From 6e46e436a2db06c7a9d44719634acb0e20b8cfdf Mon Sep 17 00:00:00 2001
From: henze <henze@jpl.nasa.gov>
Date: Thu, 7 May 2026 17:32:55 -0700
Subject: [PATCH 1/2] Add open_icechunk.ipynb

---
 notebooks/Advanced_cloud/open_icechunk.ipynb | 12104 +++++++++++++++++
 1 file changed, 12104 insertions(+)
 create mode 100644 notebooks/Advanced_cloud/open_icechunk.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ce676c6b-db54-449a-9952-a73a9a93bf39",
+   "metadata": {},
+   "source": [
+    "## Example of Opening an Icechunk Store In-Cloud\n",
+    "\n",
+    "#### *Author: Dean Henze, PO.DAAC*\n",
+    "\n",
+    "This notebook demonstrates opening a virtual dataset (VDS) stored as an Icechunk store (note this technology is currently experimental, although it is gaining momentum). For a general overview of VDS's see the [Using Virutal Datasets chapter](https://podaac.github.io/tutorials/quarto_text/UsingVirtualDatasets.html) on the PO.DAAC Cookbook.\n",
+    "\n",
+    "The example dataset(s) used here are from the SASSIE ECCO project (The e.g. the [User Guide](https://doi.org/10.5067/SEL1D-DUG11) with project overview). The ECCO model (Estimating the Circulation and Climate of the Ocean) was run over the arctic region over a seven year period in support of the SASSIE field experiment (Salinity and Stratification at the Sea Ice Edge). The SASSIE ECCO data are traditionally archived and available in netCDF format - one file per day(month) for the daily-mean(monthly-snapshot) datasets. The model output variables (totalling ~50 TBs) are large enough that they are spread out over 18 different netCDF datasets for the daily-means, and over 3 different datasets for the monthly-snapshots. One novel functionality of VDS's being tested here is the ability to combine the data across e.g. the 18 daily-mean datasets so users can interact with the data as if all variables were contained in a single dataset. Computing performance is TBD.\n",
+    "\n",
+    "The AWS S3 paths to the SASSIE ECCO icechunk stores are\n",
+    "\n",
+    "**Daily averages:**\n",
+    "\n",
+    "s3://podaac-ops-cumulus-public/virtual_collections/SASSIE_ECCO_V1R1/SASSIE_ECCO_L4_DAILY_V1R1_virtual_s3.icechunk/\n",
+    "\n",
+    "**Monthly snapshots:**\n",
+    "\n",
+    "s3://podaac-ops-cumulus-public/virtual_collections/SASSIE_ECCO_V1R1/SASSIE_ECCO_L4_SNAPSHOT_V1R1_virtual_s3.icechunk/"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81464144-bf17-4fe0-861b-b97d01aed169",
+   "metadata": {},
+   "source": [
+    "## Requirements and Python environment\n",
+    "\n",
+    "* Earthdata login account: An Earthdata Login account is required to access data from the NASA Earthdata system. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account.\n",
+    "\n",
+    "* Compute environment: This notebook is meant to be run in the cloud (AWS instance running in us-west-2).\n",
+    "\n",
+    "* The minimal working installation for Python 3.13 environment is\n",
+    "\n",
+    "```\n",
+    "earthaccess==0.16.0\n",
+    "icechunk==1.1.19\n",
+    "xarray==2025.6.1\n",
+    "zarr==3.1.1\n",
+    "matplotlib\n",
+    "jupyterlab\n",
+    "```\n",
+    "\n",
+    "***Note that the icechunk=1.1.19 dependency is important! Do not use a more recent version.***"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0a074463-73c5-4284-85d5-9679eb64158b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import icechunk\n",
+    "import xarray as xr\n",
+    "import earthaccess"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ede75846-f591-408b-9421-ac73dee84312",
+   "metadata": {},
+   "source": [
+    "### 1. Define a function to access the icechunk store\n",
+    "\n",
+    "This function does not need any modification. It defines some access keywords and a mapper to the VDS. The mapper will contain all the required access credentials and can be passed directly to xarray. In the future this step will likely be built into to earthaccess but for now we must define it in the notebook. The only inputs to the function are:\n",
+    "\n",
+    "1. Your EDS credentials\n",
+    "2. The link to the VDS reference file (in the header of this notebook and in the next section)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "db744b69-c703-4234-8fab-b5b19aaffdaf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def open_readonly_icechunkstore_s3(\n",
+    "                           s3path_store, creds_store, creds_data_chunks, \n",
+    "                           bucket_data_chunks = \"s3://podaac-ops-cumulus-protected/\"):\n",
+    "    \"\"\"\n",
+    "    Opens and returns an icechunk store with read-only capabilities.\n",
+    "\n",
+    "    Inputs\n",
+    "    ------\n",
+    "    s3path_store: str or path\n",
+    "        AWS S3 path to the icechunk store, e.g. \"s3://podaac-ops-cumulus-public/virtual_collections/...\".\n",
+    "    creds_store: dict\n",
+    "        Credentials to access the icechunk store (e.g. EDL creds for NASA Earthdata). Expected\n",
+    "        dictionary keys are \"accessKeyId\", \"secretAccessKey\", and \"sessionToken\".\n",
+    "    creds_data_chunks: dict\n",
+    "        Credentials to access the science data that the icechunk store points to (e.g. EDL creds \n",
+    "        for NASA Earthdata). Expected dictionary keys are \"accessKeyId\", \"secretAccessKey\",\n",
+    "        and \"sessionToken\".\n",
+    "    bucket_data_chunks: str\n",
+    "        Name of bucket containing the science data that the icechunk store points to. E.g. for\n",
+    "        PO.DAAC data this might be \"s3://podaac-ops-cumulus-protected/\". Note you need\n",
+    "        the \"s3://\" prefix.\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    \n",
+    "    # 1. Create the raw static credentials object for virtual chunks\n",
+    "    virtualchunk_static_creds = icechunk.s3_static_credentials(\n",
+    "        access_key_id = creds_data_chunks[\"accessKeyId\"],\n",
+    "        secret_access_key = creds_data_chunks[\"secretAccessKey\"],\n",
+    "        session_token = creds_data_chunks[\"sessionToken\"]\n",
+    "    )\n",
+    "    auth_map = icechunk.containers_credentials({\n",
+    "        bucket_data_chunks: virtualchunk_static_creds\n",
+    "    })\n",
+    "    \n",
+    "    # 2. Config for the store / metadata repository\n",
+    "    s3path_split = s3path_store.split(\"/\")\n",
+    "    bucket_store = \"/\".join(s3path_split[2:3])\n",
+    "    prefix_store = \"/\".join(s3path_split[3:])\n",
+    "\n",
+    "    storage = icechunk.s3_storage(\n",
+    "        bucket = bucket_store,\n",
+    "        prefix = prefix_store,\n",
+    "        access_key_id = creds_store['accessKeyId'],\n",
+    "        secret_access_key = creds_store['secretAccessKey'],\n",
+    "        session_token = creds_store['sessionToken']\n",
+    "    )\n",
+    "    \n",
+    "    # 3. Open the repository, passing the compiled auth_map!\n",
+    "    repo = icechunk.Repository.open(\n",
+    "        storage,\n",
+    "        authorize_virtual_chunk_access=auth_map\n",
+    "    )\n",
+    "    \n",
+    "    # 4. Return the store\n",
+    "    session = repo.readonly_session(\"main\")\n",
+    "    return session.store"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf5ba5ce-e274-4e10-9932-2624b30ab698",
+   "metadata": {},
+   "source": [
+    "### 2. Access store and open data with Xarray\n",
+    "\n",
+    "Steps are to:\n",
+    "\n",
+    "1. Login to NASA EDL.\n",
+    "2. Use the above function to get the icechunk store mapper.\n",
+    "3. Pass the mapper into Xarray to open the dataset.\n",
+    "4. Perform some sample computations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8bfe1ba3-9cf2-4c56-8f92-ade97b98f9d9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Enter your Earthdata Login username:  deanh808\n",
+      "Enter your Earthdata password:  ········\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 1. Authenticate with NASA Earthdata and dynamically fetch S3 credentials  ------------------------------------------\n",
+    "earthaccess.login() # Uses your ~/.netrc file or environment variables if configured\n",
+    "ea_creds = earthaccess.get_s3_credentials(daac=\"PODAAC\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1517fc94-f258-4920-b3e1-91269aea2ef6",
+   "metadata": {},
+   "source": [
+    "The AWS S3 paths to the SASSIE ECCO icechunk stores are\n",
+    "\n",
+    "**Daily averages:**\n",
+    "\n",
+    "s3://podaac-ops-cumulus-public/virtual_collections/SASSIE_ECCO_V1R1/SASSIE_ECCO_L4_DAILY_V1R1_virtual_s3.icechunk/\n",
+    "\n",
+    "**Monthly snapshots:**\n",
+    "\n",
+    "s3://podaac-ops-cumulus-public/virtual_collections/SASSIE_ECCO_V1R1/SASSIE_ECCO_L4_SNAPSHOT_V1R1_virtual_s3.icechunk/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "a487df69-0502-4cec-930c-297b017f998a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## 2. Get the icechunk store mapper  ----------------------------------------------------------------------------------\n",
+    "s3path_store = \"s3://podaac-ops-cumulus-public/virtual_collections/SASSIE_ECCO_V1R1/SASSIE_ECCO_L4_DAILY_V1R1_virtual_s3.icechunk/\"\n",
+    "store = open_readonly_icechunkstore_s3(\n",
+    "    s3path_store, \n",
+    "    ea_creds, ea_creds, \n",
+    "    bucket_data_chunks = \"s3://podaac-ops-cumulus-protected/\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "307180bf-277d-447a-b2d7-6c42fde93097",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/coiled/env/lib/python3.14/site-packages/zarr/codecs/numcodecs/_codecs.py:141: ZarrUserWarning: Numcodecs codecs are not in the Zarr version 3 specification and may not be supported by other zarr implementations.\n",
+      "  super().__init__(**codec_config)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 828 ms, sys: 163 ms, total: 991 ms\n",
+      "Wall time: 1.58 s\n"
+     ]
+    },
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+       "}\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",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\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",
+       "  border-color: var(--xr-background-color-row-even);\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",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\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-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\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",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 46TB\n",
+       "Dimensions:    (time: 2581, k_l: 90, j: 1080, i: 1800, i_g: 1800, j_g: 1080,\n",
+       "                k: 90, k_u: 90, nb: 4, k_p1: 91, nv: 2)\n",
+       "Coordinates: (12/23)\n",
+       "    XC         (j, i) float32 8MB dask.array&lt;chunksize=(1080, 1800), meta=np.ndarray&gt;\n",
+       "    XC_bnds    (j, i, nb) float32 31MB dask.array&lt;chunksize=(1080, 1800, 4), meta=np.ndarray&gt;\n",
+       "    YC         (j, i) float32 8MB dask.array&lt;chunksize=(1080, 1800), meta=np.ndarray&gt;\n",
+       "    XU         (j, i_g) float32 8MB dask.array&lt;chunksize=(1080, 1800), meta=np.ndarray&gt;\n",
+       "    YC_bnds    (j, i, nb) float32 31MB dask.array&lt;chunksize=(1080, 1800, 4), meta=np.ndarray&gt;\n",
+       "    YU         (j, i_g) float32 8MB dask.array&lt;chunksize=(1080, 1800), meta=np.ndarray&gt;\n",
+       "    ...         ...\n",
+       "  * j_g        (j_g) int32 4kB 0 1 2 3 4 5 6 ... 1074 1075 1076 1077 1078 1079\n",
+       "  * k_p1       (k_p1) int32 364B 0 1 2 3 4 5 6 7 8 ... 83 84 85 86 87 88 89 90\n",
+       "  * k_l        (k_l) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
+       "    time_bnds  (time, nv) datetime64[ns] 41kB dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\n",
+       "  * k_u        (k_u) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
+       "  * time       (time) datetime64[ns] 21kB 2014-01-15T12:00:00 ... 2021-02-07T...\n",
+       "Dimensions without coordinates: nb, nv\n",
+       "Data variables: (12/83)\n",
+       "    ADVr_SLT   (time, k_l, j, i) float32 2TB dask.array&lt;chunksize=(1, 15, 270, 450), meta=np.ndarray&gt;\n",
+       "    ADVr_TH    (time, k_l, j, i) float32 2TB dask.array&lt;chunksize=(1, 15, 270, 450), meta=np.ndarray&gt;\n",
+       "    ADVxHEFF   (time, j, i_g) float32 20GB dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;\n",
+       "    ADVxAREA   (time, j, i_g) float32 20GB dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;\n",
+       "    ADVyAREA   (time, j_g, i) float32 20GB dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;\n",
+       "    ADVx_SLT   (time, k, j, i_g) float32 2TB dask.array&lt;chunksize=(1, 15, 270, 450), meta=np.ndarray&gt;\n",
+       "    ...         ...\n",
+       "    oceQsw     (time, j, i) float32 20GB dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;\n",
+       "    oceTAUX    (time, j, i_g) float32 20GB dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;\n",
+       "    sIceLoad   (time, j, i) float32 20GB dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;\n",
+       "    oceFWflx   (time, j, i) float32 20GB dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;\n",
+       "    oceTAUY    (time, j_g, i) float32 20GB dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;\n",
+       "    oceQnet    (time, j, i) float32 20GB dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;\n",
+       "Attributes: (12/49)\n",
+       "    acknowledgement:                   This research was carried out by the J...\n",
+       "    author:                            Marie Zahn, Mike Wood, Ian Fenty, and ...\n",
+       "    cdm_data_type:                     Grid\n",
+       "    Conventions:                       CF-1.8, ACDD-1.3\n",
+       "    creator_email:                     marie.j.zahn@jpl.nasa.gov\n",
+       "    creator_institution:               NASA Jet Propulsion Laboratory (JPL)\n",
+       "    ...                                ...\n",
+       "    geospatial_vertical_min:           -7000.0\n",
+       "    geospatial_vertical_positive:      up\n",
+       "    geospatial_vertical_resolution:    variable\n",
+       "    geospatial_vertical_units:         meter\n",
+       "    identifier_product_doi:            https://doi.org/10.5067/SEL1D-DUG11\n",
+       "    date_created:                      2026-03-21T00:00:00Z</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-470a9def-e256-40b7-83cc-ea4718143197' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-470a9def-e256-40b7-83cc-ea4718143197' 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'>time</span>: 2581</li><li><span class='xr-has-index'>k_l</span>: 90</li><li><span class='xr-has-index'>j</span>: 1080</li><li><span class='xr-has-index'>i</span>: 1800</li><li><span class='xr-has-index'>i_g</span>: 1800</li><li><span class='xr-has-index'>j_g</span>: 1080</li><li><span class='xr-has-index'>k</span>: 90</li><li><span class='xr-has-index'>k_u</span>: 90</li><li><span>nb</span>: 4</li><li><span class='xr-has-index'>k_p1</span>: 91</li><li><span>nv</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b0247272-7845-43a3-a8f3-879a8ae3dcc8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b0247272-7845-43a3-a8f3-879a8ae3dcc8' class='xr-section-summary' >Coordinates: <span>(23)</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>XC</span></div><div class='xr-var-dims'>(j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-f47ab574-20c8-462b-bb67-7b753390b709' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f47ab574-20c8-462b-bb67-7b753390b709' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ea459ba-5f2f-4163-95d3-d26b5254a38a' class='xr-var-data-in' type='checkbox'><label for='data-3ea459ba-5f2f-4163-95d3-d26b5254a38a' 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>XC_bnds</dd><dt><span>comment :</span></dt><dd>nonuniform grid spacing</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>longitude of tracer grid cell center</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>-179.9994354248047</dd><dt><span>valid_max :</span></dt><dd>179.99996948242188</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> 7.42 MiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1080, 1800) </td>\n",
+       "                        <td> (1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"122\" 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=\"72\" x2=\"120\" y2=\"72\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"72\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"72\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,72.0 0.0,72.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"92.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1800</text>\n",
+       "  <text x=\"140.0\" y=\"36.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.0,36.0)\">1080</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XC_bnds</span></div><div class='xr-var-dims'>(j, i, nb)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1080, 1800, 4), meta=np.ndarray&gt;</div><input id='attrs-40ffa42e-39b0-49c4-868a-defdd3eda441' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40ffa42e-39b0-49c4-868a-defdd3eda441' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71f0630f-cd3e-48e1-9bd7-7c32b81fd938' class='xr-var-data-in' type='checkbox'><label for='data-71f0630f-cd3e-48e1-9bd7-7c32b81fd938' 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>comment :</span></dt><dd>Bounds array follows CF conventions. XC_bnds[i,j,0] = &#x27;southwest&#x27; corner (j-1, i-1), XC_bnds[i,j,1] = &#x27;southeast&#x27; corner (j-1, i+1), XC_bnds[i,j,2] = &#x27;northeast&#x27; corner (j+1, i+1), XC_bnds[i,j,3]  = &#x27;northwest&#x27; corner (j+1, i-1). Note: &#x27;southwest&#x27;, &#x27;southeast&#x27;, northwest&#x27;, and &#x27;northeast&#x27; do not correspond to geographic orientation but are used for convenience to describe the computational grid. See MITgcm documentation for details.</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> 29.66 MiB </td>\n",
+       "                        <td> 29.66 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1080, 1800, 4) </td>\n",
+       "                        <td> (1080, 1800, 4) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"127\" height=\"212\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"52\" y2=\"42\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"10\" y1=\"120\" x2=\"52\" y2=\"162\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"52\" y1=\"42\" x2=\"52\" y2=\"162\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"10.0,0.0 52.35294117647059,42.35294117647059 52.35294117647059,162.35294117647058 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"52\" y1=\"42\" x2=\"52\" y2=\"162\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"77\" y1=\"42\" x2=\"77\" y2=\"162\" style=\"stroke-width:2\" />\n",
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+       "  <!-- Colored Rectangle -->\n",
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+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"65.05924943376183\" y=\"182.35294117647058\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n",
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+       "  <text x=\"21.176470588235293\" y=\"161.17647058823528\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,21.176470588235293,161.17647058823528)\">1080</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>YC</span></div><div class='xr-var-dims'>(j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-82b14d5b-943b-4fb8-9115-4dc9ae1ced5d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-82b14d5b-943b-4fb8-9115-4dc9ae1ced5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38b1bbb6-5508-4c04-bcc5-c5bbbae68208' class='xr-var-data-in' type='checkbox'><label for='data-38b1bbb6-5508-4c04-bcc5-c5bbbae68208' 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>YC_bnds</dd><dt><span>comment :</span></dt><dd>nonuniform grid spacing</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>latitude of tracer grid cell center</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>48.678619384765625</dd><dt><span>valid_max :</span></dt><dd>89.97828674316406</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> 7.42 MiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1080, 1800) </td>\n",
+       "                        <td> (1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"72\" x2=\"120\" y2=\"72\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"72\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"72\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,72.0 0.0,72.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"92.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1800</text>\n",
+       "  <text x=\"140.0\" y=\"36.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.0,36.0)\">1080</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XU</span></div><div class='xr-var-dims'>(j, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-a6050e8f-8aa4-4952-9fbf-fdeab384f156' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6050e8f-8aa4-4952-9fbf-fdeab384f156' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e733854-68f6-4b64-8f3e-63df2bfb60d5' class='xr-var-data-in' type='checkbox'><label for='data-9e733854-68f6-4b64-8f3e-63df2bfb60d5' 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>comment :</span></dt><dd>The u point is at midpoint between the &#x27;southwest&#x27; and &#x27;northwest&#x27; corners of the tracer grid cell. Grid spacing is nonuniform. Note: &#x27;west&#x27; refers to the computational grid orientation and does not necessarily correspond to geographic west. See MITgcm documentation and Arakawa C grid notation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>longitude of the u point on the &#x27;west&#x27; face of tracer grid cell</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>-179.9999542236328</dd><dt><span>valid_max :</span></dt><dd>179.9999542236328</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> 7.42 MiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1080, 1800) </td>\n",
+       "                        <td> (1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"122\" 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=\"72\" x2=\"120\" y2=\"72\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"72\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"72\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,72.0 0.0,72.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"92.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1800</text>\n",
