jupyter_notebooks/cesm_oceanheat.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bc65f494-4fbe-4af0-85ca-e3a424b56454",
   "metadata": {},
   "source": [
    "# Calculate surface ocean heat content using CESM2 LENS data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d20011b4-5317-4614-a5e0-42f3e0a9ece6",
   "metadata": {},
   "source": [
    "- This notebook is adapted from the NCAR gallery in the Pangeo collection\n",
    "- https://gallery.pangeo.io/repos/NCAR/notebook-gallery/notebooks/Run-Anywhere/Ocean-Heat-Content/OHC_tutorial.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db1d6ac4-fe69-4d95-a9cb-ca2f063ea493",
   "metadata": {},
   "source": [
    "### Input Data Access\n",
    "\n",
    "- This notebook illustrates how to compute surface ocean heat content using potential temperature data from CESM2 Large Ensemble Dataset (https://www.cesm.ucar.edu/community-projects/lens2) hosted on NCAR's glade storage.\n",
    "- This data is open access and is accessed via OSDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "20444ba4-275a-45a7-bda5-c22b5cb5cee4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Display output of plots directly in Notebook\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import intake\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import seaborn as sns\n",
    "import re\n",
    "# import nest_asyncio\n",
    "# nest_asyncio.apply()\n",
    "import xesmf as xe\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "63e53607-bf0a-41a4-9f72-5769197e4570",
   "metadata": {},
   "outputs": [],
   "source": [
    "import fsspec.implementations.http as fshttp\n",
    "from pelicanfs.core import PelicanFileSystem, PelicanMap, OSDFFileSystem \n",
    "\n",
    "import cf_units as cf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9928a424-cf23-4a2f-bf16-afe7137d296f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import dask \n",
    "from dask_jobqueue import PBSCluster\n",
    "from dask.distributed import Client\n",
    "from dask.distributed import performance_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "961a7413-7c37-42a3-9891-14935fb12bd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "init_year0  = '1991'\n",
    "init_year1  = '2020'\n",
    "final_year0 = '2071'\n",
    "final_year1 = '2100'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ef9b6c48-22f6-476b-8261-7427e6186b29",
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_daily(ds):\n",
    "    year = ds.time.dt.year\n",
    "    day = ds.time.dt.dayofyear\n",
    "\n",
    "    # assign new coords\n",
    "    ds = ds.assign_coords(year=(\"time\", year.data), day=(\"time\", day.data))\n",
    "\n",
    "    # reshape the array to (..., \"day\", \"year\")\n",
    "    return ds.set_index(time=(\"year\", \"day\")).unstack(\"time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a9355d14-86f5-4236-a986-44af18453483",
   "metadata": {},
   "outputs": [],
   "source": [
    "rda_scratch = '/gpfs/csfs1/collections/rda/scratch/harshah'\n",
    "# zarr_path   = rda_scratch + \"/tas_zarr/\"\n",
    "# mean_path   = zarr_path + \"/means/\"\n",
    "# stdev_path  = zarr_path + \"/stdevs/\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfb1461c-e63d-435c-919e-b18069e47d6f",
   "metadata": {},
   "source": [
    "## Create a PBS cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "61ff9c8f-cadb-48e2-bd8d-7b5d7d2b4fe4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a PBS cluster object\n",
    "cluster = PBSCluster(\n",
    "    job_name = 'dask-wk24-hpc',\n",
    "    cores = 1,\n",
    "    memory = '8GiB',\n",
    "    processes = 1,\n",
    "    local_directory = rda_scratch+'/dask/spill',\n",
    "    log_directory = rda_scratch + '/dask/logs/',\n",
    "    resource_spec = 'select=1:ncpus=1:mem=8GB',\n",
    "    queue = 'casper',\n",
    "    walltime = '5:00:00',\n",
    "    #interface = 'ib0'\n",
    "    interface = 'ext'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e1ab50dc-0496-455e-8b02-acf4ef314b37",
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster.scale(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5c5304fb-06bc-4e37-8421-0c70f883d062",
   "metadata": {},
   "outputs": [
    {
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       "                    <strong>Dashboard:</strong> <a href=\"https://jupyterhub.hpc.ucar.edu/stable/user/harshah/proxy/42427/status\" target=\"_blank\">https://jupyterhub.hpc.ucar.edu/stable/user/harshah/proxy/42427/status</a>\n",
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       "                        <strong>Dashboard:</strong> <a href=\"https://jupyterhub.hpc.ucar.edu/stable/user/harshah/proxy/42427/status\" target=\"_blank\">https://jupyterhub.hpc.ucar.edu/stable/user/harshah/proxy/42427/status</a>\n",
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       "        <summary style=\"margin-bottom: 20px;\">\n",
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       "PBSCluster(9ea08cc7, 'tcp://128.117.208.94:33169', workers=0, threads=0, memory=0 B)"
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   "source": [
    "cluster"
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  {
   "cell_type": "markdown",
   "id": "e971bb08-6952-4e5a-8c34-5b03fe189562",
   "metadata": {},
   "source": [
    "## Load CESM LENS2 temperature data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "be45a360-624a-403b-be3f-6879ec0a5c09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><strong>aws-cesm2-le catalog with 40 dataset(s) from 322 asset(s)</strong>:</p> <div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <td>322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>variable</th>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>long_name</th>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>component</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>experiment</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forcing_variant</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frequency</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vertical_levels</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spatial_domain</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>units</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>start_time</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>end_time</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>path</th>\n",
       "      <td>313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>derived_variable</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cesm_cat = intake.open_esm_datastore(rda_scratch + '/intake_catalogs/posix/aws-cesm2-le.json')\n",
    "cesm_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9a74b61b-7ee8-460b-b165-d16319dbec2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# cesm_cat.df['variable'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b2dc1598-96bc-491a-8bae-80a90b8dbd3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><strong>aws-cesm2-le catalog with 3 dataset(s) from 3 asset(s)</strong>:</p> <div>\n",
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       "      <th>Unnamed: 0</th>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>variable</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>long_name</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>component</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>experiment</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>forcing_variant</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>frequency</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vertical_levels</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>spatial_domain</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>units</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>start_time</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>end_time</th>\n",
       "      <td>2</td>\n",
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       "      <th>path</th>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>derived_variable</th>\n",
       "      <td>0</td>\n",
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   ],
   "source": [
    "cesm_temp = cesm_cat.search(variable ='TEMP', frequency ='monthly')\n",
    "cesm_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a192979f-0901-4a6d-8399-f03c81023e29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['/glade/campaign/collections/rda/transfer/chifan_AWS/ncar-cesm2-lens/ocn/monthly/cesm2LE-historical-cmip6-TEMP.zarr',\n",
       "       '/glade/campaign/collections/rda/transfer/chifan_AWS/ncar-cesm2-lens/ocn/monthly/cesm2LE-ssp370-cmip6-TEMP.zarr',\n",
       "       '/glade/campaign/collections/rda/transfer/chifan_AWS/ncar-cesm2-lens/ocn/monthly/cesm2LE-ssp370-smbb-TEMP.zarr'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cesm_temp.df['path'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fc63c711-865e-4f21-86dc-03947dac7a04",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'component.experiment.frequency.forcing_variant'\n"
     ]
    },
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   "source": [
    "dsets_cesm = cesm_temp.to_dataset_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "02d5967d-1cb4-4d16-a2e4-f54509db39ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ocn.historical.monthly.cmip6',\n",
       " 'ocn.ssp370.monthly.cmip6',\n",
       " 'ocn.ssp370.monthly.smbb']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cesm_temp.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "63ba6df1-c054-4893-a87f-9f87a6b412d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "historical       = dsets_cesm['ocn.historical.monthly.cmip6']\n",
    "future_smbb      = dsets_cesm['ocn.ssp370.monthly.smbb']\n",
    "future_cmip6     = dsets_cesm['ocn.ssp370.monthly.cmip6']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b43bbedd-909b-4bba-bbe9-0043b6cd5fc9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 36min 32s, sys: 19min 30s, total: 56min 3s\n",
      "Wall time: 2h 5min 27s\n"
     ]
    }
   ],
   "source": [
    "# %%time\n",
    "# merge_ds_cmip6 = xr.concat([historical, future_cmip6], dim='time')\n",
    "# merge_ds_cmip6 = merge_ds_cmip6.dropna(dim='member_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "73bcb612-9703-4e7c-b42e-57c236fee7f8",
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   "outputs": [
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       "\n",
       ".xr-attrs dt,\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
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       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
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       "  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",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 3TB\n",
       "Dimensions:     (member_id: 50, time: 1980, z_t: 60, nlat: 384, nlon: 320, d2: 2)\n",
       "Coordinates:\n",
       "  * member_id   (member_id) &lt;U12 2kB &#x27;r10i1181p1f1&#x27; ... &#x27;r9i1301p1f1&#x27;\n",
       "  * time        (time) object 16kB 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "    time_bound  (time, d2) object 32kB dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;\n",
       "  * z_t         (z_t) float32 240B 500.0 1.5e+03 2.5e+03 ... 5.125e+05 5.375e+05\n",
       "Dimensions without coordinates: nlat, nlon, d2\n",
       "Data variables:\n",
       "    TEMP        (member_id, time, z_t, nlat, nlon) float32 3TB dask.array&lt;chunksize=(1, 6, 60, 384, 320), meta=np.ndarray&gt;\n",
       "Attributes: (12/25)\n",
