notebooks/cesm_osdf_stampede3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "aa4724aa",
   "metadata": {},
   "source": [
    "# Access AWS CESM2 using the AWS open data origin data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ea702c96",
   "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 re\n",
    "# import nest_asyncio\n",
    "# nest_asyncio.apply()\n",
    "import aiohttp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2cbfa9e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import fsspec.implementations.http as fshttp\n",
    "from pelicanfs.core import PelicanFileSystem, PelicanMap, OSDFFileSystem "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2a0d284d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import dask \n",
    "from dask_jobqueue import SLURMCluster\n",
    "from dask.distributed import Client\n",
    "from dask.distributed import performance_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "89075824",
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "rda_url    =  'https://data.rda.ucar.edu/'\n",
    "intake_url = rda_url + 'harshah/intake_catalogs/cesm2-lens-osdf/aws-cesm2-le.json'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a814f2a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-10-09 22:18:30,894 - tornado.application - ERROR - Exception in callback functools.partial(<bound method IOLoop._discard_future_result of <tornado.platform.asyncio.AsyncIOMainLoop object at 0x14c6f0d2e370>>, <Task finished name='Task-32' coro=<SpecCluster._correct_state_internal() done, defined at /home1/10234/hhampapura/.local/lib/python3.9/site-packages/distributed/deploy/spec.py:346> exception=RuntimeError('Command exited with non-zero exit code.\\nExit code: 1\\nCommand:\\nsbatch /tmp/tmp90uvwwy7.sh\\nstdout:\\n\\n[TACC]: Job submission is not allowed from this host. Please submit\\n[TACC]: through one of the available login resources.\\n\\n\\nstderr:\\n\\n')>)\n",
      "Traceback (most recent call last):\n",
      "  File \"/home1/10234/hhampapura/.local/lib/python3.9/site-packages/tornado/ioloop.py\", line 750, in _run_callback\n",
      "    ret = callback()\n",
      "  File \"/home1/10234/hhampapura/.local/lib/python3.9/site-packages/tornado/ioloop.py\", line 774, in _discard_future_result\n",
      "    future.result()\n",
      "  File \"/home1/10234/hhampapura/.local/lib/python3.9/site-packages/distributed/deploy/spec.py\", line 390, in _correct_state_internal\n",
      "    await asyncio.gather(*worker_futs)\n",
      "  File \"/scratch/projects/compilers/intel24.0/oneapi/intelpython/python3.9/lib/python3.9/asyncio/tasks.py\", line 688, in _wrap_awaitable\n",
      "    return (yield from awaitable.__await__())\n",
      "  File \"/home1/10234/hhampapura/.local/lib/python3.9/site-packages/distributed/deploy/spec.py\", line 74, in _\n",
      "    await self.start()\n",
      "  File \"/home1/10234/hhampapura/.local/lib/python3.9/site-packages/dask_jobqueue/core.py\", line 426, in start\n",
      "    out = await self._submit_job(fn)\n",
      "  File \"/home1/10234/hhampapura/.local/lib/python3.9/site-packages/dask_jobqueue/core.py\", line 409, in _submit_job\n",
      "    return await self._call(shlex.split(self.submit_command) + [script_filename])\n",
      "  File \"/home1/10234/hhampapura/.local/lib/python3.9/site-packages/dask_jobqueue/core.py\", line 505, in _call\n",
      "    raise RuntimeError(\n",
      "RuntimeError: Command exited with non-zero exit code.\n",
      "Exit code: 1\n",
      "Command:\n",
      "sbatch /tmp/tmp90uvwwy7.sh\n",
      "stdout:\n",
      "\n",
      "[TACC]: Job submission is not allowed from this host. Please submit\n",
      "[TACC]: through one of the available login resources.\n",
      "\n",
      "\n",
      "stderr:\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# # Spin up SLURM cluster\n",
    "#job_extra = ['--qos=icx','--account=ac_cumulus'] \n",
    "cluster =  SLURMCluster(queue=\"icx\", cores=5, walltime='6:00:00', \n",
    "                local_directory='/home1/10234/hhampapura/dask_space/', \n",
    "                log_directory='/home1/10234/hhampapura/dask_space/', memory =\"192GB\")\n",
    "                #job_extra_directives=job_extra, interface='eth0', memory=\"192GB\") \n",
    "client  = Client(cluster) \n",
    "cluster.scale_up(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "769e475d",
   "metadata": {},
   "source": [
    "### Access the data from the AWS bucket using intake"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b5a783be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<p><strong>aws-cesm2-le catalog with 40 dataset(s) from 322 asset(s)</strong>:</p> <div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>unique</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <td>322</td>\n",
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       "      <th>variable</th>\n",
       "      <td>53</td>\n",
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       "    <tr>\n",
       "      <th>long_name</th>\n",
       "      <td>51</td>\n",
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       "    <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",
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       "    <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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       "      <td>4</td>\n",
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       "    <tr>\n",
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       "      <td>7</td>\n",
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   "source": [
    "osdf_catalog = intake.open_esm_datastore(intake_url)\n",
    "osdf_catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "97279c9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['osdf:///aws-opendata/us-west-2/ncar-cesm2-lens/atm/daily/cesm2LE-historical-cmip6-FLNS.zarr',\n",
       "       'osdf:///aws-opendata/us-west-2/ncar-cesm2-lens/atm/daily/cesm2LE-historical-cmip6-FLNSC.zarr',\n",
       "       'osdf:///aws-opendata/us-west-2/ncar-cesm2-lens/atm/daily/cesm2LE-historical-cmip6-FLUT.zarr',\n",
       "       'osdf:///aws-opendata/us-west-2/ncar-cesm2-lens/atm/daily/cesm2LE-historical-cmip6-FSNS.zarr',\n",
       "       'osdf:///aws-opendata/us-west-2/ncar-cesm2-lens/atm/daily/cesm2LE-historical-cmip6-FSNSC.zarr'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "osdf_catalog.df['path'].head().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "588a90b5",
   "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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       "    <tr>\n",
       "      <th>experiment</th>\n",
       "      <td>2</td>\n",
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   "source": [
    "osdf_catalog_temp = osdf_catalog.search(variable ='TREFHTMX', frequency ='daily')\n",
    "osdf_catalog_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "85908553",
   "metadata": {},
   "outputs": [
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     "name": "stdout",
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     "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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      "CPU times: user 541 ms, sys: 29.5 ms, total: 570 ms\n",
      "Wall time: 10 s\n"
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   ],
   "source": [
    "%%time\n",
    "#dsets = osdf_catalog_temp.to_dataset_dict(storage_options={'anon':True})\n",
    "dsets = osdf_catalog_temp.to_dataset_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "759d353a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['atm.ssp370.daily.smbb', 'atm.historical.daily.smbb', 'atm.historical.daily.cmip6', 'atm.ssp370.daily.cmip6'])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
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   "source": [
    "dsets.keys()"
   ]
  },
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   "execution_count": 19,
   "id": "7bf66e97",
   "metadata": {},
   "outputs": [
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       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 666GB\n",
       "Dimensions:    (member_id: 50, time: 60225, lat: 192, lon: 288, nbnd: 2)\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 2kB &#x27;r10i1191p1f2&#x27; ... &#x27;r9i1171p1f2&#x27;\n",
       "  * time       (time) object 482kB 1850-01-01 12:00:00 ... 2014-12-31 12:00:00\n",
       "    time_bnds  (time, nbnd) object 964kB dask.array&lt;chunksize=(30113, 1), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: nbnd\n",
       "Data variables:\n",
       "    TREFHTMX   (member_id, time, lat, lon) float32 666GB dask.array&lt;chunksize=(1, 730, 192, 288), meta=np.ndarray&gt;\n",
       "Attributes: (12/22)\n",
       "    Conventions:                       CF-1.0\n",
       "    logname:                           sunseon\n",
       "    model_doi_url:                     https://doi.org/10.5065/D67H1H0V\n",
       "    source:                            CAM\n",
       "    time_period_freq:                  day_1\n",
       "    topography_file:                   /mnt/lustre/share/CESM/cesm_input/atm/...\n",
       "    ...                                ...\n",
       "    intake_esm_attrs:units:            K\n",
       "    intake_esm_attrs:start_time:       1850-01-01 12:00:00\n",
       "    intake_esm_attrs:end_time:         2014-12-31 12:00:00\n",
       "    intake_esm_attrs:path:             osdf:///aws-opendata/us-west-2/ncar-ce...\n",
       "    intake_esm_attrs:_data_format_:    zarr\n",
       "    intake_esm_dataset_key:            atm.historical.daily.smbb</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-4cf53ceb-c81a-46e6-8854-4999f7ea63c9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4cf53ceb-c81a-46e6-8854-4999f7ea63c9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>member_id</span>: 50</li><li><span class='xr-has-index'>time</span>: 60225</li><li><span class='xr-has-index'>lat</span>: 192</li><li><span class='xr-has-index'>lon</span>: 288</li><li><span>nbnd</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-4a506b26-fb2b-4f2a-a7fe-6d77c79cf9b1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4a506b26-fb2b-4f2a-a7fe-6d77c79cf9b1' 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'>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-2f6155f6-60f9-453a-809a-2960617f10bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2f6155f6-60f9-453a-809a-2960617f10bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef62af9d-6a32-4879-9ec4-dd052de66fb3' class='xr-var-data-in' type='checkbox'><label for='data-ef62af9d-6a32-4879-9ec4-dd052de66fb3' 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-b3bc580a-96de-4ee0-a0d3-8fee2e27bbd2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3bc580a-96de-4ee0-a0d3-8fee2e27bbd2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ce9bac0-a8d9-4cf3-80cf-e55930b4b892' class='xr-var-data-in' type='checkbox'><label for='data-6ce9bac0-a8d9-4cf3-80cf-e55930b4b892' 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;r10i1191p1f2&#x27; ... &#x27;r9i1171p1f2&#x27;</div><input id='attrs-10b12ee8-33e7-491d-a8fc-4be354059b16' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-10b12ee8-33e7-491d-a8fc-4be354059b16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-891bce08-a1c5-47f3-824e-94bfed974168' class='xr-var-data-in' type='checkbox'><label for='data-891bce08-a1c5-47f3-824e-94bfed974168' 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;r10i1191p1f2&#x27;, &#x27;r11i1231p1f2&#x27;, &#x27;r11i1251p1f2&#x27;, &#x27;r11i1281p1f2&#x27;,\n",
       "       &#x27;r11i1301p1f2&#x27;, &#x27;r12i1231p1f2&#x27;, &#x27;r12i1251p1f2&#x27;, &#x27;r12i1281p1f2&#x27;,\n",
       "       &#x27;r12i1301p1f2&#x27;, &#x27;r13i1231p1f2&#x27;, &#x27;r13i1251p1f2&#x27;, &#x27;r13i1281p1f2&#x27;,\n",
       "       &#x27;r13i1301p1f2&#x27;, &#x27;r14i1231p1f2&#x27;, &#x27;r14i1251p1f2&#x27;, &#x27;r14i1281p1f2&#x27;,\n",
