EN422g10_Creat24N.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1df62f37-e43c-4f83-9107-5cebfdbacca9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt  # plotting library\n",
    "import numpy as np  # numerical library\n",
    "import xarray as xr  # netCDF library\n",
    "import cartopy  # Map projections libary\n",
    "import cartopy.crs as ccrs  # Projections list\n",
    "import cftime\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be242b2c-f910-421f-bf0c-99e8c2be40a3",
   "metadata": {},
   "source": [
    "# Settings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "96ad8bc4-bb60-4873-807d-450925dbf97b",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = (12, 5)  # Default plot size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dc20665-be3e-4fef-9556-ea6b52aa9afd",
   "metadata": {},
   "source": [
    "## Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4eb855b5-d1a2-4ea6-80a1-1ce1277be1d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir='/Volumes/HD/Climatologias/EN.4.2.2.g10/NC/'\n",
    "\n",
    "files = []\n",
    "for iy in range(1950,2024):\n",
    "    for im in range(1,13):\n",
    "        files.append(data_dir+\"EN.4.2.2.f.analysis.g10.%04d%02d.nc\"%(iy,im))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5b7ee9ad-5dfd-4570-aba0-00f1c9ee98d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "DC = xr.open_mfdataset(files)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "13410dc6-02ac-4eff-9494-01291f19c162",
   "metadata": {},
   "outputs": [],
   "source": [
    "EN = DC.drop_vars(\"temperature_uncertainty\").drop_vars(\"salinity_uncertainty\")\n",
    "data = EN.drop_vars(\"temperature_observation_weights\").drop_vars(\"salinity_observation_weights\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "269d9cf5-a348-401b-bd23-fcf658a6f019",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\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: 37GB\n",
       "Dimensions:      (depth: 42, lat: 173, lon: 360, time: 888, bnds: 2)\n",
       "Coordinates:\n",
       "  * depth        (depth) float32 168B 5.022 15.08 25.16 ... 5.051e+03 5.35e+03\n",
       "  * lat          (lat) float32 692B -83.0 -82.0 -81.0 -80.0 ... 87.0 88.0 89.0\n",
       "  * lon          (lon) float32 1kB 1.0 2.0 3.0 4.0 ... 357.0 358.0 359.0 360.0\n",
       "  * time         (time) datetime64[ns] 7kB 1950-01-16T12:00:00 ... 2023-12-16...\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    temperature  (time, depth, lat, lon) float64 19GB dask.array&lt;chunksize=(1, 21, 87, 180), meta=np.ndarray&gt;\n",
       "    salinity     (time, depth, lat, lon) float64 19GB dask.array&lt;chunksize=(1, 21, 87, 180), meta=np.ndarray&gt;\n",
       "    time_bnds    (time, bnds) datetime64[ns] 14kB dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\n",
       "    depth_bnds   (time, depth, bnds) float32 298kB dask.array&lt;chunksize=(1, 42, 2), meta=np.ndarray&gt;\n",
       "Attributes: (12/22)\n",
       "    Conventions:            CF-1.0\n",
       "    title:                  Temperature and salinity analysis\n",
       "    DSD_entry_id:           UKMO-L4UHFnd-GLOB-v01\n",
       "    references:             Website and paper: https://www.metoffice.gov.uk/h...\n",
       "    institution:            UK Met Office\n",
       "    contact:                Rachel Killick - rachel.killick@metoffice.gov.uk\n",
       "    ...                     ...\n",
       "    southernmost_latitude:  -90.5\n",
       "    northernmost_latitude:  89.5\n",
       "    westernmost_longitude:  0.5\n",
       "    easternmost_longitude:  362.5\n",
       "    file_quality_index:     0\n",
       "    licence:                EN4 is distributed under the Non Commercial Gover...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8f4baae3-6e2d-40a8-be0d-f54951d145d0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8f4baae3-6e2d-40a8-be0d-f54951d145d0' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>depth</span>: 42</li><li><span class='xr-has-index'>lat</span>: 173</li><li><span class='xr-has-index'>lon</span>: 360</li><li><span class='xr-has-index'>time</span>: 888</li><li><span>bnds</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ad24e8d8-9574-4dde-905b-5fc906f8fd50' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ad24e8d8-9574-4dde-905b-5fc906f8fd50' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth</span></div><div class='xr-var-dims'>(depth)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.022 15.08 ... 5.051e+03 5.35e+03</div><input id='attrs-9dd60795-d4e6-4454-a94e-85961971c7b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9dd60795-d4e6-4454-a94e-85961971c7b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-578a3b51-c933-4911-8270-d13ce9d17f9e' class='xr-var-data-in' type='checkbox'><label for='data-578a3b51-c933-4911-8270-d13ce9d17f9e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>metres</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>bounds :</span></dt><dd>depth_bnds</dd></dl></div><div class='xr-var-data'><pre>array([5.021590e+00, 1.507854e+01, 2.516046e+01, 3.527829e+01, 4.544776e+01,\n",
