jupyter_notebooks/cesm_posix_bias.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bc65f494-4fbe-4af0-85ca-e3a424b56454",
   "metadata": {},
   "source": [
    "# Bias-correct CESM2 LENS temperature data using ERA5 reanalysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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 s3fs\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": 2,
   "id": "63e53607-bf0a-41a4-9f72-5769197e4570",
   "metadata": {},
   "outputs": [],
   "source": [
    "import fsspec.implementations.http as fshttp\n",
    "from pelicanfs.core import PelicanFileSystem, PelicanMap, OSDFFileSystem "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 4,
   "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": 5,
   "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": 6,
   "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": 7,
   "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": 8,
   "id": "e1ab50dc-0496-455e-8b02-acf4ef314b37",
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster.scale(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5c5304fb-06bc-4e37-8421-0c70f883d062",
   "metadata": {},
   "outputs": [
    {
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       "        <h3 style=\"margin-bottom: 0px; margin-top: 0px;\">PBSCluster</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">a320a331</p>\n",
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       "            <tr>\n",
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       "                    <strong>Dashboard:</strong> <a href=\"https://jupyterhub.hpc.ucar.edu/stable/user/harshah/proxy/8787/status\" target=\"_blank\">https://jupyterhub.hpc.ucar.edu/stable/user/harshah/proxy/8787/status</a>\n",
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       "            <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
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       "                        <strong>Comm:</strong> tcp://128.117.208.96:43661\n",
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       "                        <strong>Dashboard:</strong> <a href=\"https://jupyterhub.hpc.ucar.edu/stable/user/harshah/proxy/8787/status\" target=\"_blank\">https://jupyterhub.hpc.ucar.edu/stable/user/harshah/proxy/8787/status</a>\n",
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       "                        <strong>Started:</strong> Just now\n",
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       "                    <td style=\"text-align: left;\">\n",
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       "        <summary style=\"margin-bottom: 20px;\">\n",
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       "PBSCluster(a320a331, 'tcp://128.117.208.96:43661', workers=0, threads=0, memory=0 B)"
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    "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": 10,
   "id": "be45a360-624a-403b-be3f-6879ec0a5c09",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<p><strong>aws-cesm2-le catalog with 40 dataset(s) from 322 asset(s)</strong>:</p> <div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique</th>\n",
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       "  </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",
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       "      <th>start_time</th>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>end_time</th>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>path</th>\n",
       "      <td>313</td>\n",
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       "    <tr>\n",
       "      <th>derived_variable</th>\n",
       "      <td>0</td>\n",
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "cesm_cat = intake.open_esm_datastore(rda_scratch + '/intake_catalogs/posix/aws-cesm2-le.json')\n",
    "cesm_cat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b2dc1598-96bc-491a-8bae-80a90b8dbd3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><strong>aws-cesm2-le catalog with 4 dataset(s) from 4 asset(s)</strong>:</p> <div>\n",
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       "      <td>1</td>\n",
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       "      <th>component</th>\n",
       "      <td>1</td>\n",
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       "    <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",
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       "    <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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   ],
   "source": [
    "cesm_temp = cesm_cat.search(variable ='TREFHTMX', frequency ='daily')\n",
    "cesm_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a192979f-0901-4a6d-8399-f03c81023e29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['/glade/campaign/collections/rda/transfer/chifan_AWS/ncar-cesm2-lens/atm/daily/cesm2LE-historical-cmip6-TREFHTMX.zarr',\n",
       "       '/glade/campaign/collections/rda/transfer/chifan_AWS/ncar-cesm2-lens/atm/daily/cesm2LE-historical-smbb-TREFHTMX.zarr',\n",
       "       '/glade/campaign/collections/rda/transfer/chifan_AWS/ncar-cesm2-lens/atm/daily/cesm2LE-ssp370-cmip6-TREFHTMX.zarr',\n",
       "       '/glade/campaign/collections/rda/transfer/chifan_AWS/ncar-cesm2-lens/atm/daily/cesm2LE-ssp370-smbb-TREFHTMX.zarr'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cesm_temp.df['path'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": 14,
   "id": "63ba6df1-c054-4893-a87f-9f87a6b412d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "historical_smbb  = dsets_cesm['atm.historical.daily.smbb']\n",
    "future_smbb      = dsets_cesm['atm.ssp370.daily.smbb']\n",
    "\n",
    "historical_cmip6 = dsets_cesm['atm.historical.daily.cmip6']\n",
    "future_cmip6     = dsets_cesm['atm.ssp370.daily.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_smbb = xr.concat([historical_smbb, future_smbb], dim='time')\n",
    "merge_ds_smbb = merge_ds_smbb.dropna(dim='member_id')\n",
    "\n",
    "merge_ds_cmip6= xr.concat([historical_cmip6, future_cmip6], dim='time')\n",
    "merge_ds_cmip6 = merge_ds_cmip6.dropna(dim='member_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "343c5b75-1706-455c-99a1-984c666f762f",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;TREFHTMX&#x27; (member_id: 99, time: 10950, lat: 192, lon: 288)&gt; Size: 240GB\n",
       "dask.array&lt;concatenate, shape=(99, 10950, 192, 288), dtype=float32, chunksize=(1, 730, 192, 288), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 2kB -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "  * lon        (lon) float64 2kB 0.0 1.25 2.5 3.75 ... 355.0 356.2 357.5 358.8\n",
       "  * member_id  (member_id) &lt;U12 5kB &#x27;r10i1181p1f1&#x27; ... &#x27;r9i1171p1f2&#x27;\n",
       "  * time       (time) object 88kB 1991-01-01 12:00:00 ... 2020-12-31 12:00:00\n",
       "Attributes:\n",
       "    cell_methods:  time: maximum\n",
       "    long_name:     Maximum reference height temperature over output period\n",
       "    units:         K</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'>'TREFHTMX'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>member_id</span>: 99</li><li><span class='xr-has-index'>time</span>: 10950</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8cca3bfc-b923-4ee8-9fa5-9396f78f384e' class='xr-array-in' type='checkbox' checked><label for='section-8cca3bfc-b923-4ee8-9fa5-9396f78f384e' 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, 365, 192, 288), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                    <tr>\n",
       "                        <td> </td>\n",
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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 223.31 GiB </td>\n",
       "                        <td> 153.98 MiB </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1584 chunks in 15 graph layers </td>\n",
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       "  <line x1=\"156\" y1=\"61\" x2=\"156\" y2=\"89\" />\n",
       "  <line x1=\"160\" y1=\"65\" x2=\"160\" y2=\"94\" />\n",
       "  <line x1=\"165\" y1=\"70\" x2=\"165\" y2=\"98\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"95.0,0.0 165.58823529411765,70.58823529411765 165.58823529411765,98.88787243663116 95.0,28.299637142513504\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"95\" y1=\"0\" x2=\"125\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"97\" y1=\"2\" x2=\"127\" y2=\"2\" />\n",
       "  <line x1=\"102\" y1=\"7\" x2=\"132\" y2=\"7\" />\n",
       "  <line x1=\"106\" y1=\"11\" x2=\"137\" y2=\"11\" />\n",
       "  <line x1=\"111\" y1=\"16\" x2=\"142\" y2=\"16\" />\n",
       "  <line x1=\"116\" y1=\"21\" x2=\"146\" y2=\"21\" />\n",
       "  <line x1=\"120\" y1=\"25\" x2=\"151\" y2=\"25\" />\n",
       "  <line x1=\"125\" y1=\"30\" x2=\"156\" y2=\"30\" />\n",
       "  <line x1=\"130\" y1=\"35\" x2=\"160\" y2=\"35\" />\n",
       "  <line x1=\"135\" y1=\"40\" x2=\"165\" y2=\"40\" />\n",
       "  <line x1=\"139\" y1=\"44\" x2=\"170\" y2=\"44\" />\n",
       "  <line x1=\"144\" y1=\"49\" x2=\"175\" y2=\"49\" />\n",
       "  <line x1=\"149\" y1=\"54\" x2=\"179\" y2=\"54\" />\n",
       "  <line x1=\"151\" y1=\"56\" x2=\"182\" y2=\"56\" />\n",
       "  <line x1=\"156\" y1=\"61\" x2=\"186\" y2=\"61\" />\n",
       "  <line x1=\"160\" y1=\"65\" x2=\"191\" y2=\"65\" />\n",
       "  <line x1=\"165\" y1=\"70\" x2=\"196\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"95\" y1=\"0\" x2=\"165\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"125\" y1=\"0\" x2=\"196\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"95.0,0.0 125.60908677844436,0.0 196.19732207256203,70.58823529411765 165.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"165\" y1=\"70\" x2=\"196\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"165\" y1=\"98\" x2=\"196\" y2=\"98\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"165\" y1=\"70\" x2=\"165\" y2=\"98\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"196\" y1=\"70\" x2=\"196\" y2=\"98\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"165.58823529411765,70.58823529411765 196.19732207256203,70.58823529411765 196.19732207256203,98.88787243663116 165.58823529411765,98.88787243663116\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"180.892779\" y=\"118.887872\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >288</text>\n",
       "  <text x=\"216.197322\" y=\"84.738054\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,216.197322,84.738054)\">192</text>\n",
       "  <text x=\"120.294118\" y=\"83.593755\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,120.294118,83.593755)\">10950</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-c6c8d946-0618-4b99-9229-166b96491043' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c6c8d946-0618-4b99-9229-166b96491043' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-03fb987f-068c-4713-95aa-80edf3a07e32' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03fb987f-068c-4713-95aa-80edf3a07e32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e476148-f72f-41a8-9ae9-a5e82fa20a73' class='xr-var-data-in' type='checkbox'><label for='data-7e476148-f72f-41a8-9ae9-a5e82fa20a73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-b0c795d7-60b3-40b5-9108-1ef914d0d70e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0c795d7-60b3-40b5-9108-1ef914d0d70e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9a9ff69-28ff-4cf8-93ee-284bf5db541a' class='xr-var-data-in' type='checkbox'><label for='data-d9a9ff69-28ff-4cf8-93ee-284bf5db541a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>&lt;U12</div><div class='xr-var-preview xr-preview'>&#x27;r10i1181p1f1&#x27; ... &#x27;r9i1171p1f2&#x27;</div><input id='attrs-861c920b-7be3-4db2-b50c-69094d8bd2ef' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-861c920b-7be3-4db2-b50c-69094d8bd2ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0526caf4-424e-477b-b7de-33a6a3e6251d' class='xr-var-data-in' type='checkbox'><label for='data-0526caf4-424e-477b-b7de-33a6a3e6251d' 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;, &#x27;r10i1191p1f2&#x27;, &#x27;r11i1231p1f2&#x27;,\n",
       "       &#x27;r11i1251p1f2&#x27;, &#x27;r11i1281p1f2&#x27;, &#x27;r11i1301p1f2&#x27;, &#x27;r12i1231p1f2&#x27;,\n",
       "       &#x27;r12i1251p1f2&#x27;, &#x27;r12i1281p1f2&#x27;, &#x27;r12i1301p1f2&#x27;, &#x27;r13i1231p1f2&#x27;,\n",
       "       &#x27;r13i1251p1f2&#x27;, &#x27;r13i1281p1f2&#x27;, &#x27;r13i1301p1f2&#x27;, &#x27;r14i1231p1f2&#x27;,\n",
       "       &#x27;r14i1251p1f2&#x27;, &#x27;r14i1281p1f2&#x27;, &#x27;r14i1301p1f2&#x27;, &#x27;r15i1231p1f2&#x27;,\n",
       "       &#x27;r15i1251p1f2&#x27;, &#x27;r15i1281p1f2&#x27;, &#x27;r15i1301p1f2&#x27;, &#x27;r16i1231p1f2&#x27;,\n",
       "       &#x27;r16i1251p1f2&#x27;, &#x27;r16i1281p1f2&#x27;, &#x27;r16i1301p1f2&#x27;, &#x27;r17i1231p1f2&#x27;,\n",
       "       &#x27;r17i1251p1f2&#x27;, &#x27;r17i1281p1f2&#x27;, &#x27;r18i1231p1f2&#x27;, &#x27;r18i1251p1f2&#x27;,\n",
       "       &#x27;r18i1281p1f2&#x27;, &#x27;r18i1301p1f2&#x27;, &#x27;r19i1231p1f2&#x27;, &#x27;r19i1251p1f2&#x27;,\n",
       "       &#x27;r19i1281p1f2&#x27;, &#x27;r19i1301p1f2&#x27;, &#x27;r1i1011p1f2&#x27;, &#x27;r20i1231p1f2&#x27;,\n",
       "       &#x27;r20i1251p1f2&#x27;, &#x27;r20i1281p1f2&#x27;, &#x27;r20i1301p1f2&#x27;, &#x27;r2i1031p1f2&#x27;,\n",
       "       &#x27;r3i1051p1f2&#x27;, &#x27;r4i1071p1f2&#x27;, &#x27;r5i1091p1f2&#x27;, &#x27;r6i1111p1f2&#x27;,\n",