       "    Conventions:                       CF-1.0; http://www.cgd.ucar.edu/cms/ea...\n",
       "    calendar:                          All years have exactly  365 days.\n",
       "    cell_methods:                      cell_methods = time: mean ==&gt; the vari...\n",
       "    contents:                          Diagnostic and Prognostic Variables\n",
       "    model_doi_url:                     https://doi.org/10.5065/D67H1H0V\n",
       "    revision:                          $Id$\n",
       "    ...                                ...\n",
       "    intake_esm_attrs:units:            degC\n",
       "    intake_esm_attrs:start_time:       1850-01-16 12:00:00\n",
       "    intake_esm_attrs:end_time:         2014-12-16 12:00:00\n",
       "    intake_esm_attrs:path:             /glade/campaign/collections/rda/transf...\n",
       "    intake_esm_attrs:_data_format_:    zarr\n",
       "    intake_esm_dataset_key:            ocn.historical.monthly.cmip6</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-74c27735-31f9-4d03-aa9e-d6619787663c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-74c27735-31f9-4d03-aa9e-d6619787663c' 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'>member_id</span>: 50</li><li><span class='xr-has-index'>time</span>: 1980</li><li><span class='xr-has-index'>z_t</span>: 60</li><li><span>nlat</span>: 384</li><li><span>nlon</span>: 320</li><li><span>d2</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-39d7ac26-7d3f-4ba0-bc77-a248f90177fe' class='xr-section-summary-in' type='checkbox'  checked><label for='section-39d7ac26-7d3f-4ba0-bc77-a248f90177fe' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>&lt;U12</div><div class='xr-var-preview xr-preview'>&#x27;r10i1181p1f1&#x27; ... &#x27;r9i1301p1f1&#x27;</div><input id='attrs-8988f2e6-1c62-49cb-9dff-cfa19b0f8fa7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8988f2e6-1c62-49cb-9dff-cfa19b0f8fa7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ada6104-e591-45f6-9fe7-eb5f38da7f79' class='xr-var-data-in' type='checkbox'><label for='data-0ada6104-e591-45f6-9fe7-eb5f38da7f79' 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;r10i1181p1f1&#x27;, &#x27;r10i1231p1f1&#x27;, &#x27;r10i1251p1f1&#x27;, &#x27;r10i1281p1f1&#x27;,\n",
       "       &#x27;r10i1301p1f1&#x27;, &#x27;r1i1001p1f1&#x27;, &#x27;r1i1231p1f1&#x27;, &#x27;r1i1251p1f1&#x27;,\n",
       "       &#x27;r1i1281p1f1&#x27;, &#x27;r1i1301p1f1&#x27;, &#x27;r2i1021p1f1&#x27;, &#x27;r2i1231p1f1&#x27;,\n",
       "       &#x27;r2i1251p1f1&#x27;, &#x27;r2i1281p1f1&#x27;, &#x27;r2i1301p1f1&#x27;, &#x27;r3i1041p1f1&#x27;,\n",
       "       &#x27;r3i1231p1f1&#x27;, &#x27;r3i1251p1f1&#x27;, &#x27;r3i1281p1f1&#x27;, &#x27;r3i1301p1f1&#x27;,\n",
       "       &#x27;r4i1061p1f1&#x27;, &#x27;r4i1231p1f1&#x27;, &#x27;r4i1251p1f1&#x27;, &#x27;r4i1281p1f1&#x27;,\n",
       "       &#x27;r4i1301p1f1&#x27;, &#x27;r5i1081p1f1&#x27;, &#x27;r5i1231p1f1&#x27;, &#x27;r5i1251p1f1&#x27;,\n",
       "       &#x27;r5i1281p1f1&#x27;, &#x27;r5i1301p1f1&#x27;, &#x27;r6i1101p1f1&#x27;, &#x27;r6i1231p1f1&#x27;,\n",
       "       &#x27;r6i1251p1f1&#x27;, &#x27;r6i1281p1f1&#x27;, &#x27;r6i1301p1f1&#x27;, &#x27;r7i1121p1f1&#x27;,\n",
       "       &#x27;r7i1231p1f1&#x27;, &#x27;r7i1251p1f1&#x27;, &#x27;r7i1281p1f1&#x27;, &#x27;r7i1301p1f1&#x27;,\n",
       "       &#x27;r8i1141p1f1&#x27;, &#x27;r8i1231p1f1&#x27;, &#x27;r8i1251p1f1&#x27;, &#x27;r8i1281p1f1&#x27;,\n",
       "       &#x27;r8i1301p1f1&#x27;, &#x27;r9i1161p1f1&#x27;, &#x27;r9i1231p1f1&#x27;, &#x27;r9i1251p1f1&#x27;,\n",
       "       &#x27;r9i1281p1f1&#x27;, &#x27;r9i1301p1f1&#x27;], dtype=&#x27;&lt;U12&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-16 12:00:00 ... 2014-12-...</div><input id='attrs-3eb3cf9d-ba5c-40ce-a3df-c5f4e6fe7507' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3eb3cf9d-ba5c-40ce-a3df-c5f4e6fe7507' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2624591a-fa47-4c2b-8aad-686323e6b18e' class='xr-var-data-in' type='checkbox'><label for='data-2624591a-fa47-4c2b-8aad-686323e6b18e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bound</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       ...,\n",
       "       cftime.DatetimeNoLeap(2014, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bound</span></div><div class='xr-var-dims'>(time, d2)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;</div><input id='attrs-90b6d5ea-c0d1-44d6-91c3-b2e4729aaab2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90b6d5ea-c0d1-44d6-91c3-b2e4729aaab2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eaa184a5-cc77-44ea-8392-2a3a808e9d86' class='xr-var-data-in' type='checkbox'><label for='data-eaa184a5-cc77-44ea-8392-2a3a808e9d86' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>boundaries for time-averaging interval</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 30.94 kiB </td>\n",
       "                        <td> 30.94 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1980, 2) </td>\n",
       "                        <td> (1980, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> object numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
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       "\n",
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       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">1980</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'>z_t</span></div><div class='xr-var-dims'>(z_t)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 5.375e+05</div><input id='attrs-4b434bf8-b260-4b56-844a-8fb923e7bf5d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b434bf8-b260-4b56-844a-8fb923e7bf5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c38294a-4cfb-48eb-b403-d472e9e4edee' class='xr-var-data-in' type='checkbox'><label for='data-4c38294a-4cfb-48eb-b403-d472e9e4edee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>depth from surface to midpoint of layer</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>units :</span></dt><dd>centimeters</dd><dt><span>valid_max :</span></dt><dd>537500.0</dd><dt><span>valid_min :</span></dt><dd>500.0</dd></dl></div><div class='xr-var-data'><pre>array([5.000000e+02, 1.500000e+03, 2.500000e+03, 3.500000e+03, 4.500000e+03,\n",
       "       5.500000e+03, 6.500000e+03, 7.500000e+03, 8.500000e+03, 9.500000e+03,\n",
       "       1.050000e+04, 1.150000e+04, 1.250000e+04, 1.350000e+04, 1.450000e+04,\n",
       "       1.550000e+04, 1.650984e+04, 1.754790e+04, 1.862913e+04, 1.976603e+04,\n",
       "       2.097114e+04, 2.225783e+04, 2.364088e+04, 2.513702e+04, 2.676542e+04,\n",
       "       2.854837e+04, 3.051192e+04, 3.268680e+04, 3.510935e+04, 3.782276e+04,\n",
       "       4.087846e+04, 4.433777e+04, 4.827367e+04, 5.277280e+04, 5.793729e+04,\n",
       "       6.388626e+04, 7.075633e+04, 7.870025e+04, 8.788252e+04, 9.847059e+04,\n",
       "       1.106204e+05, 1.244567e+05, 1.400497e+05, 1.573946e+05, 1.764003e+05,\n",
       "       1.968944e+05, 2.186457e+05, 2.413972e+05, 2.649001e+05, 2.889385e+05,\n",
       "       3.133405e+05, 3.379793e+05, 3.627670e+05, 3.876452e+05, 4.125768e+05,\n",
       "       4.375392e+05, 4.625190e+05, 4.875083e+05, 5.125028e+05, 5.375000e+05],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-70b5b0e6-54bc-4e59-b1a4-9dc8a06bb6c4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-70b5b0e6-54bc-4e59-b1a4-9dc8a06bb6c4' 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>TEMP</span></div><div class='xr-var-dims'>(member_id, time, z_t, nlat, nlon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 6, 60, 384, 320), meta=np.ndarray&gt;</div><input id='attrs-e098ded5-f40a-4462-8c7b-848691c2a841' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e098ded5-f40a-4462-8c7b-848691c2a841' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21e5af7e-bbf2-480d-b557-1047a9282bad' class='xr-var-data-in' type='checkbox'><label for='data-21e5af7e-bbf2-480d-b557-1047a9282bad' 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_methods :</span></dt><dd>time: mean</dd><dt><span>grid_loc :</span></dt><dd>3111</dd><dt><span>long_name :</span></dt><dd>Potential Temperature</dd><dt><span>units :</span></dt><dd>degC</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.66 TiB </td>\n",
       "                        <td> 168.75 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50, 1980, 60, 384, 320) </td>\n",
       "                        <td> (1, 6, 60, 384, 320) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 16500 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-ecdf0113-8551-495b-94a6-3c4d7210de90' class='xr-section-summary-in' type='checkbox'  ><label for='section-ecdf0113-8551-495b-94a6-3c4d7210de90' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>member_id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7b0e2a8d-d209-443b-aab5-b50a15b6f8b4' class='xr-index-data-in' type='checkbox'/><label for='index-7b0e2a8d-d209-443b-aab5-b50a15b6f8b4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;r10i1181p1f1&#x27;, &#x27;r10i1231p1f1&#x27;, &#x27;r10i1251p1f1&#x27;, &#x27;r10i1281p1f1&#x27;,\n",
       "       &#x27;r10i1301p1f1&#x27;, &#x27;r1i1001p1f1&#x27;, &#x27;r1i1231p1f1&#x27;, &#x27;r1i1251p1f1&#x27;,\n",
       "       &#x27;r1i1281p1f1&#x27;, &#x27;r1i1301p1f1&#x27;, &#x27;r2i1021p1f1&#x27;, &#x27;r2i1231p1f1&#x27;,\n",
       "       &#x27;r2i1251p1f1&#x27;, &#x27;r2i1281p1f1&#x27;, &#x27;r2i1301p1f1&#x27;, &#x27;r3i1041p1f1&#x27;,\n",
       "       &#x27;r3i1231p1f1&#x27;, &#x27;r3i1251p1f1&#x27;, &#x27;r3i1281p1f1&#x27;, &#x27;r3i1301p1f1&#x27;,\n",
       "       &#x27;r4i1061p1f1&#x27;, &#x27;r4i1231p1f1&#x27;, &#x27;r4i1251p1f1&#x27;, &#x27;r4i1281p1f1&#x27;,\n",
       "       &#x27;r4i1301p1f1&#x27;, &#x27;r5i1081p1f1&#x27;, &#x27;r5i1231p1f1&#x27;, &#x27;r5i1251p1f1&#x27;,\n",
       "       &#x27;r5i1281p1f1&#x27;, &#x27;r5i1301p1f1&#x27;, &#x27;r6i1101p1f1&#x27;, &#x27;r6i1231p1f1&#x27;,\n",
       "       &#x27;r6i1251p1f1&#x27;, &#x27;r6i1281p1f1&#x27;, &#x27;r6i1301p1f1&#x27;, &#x27;r7i1121p1f1&#x27;,\n",
       "       &#x27;r7i1231p1f1&#x27;, &#x27;r7i1251p1f1&#x27;, &#x27;r7i1281p1f1&#x27;, &#x27;r7i1301p1f1&#x27;,\n",
       "       &#x27;r8i1141p1f1&#x27;, &#x27;r8i1231p1f1&#x27;, &#x27;r8i1251p1f1&#x27;, &#x27;r8i1281p1f1&#x27;,\n",
       "       &#x27;r8i1301p1f1&#x27;, &#x27;r9i1161p1f1&#x27;, &#x27;r9i1231p1f1&#x27;, &#x27;r9i1251p1f1&#x27;,\n",
       "       &#x27;r9i1281p1f1&#x27;, &#x27;r9i1301p1f1&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;member_id&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fa284976-ee64-4318-8609-b5ba15b30fcd' class='xr-index-data-in' type='checkbox'/><label for='index-fa284976-ee64-4318-8609-b5ba15b30fcd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1850-01-16 12:00:00, 1850-02-15 00:00:00, 1850-03-16 12:00:00,\n",
       "             1850-04-16 00:00:00, 1850-05-16 12:00:00, 1850-06-16 00:00:00,\n",
       "             1850-07-16 12:00:00, 1850-08-16 12:00:00, 1850-09-16 00:00:00,\n",
       "             1850-10-16 12:00:00,\n",
       "             ...\n",
       "             2014-03-16 12:00:00, 2014-04-16 00:00:00, 2014-05-16 12:00:00,\n",
       "             2014-06-16 00:00:00, 2014-07-16 12:00:00, 2014-08-16 12:00:00,\n",
       "             2014-09-16 00:00:00, 2014-10-16 12:00:00, 2014-11-16 00:00:00,\n",