+       "  <text x=\"140.0\" y=\"36.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.0,36.0)\">1080</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>YC_bnds</span></div><div class='xr-var-dims'>(j, i, nb)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1080, 1800, 4), meta=np.ndarray&gt;</div><input id='attrs-e369f9b0-c727-4e68-a3e7-0e7d63c307f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e369f9b0-c727-4e68-a3e7-0e7d63c307f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb803d7d-b61f-449b-9680-775a2427fb0a' class='xr-var-data-in' type='checkbox'><label for='data-cb803d7d-b61f-449b-9680-775a2427fb0a' 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>comment :</span></dt><dd>Bounds array follows CF conventions. YC_bnds[i,j,0] = &#x27;southwest&#x27; corner (j-1, i-1), YC_bnds[i,j,1] = &#x27;southeast&#x27; corner (j-1, i+1), YC_bnds[i,j,2] = &#x27;northeast&#x27; corner (j+1, i+1), YC_bnds[i,j,3]  = &#x27;northwest&#x27; corner (j+1, i-1). Note: &#x27;southwest&#x27;, &#x27;southeast&#x27;, northwest&#x27;, and &#x27;northeast&#x27; do not correspond to geographic orientation but are used for convenience to describe the computational grid. See MITgcm documentation for details.</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> 29.66 MiB </td>\n",
+       "                        <td> 29.66 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1080, 1800, 4) </td>\n",
+       "                        <td> (1080, 1800, 4) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"127\" height=\"212\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
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+       "\n",
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+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"0\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"52\" y1=\"42\" x2=\"77\" y2=\"42\" style=\"stroke-width:2\" />\n",
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+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"52\" y1=\"42\" x2=\"52\" y2=\"162\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"77\" y1=\"42\" x2=\"77\" y2=\"162\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"52.35294117647059,42.35294117647059 77.76555769105308,42.35294117647059 77.76555769105308,162.35294117647058 52.35294117647059,162.35294117647058\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"65.05924943376183\" y=\"182.35294117647058\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n",
+       "  <text x=\"97.76555769105308\" y=\"102.35294117647058\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,97.76555769105308,102.35294117647058)\">1800</text>\n",
+       "  <text x=\"21.176470588235293\" y=\"161.17647058823528\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,21.176470588235293,161.17647058823528)\">1080</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>YU</span></div><div class='xr-var-dims'>(j, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-84881107-8664-47de-a122-35691e09388d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84881107-8664-47de-a122-35691e09388d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-376fe789-88ba-442a-b2f1-d584cef95d44' class='xr-var-data-in' type='checkbox'><label for='data-376fe789-88ba-442a-b2f1-d584cef95d44' 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>comment :</span></dt><dd>The u point is at midpoint between the &#x27;southwest&#x27; and &#x27;northwest&#x27; corners of the tracer grid cell. Grid spacing is nonuniform. Note: &#x27;west&#x27; refers to the computational grid orientation and does not necessarily correspond to geographic west. See MITgcm documentation and Arakawa C grid notation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>latitude of the u point on the &#x27;west&#x27; face of tracer grid cell</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>48.652774810791016</dd><dt><span>valid_max :</span></dt><dd>89.9846420288086</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> 7.42 MiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1080, 1800) </td>\n",
+       "                        <td> (1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"122\" 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",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Z</span></div><div class='xr-var-dims'>(k)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(90,), meta=np.ndarray&gt;</div><input id='attrs-39298cd8-0022-4fe6-95cc-72492ad5286c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39298cd8-0022-4fe6-95cc-72492ad5286c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b89746a2-c670-4817-925c-7eb8361d5a2f' class='xr-var-data-in' type='checkbox'><label for='data-b89746a2-c670-4817-925c-7eb8361d5a2f' 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>Z_bnds</dd><dt><span>comment :</span></dt><dd>Non-uniform vertical spacing. The associated &#x27;Z_bnds&#x27; coordinate provides the depths of top and bottom faces of the tracer grid cell, with one pair of depths for each vertical level.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>depth of tracer grid cell center</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-6760.169921875</dd><dt><span>valid_max :</span></dt><dd>-0.5</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> 360 B </td>\n",
+       "                        <td> 360 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (90,) </td>\n",
+       "                        <td> (90,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" 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=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.924160051965714 0.0,25.924160051965714\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.924160051965714\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >90</text>\n",
+       "  <text x=\"140.0\" y=\"12.962080025982857\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.962080025982857)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Zl</span></div><div class='xr-var-dims'>(k_l)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(90,), meta=np.ndarray&gt;</div><input id='attrs-0ccac9ae-710f-4f03-9877-5b00afdaf225' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ccac9ae-710f-4f03-9877-5b00afdaf225' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2e902c2-a28f-471e-ad37-a84b290af5b0' class='xr-var-data-in' type='checkbox'><label for='data-c2e902c2-a28f-471e-ad37-a84b290af5b0' 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>comment :</span></dt><dd>First element is 0m, the depth of the top face of the uppermost tracer grid cell (i.e., the ocean surface). Last element is the depth of the top face of the deepest grid cell. The use of &#x27;l&#x27; in the variable name follows the MITgcm convention for naming the top face of ocean tracer grid cells.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>depth of top face of tracer grid cell</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-6520.2998046875</dd><dt><span>valid_max :</span></dt><dd>0.0</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> 360 B </td>\n",
+       "                        <td> 360 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (90,) </td>\n",
+       "                        <td> (90,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" 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=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.924160051965714 0.0,25.924160051965714\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.924160051965714\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >90</text>\n",
+       "  <text x=\"140.0\" y=\"12.962080025982857\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.962080025982857)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Zp1</span></div><div class='xr-var-dims'>(k_p1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(91,), meta=np.ndarray&gt;</div><input id='attrs-bdec09d9-5c38-45d7-8ae0-0a042bd5050e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bdec09d9-5c38-45d7-8ae0-0a042bd5050e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e53a10a-1b78-4813-87be-c6149a376ae0' class='xr-var-data-in' type='checkbox'><label for='data-4e53a10a-1b78-4813-87be-c6149a376ae0' 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>comment :</span></dt><dd>Contains one element more than the number of vertical layers. First element is 0m, the depth of the top face of the uppermost grid cell. Last element is the depth of the bottom face of the deepest grid cell.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>depth of top/bottom face of tracer grid cell</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-7000.0400390625</dd><dt><span>valid_max :</span></dt><dd>0.0</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> 364 B </td>\n",
+       "                        <td> 364 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (91,) </td>\n",
+       "                        <td> (91,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" 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=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.86984685721921 0.0,25.86984685721921\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.86984685721921\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >91</text>\n",
+       "  <text x=\"140.0\" y=\"12.934923428609604\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.934923428609604)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>YV</span></div><div class='xr-var-dims'>(j_g, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-6b6eba63-f322-40c8-851b-bf979ca81b13' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b6eba63-f322-40c8-851b-bf979ca81b13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c14e1ace-d71a-4b3a-b041-ee1a2c01d04b' class='xr-var-data-in' type='checkbox'><label for='data-c14e1ace-d71a-4b3a-b041-ee1a2c01d04b' 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>comment :</span></dt><dd>The v point is at midpoint between the &#x27;southwest&#x27; and &#x27;southeast&#x27; corners of the tracer grid cell. Grid spacing is nonuniform. Note: &#x27;south&#x27; refers to the computational grid orientation and does not necessarily correspond to geographic south. See MITgcm documentation and Arakawa C grid notation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>latitude of the v point on the &#x27;south&#x27; face of tracer grid cell</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>valid_min :</span></dt><dd>48.678611755371094</dd><dt><span>valid_max :</span></dt><dd>89.9846420288086</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> 7.42 MiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1080, 1800) </td>\n",
+       "                        <td> (1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"122\" 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=\"72\" x2=\"120\" y2=\"72\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"72\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"72\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,72.0 0.0,72.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"92.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1800</text>\n",
+       "  <text x=\"140.0\" y=\"36.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.0,36.0)\">1080</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XV</span></div><div class='xr-var-dims'>(j_g, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-3feff9d0-5f0d-4451-bcc5-ad21b6d1c23d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3feff9d0-5f0d-4451-bcc5-ad21b6d1c23d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50d6263f-6867-4f36-ae59-ad82119be006' class='xr-var-data-in' type='checkbox'><label for='data-50d6263f-6867-4f36-ae59-ad82119be006' 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>comment :</span></dt><dd>The v point is at midpoint between the &#x27;southwest&#x27; and &#x27;southeast&#x27; corners of the tracer grid cell. Grid spacing is nonuniform. Note: &#x27;south&#x27; refers to the computational grid orientation and does not necessarily correspond to geographic south. See MITgcm documentation and Arakawa C grid notation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>longitude of the v point on the &#x27;south&#x27; face of tracer grid cell</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>valid_min :</span></dt><dd>-179.99990844726562</dd><dt><span>valid_max :</span></dt><dd>180.0</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> 7.42 MiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (1080, 1800) </td>\n",
+       "                        <td> (1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"122\" 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=\"72\" x2=\"120\" y2=\"72\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Vertical lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"72\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"72\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,72.0 0.0,72.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"92.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1800</text>\n",
+       "  <text x=\"140.0\" y=\"36.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.0,36.0)\">1080</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>i</span></div><div class='xr-var-dims'>(i)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1796 1797 1798 1799</div><input id='attrs-e4663c2f-aaa2-441d-9c0c-55ddbc4acb2b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4663c2f-aaa2-441d-9c0c-55ddbc4acb2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29849a7b-0eb2-4e62-8c04-6b534d467e7b' class='xr-var-data-in' type='checkbox'><label for='data-29849a7b-0eb2-4e62-8c04-6b534d467e7b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>comment :</span></dt><dd>In the Arakawa C-grid system, tracer (e.g., THETA) and &#x27;v&#x27; variables (e.g., VVEL) have the same x coordinate on the model grid.</dd><dt><span>long_name :</span></dt><dd>grid index in x for variables at tracer and &#x27;v&#x27; locations</dd></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 1797, 1798, 1799], shape=(1800,), dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>j</span></div><div class='xr-var-dims'>(j)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1076 1077 1078 1079</div><input id='attrs-1b8e53c4-b60f-4c3c-a822-ebd0fc15d580' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b8e53c4-b60f-4c3c-a822-ebd0fc15d580' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83f633dd-80f7-47e4-9873-e5fe08894166' class='xr-var-data-in' type='checkbox'><label for='data-83f633dd-80f7-47e4-9873-e5fe08894166' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>comment :</span></dt><dd>In the Arakawa C-grid system, tracer (e.g., THETA) and &#x27;u&#x27; variables (e.g., UVEL) have the same y coordinate on the model grid.</dd><dt><span>long_name :</span></dt><dd>grid index in y for variables at tracer and &#x27;u&#x27; locations</dd></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 1077, 1078, 1079], shape=(1080,), dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>k</span></div><div class='xr-var-dims'>(k)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 84 85 86 87 88 89</div><input id='attrs-81ab6b1f-1ab8-4a5c-9531-bb3b7ae2b180' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81ab6b1f-1ab8-4a5c-9531-bb3b7ae2b180' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15bd31b9-4404-44ba-9e4d-81ac9c5d2323' class='xr-var-data-in' type='checkbox'><label for='data-15bd31b9-4404-44ba-9e4d-81ac9c5d2323' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>grid index in z for tracer variables</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
+       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
+       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
+       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
+       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Zu</span></div><div class='xr-var-dims'>(k_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(90,), meta=np.ndarray&gt;</div><input id='attrs-44fda4f7-2374-42ef-9f26-eeee2492e27c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-44fda4f7-2374-42ef-9f26-eeee2492e27c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3f6c1f4-41f2-4077-ba96-fcc4e35dec96' class='xr-var-data-in' type='checkbox'><label for='data-a3f6c1f4-41f2-4077-ba96-fcc4e35dec96' 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>comment :</span></dt><dd>First element is -1m, the depth of the bottom face of the uppermost tracer grid cell. Last element is the depth of the bottom face of the deepest grid cell. The use of &#x27;u&#x27; in the variable name follows the MITgcm convention for naming the bottom face of ocean tracer grid cells.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>depth of bottom face of tracer grid cell</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_min :</span></dt><dd>-7000.0400390625</dd><dt><span>valid_max :</span></dt><dd>-1.0</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> 360 B </td>\n",
+       "                        <td> 360 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (90,) </td>\n",
+       "                        <td> (90,) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"170\" height=\"75\" 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=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.924160051965714 0.0,25.924160051965714\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"60.0\" y=\"45.924160051965714\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >90</text>\n",
+       "  <text x=\"140.0\" y=\"12.962080025982857\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.0,12.962080025982857)\">1</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Z_bnds</span></div><div class='xr-var-dims'>(k, nv)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(90, 2), meta=np.ndarray&gt;</div><input id='attrs-3b4dd4d5-d1f0-4373-8c42-5859a96aa0d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b4dd4d5-d1f0-4373-8c42-5859a96aa0d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5a59a0d-6bca-46cc-84cc-571edbbcf6df' class='xr-var-data-in' type='checkbox'><label for='data-a5a59a0d-6bca-46cc-84cc-571edbbcf6df' 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>comment :</span></dt><dd>Provides the depths of the top and bottom faces of the tracer grid cell, with one pair of depths for each vertical level.</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> 720 B </td>\n",
+       "                        <td> 720 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (90, 2) </td>\n",
+       "                        <td> (90, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
+       "        <svg width=\"79\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
+       "\n",
+       "  <!-- Horizontal lines -->\n",
+       "  <line x1=\"0\" y1=\"0\" x2=\"29\" y2=\"0\" style=\"stroke-width:2\" />\n",
+       "  <line x1=\"0\" y1=\"120\" x2=\"29\" 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=\"29\" y1=\"0\" x2=\"29\" y2=\"120\" style=\"stroke-width:2\" />\n",
+       "\n",
+       "  <!-- Colored Rectangle -->\n",
+       "  <polygon points=\"0.0,0.0 29.62930551190937,0.0 29.62930551190937,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
+       "\n",
+       "  <!-- Text -->\n",
+       "  <text x=\"14.814652755954684\" y=\"140.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
+       "  <text x=\"49.629305511909365\" y=\"60.0\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,49.629305511909365,60.0)\">90</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>i_g</span></div><div class='xr-var-dims'>(i_g)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1796 1797 1798 1799</div><input id='attrs-dbcccdbb-9668-4a71-a346-5e45d1e7fe3c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dbcccdbb-9668-4a71-a346-5e45d1e7fe3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-340f85a1-a838-4571-9b87-da26b65faff8' class='xr-var-data-in' type='checkbox'><label for='data-340f85a1-a838-4571-9b87-da26b65faff8' 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>comment :</span></dt><dd>In the Arakawa C-grid system, &#x27;u&#x27; (e.g., UVEL) and &#x27;g&#x27; variables (e.g., XG) have the same x coordinate on the model grid.</dd><dt><span>long_name :</span></dt><dd>grid index in x for variables at &#x27;u&#x27; and &#x27;g&#x27; locations</dd></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 1797, 1798, 1799], shape=(1800,), dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>j_g</span></div><div class='xr-var-dims'>(j_g)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1076 1077 1078 1079</div><input id='attrs-cad333b7-de4f-47b2-b11d-49e05d15768c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cad333b7-de4f-47b2-b11d-49e05d15768c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0415f1b-38ef-41be-9990-6ac8d4e47d46' class='xr-var-data-in' type='checkbox'><label for='data-b0415f1b-38ef-41be-9990-6ac8d4e47d46' 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>comment :</span></dt><dd>In the Arakawa C-grid system, &#x27;v&#x27; (e.g., VVEL) and &#x27;g&#x27; variables (e.g., XG) have the same y coordinate.</dd><dt><span>long_name :</span></dt><dd>grid index in y for variables at &#x27;v&#x27; and &#x27;g&#x27; locations</dd></dl></div><div class='xr-var-data'><pre>array([   0,    1,    2, ..., 1077, 1078, 1079], shape=(1080,), dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>k_p1</span></div><div class='xr-var-dims'>(k_p1)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 85 86 87 88 89 90</div><input id='attrs-a5bc2ebe-5ed2-44ce-8815-a8e2b5bdde6f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a5bc2ebe-5ed2-44ce-8815-a8e2b5bdde6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0750b410-6dc6-43b8-a08b-b4dc5ac2685c' class='xr-var-data-in' type='checkbox'><label for='data-0750b410-6dc6-43b8-a08b-b4dc5ac2685c' 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>comment :</span></dt><dd>Includes top of uppermost model tracer cell (k_p1=0) and bottom of lowermost tracer cell (k_p1=91).</dd><dt><span>long_name :</span></dt><dd>grid index in z for variables at &#x27;w&#x27; locations</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
+       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
+       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
+       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,\n",
+       "       90], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>k_l</span></div><div class='xr-var-dims'>(k_l)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 84 85 86 87 88 89</div><input id='attrs-07778f1d-429e-425a-b988-0dd47ed137cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07778f1d-429e-425a-b988-0dd47ed137cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b566e51-ef1a-4669-a38c-4d4784b68b72' class='xr-var-data-in' type='checkbox'><label for='data-0b566e51-ef1a-4669-a38c-4d4784b68b72' 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>comment :</span></dt><dd>First index corresponds to the top face of the uppermost tracer grid cell. The use of &#x27;l&#x27; in the variable name follows the MITgcm convention for naming the top face of ocean tracer grid cells.</dd><dt><span>long_name :</span></dt><dd>grid index in z corresponding to the top face of tracer grid cells (&#x27;w&#x27; locations)</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
+       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
+       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
+       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