       "       &#x27;r14i1301p1f2&#x27;, &#x27;r15i1231p1f2&#x27;, &#x27;r15i1251p1f2&#x27;, &#x27;r15i1281p1f2&#x27;,\n",
       "       &#x27;r15i1301p1f2&#x27;, &#x27;r16i1231p1f2&#x27;, &#x27;r16i1251p1f2&#x27;, &#x27;r16i1281p1f2&#x27;,\n",
       "       &#x27;r16i1301p1f2&#x27;, &#x27;r17i1231p1f2&#x27;, &#x27;r17i1251p1f2&#x27;, &#x27;r17i1281p1f2&#x27;,\n",
       "       &#x27;r17i1301p1f2&#x27;, &#x27;r18i1231p1f2&#x27;, &#x27;r18i1251p1f2&#x27;, &#x27;r18i1281p1f2&#x27;,\n",
       "       &#x27;r18i1301p1f2&#x27;, &#x27;r19i1231p1f2&#x27;, &#x27;r19i1251p1f2&#x27;, &#x27;r19i1281p1f2&#x27;,\n",
       "       &#x27;r19i1301p1f2&#x27;, &#x27;r1i1011p1f2&#x27;, &#x27;r20i1231p1f2&#x27;, &#x27;r20i1251p1f2&#x27;,\n",
       "       &#x27;r20i1281p1f2&#x27;, &#x27;r20i1301p1f2&#x27;, &#x27;r2i1031p1f2&#x27;, &#x27;r3i1051p1f2&#x27;,\n",
       "       &#x27;r4i1071p1f2&#x27;, &#x27;r5i1091p1f2&#x27;, &#x27;r6i1111p1f2&#x27;, &#x27;r7i1131p1f2&#x27;,\n",
       "       &#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'>1850-01-01 12:00:00 ... 2014-12-...</div><input id='attrs-df8ad5a9-433d-477e-8be3-c31c3c780998' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df8ad5a9-433d-477e-8be3-c31c3c780998' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-daaae1f4-b44a-434e-a4ee-547ffba5fb40' class='xr-var-data-in' type='checkbox'><label for='data-daaae1f4-b44a-434e-a4ee-547ffba5fb40' 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(1850, 1, 1, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 1, 2, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 1, 3, 12, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.DatetimeNoLeap(2014, 12, 29, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 30, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 31, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, nbnd)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30113, 1), meta=np.ndarray&gt;</div><input id='attrs-ac395243-c351-4e02-9624-86e3c80ce402' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac395243-c351-4e02-9624-86e3c80ce402' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc4cbbad-73c1-4d3a-ab6c-af3edea95f12' class='xr-var-data-in' type='checkbox'><label for='data-bc4cbbad-73c1-4d3a-ab6c-af3edea95f12' 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 interval endpoints</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 0.92 MiB </td>\n",
       "                        <td> 235.26 kiB </td>\n",
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       "                        <th> Shape </th>\n",
       "                        <td> (60225, 2) </td>\n",
       "                        <td> (30113, 1) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 4 chunks in 2 graph layers </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-eea65c79-6b77-4d4e-9755-1a132b6380f4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eea65c79-6b77-4d4e-9755-1a132b6380f4' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>TREFHTMX</span></div><div class='xr-var-dims'>(member_id, time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 730, 192, 288), meta=np.ndarray&gt;</div><input id='attrs-3d753176-476a-4422-8d8b-97b4b2a9ad29' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d753176-476a-4422-8d8b-97b4b2a9ad29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4616fc8e-a5fd-401a-a528-4ec49e95b024' class='xr-var-data-in' type='checkbox'><label for='data-4616fc8e-a5fd-401a-a528-4ec49e95b024' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>cell_methods :</span></dt><dd>time: 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><div class='xr-var-data'><table>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-4cd68c37-fa9a-48b7-b515-769eff8c8234' class='xr-section-summary-in' type='checkbox'  ><label for='section-4cd68c37-fa9a-48b7-b515-769eff8c8234' 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-ae0907c9-3519-4f43-9ab7-33c0a5767cdd' class='xr-index-data-in' type='checkbox'/><label