       "       5.569149e+01, 6.604198e+01, 7.654591e+01, 8.727029e+01, 9.831118e+01,\n",
       "       1.098062e+02, 1.219519e+02, 1.350285e+02, 1.494337e+02, 1.657285e+02,\n",
       "       1.846975e+02, 2.074254e+02, 2.353862e+02, 2.705341e+02, 3.153741e+02,\n",
       "       3.729655e+02, 4.468009e+02, 5.405022e+02, 6.573229e+02, 7.995496e+02,\n",
       "       9.679958e+02, 1.161806e+03, 1.378661e+03, 1.615291e+03, 1.868071e+03,\n",
       "       2.133517e+03, 2.408583e+03, 2.690780e+03, 2.978166e+03, 3.269278e+03,\n",
       "       3.563041e+03, 3.858676e+03, 4.155628e+03, 4.453502e+03, 4.752021e+03,\n",
       "       5.050990e+03, 5.350272e+03], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-83.0 -82.0 -81.0 ... 88.0 89.0</div><input id='attrs-f8a7b64e-d779-4abd-bc02-6a412b3f87e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8a7b64e-d779-4abd-bc02-6a412b3f87e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1728fabe-4d4b-4939-b1e6-9bf68e66545f' class='xr-var-data-in' type='checkbox'><label for='data-1728fabe-4d4b-4939-b1e6-9bf68e66545f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-83., -82., -81., -80., -79., -78., -77., -76., -75., -74., -73., -72.,\n",
       "       -71., -70., -69., -68., -67., -66., -65., -64., -63., -62., -61., -60.,\n",
       "       -59., -58., -57., -56., -55., -54., -53., -52., -51., -50., -49., -48.,\n",
       "       -47., -46., -45., -44., -43., -42., -41., -40., -39., -38., -37., -36.,\n",
       "       -35., -34., -33., -32., -31., -30., -29., -28., -27., -26., -25., -24.,\n",
       "       -23., -22., -21., -20., -19., -18., -17., -16., -15., -14., -13., -12.,\n",
       "       -11., -10.,  -9.,  -8.,  -7.,  -6.,  -5.,  -4.,  -3.,  -2.,  -1.,   0.,\n",
       "         1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.,  12.,\n",
       "        13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,  22.,  23.,  24.,\n",
       "        25.,  26.,  27.,  28.,  29.,  30.,  31.,  32.,  33.,  34.,  35.,  36.,\n",
       "        37.,  38.,  39.,  40.,  41.,  42.,  43.,  44.,  45.,  46.,  47.,  48.,\n",
       "        49.,  50.,  51.,  52.,  53.,  54.,  55.,  56.,  57.,  58.,  59.,  60.,\n",
       "        61.,  62.,  63.,  64.,  65.,  66.,  67.,  68.,  69.,  70.,  71.,  72.,\n",
       "        73.,  74.,  75.,  76.,  77.,  78.,  79.,  80.,  81.,  82.,  83.,  84.,\n",
       "        85.,  86.,  87.,  88.,  89.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.0 2.0 3.0 ... 358.0 359.0 360.0</div><input id='attrs-21394d0a-a4f9-47aa-92f1-862378247f08' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21394d0a-a4f9-47aa-92f1-862378247f08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2afd5023-3c75-463a-ae1c-5f674b6747dd' class='xr-var-data-in' type='checkbox'><label for='data-2afd5023-3c75-463a-ae1c-5f674b6747dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  1.,   2.,   3., ..., 358., 359., 360.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1950-01-16T12:00:00 ... 2023-12-...</div><input id='attrs-b3a3d008-62cf-46b4-9a41-862c3a2d537a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3a3d008-62cf-46b4-9a41-862c3a2d537a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67c3938a-f8ce-4ebc-b1d0-d03e879b4a6d' class='xr-var-data-in' type='checkbox'><label for='data-67c3938a-f8ce-4ebc-b1d0-d03e879b4a6d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1950-01-16T12:00:00.000000000&#x27;, &#x27;1950-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1950-03-16T12:00:00.000000000&#x27;, ..., &#x27;2023-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2023-11-16T00:00:00.000000000&#x27;, &#x27;2023-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-74a60a19-a68b-44cf-ae93-fd51421bc6c1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-74a60a19-a68b-44cf-ae93-fd51421bc6c1' class='xr-section-summary' >Data variables: <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>temperature</span></div><div class='xr-var-dims'>(time, depth, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 21, 87, 180), meta=np.ndarray&gt;</div><input id='attrs-0970a46d-1f5b-434e-a469-447e97e9059c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0970a46d-1f5b-434e-a469-447e97e9059c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef0e7eff-66ff-4c82-b27c-0a4ddd2299f9' class='xr-var-data-in' type='checkbox'><label for='data-ef0e7eff-66ff-4c82-b27c-0a4ddd2299f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>valid_min :</span></dt><dd>-5.0</dd><dt><span>valid_max :</span></dt><dd>45.0</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 17.31 GiB </td>\n",
       "                        <td> 2.51 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (888, 42, 173, 360) </td>\n",