+       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, nv)</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-c1831322-a99d-4534-b618-0017cf9ab116' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c1831322-a99d-4534-b618-0017cf9ab116' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3c4e11a-80b7-41cb-919c-4adc5a7beabd' class='xr-var-data-in' type='checkbox'><label for='data-b3c4e11a-80b7-41cb-919c-4adc5a7beabd' 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>comment :</span></dt><dd>Start and end times of averaging period.</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> 40.33 kiB </td>\n",
+       "                        <td> 16 B </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 2) </td>\n",
+       "                        <td> (1, 2) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 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",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>k_u</span></div><div class='xr-var-dims'>(k_u)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 84 85 86 87 88 89</div><input id='attrs-d67adc5e-e115-4dc7-811f-02b0433f1a04' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d67adc5e-e115-4dc7-811f-02b0433f1a04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59a1303b-4622-4856-a23c-1a7a433f24fb' class='xr-var-data-in' type='checkbox'><label for='data-59a1303b-4622-4856-a23c-1a7a433f24fb' 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>comment :</span></dt><dd>First index corresponds to the bottom face of the uppermost tracer grid cell. The use of &#x27;u&#x27; in the variable name follows the MITgcm convention for naming the bottom face of ocean tracer grid cells.</dd><dt><span>long_name :</span></dt><dd>grid index in z corresponding to the bottom face of tracer grid cells (&#x27;w&#x27; locations)</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
+       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
+       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
+       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
+       "      dtype=int32)</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'>2014-01-15T12:00:00 ... 2021-02-...</div><input id='attrs-28893b0a-928e-4839-86c4-f55cd97c658d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28893b0a-928e-4839-86c4-f55cd97c658d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bebc6f27-c13f-49df-be9c-0e1697fee87b' class='xr-var-data-in' type='checkbox'><label for='data-bebc6f27-c13f-49df-be9c-0e1697fee87b' 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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>midpoint time of averaging period</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2014-01-15T12:00:00.000000000&#x27;, &#x27;2014-01-16T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-01-17T12:00:00.000000000&#x27;, ..., &#x27;2021-02-05T12:00:00.000000000&#x27;,\n",
+       "       &#x27;2021-02-06T12:00:00.000000000&#x27;, &#x27;2021-02-07T12:00:00.000000000&#x27;],\n",
+       "      shape=(2581,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0744dda5-515c-4857-b69c-4b4dcc5ad971' class='xr-section-summary-in' type='checkbox'  ><label for='section-0744dda5-515c-4857-b69c-4b4dcc5ad971' class='xr-section-summary' >Data variables: <span>(83)</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>ADVr_SLT</span></div><div class='xr-var-dims'>(time, k_l, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 15, 270, 450), meta=np.ndarray&gt;</div><input id='attrs-36181798-78de-4c67-ba56-a245160fcb60' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36181798-78de-4c67-ba56-a245160fcb60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6f2cb3c-2e61-4150-b8d6-b2c982efe280' class='xr-var-data-in' type='checkbox'><label for='data-d6f2cb3c-2e61-4150-b8d6-b2c982efe280' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 decreases salinity (SALT)</dd><dt><span>long_name :</span></dt><dd>Vertical advective flux of salinity</dd><dt><span>units :</span></dt><dd>1e-3 m3 s-1</dd><dt><span>comment :</span></dt><dd>Vertical advective flux of salinity (SALT) in the +z direction through the top &#x27;w&#x27; face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, vertical flux quantities are staggered relative to the tracer cells with indexing such that +ADVr_SLT(i,j,k_l) corresponds to upward +z fluxes through the top &#x27;w&#x27; face of the tracer cell at (i,j,k). Salinity defined using CF convention &#x27;Sea water salinity is the salt content of sea water, often on the Practical Salinity Scale of 1978. However, the unqualified term &#x27;salinity&#x27; is generic and does not necessarily imply any particular method of calculation. The units of salinity are dimensionless and the units attribute should normally be given as 1e-3 or 0.001 i.e. parts per thousand.&#x27; see https://cfconventions.org/Data/cf-standard-names/73/build/cf-standard-name-table.html</dd><dt><span>valid_min :</span></dt><dd>-14933340.0</dd><dt><span>valid_max :</span></dt><dd>12452069.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.64 TiB </td>\n",
+       "                        <td> 6.95 MiB </td>\n",
+       "                    </tr>\n",
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+       "                    <tr>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ADVr_TH</span></div><div class='xr-var-dims'>(time, k_l, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 15, 270, 450), meta=np.ndarray&gt;</div><input id='attrs-db646d53-784a-4bd0-869a-d3a487b839d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db646d53-784a-4bd0-869a-d3a487b839d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-212087cb-c8c1-43eb-b7da-542fc6cea412' class='xr-var-data-in' type='checkbox'><label for='data-212087cb-c8c1-43eb-b7da-542fc6cea412' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 decreases potential temperature (THETA)</dd><dt><span>long_name :</span></dt><dd>Vertical advective flux of potential temperature</dd><dt><span>units :</span></dt><dd>degree_C m3 s-1</dd><dt><span>comment :</span></dt><dd>Vertical advective flux of potential temperature (THETA) in the +z direction through the top &#x27;w&#x27; face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, vertical flux quantities are staggered relative to the tracer cells with indexing such that +ADVr_TH(i,j,k_l) corresponds to upward +z fluxes through the top &#x27;w&#x27; face of the tracer cell at (i,j,k)</dd><dt><span>valid_min :</span></dt><dd>-1738236.75</dd><dt><span>valid_max :</span></dt><dd>1278251.875</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",
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+       "                        <th> Chunk </th>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.64 TiB </td>\n",
+       "                        <td> 6.95 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 90, 1080, 1800) </td>\n",
+       "                        <td> (1, 15, 270, 450) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 247776 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ADVxHEFF</span></div><div class='xr-var-dims'>(time, j, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-720e7891-8521-4709-900f-a57eafc4712b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-720e7891-8521-4709-900f-a57eafc4712b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1efad2d-d889-4714-8f14-6c09774db8ff' class='xr-var-data-in' type='checkbox'><label for='data-c1efad2d-d889-4714-8f14-6c09774db8ff' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 increases mean sea-ice thickness (HEFF)</dd><dt><span>long_name :</span></dt><dd>Lateral advective flux of sea-ice thickness in the model +x direction</dd><dt><span>units :</span></dt><dd>m3 s-1</dd><dt><span>comment :</span></dt><dd>Lateral advective flux of grid cell mean sea-ice thickness (HEFF) in the +x direction through the &#x27;u&#x27; face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, horizontal flux quantities are staggered relative to the tracer cells with indexing such that +ADVxHEFF(i_g,j) corresponds to +x fluxes through the &#x27;u&#x27; face of the tracer cell at (i,j,k=0). Also, the model +x direction does not necessarily correspond to the geographical east-west direction because the x and y axes of the model&#x27;s curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles.</dd><dt><span>valid_min :</span></dt><dd>-1855.5511474609375</dd><dt><span>valid_max :</span></dt><dd>2128.9677734375</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> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ADVxAREA</span></div><div class='xr-var-dims'>(time, j, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-6d699e3f-ebc7-456c-ab4b-aede944bb8d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d699e3f-ebc7-456c-ab4b-aede944bb8d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0f9b302-6ee8-49e7-8547-0ac836b51fa8' class='xr-var-data-in' type='checkbox'><label for='data-f0f9b302-6ee8-49e7-8547-0ac836b51fa8' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Lateral advective flux of sea-ice area in the model +x direction</dd><dt><span>units :</span></dt><dd>m2 m-2 m-2 s-1</dd><dt><span>comment :</span></dt><dd>Advective flux of sea ice concentration (area fraction) in the model +x direction.</dd><dt><span>valid_min :</span></dt><dd>-2452.82666015625</dd><dt><span>valid_max :</span></dt><dd>1842.02978515625</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
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+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ADVyAREA</span></div><div class='xr-var-dims'>(time, j_g, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-f820f674-dc60-4ab3-abe7-d0c7b26278e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f820f674-dc60-4ab3-abe7-d0c7b26278e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9af912c-3d48-487f-aa3a-c6f75e895be1' class='xr-var-data-in' type='checkbox'><label for='data-d9af912c-3d48-487f-aa3a-c6f75e895be1' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Lateral advective flux of sea-ice area in the model +y direction</dd><dt><span>units :</span></dt><dd>m2 m-2 m-2 s-1</dd><dt><span>comment :</span></dt><dd>Advective flux of sea ice concentration (area fraction) in the model +y direction</dd><dt><span>valid_min :</span></dt><dd>-2564.10498046875</dd><dt><span>valid_max :</span></dt><dd>2158.25</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ADVx_SLT</span></div><div class='xr-var-dims'>(time, k, j, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 15, 270, 450), meta=np.ndarray&gt;</div><input id='attrs-379bd522-c5ae-4ca9-899f-05b52402d8d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-379bd522-c5ae-4ca9-899f-05b52402d8d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd04fdf6-ef41-4d85-bc98-e3e07ff3a1f4' class='xr-var-data-in' type='checkbox'><label for='data-dd04fdf6-ef41-4d85-bc98-e3e07ff3a1f4' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 increases salinity (SALT)</dd><dt><span>long_name :</span></dt><dd>Lateral advective flux of salinity in the model +x direction</dd><dt><span>units :</span></dt><dd>1e-3 m3 s-1</dd><dt><span>comment :</span></dt><dd>Lateral advective flux of salinity (SALT) in the +x direction through the &#x27;u&#x27; face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, horizontal flux quantities are staggered relative to the tracer cells with indexing such that +ADVx_SLT(i_g,j,k) corresponds to +x fluxes through the &#x27;u&#x27; face of the tracer cell at (i,j,k). Also, the model +x direction does not necessarily correspond to the geographical east-west direction because the x and y axes of the model&#x27;s curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles. Salinity defined using CF convention &#x27;Sea water salinity is the salt content of sea water, often on the Practical Salinity Scale of 1978. However, the unqualified term &#x27;salinity&#x27; is generic and does not necessarily imply any particular method of calculation. The units of salinity are dimensionless and the units attribute should normally be given as 1e-3 or 0.001 i.e. parts per thousand.&#x27; see https://cfconventions.org/Data/cf-standard-names/73/build/cf-standard-name-table.html</dd><dt><span>valid_min :</span></dt><dd>-30691626.0</dd><dt><span>valid_max :</span></dt><dd>16361816.0</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.64 TiB </td>\n",
+       "                        <td> 6.95 MiB </td>\n",
+       "                    </tr>\n",
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+       "                        <th> Shape </th>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 247776 chunks in 2 graph layers </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ADVxSNOW</span></div><div class='xr-var-dims'>(time, j, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-6b4f1507-3ffa-40e6-8bf6-7a43c6b684f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b4f1507-3ffa-40e6-8bf6-7a43c6b684f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eecc7712-c45c-4c65-9673-7d2af3b4b8fc' class='xr-var-data-in' type='checkbox'><label for='data-eecc7712-c45c-4c65-9673-7d2af3b4b8fc' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 increases mean snow thickness (HSNOW)</dd><dt><span>long_name :</span></dt><dd>Lateral advective flux of snow thickness in the model +x direction</dd><dt><span>units :</span></dt><dd>m3 s-1</dd><dt><span>comment :</span></dt><dd>Lateral advective flux of grid cell mean snow thickness (HSNOW) in the +x direction through the &#x27;u&#x27; face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, horizontal flux quantities are staggered relative to the tracer cells with indexing such that +ADVxSNOW(i_g,j) corresponds to +x fluxes through the &#x27;u&#x27; face of the tracer cell at (i,j,k=0). Also, the model +x direction does not necessarily correspond to the geographical east-west direction because the x and y axes of the model&#x27;s curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles.</dd><dt><span>valid_min :</span></dt><dd>-298.6290588378906</dd><dt><span>valid_max :</span></dt><dd>484.77557373046875</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
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+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
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+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ADVx_TH</span></div><div class='xr-var-dims'>(time, k, j, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 15, 270, 450), meta=np.ndarray&gt;</div><input id='attrs-c9bdefe5-6c3c-4876-a0ee-a168c14cd730' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c9bdefe5-6c3c-4876-a0ee-a168c14cd730' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5fbcf1c1-a420-427e-a280-bf76ccf2f07e' class='xr-var-data-in' type='checkbox'><label for='data-5fbcf1c1-a420-427e-a280-bf76ccf2f07e' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 increases potential temperature (THETA)</dd><dt><span>long_name :</span></dt><dd>Lateral advective flux of potential temperature in the model +x direction</dd><dt><span>units :</span></dt><dd>degree_C m3 s-1</dd><dt><span>comment :</span></dt><dd>Lateral advective flux of potential temperature (THETA) in the +x direction through the &#x27;u&#x27; face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, horizontal flux quantities are staggered relative to the tracer cells with indexing such that +ADVx_TH(i_g,j,k) corresponds to +x fluxes through the &#x27;u&#x27; face of the tracer cell at (i,j,k). Also, the model +x direction does not necessarily correspond to the geographical east-west direction because the x and y axes of the model&#x27;s lat-lon-cap (llc) curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles.</dd><dt><span>valid_min :</span></dt><dd>-882812.125</dd><dt><span>valid_max :</span></dt><dd>908109.6875</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
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+       "                        <td> </td>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.64 TiB </td>\n",
+       "                        <td> 6.95 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 90, 1080, 1800) </td>\n",
+       "                        <td> (1, 15, 270, 450) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 247776 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ADVyHEFF</span></div><div class='xr-var-dims'>(time, j_g, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-53bde839-19b9-43dc-8cd5-8490736bdaeb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53bde839-19b9-43dc-8cd5-8490736bdaeb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6cdac88-4ef9-48fa-a834-222517cd3e81' class='xr-var-data-in' type='checkbox'><label for='data-f6cdac88-4ef9-48fa-a834-222517cd3e81' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 increases mean sea-ice thickness (HEFF)</dd><dt><span>long_name :</span></dt><dd>Lateral advective flux of sea-ice thickness in the model +y direction</dd><dt><span>units :</span></dt><dd>m3 s-1</dd><dt><span>comment :</span></dt><dd>Lateral advective flux of grid cell mean sea-ice thickness (HEFF) in the +y direction through the &#x27;v&#x27; face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, horizontal flux quantities are staggered relative to the tracer cells with indexing such that +ADVyHEFF(i,j_g) corresponds to +y fluxes through the &#x27;v&#x27; face of the tracer cell at (i,j,k=0). Also, the model +y direction does not necessarily correspond to the geographical north-south direction because the x and y axes of the model&#x27;s curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles.</dd><dt><span>valid_min :</span></dt><dd>-2921.25048828125</dd><dt><span>valid_max :</span></dt><dd>1420.995361328125</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
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+       "                <thead>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ADVy_TH</span></div><div class='xr-var-dims'>(time, k, j_g, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 15, 270, 450), meta=np.ndarray&gt;</div><input id='attrs-ce6a75e1-11b9-4cec-812d-5356dbe954c1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce6a75e1-11b9-4cec-812d-5356dbe954c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ffe820db-ab04-4409-bad7-00a276d5c88f' class='xr-var-data-in' type='checkbox'><label for='data-ffe820db-ab04-4409-bad7-00a276d5c88f' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 increases potential temperature (THETA)</dd><dt><span>long_name :</span></dt><dd>Lateral advective flux of potential temperature in the model +y direction</dd><dt><span>units :</span></dt><dd>degree_C m3 s-1</dd><dt><span>comment :</span></dt><dd>Lateral advective flux of potential temperature (THETA) in the +y direction through the &#x27;v&#x27; face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, horizontal flux quantities are staggered relative to the tracer cells with indexing such that +ADVy_TH(i,j_g,k) corresponds to +y fluxes through the &#x27;v&#x27; face of the tracer cell at (i,j,k). Also, the model +y direction does not necessarily correspond to the geographical north-south direction because the x and y axes of the model&#x27;s curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles.</dd><dt><span>valid_min :</span></dt><dd>-756634.0</dd><dt><span>valid_max :</span></dt><dd>674524.1875</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> 1.64 TiB </td>\n",
+       "                        <td> 6.95 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 90, 1080, 1800) </td>\n",
+       "                        <td> (1, 15, 270, 450) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 247776 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ADVy_SLT</span></div><div class='xr-var-dims'>(time, k, j_g, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 15, 270, 450), meta=np.ndarray&gt;</div><input id='attrs-e5561c4a-df11-4289-a8ef-df58f184f8bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5561c4a-df11-4289-a8ef-df58f184f8bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c4bd7eb-e4af-4593-990e-a017f6c2e461' class='xr-var-data-in' type='checkbox'><label for='data-8c4bd7eb-e4af-4593-990e-a017f6c2e461' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 increases salinity (SALT)</dd><dt><span>long_name :</span></dt><dd>Lateral advective flux of salinity in the model +y direction</dd><dt><span>units :</span></dt><dd>1e-3 m3 s-1</dd><dt><span>comment :</span></dt><dd>Lateral advective flux of salinity (SALT) in the +y direction through the &#x27;v&#x27; face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, horizontal flux quantities are staggered relative to the tracer cells with indexing such that +ADVy_SLT(i,j_g,k) corresponds to +y fluxes through the &#x27;v&#x27; face of the tracer cell at (i,j,k). Also, the model +y direction does not necessarily correspond to the geographical north-south direction because the x and y axes of the model&#x27;s curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles. Salinity defined using CF convention &#x27;Sea water salinity is the salt content of sea water, often on the Practical Salinity Scale of 1978. However, the unqualified term &#x27;salinity&#x27; is generic and does not necessarily imply any particular method of calculation. The units of salinity are dimensionless and the units attribute should normally be given as 1e-3 or 0.001 i.e. parts per thousand.&#x27; see https://cfconventions.org/Data/cf-standard-names/73/build/cf-standard-name-table.html</dd><dt><span>valid_min :</span></dt><dd>-16362136.0</dd><dt><span>valid_max :</span></dt><dd>23420852.0</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.64 TiB </td>\n",
+       "                        <td> 6.95 MiB </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DFrI_TH</span></div><div class='xr-var-dims'>(time, k_l, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 15, 270, 450), meta=np.ndarray&gt;</div><input id='attrs-2e4f6ab5-9487-4272-bfd7-775ffbb53e12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e4f6ab5-9487-4272-bfd7-775ffbb53e12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c53971b6-570f-4461-9b73-53115aad4229' class='xr-var-data-in' type='checkbox'><label for='data-c53971b6-570f-4461-9b73-53115aad4229' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 decreases potential temperature (THETA)</dd><dt><span>long_name :</span></dt><dd>Vertical diffusive flux of potential temperature (implicit term)</dd><dt><span>units :</span></dt><dd>degree_C m3 s-1</dd><dt><span>comment :</span></dt><dd>The implicit term of the vertical diffusive flux of potential temperature (THETA) in the +z direction through the top &#x27;w&#x27; face of the tracer cell on the native model grid. There is no explicit term of the vertical diffusive flux in the SASSIE ECCO model. Note: in the Arakawa-C grid, vertical flux quantities are staggered relative to the tracer cells with indexing such that +DFrI_TH(i,j,k_l) corresponds to upward +z fluxes through the top &#x27;w&#x27; face of the tracer cell at (i,j,k)</dd><dt><span>valid_min :</span></dt><dd>-29849.361328125</dd><dt><span>valid_max :</span></dt><dd>18339.25390625</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> 1.64 TiB </td>\n",