       "       &#x27;r7i1131p1f2&#x27;, &#x27;r8i1151p1f2&#x27;, &#x27;r9i1171p1f2&#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'>1991-01-01 12:00:00 ... 2020-12-...</div><input id='attrs-0ed21622-c09c-4228-90ea-85f78884b21c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ed21622-c09c-4228-90ea-85f78884b21c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59348ec4-6351-4397-b36c-dba9809881bc' class='xr-var-data-in' type='checkbox'><label for='data-59348ec4-6351-4397-b36c-dba9809881bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1991, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1991, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1991, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.DatetimeNoLeap(2020, 12, 29, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2020, 12, 30, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2020, 12, 31, 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-c975bb33-cbc2-4a38-aa7c-e1b756d1247c' class='xr-section-summary-in' type='checkbox'  ><label for='section-c975bb33-cbc2-4a38-aa7c-e1b756d1247c' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-52ddbd08-d9a9-420f-8eef-d7e8c0e50096' class='xr-index-data-in' type='checkbox'/><label for='index-52ddbd08-d9a9-420f-8eef-d7e8c0e50096' 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([             -90.0, -89.05759162303664,  -88.1151832460733,\n",
       "       -87.17277486910994,  -86.2303664921466, -85.28795811518324,\n",
       "        -84.3455497382199, -83.40314136125654, -82.46073298429319,\n",
       "       -81.51832460732984,\n",
       "       ...\n",
       "        81.51832460732984,  82.46073298429319,  83.40314136125654,\n",
       "         84.3455497382199,  85.28795811518324,   86.2303664921466,\n",
       "        87.17277486910994,   88.1151832460733,  89.05759162303664,\n",
       "                     90.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-57b33d0e-4b95-41a9-a96c-9acb477cdf0a' class='xr-index-data-in' type='checkbox'/><label for='index-57b33d0e-4b95-41a9-a96c-9acb477cdf0a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,\n",
       "        11.25,\n",
       "       ...\n",
       "        347.5, 348.75,  350.0, 351.25,  352.5, 353.75,  355.0, 356.25,  357.5,\n",
       "       358.75],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><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-51d4673a-449c-45be-bbdb-c0a69e1c6bf8' class='xr-index-data-in' type='checkbox'/><label for='index-51d4673a-449c-45be-bbdb-c0a69e1c6bf8' 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;, &#x27;r10i1191p1f2&#x27;, &#x27;r11i1231p1f2&#x27;,\n",
       "       &#x27;r11i1251p1f2&#x27;, &#x27;r11i1281p1f2&#x27;, &#x27;r11i1301p1f2&#x27;, &#x27;r12i1231p1f2&#x27;,\n",
       "       &#x27;r12i1251p1f2&#x27;, &#x27;r12i1281p1f2&#x27;, &#x27;r12i1301p1f2&#x27;, &#x27;r13i1231p1f2&#x27;,\n",
       "       &#x27;r13i1251p1f2&#x27;, &#x27;r13i1281p1f2&#x27;, &#x27;r13i1301p1f2&#x27;, &#x27;r14i1231p1f2&#x27;,\n",
       "       &#x27;r14i1251p1f2&#x27;, &#x27;r14i1281p1f2&#x27;, &#x27;r14i1301p1f2&#x27;, &#x27;r15i1231p1f2&#x27;,\n",
       "       &#x27;r15i1251p1f2&#x27;, &#x27;r15i1281p1f2&#x27;, &#x27;r15i1301p1f2&#x27;, &#x27;r16i1231p1f2&#x27;,\n",
       "       &#x27;r16i1251p1f2&#x27;, &#x27;r16i1281p1f2&#x27;, &#x27;r16i1301p1f2&#x27;, &#x27;r17i1231p1f2&#x27;,\n",
       "       &#x27;r17i1251p1f2&#x27;, &#x27;r17i1281p1f2&#x27;, &#x27;r18i1231p1f2&#x27;, &#x27;r18i1251p1f2&#x27;,\n",
       "       &#x27;r18i1281p1f2&#x27;, &#x27;r18i1301p1f2&#x27;, &#x27;r19i1231p1f2&#x27;, &#x27;r19i1251p1f2&#x27;,\n",
       "       &#x27;r19i1281p1f2&#x27;, &#x27;r19i1301p1f2&#x27;, &#x27;r1i1011p1f2&#x27;, &#x27;r20i1231p1f2&#x27;,\n",
       "       &#x27;r20i1251p1f2&#x27;, &#x27;r20i1281p1f2&#x27;, &#x27;r20i1301p1f2&#x27;, &#x27;r2i1031p1f2&#x27;,\n",
       "       &#x27;r3i1051p1f2&#x27;, &#x27;r4i1071p1f2&#x27;, &#x27;r5i1091p1f2&#x27;, &#x27;r6i1111p1f2&#x27;,\n",
       "       &#x27;r7i1131p1f2&#x27;, &#x27;r8i1151p1f2&#x27;, &#x27;r9i1171p1f2&#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-5b95b7fd-639a-4397-88d6-59b873df70b8' class='xr-index-data-in' type='checkbox'/><label for='index-5b95b7fd-639a-4397-88d6-59b873df70b8' 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([1991-01-01 12:00:00, 1991-01-02 12:00:00, 1991-01-03 12:00:00,\n",
       "             1991-01-04 12:00:00, 1991-01-05 12:00:00, 1991-01-06 12:00:00,\n",
       "             1991-01-07 12:00:00, 1991-01-08 12:00:00, 1991-01-09 12:00:00,\n",
       "             1991-01-10 12:00:00,\n",
       "             ...\n",
       "             2020-12-22 12:00:00, 2020-12-23 12:00:00, 2020-12-24 12:00:00,\n",
       "             2020-12-25 12:00:00, 2020-12-26 12:00:00, 2020-12-27 12:00:00,\n",
       "             2020-12-28 12:00:00, 2020-12-29 12:00:00, 2020-12-30 12:00:00,\n",
       "             2020-12-31 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=10950, calendar=&#x27;noleap&#x27;, freq=&#x27;D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-20c62945-0c34-4c44-8762-c20c13100b5b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-20c62945-0c34-4c44-8762-c20c13100b5b' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: maximum</dd><dt><span>long_name :</span></dt><dd>Maximum reference height temperature over output period</dd><dt><span>units :</span></dt><dd>K</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'TREFHTMX' (member_id: 99, time: 10950, lat: 192, lon: 288)> Size: 240GB\n",
       "dask.array<concatenate, shape=(99, 10950, 192, 288), dtype=float32, chunksize=(1, 730, 192, 288), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 2kB -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "  * lon        (lon) float64 2kB 0.0 1.25 2.5 3.75 ... 355.0 356.2 357.5 358.8\n",
       "  * member_id  (member_id) <U12 5kB 'r10i1181p1f1' ... 'r9i1171p1f2'\n",
       "  * time       (time) object 88kB 1991-01-01 12:00:00 ... 2020-12-31 12:00:00\n",
       "Attributes:\n",
       "    cell_methods:  time: maximum\n",
       "    long_name:     Maximum reference height temperature over output period\n",
       "    units:         K"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_smbb      = merge_ds_smbb.TREFHTMX\n",
    "t_cmip6     = merge_ds_cmip6.TREFHTMX\n",
    "t_init_cmip6 = t_cmip6.sel(time=slice(init_year0, init_year1))\n",
    "t_init_smbb  = t_smbb.sel(time=slice(init_year0, init_year1))\n",
    "t_init       = xr.concat([t_init_cmip6,t_init_smbb],dim='member_id')\n",
    "t_init"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0dc673ff-036a-49d6-97c6-b48835a95906",
   "metadata": {},
   "outputs": [],
   "source": [
    "t_init_day = to_daily(t_init)\n",
    "#t_init_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8c3d5ac5-957b-4fa8-81b9-f6eda0601a35",
   "metadata": {},
   "outputs": [
    {
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       "\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;TREFHTMX&#x27; (member_id: 99, lat: 192, lon: 288, year: 30,\n",
       "                              day: 365)&gt; Size: 240GB\n",
       "dask.array&lt;reshape, shape=(99, 192, 288, 30, 365), dtype=float32, chunksize=(1, 192, 288, 2, 365), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * year       (year) int64 240B 2071 2072 2073 2074 ... 2097 2098 2099 2100\n",
       "  * day        (day) int64 3kB 1 2 3 4 5 6 7 8 ... 359 360 361 362 363 364 365\n",
       "  * lat        (lat) float64 2kB -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "  * lon        (lon) float64 2kB 0.0 1.25 2.5 3.75 ... 355.0 356.2 357.5 358.8\n",
       "  * member_id  (member_id) &lt;U12 5kB &#x27;r10i1181p1f1&#x27; ... &#x27;r9i1171p1f2&#x27;\n",
       "Attributes:\n",
       "    cell_methods:  time: maximum\n",
       "    long_name:     Maximum reference height temperature over output period\n",
       "    units:         K</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'>'TREFHTMX'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>member_id</span>: 99</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li><li><span class='xr-has-index'>year</span>: 30</li><li><span class='xr-has-index'>day</span>: 365</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-bd5bc177-3fff-4467-a682-94028aedf17f' class='xr-array-in' type='checkbox' checked><label for='section-bd5bc177-3fff-4467-a682-94028aedf17f' 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, 192, 288, 2, 365), 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> 223.31 GiB </td>\n",
       "                        <td> 153.98 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (99, 192, 288, 30, 365) </td>\n",
       "                        <td> (1, 192, 288, 2, 365) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1485 chunks in 17 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",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-3dcf8ff8-07de-4b60-a42d-ebadb9802200' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3dcf8ff8-07de-4b60-a42d-ebadb9802200' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2071 2072 2073 ... 2098 2099 2100</div><input id='attrs-3371b9b9-20a6-4ff1-8f94-2acd31f790a5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3371b9b9-20a6-4ff1-8f94-2acd31f790a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07057d9c-8f3f-4129-a68f-8a15ea1bfa0d' class='xr-var-data-in' type='checkbox'><label for='data-07057d9c-8f3f-4129-a68f-8a15ea1bfa0d' 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([2071, 2072, 2073, 2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2082,\n",
       "       2083, 2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094,\n",
       "       2095, 2096, 2097, 2098, 2099, 2100])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>day</span></div><div class='xr-var-dims'>(day)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 ... 361 362 363 364 365</div><input id='attrs-a1c53732-7a3d-4d3b-9d9f-1c51595694d8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a1c53732-7a3d-4d3b-9d9f-1c51595694d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c480304c-18db-4a6c-a2dc-6b23a786ecc2' class='xr-var-data-in' type='checkbox'><label for='data-c480304c-18db-4a6c-a2dc-6b23a786ecc2' 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([  1,   2,   3, ..., 363, 364, 365])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-72f15b19-270b-4fcd-a0e6-16c26c92d76d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-72f15b19-270b-4fcd-a0e6-16c26c92d76d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad21d227-ce50-45d8-b8af-6a9a91dce12f' class='xr-var-data-in' type='checkbox'><label for='data-ad21d227-ce50-45d8-b8af-6a9a91dce12f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-f64a8459-0dec-4c8d-a21c-ecd6cef1a9df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f64a8459-0dec-4c8d-a21c-ecd6cef1a9df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88ab0a40-1f47-4989-b403-b364bdb03bae' class='xr-var-data-in' type='checkbox'><label for='data-88ab0a40-1f47-4989-b403-b364bdb03bae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>&lt;U12</div><div class='xr-var-preview xr-preview'>&#x27;r10i1181p1f1&#x27; ... &#x27;r9i1171p1f2&#x27;</div><input id='attrs-13e7cea4-7269-4420-a847-63af13e256d1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-13e7cea4-7269-4420-a847-63af13e256d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2055de8d-23cd-413d-92d7-88a599f15e97' class='xr-var-data-in' type='checkbox'><label for='data-2055de8d-23cd-413d-92d7-88a599f15e97' 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;, &#x27;r10i1191p1f2&#x27;, &#x27;r11i1231p1f2&#x27;,\n",
       "       &#x27;r11i1251p1f2&#x27;, &#x27;r11i1281p1f2&#x27;, &#x27;r11i1301p1f2&#x27;, &#x27;r12i1231p1f2&#x27;,\n",
       "       &#x27;r12i1251p1f2&#x27;, &#x27;r12i1281p1f2&#x27;, &#x27;r12i1301p1f2&#x27;, &#x27;r13i1231p1f2&#x27;,\n",
       "       &#x27;r13i1251p1f2&#x27;, &#x27;r13i1281p1f2&#x27;, &#x27;r13i1301p1f2&#x27;, &#x27;r14i1231p1f2&#x27;,\n",
       "       &#x27;r14i1251p1f2&#x27;, &#x27;r14i1281p1f2&#x27;, &#x27;r14i1301p1f2&#x27;, &#x27;r15i1231p1f2&#x27;,\n",
       "       &#x27;r15i1251p1f2&#x27;, &#x27;r15i1281p1f2&#x27;, &#x27;r15i1301p1f2&#x27;, &#x27;r16i1231p1f2&#x27;,\n",
       "       &#x27;r16i1251p1f2&#x27;, &#x27;r16i1281p1f2&#x27;, &#x27;r16i1301p1f2&#x27;, &#x27;r17i1231p1f2&#x27;,\n",
       "       &#x27;r17i1251p1f2&#x27;, &#x27;r17i1281p1f2&#x27;, &#x27;r18i1231p1f2&#x27;, &#x27;r18i1251p1f2&#x27;,\n",
       "       &#x27;r18i1281p1f2&#x27;, &#x27;r18i1301p1f2&#x27;, &#x27;r19i1231p1f2&#x27;, &#x27;r19i1251p1f2&#x27;,\n",
       "       &#x27;r19i1281p1f2&#x27;, &#x27;r19i1301p1f2&#x27;, &#x27;r1i1011p1f2&#x27;, &#x27;r20i1231p1f2&#x27;,\n",
       "       &#x27;r20i1251p1f2&#x27;, &#x27;r20i1281p1f2&#x27;, &#x27;r20i1301p1f2&#x27;, &#x27;r2i1031p1f2&#x27;,\n",
       "       &#x27;r3i1051p1f2&#x27;, &#x27;r4i1071p1f2&#x27;, &#x27;r5i1091p1f2&#x27;, &#x27;r6i1111p1f2&#x27;,\n",
       "       &#x27;r7i1131p1f2&#x27;, &#x27;r8i1151p1f2&#x27;, &#x27;r9i1171p1f2&#x27;], dtype=&#x27;&lt;U12&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d9fe4b66-5926-44a6-b3f8-8214be94badf' class='xr-section-summary-in' type='checkbox'  ><label for='section-d9fe4b66-5926-44a6-b3f8-8214be94badf' class='xr-section-summary' >Indexes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ccb1a36e-3f53-42c3-9b0f-6d3cac3bf5a2' class='xr-index-data-in' type='checkbox'/><label for='index-ccb1a36e-3f53-42c3-9b0f-6d3cac3bf5a2' 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([             -90.0, -89.05759162303664,  -88.1151832460733,\n",
       "       -87.17277486910994,  -86.2303664921466, -85.28795811518324,\n",
       "        -84.3455497382199, -83.40314136125654, -82.46073298429319,\n",
       "       -81.51832460732984,\n",
       "       ...\n",
       "        81.51832460732984,  82.46073298429319,  83.40314136125654,\n",
       "         84.3455497382199,  85.28795811518324,   86.2303664921466,\n",
       "        87.17277486910994,   88.1151832460733,  89.05759162303664,\n",
       "                     90.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-38891344-0a8a-4554-96f1-74aeb085bef6' class='xr-index-data-in' type='checkbox'/><label for='index-38891344-0a8a-4554-96f1-74aeb085bef6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,\n",
       "        11.25,\n",
       "       ...\n",
       "        347.5, 348.75,  350.0, 351.25,  352.5, 353.75,  355.0, 356.25,  357.5,\n",
       "       358.75],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><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-04f71dd4-952a-4b1a-bcc2-9d91899dee43' class='xr-index-data-in' type='checkbox'/><label for='index-04f71dd4-952a-4b1a-bcc2-9d91899dee43' 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;, &#x27;r10i1191p1f2&#x27;, &#x27;r11i1231p1f2&#x27;,\n",