       "                        <td> (1, 21, 87, 180) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 7104 chunks in 1813 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"498\" height=\"111\" 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=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"3\" y1=\"0\" x2=\"3\" y2=\"25\" />\n",
       "  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n",
       "  <line x1=\"11\" y1=\"0\" x2=\"11\" y2=\"25\" />\n",
       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
       "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
       "  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n",
       "  <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
       "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"25\" />\n",
       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
       "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n",
       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n",
       "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"25\" />\n",
       "  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n",
       "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
       "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
       "  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n",
       "  <line x1=\"71\" y1=\"0\" x2=\"71\" y2=\"25\" />\n",
       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
       "  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
       "  <line x1=\"86\" y1=\"0\" x2=\"86\" y2=\"25\" />\n",
       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
       "  <line x1=\"93\" y1=\"0\" x2=\"93\" y2=\"25\" />\n",
       "  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n",
       "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n",
       "  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"25\" />\n",
       "  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n",
       "  <line x1=\"116\" y1=\"0\" x2=\"116\" y2=\"25\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >888</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"190\" y1=\"0\" x2=\"210\" y2=\"20\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"190\" y1=\"21\" x2=\"210\" y2=\"41\" />\n",
       "  <line x1=\"190\" y1=\"41\" x2=\"210\" y2=\"61\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"41\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"200\" y1=\"10\" x2=\"200\" y2=\"51\" />\n",
       "  <line x1=\"210\" y1=\"20\" x2=\"210\" y2=\"61\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"190.0,0.0 210.09444154387342,20.094441543873415 210.09444154387342,61.992685444416594 190.0,41.89824390054318\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"190\" y1=\"0\" x2=\"238\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"200\" y1=\"10\" x2=\"248\" y2=\"10\" />\n",
       "  <line x1=\"210\" y1=\"20\" x2=\"258\" y2=\"20\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"190\" y1=\"0\" x2=\"210\" y2=\"20\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"214\" y1=\"0\" x2=\"234\" y2=\"20\" />\n",
       "  <line x1=\"238\" y1=\"0\" x2=\"258\" y2=\"20\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"190.0,0.0 238.64864864864865,0.0 258.74309019252206,20.094441543873415 210.09444154387342,20.094441543873415\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"210\" y1=\"20\" x2=\"258\" y2=\"20\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"210\" y1=\"41\" x2=\"258\" y2=\"41\" />\n",
       "  <line x1=\"210\" y1=\"61\" x2=\"258\" y2=\"61\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"210\" y1=\"20\" x2=\"210\" y2=\"61\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"234\" y1=\"20\" x2=\"234\" y2=\"61\" />\n",
       "  <line x1=\"258\" y1=\"20\" x2=\"258\" y2=\"61\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"210.09444154387342,20.094441543873415 258.74309019252206,20.094441543873415 258.74309019252206,61.992685444416594 210.09444154387342,61.992685444416594\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"234.418766\" y=\"81.992685\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >360</text>\n",
       "  <text x=\"278.743090\" y=\"41.043563\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,278.743090,41.043563)\">173</text>\n",
       "  <text x=\"190.047221\" y=\"71.945465\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,190.047221,71.945465)\">42</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salinity</span></div><div class='xr-var-dims'>(time, depth, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 21, 87, 180), meta=np.ndarray&gt;</div><input id='attrs-24f16eb7-0305-447c-b052-fd607f8bf454' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24f16eb7-0305-447c-b052-fd607f8bf454' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5dfed148-3464-43d6-83cb-92f2f04df4f1' class='xr-var-data-in' type='checkbox'><label for='data-5dfed148-3464-43d6-83cb-92f2f04df4f1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>salinity</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd><dt><span>valid_min :</span></dt><dd>-5.0</dd><dt><span>valid_max :</span></dt><dd>48.0</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 17.31 GiB </td>\n",