+       "                        <td> 6.95 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 90, 1080, 1800) </td>\n",
+       "                        <td> (1, 15, 270, 450) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 247776 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EXFaqh</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-69ef0137-8154-43b3-a1d6-722978199d98' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69ef0137-8154-43b3-a1d6-722978199d98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-015f9a5c-dd55-481b-847e-791ddb274bcb' class='xr-var-data-in' type='checkbox'><label for='data-015f9a5c-dd55-481b-847e-791ddb274bcb' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Atmosphere surface (2 m) specific humidity </dd><dt><span>standard_name :</span></dt><dd>surface_specific_humidity</dd><dt><span>units :</span></dt><dd>kg kg-1</dd><dt><span>comment :</span></dt><dd>Surface (2 m) specific humidity over open water</dd><dt><span>valid_min :</span></dt><dd>6.238536298042163e-05</dd><dt><span>valid_max :</span></dt><dd>0.007049692329019308</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
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+       "                        <td> (1, 1080, 1800) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EXFatemp</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-d45c05db-abef-4b29-a27f-92eb29605fe8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d45c05db-abef-4b29-a27f-92eb29605fe8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbd312cf-2db8-45eb-87fe-d780fdb59fc2' class='xr-var-data-in' type='checkbox'><label for='data-cbd312cf-2db8-45eb-87fe-d780fdb59fc2' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Atmosphere surface (2 m) air temperature </dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>degree_K</dd><dt><span>comment :</span></dt><dd>Surface (2 m) air temperature over open water</dd><dt><span>valid_min :</span></dt><dd>228.7965545654297</dd><dt><span>valid_max :</span></dt><dd>282.72906494140625</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",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
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+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ETAN</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-8427d661-bfc9-4bdf-a6c9-bfa8c4f91b1c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8427d661-bfc9-4bdf-a6c9-bfa8c4f91b1c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4579ceb-ee0a-421e-8a4c-788b488de8dd' class='xr-var-data-in' type='checkbox'><label for='data-c4579ceb-ee0a-421e-8a4c-788b488de8dd' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Model sea level anomaly</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>comment :</span></dt><dd>Model sea level anomaly WITHOUT corrections for global mean density (steric) changes, inverted barometer effect, or volume displacement due to submerged sea-ice and snow. . Note: ETAN should NOT be used for comparisons with altimetry data products because ETAN is NOT corrected for (a) global mean steric sea level changes related to density changes in the Boussinesq volume-conserving model (Greatbatch correction, see sterGloH) nor (b) sea level displacement due to submerged sea-ice and snow (see sIceLoad). The model has no atmospheric pressure forcing so there is no need to make an inverted barometer correction.</dd><dt><span>valid_min :</span></dt><dd>-4.420732498168945</dd><dt><span>valid_max :</span></dt><dd>0.5757531523704529</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                    <tr>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ADVySNOW</span></div><div class='xr-var-dims'>(time, j_g, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-b38425de-84af-4338-a1f3-b8f8ff291adc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b38425de-84af-4338-a1f3-b8f8ff291adc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e85594c-1d43-4e03-92ae-9f1284cc208f' class='xr-var-data-in' type='checkbox'><label for='data-1e85594c-1d43-4e03-92ae-9f1284cc208f' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 increases mean snow thickness (HSNOW)</dd><dt><span>long_name :</span></dt><dd>Lateral advective flux of snow thickness in the model +y direction</dd><dt><span>units :</span></dt><dd>m3 s-1</dd><dt><span>comment :</span></dt><dd>Lateral advective flux of grid cell mean snow thickness (HSNOW) in the +y direction through the &#x27;v&#x27; face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, horizontal flux quantities are staggered relative to the tracer cells with indexing such that +ADVySNOW(i,j_g) corresponds to +y fluxes through the &#x27;v&#x27; face of the tracer cell at (i,j,k=0). Also, the model +y direction does not necessarily correspond to the geographical north-south direction because the x and y axes of the model&#x27;s curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles.</dd><dt><span>valid_min :</span></dt><dd>-717.3237915039062</dd><dt><span>valid_max :</span></dt><dd>241.27674865722656</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
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+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EXFhl</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-b60cf901-3c02-47a9-ad65-eb4cb1f75c35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b60cf901-3c02-47a9-ad65-eb4cb1f75c35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-079ccf02-3a72-41cd-b0c6-fb481a4f6434' class='xr-var-data-in' type='checkbox'><label for='data-079ccf02-3a72-41cd-b0c6-fb481a4f6434' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 increases potential temperature (THETA)</dd><dt><span>long_name :</span></dt><dd>Open ocean air-sea latent heat flux</dd><dt><span>standard_name :</span></dt><dd>surface_downward_latent_heat_flux</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>comment :</span></dt><dd>Air-sea latent heat flux per unit area of open water (not covered by sea-ice). Note: calculated from the bulk formula following Large and Yeager (2004) NCAR/TN-460+STR.</dd><dt><span>valid_min :</span></dt><dd>-201.45005798339844</dd><dt><span>valid_max :</span></dt><dd>33.64325714111328</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EXFempmr</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-402babc1-abd6-43e0-a7c3-dc3f91b8bcb2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-402babc1-abd6-43e0-a7c3-dc3f91b8bcb2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6fe94826-d0c2-49b7-a1fe-86f5758817b9' class='xr-var-data-in' type='checkbox'><label for='data-6fe94826-d0c2-49b7-a1fe-86f5758817b9' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 increases salinity (SALT)</dd><dt><span>long_name :</span></dt><dd>Open ocean net surface freshwater flux from precipitation, evaporation, and runoff</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>comment :</span></dt><dd>Net surface freshwater flux from precipitation, evaporation, and runoff per unit area in open water (not covered by sea-ice). Excludes freshwater fluxes involving sea-ice and snow. Note: calculated as EXFevap-EXFpreci-EXFroff.</dd><dt><span>valid_min :</span></dt><dd>-5.668725293617172e-07</dd><dt><span>valid_max :</span></dt><dd>8.059536327209571e-08</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
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+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
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+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
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+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
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+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DFrI_SLT</span></div><div class='xr-var-dims'>(time, k_l, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 15, 270, 450), meta=np.ndarray&gt;</div><input id='attrs-c191359e-1b02-4795-88b7-af54b93c2557' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c191359e-1b02-4795-88b7-af54b93c2557' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7606b3b6-228d-488d-ab03-17d5c09c900c' class='xr-var-data-in' type='checkbox'><label for='data-7606b3b6-228d-488d-ab03-17d5c09c900c' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 decreases salinity (SALT)</dd><dt><span>long_name :</span></dt><dd>Vertical diffusive flux of salinity (implicit term)</dd><dt><span>units :</span></dt><dd>1e-3 m3 s-1</dd><dt><span>comment :</span></dt><dd>The implicit term of the vertical diffusive flux of salinity (SALT) in the +z direction through the top &#x27;w&#x27; face of the tracer cell on the native model grid. There is no explicit term of the vertical diffusive flux in the SASSIE ECCO model. Note: in the Arakawa-C grid, vertical flux quantities are staggered relative to the tracer cells with indexing such that +DFrI_SLT(i,j,k_l) corresponds to upward +z fluxes through the top face &#x27;w&#x27; of the tracer cell at (i,j,k). Salinity defined using CF convention &#x27;Sea water salinity is the salt content of sea water, often on the Practical Salinity Scale of 1978. However, the unqualified term &#x27;salinity&#x27; is generic and does not necessarily imply any particular method of calculation. The units of salinity are dimensionless and the units attribute should normally be given as 1e-3 or 0.001 i.e. parts per thousand.&#x27; see https://cfconventions.org/Data/cf-standard-names/73/build/cf-standard-name-table.html</dd><dt><span>valid_min :</span></dt><dd>-3505.572509765625</dd><dt><span>valid_max :</span></dt><dd>11265.3486328125</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> 1.64 TiB </td>\n",
+       "                        <td> 6.95 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 15, 270, 450) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EXFtaux</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-d3ed2dba-a107-48dd-ac21-8767f895dc85' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3ed2dba-a107-48dd-ac21-8767f895dc85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e829a1df-ed87-4e24-91e0-2f3bbcdaa16a' class='xr-var-data-in' type='checkbox'><label for='data-e829a1df-ed87-4e24-91e0-2f3bbcdaa16a' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd> &gt;0 increases horizontal velocity in the +x direction (UVEL)</dd><dt><span>long_name :</span></dt><dd>Wind stress in the model +x direction</dd><dt><span>standard_name :</span></dt><dd>surface_downward_x_stress</dd><dt><span>units :</span></dt><dd>N m-2</dd><dt><span>comment :</span></dt><dd>Wind stress in the +x direction at the tracer cell on the native model grid. Note: EXFtaux is the stress applied to the ice-free ocean surface and sea-ice covered surface. When sea-ice is present, the total stress applied to the ocean surface in the +x direction is NOT EXFtaux, but a combination of EXFtaux wind stress in the open water fraction and a stress from sea-ice in the ice-covered fraction (see oceTAUX).</dd><dt><span>valid_min :</span></dt><dd>-0.2921757996082306</dd><dt><span>valid_max :</span></dt><dd>0.3709751069545746</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> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EXFhs</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-5882139c-a0b5-4289-8c21-7ef3a4379067' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5882139c-a0b5-4289-8c21-7ef3a4379067' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c591f90c-3235-4944-ba3c-e28945e5bbd1' class='xr-var-data-in' type='checkbox'><label for='data-c591f90c-3235-4944-ba3c-e28945e5bbd1' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 increases potential temperature (THETA)</dd><dt><span>long_name :</span></dt><dd>Open ocean air-sea sensible heat flux</dd><dt><span>standard_name :</span></dt><dd>surface_downward_sensible_heat_flux</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>comment :</span></dt><dd>Air-sea sensible heat flux per unit area of open water (not covered by sea-ice). Note: calculated from the bulk formula following Large and Yeager (2004) NCAR/TN-460+STR.</dd><dt><span>valid_min :</span></dt><dd>-505.2680969238281</dd><dt><span>valid_max :</span></dt><dd>38.11310958862305</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> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EXFpreci</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-28c6d8ae-302c-40ff-9a5d-575ff7d54b2b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28c6d8ae-302c-40ff-9a5d-575ff7d54b2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c89a417-2283-45e1-b33f-5f1fce617618' class='xr-var-data-in' type='checkbox'><label for='data-5c89a417-2283-45e1-b33f-5f1fce617618' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 increases salinity (SALT)</dd><dt><span>long_name :</span></dt><dd>Precipitation rate</dd><dt><span>standard_name :</span></dt><dd>lwe_precipitation_rate</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>comment :</span></dt><dd>Precipitation rate</dd><dt><span>valid_min :</span></dt><dd>3.880195396517641e-17</dd><dt><span>valid_max :</span></dt><dd>5.848427804266976e-07</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
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+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EXFqnet</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-f6073c66-4209-48c9-841b-0b898a0e8ab8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6073c66-4209-48c9-841b-0b898a0e8ab8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-669a46dd-c901-4755-917e-57a3cff55b58' class='xr-var-data-in' type='checkbox'><label for='data-669a46dd-c901-4755-917e-57a3cff55b58' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 increases potential temperature (THETA)</dd><dt><span>long_name :</span></dt><dd>Open ocean net air-sea heat flux</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>comment :</span></dt><dd>Net air-sea heat flux (turbulent and radiative) per unit area of open water (not covered by sea-ice). Note: net upward heat flux over open water, calculated as EXFlwnet+EXFswnet-EXFlh-EXFhs.</dd><dt><span>valid_min :</span></dt><dd>-81.22846221923828</dd><dt><span>valid_max :</span></dt><dd>762.6380615234375</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
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+       "                <thead>\n",
+       "                    <tr>\n",
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+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EXFswnet</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-1dd22f24-73b6-4fb1-a9d6-39e2f040f850' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1dd22f24-73b6-4fb1-a9d6-39e2f040f850' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-506e1f2f-0378-4f04-9ead-4d93aae4ea4f' class='xr-var-data-in' type='checkbox'><label for='data-506e1f2f-0378-4f04-9ead-4d93aae4ea4f' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 increases potential temperature (THETA)</dd><dt><span>long_name :</span></dt><dd>Open ocean net shortwave radiative flux</dd><dt><span>standard_name :</span></dt><dd>surface_net_downward_shortwave_flux</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>comment :</span></dt><dd>Net shortwave radiative flux per unit area of open water (not covered by sea-ice). Note: net shortwave radiation over open water calculated from downward shortwave flux (EXFswdn) and ocean surface albdeo.</dd><dt><span>valid_min :</span></dt><dd>-58.27178955078125</dd><dt><span>valid_max :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                        <td> </td>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EXFevap</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-a02c9e80-0ed8-42cc-9826-d88ee62f3644' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a02c9e80-0ed8-42cc-9826-d88ee62f3644' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-492f18c8-ae60-4de4-a4b5-268b55340500' class='xr-var-data-in' type='checkbox'><label for='data-492f18c8-ae60-4de4-a4b5-268b55340500' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 increases salinity (SALT)</dd><dt><span>long_name :</span></dt><dd>Open ocean evaporation rate</dd><dt><span>standard_name :</span></dt><dd>lwe_water_evaporation_rate</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>comment :</span></dt><dd>Evaporation rate per unit area of open water (not covered by sea-ice). Note: calculated using the bulk formula following Large and Yeager (2004) NCAR/TN-460+STR.</dd><dt><span>valid_min :</span></dt><dd>-1.3459994896436456e-08</dd><dt><span>valid_max :</span></dt><dd>8.059614486910505e-08</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EXFtauy</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-2193301c-5e6d-4a43-abfc-53d7ddbf043a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2193301c-5e6d-4a43-abfc-53d7ddbf043a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09cea2cb-0d20-4823-810d-1fad87546c75' class='xr-var-data-in' type='checkbox'><label for='data-09cea2cb-0d20-4823-810d-1fad87546c75' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd> &gt;0 increases horizontal velocity in the +y direction (VVEL)</dd><dt><span>long_name :</span></dt><dd>Wind stress in the model +y direction</dd><dt><span>standard_name :</span></dt><dd>surface_downward_y_stress</dd><dt><span>units :</span></dt><dd>N m-2</dd><dt><span>comment :</span></dt><dd>Wind stress in the +y direction at the tracer cell on the native model grid. Note: EXFtauy is the stress applied to the ice-free ocean surface and sea-ice covered surface. When sea-ice is present, the total stress applied to the ocean surface in the +y direction is NOT EXFtauy, but a combination of EXFtauy wind stress in the open water fraction and a stress from sea-ice in the ice-covered fraction (see oceTAUY).</dd><dt><span>valid_min :</span></dt><dd>-0.30194661021232605</dd><dt><span>valid_max :</span></dt><dd>0.3529888689517975</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                    <tr>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EXFroff</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-17cca0b9-7fc4-45bb-ba67-38d74b7baf1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-17cca0b9-7fc4-45bb-ba67-38d74b7baf1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-467dafd5-6eb4-4f88-a827-b69ec87b6877' class='xr-var-data-in' type='checkbox'><label for='data-467dafd5-6eb4-4f88-a827-b69ec87b6877' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 increases salinity (SALT)</dd><dt><span>long_name :</span></dt><dd>River runoff</dd><dt><span>standard_name :</span></dt><dd>surface_runoff_flux</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>comment :</span></dt><dd>River runoff freshwater flux. Note: not adjusted by the optimization.</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.6076070323833846e-06</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
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+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                        <th> Array </th>\n",
+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EXFlwdn</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-10f8050c-dcc8-4e1e-9047-a339555ddfba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10f8050c-dcc8-4e1e-9047-a339555ddfba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ac724de-fe20-4c37-8bc3-b9110fd27a27' class='xr-var-data-in' type='checkbox'><label for='data-0ac724de-fe20-4c37-8bc3-b9110fd27a27' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 increases potential temperature (THETA)</dd><dt><span>long_name :</span></dt><dd>Downward longwave radiative flux</dd><dt><span>standard_name :</span></dt><dd>surface_downwelling_longwave_flux_in_air</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>comment :</span></dt><dd>Downward longwave radiative flux</dd><dt><span>valid_min :</span></dt><dd>101.59624481201172</dd><dt><span>valid_max :</span></dt><dd>351.4042053222656</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EXFswdn</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-c2c22be0-79d5-4a12-b8d4-7d73008acd2d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2c22be0-79d5-4a12-b8d4-7d73008acd2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfa4f898-4b6e-4f1e-9bdd-c59bc593aa7a' class='xr-var-data-in' type='checkbox'><label for='data-bfa4f898-4b6e-4f1e-9bdd-c59bc593aa7a' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 increases potential temperature (THETA)</dd><dt><span>long_name :</span></dt><dd>Downwelling shortwave radiative flux</dd><dt><span>standard_name :</span></dt><dd>surface_downwelling_shortwave_flux_in_air</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>comment :</span></dt><dd>Downward shortwave radiative flux</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>72.55374908447266</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
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+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>KPPhbl</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-83d73291-ac3e-4cdc-9289-dcde14d3e363' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83d73291-ac3e-4cdc-9289-dcde14d3e363' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f97d90a-ff83-4958-a345-c0ccb24874f2' class='xr-var-data-in' type='checkbox'><label for='data-9f97d90a-ff83-4958-a345-c0ccb24874f2' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 increases depth</dd><dt><span>long_name :</span></dt><dd>KPP boundary layer depth, bulk Ri criterion</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>comment :</span></dt><dd>Depth of the ocean surface boundary layer (h) diagnosed by the KPP bulk Richardson number criterion. Represents the thickness of the layer influenced by surface buoyancy and momentum forcing.</dd><dt><span>valid_min :</span></dt><dd>0.5</dd><dt><span>valid_max :</span></dt><dd>812.6599731445312</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>KPPg_SLT</span></div><div class='xr-var-dims'>(time, k_l, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 15, 270, 450), meta=np.ndarray&gt;</div><input id='attrs-a932e9d9-cede-4e79-ad11-45ed6c5c49ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a932e9d9-cede-4e79-ad11-45ed6c5c49ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03ee59b0-8409-459d-ac5a-d902bef7a734' class='xr-var-data-in' type='checkbox'><label for='data-03ee59b0-8409-459d-ac5a-d902bef7a734' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>KPP non-local flux of salinity</dd><dt><span>units :</span></dt><dd>1e-3 m3 s-1</dd><dt><span>comment :</span></dt><dd>Non-local KPP contribution to the vertical turbulent flux of salinity (SALT); units reflect salinity (dimensionless) times transport.</dd><dt><span>valid_min :</span></dt><dd>-12002.1669921875</dd><dt><span>valid_max :</span></dt><dd>339.4745788574219</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> 1.64 TiB </td>\n",
+       "                        <td> 6.95 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 90, 1080, 1800) </td>\n",
