1\n",
       "    physics_index:           1\n",
       "    product:                 model-output\n",
       "    realization_index:       1\n",
       "    realm:                   ocnBgchem\n",
       "    source:                  IPSL-CM6A-LR (2017):  atmos: LMDZ (NPv6, N96; 14...\n",
       "    source_id:               IPSL-CM6A-LR\n",
       "    source_type:             AOGCM BGC\n",
       "    sub_experiment:          none\n",
       "    sub_experiment_id:       none\n",
       "    table_id:                Oyr\n",
       "    title:                   IPSL-CM6A-LR model output prepared for CMIP6 / C...\n",
       "    tracking_id:             hdl:21.14100/5fb53c12-afe1-499d-9039-590b6e6b150...\n",
       "    variable_id:             o2\n",
       "    variant_info:            Equivalent to r1i1p1f1 but started on a differen...\n",
       "    variant_label:           r1i2p1f1\n",
       "    status:                  2019-10-25;created;by nhn2@columbia.edu\n",
       "    netcdf_tracking_ids:     hdl:21.14100/5fb53c12-afe1-499d-9039-590b6e6b150...\n",
       "    version_id:              v20190319\n",
       "    intake_esm_varname:      [&#x27;o2&#x27;]\n",
       "    intake_esm_dataset_key:  CMIP.IPSL.IPSL-CM6A-LR.piControl.Oyr.gn</pre><div class='xr-wrap' hidden><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-0bd47a77-d878-4fde-be86-5d6eb1929854' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0bd47a77-d878-4fde-be86-5d6eb1929854' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lev</span>: 75</li><li><span class='xr-has-index'>member_id</span>: 1</li><li><span class='xr-has-index'>time</span>: 250</li><li><span>vertex</span>: 4</li><li><span>x</span>: 362</li><li><span>y</span>: 332</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-92952f96-aa52-4f70-9ff6-b5808af95a7b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-92952f96-aa52-4f70-9ff6-b5808af95a7b' class='xr-section-summary' >Coordinates: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat_bounds</span></div><div class='xr-var-dims'>(y, x, vertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(332, 362, 4), meta=np.ndarray&gt;</div><input id='attrs-eeeb1a64-e0b7-49e0-a389-25b71384595c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eeeb1a64-e0b7-49e0-a389-25b71384595c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c805a861-77b4-451f-b20f-c55d61127e75' class='xr-var-data-in' type='checkbox'><label for='data-c805a861-77b4-451f-b20f-c55d61127e75' 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>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 1.92 MB </td> <td> 1.92 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (332, 362, 4) </td> <td> (332, 362, 4) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;</div><input id='attrs-b592ded7-5369-4b8f-8723-d874d9ab104a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b592ded7-5369-4b8f-8723-d874d9ab104a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe15acb0-2b25-4222-8a33-1ce1ef1bcff3' class='xr-var-data-in' type='checkbox'><label for='data-fe15acb0-2b25-4222-8a33-1ce1ef1bcff3' 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>bounds_nav_lat</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
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       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
       "</td>\n",
       "<td>\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;</div><input id='attrs-c569df8c-716b-4c68-8868-63ea2c7f8391' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c569df8c-716b-4c68-8868-63ea2c7f8391' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec4d80eb-01e6-477d-8522-d6fb58622283' class='xr-var-data-in' type='checkbox'><label for='data-ec4d80eb-01e6-477d-8522-d6fb58622283' 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>bounds_nav_lon</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
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       "<td>\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></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'>float32</div><div class='xr-var-preview xr-preview'>0.5058 1.556 ... 5.902e+03</div><input id='attrs-f7df79ac-bee1-421b-b700-6db6c1f8b2da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f7df79ac-bee1-421b-b700-6db6c1f8b2da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a04f565c-36d0-40dc-af3f-b16e9428cd17' class='xr-var-data-in' type='checkbox'><label for='data-a04f565c-36d0-40dc-af3f-b16e9428cd17' 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>olevel_bounds</dd><dt><span>long_name :</span></dt><dd>Vertical T levels</dd><dt><span>name :</span></dt><dd>olevel</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([5.057600e-01, 1.555855e+00, 2.667682e+00, 3.856280e+00, 5.140361e+00,\n",
       "       6.543034e+00, 8.092519e+00, 9.822750e+00, 1.177368e+01, 1.399104e+01,\n",
       "       1.652532e+01, 1.942980e+01, 2.275762e+01, 2.655830e+01, 3.087456e+01,\n",
       "       3.574020e+01, 4.118002e+01, 4.721189e+01, 5.385064e+01, 6.111284e+01,\n",
       "       6.902168e+01, 7.761116e+01, 8.692943e+01, 9.704131e+01, 1.080303e+02,\n",
       "       1.200000e+02, 1.330758e+02, 1.474062e+02, 1.631645e+02, 1.805499e+02,\n",
       "       1.997900e+02, 2.211412e+02, 2.448906e+02, 2.713564e+02, 3.008875e+02,\n",
       "       3.338628e+02, 3.706885e+02, 4.117939e+02, 4.576256e+02, 5.086399e+02,\n",
       "       5.652923e+02, 6.280260e+02, 6.972587e+02, 7.733683e+02, 8.566790e+02,\n",
       "       9.474479e+02, 1.045854e+03, 1.151991e+03, 1.265861e+03, 1.387377e+03,\n",
       "       1.516364e+03, 1.652568e+03, 1.795671e+03, 1.945296e+03, 2.101027e+03,\n",
       "       2.262422e+03, 2.429025e+03, 2.600380e+03, 2.776039e+03, 2.955570e+03,\n",
       "       3.138565e+03, 3.324641e+03, 3.513446e+03, 3.704657e+03, 3.897982e+03,\n",
       "       4.093159e+03, 4.289953e+03, 4.488155e+03, 4.687581e+03, 4.888070e+03,\n",
       "       5.089479e+03, 5.291683e+03, 5.494575e+03, 5.698061e+03, 5.902058e+03],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lev_bounds</span></div><div class='xr-var-dims'>(lev, bnds)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(75, 2), meta=np.ndarray&gt;</div><input id='attrs-ba756f48-32cf-4420-b2a8-91a13e6745f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba756f48-32cf-4420-b2a8-91a13e6745f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1411b30-fa11-4d4a-8e0c-aff1dd8a2c2d' class='xr-var-data-in' type='checkbox'><label for='data-a1411b30-fa11-4d4a-8e0c-aff1dd8a2c2d' 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></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
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       "  <tbody>\n",
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       "    <tr><th> Shape </th><td> (75, 2) </td> <td> (75, 2) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></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'>int64</div><div class='xr-var-preview xr-preview'>0 8760 17532 ... 2173944 2182704</div><input id='attrs-c7e5e836-194f-4f00-90fc-9c64f7cef3aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7e5e836-194f-4f00-90fc-9c64f7cef3aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5ce8c89-df54-480b-b5dc-aa59cc55a7dc' class='xr-var-data-in' type='checkbox'><label for='data-f5ce8c89-df54-480b-b5dc-aa59cc55a7dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd><dt><span>calendar :</span></dt><dd>gregorian</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>1850-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>hours since 1850-07-02 12:00:00.000000</dd></dl></div><div class='xr-var-data'><pre>array([      0,    8760,   17532, ..., 2165184, 2173944, 2182704])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bounds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(250, 2), meta=np.ndarray&gt;</div><input id='attrs-0a7cc82f-63a2-445d-b7b5-4c23f21f9db8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a7cc82f-63a2-445d-b7b5-4c23f21f9db8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dea18eb9-50c1-4dc6-b3f1-fdfc4430e98c' class='xr-var-data-in' type='checkbox'><label for='data-dea18eb9-50c1-4dc6-b3f1-fdfc4430e98c' 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>calendar :</span></dt><dd>gregorian</dd><dt><span>units :</span></dt><dd>days since 1850-01-01</dd></dl></div><div class='xr-var-data'><table>\n",
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       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 4.00 kB </td> <td> 4.00 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (250, 2) </td> <td> (250, 2) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "hdl:21.14100/0825a99b-e57e-4796-9f3b-0d260c960c30</dd><dt><span>variable_id :</span></dt><dd>o2</dd><dt><span>variant_info :</span></dt><dd>Equivalent to r1i1p1f1 but started on a different machine. Information provided by this attribute may in some cases be flawed. Users can find more comprehensive and up-to-date documentation via the further_info_url global attribute.</dd><dt><span>variant_label :</span></dt><dd>r1i2p1f1</dd><dt><span>status :</span></dt><dd>2019-10-25;created;by nhn2@columbia.edu</dd><dt><span>netcdf_tracking_ids :</span></dt><dd>hdl:21.14100/5fb53c12-afe1-499d-9039-590b6e6b1501\n",
       "hdl:21.14100/0825a99b-e57e-4796-9f3b-0d260c960c30</dd><dt><span>version_id :</span></dt><dd>v20190319</dd><dt><span>intake_esm_varname :</span></dt><dd>[&#x27;o2&#x27;]</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>CMIP.IPSL.IPSL-CM6A-LR.piControl.Oyr.gn</dd></dl></div></li></ul></div></div>"
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       "    dr2xml_version:          1.16\n",
       "    experiment:              pre-industrial control\n",
       "    experiment_id:           piControl\n",
       "    external_variables:      areacello volcello\n",
       "    forcing_index:           1\n",
       "    frequency:               yr\n",
       "    further_info_url:        https://furtherinfo.es-doc.org/CMIP6.IPSL.IPSL-C...\n",
       "    grid:                    native ocean tri-polar grid with 105 k ocean cells\n",
       "    grid_label:              gn\n",
       "    history:                 none\n",
       "    initialization_index:    2\n",
       "    institution:             Institut Pierre Simon Laplace, Paris 75252, France\n",
       "    institution_id:          IPSL\n",
       "    license:                 CMIP6 model data produced by IPSL is licensed un...\n",
       "    mip_era:                 CMIP6\n",
       "    model_version:           6.1.8\n",
       "    name:                    /ccc/work/cont003/gencmip6/lebasn/IGCM_OUT/IPSLC...\n",
       "    nominal_resolution:      100 km\n",
       "    parent_activity_id:      CMIP\n",
       "    parent_experiment_id:    piControl-spinup\n",
       "    parent_mip_era:          CMIP6\n",
       "    parent_source_id:        IPSL-CM6A-LR\n",
       "    parent_time_units:       days since 1750-01-01 00:00:00\n",
       "    parent_variant_label:    r1i2p1f1\n",
       "    physics_index:           1\n",
       "    product:                 model-output\n",
       "    realization_index:       1\n",
       "    realm:                   ocnBgchem\n",
       "    source:                  IPSL-CM6A-LR (2017):  atmos: LMDZ (NPv6, N96; 14...\n",
       "    source_id:               IPSL-CM6A-LR\n",
       "    source_type:             AOGCM BGC\n",
       "    sub_experiment:          none\n",
       "    sub_experiment_id:       none\n",
       "    table_id:                Oyr\n",
       "    title:                   IPSL-CM6A-LR model output prepared for CMIP6 / C...\n",
       "    tracking_id:             hdl:21.14100/5fb53c12-afe1-499d-9039-590b6e6b150...\n",
       "    variable_id:             o2\n",
       "    variant_info:            Equivalent to r1i1p1f1 but started on a differen...\n",
       "    variant_label:           r1i2p1f1\n",
       "    status:                  2019-10-25;created;by nhn2@columbia.edu\n",
       "    netcdf_tracking_ids:     hdl:21.14100/5fb53c12-afe1-499d-9039-590b6e6b150...\n",
       "    version_id:              v20190319\n",
       "    intake_esm_varname:      ['o2']\n",
       "    intake_esm_dataset_key:  CMIP.IPSL.IPSL-CM6A-LR.piControl.Oyr.gn"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xmip.preprocessing import promote_empty_dims, broadcast_lonlat, replace_x_y_nominal_lat_lon\n",
    "\n",
    "# check out the previous datasets\n",
    "ds = dset_dict_renamed['CMIP.IPSL.IPSL-CM6A-LR.piControl.Oyr.gn']\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Note how the dimensions x and y dont have values (e.g. are not listed as coords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (bnds: 2, lev: 75, member_id: 1, time: 250, vertex: 4, x: 362, y: 332)\n",
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       "    lon          (y, x) float32 dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;\n",
       "  * lev          (lev) float32 0.5058 1.556 2.668 ... 5.698e+03 5.902e+03\n",
       "    lev_bounds   (lev, bnds) float32 dask.array&lt;chunksize=(75, 2), meta=np.ndarray&gt;\n",
       "  * time         (time) int64 0 8760 17532 26304 ... 2165184 2173944 2182704\n",
       "    time_bounds  (time, bnds) float64 dask.array&lt;chunksize=(250, 2), meta=np.ndarray&gt;\n",
       "  * member_id    (member_id) &lt;U8 &#x27;r1i2p1f1&#x27;\n",
       "  * bnds         (bnds) int64 0 1\n",
       "  * vertex       (vertex) int64 0 1 2 3\n",
       "  * x            (x) int64 0 1 2 3 4 5 6 7 8 ... 354 355 356 357 358 359 360 361\n",