for='index-ae0907c9-3519-4f43-9ab7-33c0a5767cdd' 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-0d2dfd46-9bf0-4bf2-ab88-c501176ea5cb' class='xr-index-data-in' type='checkbox'/><label for='index-0d2dfd46-9bf0-4bf2-ab88-c501176ea5cb' 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-617ebf9c-8e36-4ce0-baa9-cbc271eeb9c9' class='xr-index-data-in' type='checkbox'/><label for='index-617ebf9c-8e36-4ce0-baa9-cbc271eeb9c9' 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;r10i1191p1f2&#x27;, &#x27;r11i1231p1f2&#x27;, &#x27;r11i1251p1f2&#x27;, &#x27;r11i1281p1f2&#x27;,\n",
       "       &#x27;r11i1301p1f2&#x27;, &#x27;r12i1231p1f2&#x27;, &#x27;r12i1251p1f2&#x27;, &#x27;r12i1281p1f2&#x27;,\n",
       "       &#x27;r12i1301p1f2&#x27;, &#x27;r13i1231p1f2&#x27;, &#x27;r13i1251p1f2&#x27;, &#x27;r13i1281p1f2&#x27;,\n",
       "       &#x27;r13i1301p1f2&#x27;, &#x27;r14i1231p1f2&#x27;, &#x27;r14i1251p1f2&#x27;, &#x27;r14i1281p1f2&#x27;,\n",
       "       &#x27;r14i1301p1f2&#x27;, &#x27;r15i1231p1f2&#x27;, &#x27;r15i1251p1f2&#x27;, &#x27;r15i1281p1f2&#x27;,\n",
       "       &#x27;r15i1301p1f2&#x27;, &#x27;r16i1231p1f2&#x27;, &#x27;r16i1251p1f2&#x27;, &#x27;r16i1281p1f2&#x27;,\n",
       "       &#x27;r16i1301p1f2&#x27;, &#x27;r17i1231p1f2&#x27;, &#x27;r17i1251p1f2&#x27;, &#x27;r17i1281p1f2&#x27;,\n",
       "       &#x27;r17i1301p1f2&#x27;, &#x27;r18i1231p1f2&#x27;, &#x27;r18i1251p1f2&#x27;, &#x27;r18i1281p1f2&#x27;,\n",
       "       &#x27;r18i1301p1f2&#x27;, &#x27;r19i1231p1f2&#x27;, &#x27;r19i1251p1f2&#x27;, &#x27;r19i1281p1f2&#x27;,\n",
       "       &#x27;r19i1301p1f2&#x27;, &#x27;r1i1011p1f2&#x27;, &#x27;r20i1231p1f2&#x27;, &#x27;r20i1251p1f2&#x27;,\n",
       "       &#x27;r20i1281p1f2&#x27;, &#x27;r20i1301p1f2&#x27;, &#x27;r2i1031p1f2&#x27;, &#x27;r3i1051p1f2&#x27;,\n",
       "       &#x27;r4i1071p1f2&#x27;, &#x27;r5i1091p1f2&#x27;, &#x27;r6i1111p1f2&#x27;, &#x27;r7i1131p1f2&#x27;,\n",
       "       &#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-a0cc7788-9875-4919-8006-7a64f87114b4' class='xr-index-data-in' type='checkbox'/><label for='index-a0cc7788-9875-4919-8006-7a64f87114b4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(CFTimeIndex([1850-01-01 12:00:00, 1850-01-02 12:00:00, 1850-01-03 12:00:00,\n",
       "             1850-01-04 12:00:00, 1850-01-05 12:00:00, 1850-01-06 12:00:00,\n",
       "             1850-01-07 12:00:00, 1850-01-08 12:00:00, 1850-01-09 12:00:00,\n",
       "             1850-01-10 12:00:00,\n",
       "             ...\n",
       "             2014-12-22 12:00:00, 2014-12-23 12:00:00, 2014-12-24 12:00:00,\n",
       "             2014-12-25 12:00:00, 2014-12-26 12:00:00, 2014-12-27 12:00:00,\n",
       "             2014-12-28 12:00:00, 2014-12-29 12:00:00, 2014-12-30 12:00:00,\n",
       "             2014-12-31 12:00:00],\n",
       "            dtype=&#x27;object&#x27;, length=60225, calendar=&#x27;noleap&#x27;, freq=&#x27;D&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-da2f625b-fef8-4185-b589-0948edb975af' class='xr-section-summary-in' type='checkbox'  ><label for='section-da2f625b-fef8-4185-b589-0948edb975af' class='xr-section-summary' >Attributes: <span>(22)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>logname :</span></dt><dd>sunseon</dd><dt><span>model_doi_url :</span></dt><dd>https://doi.org/10.5065/D67H1H0V</dd><dt><span>source :</span></dt><dd>CAM</dd><dt><span>time_period_freq :</span></dt><dd>day_1</dd><dt><span>topography_file :</span></dt><dd>/mnt/lustre/share/CESM/cesm_input/atm/cam/topo/fv_0.9x1.25_nc3000_Nsw042_Nrs008_Co060_Fi001_ZR_sgh30_24km_GRNL_c170103.nc</dd><dt><span>intake_esm_vars :</span></dt><dd>[&#x27;TREFHTMX&#x27;]</dd><dt><span>intake_esm_attrs:Unnamed: 0 :</span></dt><dd>41</dd><dt><span>intake_esm_attrs:variable :</span></dt><dd>TREFHTMX</dd><dt><span>intake_esm_attrs:long_name :</span></dt><dd>maximum reference height temperature over output period</dd><dt><span>intake_esm_attrs:component :</span></dt><dd>atm</dd><dt><span>intake_esm_attrs:experiment :</span></dt><dd>historical</dd><dt><span>intake_esm_attrs:forcing_variant :</span></dt><dd>smbb</dd><dt><span>intake_esm_attrs:frequency :</span></dt><dd>daily</dd><dt><span>intake_esm_attrs:vertical_levels :</span></dt><dd>1.0</dd><dt><span>intake_esm_attrs:spatial_domain :</span></dt><dd>global</dd><dt><span>intake_esm_attrs:units :</span></dt><dd>K</dd><dt><span>intake_esm_attrs:start_time :</span></dt><dd>1850-01-01 12:00:00</dd><dt><span>intake_esm_attrs:end_time :</span></dt><dd>2014-12-31 12:00:00</dd><dt><span>intake_esm_attrs:path :</span></dt><dd>osdf:///aws-opendata/us-west-2/ncar-cesm2-lens/atm/daily/cesm2LE-historical-smbb-TREFHTMX.zarr</dd><dt><span>intake_esm_attrs:_data_format_ :</span></dt><dd>zarr</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>atm.historical.daily.smbb</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 666GB\n",
       "Dimensions:    (member_id: 50, time: 60225, lat: 192, lon: 288, nbnd: 2)\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 2kB 'r10i1191p1f2' ... 'r9i1171p1f2'\n",
       "  * time       (time) object 482kB 1850-01-01 12:00:00 ... 2014-12-31 12:00:00\n",
       "    time_bnds  (time, nbnd) object 964kB dask.array<chunksize=(30113, 1), meta=np.ndarray>\n",
       "Dimensions without coordinates: nbnd\n",
       "Data variables:\n",
       "    TREFHTMX   (member_id, time, lat, lon) float32 666GB dask.array<chunksize=(1, 730, 192, 288), meta=np.ndarray>\n",
       "Attributes: (12/22)\n",
       "    Conventions:                       CF-1.0\n",
       "    logname:                           sunseon\n",
       "    model_doi_url:                     https://doi.org/10.5065/D67H1H0V\n",
       "    source:                            CAM\n",
       "    time_period_freq:                  day_1\n",
       "    topography_file:                   /mnt/lustre/share/CESM/cesm_input/atm/...\n",
       "    ...                                ...\n",
       "    intake_esm_attrs:units:            K\n",
       "    intake_esm_attrs:start_time:       1850-01-01 12:00:00\n",
       "    intake_esm_attrs:end_time:         2014-12-31 12:00:00\n",
       "    intake_esm_attrs:path:             osdf:///aws-opendata/us-west-2/ncar-ce...\n",
       "    intake_esm_attrs:_data_format_:    zarr\n",
       "    intake_esm_dataset_key:            atm.historical.daily.smbb"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "historical_smbb = dsets['atm.historical.daily.smbb']\n",
    "historical_smbb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "816bd14d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 675 µs, sys: 0 ns, total: 675 µs\n",
      "Wall time: 684 µs\n"
     ]
    },
    {
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       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;TREFHTMX&#x27; (lat: 192, lon: 288)&gt; Size: 221kB\n",
       "dask.array&lt;getitem, shape=(192, 288), dtype=float32, chunksize=(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  &lt;U12 48B &#x27;r10i1191p1f2&#x27;\n",
       "    time       object 8B 1850-01-01 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'>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-7cd852f3-a371-4aa3-b4f9-12a9ebc4c9f9' class='xr-array-in' type='checkbox' checked><label for='section-7cd852f3-a371-4aa3-b4f9-12a9ebc4c9f9' 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), 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> 216.00 kiB </td>\n",
       "                        <td> 216.00 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (192, 288) </td>\n",
       "                        <td> (192, 288) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"130\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"80\" x2=\"120\" y2=\"80\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"80\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"80\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,80.0 0.0,80.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"100.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >288</text>\n",