       "                        <td> 2.51 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (888, 42, 173, 360) </td>\n",
       "                        <td> (1, 21, 87, 180) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 7104 chunks in 1813 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"498\" height=\"111\" 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=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"3\" y1=\"0\" x2=\"3\" y2=\"25\" />\n",
       "  <line x1=\"7\" y1=\"0\" x2=\"7\" y2=\"25\" />\n",
       "  <line x1=\"11\" y1=\"0\" x2=\"11\" y2=\"25\" />\n",
       "  <line x1=\"15\" y1=\"0\" x2=\"15\" y2=\"25\" />\n",
       "  <line x1=\"18\" y1=\"0\" x2=\"18\" y2=\"25\" />\n",
       "  <line x1=\"22\" y1=\"0\" x2=\"22\" y2=\"25\" />\n",
       "  <line x1=\"26\" y1=\"0\" x2=\"26\" y2=\"25\" />\n",
       "  <line x1=\"30\" y1=\"0\" x2=\"30\" y2=\"25\" />\n",
       "  <line x1=\"33\" y1=\"0\" x2=\"33\" y2=\"25\" />\n",
       "  <line x1=\"37\" y1=\"0\" x2=\"37\" y2=\"25\" />\n",
       "  <line x1=\"41\" y1=\"0\" x2=\"41\" y2=\"25\" />\n",
       "  <line x1=\"45\" y1=\"0\" x2=\"45\" y2=\"25\" />\n",
       "  <line x1=\"48\" y1=\"0\" x2=\"48\" y2=\"25\" />\n",
       "  <line x1=\"52\" y1=\"0\" x2=\"52\" y2=\"25\" />\n",
       "  <line x1=\"56\" y1=\"0\" x2=\"56\" y2=\"25\" />\n",
       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"25\" />\n",
       "  <line x1=\"63\" y1=\"0\" x2=\"63\" y2=\"25\" />\n",
       "  <line x1=\"67\" y1=\"0\" x2=\"67\" y2=\"25\" />\n",
       "  <line x1=\"71\" y1=\"0\" x2=\"71\" y2=\"25\" />\n",
       "  <line x1=\"75\" y1=\"0\" x2=\"75\" y2=\"25\" />\n",
       "  <line x1=\"78\" y1=\"0\" x2=\"78\" y2=\"25\" />\n",
       "  <line x1=\"82\" y1=\"0\" x2=\"82\" y2=\"25\" />\n",
       "  <line x1=\"86\" y1=\"0\" x2=\"86\" y2=\"25\" />\n",
       "  <line x1=\"90\" y1=\"0\" x2=\"90\" y2=\"25\" />\n",
       "  <line x1=\"93\" y1=\"0\" x2=\"93\" y2=\"25\" />\n",
       "  <line x1=\"97\" y1=\"0\" x2=\"97\" y2=\"25\" />\n",
       "  <line x1=\"101\" y1=\"0\" x2=\"101\" y2=\"25\" />\n",
       "  <line x1=\"105\" y1=\"0\" x2=\"105\" y2=\"25\" />\n",
       "  <line x1=\"108\" y1=\"0\" x2=\"108\" y2=\"25\" />\n",
       "  <line x1=\"112\" y1=\"0\" x2=\"112\" y2=\"25\" />\n",
       "  <line x1=\"116\" y1=\"0\" x2=\"116\" y2=\"25\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >888</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"190\" y1=\"0\" x2=\"210\" y2=\"20\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"190\" y1=\"21\" x2=\"210\" y2=\"41\" />\n",
       "  <line x1=\"190\" y1=\"41\" x2=\"210\" y2=\"61\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"190\" y1=\"0\" x2=\"190\" y2=\"41\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"200\" y1=\"10\" x2=\"200\" y2=\"51\" />\n",
       "  <line x1=\"210\" y1=\"20\" x2=\"210\" y2=\"61\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"190.0,0.0 210.09444154387342,20.094441543873415 210.09444154387342,61.992685444416594 190.0,41.89824390054318\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"190\" y1=\"0\" x2=\"238\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"200\" y1=\"10\" x2=\"248\" y2=\"10\" />\n",
       "  <line x1=\"210\" y1=\"20\" x2=\"258\" y2=\"20\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"190\" y1=\"0\" x2=\"210\" y2=\"20\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"214\" y1=\"0\" x2=\"234\" y2=\"20\" />\n",
       "  <line x1=\"238\" y1=\"0\" x2=\"258\" y2=\"20\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"190.0,0.0 238.64864864864865,0.0 258.74309019252206,20.094441543873415 210.09444154387342,20.094441543873415\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"210\" y1=\"20\" x2=\"258\" y2=\"20\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"210\" y1=\"41\" x2=\"258\" y2=\"41\" />\n",
       "  <line x1=\"210\" y1=\"61\" x2=\"258\" y2=\"61\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"210\" y1=\"20\" x2=\"210\" y2=\"61\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"234\" y1=\"20\" x2=\"234\" y2=\"61\" />\n",
       "  <line x1=\"258\" y1=\"20\" x2=\"258\" y2=\"61\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"210.09444154387342,20.094441543873415 258.74309019252206,20.094441543873415 258.74309019252206,61.992685444416594 210.09444154387342,61.992685444416594\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"234.418766\" y=\"81.992685\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >360</text>\n",
       "  <text x=\"278.743090\" y=\"41.043563\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,278.743090,41.043563)\">173</text>\n",
       "  <text x=\"190.047221\" y=\"71.945465\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,190.047221,71.945465)\">42</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-e06afac5-d6c3-406f-822e-a3e327ec7f61' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e06afac5-d6c3-406f-822e-a3e327ec7f61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a42cb3d-9c9d-43d0-8af7-05b0cfb8be43' class='xr-var-data-in' type='checkbox'><label for='data-9a42cb3d-9c9d-43d0-8af7-05b0cfb8be43' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 13.88 kiB </td>\n",
       "                        <td> 16 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (888, 2) </td>\n",
       "                        <td> (1, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 888 chunks in 1777 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"6\" x2=\"25\" y2=\"6\" />\n",
       "  <line x1=\"0\" y1=\"12\" x2=\"25\" y2=\"12\" />\n",
       "  <line x1=\"0\" y1=\"18\" x2=\"25\" y2=\"18\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" />\n",
       "  <line x1=\"0\" y1=\"31\" x2=\"25\" y2=\"31\" />\n",
       "  <line x1=\"0\" y1=\"37\" x2=\"25\" y2=\"37\" />\n",
       "  <line x1=\"0\" y1=\"44\" x2=\"25\" y2=\"44\" />\n",
       "  <line x1=\"0\" y1=\"50\" x2=\"25\" y2=\"50\" />\n",
       "  <line x1=\"0\" y1=\"56\" x2=\"25\" y2=\"56\" />\n",
       "  <line x1=\"0\" y1=\"63\" x2=\"25\" y2=\"63\" />\n",
       "  <line x1=\"0\" y1=\"69\" x2=\"25\" y2=\"69\" />\n",
       "  <line x1=\"0\" y1=\"75\" x2=\"25\" y2=\"75\" />\n",
       "  <line x1=\"0\" y1=\"82\" x2=\"25\" y2=\"82\" />\n",
       "  <line x1=\"0\" y1=\"88\" x2=\"25\" y2=\"88\" />\n",
       "  <line x1=\"0\" y1=\"94\" x2=\"25\" y2=\"94\" />\n",
       "  <line x1=\"0\" y1=\"100\" x2=\"25\" y2=\"100\" />\n",
       "  <line x1=\"0\" y1=\"107\" x2=\"25\" y2=\"107\" />\n",
       "  <line x1=\"0\" y1=\"113\" x2=\"25\" y2=\"113\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">888</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth_bnds</span></div><div class='xr-var-dims'>(time, depth, bnds)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 42, 2), meta=np.ndarray&gt;</div><input id='attrs-4d8bd68b-6865-4a36-bced-02de7934f566' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4d8bd68b-6865-4a36-bced-02de7934f566' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bedc7726-2c73-489f-a9bd-b5b2393ce736' class='xr-var-data-in' type='checkbox'><label for='data-bedc7726-2c73-489f-a9bd-b5b2393ce736' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 291.38 kiB </td>\n",
       "                        <td> 336 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (888, 42, 2) </td>\n",
       "                        <td> (1, 42, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 888 chunks in 2665 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"156\" height=\"154\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"34\" x2=\"80\" y2=\"104\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"34\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"37\" />\n",
       "  <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"41\" />\n",
       "  <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"45\" />\n",
       "  <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"48\" />\n",
       "  <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"52\" />\n",
       "  <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"56\" />\n",
       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"60\" />\n",
       "  <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"63\" />\n",
       "  <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"67\" />\n",
       "  <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"71\" />\n",
       "  <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"75\" />\n",
       "  <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"78\" />\n",
       "  <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"82\" />\n",
       "  <line x1=\"61\" y1=\"51\" x2=\"61\" y2=\"86\" />\n",
       "  <line x1=\"65\" y1=\"55\" x2=\"65\" y2=\"89\" />\n",
       "  <line x1=\"69\" y1=\"59\" x2=\"69\" y2=\"93\" />\n",
       "  <line x1=\"73\" y1=\"63\" x2=\"73\" y2=\"97\" />\n",
       "  <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"101\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"104\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,104.74878591870245 10.0,34.16055062458481\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"13\" y1=\"3\" x2=\"39\" y2=\"3\" />\n",
       "  <line x1=\"17\" y1=\"7\" x2=\"42\" y2=\"7\" />\n",
       "  <line x1=\"21\" y1=\"11\" x2=\"46\" y2=\"11\" />\n",
       "  <line x1=\"24\" y1=\"14\" x2=\"50\" y2=\"14\" />\n",
       "  <line x1=\"28\" y1=\"18\" x2=\"53\" y2=\"18\" />\n",
       "  <line x1=\"32\" y1=\"22\" x2=\"57\" y2=\"22\" />\n",
       "  <line x1=\"35\" y1=\"25\" x2=\"61\" y2=\"25\" />\n",