       "  * y            (y) int64 0 1 2 3 4 5 6 7 8 ... 324 325 326 327 328 329 330 331\n",
       "Data variables:\n",
       "    area         (y, x) float32 dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;\n",
       "    o2           (member_id, time, lev, y, x) float32 dask.array&lt;chunksize=(1, 9, 75, 332, 362), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    CMIP6_CV_version:        cv=6.2.15.1\n",
       "    Conventions:             CF-1.7 CMIP-6.2\n",
       "    EXPID:                   piControl\n",
       "    activity_id:             CMIP\n",
       "    branch_method:           standard\n",
       "    branch_time_in_child:    0.0\n",
       "    branch_time_in_parent:   36524.0\n",
       "    contact:                 ipsl-cmip6@listes.ipsl.fr\n",
       "    creation_date:           2019-02-11T12:01:20Z\n",
       "    data_specs_version:      01.00.28\n",
       "    description:             DECK: control\n",
       "    dr2xml_md5sum:           c2dce418e78ca835be1e2ff817c2c403\n",
       "    dr2xml_version:          1.16\n",
       "    experiment:              pre-industrial control\n",
       "    experiment_id:           piControl\n",
       "    external_variables:      areacello volcello\n",
       "    forcing_index:           1\n",
       "    frequency:               yr\n",
       "    further_info_url:        https://furtherinfo.es-doc.org/CMIP6.IPSL.IPSL-C...\n",
       "    grid:                    native ocean tri-polar grid with 105 k ocean cells\n",
       "    grid_label:              gn\n",
       "    history:                 none\n",
       "    initialization_index:    2\n",
       "    institution:             Institut Pierre Simon Laplace, Paris 75252, France\n",
       "    institution_id:          IPSL\n",
       "    license:                 CMIP6 model data produced by IPSL is licensed un...\n",
       "    mip_era:                 CMIP6\n",
       "    model_version:           6.1.8\n",
       "    name:                    /ccc/work/cont003/gencmip6/lebasn/IGCM_OUT/IPSLC...\n",
       "    nominal_resolution:      100 km\n",
       "    parent_activity_id:      CMIP\n",
       "    parent_experiment_id:    piControl-spinup\n",
       "    parent_mip_era:          CMIP6\n",
       "    parent_source_id:        IPSL-CM6A-LR\n",
       "    parent_time_units:       days since 1750-01-01 00:00:00\n",
       "    parent_variant_label:    r1i2p1f1\n",
       "    physics_index:           1\n",
       "    product:                 model-output\n",
       "    realization_index:       1\n",
       "    realm:                   ocnBgchem\n",
       "    source:                  IPSL-CM6A-LR (2017):  atmos: LMDZ (NPv6, N96; 14...\n",
       "    source_id:               IPSL-CM6A-LR\n",
       "    source_type:             AOGCM BGC\n",
       "    sub_experiment:          none\n",
       "    sub_experiment_id:       none\n",
       "    table_id:                Oyr\n",
       "    title:                   IPSL-CM6A-LR model output prepared for CMIP6 / C...\n",
       "    tracking_id:             hdl:21.14100/5fb53c12-afe1-499d-9039-590b6e6b150...\n",
       "    variable_id:             o2\n",
       "    variant_info:            Equivalent to r1i1p1f1 but started on a differen...\n",
       "    variant_label:           r1i2p1f1\n",
       "    status:                  2019-10-25;created;by nhn2@columbia.edu\n",
       "    netcdf_tracking_ids:     hdl:21.14100/5fb53c12-afe1-499d-9039-590b6e6b150...\n",
       "    version_id:              v20190319\n",
       "    intake_esm_varname:      [&#x27;o2&#x27;]\n",
       "    intake_esm_dataset_key:  CMIP.IPSL.IPSL-CM6A-LR.piControl.Oyr.gn</pre><div class='xr-wrap' hidden><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-8edf5a8a-4cfa-4720-b044-31aa3c07f9b1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8edf5a8a-4cfa-4720-b044-31aa3c07f9b1' 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'>bnds</span>: 2</li><li><span class='xr-has-index'>lev</span>: 75</li><li><span class='xr-has-index'>member_id</span>: 1</li><li><span class='xr-has-index'>time</span>: 250</li><li><span class='xr-has-index'>vertex</span>: 4</li><li><span class='xr-has-index'>x</span>: 362</li><li><span class='xr-has-index'>y</span>: 332</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3f7cb4aa-0194-4ee4-9287-cb543ea699b6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3f7cb4aa-0194-4ee4-9287-cb543ea699b6' class='xr-section-summary' >Coordinates: <span>(13)</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_bounds</span></div><div class='xr-var-dims'>(y, x, vertex)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(332, 362, 4), meta=np.ndarray&gt;</div><input id='attrs-4ab93f7c-3816-4cb1-8710-d31c32612c76' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4ab93f7c-3816-4cb1-8710-d31c32612c76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8789b5f8-d272-4aa2-92e5-3311f8a1391d' class='xr-var-data-in' type='checkbox'><label for='data-8789b5f8-d272-4aa2-92e5-3311f8a1391d' 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>\n",
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       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 1.92 MB </td> <td> 1.92 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (332, 362, 4) </td> <td> (332, 362, 4) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;</div><input id='attrs-51af690d-311b-46da-b983-8c475093868d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-51af690d-311b-46da-b983-8c475093868d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-573d68ae-5627-4240-88ee-eacb5fd86030' class='xr-var-data-in' type='checkbox'><label for='data-573d68ae-5627-4240-88ee-eacb5fd86030' 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>bounds_nav_lat</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;</div><input id='attrs-65120bc2-071c-4817-8752-144c110b6868' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-65120bc2-071c-4817-8752-144c110b6868' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1f30a53-cffe-4866-857a-cd15362b6695' class='xr-var-data-in' type='checkbox'><label for='data-d1f30a53-cffe-4866-857a-cd15362b6695' 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>bounds_nav_lon</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
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       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></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'>float32</div><div class='xr-var-preview xr-preview'>0.5058 1.556 ... 5.902e+03</div><input id='attrs-6a0c4327-5546-40ef-a903-f73684f47a91' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a0c4327-5546-40ef-a903-f73684f47a91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c782089-45e5-4033-89f3-3a32a895296f' class='xr-var-data-in' type='checkbox'><label for='data-9c782089-45e5-4033-89f3-3a32a895296f' 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>olevel_bounds</dd><dt><span>long_name :</span></dt><dd>Vertical T levels</dd><dt><span>name :</span></dt><dd>olevel</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([5.057600e-01, 1.555855e+00, 2.667682e+00, 3.856280e+00, 5.140361e+00,\n",
       "       6.543034e+00, 8.092519e+00, 9.822750e+00, 1.177368e+01, 1.399104e+01,\n",
       "       1.652532e+01, 1.942980e+01, 2.275762e+01, 2.655830e+01, 3.087456e+01,\n",
       "       3.574020e+01, 4.118002e+01, 4.721189e+01, 5.385064e+01, 6.111284e+01,\n",
       "       6.902168e+01, 7.761116e+01, 8.692943e+01, 9.704131e+01, 1.080303e+02,\n",
       "       1.200000e+02, 1.330758e+02, 1.474062e+02, 1.631645e+02, 1.805499e+02,\n",
       "       1.997900e+02, 2.211412e+02, 2.448906e+02, 2.713564e+02, 3.008875e+02,\n",
       "       3.338628e+02, 3.706885e+02, 4.117939e+02, 4.576256e+02, 5.086399e+02,\n",
       "       5.652923e+02, 6.280260e+02, 6.972587e+02, 7.733683e+02, 8.566790e+02,\n",
       "       9.474479e+02, 1.045854e+03, 1.151991e+03, 1.265861e+03, 1.387377e+03,\n",
       "       1.516364e+03, 1.652568e+03, 1.795671e+03, 1.945296e+03, 2.101027e+03,\n",
       "       2.262422e+03, 2.429025e+03, 2.600380e+03, 2.776039e+03, 2.955570e+03,\n",
       "       3.138565e+03, 3.324641e+03, 3.513446e+03, 3.704657e+03, 3.897982e+03,\n",
       "       4.093159e+03, 4.289953e+03, 4.488155e+03, 4.687581e+03, 4.888070e+03,\n",
       "       5.089479e+03, 5.291683e+03, 5.494575e+03, 5.698061e+03, 5.902058e+03],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lev_bounds</span></div><div class='xr-var-dims'>(lev, bnds)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(75, 2), meta=np.ndarray&gt;</div><input id='attrs-ab8655af-bac6-4120-a763-5b7e8c6ca112' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab8655af-bac6-4120-a763-5b7e8c6ca112' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-001e10d5-e8ae-468e-bd99-7a57f2e9fbbd' class='xr-var-data-in' type='checkbox'><label for='data-001e10d5-e8ae-468e-bd99-7a57f2e9fbbd' 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></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 600 B </td> <td> 600 B </td></tr>\n",
       "    <tr><th> Shape </th><td> (75, 2) </td> <td> (75, 2) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
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       "\n",
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       "  <text x=\"15.345186\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></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'>int64</div><div class='xr-var-preview xr-preview'>0 8760 17532 ... 2173944 2182704</div><input id='attrs-440f9334-0957-43eb-99bd-b703bb98864b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-440f9334-0957-43eb-99bd-b703bb98864b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d12ca84-2c3d-4009-807d-7a97521d6fdf' class='xr-var-data-in' type='checkbox'><label for='data-5d12ca84-2c3d-4009-807d-7a97521d6fdf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd><dt><span>calendar :</span></dt><dd>gregorian</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>time_origin :</span></dt><dd>1850-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>hours since 1850-07-02 12:00:00.000000</dd></dl></div><div class='xr-var-data'><pre>array([      0,    8760,   17532, ..., 2165184, 2173944, 2182704])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bounds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(250, 2), meta=np.ndarray&gt;</div><input id='attrs-1a0ea277-ec9a-4e98-a710-39008eb398d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a0ea277-ec9a-4e98-a710-39008eb398d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26fe352e-c231-4d61-b6f6-223e94cb89dd' class='xr-var-data-in' type='checkbox'><label for='data-26fe352e-c231-4d61-b6f6-223e94cb89dd' 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>calendar :</span></dt><dd>gregorian</dd><dt><span>units :</span></dt><dd>days since 1850-01-01</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
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       "  <thead>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 4.00 kB </td> <td> 4.00 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (250, 2) </td> <td> (250, 2) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;r1i2p1f1&#x27;</div><input id='attrs-66b8d63a-44a4-4913-a5d7-c985d631d7f3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-66b8d63a-44a4-4913-a5d7-c985d631d7f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8975f648-4739-433f-b464-249d426efafd' class='xr-var-data-in' type='checkbox'><label for='data-8975f648-4739-433f-b464-249d426efafd' 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([&#x27;r1i2p1f1&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>bnds</span></div><div class='xr-var-dims'>(bnds)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-ee18163b-a034-4ffc-a1ee-3fe2ef8037f2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee18163b-a034-4ffc-a1ee-3fe2ef8037f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27bc5672-9e59-4b83-870a-2efd404d0fed' class='xr-var-data-in' type='checkbox'><label for='data-27bc5672-9e59-4b83-870a-2efd404d0fed' 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, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>vertex</span></div><div class='xr-var-dims'>(vertex)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-5fc82cdb-c345-4f01-8caa-bde698d0d00d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5fc82cdb-c345-4f01-8caa-bde698d0d00d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-604f6e13-86ee-4d6c-88fa-cf1e8c4de130' class='xr-var-data-in' type='checkbox'><label for='data-604f6e13-86ee-4d6c-88fa-cf1e8c4de130' 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, 1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 357 358 359 360 361</div><input id='attrs-f98b5049-9f19-4119-bbc3-875150242e84' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f98b5049-9f19-4119-bbc3-875150242e84' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9538e774-7891-48d6-b7da-8a73774c2799' class='xr-var-data-in' type='checkbox'><label for='data-9538e774-7891-48d6-b7da-8a73774c2799' 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,   1,   2, ..., 359, 360, 361])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 327 328 329 330 331</div><input id='attrs-5db4c300-f1ef-4c7f-b240-cfd1a927353c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5db4c300-f1ef-4c7f-b240-cfd1a927353c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02d2402b-2aaf-415e-b225-9a511b94ce31' class='xr-var-data-in' type='checkbox'><label for='data-02d2402b-2aaf-415e-b225-9a511b94ce31' 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,   1,   2, ..., 329, 330, 331])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bd9143f5-e5db-4a56-afc2-499c4f75d6a1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bd9143f5-e5db-4a56-afc2-499c4f75d6a1' 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>area</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(332, 362), meta=np.ndarray&gt;</div><input id='attrs-f706f1a6-324e-4958-8055-6e255fb6e05d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f706f1a6-324e-4958-8055-6e255fb6e05d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5570f796-e483-4842-bd4f-08aa8ab5a80f' class='xr-var-data-in' type='checkbox'><label for='data-5570f796-e483-4842-bd4f-08aa8ab5a80f' 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>cell_area</dd><dt><span>units :</span></dt><dd>m2</dd></dl></div><div class='xr-var-data'><table>\n",