+       "                        <td> (1, 15, 270, 450) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        <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> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EXFvwind</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-3703bfa9-6a6e-408f-b025-4b4fa3ab47bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3703bfa9-6a6e-408f-b025-4b4fa3ab47bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ffc7fc1-ecbf-495c-b229-42f88ca9d5d7' class='xr-var-data-in' type='checkbox'><label for='data-7ffc7fc1-ecbf-495c-b229-42f88ca9d5d7' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Wind speed at 10m in the model +y direction</dd><dt><span>standard_name :</span></dt><dd>y_wind</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>comment :</span></dt><dd>Wind speed at 10m in the +y direction at the tracer cell on the native model grid. Note: ECCO is forced with wind stress (see EXFtauy) not vector winds converted to wind stress using bulk formulae. EXFvwind is calculated by converting wind stress to vector wind using bulk formulae.</dd><dt><span>valid_min :</span></dt><dd>-12.555680274963379</dd><dt><span>valid_max :</span></dt><dd>12.811070442199707</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
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+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>KPPdVsq</span></div><div class='xr-var-dims'>(time, k_u, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 15, 270, 450), meta=np.ndarray&gt;</div><input id='attrs-052bdc48-960e-4ea6-b200-0682bfa8eb4d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-052bdc48-960e-4ea6-b200-0682bfa8eb4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db7a2882-abfd-4003-9a24-9c880086afa4' class='xr-var-data-in' type='checkbox'><label for='data-db7a2882-abfd-4003-9a24-9c880086afa4' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Shear relative to surface (V(ksrf)-V(k))**2</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>comment :</span></dt><dd>Squared vertical shear relative to surface velocity used in KPP shear mixing.</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>2.209679365158081</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.64 TiB </td>\n",
+       "                        <td> 6.95 MiB </td>\n",
+       "                    </tr>\n",
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+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 90, 1080, 1800) </td>\n",
+       "                        <td> (1, 15, 270, 450) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 247776 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>KPPdiffS</span></div><div class='xr-var-dims'>(time, k, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 15, 270, 450), meta=np.ndarray&gt;</div><input id='attrs-3232b46a-c3f0-402f-a779-07fbecfe73d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3232b46a-c3f0-402f-a779-07fbecfe73d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e136d5ca-9d3c-4324-a9ab-56f3537c456d' class='xr-var-data-in' type='checkbox'><label for='data-e136d5ca-9d3c-4324-a9ab-56f3537c456d' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Vertical diffusion coefficient for salt and tracers</dd><dt><span>units :</span></dt><dd>m2 s-1</dd><dt><span>comment :</span></dt><dd>Vertical eddy diffusivity for salinity and passive tracers from the KPP scheme. Controls the strength of turbulent vertical mixing of scalars in the ocean boundary layer and interior.</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>3.470266580581665</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.64 TiB </td>\n",
+       "                        <td> 6.95 MiB </td>\n",
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+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 90, 1080, 1800) </td>\n",
+       "                        <td> (1, 15, 270, 450) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 247776 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "                </tbody>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>KPPg_TH</span></div><div class='xr-var-dims'>(time, k_l, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 15, 270, 450), meta=np.ndarray&gt;</div><input id='attrs-e83949b5-7058-4ad0-936c-252ddf9fcea6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e83949b5-7058-4ad0-936c-252ddf9fcea6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5c3a26e-d84e-457d-8af1-27d3e9ffa74a' class='xr-var-data-in' type='checkbox'><label for='data-c5c3a26e-d84e-457d-8af1-27d3e9ffa74a' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>KPP non-local flux of potential temperature</dd><dt><span>units :</span></dt><dd>degree_C m3 s-1</dd><dt><span>comment :</span></dt><dd>Non-local KPP contribution to the vertical turbulent flux of potential temperature (THETA); units reflect temperature times transport.</dd><dt><span>valid_min :</span></dt><dd>-6244.767578125</dd><dt><span>valid_max :</span></dt><dd>31889.953125</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> 1.64 TiB </td>\n",
+       "                        <td> 6.95 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 90, 1080, 1800) </td>\n",
+       "                        <td> (1, 15, 270, 450) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 247776 chunks in 2 graph layers </td>\n",
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+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>KPPRi</span></div><div class='xr-var-dims'>(time, k_u, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 15, 270, 450), meta=np.ndarray&gt;</div><input id='attrs-7d315784-571f-4cfe-a609-42e36a4ce5e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d315784-571f-4cfe-a609-42e36a4ce5e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c472c1d0-524a-42b5-b105-f721525a242c' class='xr-var-data-in' type='checkbox'><label for='data-c472c1d0-524a-42b5-b105-f721525a242c' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Bulk Richardson number</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>comment :</span></dt><dd>Bulk Richardson number used in KPP mixing parameterization; non-dimensional measure of stratification versus shear.</dd><dt><span>valid_min :</span></dt><dd>-29.621444702148438</dd><dt><span>valid_max :</span></dt><dd>8927583.0</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> 1.64 TiB </td>\n",
+       "                        <td> 6.95 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 15, 270, 450) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 247776 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>KPPviscA</span></div><div class='xr-var-dims'>(time, k, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 15, 270, 450), meta=np.ndarray&gt;</div><input id='attrs-78f86fa7-237c-4d18-846b-83cb8ec837d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78f86fa7-237c-4d18-846b-83cb8ec837d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5589a94b-9387-469b-a70a-e4c30d1f8bfa' class='xr-var-data-in' type='checkbox'><label for='data-5589a94b-9387-469b-a70a-e4c30d1f8bfa' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>KPP vertical eddy viscosity coefficient</dd><dt><span>units :</span></dt><dd>m2 s-1</dd><dt><span>comment :</span></dt><dd>Vertical eddy viscosity coefficient computed by the K-Profile Parameterization (KPP) scheme. Represents the turbulent vertical momentum mixing within the ocean surface boundary layer and interior. . Higher values indicate enhanced vertical mixing of momentum due to shear instability, surface forcing, or boundary layer processes.</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.781213402748108</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
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+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.64 TiB </td>\n",
+       "                        <td> 6.95 MiB </td>\n",
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+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 15, 270, 450) </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 247776 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SIatmFW</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-376343ef-3bdf-4c6c-b6de-4a01ec4a8cf9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-376343ef-3bdf-4c6c-b6de-4a01ec4a8cf9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-faa81abd-7612-41c2-abcf-f04610381f60' class='xr-var-data-in' type='checkbox'><label for='data-faa81abd-7612-41c2-abcf-f04610381f60' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 decreases salinity (SALT)</dd><dt><span>long_name :</span></dt><dd>Net freshwater flux into the open ocean, sea-ice, and snow</dd><dt><span>standard_name :</span></dt><dd>surface_downward_water_flux</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>comment :</span></dt><dd>Net freshwater flux into the combined liquid ocean, sea-ice, and snow reservoirs from the atmosphere and runoff. Note: freshwater fluxes BETWEEN the liquid ocean and sea-ice or snow reservoirs do not contribute to SIatmFW. SIatmFW counts all fluxes to/from the atmosphere that change the TOTAL freshwater stored in the combined liquid ocean, sea-ice, and snow reservoirs.</dd><dt><span>valid_min :</span></dt><dd>-8.057924424065277e-05</dd><dt><span>valid_max :</span></dt><dd>0.0016098151681944728</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> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>KPPghatK</span></div><div class='xr-var-dims'>(time, k_l, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 15, 270, 450), meta=np.ndarray&gt;</div><input id='attrs-b9c3d9f1-9000-4b5f-adc2-b9160ad5f689' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9c3d9f1-9000-4b5f-adc2-b9160ad5f689' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42a81c5a-a541-4ddb-932c-095e9fe5996c' class='xr-var-data-in' type='checkbox'><label for='data-42a81c5a-a541-4ddb-932c-095e9fe5996c' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Ratio of KPP non-local (salt) flux relative to surface-flux</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>comment :</span></dt><dd>Non-dimensional ratio (0–1) of KPP non-local salt flux relative to the surface flux.</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>22.060338973999023</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> 1.64 TiB </td>\n",
+       "                        <td> 6.95 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 90, 1080, 1800) </td>\n",
+       "                        <td> (1, 15, 270, 450) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 247776 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PHIHYD</span></div><div class='xr-var-dims'>(time, k, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 15, 270, 450), meta=np.ndarray&gt;</div><input id='attrs-8d63bde7-afe9-4582-8aa1-4a7920bab7d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d63bde7-afe9-4582-8aa1-4a7920bab7d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e92178b-2b1b-42f3-b1d2-f3f3af07307d' class='xr-var-data-in' type='checkbox'><label for='data-0e92178b-2b1b-42f3-b1d2-f3f3af07307d' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Ocean hydrostatic pressure anomaly</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>comment :</span></dt><dd>PHIHYD = p(k) / rhoConst - g z*(k,t), where p = hydrostatic ocean pressure at depth level k, rhoConst = reference density (1027.5 kg m-3) and g is acceleration due to gravity (9.81 m s-2). Units: p:[kg m-1 s-2], rhoConst:[kg m-3], g:[m s-2], H(t):[m]. Note: Quantity referred to in some contexts as hydrostatic pressure anomaly. PHIHYD is NOT corrected for global mean steric sea level changes related to density changes in the Boussinesq volume-conserving model (Greatbatch correction). </dd><dt><span>valid_min :</span></dt><dd>-40.36260223388672</dd><dt><span>valid_max :</span></dt><dd>995.8346557617188</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.64 TiB </td>\n",
+       "                        <td> 6.95 MiB </td>\n",
+       "                    </tr>\n",
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+       "                        <th> Shape </th>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 247776 chunks in 2 graph layers </td>\n",
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+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>RHOAnoma</span></div><div class='xr-var-dims'>(time, k, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 15, 270, 450), meta=np.ndarray&gt;</div><input id='attrs-be42abe5-3975-449c-a9cf-e9f425ddfff5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be42abe5-3975-449c-a9cf-e9f425ddfff5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29cbe4da-12f5-409f-92e2-ecebd5a67771' class='xr-var-data-in' type='checkbox'><label for='data-29cbe4da-12f5-409f-92e2-ecebd5a67771' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>In-situ seawater density anomaly</dd><dt><span>units :</span></dt><dd>kg m-3</dd><dt><span>comment :</span></dt><dd>In-situ seawater density anomaly relative to the reference density, rhoConst. rhoConst = 1027.5 kg m-3.</dd><dt><span>valid_min :</span></dt><dd>-27.649499893188477</dd><dt><span>valid_max :</span></dt><dd>30.177120208740234</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 1.64 TiB </td>\n",
+       "                        <td> 6.95 MiB </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 247776 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
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+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SIacSubl</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-ed4fbae8-41cc-4472-9a01-9650e5f8b919' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed4fbae8-41cc-4472-9a01-9650e5f8b919' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-819805cb-9f1f-405a-a1f9-9a7a0c7fa7bd' class='xr-var-data-in' type='checkbox'><label for='data-819805cb-9f1f-405a-a1f9-9a7a0c7fa7bd' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 decreases ice</dd><dt><span>long_name :</span></dt><dd>Actual sublimation freshwater flux</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>comment :</span></dt><dd>Realized (actual) sublimation freshwater flux from sea ice; positive values remove ice mass.</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>5.118795797898201e-06</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
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+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    <tr>\n",
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+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SIatmQnt</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-86fac2a4-35a6-4ae8-b584-09463ef033df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86fac2a4-35a6-4ae8-b584-09463ef033df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a34182f-ab01-4def-b31e-ea58c962b720' class='xr-var-data-in' type='checkbox'><label for='data-6a34182f-ab01-4def-b31e-ea58c962b720' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 decreases potential temperature (THETA)</dd><dt><span>long_name :</span></dt><dd>Net atmospheric heat flux</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>comment :</span></dt><dd>Atmospheric contribution to the net surface heat flux; positive values cool the ocean (decrease THETA).</dd><dt><span>valid_min :</span></dt><dd>-83.37039947509766</dd><dt><span>valid_max :</span></dt><dd>780.6641235351562</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
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+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
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+       "                        <th> Data type </th>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PHIBOT</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-1744808d-13b9-4f34-82dd-5be31c0c8b91' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1744808d-13b9-4f34-82dd-5be31c0c8b91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25fabc2b-743e-40db-ada0-976ff11b88dd' class='xr-var-data-in' type='checkbox'><label for='data-25fabc2b-743e-40db-ada0-976ff11b88dd' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Ocean hydrostatic bottom pressure anomaly</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>comment :</span></dt><dd>PHIBOT = p_b / rhoConst - g H(t), where p_b = hydrostatic ocean bottom pressure, rhoConst = reference density (1027.5 kg m-3), g is acceleration due to gravity (9.81 m s-2), and H(t) is model depth at time t. Units: p:[kg m-1 s-2], rhoConst:[kg m-3], g:[m s-2], H(t):[m]. Note: PHIBOT is NOT corrected for global mean steric sea level changes related to density changes in the Boussinesq volume-conserving model (Greatbatch correction), and therefore should NOT be used for comparisons with ocean bottom pressure data.</dd><dt><span>valid_min :</span></dt><dd>-77.73843383789062</dd><dt><span>valid_max :</span></dt><dd>1064.8739013671875</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> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SFLUX</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-221d9abb-a17c-46c4-a659-210cc0e147b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-221d9abb-a17c-46c4-a659-210cc0e147b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bef80f7b-c380-4e90-8bc3-8523a22e73c1' class='xr-var-data-in' type='checkbox'><label for='data-bef80f7b-c380-4e90-8bc3-8523a22e73c1' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 increases salinity (SALT)</dd><dt><span>long_name :</span></dt><dd>Rate of change of total ocean salinity per m2 accounting for mass fluxes.</dd><dt><span>units :</span></dt><dd>g m-2 s-1</dd><dt><span>comment :</span></dt><dd>The rate of change of total ocean salinity due to freshwater fluxes across the liquid surface and the addition or removal of mass. Note: the global area integral of SFLUX matches the time-derivative of total ocean salinity (psu s-1). Unlike oceFWflx, SFLUX includes the contribution to the total ocean salinity from changing ocean mass (e.g. from the addition or removal of freshwater in oceFWflx). </dd><dt><span>valid_min :</span></dt><dd>-0.13435481488704681</dd><dt><span>valid_max :</span></dt><dd>0.13753609359264374</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <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> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SALT</span></div><div class='xr-var-dims'>(time, k, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 15, 270, 450), meta=np.ndarray&gt;</div><input id='attrs-2abc2a29-3307-481c-a174-e134dc2aed04' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2abc2a29-3307-481c-a174-e134dc2aed04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-094492bc-ec8f-489f-b8e1-e1279aa8ebbe' class='xr-var-data-in' type='checkbox'><label for='data-094492bc-ec8f-489f-b8e1-e1279aa8ebbe' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Salinity</dd><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd><dt><span>units :</span></dt><dd>1e-3</dd><dt><span>comment :</span></dt><dd>Defined using CF convention &#x27;Sea water salinity is the salt content of sea water, often on the Practical Salinity Scale of 1978. However, the unqualified term &#x27;salinity&#x27; is generic and does not necessarily imply any particular method of calculation. The units of salinity are dimensionless and the units attribute should normally be given as 1e-3 or 0.001 i.e. parts per thousand.&#x27; see https://cfconventions.org/Data/cf-standard-names/73/build/cf-standard-name-table.html</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>47.211090087890625</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> 1.64 TiB </td>\n",
+       "                        <td> 6.95 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 90, 1080, 1800) </td>\n",
+       "                        <td> (1, 15, 270, 450) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 247776 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SIarea</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-2d7b0c3e-4221-419b-a480-e0e93c5c552d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d7b0c3e-4221-419b-a480-e0e93c5c552d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0109f15e-1cd0-4b6f-935e-987442c4403c' class='xr-var-data-in' type='checkbox'><label for='data-0109f15e-1cd0-4b6f-935e-987442c4403c' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Sea-ice concentration</dd><dt><span>standard_name :</span></dt><dd>sea_ice_area_fraction</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>comment :</span></dt><dd>Fraction of ocean grid cell covered with sea-ice [0 to 1]. CF Standard Name Table v73:  &#x27;Area fraction&#x27; is the fraction of a grid cell&#x27;s horizontal area that has some characteristic of interest. It is evaluated as the area of interest divided by the grid cell area. It may be expressed as a fraction, a percentage, or any other dimensionless representation of a fraction. Sea ice area fraction is area of the sea surface occupied by sea ice. It is also called &#x27;sea ice concentration&#x27;. &#x27;Sea ice&#x27; means all ice floating in the sea which has formed from freezing sea water, rather than by other processes such as calving of land ice to form icebergs. https://cfconventions.org/Data/cf-standard-names/73/build/cf-standard-name-table.html. Defined using CF Standard Name Table v73: &#x27;Area fraction&#x27; is the fraction of a grid cell&#x27;s horizontal area that has some characteristic of interest. It is evaluated as the area of interest divided by the grid cell area. It may be expressed as a fraction, a percentage, or any other dimensionless representation of a fraction. Sea ice area fraction is area of the sea surface occupied by sea ice. It is also called &#x27;sea ice concentration&#x27;. &#x27;Sea ice&#x27; means all ice floating in the sea which has formed from freezing sea water and precipitation, rather than by other processes such as calving of land ice to form icebergs. https://cfconventions.org/Data/cf-standard-names/73/build/cf-standard-name-table.html</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>1.0</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <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> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SIdAbATO</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-3ce3de39-573c-4851-930f-3f7d7d010bec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ce3de39-573c-4851-930f-3f7d7d010bec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9f7f5b3-2797-474d-8185-c97224d8d4c8' class='xr-var-data-in' type='checkbox'><label for='data-f9f7f5b3-2797-474d-8185-c97224d8d4c8' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Potential sea ice concentration rate of change by open ocean atmospheric flux</dd><dt><span>units :</span></dt><dd>s-1</dd><dt><span>comment :</span></dt><dd>Tendency of the sea ice covered fraction of a grid cell surface area due to atmospheric fluxes acting over the open ocean fraction of the grid cell.</dd><dt><span>valid_min :</span></dt><dd>-1.5272783926434386e-08</dd><dt><span>valid_max :</span></dt><dd>4.134771643293789e-06</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> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SIdAbOCN</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-76293e71-2c6e-403f-bebc-f238f80a7611' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76293e71-2c6e-403f-bebc-f238f80a7611' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebc02a03-db45-4ce3-9be3-82cd8c3c81e0' class='xr-var-data-in' type='checkbox'><label for='data-ebc02a03-db45-4ce3-9be3-82cd8c3c81e0' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Potential sea ice concentration rate of change by ocean-ice flux</dd><dt><span>units :</span></dt><dd>s-1</dd><dt><span>comment :</span></dt><dd>Tendency of the sea ice covered fraction of a grid cell surface area due to fluxes at the ice-ocean interface.</dd><dt><span>valid_min :</span></dt><dd>-5.516970759344986e-06</dd><dt><span>valid_max :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SIdA</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-486d7080-58c9-41ef-af45-3b42ccd719f5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-486d7080-58c9-41ef-af45-3b42ccd719f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d12fdd4-e02b-48d4-9b0d-e9b6cb7429e3' class='xr-var-data-in' type='checkbox'><label for='data-0d12fdd4-e02b-48d4-9b0d-e9b6cb7429e3' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Net sea ice concentration rate of change</dd><dt><span>units :</span></dt><dd>s-1</dd><dt><span>comment :</span></dt><dd>Net rate of change of the sea ice covered fraction of a grid cell surface area from all contributing processes (thermodynamic + dynamic).</dd><dt><span>valid_min :</span></dt><dd>-1.4122149877948686e-05</dd><dt><span>valid_max :</span></dt><dd>4.134771643293789e-06</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",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SIheffPT</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-2a21d3cf-289a-421a-a838-f555f980c95c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a21d3cf-289a-421a-a838-f555f980c95c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4379827-0690-436d-91a2-b07b2cf2a741' class='xr-var-data-in' type='checkbox'><label for='data-e4379827-0690-436d-91a2-b07b2cf2a741' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Area-averaged sea-ice thickness (SIheff) preceeeding thermodynamic growth/melt</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>comment :</span></dt><dd>Grid-cell mean sea ice thickness (SIheff) prior to thermodynamic growth or melt calculations.</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>4.405087471008301</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