       "  <text x=\"140.000000\" y=\"40.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,40.000000)\">192</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-fe75af47-63ec-4f9b-9def-f6a7f1d740ad' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fe75af47-63ec-4f9b-9def-f6a7f1d740ad' 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-91ac3934-2887-460e-8dfa-c409915ee96b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-91ac3934-2887-460e-8dfa-c409915ee96b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-512b39cd-c785-468e-9579-90ec71101424' class='xr-var-data-in' type='checkbox'><label for='data-512b39cd-c785-468e-9579-90ec71101424' 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-c0292a3b-6d4b-48a4-a111-b4b957f1d31b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0292a3b-6d4b-48a4-a111-b4b957f1d31b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12de407e-bc33-42b2-b546-9092ab1d45bf' class='xr-var-data-in' type='checkbox'><label for='data-12de407e-bc33-42b2-b546-9092ab1d45bf' 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>member_id</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U12</div><div class='xr-var-preview xr-preview'>&#x27;r10i1191p1f2&#x27;</div><input id='attrs-dd4e0350-e13c-4b7e-9646-25b5ab4f38bf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dd4e0350-e13c-4b7e-9646-25b5ab4f38bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ca1464e-1ffd-4942-9319-5647a525b602' class='xr-var-data-in' type='checkbox'><label for='data-6ca1464e-1ffd-4942-9319-5647a525b602' 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;r10i1191p1f2&#x27;, dtype=&#x27;&lt;U12&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1850-01-01 12:00:00</div><input id='attrs-6a88db2a-f8b3-4b9f-be53-8a542681683d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a88db2a-f8b3-4b9f-be53-8a542681683d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d9b0b87-1982-43ec-b5ea-e9404dd098f1' class='xr-var-data-in' type='checkbox'><label for='data-4d9b0b87-1982-43ec-b5ea-e9404dd098f1' 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(1850, 1, 1, 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-ec965aa5-7296-4f6e-8a85-14772f57218e' class='xr-section-summary-in' type='checkbox'  ><label for='section-ec965aa5-7296-4f6e-8a85-14772f57218e' 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-857450d1-5583-4962-abff-a768112ab5dd' class='xr-index-data-in' type='checkbox'/><label for='index-857450d1-5583-4962-abff-a768112ab5dd' 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-20a7d2e1-223e-435a-8ed4-be5de10663da' class='xr-index-data-in' type='checkbox'/><label for='index-20a7d2e1-223e-435a-8ed4-be5de10663da' 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-420f42fc-5ea5-4183-99a4-1077dfae41a1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-420f42fc-5ea5-4183-99a4-1077dfae41a1' 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' (lat: 192, lon: 288)> Size: 221kB\n",
       "dask.array<getitem, shape=(192, 288), dtype=float32, chunksize=(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  <U12 48B 'r10i1191p1f2'\n",
       "    time       object 8B 1850-01-01 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": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "historical_smbb.TREFHTMX.isel(member_id=0,time=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "07320dcd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 396 ms, sys: 173 ms, total: 570 ms\n",
      "Wall time: 1.29 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x1544ef476790>"
      ]
     },
     "execution_count": 21,
     "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",
    "historical_smbb.TREFHTMX.isel(member_id=0,time=0).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1881519b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}