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       "    <tr><th> Shape </th><td> (332, 362) </td> <td> (332, 362) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\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>o2</span></div><div class='xr-var-dims'>(member_id, time, lev, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 9, 75, 332, 362), meta=np.ndarray&gt;</div><input id='attrs-002ca0c7-86a1-47f7-b56e-42735bca7ac0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-002ca0c7-86a1-47f7-b56e-42735bca7ac0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b1c60e6-81cb-4cbf-ab98-93450fe64b30' class='xr-var-data-in' type='checkbox'><label for='data-9b1c60e6-81cb-4cbf-ab98-93450fe64b30' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_measures :</span></dt><dd>area: areacello volume: volcello</dd><dt><span>cell_methods :</span></dt><dd>area: mean where sea time: mean</dd><dt><span>description :</span></dt><dd>&#x27;Mole concentration&#x27; means number of moles per unit volume, also called&quot;molarity&quot;, and is used in the construction mole_concentration_of_X_in_Y, whereX is a material constituent of Y.  A chemical or biological species denoted by X may be described by a single term such as &#x27;nitrogen&#x27; or a phrase such as &#x27;nox_expressed_as_nitrogen&#x27;.</dd><dt><span>history :</span></dt><dd>none</dd><dt><span>interval_operation :</span></dt><dd>2700 s</dd><dt><span>interval_write :</span></dt><dd>1 yr</dd><dt><span>long_name :</span></dt><dd>Dissolved Oxygen Concentration</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>standard_name :</span></dt><dd>mole_concentration_of_dissolved_molecular_oxygen_in_sea_water</dd><dt><span>units :</span></dt><dd>mol m-3</dd></dl></div><div class='xr-var-data'><table>\n",
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       "hdl:21.14100/0825a99b-e57e-4796-9f3b-0d260c960c30</dd><dt><span>variable_id :</span></dt><dd>o2</dd><dt><span>variant_info :</span></dt><dd>Equivalent to r1i1p1f1 but started on a different machine. Information provided by this attribute may in some cases be flawed. Users can find more comprehensive and up-to-date documentation via the further_info_url global attribute.</dd><dt><span>variant_label :</span></dt><dd>r1i2p1f1</dd><dt><span>status :</span></dt><dd>2019-10-25;created;by nhn2@columbia.edu</dd><dt><span>netcdf_tracking_ids :</span></dt><dd>hdl:21.14100/5fb53c12-afe1-499d-9039-590b6e6b1501\n",
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       "    frequency:               yr\n",
       "    further_info_url:        https://furtherinfo.es-doc.org/CMIP6.IPSL.IPSL-C...\n",
       "    grid:                    native ocean tri-polar grid with 105 k ocean cells\n",
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       "    institution:             Institut Pierre Simon Laplace, Paris 75252, France\n",
       "    institution_id:          IPSL\n",
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       "    mip_era:                 CMIP6\n",
       "    model_version:           6.1.8\n",
       "    name:                    /ccc/work/cont003/gencmip6/lebasn/IGCM_OUT/IPSLC...\n",
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       "    parent_mip_era:          CMIP6\n",
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       "    parent_variant_label:    r1i2p1f1\n",
       "    physics_index:           1\n",
       "    product:                 model-output\n",
       "    realization_index:       1\n",
       "    realm:                   ocnBgchem\n",
       "    source:                  IPSL-CM6A-LR (2017):  atmos: LMDZ (NPv6, N96; 14...\n",
       "    source_id:               IPSL-CM6A-LR\n",
       "    source_type:             AOGCM BGC\n",
       "    sub_experiment:          none\n",
       "    sub_experiment_id:       none\n",
       "    table_id:                Oyr\n",
       "    title:                   IPSL-CM6A-LR model output prepared for CMIP6 / C...\n",
       "    tracking_id:             hdl:21.14100/5fb53c12-afe1-499d-9039-590b6e6b150...\n",
       "    variable_id:             o2\n",
       "    variant_info:            Equivalent to r1i1p1f1 but started on a differen...\n",
       "    variant_label:           r1i2p1f1\n",
       "    status:                  2019-10-25;created;by nhn2@columbia.edu\n",
       "    netcdf_tracking_ids:     hdl:21.14100/5fb53c12-afe1-499d-9039-590b6e6b150...\n",
       "    version_id:              v20190319\n",
       "    intake_esm_varname:      ['o2']\n",
       "    intake_esm_dataset_key:  CMIP.IPSL.IPSL-CM6A-LR.piControl.Oyr.gn"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = promote_empty_dims(ds)\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['CMIP.IPSL.IPSL-CM6A-LR.piControl.Oyr.gn', 'CMIP.NOAA-GFDL.GFDL-ESM4.piControl.Oyr.gr', 'CMIP.CCCma.CanESM5.piControl.Oyr.gn'])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dset_dict_renamed.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nice. Now check out the `GFDL` model...\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       ".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",
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       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
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       "\n",
       ".xr-has-index {\n",
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       "\n",
       ".xr-var-list,\n",
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       "\n",
       ".xr-var-item > div,\n",
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       ".xr-var-item > .xr-var-name span {\n",
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       "\n",
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       "\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",
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       "\n",
       ".xr-var-dims {\n",
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       "}\n",
       "\n",
       ".xr-var-dtype {\n",
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       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
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       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
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       "\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",
       "  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",
       "  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-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\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",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (bnds: 2, lev: 35, member_id: 1, time: 500, x: 360, y: 180)\n",
       "Coordinates:\n",
       "  * y            (y) float64 -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5\n",
       "    lat_bounds   (y, bnds) float64 dask.array&lt;chunksize=(180, 2), meta=np.ndarray&gt;\n",
       "  * lev          (lev) float64 2.5 10.0 20.0 32.5 ... 5.5e+03 6e+03 6.5e+03\n",
       "    lev_bounds   (lev, bnds) float64 dask.array&lt;chunksize=(35, 2), meta=np.ndarray&gt;\n",
       "  * x            (x) float64 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5\n",
       "    lon_bounds   (x, bnds) float64 dask.array&lt;chunksize=(360, 2), meta=np.ndarray&gt;\n",
       "  * time         (time) int64 0 365 730 1095 ... 181040 181405 181770 182135\n",
       "    time_bounds  (time, bnds) float64 dask.array&lt;chunksize=(500, 2), meta=np.ndarray&gt;\n",
       "  * member_id    (member_id) &lt;U8 &#x27;r1i1p1f1&#x27;\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    o2           (member_id, time, lev, y, x) float32 dask.array&lt;chunksize=(1, 28, 35, 180, 360), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    Conventions:             CF-1.7 CMIP-6.0 UGRID-1.0\n",
       "    activity_id:             CMIP\n",
       "    branch_method:           Coupled climate state after 400 years of spinup,...\n",
       "    branch_time_in_child:    0.0\n",
       "    branch_time_in_parent:   0.0\n",
       "    comment:                 &lt;null ref&gt;\n",
       "    contact:                 gfdl.climate.model.info@noaa.gov\n",
       "    creation_date:           2019-05-13T21:46:48Z\n",
       "    data_specs_version:      01.00.27\n",
       "    experiment:              pre-industrial control\n",
       "    experiment_id:           piControl\n",
       "    external_variables:      areacello volcello\n",
       "    forcing_index:           1\n",
       "    frequency:               yr\n",
       "    further_info_url:        https://furtherinfo.es-doc.org/CMIP6.NOAA-GFDL.G...\n",
       "    grid:                    ocean data regridded from tripolar - nominal 0.5...\n",
       "    grid_label:              gr\n",
       "    history:                 File was processed by fremetar (GFDL analog of C...\n",
       "    initialization_index:    1\n",
       "    institution:             National Oceanic and Atmospheric Administration,...\n",
       "    institution_id:          NOAA-GFDL\n",
       "    license:                 CMIP6 model data produced by NOAA-GFDL is licens...\n",
       "    mip_era:                 CMIP6\n",
       "    nominal_resolution:      1x1 degree\n",
       "    parent_activity_id:      CMIP\n",
       "    parent_experiment_id:    piControl-spinup\n",
       "    parent_mip_era:          CMIP6\n",
       "    parent_source_id:        GFDL-ESM4\n",
       "    parent_time_units:       days since 0001-1-1\n",
       "    parent_variant_label:    r1i1p1f1\n",
       "    physics_index:           1\n",
       "    product:                 model-output\n",
       "    realization_index:       1\n",
       "    realm:                   ocnBgchem\n",
       "    references:              see further_info_url attribute\n",
       "    source:                  GFDL-ESM4 (2018):\\natmos: GFDL-AM4.1 (Cubed-sphe...\n",
       "    source_id:               GFDL-ESM4\n",
       "    source_type:             AOGCM AER CHEM BGC\n",
       "    sub_experiment:          none\n",
       "    sub_experiment_id:       none\n",
       "    table_id:                Oyr\n",
       "    title:                   NOAA GFDL GFDL-ESM4 model output prepared for CM...\n",
       "    tracking_id:             hdl:21.14100/894329be-9c3c-4dae-af32-3030d451e78...\n",
       "    variable_id:             o2\n",
       "    variant_info:            N/A\n",
       "    variant_label:           r1i1p1f1\n",
       "    status:                  2019-10-25;created;by nhn2@columbia.edu\n",
       "    netcdf_tracking_ids:     hdl:21.14100/894329be-9c3c-4dae-af32-3030d451e78...\n",
       "    version_id:              v20180701\n",
       "    intake_esm_varname:      [&#x27;o2&#x27;]\n",
       "    intake_esm_dataset_key:  CMIP.NOAA-GFDL.GFDL-ESM4.piControl.Oyr.gr</pre><div class='xr-wrap' hidden><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-bab234e8-15f9-4476-80c1-a866408b1210' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bab234e8-15f9-4476-80c1-a866408b1210' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lev</span>: 35</li><li><span class='xr-has-index'>member_id</span>: 1</li><li><span class='xr-has-index'>time</span>: 500</li><li><span class='xr-has-index'>x</span>: 360</li><li><span class='xr-has-index'>y</span>: 180</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9382983a-0720-49cf-a3eb-779c53fa56a7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9382983a-0720-49cf-a3eb-779c53fa56a7' class='xr-section-summary' >Coordinates: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-84ed286a-fea6-42b8-bebb-443f66cd5f83' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84ed286a-fea6-42b8-bebb-443f66cd5f83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-87209485-54a3-42b2-a7e2-699a42dbfe7f' class='xr-var-data-in' type='checkbox'><label for='data-87209485-54a3-42b2-a7e2-699a42dbfe7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
       "       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,\n",
       "       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,\n",
       "       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,\n",
       "       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,\n",
       "        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,\n",
       "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