       "             2014-12-16 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=1980, calendar=&#x27;noleap&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>z_t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-adc6a211-5282-4439-947c-8fdde39ffa3d' class='xr-index-data-in' type='checkbox'/><label for='index-adc6a211-5282-4439-947c-8fdde39ffa3d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([          500.0,          1500.0,          2500.0,          3500.0,\n",
       "                4500.0,          5500.0,          6500.0,          7500.0,\n",
       "                8500.0,          9500.0,         10500.0,         11500.0,\n",
       "               12500.0,         13500.0,         14500.0,         15500.0,\n",
       "        16509.83984375, 17547.904296875, 18629.126953125,  19766.02734375,\n",
       "       20971.138671875,    22257.828125,   23640.8828125,    25137.015625,\n",
       "       26765.419921875, 28548.365234375,    30511.921875, 32686.798828125,\n",
       "        35109.34765625,  37822.76171875,  40878.46484375,  44337.76953125,\n",
       "          48273.671875,  52772.80078125,   57937.2890625,  63886.26171875,\n",
       "          70756.328125,        78700.25,   87882.5234375,   98470.5859375,\n",
       "         110620.421875,     124456.6875,    140049.71875,   157394.640625,\n",
       "         176400.328125,   196894.421875,    218645.65625,    241397.15625,\n",
       "            264900.125,    288938.46875,    313340.46875,    337979.34375,\n",
       "          362767.03125,     387645.1875,     412576.8125,       437539.25,\n",
       "          462519.03125,    487508.34375,     512502.8125,        537500.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;z_t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-10afa4a6-6f84-4a90-abab-0886f442e764' class='xr-section-summary-in' type='checkbox'  ><label for='section-10afa4a6-6f84-4a90-abab-0886f442e764' class='xr-section-summary' >Attributes: <span>(25)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.0; http://www.cgd.ucar.edu/cms/eaton/netcdf/CF-current.htm</dd><dt><span>calendar :</span></dt><dd>All years have exactly  365 days.</dd><dt><span>cell_methods :</span></dt><dd>cell_methods = time: mean ==&gt; the variable values are averaged over the time interval between the previous time coordinate and the current one.          cell_methods  absent  ==&gt; the variable values are at the time given by the current time coordinate.</dd><dt><span>contents :</span></dt><dd>Diagnostic and Prognostic Variables</dd><dt><span>model_doi_url :</span></dt><dd>https://doi.org/10.5065/D67H1H0V</dd><dt><span>revision :</span></dt><dd>$Id$</dd><dt><span>source :</span></dt><dd>CCSM POP2, the CCSM Ocean Component</dd><dt><span>start_time :</span></dt><dd>This dataset was created on 2020-07-18 at 07:26:52.9</dd><dt><span>time_period_freq :</span></dt><dd>month_1</dd><dt><span>intake_esm_vars :</span></dt><dd>[&#x27;TEMP&#x27;]</dd><dt><span>intake_esm_attrs:Unnamed: 0 :</span></dt><dd>271</dd><dt><span>intake_esm_attrs:variable :</span></dt><dd>TEMP</dd><dt><span>intake_esm_attrs:long_name :</span></dt><dd>potential temperature</dd><dt><span>intake_esm_attrs:component :</span></dt><dd>ocn</dd><dt><span>intake_esm_attrs:experiment :</span></dt><dd>historical</dd><dt><span>intake_esm_attrs:forcing_variant :</span></dt><dd>cmip6</dd><dt><span>intake_esm_attrs:frequency :</span></dt><dd>monthly</dd><dt><span>intake_esm_attrs:vertical_levels :</span></dt><dd>60.0</dd><dt><span>intake_esm_attrs:spatial_domain :</span></dt><dd>global_ocean</dd><dt><span>intake_esm_attrs:units :</span></dt><dd>degC</dd><dt><span>intake_esm_attrs:start_time :</span></dt><dd>1850-01-16 12:00:00</dd><dt><span>intake_esm_attrs:end_time :</span></dt><dd>2014-12-16 12:00:00</dd><dt><span>intake_esm_attrs:path :</span></dt><dd>/glade/campaign/collections/rda/transfer/chifan_AWS/ncar-cesm2-lens/ocn/monthly/cesm2LE-historical-cmip6-TEMP.zarr</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>zarr</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>ocn.historical.monthly.cmip6</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 3TB\n",
       "Dimensions:     (member_id: 50, time: 1980, z_t: 60, nlat: 384, nlon: 320, d2: 2)\n",
       "Coordinates:\n",
       "  * member_id   (member_id) <U12 2kB 'r10i1181p1f1' ... 'r9i1301p1f1'\n",
       "  * time        (time) object 16kB 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "    time_bound  (time, d2) object 32kB dask.array<chunksize=(1980, 2), meta=np.ndarray>\n",
       "  * z_t         (z_t) float32 240B 500.0 1.5e+03 2.5e+03 ... 5.125e+05 5.375e+05\n",
       "Dimensions without coordinates: nlat, nlon, d2\n",
       "Data variables:\n",
       "    TEMP        (member_id, time, z_t, nlat, nlon) float32 3TB dask.array<chunksize=(1, 6, 60, 384, 320), meta=np.ndarray>\n",
       "Attributes: (12/25)\n",
       "    Conventions:                       CF-1.0; http://www.cgd.ucar.edu/cms/ea...\n",
       "    calendar:                          All years have exactly  365 days.\n",
       "    cell_methods:                      cell_methods = time: mean ==> the vari...\n",
       "    contents:                          Diagnostic and Prognostic Variables\n",
       "    model_doi_url:                     https://doi.org/10.5065/D67H1H0V\n",
       "    revision:                          $Id$\n",
       "    ...                                ...\n",
       "    intake_esm_attrs:units:            degC\n",
       "    intake_esm_attrs:start_time:       1850-01-16 12:00:00\n",
       "    intake_esm_attrs:end_time:         2014-12-16 12:00:00\n",
       "    intake_esm_attrs:path:             /glade/campaign/collections/rda/transf...\n",
       "    intake_esm_attrs:_data_format_:    zarr\n",
       "    intake_esm_dataset_key:            ocn.historical.monthly.cmip6"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "historical"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dc90b06-edbf-4c68-9eff-4d27f29bd577",
   "metadata": {},
   "source": [
    "#### Change units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9790a5a3-b6ec-4bd4-a5f7-5d83d26a9260",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unit('centimeters')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "orig_units = cf.Unit(historical.z_t.attrs['units'])\n",
    "orig_units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0c6e0457-b341-4476-a540-8e6e3f5322a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def change_units(ds, variable_str, variable_bounds_str, target_unit_str):\n",
    "    orig_units = cf.Unit(ds[variable_str].attrs['units'])\n",
    "    target_units = cf.Unit(target_unit_str)\n",
    "    variable_in_new_units = xr.apply_ufunc(orig_units.convert, ds[variable_bounds_str], target_units, dask='parallelized', output_dtypes=[ds[variable_bounds_str].dtype])\n",
    "    return variable_in_new_units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "622940a2-eedf-471e-848b-542b9692bb52",
   "metadata": {},
   "outputs": [
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       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;z_t&#x27; (z_t: 60)&gt; Size: 240B\n",
       "array([5.000000e+02, 1.500000e+03, 2.500000e+03, 3.500000e+03, 4.500000e+03,\n",
       "       5.500000e+03, 6.500000e+03, 7.500000e+03, 8.500000e+03, 9.500000e+03,\n",
       "       1.050000e+04, 1.150000e+04, 1.250000e+04, 1.350000e+04, 1.450000e+04,\n",
       "       1.550000e+04, 1.650984e+04, 1.754790e+04, 1.862913e+04, 1.976603e+04,\n",
       "       2.097114e+04, 2.225783e+04, 2.364088e+04, 2.513702e+04, 2.676542e+04,\n",
       "       2.854837e+04, 3.051192e+04, 3.268680e+04, 3.510935e+04, 3.782276e+04,\n",
       "       4.087846e+04, 4.433777e+04, 4.827367e+04, 5.277280e+04, 5.793729e+04,\n",
       "       6.388626e+04, 7.075633e+04, 7.870025e+04, 8.788252e+04, 9.847059e+04,\n",
       "       1.106204e+05, 1.244567e+05, 1.400497e+05, 1.573946e+05, 1.764003e+05,\n",
       "       1.968944e+05, 2.186457e+05, 2.413972e+05, 2.649001e+05, 2.889385e+05,\n",
       "       3.133405e+05, 3.379793e+05, 3.627670e+05, 3.876452e+05, 4.125768e+05,\n",
       "       4.375392e+05, 4.625190e+05, 4.875083e+05, 5.125028e+05, 5.375000e+05],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "  * z_t      (z_t) float32 240B 500.0 1.5e+03 2.5e+03 ... 5.125e+05 5.375e+05\n",
       "Attributes:\n",
       "    long_name:  depth from surface to midpoint of layer\n",
       "    positive:   down\n",
       "    units:      centimeters\n",
       "    valid_max:  537500.0\n",
       "    valid_min:  500.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'z_t'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>z_t</span>: 60</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ba9fa1ff-2d42-4ad1-9454-f17b1e862848' class='xr-array-in' type='checkbox' checked><label for='section-ba9fa1ff-2d42-4ad1-9454-f17b1e862848' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>500.0 1.5e+03 2.5e+03 3.5e+03 ... 4.875e+05 5.125e+05 5.375e+05</span></div><div class='xr-array-data'><pre>array([5.000000e+02, 1.500000e+03, 2.500000e+03, 3.500000e+03, 4.500000e+03,\n",
       "       5.500000e+03, 6.500000e+03, 7.500000e+03, 8.500000e+03, 9.500000e+03,\n",
       "       1.050000e+04, 1.150000e+04, 1.250000e+04, 1.350000e+04, 1.450000e+04,\n",
       "       1.550000e+04, 1.650984e+04, 1.754790e+04, 1.862913e+04, 1.976603e+04,\n",
       "       2.097114e+04, 2.225783e+04, 2.364088e+04, 2.513702e+04, 2.676542e+04,\n",
       "       2.854837e+04, 3.051192e+04, 3.268680e+04, 3.510935e+04, 3.782276e+04,\n",
       "       4.087846e+04, 4.433777e+04, 4.827367e+04, 5.277280e+04, 5.793729e+04,\n",
       "       6.388626e+04, 7.075633e+04, 7.870025e+04, 8.788252e+04, 9.847059e+04,\n",
       "       1.106204e+05, 1.244567e+05, 1.400497e+05, 1.573946e+05, 1.764003e+05,\n",
       "       1.968944e+05, 2.186457e+05, 2.413972e+05, 2.649001e+05, 2.889385e+05,\n",
       "       3.133405e+05, 3.379793e+05, 3.627670e+05, 3.876452e+05, 4.125768e+05,\n",
       "       4.375392e+05, 4.625190e+05, 4.875083e+05, 5.125028e+05, 5.375000e+05],\n",
       "      dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-b6b748c5-8c5b-4309-9282-02f2b265326c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b6b748c5-8c5b-4309-9282-02f2b265326c' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>z_t</span></div><div class='xr-var-dims'>(z_t)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 5.375e+05</div><input id='attrs-6b0fe1b8-207b-4020-8158-59646ddc0199' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b0fe1b8-207b-4020-8158-59646ddc0199' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51a5e9c4-caf5-4bea-b1e2-7b5867310fc8' class='xr-var-data-in' type='checkbox'><label for='data-51a5e9c4-caf5-4bea-b1e2-7b5867310fc8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>depth from surface to midpoint of layer</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>units :</span></dt><dd>centimeters</dd><dt><span>valid_max :</span></dt><dd>537500.0</dd><dt><span>valid_min :</span></dt><dd>500.0</dd></dl></div><div class='xr-var-data'><pre>array([5.000000e+02, 1.500000e+03, 2.500000e+03, 3.500000e+03, 4.500000e+03,\n",
       "       5.500000e+03, 6.500000e+03, 7.500000e+03, 8.500000e+03, 9.500000e+03,\n",
       "       1.050000e+04, 1.150000e+04, 1.250000e+04, 1.350000e+04, 1.450000e+04,\n",
       "       1.550000e+04, 1.650984e+04, 1.754790e+04, 1.862913e+04, 1.976603e+04,\n",
       "       2.097114e+04, 2.225783e+04, 2.364088e+04, 2.513702e+04, 2.676542e+04,\n",
       "       2.854837e+04, 3.051192e+04, 3.268680e+04, 3.510935e+04, 3.782276e+04,\n",
       "       4.087846e+04, 4.433777e+04, 4.827367e+04, 5.277280e+04, 5.793729e+04,\n",
       "       6.388626e+04, 7.075633e+04, 7.870025e+04, 8.788252e+04, 9.847059e+04,\n",