       "       &#x27;r11i1251p1f2&#x27;, &#x27;r11i1281p1f2&#x27;, &#x27;r11i1301p1f2&#x27;, &#x27;r12i1231p1f2&#x27;,\n",
       "       &#x27;r12i1251p1f2&#x27;, &#x27;r12i1281p1f2&#x27;, &#x27;r12i1301p1f2&#x27;, &#x27;r13i1231p1f2&#x27;,\n",
       "       &#x27;r13i1251p1f2&#x27;, &#x27;r13i1281p1f2&#x27;, &#x27;r13i1301p1f2&#x27;, &#x27;r14i1231p1f2&#x27;,\n",
       "       &#x27;r14i1251p1f2&#x27;, &#x27;r14i1281p1f2&#x27;, &#x27;r14i1301p1f2&#x27;, &#x27;r15i1231p1f2&#x27;,\n",
       "       &#x27;r15i1251p1f2&#x27;, &#x27;r15i1281p1f2&#x27;, &#x27;r15i1301p1f2&#x27;, &#x27;r16i1231p1f2&#x27;,\n",
       "       &#x27;r16i1251p1f2&#x27;, &#x27;r16i1281p1f2&#x27;, &#x27;r16i1301p1f2&#x27;, &#x27;r17i1231p1f2&#x27;,\n",
       "       &#x27;r17i1251p1f2&#x27;, &#x27;r17i1281p1f2&#x27;, &#x27;r18i1231p1f2&#x27;, &#x27;r18i1251p1f2&#x27;,\n",
       "       &#x27;r18i1281p1f2&#x27;, &#x27;r18i1301p1f2&#x27;, &#x27;r19i1231p1f2&#x27;, &#x27;r19i1251p1f2&#x27;,\n",
       "       &#x27;r19i1281p1f2&#x27;, &#x27;r19i1301p1f2&#x27;, &#x27;r1i1011p1f2&#x27;, &#x27;r20i1231p1f2&#x27;,\n",
       "       &#x27;r20i1251p1f2&#x27;, &#x27;r20i1281p1f2&#x27;, &#x27;r20i1301p1f2&#x27;, &#x27;r2i1031p1f2&#x27;,\n",
       "       &#x27;r3i1051p1f2&#x27;, &#x27;r4i1071p1f2&#x27;, &#x27;r5i1091p1f2&#x27;, &#x27;r6i1111p1f2&#x27;,\n",
       "       &#x27;r7i1131p1f2&#x27;, &#x27;r8i1151p1f2&#x27;, &#x27;r9i1171p1f2&#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>year</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cd51d1c0-6134-44b8-9b3c-7b5fce9628f3' class='xr-index-data-in' type='checkbox'/><label for='index-cd51d1c0-6134-44b8-9b3c-7b5fce9628f3' 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([2071, 2072, 2073, 2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2082,\n",
       "       2083, 2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094,\n",
       "       2095, 2096, 2097, 2098, 2099, 2100],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;year&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>day</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-98ac76e7-7503-45b7-9317-4d1e70ce5654' class='xr-index-data-in' type='checkbox'/><label for='index-98ac76e7-7503-45b7-9317-4d1e70ce5654' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,\n",
       "       ...\n",
       "       356, 357, 358, 359, 360, 361, 362, 363, 364, 365],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;day&#x27;, length=365))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-21db3768-e645-4394-a6ff-44f10c963234' class='xr-section-summary-in' type='checkbox'  checked><label for='section-21db3768-e645-4394-a6ff-44f10c963234' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: maximum</dd><dt><span>long_name :</span></dt><dd>Maximum reference height temperature over output period</dd><dt><span>units :</span></dt><dd>K</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'TREFHTMX' (member_id: 99, lat: 192, lon: 288, year: 30,\n",
       "                              day: 365)> Size: 240GB\n",
       "dask.array<reshape, shape=(99, 192, 288, 30, 365), dtype=float32, chunksize=(1, 192, 288, 2, 365), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * year       (year) int64 240B 2071 2072 2073 2074 ... 2097 2098 2099 2100\n",
       "  * day        (day) int64 3kB 1 2 3 4 5 6 7 8 ... 359 360 361 362 363 364 365\n",
       "  * lat        (lat) float64 2kB -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "  * lon        (lon) float64 2kB 0.0 1.25 2.5 3.75 ... 355.0 356.2 357.5 358.8\n",
       "  * member_id  (member_id) <U12 5kB 'r10i1181p1f1' ... 'r9i1171p1f2'\n",
       "Attributes:\n",
       "    cell_methods:  time: maximum\n",
       "    long_name:     Maximum reference height temperature over output period\n",
       "    units:         K"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_fut_cmip6 = t_cmip6.sel(time=slice(final_year0, final_year1))\n",
    "t_fut_smbb  = t_smbb.sel(time=slice(final_year0, final_year1))\n",
    "t_fut       = xr.concat([t_fut_cmip6,t_fut_smbb],dim='member_id')\n",
    "t_fut_day   = to_daily(t_fut)\n",
    "t_fut_day"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec85a550-5e99-4a66-98ab-25b984cd481a",
   "metadata": {},
   "source": [
    "### Save means and standard deviations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "92dc4f5c-940c-4c86-82b4-43b2cd72f631",
   "metadata": {},
   "outputs": [],
   "source": [
    "# init_means   = t_init_day.mean({'year','member_id'})\n",
    "# init_stdevs  = t_init_day.std({'year','member_id'})\n",
    "# final_means  = t_fut_day.mean({'year','member_id'})\n",
    "# final_stdevs = t_fut_day.std({'year','member_id'})\n",
    "#\n",
    "init_ensemble_means  = t_init_day.mean({'member_id'})\n",
    "final_ensemble_means = t_fut_day.mean({'member_id'})\n",
    "init_ensemble_means  = init_ensemble_means.chunk({'lat':192,'lon':288,'year':2,'day':365})\n",
    "final_ensemble_means = final_ensemble_means.chunk({'lat':192,'lon':288,'year':2,'day':365})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ccc8ce3-a11e-4cfb-b563-2be618d4cfd2",
   "metadata": {},
   "source": [
    "- Save the overall means, standard devaitions and the ensemble means\n",
    "- We will regrid the 'final/EOC' ensemble means onto the ERA5 grid.\n",
    "- We will then compare it with the bias-corrected future predictions obtained from ERA5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "410f94d7-2754-4e5b-82b9-398288dd4842",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<xarray.backends.zarr.ZarrStore at 0x152005bace40>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# %%time\n",
    "# init_means.to_dataset().to_zarr(mean_path + 'cesm2_'+ init_year0 + '_' + init_year1+ '_means.zarr',mode='w')\n",
    "# init_stdevs.to_dataset().to_zarr(stdev_path + 'cesm2_'+ init_year0 + '_' + init_year1+ '_stdevs.zarr',mode='w') \n",
    "# final_means.to_dataset().to_zarr(mean_path + 'cesm2_'+ final_year0 + '_' + final_year1+ '_means.zarr',mode='w')\n",
    "# final_stdevs.to_dataset().to_zarr(stdev_path + 'cesm2_'+ final_year0 + '_' + final_year1+ '_stdevs.zarr',mode='w') \n",
    "# init_ensemble_means.to_dataset().to_zarr(mean_path + 'cesm2_'+ init_year0 + '_' + init_year1 \\\n",
    "#                                          + '_ensemble_means.zarr',mode='w')\n",
    "# final_ensemble_means.to_dataset().to_zarr(mean_path + 'cesm2_'+ final_year0 + '_' + final_year1 \\\n",
    "#                                           + '_ensemble_means.zarr',mode='w')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e248333-8828-46db-a491-5697a103b485",
   "metadata": {},
   "source": [
    "## Access ERA5 data and regrid CESM2 LENS data on the finer, ERA5 grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c1153f99-700e-49a8-ac1e-79c5f28fcfd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 71.9 ms, sys: 12.1 ms, total: 84 ms\n",
      "Wall time: 1.25 s\n"
     ]
    },
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;VAR_2T&#x27; (time: 10958, latitude: 721, longitude: 1440)&gt; Size: 46GB\n",
       "dask.array&lt;getitem, shape=(10958, 721, 1440), dtype=float32, chunksize=(1000, 139, 544), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float64 6kB 90.0 89.75 89.5 ... -89.5 -89.75 -90.0\n",
       "  * longitude  (longitude) float64 12kB 0.0 0.25 0.5 0.75 ... 359.2 359.5 359.8\n",
       "  * time       (time) datetime64[ns] 88kB 1991-01-01 1991-01-02 ... 2020-12-31\n",
       "Attributes: (12/14)\n",
       "    QuantizeGranularBitGroomNumberOfSignificantDigits:  7\n",
       "    ecmwf_local_table:                                  128\n",
       "    ecmwf_parameter:                                    167\n",
       "    grid_specification:                                 0.25 degree x 0.25 de...\n",
       "    long_name:                                          2 metre temperature\n",
       "    maximum_value:                                      320.42938232421875\n",
       "    ...                                                 ...\n",
       "    rda_dataset:                                        ds633.0\n",
       "    rda_dataset_doi:                                    DOI: 10.5065/BH6N-5N20\n",
       "    rda_dataset_group:                                  ERA5 atmospheric surf...\n",
       "    rda_dataset_url:                                    https:/rda.ucar.edu/d...\n",
       "    short_name:                                         2t\n",
       "    units:                                              K</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'>'VAR_2T'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 10958</li><li><span class='xr-has-index'>latitude</span>: 721</li><li><span class='xr-has-index'>longitude</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d2d8c6d7-a4ad-4cc9-9db2-15979a96a3e6' class='xr-array-in' type='checkbox' checked><label for='section-d2d8c6d7-a4ad-4cc9-9db2-15979a96a3e6' 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=(372, 139, 544), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
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       "                        <td> 42.38 GiB </td>\n",
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       "       3.5975e+02])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1991-01-01 ... 2020-12-31</div><input id='attrs-5de0b3f7-6cc7-4b69-8ce9-eaceeaf2b6e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5de0b3f7-6cc7-4b69-8ce9-eaceeaf2b6e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-718f6176-9a3b-4df6-803d-2771bb496b57' class='xr-var-data-in' type='checkbox'><label for='data-718f6176-9a3b-4df6-803d-2771bb496b57' 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>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1991-01-01T00:00:00.000000000&#x27;, &#x27;1991-01-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;1991-01-03T00:00:00.000000000&#x27;, ..., &#x27;2020-12-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-30T00:00:00.000000000&#x27;, &#x27;2020-12-31T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-17d393d7-3e3f-4caa-95b1-4e73a4dfce2a' class='xr-section-summary-in' type='checkbox'  ><label for='section-17d393d7-3e3f-4caa-95b1-4e73a4dfce2a' 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>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c413e8a9-fd89-49c4-a498-2336ddb8c441' class='xr-index-data-in' type='checkbox'/><label for='index-c413e8a9-fd89-49c4-a498-2336ddb8c441' 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([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,\n",
       "        87.75,\n",
       "       ...\n",
       "       -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5, -89.75,\n",
       "        -90.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=721))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-51462285-45ce-406b-80d4-c913d108804f' class='xr-index-data-in' type='checkbox'/><label for='index-51462285-45ce-406b-80d4-c913d108804f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,\n",
       "         2.25,\n",
       "       ...\n",
       "        357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,\n",
       "       359.75],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=1440))</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-98318632-acda-46e6-9bc7-3325d978bbe3' class='xr-index-data-in' type='checkbox'/><label for='index-98318632-acda-46e6-9bc7-3325d978bbe3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1991-01-01&#x27;, &#x27;1991-01-02&#x27;, &#x27;1991-01-03&#x27;, &#x27;1991-01-04&#x27;,\n",
       "               &#x27;1991-01-05&#x27;, &#x27;1991-01-06&#x27;, &#x27;1991-01-07&#x27;, &#x27;1991-01-08&#x27;,\n",
       "               &#x27;1991-01-09&#x27;, &#x27;1991-01-10&#x27;,\n",
       "               ...\n",
       "               &#x27;2020-12-22&#x27;, &#x27;2020-12-23&#x27;, &#x27;2020-12-24&#x27;, &#x27;2020-12-25&#x27;,\n",
       "               &#x27;2020-12-26&#x27;, &#x27;2020-12-27&#x27;, &#x27;2020-12-28&#x27;, &#x27;2020-12-29&#x27;,\n",
       "               &#x27;2020-12-30&#x27;, &#x27;2020-12-31&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=10958, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-df4d557e-83c3-4984-b524-6320166e190b' class='xr-section-summary-in' type='checkbox'  ><label for='section-df4d557e-83c3-4984-b524-6320166e190b' class='xr-section-summary' >Attributes: <span>(14)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>QuantizeGranularBitGroomNumberOfSignificantDigits :</span></dt><dd>7</dd><dt><span>ecmwf_local_table :</span></dt><dd>128</dd><dt><span>ecmwf_parameter :</span></dt><dd>167</dd><dt><span>grid_specification :</span></dt><dd>0.25 degree x 0.25 degree from 90N to 90S and 0E to 359.75E (721 x 1440 Latitude/Longitude)</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd><dt><span>maximum_value :</span></dt><dd>320.42938232421875</dd><dt><span>minimum_value :</span></dt><dd>216.13075256347656</dd><dt><span>original_format :</span></dt><dd>WMO GRIB 1 with ECMWF local table</dd><dt><span>rda_dataset :</span></dt><dd>ds633.0</dd><dt><span>rda_dataset_doi :</span></dt><dd>DOI: 10.5065/BH6N-5N20</dd><dt><span>rda_dataset_group :</span></dt><dd>ERA5 atmospheric surface analysis [netCDF4]</dd><dt><span>rda_dataset_url :</span></dt><dd>https:/rda.ucar.edu/datasets/ds633.0/</dd><dt><span>short_name :</span></dt><dd>2t</dd><dt><span>units :</span></dt><dd>K</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'VAR_2T' (time: 10958, latitude: 721, longitude: 1440)> Size: 46GB\n",
       "dask.array<getitem, shape=(10958, 721, 1440), dtype=float32, chunksize=(1000, 139, 544), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * latitude   (latitude) float64 6kB 90.0 89.75 89.5 ... -89.5 -89.75 -90.0\n",
       "  * longitude  (longitude) float64 12kB 0.0 0.25 0.5 0.75 ... 359.2 359.5 359.8\n",
       "  * time       (time) datetime64[ns] 88kB 1991-01-01 1991-01-02 ... 2020-12-31\n",
       "Attributes: (12/14)\n",
       "    QuantizeGranularBitGroomNumberOfSignificantDigits:  7\n",
       "    ecmwf_local_table:                                  128\n",
       "    ecmwf_parameter:                                    167\n",