       "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bounds</span></div><div class='xr-var-dims'>(y, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(180, 2), meta=np.ndarray&gt;</div><input id='attrs-98d3fb1d-817a-4a95-b991-e7e1c6ffa3f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98d3fb1d-817a-4a95-b991-e7e1c6ffa3f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f14a6ace-75a3-464c-9e36-2fabe7a27365' class='xr-var-data-in' type='checkbox'><label for='data-f14a6ace-75a3-464c-9e36-2fabe7a27365' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude bounds</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
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       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 2.88 kB </td> <td> 2.88 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (180, 2) </td> <td> (180, 2) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></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'>2.5 10.0 20.0 ... 6e+03 6.5e+03</div><input id='attrs-2adcb362-d895-4f22-9250-10b5d2494425' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2adcb362-d895-4f22-9250-10b5d2494425' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea176b84-b745-4236-90db-0677649b96b0' class='xr-var-data-in' type='checkbox'><label for='data-ea176b84-b745-4236-90db-0677649b96b0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>bounds :</span></dt><dd>lev_bnds</dd><dt><span>description :</span></dt><dd>generic ocean model vertical coordinate (nondimensional or dimensional)</dd><dt><span>long_name :</span></dt><dd>ocean model level</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([2.5000e+00, 1.0000e+01, 2.0000e+01, 3.2500e+01, 5.1250e+01, 7.5000e+01,\n",
       "       1.0000e+02, 1.2500e+02, 1.5625e+02, 2.0000e+02, 2.5000e+02, 3.1250e+02,\n",
       "       4.0000e+02, 5.0000e+02, 6.0000e+02, 7.0000e+02, 8.0000e+02, 9.0000e+02,\n",
       "       1.0000e+03, 1.1000e+03, 1.2000e+03, 1.3000e+03, 1.4000e+03, 1.5375e+03,\n",
       "       1.7500e+03, 2.0625e+03, 2.5000e+03, 3.0000e+03, 3.5000e+03, 4.0000e+03,\n",
       "       4.5000e+03, 5.0000e+03, 5.5000e+03, 6.0000e+03, 6.5000e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lev_bounds</span></div><div class='xr-var-dims'>(lev, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(35, 2), meta=np.ndarray&gt;</div><input id='attrs-63a99a23-fb50-49c8-9579-a495ad7ab703' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-63a99a23-fb50-49c8-9579-a495ad7ab703' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c53c1d4c-0d04-4cb2-b9e5-c15d1262d8df' class='xr-var-data-in' type='checkbox'><label for='data-c53c1d4c-0d04-4cb2-b9e5-c15d1262d8df' 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>\n",
       "  <thead>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 560 B </td> <td> 560 B </td></tr>\n",
       "    <tr><th> Shape </th><td> (35, 2) </td> <td> (35, 2) </td></tr>\n",
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       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "<td>\n",
       "<svg width=\"85\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5 1.5 2.5 ... 357.5 358.5 359.5</div><input id='attrs-c8f79cb0-6ad2-4be3-a3ce-bc8563760aeb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8f79cb0-6ad2-4be3-a3ce-bc8563760aeb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c24e8fbb-bbe7-42e8-81af-afb573a06646' class='xr-var-data-in' type='checkbox'><label for='data-c24e8fbb-bbe7-42e8-81af-afb573a06646' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.5,   1.5,   2.5, ..., 357.5, 358.5, 359.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bounds</span></div><div class='xr-var-dims'>(x, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(360, 2), meta=np.ndarray&gt;</div><input id='attrs-85ff45ab-b0c1-4b41-9419-4a7f44d98b53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85ff45ab-b0c1-4b41-9419-4a7f44d98b53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8571dece-3d95-4cb5-b53d-3e70b2b1179c' class='xr-var-data-in' type='checkbox'><label for='data-8571dece-3d95-4cb5-b53d-3e70b2b1179c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude bounds</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
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       "<td>\n",
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       "  <tbody>\n",
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       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></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'>int64</div><div class='xr-var-preview xr-preview'>0 365 730 ... 181405 181770 182135</div><input id='attrs-67f41831-2df4-4e6c-b859-8ca1283ade8f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67f41831-2df4-4e6c-b859-8ca1283ade8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-940aa9df-b011-4b11-b4ff-9233d3d356ab' class='xr-var-data-in' type='checkbox'><label for='data-940aa9df-b011-4b11-b4ff-9233d3d356ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>calendar :</span></dt><dd>noleap</dd><dt><span>calendar_type :</span></dt><dd>noleap</dd><dt><span>description :</span></dt><dd>Temporal mean</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>days since 0001-07-02 12:00:00.000000</dd></dl></div><div class='xr-var-data'><pre>array([     0,    365,    730, ..., 181405, 181770, 182135])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bounds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(500, 2), meta=np.ndarray&gt;</div><input id='attrs-cd497002-f361-4949-b6c6-7f5a581d1925' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd497002-f361-4949-b6c6-7f5a581d1925' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-946d1916-3bd1-41cb-925d-a4058fb6de30' class='xr-var-data-in' type='checkbox'><label for='data-946d1916-3bd1-41cb-925d-a4058fb6de30' 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>calendar :</span></dt><dd>noleap</dd><dt><span>long_name :</span></dt><dd>time axis boundaries</dd><dt><span>units :</span></dt><dd>days since 0001-01-01</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 class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;r1i1p1f1&#x27;</div><input id='attrs-9877dc31-23b7-4907-b636-faa950a57142' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9877dc31-23b7-4907-b636-faa950a57142' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b89376d-26eb-43d7-8862-a0e51c339e1a' class='xr-var-data-in' type='checkbox'><label for='data-1b89376d-26eb-43d7-8862-a0e51c339e1a' 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([&#x27;r1i1p1f1&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-12526e24-31ec-4977-a59b-bdd7f2c111ec' class='xr-section-summary-in' type='checkbox'  checked><label for='section-12526e24-31ec-4977-a59b-bdd7f2c111ec' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>o2</span></div><div class='xr-var-dims'>(member_id, time, lev, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 28, 35, 180, 360), meta=np.ndarray&gt;</div><input id='attrs-0a6584c9-029c-45f7-a8cc-67a1da2552d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a6584c9-029c-45f7-a8cc-67a1da2552d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-555a8dea-2312-4a1f-8208-53ff71323833' class='xr-var-data-in' type='checkbox'><label for='data-555a8dea-2312-4a1f-8208-53ff71323833' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_measures :</span></dt><dd>area: areacello volume: volcello</dd><dt><span>cell_methods :</span></dt><dd>area: mean where sea time: mean</dd><dt><span>comment :</span></dt><dd>Model data on the 1x1 grid includes values in all cells for which any ocean exists on the native grid. For mapping purposes, we recommend using a land mask such as World Ocean Atlas to cover these areas of partial land.  For calculating approximate integrals, we recommend multiplying by cell volume (volcello).</dd><dt><span>interp_method :</span></dt><dd>conserve_order1</dd><dt><span>long_name :</span></dt><dd>Dissolved Oxygen Concentration</dd><dt><span>original_name :</span></dt><dd>o2</dd><dt><span>standard_name :</span></dt><dd>mole_concentration_of_dissolved_molecular_oxygen_in_sea_water</dd><dt><span>units :</span></dt><dd>mol m-3</dd></dl></div><div class='xr-var-data'><table>\n",
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       "atmos: GFDL-AM4.1 (Cubed-sphere (c96) - 1 degree nominal horizontal resolution; 360 x 180 longitude/latitude; 49 levels; top level 1 Pa)\n",
       "ocean: GFDL-OM4p5 (GFDL-MOM6, tripolar - nominal 0.5 deg; 720 x 576 longitude/latitude; 75 levels; top grid cell 0-2 m)\n",
       "seaIce: GFDL-SIM4p5 (GFDL-SIS2.0, tripolar - nominal 0.5 deg; 720 x 576 longitude/latitude; 5 layers; 5 thickness categories)\n",
       "land: GFDL-LM4.1\n",
       "aerosol: interactive\n",
       "atmosChem: GFDL-ATMCHEM4.1 (full atmospheric chemistry)\n",
       "ocnBgchem: GFDL-COBALTv2\n",
       "landIce: GFDL-LM4.1\n",
       "(GFDL ID: 2019_0273)</dd><dt><span>source_id :</span></dt><dd>GFDL-ESM4</dd><dt><span>source_type :</span></dt><dd>AOGCM AER CHEM BGC</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>Oyr</dd><dt><span>title :</span></dt><dd>NOAA GFDL GFDL-ESM4 model output prepared for CMIP6 pre-industrial control</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/894329be-9c3c-4dae-af32-3030d451e789\n",
       "hdl:21.14100/37a9d5ed-9ce3-4b7e-8524-75a6e9c962a6\n",
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       "hdl:21.14100/51554b9d-33ef-4358-b00b-404851a2cc97</dd><dt><span>variable_id :</span></dt><dd>o2</dd><dt><span>variant_info :</span></dt><dd>N/A</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>status :</span></dt><dd>2019-10-25;created;by nhn2@columbia.edu</dd><dt><span>netcdf_tracking_ids :</span></dt><dd>hdl:21.14100/894329be-9c3c-4dae-af32-3030d451e789\n",
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       "hdl:21.14100/9ded7d69-4429-4341-97ea-21f5588c42a4\n",
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       "Dimensions:      (bnds: 2, lev: 35, member_id: 1, time: 500, x: 360, y: 180)\n",
       "Coordinates:\n",
       "  * y            (y) float64 -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5\n",
       "    lat_bounds   (y, bnds) float64 dask.array<chunksize=(180, 2), meta=np.ndarray>\n",
       "  * lev          (lev) float64 2.5 10.0 20.0 32.5 ... 5.5e+03 6e+03 6.5e+03\n",
       "    lev_bounds   (lev, bnds) float64 dask.array<chunksize=(35, 2), meta=np.ndarray>\n",
       "  * x            (x) float64 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5\n",
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       "Dimensions without coordinates: bnds\n",
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       "Attributes:\n",
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       "    activity_id:             CMIP\n",
       "    branch_method:           Coupled climate state after 400 years of spinup,...\n",
       "    branch_time_in_child:    0.0\n",
       "    branch_time_in_parent:   0.0\n",
       "    comment:                 <null ref>\n",
       "    contact:                 gfdl.climate.model.info@noaa.gov\n",
       "    creation_date:           2019-05-13T21:46:48Z\n",
       "    data_specs_version:      01.00.27\n",
       "    experiment:              pre-industrial control\n",
       "    experiment_id:           piControl\n",
       "    external_variables:      areacello volcello\n",
       "    forcing_index:           1\n",
       "    frequency:               yr\n",
       "    further_info_url:        https://furtherinfo.es-doc.org/CMIP6.NOAA-GFDL.G...\n",
       "    grid:                    ocean data regridded from tripolar - nominal 0.5...\n",
       "    grid_label:              gr\n",
       "    history:                 File was processed by fremetar (GFDL analog of C...\n",
       "    initialization_index:    1\n",
       "    institution:             National Oceanic and Atmospheric Administration,...\n",
       "    institution_id:          NOAA-GFDL\n",
       "    license:                 CMIP6 model data produced by NOAA-GFDL is licens...\n",
       "    mip_era:                 CMIP6\n",
       "    nominal_resolution:      1x1 degree\n",
       "    parent_activity_id:      CMIP\n",
       "    parent_experiment_id:    piControl-spinup\n",
       "    parent_mip_era:          CMIP6\n",
       "    parent_source_id:        GFDL-ESM4\n",
       "    parent_time_units:       days since 0001-1-1\n",
       "    parent_variant_label:    r1i1p1f1\n",
       "    physics_index:           1\n",
       "    product:                 model-output\n",
       "    realization_index:       1\n",
       "    realm:                   ocnBgchem\n",
       "    references:              see further_info_url attribute\n",
       "    source:                  GFDL-ESM4 (2018):\\natmos: GFDL-AM4.1 (Cubed-sphe...\n",
       "    source_id:               GFDL-ESM4\n",
       "    source_type:             AOGCM AER CHEM BGC\n",
       "    sub_experiment:          none\n",
       "    sub_experiment_id:       none\n",
       "    table_id:                Oyr\n",
       "    title:                   NOAA GFDL GFDL-ESM4 model output prepared for CM...\n",
       "    tracking_id:             hdl:21.14100/894329be-9c3c-4dae-af32-3030d451e78...\n",
       "    variable_id:             o2\n",
       "    variant_info:            N/A\n",
       "    variant_label:           r1i1p1f1\n",
       "    status:                  2019-10-25;created;by nhn2@columbia.edu\n",
       "    netcdf_tracking_ids:     hdl:21.14100/894329be-9c3c-4dae-af32-3030d451e78...\n",
       "    version_id:              v20180701\n",
       "    intake_esm_varname:      ['o2']\n",