       "       1.106204e+05, 1.244567e+05, 1.400497e+05, 1.573946e+05, 1.764003e+05,\n",
       "       1.968944e+05, 2.186457e+05, 2.413972e+05, 2.649001e+05, 2.889385e+05,\n",
       "       3.133405e+05, 3.379793e+05, 3.627670e+05, 3.876452e+05, 4.125768e+05,\n",
       "       4.375392e+05, 4.625190e+05, 4.875083e+05, 5.125028e+05, 5.375000e+05],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8ba25fd8-dc12-41f7-b636-456b391f396c' class='xr-section-summary-in' type='checkbox'  ><label for='section-8ba25fd8-dc12-41f7-b636-456b391f396c' class='xr-section-summary' >Indexes: <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-index-name'><div>z_t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1a42817a-0a27-47ad-b101-0b951a0f4dcd' class='xr-index-data-in' type='checkbox'/><label for='index-1a42817a-0a27-47ad-b101-0b951a0f4dcd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([          500.0,          1500.0,          2500.0,          3500.0,\n",
       "                4500.0,          5500.0,          6500.0,          7500.0,\n",
       "                8500.0,          9500.0,         10500.0,         11500.0,\n",
       "               12500.0,         13500.0,         14500.0,         15500.0,\n",
       "        16509.83984375, 17547.904296875, 18629.126953125,  19766.02734375,\n",
       "       20971.138671875,    22257.828125,   23640.8828125,    25137.015625,\n",
       "       26765.419921875, 28548.365234375,    30511.921875, 32686.798828125,\n",
       "        35109.34765625,  37822.76171875,  40878.46484375,  44337.76953125,\n",
       "          48273.671875,  52772.80078125,   57937.2890625,  63886.26171875,\n",
       "          70756.328125,        78700.25,   87882.5234375,   98470.5859375,\n",
       "         110620.421875,     124456.6875,    140049.71875,   157394.640625,\n",
       "         176400.328125,   196894.421875,    218645.65625,    241397.15625,\n",
       "            264900.125,    288938.46875,    313340.46875,    337979.34375,\n",
       "          362767.03125,     387645.1875,     412576.8125,       437539.25,\n",
       "          462519.03125,    487508.34375,     512502.8125,        537500.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;z_t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cdced7c9-5feb-4793-ad19-9962e3c95805' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cdced7c9-5feb-4793-ad19-9962e3c95805' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>depth from surface to midpoint of layer</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>units :</span></dt><dd>centimeters</dd><dt><span>valid_max :</span></dt><dd>537500.0</dd><dt><span>valid_min :</span></dt><dd>500.0</dd></dl></div></li></ul></div></div>"
      ],
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       "<xarray.DataArray 'z_t' (z_t: 60)> Size: 240B\n",
       "array([5.000000e+02, 1.500000e+03, 2.500000e+03, 3.500000e+03, 4.500000e+03,\n",
       "       5.500000e+03, 6.500000e+03, 7.500000e+03, 8.500000e+03, 9.500000e+03,\n",
       "       1.050000e+04, 1.150000e+04, 1.250000e+04, 1.350000e+04, 1.450000e+04,\n",
       "       1.550000e+04, 1.650984e+04, 1.754790e+04, 1.862913e+04, 1.976603e+04,\n",
       "       2.097114e+04, 2.225783e+04, 2.364088e+04, 2.513702e+04, 2.676542e+04,\n",
       "       2.854837e+04, 3.051192e+04, 3.268680e+04, 3.510935e+04, 3.782276e+04,\n",
       "       4.087846e+04, 4.433777e+04, 4.827367e+04, 5.277280e+04, 5.793729e+04,\n",
       "       6.388626e+04, 7.075633e+04, 7.870025e+04, 8.788252e+04, 9.847059e+04,\n",
       "       1.106204e+05, 1.244567e+05, 1.400497e+05, 1.573946e+05, 1.764003e+05,\n",
       "       1.968944e+05, 2.186457e+05, 2.413972e+05, 2.649001e+05, 2.889385e+05,\n",
       "       3.133405e+05, 3.379793e+05, 3.627670e+05, 3.876452e+05, 4.125768e+05,\n",
       "       4.375392e+05, 4.625190e+05, 4.875083e+05, 5.125028e+05, 5.375000e+05],\n",
       "      dtype=float32)\n",
       "Coordinates:\n",
       "  * z_t      (z_t) float32 240B 500.0 1.5e+03 2.5e+03 ... 5.125e+05 5.375e+05\n",
       "Attributes:\n",
       "    long_name:  depth from surface to midpoint of layer\n",
       "    positive:   down\n",
       "    units:      centimeters\n",
       "    valid_max:  537500.0\n",
       "    valid_min:  500.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "historical['z_t']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "54aba133-d9fb-4901-b012-41ddbd06d0ed",
   "metadata": {},
   "outputs": [
    {
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       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
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       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (member_id: 50, time: 1980, z_t: 60, nlat: 384, nlon: 320)&gt; Size: 3TB\n",
       "dask.array&lt;transpose, shape=(50, 1980, 60, 384, 320), dtype=float32, chunksize=(1, 6, 60, 384, 320), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * member_id  (member_id) &lt;U12 2kB &#x27;r10i1181p1f1&#x27; ... &#x27;r9i1301p1f1&#x27;\n",
       "  * time       (time) object 16kB 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "  * z_t        (z_t) float32 240B 500.0 1.5e+03 2.5e+03 ... 5.125e+05 5.375e+05\n",
       "Dimensions without coordinates: nlat, nlon</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>member_id</span>: 50</li><li><span class='xr-has-index'>time</span>: 1980</li><li><span class='xr-has-index'>z_t</span>: 60</li><li><span>nlat</span>: 384</li><li><span>nlon</span>: 320</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e3d05b27-cbd1-437f-acda-676bd7e11dd9' class='xr-array-in' type='checkbox' checked><label for='section-e3d05b27-cbd1-437f-acda-676bd7e11dd9' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 6, 60, 384, 320), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.66 TiB </td>\n",
       "                        <td> 168.75 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50, 1980, 60, 384, 320) </td>\n",
       "                        <td> (1, 6, 60, 384, 320) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 16500 chunks in 6 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "  <line x1=\"208\" y1=\"18\" x2=\"249\" y2=\"18\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"190\" y1=\"0\" x2=\"208\" y2=\"18\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"231\" y1=\"0\" x2=\"249\" y2=\"18\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"190.0,0.0 231.06088584004232,0.0 249.56121642495668,18.500330584914376 208.50033058491437,18.500330584914376\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"208\" y1=\"18\" x2=\"249\" y2=\"18\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"208\" y1=\"60\" x2=\"249\" y2=\"60\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"208\" y1=\"18\" x2=\"208\" y2=\"60\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"249\" y1=\"18\" x2=\"249\" y2=\"60\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"208.50033058491437,18.500330584914376 249.56121642495668,18.500330584914376 249.56121642495668,60.379154035269096 208.50033058491437,60.379154035269096\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"229.030774\" y=\"80.379154\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >320</text>\n",
       "  <text x=\"269.561216\" y=\"39.439742\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,269.561216,39.439742)\">384</text>\n",
       "  <text x=\"189.250165\" y=\"71.128989\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,189.250165,71.128989)\">60</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-ab2d9363-dc1a-44da-944b-27eac9c33c04' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ab2d9363-dc1a-44da-944b-27eac9c33c04' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>&lt;U12</div><div class='xr-var-preview xr-preview'>&#x27;r10i1181p1f1&#x27; ... &#x27;r9i1301p1f1&#x27;</div><input id='attrs-5bf1fb29-84f2-423a-b22f-992827bc6ecd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5bf1fb29-84f2-423a-b22f-992827bc6ecd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6db36c4-7734-43d1-bd1d-42289bd20c1b' class='xr-var-data-in' type='checkbox'><label for='data-c6db36c4-7734-43d1-bd1d-42289bd20c1b' 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;r10i1181p1f1&#x27;, &#x27;r10i1231p1f1&#x27;, &#x27;r10i1251p1f1&#x27;, &#x27;r10i1281p1f1&#x27;,\n",
       "       &#x27;r10i1301p1f1&#x27;, &#x27;r1i1001p1f1&#x27;, &#x27;r1i1231p1f1&#x27;, &#x27;r1i1251p1f1&#x27;,\n",
       "       &#x27;r1i1281p1f1&#x27;, &#x27;r1i1301p1f1&#x27;, &#x27;r2i1021p1f1&#x27;, &#x27;r2i1231p1f1&#x27;,\n",
       "       &#x27;r2i1251p1f1&#x27;, &#x27;r2i1281p1f1&#x27;, &#x27;r2i1301p1f1&#x27;, &#x27;r3i1041p1f1&#x27;,\n",
       "       &#x27;r3i1231p1f1&#x27;, &#x27;r3i1251p1f1&#x27;, &#x27;r3i1281p1f1&#x27;, &#x27;r3i1301p1f1&#x27;,\n",
       "       &#x27;r4i1061p1f1&#x27;, &#x27;r4i1231p1f1&#x27;, &#x27;r4i1251p1f1&#x27;, &#x27;r4i1281p1f1&#x27;,\n",
       "       &#x27;r4i1301p1f1&#x27;, &#x27;r5i1081p1f1&#x27;, &#x27;r5i1231p1f1&#x27;, &#x27;r5i1251p1f1&#x27;,\n",
       "       &#x27;r5i1281p1f1&#x27;, &#x27;r5i1301p1f1&#x27;, &#x27;r6i1101p1f1&#x27;, &#x27;r6i1231p1f1&#x27;,\n",
       "       &#x27;r6i1251p1f1&#x27;, &#x27;r6i1281p1f1&#x27;, &#x27;r6i1301p1f1&#x27;, &#x27;r7i1121p1f1&#x27;,\n",
       "       &#x27;r7i1231p1f1&#x27;, &#x27;r7i1251p1f1&#x27;, &#x27;r7i1281p1f1&#x27;, &#x27;r7i1301p1f1&#x27;,\n",
       "       &#x27;r8i1141p1f1&#x27;, &#x27;r8i1231p1f1&#x27;, &#x27;r8i1251p1f1&#x27;, &#x27;r8i1281p1f1&#x27;,\n",
       "       &#x27;r8i1301p1f1&#x27;, &#x27;r9i1161p1f1&#x27;, &#x27;r9i1231p1f1&#x27;, &#x27;r9i1251p1f1&#x27;,\n",
       "       &#x27;r9i1281p1f1&#x27;, &#x27;r9i1301p1f1&#x27;], dtype=&#x27;&lt;U12&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-16 12:00:00 ... 2014-12-...</div><input id='attrs-20b7460f-2740-4283-8fc1-0a4445e932d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20b7460f-2740-4283-8fc1-0a4445e932d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2634836-6769-48e2-b4c6-1b99527ba828' class='xr-var-data-in' type='checkbox'><label for='data-b2634836-6769-48e2-b4c6-1b99527ba828' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bound</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       ...,\n",
       "       cftime.DatetimeNoLeap(2014, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z_t</span></div><div class='xr-var-dims'>(z_t)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 5.375e+05</div><input id='attrs-ef537524-3117-4418-b8eb-903e378a8622' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef537524-3117-4418-b8eb-903e378a8622' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b452a23-9248-4601-9011-0ca48c0edacd' class='xr-var-data-in' type='checkbox'><label for='data-5b452a23-9248-4601-9011-0ca48c0edacd' 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'><pre>array([5.000000e+02, 1.500000e+03, 2.500000e+03, 3.500000e+03, 4.500000e+03,\n",
       "       5.500000e+03, 6.500000e+03, 7.500000e+03, 8.500000e+03, 9.500000e+03,\n",
       "       1.050000e+04, 1.150000e+04, 1.250000e+04, 1.350000e+04, 1.450000e+04,\n",
       "       1.550000e+04, 1.650984e+04, 1.754790e+04, 1.862913e+04, 1.976603e+04,\n",
       "       2.097114e+04, 2.225783e+04, 2.364088e+04, 2.513702e+04, 2.676542e+04,\n",
       "       2.854837e+04, 3.051192e+04, 3.268680e+04, 3.510935e+04, 3.782276e+04,\n",
       "       4.087846e+04, 4.433777e+04, 4.827367e+04, 5.277280e+04, 5.793729e+04,\n",
       "       6.388626e+04, 7.075633e+04, 7.870025e+04, 8.788252e+04, 9.847059e+04,\n",