       "    grid_specification:                                 0.25 degree x 0.25 de...\n",
       "    long_name:                                          2 metre temperature\n",
       "    maximum_value:                                      320.42938232421875\n",
       "    ...                                                 ...\n",
       "    rda_dataset:                                        ds633.0\n",
       "    rda_dataset_doi:                                    DOI: 10.5065/BH6N-5N20\n",
       "    rda_dataset_group:                                  ERA5 atmospheric surf...\n",
       "    rda_dataset_url:                                    https:/rda.ucar.edu/d...\n",
       "    short_name:                                         2t\n",
       "    units:                                              K"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "tas_obs_daily = xr.open_zarr(zarr_path + \"e5_tas2m_daily_1940_2023.zarr\").VAR_2T\n",
    "tas_obs_init = tas_obs_daily.sel(time=slice(init_year0, init_year1))\n",
    "tas_obs_init"
   ]
  },
  {
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   "execution_count": 30,
   "id": "8a0e4932-7f2f-44a8-a934-39853a1667ec",
   "metadata": {},
   "outputs": [
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       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
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       "}\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",
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       "\n",
       ".xr-section-summary > span {\n",
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       "  padding-left: 0.5em;\n",
       "}\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",
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       "}\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",
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       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-array-preview,\n",
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       "\n",
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       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
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       "\n",
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       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
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       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
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       "\n",
       ".xr-dim-list:after {\n",
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       "\n",
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       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
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       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
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       "\n",
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       ".xr-attrs {\n",
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       "\n",
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       ".xr-var-attrs,\n",
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       "\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
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       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
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       "\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;TREFHTMX&#x27; (lat: 192, lon: 288, day: 365)&gt; Size: 81MB\n",
       "dask.array&lt;open_dataset-TREFHTMX, shape=(192, 288, 365), dtype=float32, chunksize=(192, 288, 365), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * day      (day) int64 3kB 1 2 3 4 5 6 7 8 ... 358 359 360 361 362 363 364 365\n",
       "  * lat      (lat) float64 2kB -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "  * lon      (lon) float64 2kB 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8</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'>'TREFHTMX'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li><li><span class='xr-has-index'>day</span>: 365</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-07a711af-3881-4a00-8afd-e388a5c35317' class='xr-array-in' type='checkbox' checked><label for='section-07a711af-3881-4a00-8afd-e388a5c35317' 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=(192, 288, 365), 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> 76.99 MiB </td>\n",
       "                        <td> 76.99 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (192, 288, 365) </td>\n",
       "                        <td> (192, 288, 365) </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\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
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       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-84457fc4-6f15-46cb-a0b5-0303248dc12b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84457fc4-6f15-46cb-a0b5-0303248dc12b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8bcc682-f117-4a71-b722-dc6ebf943d3a' class='xr-var-data-in' type='checkbox'><label for='data-a8bcc682-f117-4a71-b722-dc6ebf943d3a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-caa97521-ddd7-42d7-8aa5-682491656314' class='xr-section-summary-in' type='checkbox'  ><label for='section-caa97521-ddd7-42d7-8aa5-682491656314' 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>day</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-72bda04a-3612-451f-a5c6-6048b3f42f7c' class='xr-index-data-in' type='checkbox'/><label for='index-72bda04a-3612-451f-a5c6-6048b3f42f7c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,\n",
       "       ...\n",
       "       356, 357, 358, 359, 360, 361, 362, 363, 364, 365],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;day&#x27;, length=365))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5ebb582c-bfc5-4715-921e-348b571e3d8c' class='xr-index-data-in' type='checkbox'/><label for='index-5ebb582c-bfc5-4715-921e-348b571e3d8c' 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([             -90.0, -89.05759162303664,  -88.1151832460733,\n",
       "       -87.17277486910994,  -86.2303664921466, -85.28795811518324,\n",
       "        -84.3455497382199, -83.40314136125654, -82.46073298429319,\n",
       "       -81.51832460732984,\n",
       "       ...\n",
       "        81.51832460732984,  82.46073298429319,  83.40314136125654,\n",
       "         84.3455497382199,  85.28795811518324,   86.2303664921466,\n",
       "        87.17277486910994,   88.1151832460733,  89.05759162303664,\n",
       "                     90.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-eaabef3a-20cc-4c4c-b814-8a31281b9a32' class='xr-index-data-in' type='checkbox'/><label for='index-eaabef3a-20cc-4c4c-b814-8a31281b9a32' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,\n",
       "        11.25,\n",
       "       ...\n",
       "        347.5, 348.75,  350.0, 351.25,  352.5, 353.75,  355.0, 356.25,  357.5,\n",
       "       358.75],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f6ec67b3-e7f1-48d4-b325-89c418931e5d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f6ec67b3-e7f1-48d4-b325-89c418931e5d' 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 'TREFHTMX' (lat: 192, lon: 288, day: 365)> Size: 81MB\n",
       "dask.array<open_dataset-TREFHTMX, shape=(192, 288, 365), dtype=float32, chunksize=(192, 288, 365), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * day      (day) int64 3kB 1 2 3 4 5 6 7 8 ... 358 359 360 361 362 363 364 365\n",
       "  * lat      (lat) float64 2kB -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "  * lon      (lon) float64 2kB 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "init_means_ds = xr.open_zarr(mean_path + 'cesm2_'+ init_year0 + '_' + init_year1+ '_means.zarr')\n",
    "init_means    = init_means_ds.TREFHTMX\n",
    "init_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "686d452c-c1ea-4b67-8f0a-0301ac92763a",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_means  = xr.open_zarr(mean_path  + 'cesm2_'+ final_year0 + '_' + final_year1+ '_means.zarr').TREFHTMX\n",
    "init_stdevs  = xr.open_zarr(stdev_path + 'cesm2_'+ init_year0 + '_' + init_year1+ '_stdevs.zarr').TREFHTMX\n",
    "final_stdevs = xr.open_zarr(stdev_path + 'cesm2_'+ final_year0 + '_' + final_year1+ '_stdevs.zarr').TREFHTMX"
   ]
  },
  {
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   "execution_count": 37,
   "id": "73737f3e-ee1d-4bf8-88cc-a4773158b2a0",
   "metadata": {},
   "outputs": [
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       "  * lon      (lon) float64 12kB 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
       "Data variables:\n",
       "    *empty*</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-d559974d-e589-4add-ac72-31b50d15b60c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d559974d-e589-4add-ac72-31b50d15b60c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 721</li><li><span class='xr-has-index'>lon</span>: 1440</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b129a4c3-4d68-4e15-97cb-9d9c5c785a5c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b129a4c3-4d68-4e15-97cb-9d9c5c785a5c' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>90.0 89.75 89.5 ... -89.75 -90.0</div><input id='attrs-12eb852d-218c-4be2-9535-2fc7a6dde40c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12eb852d-218c-4be2-9535-2fc7a6dde40c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16dc7dc8-ab99-4293-a940-f207be42f950' class='xr-var-data-in' type='checkbox'><label for='data-16dc7dc8-ab99-4293-a940-f207be42f950' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>short_name :</span></dt><dd>lat</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.25 0.5 ... 359.2 359.5 359.8</div><input id='attrs-0dbcd27d-8372-44e0-b420-731165eacd61' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0dbcd27d-8372-44e0-b420-731165eacd61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c3fce34-67fb-4b82-a518-fb1224d132df' class='xr-var-data-in' type='checkbox'><label for='data-5c3fce34-67fb-4b82-a518-fb1224d132df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>short_name :</span></dt><dd>lon</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,\n",
       "       3.5975e+02])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-aaa277e9-975f-429d-9904-7f354645f4c3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-aaa277e9-975f-429d-9904-7f354645f4c3' class='xr-section-summary'  title='Expand/collapse section'>Data variables: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-a933e444-be4c-4b34-a80f-362647751597' class='xr-section-summary-in' type='checkbox'  ><label for='section-a933e444-be4c-4b34-a80f-362647751597' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8bc78c95-9123-46e6-a0b6-06d68e83279b' class='xr-index-data-in' type='checkbox'/><label for='index-8bc78c95-9123-46e6-a0b6-06d68e83279b' 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([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,\n",
       "        87.75,\n",
       "       ...\n",
       "       -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5, -89.75,\n",
       "        -90.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=721))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e029668f-a542-48b5-8791-f3b9a638edfb' class='xr-index-data-in' type='checkbox'/><label for='index-e029668f-a542-48b5-8791-f3b9a638edfb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,\n",
       "         2.25,\n",
       "       ...\n",
       "        357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,\n",
       "       359.75],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=1440))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ffa05291-29f8-40e7-8eba-9dd808cc039e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ffa05291-29f8-40e7-8eba-9dd808cc039e' 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.Dataset> Size: 17kB\n",
       "Dimensions:  (lat: 721, lon: 1440)\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 6kB 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
       "  * lon      (lon) float64 12kB 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
       "Data variables:\n",
       "    *empty*"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create output grid\n",
    "ds_out = xr.Dataset(\n",
    "    coords={\n",
    "        'latitude': tas_obs_init.coords['latitude'],\n",
    "        'longitude': tas_obs_init.coords['longitude']\n",
    "    }\n",
    ")\n",
    "ds_out = ds_out.rename({'latitude':'lat','longitude':'lon'})\n",
    "ds_out"
   ]
  },
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   "execution_count": 32,
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       "  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;VAR_2T&#x27; (lat: 721, lon: 1440, year: 30, day: 366)&gt; Size: 46GB\n",
       "dask.array&lt;rechunk-merge, shape=(721, 1440, 30, 366), dtype=float32, chunksize=(139, 544, 3, 90), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * year     (year) int64 240B 1991 1992 1993 1994 1995 ... 2017 2018 2019 2020\n",
       "  * day      (day) int64 3kB 1 2 3 4 5 6 7 8 ... 359 360 361 362 363 364 365 366\n",
       "  * lat      (lat) float64 6kB 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
       "  * lon      (lon) float64 12kB 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
       "Attributes: (12/14)\n",
       "    QuantizeGranularBitGroomNumberOfSignificantDigits:  7\n",
       "    ecmwf_local_table:                                  128\n",
       "    ecmwf_parameter:                                    167\n",
       "    grid_specification:                                 0.25 degree x 0.25 de...\n",
       "    long_name:                                          2 metre temperature\n",
       "    maximum_value:                                      320.42938232421875\n",
       "    ...                                                 ...\n",
       "    rda_dataset:                                        ds633.0\n",
       "    rda_dataset_doi:                                    DOI: 10.5065/BH6N-5N20\n",