       "    intake_esm_dataset_key:  CMIP.NOAA-GFDL.GFDL-ESM4.piControl.Oyr.gr"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = dset_dict_renamed['CMIP.NOAA-GFDL.GFDL-ESM4.piControl.Oyr.gr']\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset is from regridded output and has thus only 1D longitude and latitude values (which are called `x` and `y` due to the previous renaming step. `broadcast_lonlat` adds the `lon` and `lat` arrays back as 2d arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "  display: none;\n",
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       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
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       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
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       ".xr-var-data,\n",
       ".xr-attrs {\n",
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       "\n",
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       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (bnds: 2, lev: 35, member_id: 1, time: 500, x: 360, y: 180)\n",
       "Coordinates:\n",
       "  * y            (y) float64 -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5\n",
       "    lat_bounds   (y, bnds) float64 dask.array&lt;chunksize=(180, 2), meta=np.ndarray&gt;\n",
       "  * lev          (lev) float64 2.5 10.0 20.0 32.5 ... 5.5e+03 6e+03 6.5e+03\n",
       "    lev_bounds   (lev, bnds) float64 dask.array&lt;chunksize=(35, 2), meta=np.ndarray&gt;\n",
       "  * x            (x) float64 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5\n",
       "    lon_bounds   (x, bnds) float64 dask.array&lt;chunksize=(360, 2), meta=np.ndarray&gt;\n",
       "  * time         (time) int64 0 365 730 1095 ... 181040 181405 181770 182135\n",
       "    time_bounds  (time, bnds) float64 dask.array&lt;chunksize=(500, 2), meta=np.ndarray&gt;\n",
       "  * member_id    (member_id) &lt;U8 &#x27;r1i1p1f1&#x27;\n",
       "    lon          (x, y) float64 0.5 0.5 0.5 0.5 0.5 ... 359.5 359.5 359.5 359.5\n",
       "    lat          (x, y) float64 -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    o2           (member_id, time, lev, y, x) float32 dask.array&lt;chunksize=(1, 28, 35, 180, 360), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    Conventions:             CF-1.7 CMIP-6.0 UGRID-1.0\n",
       "    activity_id:             CMIP\n",
       "    branch_method:           Coupled climate state after 400 years of spinup,...\n",
       "    branch_time_in_child:    0.0\n",
       "    branch_time_in_parent:   0.0\n",
       "    comment:                 &lt;null ref&gt;\n",
       "    contact:                 gfdl.climate.model.info@noaa.gov\n",
       "    creation_date:           2019-05-13T21:46:48Z\n",
       "    data_specs_version:      01.00.27\n",
       "    experiment:              pre-industrial control\n",
       "    experiment_id:           piControl\n",
       "    external_variables:      areacello volcello\n",
       "    forcing_index:           1\n",
       "    frequency:               yr\n",
       "    further_info_url:        https://furtherinfo.es-doc.org/CMIP6.NOAA-GFDL.G...\n",
       "    grid:                    ocean data regridded from tripolar - nominal 0.5...\n",
       "    grid_label:              gr\n",
       "    history:                 File was processed by fremetar (GFDL analog of C...\n",
       "    initialization_index:    1\n",
       "    institution:             National Oceanic and Atmospheric Administration,...\n",
       "    institution_id:          NOAA-GFDL\n",
       "    license:                 CMIP6 model data produced by NOAA-GFDL is licens...\n",
       "    mip_era:                 CMIP6\n",
       "    nominal_resolution:      1x1 degree\n",
       "    parent_activity_id:      CMIP\n",
       "    parent_experiment_id:    piControl-spinup\n",
       "    parent_mip_era:          CMIP6\n",
       "    parent_source_id:        GFDL-ESM4\n",
       "    parent_time_units:       days since 0001-1-1\n",
       "    parent_variant_label:    r1i1p1f1\n",
       "    physics_index:           1\n",
       "    product:                 model-output\n",
       "    realization_index:       1\n",
       "    realm:                   ocnBgchem\n",
       "    references:              see further_info_url attribute\n",
       "    source:                  GFDL-ESM4 (2018):\\natmos: GFDL-AM4.1 (Cubed-sphe...\n",
       "    source_id:               GFDL-ESM4\n",
       "    source_type:             AOGCM AER CHEM BGC\n",
       "    sub_experiment:          none\n",
       "    sub_experiment_id:       none\n",
       "    table_id:                Oyr\n",
       "    title:                   NOAA GFDL GFDL-ESM4 model output prepared for CM...\n",
       "    tracking_id:             hdl:21.14100/894329be-9c3c-4dae-af32-3030d451e78...\n",
       "    variable_id:             o2\n",
       "    variant_info:            N/A\n",
       "    variant_label:           r1i1p1f1\n",
       "    status:                  2019-10-25;created;by nhn2@columbia.edu\n",
       "    netcdf_tracking_ids:     hdl:21.14100/894329be-9c3c-4dae-af32-3030d451e78...\n",
       "    version_id:              v20180701\n",
       "    intake_esm_varname:      [&#x27;o2&#x27;]\n",
       "    intake_esm_dataset_key:  CMIP.NOAA-GFDL.GFDL-ESM4.piControl.Oyr.gr</pre><div class='xr-wrap' hidden><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-3232e66b-1277-4a4a-a622-f99d0af21503' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3232e66b-1277-4a4a-a622-f99d0af21503' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>lev</span>: 35</li><li><span class='xr-has-index'>member_id</span>: 1</li><li><span class='xr-has-index'>time</span>: 500</li><li><span class='xr-has-index'>x</span>: 360</li><li><span class='xr-has-index'>y</span>: 180</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7139330b-c7ab-4161-b8a6-e3f0d1da6b1f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7139330b-c7ab-4161-b8a6-e3f0d1da6b1f' class='xr-section-summary' >Coordinates: <span>(11)</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'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-2816dd89-c2cc-44f4-b6be-a5d573967c27' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2816dd89-c2cc-44f4-b6be-a5d573967c27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0e24c22-8ab8-42e7-ba76-e93a609de345' class='xr-var-data-in' type='checkbox'><label for='data-e0e24c22-8ab8-42e7-ba76-e93a609de345' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
       "       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,\n",
       "       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,\n",
       "       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,\n",
       "       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,\n",
       "        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,\n",
       "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
       "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bounds</span></div><div class='xr-var-dims'>(y, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(180, 2), meta=np.ndarray&gt;</div><input id='attrs-58110f48-1fe2-4586-a6ab-77127c752df0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58110f48-1fe2-4586-a6ab-77127c752df0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d8eadbc-e931-4a64-9ea6-9d5e08608124' class='xr-var-data-in' type='checkbox'><label for='data-5d8eadbc-e931-4a64-9ea6-9d5e08608124' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude bounds</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 2.88 kB </td> <td> 2.88 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (180, 2) </td> <td> (180, 2) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></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",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 25.924160051965714,0.0 25.924160051965714,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"12.962080\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
       "  <text x=\"45.924160\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.924160,60.000000)\">180</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></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'>2.5 10.0 20.0 ... 6e+03 6.5e+03</div><input id='attrs-bf395fb0-fb7f-4b91-b103-fe2b38a4a734' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf395fb0-fb7f-4b91-b103-fe2b38a4a734' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99d4af79-a037-4670-85d9-9159639fb836' class='xr-var-data-in' type='checkbox'><label for='data-99d4af79-a037-4670-85d9-9159639fb836' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>bounds :</span></dt><dd>lev_bnds</dd><dt><span>description :</span></dt><dd>generic ocean model vertical coordinate (nondimensional or dimensional)</dd><dt><span>long_name :</span></dt><dd>ocean model level</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([2.5000e+00, 1.0000e+01, 2.0000e+01, 3.2500e+01, 5.1250e+01, 7.5000e+01,\n",
       "       1.0000e+02, 1.2500e+02, 1.5625e+02, 2.0000e+02, 2.5000e+02, 3.1250e+02,\n",
       "       4.0000e+02, 5.0000e+02, 6.0000e+02, 7.0000e+02, 8.0000e+02, 9.0000e+02,\n",
       "       1.0000e+03, 1.1000e+03, 1.2000e+03, 1.3000e+03, 1.4000e+03, 1.5375e+03,\n",
       "       1.7500e+03, 2.0625e+03, 2.5000e+03, 3.0000e+03, 3.5000e+03, 4.0000e+03,\n",
       "       4.5000e+03, 5.0000e+03, 5.5000e+03, 6.0000e+03, 6.5000e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lev_bounds</span></div><div class='xr-var-dims'>(lev, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(35, 2), meta=np.ndarray&gt;</div><input id='attrs-581a69bb-7ecf-40b1-98b2-5e49481a8a1d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-581a69bb-7ecf-40b1-98b2-5e49481a8a1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-452942a0-2b6d-42bc-856e-08a6d1591f1a' class='xr-var-data-in' type='checkbox'><label for='data-452942a0-2b6d-42bc-856e-08a6d1591f1a' 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>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 560 B </td> <td> 560 B </td></tr>\n",
       "    <tr><th> Shape </th><td> (35, 2) </td> <td> (35, 2) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
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       "  <line x1=\"0\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5 1.5 2.5 ... 357.5 358.5 359.5</div><input id='attrs-ae5f954c-9d53-4ab6-aab7-539fb26bdd5a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae5f954c-9d53-4ab6-aab7-539fb26bdd5a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d5752e1-0eb6-486e-9992-029e97dcfb3a' class='xr-var-data-in' type='checkbox'><label for='data-4d5752e1-0eb6-486e-9992-029e97dcfb3a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.5,   1.5,   2.5, ..., 357.5, 358.5, 359.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_bounds</span></div><div class='xr-var-dims'>(x, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(360, 2), meta=np.ndarray&gt;</div><input id='attrs-a12c8138-a2c2-49a4-8d09-3725930d399c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a12c8138-a2c2-49a4-8d09-3725930d399c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-758f3f89-68b3-4bf9-8c9d-c6f16b4d5979' class='xr-var-data-in' type='checkbox'><label for='data-758f3f89-68b3-4bf9-8c9d-c6f16b4d5979' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude bounds</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
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       "  <thead>\n",
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       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 5.76 kB </td> <td> 5.76 kB </td></tr>\n",
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       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
       "</td>\n",
       "<td>\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></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'>int64</div><div class='xr-var-preview xr-preview'>0 365 730 ... 181405 181770 182135</div><input id='attrs-0d2b6673-5cef-49ce-9826-faf62fbd3c27' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d2b6673-5cef-49ce-9826-faf62fbd3c27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f944d184-f3ec-48e9-8c03-6d2537dbcc4c' class='xr-var-data-in' type='checkbox'><label for='data-f944d184-f3ec-48e9-8c03-6d2537dbcc4c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>calendar :</span></dt><dd>noleap</dd><dt><span>calendar_type :</span></dt><dd>noleap</dd><dt><span>description :</span></dt><dd>Temporal mean</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>days since 0001-07-02 12:00:00.000000</dd></dl></div><div class='xr-var-data'><pre>array([     0,    365,    730, ..., 181405, 181770, 182135])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bounds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(500, 2), meta=np.ndarray&gt;</div><input id='attrs-07f2efad-76ab-41dd-943f-d36f27fc2719' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07f2efad-76ab-41dd-943f-d36f27fc2719' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69ca4d57-6ff5-4c66-be5d-0064d5eb280a' class='xr-var-data-in' type='checkbox'><label for='data-69ca4d57-6ff5-4c66-be5d-0064d5eb280a' 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>calendar :</span></dt><dd>noleap</dd><dt><span>long_name :</span></dt><dd>time axis boundaries</dd><dt><span>units :</span></dt><dd>days since 0001-01-01</dd></dl></div><div class='xr-var-data'><table>\n",
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       "  <tbody>\n",
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       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
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       "<td>\n",
       "<svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "\n",