       "       1.106204e+05, 1.244567e+05, 1.400497e+05, 1.573946e+05, 1.764003e+05,\n",
       "       1.968944e+05, 2.186457e+05, 2.413972e+05, 2.649001e+05, 2.889385e+05,\n",
       "       3.133405e+05, 3.379793e+05, 3.627670e+05, 3.876452e+05, 4.125768e+05,\n",
       "       4.375392e+05, 4.625190e+05, 4.875083e+05, 5.125028e+05, 5.375000e+05],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c5a5156a-31b5-4862-8977-59439f666218' class='xr-section-summary-in' type='checkbox'  ><label for='section-c5a5156a-31b5-4862-8977-59439f666218' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>member_id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8e2a3916-3745-4c01-bb1b-cb24c2dc4873' class='xr-index-data-in' type='checkbox'/><label for='index-8e2a3916-3745-4c01-bb1b-cb24c2dc4873' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;r10i1181p1f1&#x27;, &#x27;r10i1231p1f1&#x27;, &#x27;r10i1251p1f1&#x27;, &#x27;r10i1281p1f1&#x27;,\n",
       "       &#x27;r10i1301p1f1&#x27;, &#x27;r1i1001p1f1&#x27;, &#x27;r1i1231p1f1&#x27;, &#x27;r1i1251p1f1&#x27;,\n",
       "       &#x27;r1i1281p1f1&#x27;, &#x27;r1i1301p1f1&#x27;, &#x27;r2i1021p1f1&#x27;, &#x27;r2i1231p1f1&#x27;,\n",
       "       &#x27;r2i1251p1f1&#x27;, &#x27;r2i1281p1f1&#x27;, &#x27;r2i1301p1f1&#x27;, &#x27;r3i1041p1f1&#x27;,\n",
       "       &#x27;r3i1231p1f1&#x27;, &#x27;r3i1251p1f1&#x27;, &#x27;r3i1281p1f1&#x27;, &#x27;r3i1301p1f1&#x27;,\n",
       "       &#x27;r4i1061p1f1&#x27;, &#x27;r4i1231p1f1&#x27;, &#x27;r4i1251p1f1&#x27;, &#x27;r4i1281p1f1&#x27;,\n",
       "       &#x27;r4i1301p1f1&#x27;, &#x27;r5i1081p1f1&#x27;, &#x27;r5i1231p1f1&#x27;, &#x27;r5i1251p1f1&#x27;,\n",
       "       &#x27;r5i1281p1f1&#x27;, &#x27;r5i1301p1f1&#x27;, &#x27;r6i1101p1f1&#x27;, &#x27;r6i1231p1f1&#x27;,\n",
       "       &#x27;r6i1251p1f1&#x27;, &#x27;r6i1281p1f1&#x27;, &#x27;r6i1301p1f1&#x27;, &#x27;r7i1121p1f1&#x27;,\n",
       "       &#x27;r7i1231p1f1&#x27;, &#x27;r7i1251p1f1&#x27;, &#x27;r7i1281p1f1&#x27;, &#x27;r7i1301p1f1&#x27;,\n",
       "       &#x27;r8i1141p1f1&#x27;, &#x27;r8i1231p1f1&#x27;, &#x27;r8i1251p1f1&#x27;, &#x27;r8i1281p1f1&#x27;,\n",
       "       &#x27;r8i1301p1f1&#x27;, &#x27;r9i1161p1f1&#x27;, &#x27;r9i1231p1f1&#x27;, &#x27;r9i1251p1f1&#x27;,\n",
       "       &#x27;r9i1281p1f1&#x27;, &#x27;r9i1301p1f1&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;member_id&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2205a635-1106-4688-b968-80bec8777f87' class='xr-index-data-in' type='checkbox'/><label for='index-2205a635-1106-4688-b968-80bec8777f87' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1850-01-16 12:00:00, 1850-02-15 00:00:00, 1850-03-16 12:00:00,\n",
       "             1850-04-16 00:00:00, 1850-05-16 12:00:00, 1850-06-16 00:00:00,\n",
       "             1850-07-16 12:00:00, 1850-08-16 12:00:00, 1850-09-16 00:00:00,\n",
       "             1850-10-16 12:00:00,\n",
       "             ...\n",
       "             2014-03-16 12:00:00, 2014-04-16 00:00:00, 2014-05-16 12:00:00,\n",
       "             2014-06-16 00:00:00, 2014-07-16 12:00:00, 2014-08-16 12:00:00,\n",
       "             2014-09-16 00:00:00, 2014-10-16 12:00:00, 2014-11-16 00:00:00,\n",
       "             2014-12-16 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=1980, calendar=&#x27;noleap&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>z_t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b01829ab-7b55-4bf7-919b-59595ad9ec42' class='xr-index-data-in' type='checkbox'/><label for='index-b01829ab-7b55-4bf7-919b-59595ad9ec42' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([          500.0,          1500.0,          2500.0,          3500.0,\n",
       "                4500.0,          5500.0,          6500.0,          7500.0,\n",
       "                8500.0,          9500.0,         10500.0,         11500.0,\n",
       "               12500.0,         13500.0,         14500.0,         15500.0,\n",
       "        16509.83984375, 17547.904296875, 18629.126953125,  19766.02734375,\n",
       "       20971.138671875,    22257.828125,   23640.8828125,    25137.015625,\n",
       "       26765.419921875, 28548.365234375,    30511.921875, 32686.798828125,\n",
       "        35109.34765625,  37822.76171875,  40878.46484375,  44337.76953125,\n",
       "          48273.671875,  52772.80078125,   57937.2890625,  63886.26171875,\n",
       "          70756.328125,        78700.25,   87882.5234375,   98470.5859375,\n",
       "         110620.421875,     124456.6875,    140049.71875,   157394.640625,\n",
       "         176400.328125,   196894.421875,    218645.65625,    241397.15625,\n",
       "            264900.125,    288938.46875,    313340.46875,    337979.34375,\n",
       "          362767.03125,     387645.1875,     412576.8125,       437539.25,\n",
       "          462519.03125,    487508.34375,     512502.8125,        537500.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;z_t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-da85e4a9-a874-4af8-b941-15bdfee4d489' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-da85e4a9-a874-4af8-b941-15bdfee4d489' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (member_id: 50, time: 1980, z_t: 60, nlat: 384, nlon: 320)> Size: 3TB\n",
       "dask.array<transpose, shape=(50, 1980, 60, 384, 320), dtype=float32, chunksize=(1, 6, 60, 384, 320), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * member_id  (member_id) <U12 2kB 'r10i1181p1f1' ... 'r9i1301p1f1'\n",
       "  * time       (time) object 16kB 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "  * z_t        (z_t) float32 240B 500.0 1.5e+03 2.5e+03 ... 5.125e+05 5.375e+05\n",
       "Dimensions without coordinates: nlat, nlon"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depth_levels_in_m = change_units(historical, 'z_t', 'z_t', 'm')\n",
    "hist_temp_in_degK = change_units(historical, 'TEMP', 'TEMP', 'degK')\n",
    "fut_cmip6_temp_in_degK = change_units(future_cmip6, 'TEMP', 'TEMP', 'degK')\n",
    "fut_smbb_temp_in_degK = change_units(future_smbb, 'TEMP', 'TEMP', 'degK')\n",
    "#\n",
    "hist_temp_in_degK  = hist_temp_in_degK.assign_coords(z_t=(\"z_t\", depth_levels_in_m['z_t'].data))\n",
    "hist_temp_in_degK[\"z_t\"].attrs[\"units\"] = \"m\"\n",
    "hist_temp_in_degK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "76b72042-5cbc-4aa6-9b34-e243b57d8fb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
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       "\n",
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       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
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       "\n",
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       "\n",
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       "  display: grid;\n",
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       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
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       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
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       "\n",
       ".xr-section-item input + label {\n",
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       "\n",
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       "\n",
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       "\n",
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       "  font-weight: 500;\n",
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       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
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       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
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       "\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",
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       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
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       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
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       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
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       "\n",
       ".xr-array-preview,\n",
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       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
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       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
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       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
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       "}\n",
       "\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",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
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       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (z_t: 59)&gt; Size: 236B\n",
       "array([5.0000000e+00, 1.5000000e+01, 2.5000000e+01, 3.5000000e+01,\n",
       "       4.5000000e+01, 5.5000000e+01, 6.5000000e+01, 7.5000000e+01,\n",
       "       8.5000000e+01, 9.5000000e+01, 1.0500000e+02, 1.1500000e+02,\n",
       "       1.2500000e+02, 1.3500000e+02, 1.4500000e+02, 1.5500000e+02,\n",
       "       1.6509840e+02, 1.7547905e+02, 1.8629128e+02, 1.9766028e+02,\n",
       "       2.0971138e+02, 2.2257828e+02, 2.3640883e+02, 2.5137016e+02,\n",
       "       2.6765421e+02, 2.8548364e+02, 3.0511923e+02, 3.2686798e+02,\n",
       "       3.5109348e+02, 3.7822763e+02, 4.0878464e+02, 4.4337769e+02,\n",
       "       4.8273672e+02, 5.2772803e+02, 5.7937286e+02, 6.3886261e+02,\n",
       "       7.0756329e+02, 7.8700250e+02, 8.7882526e+02, 9.8470587e+02,\n",
       "       1.1062042e+03, 1.2445669e+03, 1.4004972e+03, 1.5739464e+03,\n",
       "       1.7640033e+03, 1.9689442e+03, 2.1864565e+03, 2.4139717e+03,\n",
       "       2.6490012e+03, 2.8893848e+03, 3.1334048e+03, 3.3797935e+03,\n",
       "       3.6276704e+03, 3.8764519e+03, 4.1257681e+03, 4.3753926e+03,\n",
       "       4.6251904e+03, 4.8750835e+03, 5.1250283e+03], dtype=float32)\n",
       "Coordinates:\n",
       "  * z_t      (z_t) float32 236B 500.0 1.5e+03 2.5e+03 ... 4.875e+05 5.125e+05</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>z_t</span>: 59</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-4e5d99a9-09ff-46ad-a441-8005ee7797db' class='xr-array-in' type='checkbox' checked><label for='section-4e5d99a9-09ff-46ad-a441-8005ee7797db' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>5.0 15.0 25.0 35.0 45.0 ... 4.375e+03 4.625e+03 4.875e+03 5.125e+03</span></div><div class='xr-array-data'><pre>array([5.0000000e+00, 1.5000000e+01, 2.5000000e+01, 3.5000000e+01,\n",
       "       4.5000000e+01, 5.5000000e+01, 6.5000000e+01, 7.5000000e+01,\n",
       "       8.5000000e+01, 9.5000000e+01, 1.0500000e+02, 1.1500000e+02,\n",
       "       1.2500000e+02, 1.3500000e+02, 1.4500000e+02, 1.5500000e+02,\n",
       "       1.6509840e+02, 1.7547905e+02, 1.8629128e+02, 1.9766028e+02,\n",
       "       2.0971138e+02, 2.2257828e+02, 2.3640883e+02, 2.5137016e+02,\n",
       "       2.6765421e+02, 2.8548364e+02, 3.0511923e+02, 3.2686798e+02,\n",
       "       3.5109348e+02, 3.7822763e+02, 4.0878464e+02, 4.4337769e+02,\n",
       "       4.8273672e+02, 5.2772803e+02, 5.7937286e+02, 6.3886261e+02,\n",
       "       7.0756329e+02, 7.8700250e+02, 8.7882526e+02, 9.8470587e+02,\n",
       "       1.1062042e+03, 1.2445669e+03, 1.4004972e+03, 1.5739464e+03,\n",
       "       1.7640033e+03, 1.9689442e+03, 2.1864565e+03, 2.4139717e+03,\n",