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+       "                        <td> 7.42 MiB </td>\n",
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+       "                    \n",
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+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SIdAbATC</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-be9671dc-cab9-4901-b666-a657ff9006e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be9671dc-cab9-4901-b666-a657ff9006e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50e3868d-e944-42e2-83ea-371f72cd30af' class='xr-var-data-in' type='checkbox'><label for='data-50e3868d-e944-42e2-83ea-371f72cd30af' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Potential sea ice concentration rate of change by atmospheric flux over ice</dd><dt><span>units :</span></dt><dd>s-1</dd><dt><span>comment :</span></dt><dd>Tendency of the sea ice covered fraction of a grid cell surface area due to atmospheric fluxes acting over the ice-covered fraction of the grid cell.</dd><dt><span>valid_min :</span></dt><dd>-8.887282576530708e-11</dd><dt><span>valid_max :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
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+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SIqnet</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-0eacc7fa-95e4-49de-829f-69cfdc6e5eb4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0eacc7fa-95e4-49de-829f-69cfdc6e5eb4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52744ab2-5e5b-4829-b6e5-04b61513950f' class='xr-var-data-in' type='checkbox'><label for='data-52744ab2-5e5b-4829-b6e5-04b61513950f' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 decreases potential temperature (THETA)</dd><dt><span>long_name :</span></dt><dd>Ocean surface heat flux (turbulent and radiative)</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>comment :</span></dt><dd>Total surface heat flux (turbulent and radiative) applied to the ocean; positive values cool the ocean (decrease THETA).</dd><dt><span>valid_min :</span></dt><dd>-1412.13818359375</dd><dt><span>valid_max :</span></dt><dd>2275.68701171875</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SIfwSubl</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-e30e1701-3861-421d-9005-1cecd2269eea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e30e1701-3861-421d-9005-1cecd2269eea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16bf41b5-e679-4028-9c82-b039bca8c178' class='xr-var-data-in' type='checkbox'><label for='data-16bf41b5-e679-4028-9c82-b039bca8c178' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 decreases ice</dd><dt><span>long_name :</span></dt><dd>Potential sublimation freshwater flux</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>comment :</span></dt><dd>Potential sublimation freshwater flux from sea ice; positive values remove ice mass.</dd><dt><span>valid_min :</span></dt><dd>-4.757612714456627e-06</dd><dt><span>valid_max :</span></dt><dd>5.118795797898201e-06</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SIheff</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-914d6fbc-c9b0-40e0-b869-a9bc0f911647' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-914d6fbc-c9b0-40e0-b869-a9bc0f911647' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca2e2545-08ad-46c4-a3f7-23b3a68233e4' class='xr-var-data-in' type='checkbox'><label for='data-ca2e2545-08ad-46c4-a3f7-23b3a68233e4' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Area-averaged sea-ice thickness</dd><dt><span>standard_name :</span></dt><dd>sea_ice_thickness</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>comment :</span></dt><dd>Sea-ice thickness averaged over the entire model grid cell, including open water where sea-ice thickness is zero. Note: sea-ice thickness over the ICE-COVERED fraction of the grid cell is SIheff/SIarea</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>4.405087471008301</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> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SIhsnow</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-bf3dc173-bf89-4cbc-9e52-ce3d8bfc912a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf3dc173-bf89-4cbc-9e52-ce3d8bfc912a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4004e43c-640f-412f-8726-d40bc6ec49ea' class='xr-var-data-in' type='checkbox'><label for='data-4004e43c-640f-412f-8726-d40bc6ec49ea' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Area-averaged snow thickness</dd><dt><span>standard_name :</span></dt><dd>surface_snow_thickness</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>comment :</span></dt><dd>Snow thickness averaged over the entire model grid cell, including open water where snow thickness is zero. Note: snow thickness over the ICE-COVERED fraction of the grid cell is SIhsnow/SIarea</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>0.8147290349006653</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SIqneto</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-fcf62377-8e94-4e02-b033-a66d474bfac7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fcf62377-8e94-4e02-b033-a66d474bfac7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9afac6cf-ef77-43e3-ab6d-a06fe5ea9b9a' class='xr-var-data-in' type='checkbox'><label for='data-9afac6cf-ef77-43e3-ab6d-a06fe5ea9b9a' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 decreases potential temperature (THETA)</dd><dt><span>long_name :</span></dt><dd>Open ocean part of ocean surface heat flux (SIqnet)</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>comment :</span></dt><dd>Portion of the net surface heat flux (SIqnet) applied over the open-ocean fraction of the grid cell; positive values cool the ocean (decrease THETA).</dd><dt><span>valid_min :</span></dt><dd>-83.37039947509766</dd><dt><span>valid_max :</span></dt><dd>574.0071411132812</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SIrsSubl</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-eb0b2fb2-2b87-4e46-8348-97a58c966444' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb0b2fb2-2b87-4e46-8348-97a58c966444' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fd8e61f-68b7-464a-9189-10df5a59fb62' class='xr-var-data-in' type='checkbox'><label for='data-0fd8e61f-68b7-464a-9189-10df5a59fb62' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 taken from ocean</dd><dt><span>long_name :</span></dt><dd>Residual sublimation freshwater flux</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>comment :</span></dt><dd>Residual sublimation freshwater flux term ensuring conservation of mass; positive values correspond to flux taken from the ocean.</dd><dt><span>valid_min :</span></dt><dd>-4.757612714456627e-06</dd><dt><span>valid_max :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
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+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
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+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SIuice</span></div><div class='xr-var-dims'>(time, j, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-4c3a5dbd-b12a-4a45-8da3-948ff04206ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c3a5dbd-b12a-4a45-8da3-948ff04206ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a3f159a-a854-4a5a-91d7-f8236965eb32' class='xr-var-data-in' type='checkbox'><label for='data-5a3f159a-a854-4a5a-91d7-f8236965eb32' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Sea-ice velocity in the model +x direction </dd><dt><span>standard_name :</span></dt><dd>sea_ice_x_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>comment :</span></dt><dd>Horizontal sea-ice velocity in the +x direction at the &#x27;u&#x27; face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, horizontal velocities are staggered relative to the tracer cells with indexing such that +SIuice(i_g,j) corresponds to +x fluxes through the &#x27;u&#x27; face of the tracer cell at (i,j,k=0). Also, the model +x direction does not necessarily correspond to the geographical east-west direction because the x and y axes of the model&#x27;s curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles.</dd><dt><span>valid_min :</span></dt><dd>-0.8023905754089355</dd><dt><span>valid_max :</span></dt><dd>0.9455509781837463</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
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+       "                    <tr>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
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+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SIhsnoPT</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-77f35207-dd57-46d6-8c72-7b2204737a52' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-77f35207-dd57-46d6-8c72-7b2204737a52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d099f72-a524-472f-a443-ab7350d6551d' class='xr-var-data-in' type='checkbox'><label for='data-3d099f72-a524-472f-a443-ab7350d6551d' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Area-averaged snow thickness (SIhsnow) preceeeding thermodynamic growth/melt</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>comment :</span></dt><dd>Grid-cell mean snow thickness (SIhsnow) prior to thermodynamic growth or melt calculations.</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>0.8147248029708862</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                <tbody>\n",
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+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
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+       "                        <td> 7.42 MiB </td>\n",
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+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SIqsw</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-5f1c087a-6ef7-4841-b6fa-67f06eeb1e78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f1c087a-6ef7-4841-b6fa-67f06eeb1e78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b00ce3f-f304-4131-852c-21afb0ff31f0' class='xr-var-data-in' type='checkbox'><label for='data-9b00ce3f-f304-4131-852c-21afb0ff31f0' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 decreases potential temperature (THETA)</dd><dt><span>long_name :</span></dt><dd>Ocean surface shortwave radiation</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>comment :</span></dt><dd>Shortwave radiative flux at the ocean surface; positive values cool the ocean (decrease THETA).</dd><dt><span>valid_min :</span></dt><dd>-58.27178955078125</dd><dt><span>valid_max :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
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+       "                    \n",
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+       "                        <th> Shape </th>\n",
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+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SIvice</span></div><div class='xr-var-dims'>(time, j_g, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-a03065f4-0cc5-47da-b769-abe67bc76ca7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a03065f4-0cc5-47da-b769-abe67bc76ca7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8db5520-a743-45b5-b8a8-f5698d26495f' class='xr-var-data-in' type='checkbox'><label for='data-a8db5520-a743-45b5-b8a8-f5698d26495f' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Sea-ice velocity in the model +y direction </dd><dt><span>standard_name :</span></dt><dd>sea_ice_y_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>comment :</span></dt><dd>Horizontal sea-ice velocity in the +y direction at the &#x27;v&#x27; face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, horizontal velocities are staggered relative to the tracer cells with indexing such that +SIvice(i,j_g) corresponds to +y fluxes through the &#x27;v&#x27; face of the tracer cell at (i,j,k=0). Also, the model +y direction does not necessarily correspond to the geographical north-south direction because the x and y axes of the model&#x27;s curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles.</dd><dt><span>valid_min :</span></dt><dd>-1.319574236869812</dd><dt><span>valid_max :</span></dt><dd>0.8663744330406189</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> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "  <text x=\"35.294117647058826\" y=\"105.50721334640018\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294117647058826,105.50721334640018)\">2581</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SSH</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-98229c0f-a59b-4d7d-a71a-963757a0f53e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98229c0f-a59b-4d7d-a71a-963757a0f53e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c60f1e5-5a2c-4a24-8bc5-e70be0d7ce0a' class='xr-var-data-in' type='checkbox'><label for='data-1c60f1e5-5a2c-4a24-8bc5-e70be0d7ce0a' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Dynamic sea surface height anomaly</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>comment :</span></dt><dd>Dynamic sea surface height anomaly above the geoid. Note: SSH is calculated by correcting model sea level anomaly ETAN for two effects: a) sea level displacement due to sea-ice and snow pressure loading (see sIceLoad), and b) the linear trend in ETAN. The linear trend is removed because the open boundary conditions have net zero inflow and outflow by model construction, so there is an accumulation of volume. This term can be compared with detrended altimetry data that has the inverse barometer (IB) correction.</dd><dt><span>valid_min :</span></dt><dd>0.29679974913597107</dd><dt><span>valid_max :</span></dt><dd>1.858204960823059</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> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>THETA</span></div><div class='xr-var-dims'>(time, k, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 15, 270, 450), meta=np.ndarray&gt;</div><input id='attrs-8b4227c1-5b0e-4212-a4a5-b3ff0485b6f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b4227c1-5b0e-4212-a4a5-b3ff0485b6f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-987b6a0f-dd61-436a-9a18-3b1e50deb0cc' class='xr-var-data-in' type='checkbox'><label for='data-987b6a0f-dd61-436a-9a18-3b1e50deb0cc' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Potential temperature </dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>comment :</span></dt><dd>Sea water potential temperature is the temperature a parcel of sea water would have if moved adiabatically to sea level pressure. Note: the equation of state is a modified UNESCO formula by Jackett and McDougall (1995), which uses the model variable potential temperature as input assuming a horizontally and temporally constant pressure of $p_0=-g \r",
+       "ho_{0} z$.</dd><dt><span>valid_min :</span></dt><dd>-2.6241772174835205</dd><dt><span>valid_max :</span></dt><dd>10.097210884094238</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> 1.64 TiB </td>\n",
+       "                        <td> 6.95 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 90, 1080, 1800) </td>\n",
+       "                        <td> (1, 15, 270, 450) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UVELMASS</span></div><div class='xr-var-dims'>(time, k, j, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 15, 270, 450), meta=np.ndarray&gt;</div><input id='attrs-c6748a1b-5f0c-4530-a5b4-06ff587bef95' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6748a1b-5f0c-4530-a5b4-06ff587bef95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-628254b9-9b1a-4252-ad01-900c8c8a8111' class='xr-var-data-in' type='checkbox'><label for='data-628254b9-9b1a-4252-ad01-900c8c8a8111' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 increases volume</dd><dt><span>long_name :</span></dt><dd>Horizontal velocity in the model +x direction per unit area of the grid cell &#x27;u&#x27; face</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>comment :</span></dt><dd>Horizontal velocity in the model +x direction multiplied by the open water fraction (hFacW) of the grid cell &#x27;u&#x27; face on the native model grid (&#x27;u&#x27; grid cell face area = drF dyG). Accounts for partial cells (hFacW &lt; 1). Volume flux in +x = UVELMASS drF dyG. Although UVELMASS is expressed in units of [m s-1], it is important to note that it is a normalized transport: volume flux [m3 s-1] per square meter. UVELMASS(t) = UVEL(t) x (drF x dyG) x hFacW / (drF x dyG). Note: in the Arakawa-C grid, horizontal velocities are staggered relative to the tracer cells with indexing such that +UVELMASS(i,j,k) corresponds to +x fluxes through the &#x27;u&#x27; face of the tracer cell at (i,j,k). UVELMASS can be used for volume flux calculations. Also, the model +x direction does not necessarily correspond to the geographical east-west direction because the x and y axes of the model&#x27;s curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles. See VVELMASS and WVELMASS</dd><dt><span>valid_min :</span></dt><dd>-1.058140516281128</dd><dt><span>valid_max :</span></dt><dd>1.7317922115325928</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> 1.64 TiB </td>\n",
+       "                        <td> 6.95 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 90, 1080, 1800) </td>\n",
+       "                        <td> (1, 15, 270, 450) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 247776 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>VVEL</span></div><div class='xr-var-dims'>(time, k, j_g, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 15, 270, 450), meta=np.ndarray&gt;</div><input id='attrs-3ff65f22-3f1b-4187-8638-e40457baeb23' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ff65f22-3f1b-4187-8638-e40457baeb23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1b94c89-79e7-4420-a6d3-34114600da13' class='xr-var-data-in' type='checkbox'><label for='data-d1b94c89-79e7-4420-a6d3-34114600da13' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 increases volume</dd><dt><span>long_name :</span></dt><dd>Horizontal velocity in the model +y direction</dd><dt><span>standard_name :</span></dt><dd>sea_water_y_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>comment :</span></dt><dd>Horizontal velocity in the +y direction at the &#x27;v&#x27; face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, horizontal velocities are staggered relative to the tracer cells with indexing such that +VVEL(i,j_g,k) corresponds to +y fluxes through the &#x27;v&#x27; face of the tracer cell at (i,j,k). Also, the model +y direction does not necessarily correspond to the geographical north-south direction because the x and y axes of the model&#x27;s curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles.</dd><dt><span>valid_min :</span></dt><dd>-1.5160799026489258</dd><dt><span>valid_max :</span></dt><dd>1.1261661052703857</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> 1.64 TiB </td>\n",
+       "                        <td> 6.95 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 90, 1080, 1800) </td>\n",
+       "                        <td> (1, 15, 270, 450) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 247776 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>VVELMASS</span></div><div class='xr-var-dims'>(time, k, j_g, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 15, 270, 450), meta=np.ndarray&gt;</div><input id='attrs-774ccb24-5c3f-4b54-8903-58a234fe1074' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-774ccb24-5c3f-4b54-8903-58a234fe1074' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf328a19-6421-4fd5-a95e-1e270ebf753f' class='xr-var-data-in' type='checkbox'><label for='data-cf328a19-6421-4fd5-a95e-1e270ebf753f' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 increases volume</dd><dt><span>long_name :</span></dt><dd>Horizontal velocity in the model +y direction per unit area of the grid cell &#x27;v&#x27; face</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>comment :</span></dt><dd>Horizontal velocity in the model +y direction multiplied by the open water fraction (hFacS) of the grid cell &#x27;v&#x27; face on the native model grid (&#x27;v&#x27; grid cell face area = drF dxG). Accounts for partial cells (hFacS &lt; 1). Volume flux in +y = VVELMASS drF dxG. Although VVELMASS is expressed in units of [m s-1], it is important to note that it is a normalized transport: volume flux [m3 s-1] per square meter. VVELMASS(t) = VVEL(t) x (drF x dxG) x hFacS / (drF x dxG). Note: in the Arakawa-C grid, horizontal velocities are staggered relative to the tracer cells with indexing such that +VVELMASS(i,j,k) corresponds to +y fluxes through the &#x27;v&#x27; face of the tracer cell at (i,j,k). VVELMASS can be used for volume flux calculations. Also, the model +y direction does not necessarily correspond to the geographical north-south direction because the x and y axes of the model&#x27;s curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles. See UVELMASS and WVELMASS.</dd><dt><span>valid_min :</span></dt><dd>-1.517075777053833</dd><dt><span>valid_max :</span></dt><dd>1.1261779069900513</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> 1.64 TiB </td>\n",