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       "\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;r1i1p1f1&#x27;</div><input id='attrs-012141d8-e9cc-4fa3-a1c2-39dfd7c7ffe9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-012141d8-e9cc-4fa3-a1c2-39dfd7c7ffe9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85e9c2a4-80da-4030-98be-d76226086c0c' class='xr-var-data-in' type='checkbox'><label for='data-85e9c2a4-80da-4030-98be-d76226086c0c' 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([&#x27;r1i1p1f1&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(x, y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5 0.5 0.5 ... 359.5 359.5 359.5</div><input id='attrs-032c675d-1a21-4a64-b7f4-4e086842d00c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-032c675d-1a21-4a64-b7f4-4e086842d00c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-689dd6a5-ef86-4255-858d-5c48b43c1eab' class='xr-var-data-in' type='checkbox'><label for='data-689dd6a5-ef86-4255-858d-5c48b43c1eab' 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([[  0.5,   0.5,   0.5, ...,   0.5,   0.5,   0.5],\n",
       "       [  1.5,   1.5,   1.5, ...,   1.5,   1.5,   1.5],\n",
       "       [  2.5,   2.5,   2.5, ...,   2.5,   2.5,   2.5],\n",
       "       ...,\n",
       "       [357.5, 357.5, 357.5, ..., 357.5, 357.5, 357.5],\n",
       "       [358.5, 358.5, 358.5, ..., 358.5, 358.5, 358.5],\n",
       "       [359.5, 359.5, 359.5, ..., 359.5, 359.5, 359.5]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(x, y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-511f4be7-7b07-454c-b06c-3a40f4c949a1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-511f4be7-7b07-454c-b06c-3a40f4c949a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-463db099-38ec-442f-adeb-24e0901bc4a6' class='xr-var-data-in' type='checkbox'><label for='data-463db099-38ec-442f-adeb-24e0901bc4a6' 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([[-89.5, -88.5, -87.5, ...,  87.5,  88.5,  89.5],\n",
       "       [-89.5, -88.5, -87.5, ...,  87.5,  88.5,  89.5],\n",
       "       [-89.5, -88.5, -87.5, ...,  87.5,  88.5,  89.5],\n",
       "       ...,\n",
       "       [-89.5, -88.5, -87.5, ...,  87.5,  88.5,  89.5],\n",
       "       [-89.5, -88.5, -87.5, ...,  87.5,  88.5,  89.5],\n",
       "       [-89.5, -88.5, -87.5, ...,  87.5,  88.5,  89.5]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-25bd42a0-87a1-4537-adac-e98e9525a6e0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-25bd42a0-87a1-4537-adac-e98e9525a6e0' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>o2</span></div><div class='xr-var-dims'>(member_id, time, lev, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 28, 35, 180, 360), meta=np.ndarray&gt;</div><input id='attrs-80a166fe-1ded-44dd-9a81-78bed17ca342' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-80a166fe-1ded-44dd-9a81-78bed17ca342' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63a214f0-68a0-4967-89ac-2230aea12bf4' class='xr-var-data-in' type='checkbox'><label for='data-63a214f0-68a0-4967-89ac-2230aea12bf4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_measures :</span></dt><dd>area: areacello volume: volcello</dd><dt><span>cell_methods :</span></dt><dd>area: mean where sea time: mean</dd><dt><span>comment :</span></dt><dd>Model data on the 1x1 grid includes values in all cells for which any ocean exists on the native grid. For mapping purposes, we recommend using a land mask such as World Ocean Atlas to cover these areas of partial land.  For calculating approximate integrals, we recommend multiplying by cell volume (volcello).</dd><dt><span>interp_method :</span></dt><dd>conserve_order1</dd><dt><span>long_name :</span></dt><dd>Dissolved Oxygen Concentration</dd><dt><span>original_name :</span></dt><dd>o2</dd><dt><span>standard_name :</span></dt><dd>mole_concentration_of_dissolved_molecular_oxygen_in_sea_water</dd><dt><span>units :</span></dt><dd>mol m-3</dd></dl></div><div class='xr-var-data'><table>\n",
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       "    <tr><th> Count </th><td> 37 Tasks </td><td> 18 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-48848a23-dd39-4b1f-961e-a84845ae9dc5' class='xr-section-summary-in' type='checkbox'  ><label for='section-48848a23-dd39-4b1f-961e-a84845ae9dc5' class='xr-section-summary' >Attributes: <span>(51)</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-6.0 UGRID-1.0</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>branch_method :</span></dt><dd>Coupled climate state after 400 years of spinup, except land state after 800 years of spinup.</dd><dt><span>branch_time_in_child :</span></dt><dd>0.0</dd><dt><span>branch_time_in_parent :</span></dt><dd>0.0</dd><dt><span>comment :</span></dt><dd>&lt;null ref&gt;</dd><dt><span>contact :</span></dt><dd>gfdl.climate.model.info@noaa.gov</dd><dt><span>creation_date :</span></dt><dd>2019-05-13T21:46:48Z</dd><dt><span>data_specs_version :</span></dt><dd>01.00.27</dd><dt><span>experiment :</span></dt><dd>pre-industrial control</dd><dt><span>experiment_id :</span></dt><dd>piControl</dd><dt><span>external_variables :</span></dt><dd>areacello volcello</dd><dt><span>forcing_index :</span></dt><dd>1</dd><dt><span>frequency :</span></dt><dd>yr</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.NOAA-GFDL.GFDL-ESM4.piControl.none.r1i1p1f1</dd><dt><span>grid :</span></dt><dd>ocean data regridded from tripolar - nominal 0.5 deg latitude/longitude to 180,360; interpolation method: conserve_order1</dd><dt><span>grid_label :</span></dt><dd>gr</dd><dt><span>history :</span></dt><dd>File was processed by fremetar (GFDL analog of CMOR). TripleID: [exper_id_Nl8y6FcDL6,realiz_id_mNbn4PTB8f,run_id_0l1ykpVYGy]</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>institution :</span></dt><dd>National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08540, USA</dd><dt><span>institution_id :</span></dt><dd>NOAA-GFDL</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by NOAA-GFDL 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). 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>mip_era :</span></dt><dd>CMIP6</dd><dt><span>nominal_resolution :</span></dt><dd>1x1 degree</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl-spinup</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_source_id :</span></dt><dd>GFDL-ESM4</dd><dt><span>parent_time_units :</span></dt><dd>days since 0001-1-1</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>realization_index :</span></dt><dd>1</dd><dt><span>realm :</span></dt><dd>ocnBgchem</dd><dt><span>references :</span></dt><dd>see further_info_url attribute</dd><dt><span>source :</span></dt><dd>GFDL-ESM4 (2018):\n",
       "atmos: GFDL-AM4.1 (Cubed-sphere (c96) - 1 degree nominal horizontal resolution; 360 x 180 longitude/latitude; 49 levels; top level 1 Pa)\n",
       "ocean: GFDL-OM4p5 (GFDL-MOM6, tripolar - nominal 0.5 deg; 720 x 576 longitude/latitude; 75 levels; top grid cell 0-2 m)\n",
       "seaIce: GFDL-SIM4p5 (GFDL-SIS2.0, tripolar - nominal 0.5 deg; 720 x 576 longitude/latitude; 5 layers; 5 thickness categories)\n",
       "land: GFDL-LM4.1\n",
       "aerosol: interactive\n",
       "atmosChem: GFDL-ATMCHEM4.1 (full atmospheric chemistry)\n",
       "ocnBgchem: GFDL-COBALTv2\n",
       "landIce: GFDL-LM4.1\n",
       "(GFDL ID: 2019_0273)</dd><dt><span>source_id :</span></dt><dd>GFDL-ESM4</dd><dt><span>source_type :</span></dt><dd>AOGCM AER CHEM BGC</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>Oyr</dd><dt><span>title :</span></dt><dd>NOAA GFDL GFDL-ESM4 model output prepared for CMIP6 pre-industrial control</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/894329be-9c3c-4dae-af32-3030d451e789\n",
       "hdl:21.14100/37a9d5ed-9ce3-4b7e-8524-75a6e9c962a6\n",
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       "hdl:21.14100/51554b9d-33ef-4358-b00b-404851a2cc97</dd><dt><span>variable_id :</span></dt><dd>o2</dd><dt><span>variant_info :</span></dt><dd>N/A</dd><dt><span>variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>status :</span></dt><dd>2019-10-25;created;by nhn2@columbia.edu</dd><dt><span>netcdf_tracking_ids :</span></dt><dd>hdl:21.14100/894329be-9c3c-4dae-af32-3030d451e789\n",
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       "hdl:21.14100/9ded7d69-4429-4341-97ea-21f5588c42a4\n",
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       "<xarray.Dataset>\n",
       "Dimensions:      (bnds: 2, lev: 35, member_id: 1, time: 500, x: 360, y: 180)\n",
       "Coordinates:\n",
       "  * y            (y) float64 -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5\n",
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       "    lev_bounds   (lev, bnds) float64 dask.array<chunksize=(35, 2), meta=np.ndarray>\n",
       "  * x            (x) float64 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5\n",
       "    lon_bounds   (x, bnds) float64 dask.array<chunksize=(360, 2), meta=np.ndarray>\n",
       "  * time         (time) int64 0 365 730 1095 ... 181040 181405 181770 182135\n",
       "    time_bounds  (time, bnds) float64 dask.array<chunksize=(500, 2), meta=np.ndarray>\n",
       "  * member_id    (member_id) <U8 'r1i1p1f1'\n",
       "    lon          (x, y) float64 0.5 0.5 0.5 0.5 0.5 ... 359.5 359.5 359.5 359.5\n",
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       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
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       "Attributes:\n",
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       "    activity_id:             CMIP\n",
       "    branch_method:           Coupled climate state after 400 years of spinup,...\n",
       "    branch_time_in_child:    0.0\n",
       "    branch_time_in_parent:   0.0\n",
       "    comment:                 <null ref>\n",
       "    contact:                 gfdl.climate.model.info@noaa.gov\n",
       "    creation_date:           2019-05-13T21:46:48Z\n",
       "    data_specs_version:      01.00.27\n",
       "    experiment:              pre-industrial control\n",
       "    experiment_id:           piControl\n",
       "    external_variables:      areacello volcello\n",
       "    forcing_index:           1\n",
       "    frequency:               yr\n",
       "    further_info_url:        https://furtherinfo.es-doc.org/CMIP6.NOAA-GFDL.G...\n",
       "    grid:                    ocean data regridded from tripolar - nominal 0.5...\n",
       "    grid_label:              gr\n",
       "    history:                 File was processed by fremetar (GFDL analog of C...\n",
       "    initialization_index:    1\n",
       "    institution:             National Oceanic and Atmospheric Administration,...\n",
       "    institution_id:          NOAA-GFDL\n",
       "    license:                 CMIP6 model data produced by NOAA-GFDL is licens...\n",
       "    mip_era:                 CMIP6\n",
       "    nominal_resolution:      1x1 degree\n",
       "    parent_activity_id:      CMIP\n",
       "    parent_experiment_id:    piControl-spinup\n",
       "    parent_mip_era:          CMIP6\n",
       "    parent_source_id:        GFDL-ESM4\n",
       "    parent_time_units:       days since 0001-1-1\n",
       "    parent_variant_label:    r1i1p1f1\n",
       "    physics_index:           1\n",
       "    product:                 model-output\n",
       "    realization_index:       1\n",
       "    realm:                   ocnBgchem\n",
       "    references:              see further_info_url attribute\n",
       "    source:                  GFDL-ESM4 (2018):\\natmos: GFDL-AM4.1 (Cubed-sphe...\n",
       "    source_id:               GFDL-ESM4\n",
       "    source_type:             AOGCM AER CHEM BGC\n",
       "    sub_experiment:          none\n",
       "    sub_experiment_id:       none\n",
       "    table_id:                Oyr\n",
       "    title:                   NOAA GFDL GFDL-ESM4 model output prepared for CM...\n",
       "    tracking_id:             hdl:21.14100/894329be-9c3c-4dae-af32-3030d451e78...\n",
       "    variable_id:             o2\n",
       "    variant_info:            N/A\n",
       "    variant_label:           r1i1p1f1\n",
       "    status:                  2019-10-25;created;by nhn2@columbia.edu\n",
       "    netcdf_tracking_ids:     hdl:21.14100/894329be-9c3c-4dae-af32-3030d451e78...\n",
       "    version_id:              v20180701\n",
       "    intake_esm_varname:      ['o2']\n",
       "    intake_esm_dataset_key:  CMIP.NOAA-GFDL.GFDL-ESM4.piControl.Oyr.gr"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = broadcast_lonlat(ds)\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you look back at the `IPSL` model you notice that the `x` and `y` values are given just as indicies, making rough selection of regions using xarrays `.sel` rather useless. To gain back this functionality, we replace `x` and `y` with nominal longitudes and latitudes using `replace_x_y_nominal_lat_lon`:\n",
    "\n",
    "> As of version '0.2' this is not part of the `combined_preprocessing` anymore due to performance issue, when applied to a full dataset. You can still apply this function before plotting as shown in the final part of the tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
      "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
      "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
      "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
      "  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n",
      " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
      " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
      " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n",
      " 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179\n",
      " 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197\n",
      " 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215\n",
      " 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233\n",
      " 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251\n",
      " 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269\n",
      " 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287\n",
      " 288 289 290]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/juliusbusecke/miniconda/envs/xmip_docs/lib/python3.9/site-packages/xarray/core/indexing.py:1369: PerformanceWarning: Slicing is producing a large chunk. To accept the large\n",
      "chunk and silence this warning, set the option\n",
      "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):\n",
      "    ...     array[indexer]\n",
      "\n",
      "To avoid creating the large chunks, set the option\n",
      "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n",
      "    ...     array[indexer]\n",
      "  return self.array[key]\n"
     ]
    },
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       "         2.00046086,   2.33502841,   2.67085862,   3.00865722,\n",
       "         3.3493495 ,   3.69411659,   4.04443884,   4.40214634,\n",
       "         4.76948118,   5.1491704 ,   5.54450321,   5.95941591,\n",
       "         6.39854765,   6.86724281,   7.3714118 ,   7.91714287,\n",
       "         8.50995922,   9.1537075 ,   9.84941292,  10.59480476,\n",
       "        11.3851366 ,  12.21489906,  13.07900047,  13.97249222,\n",
       "        14.88963032,  15.82362843,  16.76747131,  17.71509743,\n",
       "        18.66210938,  19.605793  ,  20.54502678,  21.4785862 ,\n",
       "        22.40633011,  23.32811546,  24.24381447,  25.15330124,\n",
       "        26.05646706,  26.95320511,  27.84342003,  28.72702217,\n",
       "        29.60393143,  30.4740715 ,  31.33737755,  32.19379044,\n",
       "        33.04325867,  33.88573456,  34.72118759,  35.54957962,\n",
       "        36.37088776,  37.18510056,  37.99220276,  38.79219818,\n",
       "        39.58508682,  40.37087631,  41.14958954,  41.92126465,\n",
       "        42.68597031,  43.44370651,  44.19451523,  44.93845367,\n",
       "        45.67557907,  46.40594864,  47.12963867,  47.84671783,\n",
       "        48.55727386,  49.26138687,  49.95915222,  50.65066147,\n",
       "        51.33601761,  52.01532364,  52.68869019,  53.35645294,\n",
       "        54.01857376,  54.67513275,  55.3262558 ,  55.97206116,\n",
       "        56.61268616,  57.24825668,  57.87890625,  58.50476456,\n",
       "        59.12596893,  59.74265289,  60.35513306,  60.96369171,\n",
       "        61.56817245,  62.16870499,  62.76543045,  63.35847473,\n",
       "        63.94796753,  64.53403473,  65.11679077,  65.69636536,\n",
       "        66.2728653 ,  66.84711456,  67.41855621,  67.9872818 ,\n",
       "        68.55338287,  69.11695099,  69.67805481,  70.23677063,\n",
       "        70.79315948,  71.34726715,  71.89914703,  72.44923401,\n",
       "        72.99770355,  73.54405212,  74.08827972,  74.63038635,\n",
       "        75.17033386,  75.70809174,  76.24359894,  76.77680969,\n",
       "        77.30763245,  77.83597565,  78.36173248,  78.88496399,\n",
       "        79.40639496,  79.92492676,  80.44039154,  80.95262909,\n",
       "        81.46146393,  81.96670532,  82.46815491,  82.96560669,\n",
       "        83.45883179,  83.94761658,  84.43172455,  84.91091156,\n",
       "        85.38493347,  85.85354614,  86.31648254,  86.77348328,\n",
       "        87.22425842,  87.66850281,  88.10582733,  88.53568268,\n",
       "        88.95700073,  89.36653137,  89.74176788])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = dset_dict_renamed['CMIP.CCCma.CanESM5.piControl.Oyr.gn']\n",
    "print(ds.y.data)\n",
    "\n",
    "ds = replace_x_y_nominal_lat_lon(ds)\n",
    "ds.y.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can put all of this together in a wrapper function and plot some data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='3' class='' max='3' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [3/3 00:00<00:00]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/juliusbusecke/miniconda/envs/xmip_docs/lib/python3.9/site-packages/dask/array/core.py:4241: PerformanceWarning: Increasing number of chunks by factor of 60\n",
      "  result = blockwise(\n"
     ]
    }
   ],
   "source": [
    "def wrapper(ds):\n",
    "    ds = ds.copy()\n",
    "    ds = rename_cmip6(ds)\n",
    "    ds = promote_empty_dims(ds)\n",
    "    ds = broadcast_lonlat(ds)\n",
    "    ds = replace_x_y_nominal_lat_lon(ds)\n",
    "    return ds\n",
    "\n",
    "# pass the preprocessing directly\n",
    "with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n",
    "    dset_dict_processed1 = cat.to_dataset_dict(zarr_kwargs=z_kwargs,\n",
    "                                               preprocess=wrapper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axarr = plt.subplots(nrows=3, figsize=[10,15])\n",
    "for ax, (k, ds) in zip(axarr.flat, dset_dict_processed1.items()):\n",
    "    if 'member_id' in ds.dims:\n",
    "        ds = ds.isel(member_id=-1)\n",
    "    ds.o2.isel(time=0, lev=0).sel(y=slice(-15,15)).plot(ax=ax)\n",
    "    ax.set_title(k)\n",
    "    ax.set_aspect(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The naming and units are still inconsistent (not implemented yet) and the longitude is not consistent (we will deal with this below) But this is a big step forward. With the 'unprocessed' datasets this would have needed a lot more logic in the print loop."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"lon\"></a>\n",
    "## Inconsistent longitude conventions\n",
    "We saw above that not all models have a '0-360' longitude convention. We can fix this very quickly using `correct_lon`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
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       "      <progress value='3' class='' max='3' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [3/3 00:00<00:00]\n",
       "    </div>\n",
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      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/juliusbusecke/miniconda/envs/xmip_docs/lib/python3.9/site-packages/dask/array/core.py:4241: PerformanceWarning: Increasing number of chunks by factor of 60\n",
      "  result = blockwise(\n"
     ]
    }
   ],
   "source": [
    "from xmip.preprocessing import correct_lon\n",
    "\n",
    "# same as above\n",
    "def wrapper(ds):\n",
    "    ds = ds.copy()\n",
    "    ds = rename_cmip6(ds)\n",
    "    ds = promote_empty_dims(ds)\n",
    "    ds = broadcast_lonlat(ds)\n",
    "    ds = correct_lon(ds)\n",
    "    ds = replace_x_y_nominal_lat_lon(ds)\n",
    "    return ds\n",
    "\n",
    "# pass the preprocessing directly\n",
    "with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n",
    "    dset_dict_processed2 = cat.to_dataset_dict(zarr_kwargs=z_kwargs,\n",
    "                                               preprocess=wrapper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axarr = plt.subplots(nrows=3, figsize=[10,15])\n",
    "for ax, (k, ds) in zip(axarr.flat, dset_dict_processed2.items()):\n",
    "    if 'member_id' in ds.dims:\n",
    "        ds = ds.isel(member_id=-1)\n",
    "    ds.o2.isel(time=0, lev=0).sel(y=slice(-15,15)).plot(ax=ax)\n",
    "    ax.set_title(k)\n",
    "    ax.set_aspect(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://media.giphy.com/media/xT5LMQ8rHYTDGFG07e/giphy.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"units\"></a>\n",
    "## Inconsistent units\n",
    "\n",
    "But of course this is not all. Some models, give the depth in centimeters (so far I have only seen this in the NCAR models). We can fix this with `correct_units`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='2' class='' max='2' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [2/2 00:00<00:00]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='2' class='' max='2' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [2/2 00:00<00:00]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from xmip.preprocessing import correct_units\n",
    "query = dict(experiment_id = ['historical'],variable_id='thetao', grid_label=['gn'],source_id=['CESM2', 'CanESM5'], member_id='r1i1p1f1',\n",
    "             )\n",
    "cat = col.search(**query)\n",
    "# raw data read in\n",
    "dset_dict = cat.to_dataset_dict(zarr_kwargs=z_kwargs)\n",
    "# fixed units\n",
    "dset_dict_fixed_unit = cat.to_dataset_dict(zarr_kwargs=z_kwargs, preprocess=correct_units)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x170297640>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dset_dict['CMIP.NCAR.CESM2.historical.Omon.gn'].lev.plot()\n",
    "plt.figure()\n",
    "dset_dict_fixed_unit['CMIP.NCAR.CESM2.historical.Omon.gn'].lev.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This helps tremendously when you are trying to slice a common depth from a series of models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axarr = plt.subplots(nrows=2, figsize=[10,10])\n",
    "for ax, (k, ds) in zip(axarr.flat, dset_dict_fixed_unit.items()):\n",
    "    ds.thetao.isel(time=0).sel(lev=5000, method='nearest').plot(ax=ax, vmin=-1, vmax=5)\n",
    "    ax.set_title(k)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a comparison, for the unprocessed data this would have picked the depth at 50m for the `CESM2` model instead of 5000m:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axarr = plt.subplots(nrows=2, figsize=[10,10])\n",
    "for ax, (k, ds) in zip(axarr.flat, dset_dict.items()):\n",
    "    ds.thetao.isel(time=0).sel(lev=5000, method='nearest').plot(ax=ax, vmin=-1, vmax=5)\n",
    "    ax.set_title(k)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"units\"></a>\n",
    "## Consistent CF bounds\n",
    "Many of the CMIP6 models come with 'bound' dataarrays, that describe the extent of the finite grid cells.\n",
    "For the longitude and latitude there are two conventions: 2-element 'bounds' (describing the width of a cell along the center) and 4 element 'verticies' (describing the 4 corner coordinates of the cell).\n",
    "`xmip` automatically renames these variables consistently and converts them so that every dataset has both conventions available.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/juliusbusecke/miniconda/envs/xmip_docs/lib/python3.9/site-packages/xarray/core/indexing.py:1369: PerformanceWarning: Slicing is producing a large chunk. To accept the large\n",
      "chunk and silence this warning, set the option\n",
      "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):\n",
      "    ...     array[indexer]\n",
      "\n",
      "To avoid creating the large chunks, set the option\n",
      "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n",
      "    ...     array[indexer]\n",
      "  return self.array[key]\n",
      "/Users/juliusbusecke/miniconda/envs/xmip_docs/lib/python3.9/site-packages/xarray/core/indexing.py:1369: PerformanceWarning: Slicing is producing a large chunk. To accept the large\n",
      "chunk and silence this warning, set the option\n",
      "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):\n",
      "    ...     array[indexer]\n",
      "\n",
      "To avoid creating the large chunks, set the option\n",
      "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n",
      "    ...     array[indexer]\n",
      "  return self.array[key]\n"
     ]
    },
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       "            }\n",
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     "text": [
      "/Users/juliusbusecke/miniconda/envs/xmip_docs/lib/python3.9/site-packages/xarray/core/indexing.py:1369: PerformanceWarning: Slicing is producing a large chunk. To accept the large\n",
      "chunk and silence this warning, set the option\n",
      "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': False}):\n",
      "    ...     array[indexer]\n",
      "\n",
      "To avoid creating the large chunks, set the option\n",
      "    >>> with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n",
      "    ...     array[indexer]\n",
      "  return self.array[key]\n"
     ]
    }
   ],
   "source": [
    "from xmip.preprocessing import correct_coordinates,parse_lon_lat_bounds, maybe_convert_bounds_to_vertex, maybe_convert_vertex_to_bounds\n",
    "\n",
    "# same as above\n",
    "def wrapper(ds):\n",
    "    ds = ds.copy()\n",
    "    ds = rename_cmip6(ds)\n",
    "    ds = promote_empty_dims(ds)\n",
    "    ds = broadcast_lonlat(ds)\n",
    "    ds = replace_x_y_nominal_lat_lon(ds)\n",
    "    ds = correct_lon(ds)\n",
    "    ds = correct_coordinates(ds)\n",
    "    ds = parse_lon_lat_bounds(ds)\n",
    "    ds = maybe_convert_bounds_to_vertex(ds)\n",
    "    ds = maybe_convert_vertex_to_bounds(ds)\n",
    "    return ds\n",
    "\n",
    "# pass the preprocessing directly\n",
    "\n",
    "dset_dict_processed3 = cat.to_dataset_dict(zarr_kwargs=z_kwargs,\n",
    "                                           preprocess=wrapper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:        (bnds: 2, lev: 60, member_id: 1, time: 1980, vertex: 4, x: 320, y: 384)\n",
      "Coordinates:\n",
      "    lat            (y, x) float64 dask.array<chunksize=(384, 320), meta=np.ndarray>\n",