       "       2.6490012e+03, 2.8893848e+03, 3.1334048e+03, 3.3797935e+03,\n",
       "       3.6276704e+03, 3.8764519e+03, 4.1257681e+03, 4.3753926e+03,\n",
       "       4.6251904e+03, 4.8750835e+03, 5.1250283e+03], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-dde8a97e-9a3e-44c4-b2a4-91e10ad431ea' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dde8a97e-9a3e-44c4-b2a4-91e10ad431ea' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>z_t</span></div><div class='xr-var-dims'>(z_t)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 5.125e+05</div><input id='attrs-8defd368-f943-4096-80e3-1f0e14c1b3ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8defd368-f943-4096-80e3-1f0e14c1b3ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-502f6fd4-82a5-47aa-930c-8eaba324b92d' class='xr-var-data-in' type='checkbox'><label for='data-502f6fd4-82a5-47aa-930c-8eaba324b92d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>depth from surface to midpoint of layer</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>units :</span></dt><dd>centimeters</dd><dt><span>valid_max :</span></dt><dd>537500.0</dd><dt><span>valid_min :</span></dt><dd>500.0</dd></dl></div><div class='xr-var-data'><pre>array([5.000000e+02, 1.500000e+03, 2.500000e+03, 3.500000e+03, 4.500000e+03,\n",
       "       5.500000e+03, 6.500000e+03, 7.500000e+03, 8.500000e+03, 9.500000e+03,\n",
       "       1.050000e+04, 1.150000e+04, 1.250000e+04, 1.350000e+04, 1.450000e+04,\n",
       "       1.550000e+04, 1.650984e+04, 1.754790e+04, 1.862913e+04, 1.976603e+04,\n",
       "       2.097114e+04, 2.225783e+04, 2.364088e+04, 2.513702e+04, 2.676542e+04,\n",
       "       2.854837e+04, 3.051192e+04, 3.268680e+04, 3.510935e+04, 3.782276e+04,\n",
       "       4.087846e+04, 4.433777e+04, 4.827367e+04, 5.277280e+04, 5.793729e+04,\n",
       "       6.388626e+04, 7.075633e+04, 7.870025e+04, 8.788252e+04, 9.847059e+04,\n",
       "       1.106204e+05, 1.244567e+05, 1.400497e+05, 1.573946e+05, 1.764003e+05,\n",
       "       1.968944e+05, 2.186457e+05, 2.413972e+05, 2.649001e+05, 2.889385e+05,\n",
       "       3.133405e+05, 3.379793e+05, 3.627670e+05, 3.876452e+05, 4.125768e+05,\n",
       "       4.375392e+05, 4.625190e+05, 4.875083e+05, 5.125028e+05], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d9d2cbd0-ddee-44dd-a9ca-2b1e13a14826' class='xr-section-summary-in' type='checkbox'  ><label for='section-d9d2cbd0-ddee-44dd-a9ca-2b1e13a14826' class='xr-section-summary' >Indexes: <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-index-name'><div>z_t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-41d95bb3-5e96-4f69-b041-760cace79414' class='xr-index-data-in' type='checkbox'/><label for='index-41d95bb3-5e96-4f69-b041-760cace79414' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([          500.0,          1500.0,          2500.0,          3500.0,\n",
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       "            264900.125,    288938.46875,    313340.46875,    337979.34375,\n",
       "          362767.03125,     387645.1875,     412576.8125,       437539.25,\n",
       "          462519.03125,    487508.34375,     512502.8125],\n",
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       "  * z_t      (z_t) float32 236B 500.0 1.5e+03 2.5e+03 ... 4.875e+05 5.125e+05</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>z_t</span>: 59</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ea37ec10-4128-4eeb-8b5b-107f6362730c' class='xr-array-in' type='checkbox' checked><label for='section-ea37ec10-4128-4eeb-8b5b-107f6362730c' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>10.0 10.0 10.0 10.0 10.0 10.0 ... 249.3 249.6 249.8 249.9 249.9 250.0</span></div><div class='xr-array-data'><pre>array([ 10.      ,  10.      ,  10.      ,  10.      ,  10.      ,\n",
       "        10.      ,  10.      ,  10.      ,  10.      ,  10.      ,\n",
       "        10.      ,  10.      ,  10.      ,  10.      ,  10.      ,\n",
       "        10.098404,  10.380646,  10.812225,  11.369003,  12.051102,\n",
       "        12.866898,  13.830551,  14.961334,  16.284042,  17.829437,\n",
       "        19.63559 ,  21.748749,  24.225494,  27.134155,  30.557007,\n",
       "        34.59305 ,  39.35904 ,  44.991302,  51.644836,  59.489746,\n",
       "        68.70068 ,  79.43921 ,  91.822754, 105.880615, 121.49835 ,\n",
       "       138.36267 , 155.9303  , 173.44922 , 190.05688 , 204.94092 ,\n",
       "       217.51233 , 227.51514 , 235.02954 , 240.38354 , 244.02002 ,\n",
       "       246.38867 , 247.87695 , 248.7815  , 249.31616 , 249.62451 ,\n",
       "       249.79785 , 249.89307 , 249.94482 , 249.97168 ], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-4d491d2f-5c5c-4508-8d99-3d014191235c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4d491d2f-5c5c-4508-8d99-3d014191235c' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>z_t</span></div><div class='xr-var-dims'>(z_t)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 5.125e+05</div><input id='attrs-87178f45-c5dc-4b5c-acf9-6746dd2b15de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87178f45-c5dc-4b5c-acf9-6746dd2b15de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61f6c532-db67-4732-8694-09e9c3f10cda' class='xr-var-data-in' type='checkbox'><label for='data-61f6c532-db67-4732-8694-09e9c3f10cda' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>depth from surface to midpoint of layer</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>units :</span></dt><dd>centimeters</dd><dt><span>valid_max :</span></dt><dd>537500.0</dd><dt><span>valid_min :</span></dt><dd>500.0</dd></dl></div><div class='xr-var-data'><pre>array([5.000000e+02, 1.500000e+03, 2.500000e+03, 3.500000e+03, 4.500000e+03,\n",
       "       5.500000e+03, 6.500000e+03, 7.500000e+03, 8.500000e+03, 9.500000e+03,\n",
       "       1.050000e+04, 1.150000e+04, 1.250000e+04, 1.350000e+04, 1.450000e+04,\n",
       "       1.550000e+04, 1.650984e+04, 1.754790e+04, 1.862913e+04, 1.976603e+04,\n",
       "       2.097114e+04, 2.225783e+04, 2.364088e+04, 2.513702e+04, 2.676542e+04,\n",
       "       2.854837e+04, 3.051192e+04, 3.268680e+04, 3.510935e+04, 3.782276e+04,\n",
       "       4.087846e+04, 4.433777e+04, 4.827367e+04, 5.277280e+04, 5.793729e+04,\n",
       "       6.388626e+04, 7.075633e+04, 7.870025e+04, 8.788252e+04, 9.847059e+04,\n",
       "       1.106204e+05, 1.244567e+05, 1.400497e+05, 1.573946e+05, 1.764003e+05,\n",
       "       1.968944e+05, 2.186457e+05, 2.413972e+05, 2.649001e+05, 2.889385e+05,\n",
       "       3.133405e+05, 3.379793e+05, 3.627670e+05, 3.876452e+05, 4.125768e+05,\n",
       "       4.375392e+05, 4.625190e+05, 4.875083e+05, 5.125028e+05], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-085c8446-1186-4544-a824-4b7a75164c09' class='xr-section-summary-in' type='checkbox'  ><label for='section-085c8446-1186-4544-a824-4b7a75164c09' class='xr-section-summary' >Indexes: <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-index-name'><div>z_t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e71ad179-9f3d-4ed1-b0e0-9fbda075a500' class='xr-index-data-in' type='checkbox'/><label for='index-e71ad179-9f3d-4ed1-b0e0-9fbda075a500' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([          500.0,          1500.0,          2500.0,          3500.0,\n",
       "                4500.0,          5500.0,          6500.0,          7500.0,\n",
       "                8500.0,          9500.0,         10500.0,         11500.0,\n",
       "               12500.0,         13500.0,         14500.0,         15500.0,\n",
       "        16509.83984375, 17547.904296875, 18629.126953125,  19766.02734375,\n",
       "       20971.138671875,    22257.828125,   23640.8828125,    25137.015625,\n",
       "       26765.419921875, 28548.365234375,    30511.921875, 32686.798828125,\n",
       "        35109.34765625,  37822.76171875,  40878.46484375,  44337.76953125,\n",
       "          48273.671875,  52772.80078125,   57937.2890625,  63886.26171875,\n",
       "          70756.328125,        78700.25,   87882.5234375,   98470.5859375,\n",
       "         110620.421875,     124456.6875,    140049.71875,   157394.640625,\n",
       "         176400.328125,   196894.421875,    218645.65625,    241397.15625,\n",
       "            264900.125,    288938.46875,    313340.46875,    337979.34375,\n",
       "          362767.03125,     387645.1875,     412576.8125,       437539.25,\n",
       "          462519.03125,    487508.34375,     512502.8125],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;z_t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4e64464f-a43b-4cff-aa62-d82886718289' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4e64464f-a43b-4cff-aa62-d82886718289' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (z_t: 59)> Size: 236B\n",
       "array([ 10.      ,  10.      ,  10.      ,  10.      ,  10.      ,\n",
       "        10.      ,  10.      ,  10.      ,  10.      ,  10.      ,\n",
       "        10.      ,  10.      ,  10.      ,  10.      ,  10.      ,\n",
       "        10.098404,  10.380646,  10.812225,  11.369003,  12.051102,\n",
       "        12.866898,  13.830551,  14.961334,  16.284042,  17.829437,\n",
       "        19.63559 ,  21.748749,  24.225494,  27.134155,  30.557007,\n",
       "        34.59305 ,  39.35904 ,  44.991302,  51.644836,  59.489746,\n",
       "        68.70068 ,  79.43921 ,  91.822754, 105.880615, 121.49835 ,\n",
       "       138.36267 , 155.9303  , 173.44922 , 190.05688 , 204.94092 ,\n",
       "       217.51233 , 227.51514 , 235.02954 , 240.38354 , 244.02002 ,\n",
       "       246.38867 , 247.87695 , 248.7815  , 249.31616 , 249.62451 ,\n",
       "       249.79785 , 249.89307 , 249.94482 , 249.97168 ], dtype=float32)\n",
       "Coordinates:\n",
       "  * z_t      (z_t) float32 236B 500.0 1.5e+03 2.5e+03 ... 4.875e+05 5.125e+05"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Compute depth level deltas using z_t levels\n",
    "depth_level_deltas = depth_levels_in_m.isel(z_t=slice(1, None)).values - depth_levels_in_m.isel(z_t=slice(0, -1)).values\n",
    "# Optionally, if you want to keep it as an xarray DataArray, re-wrap the result\n",
    "depth_level_deltas = xr.DataArray(depth_level_deltas, dims=[\"z_t\"], coords={\"z_t\": depth_levels_in_m.z_t.isel(z_t=slice(0, -1))})\n",
    "depth_level_deltas                                                                                        "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c0e2457-0993-4db3-8299-cb3ab1ebd64b",
   "metadata": {},
   "source": [
    "# Compute Ocean Heat content for ocean surface\n",
    "- Ocean surface is considered to be the top 100m\n",
    "- The formula for this is: $$ H = \\rho C \\int_0^z T(z) dz $$\n",
    "\n",
    "\n",
    "Where H is ocean heat content, the value we are trying to calculate,\n",
    "\n",
    "$\\rho$ is the density of sea water, $1026 kg/m^3$  ,\n",
    "\n",
    "$C$ is the specific heat of sea water, $3990 J/(kg K)$  ,\n",
    "\n",
    "$z$ is the depth limit of the calculation in meters,\n",
    "\n",
    "and $T(z)$ is the temperature at each depth in degrees Kelvin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "f56c6713-273d-45c5-a540-2a893b37c7bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_ocean_heat(delta_level, temperature):\n",
    "    rho = 1026 #kg/m^3\n",
    "    c_p = 3990 #J/(kg K)\n",
    "    weighted_temperature = delta_level * temperature\n",
    "    heat = weighted_temperature.sum(dim=\"z_t\")*rho*c_p\n",
    "    return heat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "0a432eba-dab8-45d5-a519-f9fd6e64ef26",
   "metadata": {},
   "outputs": [
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       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (member_id: 50, time: 1980, z_t: 10, nlat: 384, nlon: 320)&gt; Size: 487GB\n",
       "dask.array&lt;where, shape=(50, 1980, 10, 384, 320), dtype=float32, chunksize=(1, 6, 10, 384, 320), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * member_id  (member_id) &lt;U12 2kB &#x27;r10i1181p1f1&#x27; ... &#x27;r9i1301p1f1&#x27;\n",
       "  * time       (time) object 16kB 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "  * z_t        (z_t) float32 40B 500.0 1.5e+03 2.5e+03 ... 8.5e+03 9.5e+03\n",
       "Dimensions without coordinates: nlat, nlon</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>member_id</span>: 50</li><li><span class='xr-has-index'>time</span>: 1980</li><li><span class='xr-has-index'>z_t</span>: 10</li><li><span>nlat</span>: 384</li><li><span>nlon</span>: 320</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d2cd84a4-9bb2-42bb-8dcd-35dd03ec066b' class='xr-array-in' type='checkbox' checked><label for='section-d2cd84a4-9bb2-42bb-8dcd-35dd03ec066b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 6, 10, 384, 320), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 453.19 GiB </td>\n",
       "                        <td> 28.12 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50, 1980, 10, 384, 320) </td>\n",
       "                        <td> (1, 6, 10, 384, 320) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 16500 chunks in 9 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-a12ac70b-dc3b-40fd-b514-a70b68f0996a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a12ac70b-dc3b-40fd-b514-a70b68f0996a' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>&lt;U12</div><div class='xr-var-preview xr-preview'>&#x27;r10i1181p1f1&#x27; ... &#x27;r9i1301p1f1&#x27;</div><input id='attrs-c1eead4f-d6e9-47eb-8300-79d370f50046' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c1eead4f-d6e9-47eb-8300-79d370f50046' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0123fe8d-844d-4cd1-85c9-8169aa6fdc13' class='xr-var-data-in' type='checkbox'><label for='data-0123fe8d-844d-4cd1-85c9-8169aa6fdc13' 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;r10i1181p1f1&#x27;, &#x27;r10i1231p1f1&#x27;, &#x27;r10i1251p1f1&#x27;, &#x27;r10i1281p1f1&#x27;,\n",
       "       &#x27;r10i1301p1f1&#x27;, &#x27;r1i1001p1f1&#x27;, &#x27;r1i1231p1f1&#x27;, &#x27;r1i1251p1f1&#x27;,\n",
       "       &#x27;r1i1281p1f1&#x27;, &#x27;r1i1301p1f1&#x27;, &#x27;r2i1021p1f1&#x27;, &#x27;r2i1231p1f1&#x27;,\n",
       "       &#x27;r2i1251p1f1&#x27;, &#x27;r2i1281p1f1&#x27;, &#x27;r2i1301p1f1&#x27;, &#x27;r3i1041p1f1&#x27;,\n",
       "       &#x27;r3i1231p1f1&#x27;, &#x27;r3i1251p1f1&#x27;, &#x27;r3i1281p1f1&#x27;, &#x27;r3i1301p1f1&#x27;,\n",
       "       &#x27;r4i1061p1f1&#x27;, &#x27;r4i1231p1f1&#x27;, &#x27;r4i1251p1f1&#x27;, &#x27;r4i1281p1f1&#x27;,\n",
       "       &#x27;r4i1301p1f1&#x27;, &#x27;r5i1081p1f1&#x27;, &#x27;r5i1231p1f1&#x27;, &#x27;r5i1251p1f1&#x27;,\n",
       "       &#x27;r5i1281p1f1&#x27;, &#x27;r5i1301p1f1&#x27;, &#x27;r6i1101p1f1&#x27;, &#x27;r6i1231p1f1&#x27;,\n",
       "       &#x27;r6i1251p1f1&#x27;, &#x27;r6i1281p1f1&#x27;, &#x27;r6i1301p1f1&#x27;, &#x27;r7i1121p1f1&#x27;,\n",
       "       &#x27;r7i1231p1f1&#x27;, &#x27;r7i1251p1f1&#x27;, &#x27;r7i1281p1f1&#x27;, &#x27;r7i1301p1f1&#x27;,\n",
       "       &#x27;r8i1141p1f1&#x27;, &#x27;r8i1231p1f1&#x27;, &#x27;r8i1251p1f1&#x27;, &#x27;r8i1281p1f1&#x27;,\n",
       "       &#x27;r8i1301p1f1&#x27;, &#x27;r9i1161p1f1&#x27;, &#x27;r9i1231p1f1&#x27;, &#x27;r9i1251p1f1&#x27;,\n",
       "       &#x27;r9i1281p1f1&#x27;, &#x27;r9i1301p1f1&#x27;], dtype=&#x27;&lt;U12&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-16 12:00:00 ... 2014-12-...</div><input id='attrs-0e06567f-0ceb-4a76-9a62-04d456b3f520' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e06567f-0ceb-4a76-9a62-04d456b3f520' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c8642e0-431d-4192-b090-e9ca03514e31' class='xr-var-data-in' type='checkbox'><label for='data-6c8642e0-431d-4192-b090-e9ca03514e31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bound</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       ...,\n",
       "       cftime.DatetimeNoLeap(2014, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z_t</span></div><div class='xr-var-dims'>(z_t)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 8.5e+03 9.5e+03</div><input id='attrs-eaff64cb-79b9-4334-9d1a-d983c3f2ed4c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eaff64cb-79b9-4334-9d1a-d983c3f2ed4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84ca4467-7575-4498-bb93-d91db76af591' class='xr-var-data-in' type='checkbox'><label for='data-84ca4467-7575-4498-bb93-d91db76af591' 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'><pre>array([ 500., 1500., 2500., 3500., 4500., 5500., 6500., 7500., 8500., 9500.],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3a4aeecd-6ef6-465a-b602-10c9debde989' class='xr-section-summary-in' type='checkbox'  ><label for='section-3a4aeecd-6ef6-465a-b602-10c9debde989' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>member_id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-49c2d15c-2874-4287-a766-adbd35d6a5d1' class='xr-index-data-in' type='checkbox'/><label for='index-49c2d15c-2874-4287-a766-adbd35d6a5d1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;r10i1181p1f1&#x27;, &#x27;r10i1231p1f1&#x27;, &#x27;r10i1251p1f1&#x27;, &#x27;r10i1281p1f1&#x27;,\n",
       "       &#x27;r10i1301p1f1&#x27;, &#x27;r1i1001p1f1&#x27;, &#x27;r1i1231p1f1&#x27;, &#x27;r1i1251p1f1&#x27;,\n",
       "       &#x27;r1i1281p1f1&#x27;, &#x27;r1i1301p1f1&#x27;, &#x27;r2i1021p1f1&#x27;, &#x27;r2i1231p1f1&#x27;,\n",
       "       &#x27;r2i1251p1f1&#x27;, &#x27;r2i1281p1f1&#x27;, &#x27;r2i1301p1f1&#x27;, &#x27;r3i1041p1f1&#x27;,\n",
       "       &#x27;r3i1231p1f1&#x27;, &#x27;r3i1251p1f1&#x27;, &#x27;r3i1281p1f1&#x27;, &#x27;r3i1301p1f1&#x27;,\n",
       "       &#x27;r4i1061p1f1&#x27;, &#x27;r4i1231p1f1&#x27;, &#x27;r4i1251p1f1&#x27;, &#x27;r4i1281p1f1&#x27;,\n",
       "       &#x27;r4i1301p1f1&#x27;, &#x27;r5i1081p1f1&#x27;, &#x27;r5i1231p1f1&#x27;, &#x27;r5i1251p1f1&#x27;,\n",
       "       &#x27;r5i1281p1f1&#x27;, &#x27;r5i1301p1f1&#x27;, &#x27;r6i1101p1f1&#x27;, &#x27;r6i1231p1f1&#x27;,\n",
       "       &#x27;r6i1251p1f1&#x27;, &#x27;r6i1281p1f1&#x27;, &#x27;r6i1301p1f1&#x27;, &#x27;r7i1121p1f1&#x27;,\n",
       "       &#x27;r7i1231p1f1&#x27;, &#x27;r7i1251p1f1&#x27;, &#x27;r7i1281p1f1&#x27;, &#x27;r7i1301p1f1&#x27;,\n",
       "       &#x27;r8i1141p1f1&#x27;, &#x27;r8i1231p1f1&#x27;, &#x27;r8i1251p1f1&#x27;, &#x27;r8i1281p1f1&#x27;,\n",
       "       &#x27;r8i1301p1f1&#x27;, &#x27;r9i1161p1f1&#x27;, &#x27;r9i1231p1f1&#x27;, &#x27;r9i1251p1f1&#x27;,\n",
       "       &#x27;r9i1281p1f1&#x27;, &#x27;r9i1301p1f1&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;member_id&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d995333c-92f3-41af-9fdd-9da8cc04de65' class='xr-index-data-in' type='checkbox'/><label for='index-d995333c-92f3-41af-9fdd-9da8cc04de65' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1850-01-16 12:00:00, 1850-02-15 00:00:00, 1850-03-16 12:00:00,\n",
       "             1850-04-16 00:00:00, 1850-05-16 12:00:00, 1850-06-16 00:00:00,\n",
       "             1850-07-16 12:00:00, 1850-08-16 12:00:00, 1850-09-16 00:00:00,\n",
       "             1850-10-16 12:00:00,\n",
       "             ...\n",
       "             2014-03-16 12:00:00, 2014-04-16 00:00:00, 2014-05-16 12:00:00,\n",
       "             2014-06-16 00:00:00, 2014-07-16 12:00:00, 2014-08-16 12:00:00,\n",
       "             2014-09-16 00:00:00, 2014-10-16 12:00:00, 2014-11-16 00:00:00,\n",
       "             2014-12-16 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=1980, calendar=&#x27;noleap&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>z_t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a6991f47-4570-4f41-abb3-57570489b5e0' class='xr-index-data-in' type='checkbox'/><label for='index-a6991f47-4570-4f41-abb3-57570489b5e0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([500.0, 1500.0, 2500.0, 3500.0, 4500.0, 5500.0, 6500.0, 7500.0, 8500.0,\n",
       "       9500.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;z_t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f4ff8db5-6702-4efd-bb70-a4bc871ce27f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f4ff8db5-6702-4efd-bb70-a4bc871ce27f' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (member_id: 50, time: 1980, z_t: 10, nlat: 384, nlon: 320)> Size: 487GB\n",
       "dask.array<where, shape=(50, 1980, 10, 384, 320), dtype=float32, chunksize=(1, 6, 10, 384, 320), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * member_id  (member_id) <U12 2kB 'r10i1181p1f1' ... 'r9i1301p1f1'\n",
       "  * time       (time) object 16kB 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "  * z_t        (z_t) float32 40B 500.0 1.5e+03 2.5e+03 ... 8.5e+03 9.5e+03\n",
       "Dimensions without coordinates: nlat, nlon"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remember that the coordinate z_t still has values in cm\n",
    "hist_temp_ocean_surface = hist_temp_in_degK.where(hist_temp_in_degK['z_t'] < 1e4,drop=True)\n",
    "hist_temp_ocean_surface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "a0a623ed-4870-4fc0-bbc5-c33add47d212",
   "metadata": {},
   "outputs": [
    {
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-attrs dt {\n",
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       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
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       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (z_t: 10)&gt; Size: 40B\n",
       "array([10., 10., 10., 10., 10., 10., 10., 10., 10., 10.], dtype=float32)\n",
       "Coordinates:\n",
       "  * z_t      (z_t) float32 40B 500.0 1.5e+03 2.5e+03 ... 7.5e+03 8.5e+03 9.5e+03</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>z_t</span>: 10</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2cc60fd1-b09f-45cd-9062-9e139af79e15' class='xr-array-in' type='checkbox' checked><label for='section-2cc60fd1-b09f-45cd-9062-9e139af79e15' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0</span></div><div class='xr-array-data'><pre>array([10., 10., 10., 10., 10., 10., 10., 10., 10., 10.], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-2cbbafa1-a553-4230-981c-634fbf3ace7c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2cbbafa1-a553-4230-981c-634fbf3ace7c' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>z_t</span></div><div class='xr-var-dims'>(z_t)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 8.5e+03 9.5e+03</div><input id='attrs-3771c19a-56ca-4a66-940e-b5dee0a960d4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3771c19a-56ca-4a66-940e-b5dee0a960d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c057ee9-ba3c-4db1-8c79-bfcc143a1c2f' class='xr-var-data-in' type='checkbox'><label for='data-9c057ee9-ba3c-4db1-8c79-bfcc143a1c2f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>depth from surface to midpoint of layer</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>units :</span></dt><dd>centimeters</dd><dt><span>valid_max :</span></dt><dd>537500.0</dd><dt><span>valid_min :</span></dt><dd>500.0</dd></dl></div><div class='xr-var-data'><pre>array([ 500., 1500., 2500., 3500., 4500., 5500., 6500., 7500., 8500., 9500.],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-abf3b230-1111-46b8-82c9-fbc73f6e2ec1' class='xr-section-summary-in' type='checkbox'  ><label for='section-abf3b230-1111-46b8-82c9-fbc73f6e2ec1' class='xr-section-summary' >Indexes: <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-index-name'><div>z_t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f34d542e-3cd5-4fbe-bae1-8b4c57fbc703' class='xr-index-data-in' type='checkbox'/><label for='index-f34d542e-3cd5-4fbe-bae1-8b4c57fbc703' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([500.0, 1500.0, 2500.0, 3500.0, 4500.0, 5500.0, 6500.0, 7500.0, 8500.0,\n",
       "       9500.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;z_t&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5341c535-1331-44be-afb2-c458d0cbea39' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5341c535-1331-44be-afb2-c458d0cbea39' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (z_t: 10)> Size: 40B\n",
       "array([10., 10., 10., 10., 10., 10., 10., 10., 10., 10.], dtype=float32)\n",
       "Coordinates:\n",
       "  * z_t      (z_t) float32 40B 500.0 1.5e+03 2.5e+03 ... 7.5e+03 8.5e+03 9.5e+03"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "depth_level_deltas_surface = depth_level_deltas.where(depth_level_deltas['z_t'] <1e4, drop= True)\n",
    "depth_level_deltas_surface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "26350fad-caa4-4c49-91f9-fb81633ca9c8",
   "metadata": {},
   "outputs": [
    {
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       ".xr-icon-database,\n",
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       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (member_id: 50, time: 1980, nlat: 384, nlon: 320)&gt; Size: 49GB\n",
       "dask.array&lt;mul, shape=(50, 1980, 384, 320), dtype=float32, chunksize=(1, 6, 384, 320), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * member_id  (member_id) &lt;U12 2kB &#x27;r10i1181p1f1&#x27; ... &#x27;r9i1301p1f1&#x27;\n",
       "  * time       (time) object 16kB 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "Dimensions without coordinates: nlat, nlon</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>member_id</span>: 50</li><li><span class='xr-has-index'>time</span>: 1980</li><li><span>nlat</span>: 384</li><li><span>nlon</span>: 320</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d511e81f-e56c-4e64-ae27-bc1485b93718' class='xr-array-in' type='checkbox' checked><label for='section-d511e81f-e56c-4e64-ae27-bc1485b93718' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 6, 384, 320), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 45.32 GiB </td>\n",
       "                        <td> 2.81 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50, 1980, 384, 320) </td>\n",
       "                        <td> (1, 6, 384, 320) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 16500 chunks in 19 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-c6f579c9-70f7-4ee5-a52f-7e671013b501' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c6f579c9-70f7-4ee5-a52f-7e671013b501' class='xr-section-summary' >Coordinates: <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 class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>&lt;U12</div><div class='xr-var-preview xr-preview'>&#x27;r10i1181p1f1&#x27; ... &#x27;r9i1301p1f1&#x27;</div><input id='attrs-feb6f3a7-0598-4015-a57c-651beefda63f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-feb6f3a7-0598-4015-a57c-651beefda63f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5432541f-a55e-4f33-b472-68caca02b505' class='xr-var-data-in' type='checkbox'><label for='data-5432541f-a55e-4f33-b472-68caca02b505' 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;r10i1181p1f1&#x27;, &#x27;r10i1231p1f1&#x27;, &#x27;r10i1251p1f1&#x27;, &#x27;r10i1281p1f1&#x27;,\n",
       "       &#x27;r10i1301p1f1&#x27;, &#x27;r1i1001p1f1&#x27;, &#x27;r1i1231p1f1&#x27;, &#x27;r1i1251p1f1&#x27;,\n",
       "       &#x27;r1i1281p1f1&#x27;, &#x27;r1i1301p1f1&#x27;, &#x27;r2i1021p1f1&#x27;, &#x27;r2i1231p1f1&#x27;,\n",
       "       &#x27;r2i1251p1f1&#x27;, &#x27;r2i1281p1f1&#x27;, &#x27;r2i1301p1f1&#x27;, &#x27;r3i1041p1f1&#x27;,\n",
       "       &#x27;r3i1231p1f1&#x27;, &#x27;r3i1251p1f1&#x27;, &#x27;r3i1281p1f1&#x27;, &#x27;r3i1301p1f1&#x27;,\n",
       "       &#x27;r4i1061p1f1&#x27;, &#x27;r4i1231p1f1&#x27;, &#x27;r4i1251p1f1&#x27;, &#x27;r4i1281p1f1&#x27;,\n",
       "       &#x27;r4i1301p1f1&#x27;, &#x27;r5i1081p1f1&#x27;, &#x27;r5i1231p1f1&#x27;, &#x27;r5i1251p1f1&#x27;,\n",
       "       &#x27;r5i1281p1f1&#x27;, &#x27;r5i1301p1f1&#x27;, &#x27;r6i1101p1f1&#x27;, &#x27;r6i1231p1f1&#x27;,\n",
       "       &#x27;r6i1251p1f1&#x27;, &#x27;r6i1281p1f1&#x27;, &#x27;r6i1301p1f1&#x27;, &#x27;r7i1121p1f1&#x27;,\n",
       "       &#x27;r7i1231p1f1&#x27;, &#x27;r7i1251p1f1&#x27;, &#x27;r7i1281p1f1&#x27;, &#x27;r7i1301p1f1&#x27;,\n",
       "       &#x27;r8i1141p1f1&#x27;, &#x27;r8i1231p1f1&#x27;, &#x27;r8i1251p1f1&#x27;, &#x27;r8i1281p1f1&#x27;,\n",
       "       &#x27;r8i1301p1f1&#x27;, &#x27;r9i1161p1f1&#x27;, &#x27;r9i1231p1f1&#x27;, &#x27;r9i1251p1f1&#x27;,\n",
       "       &#x27;r9i1281p1f1&#x27;, &#x27;r9i1301p1f1&#x27;], dtype=&#x27;&lt;U12&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-16 12:00:00 ... 2014-12-...</div><input id='attrs-853466c8-1b68-4086-aff0-99784ce9a626' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-853466c8-1b68-4086-aff0-99784ce9a626' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cda1e897-fdcf-4931-b9c6-ab4f64fec7a8' class='xr-var-data-in' type='checkbox'><label for='data-cda1e897-fdcf-4931-b9c6-ab4f64fec7a8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bound</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       ...,\n",
       "       cftime.DatetimeNoLeap(2014, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cd5c870b-9126-4269-a8f7-2c59ad19b9f4' class='xr-section-summary-in' type='checkbox'  ><label for='section-cd5c870b-9126-4269-a8f7-2c59ad19b9f4' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>member_id</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f6670c25-ca1f-4ca5-b365-ab6ae665c12c' class='xr-index-data-in' type='checkbox'/><label for='index-f6670c25-ca1f-4ca5-b365-ab6ae665c12c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;r10i1181p1f1&#x27;, &#x27;r10i1231p1f1&#x27;, &#x27;r10i1251p1f1&#x27;, &#x27;r10i1281p1f1&#x27;,\n",
       "       &#x27;r10i1301p1f1&#x27;, &#x27;r1i1001p1f1&#x27;, &#x27;r1i1231p1f1&#x27;, &#x27;r1i1251p1f1&#x27;,\n",
       "       &#x27;r1i1281p1f1&#x27;, &#x27;r1i1301p1f1&#x27;, &#x27;r2i1021p1f1&#x27;, &#x27;r2i1231p1f1&#x27;,\n",
       "       &#x27;r2i1251p1f1&#x27;, &#x27;r2i1281p1f1&#x27;, &#x27;r2i1301p1f1&#x27;, &#x27;r3i1041p1f1&#x27;,\n",
       "       &#x27;r3i1231p1f1&#x27;, &#x27;r3i1251p1f1&#x27;, &#x27;r3i1281p1f1&#x27;, &#x27;r3i1301p1f1&#x27;,\n",
       "       &#x27;r4i1061p1f1&#x27;, &#x27;r4i1231p1f1&#x27;, &#x27;r4i1251p1f1&#x27;, &#x27;r4i1281p1f1&#x27;,\n",
       "       &#x27;r4i1301p1f1&#x27;, &#x27;r5i1081p1f1&#x27;, &#x27;r5i1231p1f1&#x27;, &#x27;r5i1251p1f1&#x27;,\n",
       "       &#x27;r5i1281p1f1&#x27;, &#x27;r5i1301p1f1&#x27;, &#x27;r6i1101p1f1&#x27;, &#x27;r6i1231p1f1&#x27;,\n",
       "       &#x27;r6i1251p1f1&#x27;, &#x27;r6i1281p1f1&#x27;, &#x27;r6i1301p1f1&#x27;, &#x27;r7i1121p1f1&#x27;,\n",
       "       &#x27;r7i1231p1f1&#x27;, &#x27;r7i1251p1f1&#x27;, &#x27;r7i1281p1f1&#x27;, &#x27;r7i1301p1f1&#x27;,\n",
       "       &#x27;r8i1141p1f1&#x27;, &#x27;r8i1231p1f1&#x27;, &#x27;r8i1251p1f1&#x27;, &#x27;r8i1281p1f1&#x27;,\n",
       "       &#x27;r8i1301p1f1&#x27;, &#x27;r9i1161p1f1&#x27;, &#x27;r9i1231p1f1&#x27;, &#x27;r9i1251p1f1&#x27;,\n",
       "       &#x27;r9i1281p1f1&#x27;, &#x27;r9i1301p1f1&#x27;],\n",
       "      dtype=&#x27;object&#x27;, name=&#x27;member_id&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-624d5017-4dbc-4386-8bbc-75ab413858f6' class='xr-index-data-in' type='checkbox'/><label for='index-624d5017-4dbc-4386-8bbc-75ab413858f6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1850-01-16 12:00:00, 1850-02-15 00:00:00, 1850-03-16 12:00:00,\n",
       "             1850-04-16 00:00:00, 1850-05-16 12:00:00, 1850-06-16 00:00:00,\n",
       "             1850-07-16 12:00:00, 1850-08-16 12:00:00, 1850-09-16 00:00:00,\n",
       "             1850-10-16 12:00:00,\n",
       "             ...\n",
       "             2014-03-16 12:00:00, 2014-04-16 00:00:00, 2014-05-16 12:00:00,\n",
       "             2014-06-16 00:00:00, 2014-07-16 12:00:00, 2014-08-16 12:00:00,\n",
       "             2014-09-16 00:00:00, 2014-10-16 12:00:00, 2014-11-16 00:00:00,\n",
       "             2014-12-16 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=1980, calendar=&#x27;noleap&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a79e1380-3057-4f7d-8980-c736eb23d155' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a79e1380-3057-4f7d-8980-c736eb23d155' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (member_id: 50, time: 1980, nlat: 384, nlon: 320)> Size: 49GB\n",
       "dask.array<mul, shape=(50, 1980, 384, 320), dtype=float32, chunksize=(1, 6, 384, 320), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * member_id  (member_id) <U12 2kB 'r10i1181p1f1' ... 'r9i1301p1f1'\n",
       "  * time       (time) object 16kB 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "Dimensions without coordinates: nlat, nlon"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist_ocean_heat = calc_ocean_heat(depth_level_deltas_surface,hist_temp_ocean_surface)\n",
    "hist_ocean_heat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d00d1c88-7a12-46b4-98c2-1fcc6d6d4da2",
   "metadata": {},
   "source": [
    "### Plot Ocean Heat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "e94224a4-80e3-4aae-abb9-4c8306ad020e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 15.3 s, sys: 8.12 s, total: 23.4 s\n",
      "Wall time: 29.6 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x1526a874b890>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "# Jan, 1850 average over all memebers\n",
    "hist_ocean_avgheat = hist_ocean_heat.mean('member_id')\n",
    "hist_ocean_avgheat.isel(time=0).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "57e84ab0-722e-4dd3-8316-4904b3906f96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 15.4 s, sys: 8.08 s, total: 23.5 s\n",
      "Wall time: 36.2 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x1526aab1c920>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "#Plot ocean heat for Jan 2014\n",
    "hist_ocean_avgheat.isel(time=-12).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "164b1946-c67f-4b94-9732-5011b2c22a71",
   "metadata": {},
   "source": [
    "### Has the surface ocean heat content increased with time for January ? (Due to Global Warming!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "25af23e2-3bd6-4f65-8e8b-3ae8f6f90cd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "hist_ocean_avgheat_ano = hist_ocean_avgheat.isel(time=-12) - hist_ocean_avgheat.isel(time=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "8bdd763e-7f26-48ef-8388-44e6e5890137",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 30.3 s, sys: 16.4 s, total: 46.7 s\n",
      "Wall time: 58.7 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x1526a36b1f10>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "hist_ocean_avgheat_ano.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:arco_experiments]",
   "language": "python",
   "name": "conda-env-arco_experiments-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": "3.12.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}