       "  <line x1=\"39\" y1=\"29\" x2=\"65\" y2=\"29\" />\n",
       "  <line x1=\"43\" y1=\"33\" x2=\"68\" y2=\"33\" />\n",
       "  <line x1=\"47\" y1=\"37\" x2=\"72\" y2=\"37\" />\n",
       "  <line x1=\"50\" y1=\"40\" x2=\"76\" y2=\"40\" />\n",
       "  <line x1=\"54\" y1=\"44\" x2=\"79\" y2=\"44\" />\n",
       "  <line x1=\"58\" y1=\"48\" x2=\"83\" y2=\"48\" />\n",
       "  <line x1=\"61\" y1=\"51\" x2=\"87\" y2=\"51\" />\n",
       "  <line x1=\"65\" y1=\"55\" x2=\"91\" y2=\"55\" />\n",
       "  <line x1=\"69\" y1=\"59\" x2=\"94\" y2=\"59\" />\n",
       "  <line x1=\"73\" y1=\"63\" x2=\"98\" y2=\"63\" />\n",
       "  <line x1=\"76\" y1=\"66\" x2=\"102\" y2=\"66\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"35\" y1=\"0\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 35.41261651458248,0.0 106.00085180870013,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"106\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"104\" x2=\"106\" y2=\"104\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"104\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"106\" y1=\"70\" x2=\"106\" y2=\"104\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.58823529411765,70.58823529411765 106.00085180870013,70.58823529411765 106.00085180870013,104.74878591870245 80.58823529411765,104.74878591870245\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"93.294544\" y=\"124.748786\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
       "  <text x=\"126.000852\" y=\"87.668511\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,126.000852,87.668511)\">42</text>\n",
       "  <text x=\"35.294118\" y=\"89.454668\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,89.454668)\">888</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-928c4c6c-243c-4903-b09e-98fc83f785d8' class='xr-section-summary-in' type='checkbox'  ><label for='section-928c4c6c-243c-4903-b09e-98fc83f785d8' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>depth</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9982bb88-73dd-4819-9578-92c6957fac03' class='xr-index-data-in' type='checkbox'/><label for='index-9982bb88-73dd-4819-9578-92c6957fac03' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 5.021589756011963, 15.078539848327637, 25.160459518432617,\n",
       "       35.278289794921875,  45.44776153564453,  55.69149398803711,\n",
       "        66.04198455810547,  76.54591369628906,   87.2702865600586,\n",
       "         98.3111801147461,  109.8061752319336, 121.95189666748047,\n",
       "       135.02854919433594, 149.43373107910156, 165.72845458984375,\n",
       "        184.6974639892578, 207.42544555664062, 235.38616943359375,\n",
       "       270.53411865234375, 315.37408447265625,  372.9654541015625,\n",
       "        446.8009338378906,   540.502197265625,  657.3229370117188,\n",
       "        799.5496215820312,   967.995849609375,  1161.805908203125,\n",
       "       1378.6610107421875,   1615.29052734375, 1868.0706787109375,\n",
       "         2133.51708984375,   2408.58349609375,    2690.7802734375,\n",
       "           2978.166015625,  3269.278076171875,  3563.040771484375,\n",
       "         3858.67626953125,    4155.6279296875,     4453.501953125,\n",
       "         4752.02099609375,   5050.98974609375,   5350.27197265625],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;depth&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-41b3f7a2-bef6-4485-81c6-72f09475b4f0' class='xr-index-data-in' type='checkbox'/><label for='index-41b3f7a2-bef6-4485-81c6-72f09475b4f0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-83.0, -82.0, -81.0, -80.0, -79.0, -78.0, -77.0, -76.0, -75.0, -74.0,\n",
       "       ...\n",
       "        80.0,  81.0,  82.0,  83.0,  84.0,  85.0,  86.0,  87.0,  88.0,  89.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=173))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8ce3b623-33a4-4b49-8bb3-d102a471c9a9' class='xr-index-data-in' type='checkbox'/><label for='index-8ce3b623-33a4-4b49-8bb3-d102a471c9a9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  1.0,   2.0,   3.0,   4.0,   5.0,   6.0,   7.0,   8.0,   9.0,  10.0,\n",
       "       ...\n",
       "       351.0, 352.0, 353.0, 354.0, 355.0, 356.0, 357.0, 358.0, 359.0, 360.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=360))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d5dfc4ed-a946-49dc-b562-1107efb31b05' class='xr-index-data-in' type='checkbox'/><label for='index-d5dfc4ed-a946-49dc-b562-1107efb31b05' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1950-01-16 12:00:00&#x27;, &#x27;1950-02-15 00:00:00&#x27;,\n",
       "               &#x27;1950-03-16 12:00:00&#x27;, &#x27;1950-04-16 00:00:00&#x27;,\n",
       "               &#x27;1950-05-16 12:00:00&#x27;, &#x27;1950-06-16 00:00:00&#x27;,\n",
       "               &#x27;1950-07-16 12:00:00&#x27;, &#x27;1950-08-16 12:00:00&#x27;,\n",
       "               &#x27;1950-09-16 00:00:00&#x27;, &#x27;1950-10-16 12:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2023-03-16 12:00:00&#x27;, &#x27;2023-04-16 00:00:00&#x27;,\n",
       "               &#x27;2023-05-16 12:00:00&#x27;, &#x27;2023-06-16 00:00:00&#x27;,\n",
       "               &#x27;2023-07-16 12:00:00&#x27;, &#x27;2023-08-16 12:00:00&#x27;,\n",
       "               &#x27;2023-09-16 00:00:00&#x27;, &#x27;2023-10-16 12:00:00&#x27;,\n",
       "               &#x27;2023-11-16 00:00:00&#x27;, &#x27;2023-12-16 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=888, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a8d6845e-9463-4978-9a44-ceb7b1bfe8ba' class='xr-section-summary-in' type='checkbox'  ><label for='section-a8d6845e-9463-4978-9a44-ceb7b1bfe8ba' class='xr-section-summary' >Attributes: <span>(22)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.0</dd><dt><span>title :</span></dt><dd>Temperature and salinity analysis</dd><dt><span>DSD_entry_id :</span></dt><dd>UKMO-L4UHFnd-GLOB-v01</dd><dt><span>references :</span></dt><dd>Website and paper: https://www.metoffice.gov.uk/hadobs/en4/; Good, S. A., M. J. Martin and N. A. Rayner, 2013. EN4: quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates, Journal of Geophysical Research: Oceans, 118, 6704-6716, doi:10.1002/2013JC009067</dd><dt><span>institution :</span></dt><dd>UK Met Office</dd><dt><span>contact :</span></dt><dd>Rachel Killick - rachel.killick@metoffice.gov.uk</dd><dt><span>GDS_version_id :</span></dt><dd>v1.7</dd><dt><span>netcdf_version_id :</span></dt><dd>3.5</dd><dt><span>creation_date :</span></dt><dd>2021-03-16 15:08:37.447 -00:00</dd><dt><span>product_version :</span></dt><dd>1.0</dd><dt><span>history :</span></dt><dd></dd><dt><span>grid_resolution :</span></dt><dd>   1.00000 degree</dd><dt><span>start_date :</span></dt><dd>2001-01-01 UTC</dd><dt><span>start_time :</span></dt><dd>00:00:00 UTC</dd><dt><span>stop_date :</span></dt><dd>2001-01-01 UTC</dd><dt><span>stop_time :</span></dt><dd>00:00:00 UTC</dd><dt><span>southernmost_latitude :</span></dt><dd>-90.5</dd><dt><span>northernmost_latitude :</span></dt><dd>89.5</dd><dt><span>westernmost_longitude :</span></dt><dd>0.5</dd><dt><span>easternmost_longitude :</span></dt><dd>362.5</dd><dt><span>file_quality_index :</span></dt><dd>0</dd><dt><span>licence :</span></dt><dd>EN4 is distributed under the Non Commercial Government Licence: http://www.nationalarchives.gov.uk/doc/non-commercial-government-licence/version/2/. The data are available for non-commercial use with attribution to the data providers, please see the product website (see global attribute: references) for terms and conditions.</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 37GB\n",
       "Dimensions:      (depth: 42, lat: 173, lon: 360, time: 888, bnds: 2)\n",
       "Coordinates:\n",
       "  * depth        (depth) float32 168B 5.022 15.08 25.16 ... 5.051e+03 5.35e+03\n",
       "  * lat          (lat) float32 692B -83.0 -82.0 -81.0 -80.0 ... 87.0 88.0 89.0\n",
       "  * lon          (lon) float32 1kB 1.0 2.0 3.0 4.0 ... 357.0 358.0 359.0 360.0\n",
       "  * time         (time) datetime64[ns] 7kB 1950-01-16T12:00:00 ... 2023-12-16...\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    temperature  (time, depth, lat, lon) float64 19GB dask.array<chunksize=(1, 21, 87, 180), meta=np.ndarray>\n",
       "    salinity     (time, depth, lat, lon) float64 19GB dask.array<chunksize=(1, 21, 87, 180), meta=np.ndarray>\n",
       "    time_bnds    (time, bnds) datetime64[ns] 14kB dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "    depth_bnds   (time, depth, bnds) float32 298kB dask.array<chunksize=(1, 42, 2), meta=np.ndarray>\n",
       "Attributes: (12/22)\n",
       "    Conventions:            CF-1.0\n",
       "    title:                  Temperature and salinity analysis\n",
       "    DSD_entry_id:           UKMO-L4UHFnd-GLOB-v01\n",
       "    references:             Website and paper: https://www.metoffice.gov.uk/h...\n",
       "    institution:            UK Met Office\n",
       "    contact:                Rachel Killick - rachel.killick@metoffice.gov.uk\n",
       "    ...                     ...\n",
       "    southernmost_latitude:  -90.5\n",
       "    northernmost_latitude:  89.5\n",
       "    westernmost_longitude:  0.5\n",
       "    easternmost_longitude:  362.5\n",
       "    file_quality_index:     0\n",
       "    licence:                EN4 is distributed under the Non Commercial Gover..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c9723885-5aea-4ca4-b9c1-9ea2101c2cab",
   "metadata": {},
   "outputs": [],
   "source": [
    "loni=-70+360\n",
    "lonf=-23+360"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3e27369-8435-4029-a90a-8ba1d6eee38f",
   "metadata": {},
   "source": [
    "## Extract data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6b2137c2-843f-4266-ab5d-9e01e5e97851",
   "metadata": {},
   "outputs": [],
   "source": [