      "    lat_verticies  (y, x, vertex) float32 dask.array<chunksize=(384, 320, 4), meta=np.ndarray>\n",
      "  * lev            (lev) float64 500.0 1.5e+03 2.5e+03 ... 5.125e+05 5.375e+05\n",
      "    lev_bounds     (lev, bnds) float32 dask.array<chunksize=(60, 2), meta=np.ndarray>\n",
      "    lon            (y, x) float64 dask.array<chunksize=(384, 320), meta=np.ndarray>\n",
      "    lon_verticies  (y, x, vertex) float32 dask.array<chunksize=(384, 320, 4), meta=np.ndarray>\n",
      "  * y              (y) float64 -79.22 -78.69 -78.15 -77.62 ... 89.11 89.66 89.71\n",
      "  * x              (x) float64 1.062 2.187 3.312 4.437 ... 357.7 358.8 359.9\n",
      "  * time           (time) float64 6.749e+05 6.749e+05 ... 7.351e+05 7.351e+05\n",
      "    time_bounds    (time, bnds) float64 dask.array<chunksize=(1980, 2), meta=np.ndarray>\n",
      "  * bnds           (bnds) int64 0 1\n",
      "  * vertex         (vertex) int64 0 1 2 3\n",
      "    lon_bounds     (bnds, y, x) float32 dask.array<chunksize=(1, 384, 320), meta=np.ndarray>\n",
      "    lat_bounds     (bnds, y, x) float32 dask.array<chunksize=(1, 384, 320), meta=np.ndarray>\n",
      "  * member_id      (member_id) <U8 'r1i1p1f1'\n",
      "Data variables:\n",
      "    thetao         (member_id, time, lev, y, x) float32 dask.array<chunksize=(1, 8, 60, 384, 320), meta=np.ndarray>\n",
      "Attributes:\n",
      "    Conventions:             CF-1.7 CMIP-6.2\n",
      "    activity_id:             CMIP\n",
      "    branch_method:           standard\n",
      "    branch_time_in_child:    674885.0\n",
      "    branch_time_in_parent:   219000.0\n",
      "    case_id:                 15\n",
      "    cesm_casename:           b.e21.BHIST.f09_g17.CMIP6-historical.001\n",
      "    contact:                 cesm_cmip6@ucar.edu\n",
      "    creation_date:           2019-01-16T22:01:19Z\n",
      "    data_specs_version:      01.00.29\n",
      "    experiment:              all-forcing simulation of the recent past\n",
      "    experiment_id:           historical\n",
      "    external_variables:      areacello volcello\n",
      "    forcing_index:           1\n",
      "    frequency:               mon\n",
      "    further_info_url:        https://furtherinfo.es-doc.org/CMIP6.NCAR.CESM2....\n",
      "    grid:                    native gx1v7 displaced pole grid (384x320 latxlon)\n",
      "    grid_label:              gn\n",
      "    initialization_index:    1\n",
      "    institution:             National Center for Atmospheric Research, Climat...\n",
      "    institution_id:          NCAR\n",
      "    license:                 CMIP6 model data produced by <The National Cente...\n",
      "    mip_era:                 CMIP6\n",
      "    model_doi_url:           https://doi.org/10.5065/D67H1H0V\n",
      "    nominal_resolution:      100 km\n",
      "    parent_activity_id:      CMIP\n",
      "    parent_experiment_id:    piControl\n",
      "    parent_mip_era:          CMIP6\n",
      "    parent_source_id:        CESM2\n",
      "    parent_time_units:       days since 0001-01-01 00:00:00\n",
      "    parent_variant_label:    r1i1p1f1\n",
      "    physics_index:           1\n",
      "    product:                 model-output\n",
      "    realization_index:       1\n",
      "    realm:                   ocean\n",
      "    source:                  CESM2 (2017): atmosphere: CAM6 (0.9x1.25 finite ...\n",
      "    source_id:               CESM2\n",
      "    source_type:             AOGCM BGC\n",
      "    sub_experiment:          none\n",
      "    sub_experiment_id:       none\n",
      "    table_id:                Omon\n",
      "    tracking_id:             hdl:21.14100/672d35cd-e662-4807-8dee-7d7d5e1d4d1c\n",
      "    variable_id:             thetao\n",
      "    variant_info:            CMIP6 20th century experiments (1850-2014) with ...\n",
      "    variant_label:           r1i1p1f1\n",
      "    status:                  2019-10-25;created;by nhn2@columbia.edu\n",
      "    netcdf_tracking_ids:     hdl:21.14100/672d35cd-e662-4807-8dee-7d7d5e1d4d1c\n",
      "    version_id:              v20190308\n",
      "    intake_esm_varname:      ['thetao']\n",
      "    intake_esm_dataset_key:  CMIP.NCAR.CESM2.historical.Omon.gn\n",
      "<xarray.Dataset>\n",
      "Dimensions:        (bnds: 2, lev: 45, member_id: 1, time: 1980, vertex: 4, x: 360, y: 291)\n",
      "Coordinates:\n",
      "  * x              (x) float64 0.5 1.5 2.5 3.5 4.5 ... 356.5 357.5 358.5 359.5\n",
      "  * y              (y) float32 -78.39 -78.19 -77.98 -77.77 ... 88.96 89.37 89.74\n",
      "    lat            (y, x) float32 dask.array<chunksize=(291, 360), meta=np.ndarray>\n",
      "  * lev            (lev) float64 3.047 9.454 16.36 ... 5.375e+03 5.625e+03\n",
      "    lev_bounds     (lev, bnds) float64 dask.array<chunksize=(45, 2), meta=np.ndarray>\n",
      "    lon            (y, x) float32 dask.array<chunksize=(291, 360), meta=np.ndarray>\n",
      "  * time           (time) int64 0 708 1416 2148 ... 1443192 1443924 1444656\n",
      "    time_bounds    (time, bnds) float64 dask.array<chunksize=(1980, 2), meta=np.ndarray>\n",
      "    lat_verticies  (y, x, vertex) float32 dask.array<chunksize=(291, 360, 4), meta=np.ndarray>\n",
      "    lon_verticies  (y, x, vertex) float32 dask.array<chunksize=(291, 360, 4), meta=np.ndarray>\n",
      "  * bnds           (bnds) int64 0 1\n",
      "  * vertex         (vertex) int64 0 1 2 3\n",
      "    lon_bounds     (bnds, y, x) float32 dask.array<chunksize=(1, 291, 360), meta=np.ndarray>\n",
      "    lat_bounds     (bnds, y, x) float32 dask.array<chunksize=(1, 291, 360), meta=np.ndarray>\n",
      "  * member_id      (member_id) <U8 'r1i1p1f1'\n",
      "Data variables:\n",
      "    thetao         (member_id, time, lev, y, x) float32 dask.array<chunksize=(1, 6, 45, 291, 360), meta=np.ndarray>\n",
      "Attributes:\n",
      "    CCCma_model_hash:            3dedf95315d603326fde4f5340dc0519d80d10c0\n",
      "    CCCma_parent_runid:          rc3-pictrl\n",
      "    CCCma_pycmor_hash:           094aa868e787693cfe55b2f1665f6a6b0880b03a\n",
      "    CCCma_runid:                 rc3.1-his01\n",
      "    Conventions:                 CF-1.7 CMIP-6.2\n",
      "    YMDH_branch_time_in_child:   1850:01:01:00\n",
      "    YMDH_branch_time_in_parent:  5201:01:01:00\n",
      "    activity_id:                 CMIP\n",
      "    branch_method:               Spin-up documentation\n",
      "    branch_time_in_child:        0.0\n",
      "    branch_time_in_parent:       1223115.0\n",
      "    cmor_version:                3.4.0\n",
      "    contact:                     ec.cccma.info-info.ccmac.ec@canada.ca\n",
      "    creation_date:               2019-03-14T05:00:40Z\n",
      "    data_specs_version:          01.00.29\n",
      "    experiment:                  all-forcing simulation of the recent past\n",
      "    experiment_id:               historical\n",
      "    external_variables:          areacello volcello\n",
      "    forcing_index:               1\n",
      "    frequency:                   mon\n",
      "    further_info_url:            https://furtherinfo.es-doc.org/CMIP6.CCCma.C...\n",
      "    grid:                        ORCA1 tripolar grid, 1 deg with refinement t...\n",
      "    grid_label:                  gn\n",
      "    history:                     2019-03-14T05:00:40Z ;rewrote data to be con...\n",
      "    initialization_index:        1\n",
      "    institution:                 Canadian Centre for Climate Modelling and An...\n",
      "    institution_id:              CCCma\n",
      "    license:                     CMIP6 model data produced by The Government ...\n",
      "    mip_era:                     CMIP6\n",
      "    nominal_resolution:          100 km\n",
      "    parent_activity_id:          CMIP\n",
      "    parent_experiment_id:        piControl\n",
      "    parent_mip_era:              CMIP6\n",
      "    parent_source_id:            CanESM5\n",
      "    parent_time_units:           days since 1850-01-01 0:0:0.0\n",
      "    parent_variant_label:        r1i1p1f1\n",
      "    physics_index:               1\n",
      "    product:                     model-output\n",
      "    realization_index:           1\n",
      "    realm:                       ocean\n",
      "    references:                  Geophysical Model Development Special issue ...\n",
      "    source:                      CanESM5 (2017): \\naerosol: interactive\\natmo...\n",
      "    source_id:                   CanESM5\n",
      "    source_type:                 AOGCM\n",
      "    sub_experiment:              none\n",
      "    sub_experiment_id:           none\n",
      "    table_id:                    Omon\n",
      "    table_info:                  Creation Date:(13 December 2018) MD5:e84cb97...\n",
      "    title:                       CanESM5 output prepared for CMIP6\n",
      "    tracking_id:                 hdl:21.14100/5446945e-4a3c-43a1-babd-af08607...\n",
      "    variable_id:                 thetao\n",
      "    variant_label:               r1i1p1f1\n",
      "    version:                     v20190306\n",
      "    status:                      2019-11-06;created;by nhn2@columbia.edu\n",
      "    netcdf_tracking_ids:         hdl:21.14100/5446945e-4a3c-43a1-babd-af08607...\n",
      "    version_id:                  v20190306\n",
      "    intake_esm_varname:          ['thetao']\n",
      "    intake_esm_dataset_key:      CMIP.CCCma.CanESM5.historical.Omon.gn\n"
     ]
    }
   ],
   "source": [
    "for k, ds in dset_dict_processed3.items():\n",
    "    print(ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The vertex convention is consistent across models. The points are sorted from lower-left, upper-left, upper-right to lower-right."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"combo\"></a>\n",
    "## TL;DR How to put it all together\n",
    "\n",
    "To combine all these (or just some you like), you can create a wrapper function as above, or you can use the provided `combined_preprocessing`, which does all the above.\n",
    "> Due to concerns regarding the [dask performance of large datasets](https://github.com/jbusecke/xmip/issues/94), the latest version of `combined_preprocessing` does not apply `replace_x_y_nominal_lat_lon` anymore. You can still apply this at a later point, preferrably at the end of a processing step, to enable rough selection of regions (see example below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['GFDL-ESM4' 'IPSL-CM6A-LR' 'CanESM5' 'CanESM5-CanOE' 'MPI-ESM-1-2-HAM'\n",
      " 'MPI-ESM1-2-HR' 'MPI-ESM1-2-LR' 'ACCESS-ESM1-5' 'MRI-ESM2-0'\n",
      " 'IPSL-CM5A2-INCA' 'EC-Earth3-CC']\n",
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n"
     ]
    },
    {
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       "            }\n",
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    }
   ],
   "source": [
    "from xmip.preprocessing import combined_preprocessing\n",
    "\n",
    "# lets load a bunch more models this time\n",
    "query = dict(experiment_id=['piControl', 'historical'],\n",
    "             table_id='Oyr', \n",
    "             source_id=[\n",
    "                 'GFDL-ESM4',\n",
    "                 'IPSL-CM6A-LR',\n",
    "                 'CanESM5',\n",
    "                 'CanESM5-CanOE',\n",
    "                 'MPI-ESM-1-2-HAM',\n",
    "                 'MPI-ESM1-2-HR',\n",
    "                 'MPI-ESM1-2-LR',\n",
    "                 'ACCESS-ESM1-5',\n",
    "                 'MRI-ESM2-0',\n",
    "                 'IPSL-CM5A2-INCA',\n",
    "                 'EC-Earth3-CC'\n",
    "             ],\n",
    "             variable_id='o2',\n",
    "             grid_label=['gn', 'gr'])\n",
    "cat = col.search(**query)\n",
    "\n",
    "print(cat.df['source_id'].unique())\n",
    "with dask.config.set(**{'array.slicing.split_large_chunks': True}):\n",
    "    dset_dict = cat.to_dataset_dict(zarr_kwargs=z_kwargs,\n",
    "                                    preprocess=combined_preprocessing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 24 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axarr = plt.subplots(nrows=4, ncols=3, figsize=[25,15])\n",
    "for ax,(k, ds) in zip(axarr.flat,dset_dict.items()):\n",
    "    if 'member_id' in ds.dims:\n",
    "        ds = ds.isel(member_id=0)\n",
    "    da = ds.o2.isel(time=0).interp(lev=2500)\n",
    "    # this step is necessary to order the longitudes properly for simple plotting. Alternatively you could use a proper map projection \n",
    "    # with e.g. cartopy and would not need this step\n",
    "    da = replace_x_y_nominal_lat_lon(da)\n",
    "    da = da.sel(x=slice(100, 200), y=slice(-20,20))\n",
    "    try:\n",
    "        da.plot(ax=ax, vmax=0.25, vmin=0.05)\n",
    "    except:\n",
    "        print(k)\n",
    "        pass\n",
    "    ax.set_title(k)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Appeal\n",
    "**`xmip` is still under very active development, and its true strength will be to 'crowdsource' fixes for common problems. If you notice errors, please try to isolate them by applying the single untility functions and then raise an issue on [github](https://github.com/jbusecke/xmip/issues)**"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:xmip_docs]",
   "language": "python",
   "name": "conda-env-xmip_docs-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}