+       "                        <td> 6.95 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 90, 1080, 1800) </td>\n",
+       "                        <td> (1, 15, 270, 450) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 247776 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>UVEL</span></div><div class='xr-var-dims'>(time, k, j, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 15, 270, 450), meta=np.ndarray&gt;</div><input id='attrs-18ffe8d3-2737-463b-9c77-415b1dac73a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18ffe8d3-2737-463b-9c77-415b1dac73a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0289441-d887-4a0b-882e-53a61538310c' class='xr-var-data-in' type='checkbox'><label for='data-e0289441-d887-4a0b-882e-53a61538310c' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 increases volume</dd><dt><span>long_name :</span></dt><dd>Horizontal velocity in the model +x direction</dd><dt><span>standard_name :</span></dt><dd>sea_water_x_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>comment :</span></dt><dd>Horizontal velocity in the +x direction at the &#x27;u&#x27; face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, horizontal velocities are staggered relative to the tracer cells with indexing such that +UVEL(i_g,j,k) corresponds to +x fluxes through the &#x27;u&#x27; face of the tracer cell at (i,j,k). Also, the model +x direction does not necessarily correspond to the geographical east-west direction because the x and y axes of the model&#x27;s curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles.</dd><dt><span>valid_min :</span></dt><dd>-1.0584291219711304</dd><dt><span>valid_max :</span></dt><dd>1.731411099433899</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> 1.64 TiB </td>\n",
+       "                        <td> 6.95 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 90, 1080, 1800) </td>\n",
+       "                        <td> (1, 15, 270, 450) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 247776 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SIqneti</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-bcc122c7-4534-42d8-93f4-352e221d4000' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bcc122c7-4534-42d8-93f4-352e221d4000' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fb7c12b-2f36-45ee-b79e-c5f4c1ec80bb' class='xr-var-data-in' type='checkbox'><label for='data-3fb7c12b-2f36-45ee-b79e-c5f4c1ec80bb' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 decreases potential temperature (THETA)</dd><dt><span>long_name :</span></dt><dd>Ice covered part of ocean surface heat flux (SIqnet)</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>comment :</span></dt><dd>Portion of the net surface heat flux (SIqnet) applied over the ice-covered fraction of the grid cell; positive values cool the ocean (decrease THETA).</dd><dt><span>valid_min :</span></dt><dd>-0.00023875513579696417</dd><dt><span>valid_max :</span></dt><dd>9.301651954650879</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> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>TFLUX</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-3b06fba0-ba97-42c7-af79-a0ad5e45c52d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b06fba0-ba97-42c7-af79-a0ad5e45c52d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50b8487a-1366-49db-9cb6-3427a493245a' class='xr-var-data-in' type='checkbox'><label for='data-50b8487a-1366-49db-9cb6-3427a493245a' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 increases potential temperature (THETA)</dd><dt><span>long_name :</span></dt><dd>Rate of change of ocean heat content per m2 accounting for mass fluxes.</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>comment :</span></dt><dd>The rate of change of ocean heat content due to heat fluxes across the liquid surface and the addition or removal of mass. . Note: the global area integral of TFLUX and geothermal flux (geothermalFlux.bin) matches the time-derivative of ocean heat content (J/s). Unlike oceQnet, TFLUX includes the contribution to the ocean heat content from changing ocean mass (e.g. from oceFWflx).</dd><dt><span>valid_min :</span></dt><dd>-2254.427734375</dd><dt><span>valid_max :</span></dt><dd>1379.521240234375</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> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>WSLTMASS</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-ffdc01bb-0b96-4346-bd93-908f5c2c0fa4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ffdc01bb-0b96-4346-bd93-908f5c2c0fa4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57dc9ff2-4bc1-4f39-bf7a-6996d438cf32' class='xr-var-data-in' type='checkbox'><label for='data-57dc9ff2-4bc1-4f39-bf7a-6996d438cf32' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Ocean surface salinity flux correction for linear free surface</dd><dt><span>units :</span></dt><dd>1e-3 m s-1</dd><dt><span>comment :</span></dt><dd>Surface flux correction of salinity associated with a linear free-surface configuration; applied as a mass flux term at the ocean surface.</dd><dt><span>valid_min :</span></dt><dd>-0.0007130480953492224</dd><dt><span>valid_max :</span></dt><dd>0.0007677259854972363</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
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+       "                <thead>\n",
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+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>WVEL</span></div><div class='xr-var-dims'>(time, k_l, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 15, 270, 450), meta=np.ndarray&gt;</div><input id='attrs-fd98517e-dfb5-48d3-beee-f05ac0e354c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd98517e-dfb5-48d3-beee-f05ac0e354c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-158f9916-43b9-4879-9859-a09b88ff6dca' class='xr-var-data-in' type='checkbox'><label for='data-158f9916-43b9-4879-9859-a09b88ff6dca' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 decreases volume</dd><dt><span>long_name :</span></dt><dd>Vertical velocity</dd><dt><span>standard_name :</span></dt><dd>upward_sea_water_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>comment :</span></dt><dd>Vertical velocity in the +z direction at the top &#x27;w&#x27; face of the tracer cell on the native model grid. Note: in the Arakawa-C grid, vertical velocities are staggered relative to the tracer cells with indexing such that +WVEL(i,j,k_l) corresponds to upward +z motion through the top &#x27;w&#x27; face of the tracer cell at (i,j,k). WVEL is identical to WVELMASS.</dd><dt><span>valid_min :</span></dt><dd>-0.03672891482710838</dd><dt><span>valid_max :</span></dt><dd>0.03133847936987877</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> 1.64 TiB </td>\n",
+       "                        <td> 6.95 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 90, 1080, 1800) </td>\n",
+       "                        <td> (1, 15, 270, 450) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 247776 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>WTHMASS</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-7a9dfed6-01e9-413f-a0e6-39edde937158' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7a9dfed6-01e9-413f-a0e6-39edde937158' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e679e8cd-eeaf-45e1-99b3-6b7195f74a09' class='xr-var-data-in' type='checkbox'><label for='data-e679e8cd-eeaf-45e1-99b3-6b7195f74a09' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Ocean surface potential temperature flux correction for linear free surface</dd><dt><span>units :</span></dt><dd>degree_C m s-1</dd><dt><span>comment :</span></dt><dd>Surface flux correction of potential temperature associated with a linear free-surface configuration; applied as a mass flux term at the ocean surface.</dd><dt><span>valid_min :</span></dt><dd>-4.102657112525776e-05</dd><dt><span>valid_max :</span></dt><dd>3.8994727219687775e-05</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> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>WVELMASS</span></div><div class='xr-var-dims'>(time, k_l, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 15, 270, 450), meta=np.ndarray&gt;</div><input id='attrs-415f1323-a969-44cb-8c41-93052519e4b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-415f1323-a969-44cb-8c41-93052519e4b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8436dec7-719d-4837-9757-f61cd2dfa2f4' class='xr-var-data-in' type='checkbox'><label for='data-8436dec7-719d-4837-9757-f61cd2dfa2f4' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 decreases volume</dd><dt><span>long_name :</span></dt><dd>Grid cell face-averaged vertical velocity in the model +z direction.</dd><dt><span>standard_name :</span></dt><dd>upward_sea_water_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>comment :</span></dt><dd>Vertical velocity in the +z direction at the top &#x27;w&#x27; face of the tracer cell on the native model grid. Volume flux in +z = WVELMASS drA. Note: in the Arakawa-C grid, vertical velocities are staggered relative to the tracer cells with indexing such that +WVELMASS(i,j,k) corresponds to upward +z motion through the top &#x27;w&#x27; face of the tracer cell at (i,j,k). Unlike UVELMASS and VVELMASS, WVELMASS is not scaled by a time-varying open water fraction because the open water fraction of the &#x27;w&#x27; face is always 1, thus WVELMASS is identical to WVEL.</dd><dt><span>valid_min :</span></dt><dd>-0.036736056208610535</dd><dt><span>valid_max :</span></dt><dd>0.031296662986278534</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> 1.64 TiB </td>\n",
+       "                        <td> 6.95 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 90, 1080, 1800) </td>\n",
+       "                        <td> (1, 15, 270, 450) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 247776 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SItflux</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-141c084f-faf2-421b-94bf-9ec05a2e71d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-141c084f-faf2-421b-94bf-9ec05a2e71d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4463e3b-ed55-4555-bf93-a99af3d6f472' class='xr-var-data-in' type='checkbox'><label for='data-b4463e3b-ed55-4555-bf93-a99af3d6f472' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 increases potential temperature (THETA)</dd><dt><span>long_name :</span></dt><dd>Rate of change of ocean heat content per m2 accounting for mass fluxes, including sea ice</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>comment :</span></dt><dd>Rate of change of ocean heat content per unit area including effects of surface mass fluxes (precipitation, evaporation, sea ice); positive values warm the ocean (increase THETA).</dd><dt><span>valid_min :</span></dt><dd>-81.86585998535156</dd><dt><span>valid_max :</span></dt><dd>781.0896606445312</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> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>oceQsw</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-790659d6-c64d-48b2-8291-958af8352fb5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-790659d6-c64d-48b2-8291-958af8352fb5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ec33cc2-5143-432a-a8c4-38d770337bde' class='xr-var-data-in' type='checkbox'><label for='data-4ec33cc2-5143-432a-a8c4-38d770337bde' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 increases potential temperature (THETA)</dd><dt><span>long_name :</span></dt><dd>Net shortwave radiative flux across the ocean surface</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>comment :</span></dt><dd>Net shortwave radiative flux across the ocean surface. Note: Shortwave radiation penetrates below the surface grid cell.</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>58.27178955078125</dd></dl></div><div class='xr-var-data'><table>\n",
+       "    <tr>\n",
+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>oceTAUX</span></div><div class='xr-var-dims'>(time, j, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-15b0e0df-ee42-4fc2-b2b1-ea792bad5f82' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15b0e0df-ee42-4fc2-b2b1-ea792bad5f82' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ad1d159-1705-4490-bec7-2448a9843b54' class='xr-var-data-in' type='checkbox'><label for='data-2ad1d159-1705-4490-bec7-2448a9843b54' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd> &gt;0 increases horizontal velocity in the +x direction (UVEL)</dd><dt><span>long_name :</span></dt><dd>Ocean surface stress in the model +x direction</dd><dt><span>standard_name :</span></dt><dd>downward_x_stress_at_sea_water_surface</dd><dt><span>units :</span></dt><dd>N m-2</dd><dt><span>comment :</span></dt><dd>Ocean surface stress due to wind and sea-ice in the +x direction centered over the &#x27;u&#x27; side of the the native model grid. Note: in the Arakawa-C grid, wind stress acts on horizontal velocities which are staggered relative to the tracer cells with indexing such that +oceTAUX(i_g,j) corresponds to +x momentum fluxes at &#x27;u&#x27; edge of the tracer cell at (i,j,k=0). Also, the model +x direction does not necessarily correspond to the geographical east-west direction because the x and y axes of the model&#x27;s curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles.</dd><dt><span>valid_min :</span></dt><dd>-0.33684223890304565</dd><dt><span>valid_max :</span></dt><dd>0.7831786870956421</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",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sIceLoad</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-2e5fc910-7e1e-4481-a281-c75808259d79' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e5fc910-7e1e-4481-a281-c75808259d79' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34469db1-994f-4e79-abc5-50c4e963ee31' class='xr-var-data-in' type='checkbox'><label for='data-34469db1-994f-4e79-abc5-50c4e963ee31' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Average sea-ice and snow mass per unit area</dd><dt><span>standard_name :</span></dt><dd>sea_ice_and_surface_snow_amount</dd><dt><span>units :</span></dt><dd>kg m-2</dd><dt><span>comment :</span></dt><dd>Total mass of sea-ice and snow in a model grid cell averaged over model grid cell area. Note: sIceLoad is used to correct model sea level anomaly, ETAN, to calculate dynamic sea surface height, SSH. In the model, sea-ice is treated as floating above the sea level with ETAN tracing the location of the ocean-ice interface. Consequently, sea-ice growth in the model lowers ETAN and sea-ice melting raises ETAN. Dynamic sea surface height is obtained by correcting ETAN by the weight of ice and snow directly above following Archimedes’ principle.</dd><dt><span>valid_min :</span></dt><dd>0.0</dd><dt><span>valid_max :</span></dt><dd>4180.583984375</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "        <td>\n",
+       "            <table style=\"border-collapse: collapse;\">\n",
+       "                <thead>\n",
+       "                    <tr>\n",
+       "                        <td> </td>\n",
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+       "                        <th> Chunk </th>\n",
+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
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+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>oceFWflx</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-844063be-7464-42fa-94f9-b90059ad9502' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-844063be-7464-42fa-94f9-b90059ad9502' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22527185-e0a5-4eb8-8ed2-4208364eedf9' class='xr-var-data-in' type='checkbox'><label for='data-22527185-e0a5-4eb8-8ed2-4208364eedf9' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 decreases salinity (SALT)</dd><dt><span>long_name :</span></dt><dd>Net freshwater flux into the ocean</dd><dt><span>standard_name :</span></dt><dd>water_flux_into_sea_water</dd><dt><span>units :</span></dt><dd>kg m-2 s-1</dd><dt><span>comment :</span></dt><dd>Net freshwater flux into the ocean including contributions from runoff, evaporation, precipitation, and mass exchange with sea-ice due to melting and freezing and snow melting. Note: oceFWflx does NOT include freshwater fluxes between the atmosphere and sea-ice and snow. The variable &#x27;SIatmFW&#x27; accounts for freshwater fluxes out of the combined ocean+sea-ice+snow reservoir.</dd><dt><span>valid_min :</span></dt><dd>-0.0055723050609230995</dd><dt><span>valid_max :</span></dt><dd>0.005259144585579634</dd></dl></div><div class='xr-var-data'><table>\n",
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+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>oceTAUY</span></div><div class='xr-var-dims'>(time, j_g, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-74ac0318-59e0-4a37-a430-202f5045e42b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74ac0318-59e0-4a37-a430-202f5045e42b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45ad235f-e7ad-495f-b946-73b2df92de24' class='xr-var-data-in' type='checkbox'><label for='data-45ad235f-e7ad-495f-b946-73b2df92de24' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd> &gt;0 increases horizontal velocity in the +y direction (VVEL)</dd><dt><span>long_name :</span></dt><dd>Ocean surface stress in the model +y direction</dd><dt><span>standard_name :</span></dt><dd>downward_y_stress_at_sea_water_surface</dd><dt><span>units :</span></dt><dd>N m-2</dd><dt><span>comment :</span></dt><dd>Ocean surface stress due to wind and sea-ice in the +y direction centered over the &#x27;v&#x27; side of the the native model grid. Note: in the Arakawa-C grid, wind stress acts on horizontal velocities which are staggered relative to the tracer cells with indexing such that +oceTAUY(i_g,j) corresponds to +y momentum fluxes at &#x27;v&#x27; edge of the tracer cell at (i,j,k=0). Also, the model +y direction does not necessarily correspond to the geographical north-south direction because the x and y axes of the model&#x27;s curvilinear lat-lon-cap (llc) grid have arbitrary orientations which vary within and across tiles.</dd><dt><span>valid_min :</span></dt><dd>-0.5217135548591614</dd><dt><span>valid_max :</span></dt><dd>0.386140376329422</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",
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+       "                    </tr>\n",
+       "                </thead>\n",
+       "                <tbody>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Bytes </th>\n",
+       "                        <td> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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+       "  <text x=\"122.43248171023544\" y=\"140.801330993459\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1800</text>\n",
+       "  <text x=\"184.27672812635322\" y=\"95.69478314378833\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,184.27672812635322,95.69478314378833)\">1080</text>\n",
+       "  <text x=\"35.294117647058826\" y=\"105.50721334640018\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294117647058826,105.50721334640018)\">2581</text>\n",
+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>oceQnet</span></div><div class='xr-var-dims'>(time, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 1080, 1800), meta=np.ndarray&gt;</div><input id='attrs-d9e0d6aa-3749-4304-bf5d-afa649b6a9de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9e0d6aa-3749-4304-bf5d-afa649b6a9de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-955b4a96-1030-49ba-8579-e1ef4b2bc61b' class='xr-var-data-in' type='checkbox'><label for='data-955b4a96-1030-49ba-8579-e1ef4b2bc61b' 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>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>direction :</span></dt><dd>&gt;0 increases potential temperature (THETA)</dd><dt><span>long_name :</span></dt><dd>Net heat flux into the ocean surface</dd><dt><span>standard_name :</span></dt><dd>surface_downward_heat_flux_in_sea_water</dd><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>comment :</span></dt><dd>Net heat flux into the ocean surface from all processes: air-sea turbulent and radiative fluxes and turbulent and conductive fluxes between the ocean and sea-ice and snow. Note: oceQnet does not include the change in ocean heat content due to changing ocean ocean mass (oceFWflx). Mass fluxes from evaporation, precipitation, and runoff (EXFempmr) happen at the same temperature as the ocean surface temperature. Consequently, EmPmR does not change ocean surface temperature. Conversely, mass fluxes due to sea-ice thickening/thinning and snow melt in the model are assumed to happen at a fixed 0C. Consequently, mass fluxes due to phase changes between seawater and sea-ice and snow induce a heat flux when the ocean surface temperaure is not 0C. The variable TFLUX does include the change in ocean heat content due to changing ocean mass.</dd><dt><span>valid_min :</span></dt><dd>-2275.67529296875</dd><dt><span>valid_max :</span></dt><dd>1412.1373291015625</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> 18.69 GiB </td>\n",
+       "                        <td> 7.42 MiB </td>\n",
+       "                    </tr>\n",
+       "                    \n",
+       "                    <tr>\n",
+       "                        <th> Shape </th>\n",
+       "                        <td> (2581, 1080, 1800) </td>\n",
+       "                        <td> (1, 1080, 1800) </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Dask graph </th>\n",
+       "                        <td colspan=\"2\"> 2581 chunks in 2 graph layers </td>\n",
+       "                    </tr>\n",
+       "                    <tr>\n",
+       "                        <th> Data type </th>\n",
+       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
+       "                    </tr>\n",
+       "                </tbody>\n",
+       "            </table>\n",
+       "        </td>\n",
+       "        <td>\n",
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+       "</svg>\n",
+       "        </td>\n",
+       "    </tr>\n",
+       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-617b2c9e-2e42-4d95-b1ef-eac21cc6498b' class='xr-section-summary-in' type='checkbox'  ><label for='section-617b2c9e-2e42-4d95-b1ef-eac21cc6498b' class='xr-section-summary' >Indexes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>i</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-8697e8c9-be0d-48a7-837c-ea6b2064f096' class='xr-index-data-in' type='checkbox'/><label for='index-8697e8c9-be0d-48a7-837c-ea6b2064f096' 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,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