       "    rda_dataset_group:                                  ERA5 atmospheric surf...\n",
       "    rda_dataset_url:                                    https:/rda.ucar.edu/d...\n",
       "    short_name:                                         2t\n",
       "    units:                                              K</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'>'VAR_2T'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 721</li><li><span class='xr-has-index'>lon</span>: 1440</li><li><span class='xr-has-index'>year</span>: 30</li><li><span class='xr-has-index'>day</span>: 366</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-45b038bf-cf72-4155-b502-7095cd98cf0d' class='xr-array-in' type='checkbox' checked><label for='section-45b038bf-cf72-4155-b502-7095cd98cf0d' 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=(139, 544, 3, 90), 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> 42.47 GiB </td>\n",
       "                        <td> 77.88 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (721, 1440, 30, 366) </td>\n",
       "                        <td> (139, 544, 3, 90) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 900 chunks in 18 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-fb15f2c8-486f-4070-833a-618ce5b1a6d8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fb15f2c8-486f-4070-833a-618ce5b1a6d8' 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'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1991 1992 1993 ... 2018 2019 2020</div><input id='attrs-ed73652c-ee2d-4882-849f-4c3197e78929' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ed73652c-ee2d-4882-849f-4c3197e78929' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2debb5c4-a0de-4d22-bfae-5c459b43e3ef' class='xr-var-data-in' type='checkbox'><label for='data-2debb5c4-a0de-4d22-bfae-5c459b43e3ef' 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([1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,\n",
       "       2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,\n",
       "       2015, 2016, 2017, 2018, 2019, 2020])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>day</span></div><div class='xr-var-dims'>(day)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 ... 362 363 364 365 366</div><input id='attrs-777b089d-e365-40fa-a703-01106523eeb6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-777b089d-e365-40fa-a703-01106523eeb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b58a963c-d795-43f7-a029-1ba8316ac29f' class='xr-var-data-in' type='checkbox'><label for='data-b58a963c-d795-43f7-a029-1ba8316ac29f' 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([  1,   2,   3, ..., 364, 365, 366])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>90.0 89.75 89.5 ... -89.75 -90.0</div><input id='attrs-6bb5b3fa-def4-4fcb-a177-3fb3f9fa80b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6bb5b3fa-def4-4fcb-a177-3fb3f9fa80b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8053ac49-abe2-429f-8c83-1a93974793d7' class='xr-var-data-in' type='checkbox'><label for='data-8053ac49-abe2-429f-8c83-1a93974793d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>short_name :</span></dt><dd>lat</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.25 0.5 ... 359.2 359.5 359.8</div><input id='attrs-a2af32f8-9b84-4972-8954-995fc2ae4948' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a2af32f8-9b84-4972-8954-995fc2ae4948' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e6ef240-4a04-4b30-baf8-85daed766c60' class='xr-var-data-in' type='checkbox'><label for='data-4e6ef240-4a04-4b30-baf8-85daed766c60' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>short_name :</span></dt><dd>lon</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,\n",
       "       3.5975e+02])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c08efa55-0f87-4d3d-9cf4-8f4377bb7bcc' class='xr-section-summary-in' type='checkbox'  ><label for='section-c08efa55-0f87-4d3d-9cf4-8f4377bb7bcc' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-998644c4-ce4f-466c-9458-32c6000d799c' class='xr-index-data-in' type='checkbox'/><label for='index-998644c4-ce4f-466c-9458-32c6000d799c' 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([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,\n",
       "        87.75,\n",
       "       ...\n",
       "       -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5, -89.75,\n",
       "        -90.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=721))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8ab801f1-bed8-4030-991f-214e6feb197b' class='xr-index-data-in' type='checkbox'/><label for='index-8ab801f1-bed8-4030-991f-214e6feb197b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,\n",
       "         2.25,\n",
       "       ...\n",
       "        357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,\n",
       "       359.75],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=1440))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>year</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-91262a98-bd8a-461f-8c98-1e8a1ab0e539' class='xr-index-data-in' type='checkbox'/><label for='index-91262a98-bd8a-461f-8c98-1e8a1ab0e539' 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([1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,\n",
       "       2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,\n",
       "       2015, 2016, 2017, 2018, 2019, 2020],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;year&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>day</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-35bb931d-156f-4d85-a1d5-634cfbc71456' class='xr-index-data-in' type='checkbox'/><label for='index-35bb931d-156f-4d85-a1d5-634cfbc71456' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,\n",
       "       ...\n",
       "       357, 358, 359, 360, 361, 362, 363, 364, 365, 366],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;day&#x27;, length=366))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-70480aba-5a37-4a46-ba6b-202b74b931fb' class='xr-section-summary-in' type='checkbox'  ><label for='section-70480aba-5a37-4a46-ba6b-202b74b931fb' class='xr-section-summary' >Attributes: <span>(14)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>QuantizeGranularBitGroomNumberOfSignificantDigits :</span></dt><dd>7</dd><dt><span>ecmwf_local_table :</span></dt><dd>128</dd><dt><span>ecmwf_parameter :</span></dt><dd>167</dd><dt><span>grid_specification :</span></dt><dd>0.25 degree x 0.25 degree from 90N to 90S and 0E to 359.75E (721 x 1440 Latitude/Longitude)</dd><dt><span>long_name :</span></dt><dd>2 metre temperature</dd><dt><span>maximum_value :</span></dt><dd>320.42938232421875</dd><dt><span>minimum_value :</span></dt><dd>216.13075256347656</dd><dt><span>original_format :</span></dt><dd>WMO GRIB 1 with ECMWF local table</dd><dt><span>rda_dataset :</span></dt><dd>ds633.0</dd><dt><span>rda_dataset_doi :</span></dt><dd>DOI: 10.5065/BH6N-5N20</dd><dt><span>rda_dataset_group :</span></dt><dd>ERA5 atmospheric surface analysis [netCDF4]</dd><dt><span>rda_dataset_url :</span></dt><dd>https:/rda.ucar.edu/datasets/ds633.0/</dd><dt><span>short_name :</span></dt><dd>2t</dd><dt><span>units :</span></dt><dd>K</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'VAR_2T' (lat: 721, lon: 1440, year: 30, day: 366)> Size: 46GB\n",
       "dask.array<rechunk-merge, shape=(721, 1440, 30, 366), dtype=float32, chunksize=(139, 544, 3, 90), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * year     (year) int64 240B 1991 1992 1993 1994 1995 ... 2017 2018 2019 2020\n",
       "  * day      (day) int64 3kB 1 2 3 4 5 6 7 8 ... 359 360 361 362 363 364 365 366\n",
       "  * lat      (lat) float64 6kB 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
       "  * lon      (lon) float64 12kB 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
       "Attributes: (12/14)\n",
       "    QuantizeGranularBitGroomNumberOfSignificantDigits:  7\n",
       "    ecmwf_local_table:                                  128\n",
       "    ecmwf_parameter:                                    167\n",
       "    grid_specification:                                 0.25 degree x 0.25 de...\n",
       "    long_name:                                          2 metre temperature\n",
       "    maximum_value:                                      320.42938232421875\n",
       "    ...                                                 ...\n",
       "    rda_dataset:                                        ds633.0\n",
       "    rda_dataset_doi:                                    DOI: 10.5065/BH6N-5N20\n",
       "    rda_dataset_group:                                  ERA5 atmospheric surf...\n",
       "    rda_dataset_url:                                    https:/rda.ucar.edu/d...\n",
       "    short_name:                                         2t\n",
       "    units:                                              K"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tas_obs_initial    = to_daily(tas_obs_init)\n",
    "tas_obs_initial    = tas_obs_initial.rename({'latitude':'lat','longitude':'lon'})\n",
    "tas_obs_initial    = tas_obs_initial.chunk({'lat':139,'lon':544,'year':3,'day':90})\n",
    "tas_obs_initial "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5e76d693-5ec0-4aa6-8c7c-a9dbb935a92d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min 16s, sys: 1min 28s, total: 3min 44s\n",
      "Wall time: 4min 34s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xarray.backends.zarr.ZarrStore at 0x14920a861140>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# %%time\n",
    "# tas_obs_initial.to_dataset().to_zarr(zarr_path + \"e5_tas2m_initial_1991_2020.zarr\",mode='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7bd94a5f-d6cb-4f24-a71b-d7940e66bdaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "tas_obs_initial = xr.open_zarr(zarr_path + \"e5_tas2m_initial_1991_2020.zarr\").VAR_2T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "5667476c-d546-4721-a221-c3febf878ac9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 16.6 s, sys: 553 ms, total: 17.1 s\n",
      "Wall time: 19.6 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "xESMF Regridder \n",
       "Regridding algorithm:       bilinear \n",
       "Weight filename:            bilinear_192x288_721x1440.nc \n",
       "Reuse pre-computed weights? False \n",
       "Input grid shape:           (192, 288) \n",
       "Output grid shape:          (721, 1440) \n",
       "Periodic in longitude?      False"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time \n",
    "regridder = xe.Regridder(init_means_ds, ds_out, \"bilinear\")\n",
    "regridder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ca98aefa-61b8-4e30-b34c-a3c716ef2334",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;TREFHTMX&#x27; (day: 365, lat: 721, lon: 1440)&gt; Size: 2GB\n",
       "dask.array&lt;astype, shape=(365, 721, 1440), dtype=float32, chunksize=(365, 721, 1440), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * day      (day) int64 3kB 1 2 3 4 5 6 7 8 ... 358 359 360 361 362 363 364 365\n",
       "  * lat      (lat) float64 6kB 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
       "  * lon      (lon) float64 12kB 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
       "Attributes:\n",
       "    regrid_method:  bilinear</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'>'TREFHTMX'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>day</span>: 365</li><li><span class='xr-has-index'>lat</span>: 721</li><li><span class='xr-has-index'>lon</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b35c3847-5c17-4f4f-ac02-30930162ef8f' class='xr-array-in' type='checkbox' checked><label for='section-b35c3847-5c17-4f4f-ac02-30930162ef8f' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(365, 721, 1440), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                        <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> 1.41 GiB </td>\n",
       "                        <td> 1.41 GiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (365, 721, 1440) </td>\n",
       "                        <td> (365, 721, 1440) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 9 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></div></li><li class='xr-section-item'><input id='section-111f6e45-ed5f-4af0-a231-e4d7a2852248' class='xr-section-summary-in' type='checkbox'  checked><label for='section-111f6e45-ed5f-4af0-a231-e4d7a2852248' 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'>day</span></div><div class='xr-var-dims'>(day)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 ... 361 362 363 364 365</div><input id='attrs-d1c8294f-de70-4906-a731-9717fd08f3d5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d1c8294f-de70-4906-a731-9717fd08f3d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8e53e05-472e-4472-b7a4-1e4b32b0f6e7' class='xr-var-data-in' type='checkbox'><label for='data-d8e53e05-472e-4472-b7a4-1e4b32b0f6e7' 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([  1,   2,   3, ..., 363, 364, 365])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>90.0 89.75 89.5 ... -89.75 -90.0</div><input id='attrs-37981ca9-a612-4bad-9d98-b17c23bcf644' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37981ca9-a612-4bad-9d98-b17c23bcf644' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0c1a3dd-a63e-45e0-81ec-d9b2271b8339' class='xr-var-data-in' type='checkbox'><label for='data-c0c1a3dd-a63e-45e0-81ec-d9b2271b8339' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>short_name :</span></dt><dd>lat</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.25 0.5 ... 359.2 359.5 359.8</div><input id='attrs-b8b755e2-3c75-4761-bf0d-f17d70699bad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8b755e2-3c75-4761-bf0d-f17d70699bad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20cdbf9f-0b3d-4c9e-ac97-d325a46d912e' class='xr-var-data-in' type='checkbox'><label for='data-20cdbf9f-0b3d-4c9e-ac97-d325a46d912e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>short_name :</span></dt><dd>lon</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,\n",
       "       3.5975e+02])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-64b520b4-a6fb-46c0-9929-2e18d52fa43e' class='xr-section-summary-in' type='checkbox'  ><label for='section-64b520b4-a6fb-46c0-9929-2e18d52fa43e' 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>day</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f4cd9980-7bf9-4351-b607-e2056468c600' class='xr-index-data-in' type='checkbox'/><label for='index-f4cd9980-7bf9-4351-b607-e2056468c600' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,\n",
       "       ...\n",
       "       356, 357, 358, 359, 360, 361, 362, 363, 364, 365],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;day&#x27;, length=365))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e9375aba-333e-4259-a93c-bed96efb1089' class='xr-index-data-in' type='checkbox'/><label for='index-e9375aba-333e-4259-a93c-bed96efb1089' 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([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,\n",
       "        87.75,\n",
       "       ...\n",
       "       -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5, -89.75,\n",
       "        -90.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=721))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-21928fff-d1b3-49cb-a771-c1b850d45e23' class='xr-index-data-in' type='checkbox'/><label for='index-21928fff-d1b3-49cb-a771-c1b850d45e23' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,\n",
       "         2.25,\n",
       "       ...\n",
       "        357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,\n",
       "       359.75],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=1440))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4e67f161-5be7-452d-9408-568103931165' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4e67f161-5be7-452d-9408-568103931165' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>regrid_method :</span></dt><dd>bilinear</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'TREFHTMX' (day: 365, lat: 721, lon: 1440)> Size: 2GB\n",
       "dask.array<astype, shape=(365, 721, 1440), dtype=float32, chunksize=(365, 721, 1440), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * day      (day) int64 3kB 1 2 3 4 5 6 7 8 ... 358 359 360 361 362 363 364 365\n",
       "  * lat      (lat) float64 6kB 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
       "  * lon      (lon) float64 12kB 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
       "Attributes:\n",
       "    regrid_method:  bilinear"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "init_means_regrid = regridder(init_means, keep_attrs=True)\n",
    "init_means_regrid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "584bdf59-6f0f-403d-b8fa-9cc08c4d925a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 928 ms, sys: 15.4 ms, total: 944 ms\n",
      "Wall time: 1.05 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Regrid other variables\n",
    "init_stdevs_regrid  = regridder(init_stdevs, keep_attrs=True)\n",
    "final_means_regrid  = regridder(final_means, keep_attrs=True)\n",
    "final_stdevs_regrid = regridder(final_stdevs, keep_attrs=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "665b1cd5-0d89-48ae-b5af-9091997d43c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 33.8 s, sys: 14.2 s, total: 48 s\n",
      "Wall time: 53.4 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xarray.backends.zarr.ZarrStore at 0x14920a7e6c40>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# %%time\n",
    "# #Save regridded data\n",
    "# init_means_regrid.to_dataset().to_zarr(mean_path + 'cesm2_'+ init_year0 + '_' + init_year1+ '_means_regridded.zarr',mode='w')\n",
    "# init_stdevs_regrid.to_dataset().to_zarr(stdev_path + 'cesm2_'+ init_year0 + '_' + init_year1+ '_stdevs_regridded.zarr',mode='w') \n",
    "# final_means_regrid.to_dataset().to_zarr(mean_path + 'cesm2_'+ final_year0 + '_' + final_year1+ '_means_regridded.zarr',mode='w')\n",
    "# final_stdevs_regrid.to_dataset().to_zarr(stdev_path + 'cesm2_'+ final_year0 + '_' + final_year1+ '_stdevs_regridded.zarr',mode='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5d1d097d-32bd-4165-9e62-10c006513435",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 42.6 ms, sys: 5.07 ms, total: 47.7 ms\n",
      "Wall time: 348 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Open regridded data\n",
    "init_means_regrid  = xr.open_zarr(mean_path + 'cesm2_'+ init_year0 + '_' + init_year1+ '_means_regridded.zarr').TREFHTMX\n",
    "init_stdevs_regrid = xr.open_zarr(stdev_path + 'cesm2_'+ init_year0 + '_' + init_year1+ '_stdevs_regridded.zarr').TREFHTMX\n",
    "final_means_regrid  = xr.open_zarr(mean_path + 'cesm2_'+ final_year0 + '_' + final_year1+ '_means_regridded.zarr').TREFHTMX\n",
    "final_stdevs_regrid = xr.open_zarr(stdev_path + 'cesm2_'+ final_year0 + '_' + final_year1+ '_stdevs_regridded.zarr').TREFHTMX"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ca7a7ca-30b1-4048-acf4-161d9cdc91ea",
   "metadata": {},
   "source": [
    "## Now, perform bias correction by only adjusting the first moment, i.e, mean and plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "39e94183-bcf4-4803-84c0-aad9c2cac2d2",
   "metadata": {},
   "outputs": [
    {
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       "  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",
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       "}\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",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
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       "\n",
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       "\n",
       ".xr-attrs dt:hover span {\n",
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       "\n",
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       "  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",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
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       "  stroke: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (lat: 721, lon: 1440, year: 30, day: 365)&gt; Size: 45GB\n",
       "dask.array&lt;add, shape=(721, 1440, 30, 365), dtype=float32, chunksize=(139, 544, 3, 90), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * day      (day) int64 3kB 1 2 3 4 5 6 7 8 ... 358 359 360 361 362 363 364 365\n",
       "  * lat      (lat) float64 6kB 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
       "  * lon      (lon) float64 12kB 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
       "  * year     (year) int64 240B 1991 1992 1993 1994 1995 ... 2017 2018 2019 2020</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'>lat</span>: 721</li><li><span class='xr-has-index'>lon</span>: 1440</li><li><span class='xr-has-index'>year</span>: 30</li><li><span class='xr-has-index'>day</span>: 365</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-91ffc1af-600f-458e-923a-a662b809ce25' class='xr-array-in' type='checkbox' checked><label for='section-91ffc1af-600f-458e-923a-a662b809ce25' 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=(139, 544, 3, 90), 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> 42.35 GiB </td>\n",
       "                        <td> 77.88 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (721, 1440, 30, 365) </td>\n",
       "                        <td> (139, 544, 3, 90) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 900 chunks in 12 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></div></li><li class='xr-section-item'><input id='section-b02880cd-9292-4dac-9a9c-82540dfff30c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b02880cd-9292-4dac-9a9c-82540dfff30c' 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'>day</span></div><div class='xr-var-dims'>(day)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 ... 361 362 363 364 365</div><input id='attrs-e9e1b4fc-f7aa-4895-bdb7-553184a59a7a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e9e1b4fc-f7aa-4895-bdb7-553184a59a7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d00af344-d3fc-4cfe-825d-0b917290381c' class='xr-var-data-in' type='checkbox'><label for='data-d00af344-d3fc-4cfe-825d-0b917290381c' 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([  1,   2,   3, ..., 363, 364, 365])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>90.0 89.75 89.5 ... -89.75 -90.0</div><input id='attrs-4e24594d-903b-4abc-90bf-49885e46b701' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e24594d-903b-4abc-90bf-49885e46b701' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b1c38f1-a5d7-4bd3-8612-328b31582d32' class='xr-var-data-in' type='checkbox'><label for='data-1b1c38f1-a5d7-4bd3-8612-328b31582d32' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>short_name :</span></dt><dd>lat</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.25 0.5 ... 359.2 359.5 359.8</div><input id='attrs-3719d4cc-d7de-4673-9fa6-c542ea7b5ee3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3719d4cc-d7de-4673-9fa6-c542ea7b5ee3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5a24196-9483-433e-95f9-b90085d1719f' class='xr-var-data-in' type='checkbox'><label for='data-d5a24196-9483-433e-95f9-b90085d1719f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>short_name :</span></dt><dd>lon</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,\n",
       "       3.5975e+02])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1991 1992 1993 ... 2018 2019 2020</div><input id='attrs-263c9207-e18f-4384-929b-c36d19638c91' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-263c9207-e18f-4384-929b-c36d19638c91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afb02a8f-06de-43e8-87ff-a11b118e7df1' class='xr-var-data-in' type='checkbox'><label for='data-afb02a8f-06de-43e8-87ff-a11b118e7df1' 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([1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,\n",
       "       2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,\n",
       "       2015, 2016, 2017, 2018, 2019, 2020])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b01d03c7-eb31-47fc-8534-ae9d9117dfde' class='xr-section-summary-in' type='checkbox'  ><label for='section-b01d03c7-eb31-47fc-8534-ae9d9117dfde' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>day</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5d0f93e8-2b8d-4ca6-9eba-32439469d8b0' class='xr-index-data-in' type='checkbox'/><label for='index-5d0f93e8-2b8d-4ca6-9eba-32439469d8b0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,\n",
       "       ...\n",
       "       356, 357, 358, 359, 360, 361, 362, 363, 364, 365],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;day&#x27;, length=365))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1ee33668-d9a0-4989-b0b7-c40987f1a8be' class='xr-index-data-in' type='checkbox'/><label for='index-1ee33668-d9a0-4989-b0b7-c40987f1a8be' 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([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,\n",
       "        87.75,\n",
       "       ...\n",
       "       -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5, -89.75,\n",
       "        -90.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=721))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0b7b60b8-38eb-4b67-b916-954ac42901cb' class='xr-index-data-in' type='checkbox'/><label for='index-0b7b60b8-38eb-4b67-b916-954ac42901cb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,\n",
       "         2.25,\n",
       "       ...\n",
       "        357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,\n",
       "       359.75],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=1440))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>year</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-44f66c7a-9e42-4e97-9c97-b450ca733713' class='xr-index-data-in' type='checkbox'/><label for='index-44f66c7a-9e42-4e97-9c97-b450ca733713' 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([1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002,\n",
       "       2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014,\n",
       "       2015, 2016, 2017, 2018, 2019, 2020],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;year&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2a1142ee-ad9a-418c-a6e8-115ed1d7ea41' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2a1142ee-ad9a-418c-a6e8-115ed1d7ea41' 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 (lat: 721, lon: 1440, year: 30, day: 365)> Size: 45GB\n",
       "dask.array<add, shape=(721, 1440, 30, 365), dtype=float32, chunksize=(139, 544, 3, 90), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * day      (day) int64 3kB 1 2 3 4 5 6 7 8 ... 358 359 360 361 362 363 364 365\n",
       "  * lat      (lat) float64 6kB 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
       "  * lon      (lon) float64 12kB 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
       "  * year     (year) int64 240B 1991 1992 1993 1994 1995 ... 2017 2018 2019 2020"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tas_bc = tas_obs_initial  + (final_means_regrid - init_means_regrid)\n",
    "tas_bc = tas_bc.chunk({'lat':139,'lon':544,'year':3,'day':90})\n",
    "tas_bc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad63b9e2-6e4b-465d-bc60-684429a46e0c",
   "metadata": {},
   "source": [
    "### Plot bias corrected temperature and CESM model's predictions for the End of the 21st century (2100)\n",
    "- Since tas_bc are predictions for the years 2070-2100, we need to change the year coordinated\n",
    "- We will then save the bias-corrected surface air temperatures (tas) to a zarr store.\n",
    "- Finally, we will read from this zarr store and plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d77ede3c-1674-4d32-b34b-5474de11411f",
   "metadata": {},
   "outputs": [
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       "\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",
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       "\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",
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       "}\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;bias_corrected_tas&#x27; (lat: 721, lon: 1440, year: 30, day: 365)&gt; Size: 45GB\n",
       "dask.array&lt;add, shape=(721, 1440, 30, 365), dtype=float32, chunksize=(139, 544, 3, 90), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * day      (day) int64 3kB 1 2 3 4 5 6 7 8 ... 358 359 360 361 362 363 364 365\n",
       "  * lat      (lat) float64 6kB 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
       "  * lon      (lon) float64 12kB 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
       "  * year     (year) int64 240B 2071 2072 2073 2074 2075 ... 2097 2098 2099 2100</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'>'bias_corrected_tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 721</li><li><span class='xr-has-index'>lon</span>: 1440</li><li><span class='xr-has-index'>year</span>: 30</li><li><span class='xr-has-index'>day</span>: 365</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-bb1f5611-3350-4c01-85ac-84a51e8addcd' class='xr-array-in' type='checkbox' checked><label for='section-bb1f5611-3350-4c01-85ac-84a51e8addcd' 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=(139, 544, 3, 90), 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> 42.35 GiB </td>\n",
       "                        <td> 77.88 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (721, 1440, 30, 365) </td>\n",
       "                        <td> (139, 544, 3, 90) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 900 chunks in 12 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",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-dd5430cc-27c5-4aa9-bfed-dd5cc965c82f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dd5430cc-27c5-4aa9-bfed-dd5cc965c82f' 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'>day</span></div><div class='xr-var-dims'>(day)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 ... 361 362 363 364 365</div><input id='attrs-1694a695-f58c-4b2a-87dc-624faddb09cf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1694a695-f58c-4b2a-87dc-624faddb09cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50e0ab04-8061-4b72-b1a9-3d2731896c59' class='xr-var-data-in' type='checkbox'><label for='data-50e0ab04-8061-4b72-b1a9-3d2731896c59' 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([  1,   2,   3, ..., 363, 364, 365])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>90.0 89.75 89.5 ... -89.75 -90.0</div><input id='attrs-101de4fd-b318-497f-8f01-88325da6f532' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-101de4fd-b318-497f-8f01-88325da6f532' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb23a08e-dee3-46e2-bf0c-3ef2d7a98edb' class='xr-var-data-in' type='checkbox'><label for='data-cb23a08e-dee3-46e2-bf0c-3ef2d7a98edb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>short_name :</span></dt><dd>lat</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.25 0.5 ... 359.2 359.5 359.8</div><input id='attrs-588c551d-bd09-40f6-a025-29f4634a5134' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-588c551d-bd09-40f6-a025-29f4634a5134' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5367535-7475-469d-ab3c-bbc86de44da4' class='xr-var-data-in' type='checkbox'><label for='data-a5367535-7475-469d-ab3c-bbc86de44da4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>short_name :</span></dt><dd>lon</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,\n",
       "       3.5975e+02])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2071 2072 2073 ... 2098 2099 2100</div><input id='attrs-939de65b-5831-41da-b8ee-d211033b7c01' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-939de65b-5831-41da-b8ee-d211033b7c01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50873a3d-cea8-4c91-a910-cacf4d37d320' class='xr-var-data-in' type='checkbox'><label for='data-50873a3d-cea8-4c91-a910-cacf4d37d320' 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([2071, 2072, 2073, 2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2082,\n",
       "       2083, 2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094,\n",
       "       2095, 2096, 2097, 2098, 2099, 2100])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c0606f3b-1a3a-4c98-acbc-48fa2c98dd1e' class='xr-section-summary-in' type='checkbox'  ><label for='section-c0606f3b-1a3a-4c98-acbc-48fa2c98dd1e' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>day</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-911a3e08-abf3-42ab-9b65-147564e61526' class='xr-index-data-in' type='checkbox'/><label for='index-911a3e08-abf3-42ab-9b65-147564e61526' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,\n",
       "       ...\n",
       "       356, 357, 358, 359, 360, 361, 362, 363, 364, 365],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;day&#x27;, length=365))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5325af98-f14e-4d9d-98ff-9b670c5d98d1' class='xr-index-data-in' type='checkbox'/><label for='index-5325af98-f14e-4d9d-98ff-9b670c5d98d1' 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([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,\n",
       "        87.75,\n",
       "       ...\n",
       "       -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5, -89.75,\n",
       "        -90.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=721))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-440ce1bc-3772-4e57-9a65-a0baed3a695f' class='xr-index-data-in' type='checkbox'/><label for='index-440ce1bc-3772-4e57-9a65-a0baed3a695f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,\n",
       "         2.25,\n",
       "       ...\n",
       "        357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,\n",
       "       359.75],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=1440))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>year</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b9798d3e-830f-473d-b2f7-3ac26e423c7f' class='xr-index-data-in' type='checkbox'/><label for='index-b9798d3e-830f-473d-b2f7-3ac26e423c7f' 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([2071, 2072, 2073, 2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2082,\n",
       "       2083, 2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094,\n",
       "       2095, 2096, 2097, 2098, 2099, 2100],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;year&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-47460c89-1e4d-4fad-bc0d-679854cd958e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-47460c89-1e4d-4fad-bc0d-679854cd958e' 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 'bias_corrected_tas' (lat: 721, lon: 1440, year: 30, day: 365)> Size: 45GB\n",
       "dask.array<add, shape=(721, 1440, 30, 365), dtype=float32, chunksize=(139, 544, 3, 90), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * day      (day) int64 3kB 1 2 3 4 5 6 7 8 ... 358 359 360 361 362 363 364 365\n",
       "  * lat      (lat) float64 6kB 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
       "  * lon      (lon) float64 12kB 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
       "  * year     (year) int64 240B 2071 2072 2073 2074 2075 ... 2097 2098 2099 2100"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Change the year coordinate\n",
    "tas_bc['year'] = tas_bc['year'] + 80\n",
    "tas_bc         = tas_bc.rename('bias_corrected_tas')\n",
    "tas_bc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "bed64597-bd12-4ba8-b53c-d91987377c5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3min 50s, sys: 1min 1s, total: 4min 51s\n",
      "Wall time: 10min 16s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xarray.backends.zarr.ZarrStore at 0x152007289fc0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# %%time\n",
    "# tas_bc.to_dataset().to_zarr(zarr_path + 'bias_corrected_tas_1991_2020.zarr',mode='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1c987333-de8b-45f6-b690-7c7f3c42ff23",
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;bias_corrected_tas&#x27; (lat: 721, lon: 1440, year: 30, day: 365)&gt; Size: 45GB\n",
       "dask.array&lt;open_dataset-bias_corrected_tas, shape=(721, 1440, 30, 365), dtype=float32, chunksize=(139, 544, 3, 90), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * day      (day) int64 3kB 1 2 3 4 5 6 7 8 ... 358 359 360 361 362 363 364 365\n",
       "  * lat      (lat) float64 6kB 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
       "  * lon      (lon) float64 12kB 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
       "  * year     (year) int64 240B 2071 2072 2073 2074 2075 ... 2097 2098 2099 2100</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'>'bias_corrected_tas'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 721</li><li><span class='xr-has-index'>lon</span>: 1440</li><li><span class='xr-has-index'>year</span>: 30</li><li><span class='xr-has-index'>day</span>: 365</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ffb1e51f-d8a7-4140-b142-475a3983c816' class='xr-array-in' type='checkbox' checked><label for='section-ffb1e51f-d8a7-4140-b142-475a3983c816' 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=(139, 544, 3, 90), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 42.35 GiB </td>\n",
       "                        <td> 77.88 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
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       "                    <tr>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-d8a6da99-cc9e-4cbb-ae44-19673b6a4801' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d8a6da99-cc9e-4cbb-ae44-19673b6a4801' 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'>day</span></div><div class='xr-var-dims'>(day)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 ... 361 362 363 364 365</div><input id='attrs-81f1e84b-3846-49ea-9895-76f8ee7dd76b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-81f1e84b-3846-49ea-9895-76f8ee7dd76b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9473daf7-b4bc-4d75-a278-ae748a1d3bbd' class='xr-var-data-in' type='checkbox'><label for='data-9473daf7-b4bc-4d75-a278-ae748a1d3bbd' 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([  1,   2,   3, ..., 363, 364, 365])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>90.0 89.75 89.5 ... -89.75 -90.0</div><input id='attrs-2810c86a-1f8a-4d80-bd03-a6c00ab88e6a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2810c86a-1f8a-4d80-bd03-a6c00ab88e6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-351803d4-c607-4eea-9946-9a0319ee6cbd' class='xr-var-data-in' type='checkbox'><label for='data-351803d4-c607-4eea-9946-9a0319ee6cbd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>short_name :</span></dt><dd>lat</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 90.  ,  89.75,  89.5 , ..., -89.5 , -89.75, -90.  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.25 0.5 ... 359.2 359.5 359.8</div><input id='attrs-dcb5e328-9bc4-4e80-baab-0b77887e61a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dcb5e328-9bc4-4e80-baab-0b77887e61a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fc39d15-690f-4cff-b4b9-d343c86ac3e0' class='xr-var-data-in' type='checkbox'><label for='data-3fc39d15-690f-4cff-b4b9-d343c86ac3e0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>short_name :</span></dt><dd>lon</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([0.0000e+00, 2.5000e-01, 5.0000e-01, ..., 3.5925e+02, 3.5950e+02,\n",
       "       3.5975e+02])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2071 2072 2073 ... 2098 2099 2100</div><input id='attrs-e7d70152-a856-4252-b63a-2b359ca20b79' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e7d70152-a856-4252-b63a-2b359ca20b79' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81635480-65b0-4a1b-b035-ff8a29448a1b' class='xr-var-data-in' type='checkbox'><label for='data-81635480-65b0-4a1b-b035-ff8a29448a1b' 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([2071, 2072, 2073, 2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2082,\n",
       "       2083, 2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094,\n",
       "       2095, 2096, 2097, 2098, 2099, 2100])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b14376ed-8248-4796-ab4d-696d43007b24' class='xr-section-summary-in' type='checkbox'  ><label for='section-b14376ed-8248-4796-ab4d-696d43007b24' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>day</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-54e5ad66-ef0a-41bb-8889-e8508c76dc1c' class='xr-index-data-in' type='checkbox'/><label for='index-54e5ad66-ef0a-41bb-8889-e8508c76dc1c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,\n",
       "       ...\n",
       "       356, 357, 358, 359, 360, 361, 362, 363, 364, 365],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;day&#x27;, length=365))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-54ad9e8e-4a6f-4fde-a299-53fff05fafb0' class='xr-index-data-in' type='checkbox'/><label for='index-54ad9e8e-4a6f-4fde-a299-53fff05fafb0' 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([  90.0,  89.75,   89.5,  89.25,   89.0,  88.75,   88.5,  88.25,   88.0,\n",
       "        87.75,\n",
       "       ...\n",
       "       -87.75,  -88.0, -88.25,  -88.5, -88.75,  -89.0, -89.25,  -89.5, -89.75,\n",
       "        -90.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=721))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bec2b1bd-9882-437f-9668-ecc371651790' class='xr-index-data-in' type='checkbox'/><label for='index-bec2b1bd-9882-437f-9668-ecc371651790' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   0.25,    0.5,   0.75,    1.0,   1.25,    1.5,   1.75,    2.0,\n",
       "         2.25,\n",
       "       ...\n",
       "        357.5, 357.75,  358.0, 358.25,  358.5, 358.75,  359.0, 359.25,  359.5,\n",
       "       359.75],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=1440))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>year</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ef2a1199-0efd-4a64-bcc9-2e34e44ae591' class='xr-index-data-in' type='checkbox'/><label for='index-ef2a1199-0efd-4a64-bcc9-2e34e44ae591' 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([2071, 2072, 2073, 2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2082,\n",
       "       2083, 2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094,\n",
       "       2095, 2096, 2097, 2098, 2099, 2100],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;year&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-30b20177-aabd-4ba4-90f9-115cbb9a9d83' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-30b20177-aabd-4ba4-90f9-115cbb9a9d83' 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 'bias_corrected_tas' (lat: 721, lon: 1440, year: 30, day: 365)> Size: 45GB\n",
       "dask.array<open_dataset-bias_corrected_tas, shape=(721, 1440, 30, 365), dtype=float32, chunksize=(139, 544, 3, 90), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * day      (day) int64 3kB 1 2 3 4 5 6 7 8 ... 358 359 360 361 362 363 364 365\n",
       "  * lat      (lat) float64 6kB 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
       "  * lon      (lon) float64 12kB 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
       "  * year     (year) int64 240B 2071 2072 2073 2074 2075 ... 2097 2098 2099 2100"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tas_bc = xr.open_zarr(zarr_path + 'bias_corrected_tas_1991_2020.zarr').bias_corrected_tas\n",
    "tas_bc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "e697119b-d081-4581-9452-71833ac59fba",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;TREFHTMX&#x27; (lat: 192, lon: 288, year: 30, day: 365)&gt; Size: 2GB\n",
       "dask.array&lt;open_dataset-TREFHTMX, shape=(192, 288, 30, 365), dtype=float32, chunksize=(192, 288, 2, 365), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * day      (day) int64 3kB 1 2 3 4 5 6 7 8 ... 358 359 360 361 362 363 364 365\n",
       "  * lat      (lat) float64 2kB -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "  * lon      (lon) float64 2kB 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
       "  * year     (year) int64 240B 2071 2072 2073 2074 2075 ... 2097 2098 2099 2100</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'>'TREFHTMX'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li><li><span class='xr-has-index'>year</span>: 30</li><li><span class='xr-has-index'>day</span>: 365</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b7e8b8ae-1233-4dbc-9c40-989038bf53d9' class='xr-array-in' type='checkbox' checked><label for='section-b7e8b8ae-1233-4dbc-9c40-989038bf53d9' 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=(192, 288, 2, 365), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "        <td>\n",
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       "                <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> 2.26 GiB </td>\n",
       "                        <td> 153.98 MiB </td>\n",
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       "                        <td> (192, 288, 2, 365) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 15 chunks in 2 graph layers </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-7e44b2a4-7af4-4cb8-b594-4a23d28ccd82' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7e44b2a4-7af4-4cb8-b594-4a23d28ccd82' 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'>day</span></div><div class='xr-var-dims'>(day)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 ... 361 362 363 364 365</div><input id='attrs-aa927f3e-6436-48fe-a451-6a4d8a74503d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aa927f3e-6436-48fe-a451-6a4d8a74503d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90075515-8618-48cf-8bb8-10267119eec9' class='xr-var-data-in' type='checkbox'><label for='data-90075515-8618-48cf-8bb8-10267119eec9' 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([  1,   2,   3, ..., 363, 364, 365])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -89.06 -88.12 ... 89.06 90.0</div><input id='attrs-6d073a7b-5bdd-4032-b9e9-bb46717fc3ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d073a7b-5bdd-4032-b9e9-bb46717fc3ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de70558e-030f-4d55-bd0e-b28bdde930e1' class='xr-var-data-in' type='checkbox'><label for='data-de70558e-030f-4d55-bd0e-b28bdde930e1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.      , -89.057592, -88.115183, -87.172775, -86.230366, -85.287958,\n",
       "       -84.34555 , -83.403141, -82.460733, -81.518325, -80.575916, -79.633508,\n",
       "       -78.691099, -77.748691, -76.806283, -75.863874, -74.921466, -73.979058,\n",
       "       -73.036649, -72.094241, -71.151832, -70.209424, -69.267016, -68.324607,\n",
       "       -67.382199, -66.439791, -65.497382, -64.554974, -63.612565, -62.670157,\n",
       "       -61.727749, -60.78534 , -59.842932, -58.900524, -57.958115, -57.015707,\n",
       "       -56.073298, -55.13089 , -54.188482, -53.246073, -52.303665, -51.361257,\n",
       "       -50.418848, -49.47644 , -48.534031, -47.591623, -46.649215, -45.706806,\n",
       "       -44.764398, -43.82199 , -42.879581, -41.937173, -40.994764, -40.052356,\n",
       "       -39.109948, -38.167539, -37.225131, -36.282723, -35.340314, -34.397906,\n",
       "       -33.455497, -32.513089, -31.570681, -30.628272, -29.685864, -28.743455,\n",
       "       -27.801047, -26.858639, -25.91623 , -24.973822, -24.031414, -23.089005,\n",
       "       -22.146597, -21.204188, -20.26178 , -19.319372, -18.376963, -17.434555,\n",
       "       -16.492147, -15.549738, -14.60733 , -13.664921, -12.722513, -11.780105,\n",
       "       -10.837696,  -9.895288,  -8.95288 ,  -8.010471,  -7.068063,  -6.125654,\n",
       "        -5.183246,  -4.240838,  -3.298429,  -2.356021,  -1.413613,  -0.471204,\n",
       "         0.471204,   1.413613,   2.356021,   3.298429,   4.240838,   5.183246,\n",
       "         6.125654,   7.068063,   8.010471,   8.95288 ,   9.895288,  10.837696,\n",
       "        11.780105,  12.722513,  13.664921,  14.60733 ,  15.549738,  16.492147,\n",
       "        17.434555,  18.376963,  19.319372,  20.26178 ,  21.204188,  22.146597,\n",
       "        23.089005,  24.031414,  24.973822,  25.91623 ,  26.858639,  27.801047,\n",
       "        28.743455,  29.685864,  30.628272,  31.570681,  32.513089,  33.455497,\n",
       "        34.397906,  35.340314,  36.282723,  37.225131,  38.167539,  39.109948,\n",
       "        40.052356,  40.994764,  41.937173,  42.879581,  43.82199 ,  44.764398,\n",
       "        45.706806,  46.649215,  47.591623,  48.534031,  49.47644 ,  50.418848,\n",
       "        51.361257,  52.303665,  53.246073,  54.188482,  55.13089 ,  56.073298,\n",
       "        57.015707,  57.958115,  58.900524,  59.842932,  60.78534 ,  61.727749,\n",
       "        62.670157,  63.612565,  64.554974,  65.497382,  66.439791,  67.382199,\n",
       "        68.324607,  69.267016,  70.209424,  71.151832,  72.094241,  73.036649,\n",
       "        73.979058,  74.921466,  75.863874,  76.806283,  77.748691,  78.691099,\n",
       "        79.633508,  80.575916,  81.518325,  82.460733,  83.403141,  84.34555 ,\n",
       "        85.287958,  86.230366,  87.172775,  88.115183,  89.057592,  90.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.25 2.5 ... 356.2 357.5 358.8</div><input id='attrs-bd6e2580-8757-44cf-af3e-9d2728d91e90' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd6e2580-8757-44cf-af3e-9d2728d91e90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79a2a01f-7586-4f78-abb8-c48fb2fba4e0' class='xr-var-data-in' type='checkbox'><label for='data-79a2a01f-7586-4f78-abb8-c48fb2fba4e0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   1.25,   2.5 , ..., 356.25, 357.5 , 358.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>year</span></div><div class='xr-var-dims'>(year)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>2071 2072 2073 ... 2098 2099 2100</div><input id='attrs-fbd5ccbf-8087-4d82-9c1e-90b818c39dca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fbd5ccbf-8087-4d82-9c1e-90b818c39dca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17cdb5a3-e36d-41b8-a308-b553f32ebf65' class='xr-var-data-in' type='checkbox'><label for='data-17cdb5a3-e36d-41b8-a308-b553f32ebf65' 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([2071, 2072, 2073, 2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2082,\n",
       "       2083, 2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094,\n",
       "       2095, 2096, 2097, 2098, 2099, 2100])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fba033eb-913d-499a-a2be-d240f3f85dd6' class='xr-section-summary-in' type='checkbox'  ><label for='section-fba033eb-913d-499a-a2be-d240f3f85dd6' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>day</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fe268a2e-f325-4e09-98e3-0a66c0d8359f' class='xr-index-data-in' type='checkbox'/><label for='index-fe268a2e-f325-4e09-98e3-0a66c0d8359f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  1,   2,   3,   4,   5,   6,   7,   8,   9,  10,\n",
       "       ...\n",
       "       356, 357, 358, 359, 360, 361, 362, 363, 364, 365],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;day&#x27;, length=365))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c9d4d84d-1462-4c1f-82b0-52b19a3bab4f' class='xr-index-data-in' type='checkbox'/><label for='index-c9d4d84d-1462-4c1f-82b0-52b19a3bab4f' 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([             -90.0, -89.05759162303664,  -88.1151832460733,\n",
       "       -87.17277486910994,  -86.2303664921466, -85.28795811518324,\n",
       "        -84.3455497382199, -83.40314136125654, -82.46073298429319,\n",
       "       -81.51832460732984,\n",
       "       ...\n",
       "        81.51832460732984,  82.46073298429319,  83.40314136125654,\n",
       "         84.3455497382199,  85.28795811518324,   86.2303664921466,\n",
       "        87.17277486910994,   88.1151832460733,  89.05759162303664,\n",
       "                     90.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=192))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8717aa50-2ea7-4eb8-98f1-7a6070bfe530' class='xr-index-data-in' type='checkbox'/><label for='index-8717aa50-2ea7-4eb8-98f1-7a6070bfe530' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   0.0,   1.25,    2.5,   3.75,    5.0,   6.25,    7.5,   8.75,   10.0,\n",
       "        11.25,\n",
       "       ...\n",
       "        347.5, 348.75,  350.0, 351.25,  352.5, 353.75,  355.0, 356.25,  357.5,\n",
       "       358.75],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=288))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>year</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-933683a0-4642-4e93-943d-72716daefc85' class='xr-index-data-in' type='checkbox'/><label for='index-933683a0-4642-4e93-943d-72716daefc85' 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([2071, 2072, 2073, 2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2082,\n",
       "       2083, 2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094,\n",
       "       2095, 2096, 2097, 2098, 2099, 2100],\n",
       "      dtype=&#x27;int64&#x27;, name=&#x27;year&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-87f3bbe2-1b37-4e3b-919c-7c04d2fa385b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-87f3bbe2-1b37-4e3b-919c-7c04d2fa385b' 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 'TREFHTMX' (lat: 192, lon: 288, year: 30, day: 365)> Size: 2GB\n",
       "dask.array<open_dataset-TREFHTMX, shape=(192, 288, 30, 365), dtype=float32, chunksize=(192, 288, 2, 365), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * day      (day) int64 3kB 1 2 3 4 5 6 7 8 ... 358 359 360 361 362 363 364 365\n",
       "  * lat      (lat) float64 2kB -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
       "  * lon      (lon) float64 2kB 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8\n",
       "  * year     (year) int64 240B 2071 2072 2073 2074 2075 ... 2097 2098 2099 2100"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_ensemble_means = xr.open_zarr(mean_path + 'cesm2_'+ final_year0 + '_' + final_year1 + '_ensemble_means.zarr').TREFHTMX\n",
    "final_ensemble_means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "93349d05-ec38-48d9-95a5-55715c435aad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1.23 s, sys: 443 ms, total: 1.68 s\n",
      "Wall time: 5.44 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x151ffef912b0>"
      ]
     },
     "execution_count": 45,
     "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",
    "tas_bc.sel(year = 2100, day = 211).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "abed4842-ead0-4a97-b04e-f3069f4bf1e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 822 ms, sys: 445 ms, total: 1.27 s\n",
      "Wall time: 1.35 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "fig, axs = plt.subplots(1, 2, figsize=(10, 5))\n",
    "\n",
    "# Plot the first array\n",
    "tas_bc.sel(year = 2100, day = 211).plot(ax = axs[0],cmap='RdBu_r',add_colorbar=False)\n",
    "axs[0].set_title('Bias-corrected projections')\n",
    "#axs[0].coastlines(color=\"black\")\n",
    "\n",
    "# Plot the second array\n",
    "final_ensemble_means.sel(year = 2100, day = 211).plot(ax = axs[1],cmap='RdBu_r')\n",
    "axs[1].set_title('EOC model ensemble means')\n",
    "\n",
    "# Display the plots\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3a4454e-4fd3-4903-bf8d-d46184d29a9e",
   "metadata": {},
   "source": [
    "### Calculate GMST "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1f258cf5-5d20-4ecc-b624-067860f3bc56",
   "metadata": {},
   "outputs": [],
   "source": [
    "# GMST function ###\n",
    "# calculate global means\n",
    "\n",
    "def get_lat_name(ds):\n",
    "    for lat_name in ['lat', 'latitude']:\n",
    "        if lat_name in ds.coords:\n",
    "            return lat_name\n",
    "    raise RuntimeError(\"Couldn't find a latitude coordinate\")\n",
    "\n",
    "def global_mean(ds):\n",
    "    lat = ds[get_lat_name(ds)]\n",
    "    weight = np.cos(np.deg2rad(lat))\n",
    "    weight /= weight.mean()\n",
    "    other_dims = set(ds.dims) - {'time','member_id'}\n",
    "    return (ds * weight).mean(other_dims)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9340cee-ff57-446c-9139-cdbe4fd6d0a9",
   "metadata": {},
   "source": [
    "#### Now compute (spatially weighted) Global Mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5985e661-464f-40b2-a17d-cf5f4dcb4b81",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}