    "data2=data.interp(lat=[24.5], method=\"linear\").squeeze('lat').drop_vars('lat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7085fdea-4956-432b-aac3-547083c2e831",
   "metadata": {},
   "outputs": [],
   "source": [
    "data3=data2.interp(lon=np.arange(loni,lonf,0.5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0f998fa9-4ae7-497d-afa1-ff3c0c65eef7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\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;lon&#x27; (lon: 360)&gt; Size: 1kB\n",
       "array([  1.,   2.,   3., ..., 358., 359., 360.], dtype=float32)\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 1kB 1.0 2.0 3.0 4.0 5.0 ... 357.0 358.0 359.0 360.0\n",
       "Attributes:\n",
       "    long_name:      longitude\n",
       "    units:          degrees_east\n",
       "    standard_name:  longitude</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'>'lon'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lon</span>: 360</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-129fc67a-0f36-4b86-86d1-0afd5281da1f' class='xr-array-in' type='checkbox' checked><label for='section-129fc67a-0f36-4b86-86d1-0afd5281da1f' 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>1.0 2.0 3.0 4.0 5.0 6.0 7.0 ... 355.0 356.0 357.0 358.0 359.0 360.0</span></div><div class='xr-array-data'><pre>array([  1.,   2.,   3., ..., 358., 359., 360.], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-16dc0c6a-9b2b-4a0e-aa4b-c6ea3bb0ce4a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-16dc0c6a-9b2b-4a0e-aa4b-c6ea3bb0ce4a' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.0 2.0 3.0 ... 358.0 359.0 360.0</div><input id='attrs-c5036407-6980-417a-8a8b-0346931443da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c5036407-6980-417a-8a8b-0346931443da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-974bc55f-93a4-490e-a0dc-5c590682b478' class='xr-var-data-in' type='checkbox'><label for='data-974bc55f-93a4-490e-a0dc-5c590682b478' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  1.,   2.,   3., ..., 358., 359., 360.], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-00ff0ce0-8e9e-4400-8321-1b19a96d6773' class='xr-section-summary-in' type='checkbox'  ><label for='section-00ff0ce0-8e9e-4400-8321-1b19a96d6773' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e86e0fff-197e-491c-90cb-1a96e98619b6' class='xr-index-data-in' type='checkbox'/><label for='index-e86e0fff-197e-491c-90cb-1a96e98619b6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  1.0,   2.0,   3.0,   4.0,   5.0,   6.0,   7.0,   8.0,   9.0,  10.0,\n",
       "       ...\n",
       "       351.0, 352.0, 353.0, 354.0, 355.0, 356.0, 357.0, 358.0, 359.0, 360.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=360))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-74025f8f-89b3-40f0-be16-58be64f4d335' class='xr-section-summary-in' type='checkbox'  checked><label for='section-74025f8f-89b3-40f0-be16-58be64f4d335' 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>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'lon' (lon: 360)> Size: 1kB\n",
       "array([  1.,   2.,   3., ..., 358., 359., 360.], dtype=float32)\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 1kB 1.0 2.0 3.0 4.0 5.0 ... 357.0 358.0 359.0 360.0\n",
       "Attributes:\n",
       "    long_name:      longitude\n",
       "    units:          degrees_east\n",
       "    standard_name:  longitude"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2.lon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cd95e11f-c68c-4502-94f4-18d3a16d525b",
   "metadata": {},
   "outputs": [],
   "source": [
    "data24N = data.interp(lat=[24.5], method=\"nearest\").interp(lon=np.arange(loni,lonf,0.5), method=\"linear\").squeeze('lat').drop_vars('lat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0b5eb6b1-6f80-4b3a-9236-e00d26fe3c30",
   "metadata": {},
   "outputs": [],
   "source": [
    "data24N_2000 = data24N.interp(depth = np.arange(10,1900,10), method=\"linear\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2432d03e-52d7-45bf-85c9-4cabe3251c7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x318c720d0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ig, ax = plt.subplots(figsize = (14,6))\n",
    "ax.contourf(data24N_2000.temperature.lon,-data24N_2000.temperature.depth,data24N_2000.temperature.mean('time'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d45667ca-e8f2-4505-89d2-3a3b9ac71a3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x319e45390>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data24N_2000.temperature.mean('depth').mean('lon').plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "32ce9a74-be90-419c-a1bd-52e62a033215",
   "metadata": {},
   "outputs": [],
   "source": [
    "data24N.to_netcdf('./Data/EN422g10_interp_24N.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97bb6957-6a1b-4af7-81d9-a52d1712ffb1",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}