+       "       ...\n",
+       "       1790, 1791, 1792, 1793, 1794, 1795, 1796, 1797, 1798, 1799],\n",
+       "      dtype=&#x27;int32&#x27;, name=&#x27;i&#x27;, length=1800))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>j</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-78517e9e-0840-4abb-bc37-5a4b1ff3ac33' class='xr-index-data-in' type='checkbox'/><label for='index-78517e9e-0840-4abb-bc37-5a4b1ff3ac33' 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,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
+       "       ...\n",
+       "       1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079],\n",
+       "      dtype=&#x27;int32&#x27;, name=&#x27;j&#x27;, length=1080))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>k</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-a862d213-4f63-4017-95fe-a87d17425f81' class='xr-index-data-in' type='checkbox'/><label for='index-a862d213-4f63-4017-95fe-a87d17425f81' 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,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
+       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
+       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
+       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
+       "      dtype=&#x27;int32&#x27;, name=&#x27;k&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>i_g</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-1311f298-0add-4af4-95cc-0f85329cc27b' class='xr-index-data-in' type='checkbox'/><label for='index-1311f298-0add-4af4-95cc-0f85329cc27b' 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,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
+       "       ...\n",
+       "       1790, 1791, 1792, 1793, 1794, 1795, 1796, 1797, 1798, 1799],\n",
+       "      dtype=&#x27;int32&#x27;, name=&#x27;i_g&#x27;, length=1800))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>j_g</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-c0cf3e5d-0757-40a7-be9f-834400ed3bb0' class='xr-index-data-in' type='checkbox'/><label for='index-c0cf3e5d-0757-40a7-be9f-834400ed3bb0' 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,    1,    2,    3,    4,    5,    6,    7,    8,    9,\n",
+       "       ...\n",
+       "       1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079],\n",
+       "      dtype=&#x27;int32&#x27;, name=&#x27;j_g&#x27;, length=1080))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>k_p1</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-c0e5c1af-c4a0-469c-86cd-dfcaab902057' class='xr-index-data-in' type='checkbox'/><label for='index-c0e5c1af-c4a0-469c-86cd-dfcaab902057' 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,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
+       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
+       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
+       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89,\n",
+       "       90],\n",
+       "      dtype=&#x27;int32&#x27;, name=&#x27;k_p1&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>k_l</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-31cf724c-e498-4a33-bfb2-12055bd71d89' class='xr-index-data-in' type='checkbox'/><label for='index-31cf724c-e498-4a33-bfb2-12055bd71d89' 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,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
+       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
+       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
+       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
+       "      dtype=&#x27;int32&#x27;, name=&#x27;k_l&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>k_u</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-aa52c8ae-1af2-4479-8bcd-93fb72a044cf' class='xr-index-data-in' type='checkbox'/><label for='index-aa52c8ae-1af2-4479-8bcd-93fb72a044cf' 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,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
+       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
+       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
+       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
+       "       72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89],\n",
+       "      dtype=&#x27;int32&#x27;, name=&#x27;k_u&#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><input type='checkbox' disabled/><label></label><input id='index-b3635afc-727d-4639-8e28-3e1dc87191df' class='xr-index-data-in' type='checkbox'/><label for='index-b3635afc-727d-4639-8e28-3e1dc87191df' 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;2014-01-15 12:00:00&#x27;, &#x27;2014-01-16 12:00:00&#x27;,\n",
+       "               &#x27;2014-01-17 12:00:00&#x27;, &#x27;2014-01-18 12:00:00&#x27;,\n",
+       "               &#x27;2014-01-19 12:00:00&#x27;, &#x27;2014-01-20 12:00:00&#x27;,\n",
+       "               &#x27;2014-01-21 12:00:00&#x27;, &#x27;2014-01-22 12:00:00&#x27;,\n",
+       "               &#x27;2014-01-23 12:00:00&#x27;, &#x27;2014-01-24 12:00:00&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2021-01-29 12:00:00&#x27;, &#x27;2021-01-30 12:00:00&#x27;,\n",
+       "               &#x27;2021-01-31 12:00:00&#x27;, &#x27;2021-02-01 12:00:00&#x27;,\n",
+       "               &#x27;2021-02-02 12:00:00&#x27;, &#x27;2021-02-03 12:00:00&#x27;,\n",
+       "               &#x27;2021-02-04 12:00:00&#x27;, &#x27;2021-02-05 12:00:00&#x27;,\n",
+       "               &#x27;2021-02-06 12:00:00&#x27;, &#x27;2021-02-07 12:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=2581, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fc7771c5-4881-4dda-bca9-d0ee63bd9db6' class='xr-section-summary-in' type='checkbox'  ><label for='section-fc7771c5-4881-4dda-bca9-d0ee63bd9db6' class='xr-section-summary' >Attributes: <span>(49)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>acknowledgement :</span></dt><dd>This research was carried out by the Jet Propulsion Laboratory, managed by the California Institute of Technology under a contract with the National Aeronautics and Space Administration.</dd><dt><span>author :</span></dt><dd>Marie Zahn, Mike Wood, Ian Fenty, and Severine Fournier</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>Conventions :</span></dt><dd>CF-1.8, ACDD-1.3</dd><dt><span>creator_email :</span></dt><dd>marie.j.zahn@jpl.nasa.gov</dd><dt><span>creator_institution :</span></dt><dd>NASA Jet Propulsion Laboratory (JPL)</dd><dt><span>creator_name :</span></dt><dd>Marie Zahn</dd><dt><span>creator_type :</span></dt><dd>Person</dd><dt><span>creator_url :</span></dt><dd>https://salinity.oceansciences.org/sassie.htm</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>48.70000076293945</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_max :</span></dt><dd>180.0</dd><dt><span>geospatial_lon_min :</span></dt><dd>-180.0</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_bounds_crs :</span></dt><dd>EPSG:4326</dd><dt><span>history :</span></dt><dd>Virtual layer on top of the initial release of the ECCO N1 SASSIE Ocean-Sea Ice Simulation data, for all monthly snapshot collections.</dd><dt><span>institution :</span></dt><dd>NASA Jet Propulsion Laboratory (JPL)</dd><dt><span>instrument_vocabulary :</span></dt><dd>GCMD instrument keywords</dd><dt><span>keywords :</span></dt><dd>EARTH SCIENCE SERVICES &gt; MODELS &gt; EARTH SCIENCE REANALYSES/ASSIMILATION MODELS</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>license :</span></dt><dd>Public Domain</dd><dt><span>naming_authority :</span></dt><dd>gov.nasa.jpl</dd><dt><span>platform_vocabulary :</span></dt><dd>GCMD platform keywords</dd><dt><span>processing_level :</span></dt><dd>L4</dd><dt><span>product_version :</span></dt><dd>Version 1, Release 1</dd><dt><span>program :</span></dt><dd>NASA Physical Oceanography</dd><dt><span>project :</span></dt><dd>Estimating the Circulation and Climate of the Ocean (ECCO)</dd><dt><span>publisher_email :</span></dt><dd>podaac@podaac.jpl.nasa.gov</dd><dt><span>publisher_institution :</span></dt><dd>PO.DAAC</dd><dt><span>publisher_name :</span></dt><dd>Physical Oceanography Distributed Active Archive Center (PO.DAAC)</dd><dt><span>publisher_type :</span></dt><dd>institution</dd><dt><span>publisher_url :</span></dt><dd>https://podaac.jpl.nasa.gov</dd><dt><span>platform :</span></dt><dd>ERS-1/2, TOPEX/Poseidon, Geosat Follow-On (GFO), ENVISAT, Jason-1, Jason-2, CryoSat-2, SARAL/AltiKa, Jason-3, AVHRR, Aquarius, SSM/I, SSMIS, GRACE, DTU17MDT, Argo, WOCE, GO-SHIP, MEOP, Ice Tethered Profilers (ITP)</dd><dt><span>source :</span></dt><dd>The SASSIE ocean model simulation was produced by downscaling the global ECCO state estimate from 1/3 to 1/12 degree grid cells. The ECCO global solution provided initial and boundary conditions and atmospheric forcing.</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention</dd><dt><span>time_coverage_end :</span></dt><dd>2021-02-08T00:00:00Z</dd><dt><span>time_coverage_start :</span></dt><dd>2014-01-15T00:00:00Z</dd><dt><span>comment :</span></dt><dd>SASSIE llc1080 V1R1 fields are consolidated onto a single curvilinear grid face focusing on the Arctic domain using fields from the 5 faces of the lat-lon-cap 1080 (llc1080) native grid used in the original simulation. This is a virtual layer on top of the original data, for most of the daily mean datasets - it allows the user to access all the files from those datasets as if they were in a single, composite dataset. The variables contained here are from the following SASSIE ECCO datasets: SASSIE_ECCO_L4_ATM_STATE_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_FRESH_FLUX_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_HEAT_FLUX_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_OBP_SSH_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_DENS_PRESS_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_OCN_3D_SALINITY_ADV_FLUX_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_OCN_3D_SALINITY_DIFF_FLUX_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_OCN_3D_TEMP_ADV_FLUX_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_OCN_3D_TEMP_DIFF_FLUX_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_OCN_3D_VOL_FLUX_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_OCN_VEL_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_SEA_ICE_CONC_THICK_VEL_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_STRESS_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_TEMP_SALINITY_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_KPP_DIAGS_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_KPP_BOUNDARY_LAYER_LLC1080GRID_DAILY_V1R1,SASSIE_ECCO_L4_SEA_ICE_VOL_AREA_TEND_LLC1080GRID_DAILY_V1R1, SASSIE_ECCO_L4_SEA_ICE_VOL_AREA_FLUX_LLC1080GRID_DAILY_V1R1.</dd><dt><span>identifier_product_doi_authority :</span></dt><dd>org.doi</dd><dt><span>time_coverage_duration :</span></dt><dd>P1D</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1D</dd><dt><span>geospatial_vertical_max :</span></dt><dd>0.0</dd><dt><span>geospatial_vertical_min :</span></dt><dd>-7000.0</dd><dt><span>geospatial_vertical_positive :</span></dt><dd>up</dd><dt><span>geospatial_vertical_resolution :</span></dt><dd>variable</dd><dt><span>geospatial_vertical_units :</span></dt><dd>meter</dd><dt><span>identifier_product_doi :</span></dt><dd>https://doi.org/10.5067/SEL1D-DUG11</dd><dt><span>date_created :</span></dt><dd>2026-03-21T00:00:00Z</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 46TB\n",
+       "Dimensions:    (time: 2581, k_l: 90, j: 1080, i: 1800, i_g: 1800, j_g: 1080,\n",
+       "                k: 90, k_u: 90, nb: 4, k_p1: 91, nv: 2)\n",
+       "Coordinates: (12/23)\n",
+       "    XC         (j, i) float32 8MB dask.array<chunksize=(1080, 1800), meta=np.ndarray>\n",
+       "    XC_bnds    (j, i, nb) float32 31MB dask.array<chunksize=(1080, 1800, 4), meta=np.ndarray>\n",
+       "    YC         (j, i) float32 8MB dask.array<chunksize=(1080, 1800), meta=np.ndarray>\n",
+       "    XU         (j, i_g) float32 8MB dask.array<chunksize=(1080, 1800), meta=np.ndarray>\n",
+       "    YC_bnds    (j, i, nb) float32 31MB dask.array<chunksize=(1080, 1800, 4), meta=np.ndarray>\n",
+       "    YU         (j, i_g) float32 8MB dask.array<chunksize=(1080, 1800), meta=np.ndarray>\n",
+       "    ...         ...\n",
+       "  * j_g        (j_g) int32 4kB 0 1 2 3 4 5 6 ... 1074 1075 1076 1077 1078 1079\n",
+       "  * k_p1       (k_p1) int32 364B 0 1 2 3 4 5 6 7 8 ... 83 84 85 86 87 88 89 90\n",
+       "  * k_l        (k_l) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
+       "    time_bnds  (time, nv) datetime64[ns] 41kB dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
+       "  * k_u        (k_u) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
+       "  * time       (time) datetime64[ns] 21kB 2014-01-15T12:00:00 ... 2021-02-07T...\n",
+       "Dimensions without coordinates: nb, nv\n",
+       "Data variables: (12/83)\n",
+       "    ADVr_SLT   (time, k_l, j, i) float32 2TB dask.array<chunksize=(1, 15, 270, 450), meta=np.ndarray>\n",
+       "    ADVr_TH    (time, k_l, j, i) float32 2TB dask.array<chunksize=(1, 15, 270, 450), meta=np.ndarray>\n",
+       "    ADVxHEFF   (time, j, i_g) float32 20GB dask.array<chunksize=(1, 1080, 1800), meta=np.ndarray>\n",
+       "    ADVxAREA   (time, j, i_g) float32 20GB dask.array<chunksize=(1, 1080, 1800), meta=np.ndarray>\n",
+       "    ADVyAREA   (time, j_g, i) float32 20GB dask.array<chunksize=(1, 1080, 1800), meta=np.ndarray>\n",
+       "    ADVx_SLT   (time, k, j, i_g) float32 2TB dask.array<chunksize=(1, 15, 270, 450), meta=np.ndarray>\n",
+       "    ...         ...\n",
+       "    oceQsw     (time, j, i) float32 20GB dask.array<chunksize=(1, 1080, 1800), meta=np.ndarray>\n",
+       "    oceTAUX    (time, j, i_g) float32 20GB dask.array<chunksize=(1, 1080, 1800), meta=np.ndarray>\n",
+       "    sIceLoad   (time, j, i) float32 20GB dask.array<chunksize=(1, 1080, 1800), meta=np.ndarray>\n",
+       "    oceFWflx   (time, j, i) float32 20GB dask.array<chunksize=(1, 1080, 1800), meta=np.ndarray>\n",
+       "    oceTAUY    (time, j_g, i) float32 20GB dask.array<chunksize=(1, 1080, 1800), meta=np.ndarray>\n",
+       "    oceQnet    (time, j, i) float32 20GB dask.array<chunksize=(1, 1080, 1800), meta=np.ndarray>\n",
+       "Attributes: (12/49)\n",
+       "    acknowledgement:                   This research was carried out by the J...\n",
+       "    author:                            Marie Zahn, Mike Wood, Ian Fenty, and ...\n",
+       "    cdm_data_type:                     Grid\n",
+       "    Conventions:                       CF-1.8, ACDD-1.3\n",
+       "    creator_email:                     marie.j.zahn@jpl.nasa.gov\n",
+       "    creator_institution:               NASA Jet Propulsion Laboratory (JPL)\n",
+       "    ...                                ...\n",
+       "    geospatial_vertical_min:           -7000.0\n",
+       "    geospatial_vertical_positive:      up\n",
+       "    geospatial_vertical_resolution:    variable\n",
+       "    geospatial_vertical_units:         meter\n",
+       "    identifier_product_doi:            https://doi.org/10.5067/SEL1D-DUG11\n",
+       "    date_created:                      2026-03-21T00:00:00Z"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "## 3. Open dataset  ---------------------------------------------------------------------------------------------------\n",
+    "ds = xr.open_zarr(store, consolidated=False)\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c21297bd-71c8-4153-9aa1-8d34316d6731",
+   "metadata": {},
+   "source": [
+    "#### 4. Sample computations\n",
+    "\n",
+    "#### Map of salinity at a single depth and time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a778944e-5699-4605-90df-d30e6eebf6c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 1.73 s, sys: 341 ms, total: 2.07 s\n",
+      "Wall time: 2.44 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x765232551010>"
+      ]
+     },
+     "execution_count": 6,
+     "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",
+    "ds[\"SALT\"].isel(time=0, k=10).plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ebd5a76-fbc8-449d-8626-ec18dd5e497b",
+   "metadata": {},
+   "source": [
+    "#### Time series of salinity at a single grid point and depth, over the entire record"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "097379df-5141-4cf7-be6e-68a7d5073582",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 2min 7s, sys: 21.6 s, total: 2min 29s\n",
+      "Wall time: 3min 27s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7652325fa3c0>]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "ds[\"SALT\"].isel(i=600, j=600, k=10).plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51603a02-b933-46cd-9808-0d9b322ea882",
+   "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": "3.14.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 636146d7371ec02460dfaf0c38ad4e1327665991 Mon Sep 17 00:00:00 2001
From: henze <henze@jpl.nasa.gov>
Date: Thu, 7 May 2026 18:10:37 -0700
Subject: [PATCH 2/2] Fix typo in notebook header. Add link to notebook in
 sidebar. Add link to notebook in VDS chapter.

---
 _quarto.yml                                  | 2 ++
 notebooks/Advanced_cloud/open_icechunk.ipynb | 4 ++--
 quarto_text/UsingVirtualDatasets.qmd         | 2 +-
 3 files changed, 5 insertions(+), 3 deletions(-)

diff --git a/_quarto.yml b/_quarto.yml
index 95319b76..bdbf0053 100644
--- a/_quarto.yml
+++ b/_quarto.yml
@@ -276,6 +276,8 @@ website:
                 contents:
                 - text: "Using VDS Starter Notebook"
                   href: notebooks/Advanced_cloud/using_vds_starter.ipynb
+                - text: "Opening an Icechunk Store"
+                  href: notebooks/Advanced_cloud/open_icechunk.ipynb
                 - text: "Gulf of Tehuantepec Upwelling Example"
                   href: notebooks/Advanced_cloud/GulfofTehuantepec_Ocean_Response.ipynb
               - text: "Virtualizarr Recipes"
diff --git a/notebooks/Advanced_cloud/open_icechunk.ipynb b/notebooks/Advanced_cloud/open_icechunk.ipynb
index efb301ad..7aa3dcd9 100644
--- a/notebooks/Advanced_cloud/open_icechunk.ipynb
+++ b/notebooks/Advanced_cloud/open_icechunk.ipynb
@@ -11,7 +11,7 @@
     "\n",
     "This notebook demonstrates opening a virtual dataset (VDS) stored as an Icechunk store (note this technology is currently experimental, although it is gaining momentum). For a general overview of VDS's see the [Using Virutal Datasets chapter](https://podaac.github.io/tutorials/quarto_text/UsingVirtualDatasets.html) on the PO.DAAC Cookbook.\n",
     "\n",
-    "The example dataset(s) used here are from the SASSIE ECCO project (The e.g. the [User Guide](https://doi.org/10.5067/SEL1D-DUG11) with project overview). The ECCO model (Estimating the Circulation and Climate of the Ocean) was run over the arctic region over a seven year period in support of the SASSIE field experiment (Salinity and Stratification at the Sea Ice Edge). The SASSIE ECCO data are traditionally archived and available in netCDF format - one file per day(month) for the daily-mean(monthly-snapshot) datasets. The model output variables (totalling ~50 TBs) are large enough that they are spread out over 18 different netCDF datasets for the daily-means, and over 3 different datasets for the monthly-snapshots. One novel functionality of VDS's being tested here is the ability to combine the data across e.g. the 18 daily-mean datasets so users can interact with the data as if all variables were contained in a single dataset. Computing performance is TBD.\n",
+    "The example dataset(s) used here are from the SASSIE ECCO project (the [User Guide](https://doi.org/10.5067/SEL1D-DUG11) has a project overview). The ECCO model (Estimating the Circulation and Climate of the Ocean) was run over the arctic region over a seven year period in support of the SASSIE field experiment (Salinity and Stratification at the Sea Ice Edge). The SASSIE ECCO data are traditionally archived and available in netCDF format - one file per day(month) for the daily-mean(monthly-snapshot) datasets. The model output variables (totalling ~50 TBs) are large enough that they are spread out over 18 different netCDF datasets for the daily-means, and over 3 different datasets for the monthly-snapshots. One novel functionality of VDS's being tested here is the ability to combine the data across e.g. the 18 daily-mean datasets so users can interact with the data as if all variables were contained in a single dataset. Computing performance is TBD.\n",
     "\n",
     "The AWS S3 paths to the SASSIE ECCO icechunk stores are\n",
     "\n",
@@ -12096,7 +12096,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.14.4"
+   "version": "3.13.5"
   }
  },
  "nbformat": 4,
diff --git a/quarto_text/UsingVirtualDatasets.qmd b/quarto_text/UsingVirtualDatasets.qmd
index 7eaad807..cf5c485b 100644
--- a/quarto_text/UsingVirtualDatasets.qmd
+++ b/quarto_text/UsingVirtualDatasets.qmd
@@ -44,7 +44,7 @@ Summarizing the points above, and adding some additional ones, the benefits of V
 
 ### Using Virtual Datasets
 
-To date we have explored accessing VDS’s programmatically via Jupyter notebooks, using the Xarray package for opening data. The quickest way to get up to speed with this method is to check out the [**starter notebook here**](../notebooks/Advanced_cloud/using_vds_starter.ipynb). **As with other access methods, you need an Earthdata Login account**. Other than that, the majority of the code to obtain credentials is boiler-plate and the only action item on the user is to find the VDS link for the dataset they want to use. Currently you can find the link using the table in the [Available Products](./UsingVirtualDatasets.html#available-products) section below. In the future, the links will be advertised on dataset landing pages and searchable in CMR metadata.
+To date we have explored accessing VDS’s programmatically via Jupyter notebooks, using the Xarray package for opening data. The quickest way to get up to speed with this method is to check out the [**starter notebook**](../notebooks/Advanced_cloud/using_vds_starter.ipynb). For those looking to open a VDS in `icechunk` format, see the [**opening icechunk notebook**](../notebooks/Advanced_cloud/open_icechunk.ipynb) (currently in initial tests and only for in-cloud use). **As with other access methods, you need an Earthdata Login account**. Other than that, the majority of the code to obtain credentials is boiler-plate and the only action item on the user is to find the VDS link for the dataset they want to use. Currently you can find the link using the table in the [Available Products](./UsingVirtualDatasets.html#available-products) section below. In the future, the links will be advertised on dataset landing pages and searchable in CMR metadata.
 
 Additionally, the following notebook demonstrates using VDS’s for more involved computations: