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    "# The ecco_access library\n",
    "\n",
    "Andrew Delman, updated 2025-11-13\n",
    "\n",
    "> **Important Note:** The `ecco_access` modules are now available in their own Python package, which can be installed using *conda* or *pip*! [Supporting documentation can be found here](https://ecco-access.readthedocs.io). **Hence, the** `ecco_access` **functions are no longer included in** `ecco_v4_py` **as of version 1.8**. This tutorial is for users of earlier versions of `ecco_v4_py`, but will be removed at a later date.\n",
    "\n",
    "## The `ecco_access` library for users of `ecco_v4_py` >=1.7.4,<1.8\n",
    "\n",
    "[Introduction](https://ecco-v4-python-tutorial.readthedocs.io/ECCO_access_intro.html#Introduction)\\\n",
    "[ECCO output on PO.DAAC](https://ecco-v4-python-tutorial.readthedocs.io/ECCO_access_intro.html#ECCO-output-on-PODAAC)\\\n",
    "[Importing ecco_access to your workspace](https://ecco-v4-python-tutorial.readthedocs.io/ECCO_access_intro.html#Importing-ecco_access-to-your-workspace)\\\n",
    "[Using the ecco_podaac_to_xrdataset function](https://ecco-v4-python-tutorial.readthedocs.io/ECCO_access_intro.html#Using-the-ecco_podaac_to_xrdataset-function)\\\n",
    "[What if you don't know the ShortName already?](https://ecco-v4-python-tutorial.readthedocs.io/ECCO_access_intro.html#What-if-you-don't-know-the-ShortName-already?)\\\n",
    "[Using the ecco_podaac_access function](https://ecco-v4-python-tutorial.readthedocs.io/ECCO_access_intro.html#Using-the-ecco_podaac_access-function)\\\n",
    "[Accessing ECCOv4 release 5 output in the AWS Cloud](https://ecco-v4-python-tutorial.readthedocs.io/ECCO_access_intro.html#Accessing-ECCOv4-release-5-output-in-the-AWS-Cloud)\n",
    "\n",
    "## Introduction\n",
    "\n",
    "In the past several years since ECCOv4 release 4 output was made available on the Physical Oceanography Distributed Active Archive Center or [PO.DAAC](https://podaac.jpl.nasa.gov), a number of Python scripts/functions have been written to facilitate requests of this output, authored by Jack McNelis, Ian Fenty, and Andrew Delman. These codes have been consolidated into the **ecco_access** modules, in order to standardize the format of the requests.\n",
    "\n",
    "This tutorial will help you to start using the **ecco_access** library, which is comprised of a set of Python modules. Here we introduce the two top-level functions in **ecco_access**:\n",
    "\n",
    "- `ecco_podaac_to_xrdataset`: takes as input a text query or ECCO dataset identifier, and returns an *xarray* Dataset\n",
    "\n",
    "- `ecco_podaac_access`: takes the same input, but returns the URLs/paths or local files where the data is located\n",
    "\n",
    "\n",
    "## ECCO output on PO.DAAC\n",
    "\n",
    "### Setting up Earthdata login credentials\n",
    "\n",
    "An account with NASA Earthdata is required to access ECCO output hosted in the AWS Cloud by PO.DAAC. Please visit [https://urs.earthdata.nasa.gov/home](https://urs.earthdata.nasa.gov/home) to make an account.\n",
    "\n",
    "*The Earthdata Login provides a single mechanism for user registration and profile management for all EOSDIS system components (DAACs, Tools, Services). Your Earthdata login also helps the EOSDIS program better understand the usage of EOSDIS services to improve user experience through customization of tools and improvement of services. EOSDIS data are openly available to all and free of charge except where governed by international agreements.*\n",
    "\n",
    "> **Note:**  _some Earthdata password characters may cause problems depending on your system_. To be safe, do not use any of the following characters in your password: backslash (\\\\), space, hash (#), quotes (single or double), or greater than (>).  Set/change your Earthdata password here: [https://urs.earthdata.nasa.gov/change_password](https://urs.earthdata.nasa.gov/change_password)\n",
    "\n",
    "\n",
    "1. After creating a NASA Earthdata account, create a file called ```.netrc``` in your home directory (linux, Mac):\n",
    "```\n",
    "/home/<username>/.netrc\n",
    "```\n",
    "or ```_netrc``` (Windows):\n",
    "```\n",
    "C:\\Users\\<username>\\_netrc\n",
    "```\n",
    "\n",
    "The ```netrc``` file must have the following structure and must include your Earthdata account login name and password:\n",
    "\n",
    "```\n",
    "machine urs.earthdata.nasa.gov\n",
    "    login <your username>\n",
    "    password <your password>\n",
    "```\n",
    "\n",
    "2. Set permissions on your ```netrc``` file to be readable only by the current user.  If not, you will receive the error \"netrc access too permissive.\" \n",
    "\n",
    "```shell\n",
    "$ chmod 0600 ~/.netrc\n",
    "```\n",
    "\n",
    "### ECCO output structure on PO.DAAC\n",
    "\n",
    "On PO.DAAC and in the NASA Earthdata Cloud, ECCO output is organized in the following hierarchy:\n",
    "\n",
    "- **Dataset:** Typically contains a few variables, spanning the time range of the ECCO v4r4 output (currently 1992-2017). Most datasets are divided (in the time dimension) into hundreds or thousands of granules.\n",
    "\n",
    "    - **Granule:** Dataset variables at a specific time (monthly mean, daily mean, or snapshot). Exceptions are 1-D time series where the entire dataset only consists of one granule.\n",
    "\n",
    "        - **Variable:** A specific geophysical parameter (or flux) representing the state of the ocean, atmosphere, or sea ice/snow cover. Individual variables are not visible through the NASA Earthdata website, but can be seen after a granule file has been opened.\n",
    "\n",
    "\n",
    "Each dataset has a dataset code called a `ShortName` which is used to identify it on the cloud. In order to download particular variable(s), you need to identify the `ShortName` associated with the dataset containing those variables. You can search for the variables in the linked text files below, or download these files for your reference.\n",
    "\n",
    "### Dataset ShortNames and variables associated with them\n",
    "\n",
    "[ECCO v4r4 llc90 Grid Dataset Variables - Monthly Means](https://raw.githubusercontent.com/ECCO-GROUP/ECCO-ACCESS/main/varlist/v4r4/v4r4_nctiles_monthly_varlist.txt)\n",
    "\n",
    "[ECCO v4r4 llc90 Grid Dataset Variables - Daily Means](https://raw.githubusercontent.com/ECCO-GROUP/ECCO-ACCESS/main/varlist/v4r4/v4r4_nctiles_daily_varlist.txt)\n",
    "\n",
    "[ECCO v4r4 llc90 Grid Dataset Variables - Daily Snapshots](https://raw.githubusercontent.com/ECCO-GROUP/ECCO-ACCESS/main/varlist/v4r4/v4r4_nctiles_snapshots_varlist.txt)\n",
    "\n",
    "[ECCO v4r4 0.5-Deg Interp Grid Dataset Variables - Monthly Means](https://raw.githubusercontent.com/ECCO-GROUP/ECCO-ACCESS/main/varlist/v4r4/v4r4_latlon_monthly_varlist.txt)\n",
    "\n",
    "[ECCO v4r4 0.5-Deg Interp Grid Dataset Variables - Daily Means](https://raw.githubusercontent.com/ECCO-GROUP/ECCO-ACCESS/main/varlist/v4r4/v4r4_latlon_daily_varlist.txt)\n",
    "\n",
    "[ECCO v4r4 Time Series and Grid Parameters](https://raw.githubusercontent.com/ECCO-GROUP/ECCO-ACCESS/main/varlist/v4r4/v4r4_tseries_grid_varlist.txt)\n",
    "\n",
    "\n",
    "Note that unlike earlier releases of ECCO v4, in v4r4 all monthly mean variables are also available for download as daily means. Snapshots (typically at daily intervals) are available for a few variables, and can be used to help close budgets as shown in later tutorials.\n",
    "\n",
    "\n",
    "\n",
    "## Importing `ecco_access` to your workspace\n",
    "\n",
    "As of the April 2025 update, `ecco_access` modules are included in `ecco_v4_py`. To use them, you should [install](https://ecco-v4-python-tutorial.readthedocs.io/Installing_Python_and_Python_Packages.html#downloading-the-ecco-v4-py-python-package) the `ecco_v4_py` Python package (version 1.7.8 or later), e.g.,\n",
    "\n",
    "```{python}\n",
    "conda install \"ecco_v4_py>=1.7.8\"\n",
    "```\n",
    "\n",
    "Then you can import the `ecco_access` modules in your Python code and notebooks:\n",
    "\n",
    "```{python}\n",
    "import ecco_v4_py.ecco_access as ea\n",
    "```\n",
    "\n",
    "> **Note:** You can also import *ecco_access* into your workspace just by importing `ecco_v4_py` rather than `ecco_v4_py.ecco_access`. The high-level functions discussed below should work either way. Importing `ecco_v4_py.ecco_access` is just a little more direct, especially if you are only using the access-related functions of `ecco_v4_py`.\n",
    "\n",
    "\n",
    "If you installed a recent version of `ecco_v4_py` using `conda install ecco_v4_py` or `pip install ecco_v4_py` and you are encountering errors, try [cloning](https://ecco-v4-python-tutorial.readthedocs.io/Installing_Python_and_Python_Packages.html#option-1-clone-into-the-repository-using-git-recommended) the *ECCOv4-py* Github repository that contains `ecco_v4_py`, as the Github repository may have bug fixes that have not been pushed to `conda-forge` or `pypi` yet. Then add the repository to the beginning of your Python path by putting the following code in your notebooks:\n",
    "\n",
    "```{python}\n",
    "import sys\n",
    "sys.path.insert(0,'path/to/ECCOv4-py')\n",
    "import ecco_v4_py.ecco_access as ea\n",
    "```\n",
    "\n",
    "\n",
    "## Using the `ecco_podaac_to_xrdataset` function\n",
    "\n",
    "Perhaps the most convenient way to use *ecco_access* is the `ecco_podaac_to_xrdataset`; it takes as input a *query* consisting of NASA Earthdata dataset ShortName(s), ECCO variables, or text strings in the variable descriptions, and outputs an *xarray* Dataset. Let's look at the syntax:"
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   "source": [
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "from os.path import join,expanduser\n",
    "\n",
    "import ecco_v4_py as ecco\n",
    "import ecco_v4_py.ecco_access as ea\n",
    "\n",
    "# identify user's home directory\n",
    "user_home_dir = expanduser('~')"
   ]
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     "text": [
      "Help on function ecco_podaac_to_xrdataset in module ecco_v4_py.ecco_access:\n",
      "\n",
      "ecco_podaac_to_xrdataset(query, version='v4r4', grid=None, time_res='all', StartDate=None, EndDate=None, snapshot_interval=None, mode='download_ifspace', download_root_dir=None, **kwargs)\n",
      "    This function queries and accesses ECCO datasets from PO.DAAC. The core query and download functions\n",
      "    are adapted from Jupyter notebooks created by Jack McNelis and Ian Fenty\n",
      "    (https://github.com/ECCO-GROUP/ECCO-ACCESS/blob/master/PODAAC/Downloading_ECCO_datasets_from_PODAAC/README.md)\n",
      "    and modified by Andrew Delman (https://ecco-v4-python-tutorial.readthedocs.io).\n",
      "    It is similar to ecco_podaac_access, except instead of a list of URLs or files,\n",
      "    an xarray Dataset with all of the queried ECCO datasets is returned.\n",
      "\n",
      "    Parameters\n",
      "    ----------\n",
      "    query: str, list, or dict, defines datasets or variables to access.\n",
      "           If query is str, it specifies either a dataset ShortName (if query\n",
      "           matches a NASA Earthdata ShortName), or a text string that can be\n",
      "           used to search the ECCO ShortNames, variable names, and descriptions.\n",
      "           A query may also be a list of multiple ShortNames and/or text searches,\n",
      "           or a dict that contains grid,time_res specifiers as keys and ShortNames\n",
      "           or text searches as values, e.g.,\n",
      "           {'native,monthly':['ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4',\n",
      "                              'THETA']}\n",
      "           will query the native grid monthly SSH datasets, and all native grid\n",
      "           monthly datasets with variables or descriptions matching 'THETA'.\n",
      "\n",
      "    version: ('v4r4','v4r5'), specifies ECCO version to query.\n",
      "             Currently 'v4r5' only works with ['s3_open','s3_get','s3_get_ifspace'] modes,\n",
      "             or if the files are already stored in download_root_dir/ShortName/.\n",
      "             Otherwise an error is returned.\n",
      "\n",
      "    grid: ('native','latlon',None), specifies whether to query datasets with output\n",
      "          on the native grid or the interpolated lat/lon grid.\n",
      "          The default None will query both types of grids, unless specified\n",
      "          otherwise in a query dict (e.g., the example above).\n",
      "\n",
      "    time_res: ('monthly','daily','snapshot','all'), specifies which time resolution\n",
      "              to include in query and downloads. 'all' includes all time resolutions,\n",
      "              and datasets that have no time dimension, such as the grid parameter\n",
      "              and mixing coefficient datasets.\n",
      "\n",
      "\n",
      "    StartDate,EndDate: str, in 'YYYY', 'YYYY-MM', or 'YYYY-MM-DD' format,\n",
      "                       define date range [StartDate,EndDate] for download.\n",
      "                       EndDate is included in the time range (unlike typical Python ranges).\n",
      "                       Full ECCOv4r4 date range (default) is '1992-01-01' to '2017-12-31'.\n",
      "                       For 'SNAPSHOT' datasets, an additional day is added to EndDate to enable\n",
      "                       closed budgets within the specified date range.\n",
      "\n",
      "    snapshot_interval: ('monthly', 'daily', or None), if snapshot datasets are included in ShortNames,\n",
      "                       this determines whether snapshots are included for only the beginning/end\n",
      "                       of each month ('monthly'), or for every day ('daily').\n",
      "                       If None or not specified, defaults to 'daily' if any daily mean ShortNames\n",
      "                       are included and 'monthly' otherwise.\n",
      "\n",
      "    mode: str, one of the following:\n",
      "          'download': Download datasets using NASA Earthdata URLs\n",
      "          'download_ifspace': Check storage availability before downloading.\n",
      "                              Download only if storage footprint of downloads\n",
      "                              <= max_avail_frac*(available storage)\n",
      "          'download_subset': Download spatial and temporal subsets of datasets\n",
      "                             via Opendap; query help(ecco_access.ecco_podaac_download_subset)\n",
      "                             to see keyword arguments that can be used in this mode.\n",
      "          The following modes work within the AWS cloud only:\n",
      "          's3_open': Access datasets on S3 without downloading.\n",
      "          's3_open_fsspec': Use json files (generated with `fsspec` and `kerchunk`)\n",
      "                            for expedited opening of datasets.\n",
      "          's3_get': Download from S3 (to AWS EC2 instance).\n",
      "          's3_get_ifspace': Check storage availability before downloading;\n",
      "                            download if storage footprint\n",
      "                            <= max_avail_frac*(available storage).\n",
      "                            Otherwise data are opened \"remotely\" from S3 bucket.\n",
      "\n",
      "    download_root_dir: str, defines parent directory to download files to.\n",
      "                       Files will be downloaded to directory download_root_dir/ShortName/.\n",
      "                       If not specified, parent directory defaults to '~/Downloads/ECCO_V4r4_PODAAC/',\n",
      "                       or '~/Downloads/ECCO_V4r5_PODAAC/' if version == 'v4r5'.\n",
      "\n",
      "    Additional keyword arguments*:\n",
      "    *This is not an exhaustive list, especially for\n",
      "    'download_subset' mode; use help(ecco_access.ecco_podaac_download_subset) to display\n",
      "    options specific to that mode\n",
      "\n",
      "    max_avail_frac: float, maximum fraction of remaining available disk space to\n",
      "                    use in storing ECCO datasets.\n",
      "                    If storing the datasets exceeds this fraction, an error is returned.\n",
      "                    Valid range is [0,0.9]. If number provided is outside this range, it is replaced\n",
      "                    by the closer endpoint of the range.\n",
      "\n",
      "    jsons_root_dir: str, for s3_open_fsspec mode only, the root/parent directory where the\n",
      "                    fsspec/kerchunk-generated jsons are found.\n",
      "                    jsons are generated using the steps described here:\n",
      "                    https://medium.com/pangeo/fake-it-until-you-make-it-reading-goes-netcdf4-data-on-aws-s3\n",
      "                    as-zarr-for-rapid-data-access-61e33f8fe685.\n",
      "                    If None (default), jsons_root_dir will be set to ~/MZZ/{version}/.\n",
      "                    The jsons need to be stored in the following directories/formats:\n",
      "                    For v4r4: {jsons_root_dir}/MZZ_{GRIDTYPE}_{TIME_RES}/{SHORTNAME}.json.\n",
      "                    GRIDTYPE is '05DEG' or 'LLC0090GRID' for v4r4;\n",
      "                    TIME_RES is one of: ('MONTHLY','DAILY','SNAPSHOT','GEOMETRY','MIXING_COEFFS').\n",
      "                    For v4r5: {jsons_root_dir}/MZZ_{TIME_RES}_{GRIDTYPE}/{SHORTNAME_core}_{TIME_RES}_{GRIDTYPE}_llc090_ECCOV4r5.json.\n",
      "                    TIME_RES is one of: ('mon_mean','day_mean','snap');\n",
      "                    GRIDTYPE is one of: ('native','latlon').\n",
      "                    The SHORTNAME_core is the SHORTNAME without the 'ECCO_L4_' at the beginning,\n",
      "                    or the grid/time/version IDs at the end (e.g., 'LLC0090GRID_MONTHLY_V4R5').\n",
      "\n",
      "    jsons_retrieve: bool, if True (default), and jsons are not already stored in jsons_root_dir,\n",
      "                    retrieve from S3 bucket and store locally in jsons_root_dir.\n",
      "                    If False, and jsons are not stored in jsons_root_dir,\n",
      "                    the data retrieval will fail.\n",
      "\n",
      "    n_workers: int, number of workers to use in concurrent downloads. Benefits typically taper off above 5-6.\n",
      "\n",
      "    force_redownload: bool, if True, existing files will be redownloaded and replaced;\n",
      "                            if False (default), existing files will not be replaced.\n",
      "\n",
      "    show_noredownload_msg: bool, if True (default), and force_redownload=False,\n",
      "                               display message for each file that is already\n",
      "                               downloaded (and therefore not re-downloaded);\n",
      "                               if False, these messages are not shown.\n",
      "\n",
      "    prompt_request_payer: bool, if True (default), user is prompted to approve\n",
      "                                (by entering \"y\" or \"Y\") any access to a\n",
      "                                requester pays bucket, otherwise request is canceled;\n",
      "                                if False, data access proceeds without prompting.\n",
      "\n",
      "    Returns\n",
      "    -------\n",
      "    ds_out: xarray Dataset or dict of xarray Datasets (with ShortNames as keys),\n",
      "            containing all of the accessed datasets.\n",
      "            This function does not work with the query modes: 'ls','query','s3_ls','s3_query'.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ea.ecco_podaac_to_xrdataset)"
   ]
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    "There are a lot of options that you *can* use to \"submit\" a query with this function. Let's consider a simple case, where we already have the ShortName for the [monthly native grid SSH](https://raw.githubusercontent.com/ECCO-GROUP/ECCO-ACCESS/main/varlist/v4r4/v4r4_nctiles_monthly_varlist.txt) from ECCOv4r4 (**ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4**), and we want to access output from the year 2017. The ShortName goes in the query field, and we can specify start and end dates (in *YYYY-MM* or *YYYY-MM-DD* format). The other options that matter most for this request are the *mode*, and depending on the mode, the *download_root_dir* or the *jsons_root_dir*.\n",
    "\n",
    "### Direct download over the internet (mode = 'download')\n",
    "\n",
    "Let's try the **download** mode, which retrieves the data over the Internet using NASA Earthdata URLs (this should work on any machine with Internet access, including cloud environments):"
   ]
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   "id": "bc8c0993-06ef-4e5f-8d48-39d24918cbf1",
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     "text": [
      "Creating download directory /home/jpluser/Downloads/ECCO_V4r4_PODAAC/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4\n",
      "DL Progress: 100%|#########################| 12/12 [00:04<00:00,  2.47it/s]\n",
      "\n",
      "=====================================\n",
      "total downloaded: 71.02 Mb\n",
      "avg download speed: 14.6 Mb/s\n",
      "Time spent = 4.863487720489502 seconds\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# download data and open xarray dataset\n",
    "curr_shortname = 'ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4'\n",
    "ds_SSH = ea.ecco_podaac_to_xrdataset(curr_shortname,\\\n",
    "                                        StartDate='2017-01',EndDate='2017-12',\\\n",
    "                                        mode='download',\\\n",
    "                                        download_root_dir=join(user_home_dir,'Downloads','ECCO_V4r4_PODAAC'))"
   ]
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   "source": [
    "We specified a *root* directory for the download (which also happens to be the default setting), and the data files are then placed under *download_root_dir* / ShortName. We can verify that the contents of the file are what we queried:"
   ]
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   "execution_count": 4,
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 25MB\n",
       "Dimensions:    (time: 12, tile: 13, j: 90, i: 90, i_g: 90, j_g: 90, nv: 2, nb: 4)\n",
       "Coordinates: (12/13)\n",
       "  * i          (i) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * i_g        (i_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j          (j) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j_g        (j_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
       "  * tile       (tile) int32 52B 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "  * time       (time) datetime64[ns] 96B 2017-01-16T12:00:00 ... 2017-12-16T0...\n",
       "    ...         ...\n",
       "    YC         (tile, j, i) float32 421kB dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;\n",
       "    XG         (tile, j_g, i_g) float32 421kB dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;\n",
       "    YG         (tile, j_g, i_g) float32 421kB dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;\n",
       "    time_bnds  (time, nv) datetime64[ns] 192B dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\n",
       "    XC_bnds    (tile, j, i, nb) float32 2MB dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;\n",
       "    YC_bnds    (tile, j, i, nb) float32 2MB dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: nv, nb\n",
       "Data variables:\n",
       "    SSH        (time, tile, j, i) float32 5MB dask.array&lt;chunksize=(1, 13, 90, 90), meta=np.ndarray&gt;\n",
       "    SSHIBC     (time, tile, j, i) float32 5MB dask.array&lt;chunksize=(1, 13, 90, 90), meta=np.ndarray&gt;\n",
       "    SSHNOIBC   (time, tile, j, i) float32 5MB dask.array&lt;chunksize=(1, 13, 90, 90), meta=np.ndarray&gt;\n",
       "    ETAN       (time, tile, j, i) float32 5MB dask.array&lt;chunksize=(1, 13, 90, 90), meta=np.ndarray&gt;\n",
       "Attributes: (12/57)\n",
       "    acknowledgement:              This research was carried out by the Jet Pr...\n",
       "    author:                       Ian Fenty and Ou Wang\n",
       "    cdm_data_type:                Grid\n",
       "    comment:                      Fields provided on the curvilinear lat-lon-...\n",
       "    Conventions:                  CF-1.8, ACDD-1.3\n",
       "    coordinates_comment:          Note: the global &#x27;coordinates&#x27; attribute de...\n",
       "    ...                           ...\n",
       "    time_coverage_duration:       P1M\n",
       "    time_coverage_end:            2017-02-01T00:00:00\n",
       "    time_coverage_resolution:     P1M\n",
       "    time_coverage_start:          2017-01-01T00:00:00\n",
       "    title:                        ECCO Sea Surface Height - Monthly Mean llc9...\n",
       "    uuid:                         a21a5c30-400c-11eb-a9e0-0cc47a3f49c3</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-6a92c9a0-83eb-4100-a551-275edd4a3158' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6a92c9a0-83eb-4100-a551-275edd4a3158' 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>: 12</li><li><span class='xr-has-index'>tile</span>: 13</li><li><span class='xr-has-index'>j</span>: 90</li><li><span class='xr-has-index'>i</span>: 90</li><li><span class='xr-has-index'>i_g</span>: 90</li><li><span class='xr-has-index'>j_g</span>: 90</li><li><span>nv</span>: 2</li><li><span>nb</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ce18b141-2afa-443c-89ab-5de42861b39f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ce18b141-2afa-443c-89ab-5de42861b39f' class='xr-section-summary' >Coordinates: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>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 5 6 ... 84 85 86 87 88 89</div><input id='attrs-b74b586e-8ae7-467e-8226-795b0dc59de4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b74b586e-8ae7-467e-8226-795b0dc59de4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6358d045-df5c-4807-9ec8-61d1bfe8c305' class='xr-var-data-in' type='checkbox'><label for='data-6358d045-df5c-4807-9ec8-61d1bfe8c305' 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>long_name :</span></dt><dd>grid index in x for variables at tracer and &#x27;v&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>XC</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>coverage_content_type :</span></dt><dd>coordinate</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'>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 5 6 ... 84 85 86 87 88 89</div><input id='attrs-0a4e7823-a1bd-411a-8465-93fe80296d16' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a4e7823-a1bd-411a-8465-93fe80296d16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb8584c8-bcd0-4b0c-a6ac-74d29cefffa1' class='xr-var-data-in' type='checkbox'><label for='data-cb8584c8-bcd0-4b0c-a6ac-74d29cefffa1' 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>long_name :</span></dt><dd>grid index in x for variables at &#x27;u&#x27; and &#x27;g&#x27; locations</dd><dt><span>c_grid_axis_shift :</span></dt><dd>-0.5</dd><dt><span>swap_dim :</span></dt><dd>XG</dd><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>coverage_content_type :</span></dt><dd>coordinate</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'>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 5 6 ... 84 85 86 87 88 89</div><input id='attrs-4c4e95a0-a388-40cb-8045-24f52aa32a69' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c4e95a0-a388-40cb-8045-24f52aa32a69' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca56ee0f-8dc8-478a-b8b3-c5c538aa4d64' class='xr-var-data-in' type='checkbox'><label for='data-ca56ee0f-8dc8-478a-b8b3-c5c538aa4d64' 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>long_name :</span></dt><dd>grid index in y for variables at tracer and &#x27;u&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>YC</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>coverage_content_type :</span></dt><dd>coordinate</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'>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 5 6 ... 84 85 86 87 88 89</div><input id='attrs-2633ce8c-f05f-4259-9271-3d3e11edbedd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2633ce8c-f05f-4259-9271-3d3e11edbedd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e80add8-3100-4c30-a660-8ee3b9c30444' class='xr-var-data-in' type='checkbox'><label for='data-0e80add8-3100-4c30-a660-8ee3b9c30444' 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>long_name :</span></dt><dd>grid index in y for variables at &#x27;v&#x27; and &#x27;g&#x27; locations</dd><dt><span>c_grid_axis_shift :</span></dt><dd>-0.5</dd><dt><span>swap_dim :</span></dt><dd>YG</dd><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>coverage_content_type :</span></dt><dd>coordinate</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'>tile</span></div><div class='xr-var-dims'>(tile)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-fa046277-a49b-4fea-a604-27d31a1a68d0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa046277-a49b-4fea-a604-27d31a1a68d0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92701026-b123-41b2-bca6-bab385ff5d88' class='xr-var-data-in' type='checkbox'><label for='data-92701026-b123-41b2-bca6-bab385ff5d88' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>lat-lon-cap tile index</dd><dt><span>comment :</span></dt><dd>The ECCO V4 horizontal model grid is divided into 13 tiles of 90x90 cells for convenience.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12], 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'>2017-01-16T12:00:00 ... 2017-12-...</div><input id='attrs-a356fd34-21dd-41aa-b143-4cdc7bdb6325' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a356fd34-21dd-41aa-b143-4cdc7bdb6325' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-013439e8-faf7-4187-aac1-ccd24779ec6c' class='xr-var-data-in' type='checkbox'><label for='data-013439e8-faf7-4187-aac1-ccd24779ec6c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>center time of averaging period</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-01-16T12:00:00.000000000&#x27;, &#x27;2017-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-03-16T12:00:00.000000000&#x27;, &#x27;2017-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-05-16T12:00:00.000000000&#x27;, &#x27;2017-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-07-16T12:00:00.000000000&#x27;, &#x27;2017-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2017-09-16T00:00:00.000000000&#x27;, &#x27;2017-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2017-11-16T00:00:00.000000000&#x27;, &#x27;2017-12-16T06:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XC</span></div><div class='xr-var-dims'>(tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-9621ce03-d330-486e-b873-e33a8c474bd6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9621ce03-d330-486e-b873-e33a8c474bd6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3875c5d1-9cc3-4ad8-922b-c838a6d11899' class='xr-var-data-in' type='checkbox'><label for='data-3875c5d1-9cc3-4ad8-922b-c838a6d11899' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of tracer grid cell center</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>coordinate :</span></dt><dd>YC XC</dd><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>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 411.33 kiB </td>\n",
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       "                        <td> (13, 90, 90) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <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>YC</span></div><div class='xr-var-dims'>(tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-463181f0-9f66-44f8-9eda-4b43ced0093f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-463181f0-9f66-44f8-9eda-4b43ced0093f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0d52059-34e5-4504-8139-034bca1ece27' class='xr-var-data-in' type='checkbox'><label for='data-c0d52059-34e5-4504-8139-034bca1ece27' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of tracer grid cell center</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>coordinate :</span></dt><dd>YC XC</dd><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>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
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       "                        <td> 411.33 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 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>XG</span></div><div class='xr-var-dims'>(tile, j_g, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-a518aa53-c464-4e31-b134-13efb74085b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a518aa53-c464-4e31-b134-13efb74085b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f093a245-462a-4d7b-a8dd-f4fbaa96bb94' class='xr-var-data-in' type='checkbox'><label for='data-f093a245-462a-4d7b-a8dd-f4fbaa96bb94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of &#x27;southwest&#x27; corner of tracer grid cell</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>coordinate :</span></dt><dd>YG XG</dd><dt><span>comment :</span></dt><dd>Nonuniform grid spacing. Note: &#x27;southwest&#x27; does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm dcoumentation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>longitude</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> 411.33 kiB </td>\n",
       "                        <td> 411.33 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13, 90, 90) </td>\n",
       "                        <td> (13, 90, 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",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>YG</span></div><div class='xr-var-dims'>(tile, j_g, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-af1aa0d5-20b9-4968-9095-3fb34a7a3cd9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af1aa0d5-20b9-4968-9095-3fb34a7a3cd9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55f364c0-67a1-453d-ae1c-0cebc4719a33' class='xr-var-data-in' type='checkbox'><label for='data-55f364c0-67a1-453d-ae1c-0cebc4719a33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of &#x27;southwest&#x27; corner of tracer grid cell</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>coordinate :</span></dt><dd>YG XG</dd><dt><span>comment :</span></dt><dd>Nonuniform grid spacing. Note: &#x27;southwest&#x27; does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm dcoumentation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>latitude</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> 411.33 kiB </td>\n",
       "                        <td> 411.33 kiB </td>\n",
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       "                    <tr>\n",
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       "                        <td> (13, 90, 90) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <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>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-d8617cf6-d695-40a4-bf99-fdc53c542de9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8617cf6-d695-40a4-bf99-fdc53c542de9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54160386-a30e-4067-85d1-065a9c8cd52b' class='xr-var-data-in' type='checkbox'><label for='data-54160386-a30e-4067-85d1-065a9c8cd52b' 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><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>time bounds of averaging period</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",
       "                        <td> 192 B </td>\n",
       "                        <td> 16 B </td>\n",
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       "                    \n",
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       "                        <th> Shape </th>\n",
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       "                        <td> (1, 2) </td>\n",
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       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XC_bnds</span></div><div class='xr-var-dims'>(tile, j, i, nb)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;</div><input id='attrs-2cdcb42e-4894-4f12-905b-e32e514b959b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2cdcb42e-4894-4f12-905b-e32e514b959b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a83f388-45d0-4c7d-a0bf-2410a060f956' class='xr-var-data-in' type='checkbox'><label for='data-8a83f388-45d0-4c7d-a0bf-2410a060f956' 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 dcoumentation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>longitudes of tracer grid cell corners</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 4.82 MiB </td>\n",
       "                        <td> 411.33 kiB </td>\n",
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       "                        <th> Shape </th>\n",
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       "                        <td> (1, 13, 90, 90) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 25 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>SSHIBC</span></div><div class='xr-var-dims'>(time, tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-d9df37a1-7d8d-4ef5-b6d8-d5bab4d3f54c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9df37a1-7d8d-4ef5-b6d8-d5bab4d3f54c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38ba99ce-b06e-4087-8316-738793a8bf35' class='xr-var-data-in' type='checkbox'><label for='data-38ba99ce-b06e-4087-8316-738793a8bf35' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The inverted barometer (IB) correction to sea surface height due to atmospheric pressure loading</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>comment :</span></dt><dd>Not an SSH itself, but a correction to model sea level anomaly (ETAN) required to account for the static part of sea surface displacement by atmosphere pressure loading: SSH = SSHNOIBC - SSHIBC. Note: Use SSH for model-data comparisons with altimetry data products that DO apply the IB correction and SSHNOIBC for comparisons with altimetry data products that do NOT apply the IB correction.</dd><dt><span>valid_min :</span></dt><dd>-0.30144819617271423</dd><dt><span>valid_max :</span></dt><dd>0.5248842239379883</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SSHNOIBC</span></div><div class='xr-var-dims'>(time, tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-1c1001be-202d-4d2f-bd57-46c20f7e3ee0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c1001be-202d-4d2f-bd57-46c20f7e3ee0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d21e4493-331a-4e6a-aade-69c1565261da' class='xr-var-data-in' type='checkbox'><label for='data-d21e4493-331a-4e6a-aade-69c1565261da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Sea surface height anomaly without the inverted barometer (IB) correction</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>comment :</span></dt><dd>Sea surface height anomaly above the geoid without the inverse barometer (IB) correction, suitable for comparisons with altimetry sea surface height data products that do NOT apply the inverse barometer (IB) correction. Note: SSHNOIBC is calculated by correcting model sea level anomaly ETAN for two effects: a) global mean steric sea level changes related to density changes in the Boussinesq volume-conserving model (Greatbatch correction, see sterGloH), b) sea level displacement due to sea-ice and snow pressure loading (see sIceLoad). In ECCO Version 4 Release 4 the model is forced with atmospheric pressure loading. SSHNOIBC does not correct for the static part of the effect of atmosphere pressure loading on sea surface height (the so-called inverse barometer (IB) correction). Use SSH for comparisons with altimetry data products that DO apply the IB correction.</dd><dt><span>valid_min :</span></dt><dd>-1.6654272079467773</dd><dt><span>valid_max :</span></dt><dd>1.4550364017486572</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 4.82 MiB </td>\n",
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       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\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, tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-3d819a6b-23ac-453a-a4aa-5748ce2acd7f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d819a6b-23ac-453a-a4aa-5748ce2acd7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c215281-bb76-41ea-ac69-fa5b64fb0450' class='xr-var-data-in' type='checkbox'><label for='data-4c215281-bb76-41ea-ac69-fa5b64fb0450' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Model sea level anomaly</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</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). These corrections ARE made for the variables SSH and SSHNOIBC.</dd><dt><span>valid_min :</span></dt><dd>-8.304216384887695</dd><dt><span>valid_max :</span></dt><dd>1.460192084312439</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 4.82 MiB </td>\n",
       "                        <td> 411.33 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-9744852f-682b-460b-8908-68d506fcfe8e' class='xr-section-summary-in' type='checkbox'  ><label for='section-9744852f-682b-460b-8908-68d506fcfe8e' class='xr-section-summary' >Indexes: <span>(6)</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><div></div><input id='index-d9903882-5c08-4916-9cb7-d08c5aa5c8dd' class='xr-index-data-in' type='checkbox'/><label for='index-d9903882-5c08-4916-9cb7-d08c5aa5c8dd' 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;i&#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><div></div><input id='index-9d56dfca-168a-4179-bd3c-c3aa55055032' class='xr-index-data-in' type='checkbox'/><label for='index-9d56dfca-168a-4179-bd3c-c3aa55055032' 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;i_g&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>j</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cdc85564-4bf2-4ace-9b23-b3903fef5445' class='xr-index-data-in' type='checkbox'/><label for='index-cdc85564-4bf2-4ace-9b23-b3903fef5445' 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;j&#x27;))</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><div></div><input id='index-df20467a-0b6b-4f53-93e0-a07ea2e614cb' class='xr-index-data-in' type='checkbox'/><label for='index-df20467a-0b6b-4f53-93e0-a07ea2e614cb' 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;j_g&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>tile</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c53b8aa4-2b88-437d-8cef-cc7703d7bf78' class='xr-index-data-in' type='checkbox'/><label for='index-c53b8aa4-2b88-437d-8cef-cc7703d7bf78' 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], dtype=&#x27;int32&#x27;, name=&#x27;tile&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-410c8d28-0288-466d-8786-4bd8dda135e7' class='xr-index-data-in' type='checkbox'/><label for='index-410c8d28-0288-466d-8786-4bd8dda135e7' 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;2017-01-16 12:00:00&#x27;, &#x27;2017-02-15 00:00:00&#x27;,\n",
       "               &#x27;2017-03-16 12:00:00&#x27;, &#x27;2017-04-16 00:00:00&#x27;,\n",
       "               &#x27;2017-05-16 12:00:00&#x27;, &#x27;2017-06-16 00:00:00&#x27;,\n",
       "               &#x27;2017-07-16 12:00:00&#x27;, &#x27;2017-08-16 12:00:00&#x27;,\n",
       "               &#x27;2017-09-16 00:00:00&#x27;, &#x27;2017-10-16 12:00:00&#x27;,\n",
       "               &#x27;2017-11-16 00:00:00&#x27;, &#x27;2017-12-16 06:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3267c255-5791-48fc-b93b-d4361539460a' class='xr-section-summary-in' type='checkbox'  ><label for='section-3267c255-5791-48fc-b93b-d4361539460a' class='xr-section-summary' >Attributes: <span>(57)</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>Ian Fenty and Ou Wang</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>comment :</span></dt><dd>Fields provided on the curvilinear lat-lon-cap 90 (llc90) native grid used in the ECCO model. SSH (dynamic sea surface height) = SSHNOIBC (dynamic sea surface without the inverse barometer correction) - SSHIBC (inverse barometer correction). The inverted barometer correction accounts for variations in sea surface height due to atmospheric pressure variations. Note: ETAN is model sea level anomaly and should not be compared with satellite altimetery products, see SSH and ETAN for more details.</dd><dt><span>Conventions :</span></dt><dd>CF-1.8, ACDD-1.3</dd><dt><span>coordinates_comment :</span></dt><dd>Note: the global &#x27;coordinates&#x27; attribute describes auxillary coordinates.</dd><dt><span>creator_email :</span></dt><dd>ecco-group@mit.edu</dd><dt><span>creator_institution :</span></dt><dd>NASA Jet Propulsion Laboratory (JPL)</dd><dt><span>creator_name :</span></dt><dd>ECCO Consortium</dd><dt><span>creator_type :</span></dt><dd>group</dd><dt><span>creator_url :</span></dt><dd>https://ecco-group.org</dd><dt><span>date_created :</span></dt><dd>2020-12-16T18:07:36</dd><dt><span>date_issued :</span></dt><dd>2020-12-16T18:07:36</dd><dt><span>date_metadata_modified :</span></dt><dd>2021-03-15T21:57:04</dd><dt><span>date_modified :</span></dt><dd>2021-03-15T21:57:04</dd><dt><span>geospatial_bounds_crs :</span></dt><dd>EPSG:4326</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-90.0</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>variable</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_resolution :</span></dt><dd>variable</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>history :</span></dt><dd>Inaugural release of an ECCO Central Estimate solution to PO.DAAC</dd><dt><span>id :</span></dt><dd>10.5067/ECL5M-SSH44</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 &gt; OCEANS &gt; SEA SURFACE TOPOGRAPHY &gt; SEA SURFACE HEIGHT, 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>metadata_link :</span></dt><dd>https://cmr.earthdata.nasa.gov/search/collections.umm_json?ShortName=ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4</dd><dt><span>naming_authority :</span></dt><dd>gov.nasa.jpl</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>platform_vocabulary :</span></dt><dd>GCMD platform keywords</dd><dt><span>processing_level :</span></dt><dd>L4</dd><dt><span>product_name :</span></dt><dd>SEA_SURFACE_HEIGHT_mon_mean_2017-01_ECCO_V4r4_native_llc0090.nc</dd><dt><span>product_time_coverage_end :</span></dt><dd>2018-01-01T00:00:00</dd><dt><span>product_time_coverage_start :</span></dt><dd>1992-01-01T12:00:00</dd><dt><span>product_version :</span></dt><dd>Version 4, Release 4</dd><dt><span>program :</span></dt><dd>NASA Physical Oceanography, Cryosphere, Modeling, Analysis, and Prediction (MAP)</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>references :</span></dt><dd>ECCO Consortium, Fukumori, I., Wang, O., Fenty, I., Forget, G., Heimbach, P., &amp; Ponte, R. M. 2020. Synopsis of the ECCO Central Production Global Ocean and Sea-Ice State Estimate (Version 4 Release 4). doi:10.5281/zenodo.3765928</dd><dt><span>source :</span></dt><dd>The ECCO V4r4 state estimate was produced by fitting a free-running solution of the MITgcm (checkpoint 66g) to satellite and in situ observational data in a least squares sense using the adjoint method</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention</dd><dt><span>summary :</span></dt><dd>This dataset provides monthly-averaged dynamic sea surface height and model sea level anomaly on the lat-lon-cap 90 (llc90) native model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea-ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.</dd><dt><span>time_coverage_duration :</span></dt><dd>P1M</dd><dt><span>time_coverage_end :</span></dt><dd>2017-02-01T00:00:00</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1M</dd><dt><span>time_coverage_start :</span></dt><dd>2017-01-01T00:00:00</dd><dt><span>title :</span></dt><dd>ECCO Sea Surface Height - Monthly Mean llc90 Grid (Version 4 Release 4)</dd><dt><span>uuid :</span></dt><dd>a21a5c30-400c-11eb-a9e0-0cc47a3f49c3</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 25MB\n",
       "Dimensions:    (time: 12, tile: 13, j: 90, i: 90, i_g: 90, j_g: 90, nv: 2, nb: 4)\n",
       "Coordinates: (12/13)\n",
       "  * i          (i) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * i_g        (i_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j          (j) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j_g        (j_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
       "  * tile       (tile) int32 52B 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "  * time       (time) datetime64[ns] 96B 2017-01-16T12:00:00 ... 2017-12-16T0...\n",
       "    ...         ...\n",
       "    YC         (tile, j, i) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>\n",
       "    XG         (tile, j_g, i_g) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>\n",
       "    YG         (tile, j_g, i_g) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>\n",
       "    time_bnds  (time, nv) datetime64[ns] 192B dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "    XC_bnds    (tile, j, i, nb) float32 2MB dask.array<chunksize=(13, 90, 90, 4), meta=np.ndarray>\n",
       "    YC_bnds    (tile, j, i, nb) float32 2MB dask.array<chunksize=(13, 90, 90, 4), meta=np.ndarray>\n",
       "Dimensions without coordinates: nv, nb\n",
       "Data variables:\n",
       "    SSH        (time, tile, j, i) float32 5MB dask.array<chunksize=(1, 13, 90, 90), meta=np.ndarray>\n",
       "    SSHIBC     (time, tile, j, i) float32 5MB dask.array<chunksize=(1, 13, 90, 90), meta=np.ndarray>\n",
       "    SSHNOIBC   (time, tile, j, i) float32 5MB dask.array<chunksize=(1, 13, 90, 90), meta=np.ndarray>\n",
       "    ETAN       (time, tile, j, i) float32 5MB dask.array<chunksize=(1, 13, 90, 90), meta=np.ndarray>\n",
       "Attributes: (12/57)\n",
       "    acknowledgement:              This research was carried out by the Jet Pr...\n",
       "    author:                       Ian Fenty and Ou Wang\n",
       "    cdm_data_type:                Grid\n",
       "    comment:                      Fields provided on the curvilinear lat-lon-...\n",
       "    Conventions:                  CF-1.8, ACDD-1.3\n",
       "    coordinates_comment:          Note: the global 'coordinates' attribute de...\n",
       "    ...                           ...\n",
       "    time_coverage_duration:       P1M\n",
       "    time_coverage_end:            2017-02-01T00:00:00\n",
       "    time_coverage_resolution:     P1M\n",
       "    time_coverage_start:          2017-01-01T00:00:00\n",
       "    title:                        ECCO Sea Surface Height - Monthly Mean llc9...\n",
       "    uuid:                         a21a5c30-400c-11eb-a9e0-0cc47a3f49c3"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_SSH"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b975e41-1521-46fe-bc62-d52b2a9a8c64",
   "metadata": {},
   "source": [
    "### In-cloud direct access with pre-generated json files (mode = 's3_open_fsspec')\n",
    "\n",
    "If you are working on an instance in the Amazon Web Services (AWS) Cloud (region *us-west-2*), you can use one of the [access modes](https://ecco-access.readthedocs.io/en/latest/ECCO_access_modes.html#In-cloud-only-access-modes) specifically intended for in-cloud S3 data access. One of these modes is **s3_open_fsspec**, which opens the files from S3 (no download necessary), and uses json files with the data chunking information to open the files exceptionally fast. If you have these files already stored in a specific directory on your instance, or want to specify where to store them, you can use the `jsons_root_dir` option. Otherwise, by default the json files will be accessed from the `s3://ecco-model-granules` bucket and stored under your user home directory in `~/MZZ/v4r4`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d6081687-a487-4994-8c30-d9896d142d7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "curr_shortname = 'ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4'\n",
    "ds_SSH_s3 = ea.ecco_podaac_to_xrdataset(curr_shortname,\\\n",
    "                                        StartDate='2017-01',EndDate='2017-12',\\\n",
    "                                        mode='s3_open_fsspec')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5824abfd-ffb0-4167-8e3e-6b5bcc219933",
   "metadata": {},
   "outputs": [
    {
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       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 25MB\n",
       "Dimensions:    (time: 12, tile: 13, j: 90, i: 90, nb: 4, j_g: 90, i_g: 90, nv: 2)\n",
       "Coordinates: (12/13)\n",
       "  * time       (time) datetime64[ns] 96B 2017-01-16T12:00:00 ... 2017-12-16T0...\n",
       "    XC         (tile, j, i) float32 421kB dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;\n",
       "    XC_bnds    (tile, j, i, nb) float32 2MB dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;\n",
       "    XG         (tile, j_g, i_g) float32 421kB dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;\n",
       "    YC         (tile, j, i) float32 421kB dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;\n",
       "    YC_bnds    (tile, j, i, nb) float32 2MB dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;\n",
       "    ...         ...\n",
       "  * i          (i) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * i_g        (i_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j          (j) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j_g        (j_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
       "  * tile       (tile) int32 52B 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "    time_bnds  (time, nv) datetime64[ns] 192B dask.array&lt;chunksize=(12, 2), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: nb, nv\n",
       "Data variables:\n",
       "    ETAN       (time, tile, j, i) float32 5MB dask.array&lt;chunksize=(12, 13, 90, 90), meta=np.ndarray&gt;\n",
       "    SSH        (time, tile, j, i) float32 5MB dask.array&lt;chunksize=(12, 13, 90, 90), meta=np.ndarray&gt;\n",
       "    SSHIBC     (time, tile, j, i) float32 5MB dask.array&lt;chunksize=(12, 13, 90, 90), meta=np.ndarray&gt;\n",
       "    SSHNOIBC   (time, tile, j, i) float32 5MB dask.array&lt;chunksize=(12, 13, 90, 90), meta=np.ndarray&gt;\n",
       "Attributes: (12/57)\n",
       "    Conventions:                  CF-1.8, ACDD-1.3\n",
       "    acknowledgement:              This research was carried out by the Jet Pr...\n",
       "    author:                       Ian Fenty and Ou Wang\n",
       "    cdm_data_type:                Grid\n",
       "    comment:                      Fields provided on the curvilinear lat-lon-...\n",
       "    coordinates_comment:          Note: the global &#x27;coordinates&#x27; attribute de...\n",
       "    ...                           ...\n",
       "    time_coverage_duration:       P1M\n",
       "    time_coverage_end:            1992-02-01T00:00:00\n",
       "    time_coverage_resolution:     P1M\n",
       "    time_coverage_start:          1992-01-01T12:00:00\n",
       "    title:                        ECCO Sea Surface Height - Monthly Mean llc9...\n",
       "    uuid:                         9302811e-400c-11eb-b69e-0cc47a3f49c3</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-f70b20ff-ca9b-4cf7-8aa1-38af566def97' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f70b20ff-ca9b-4cf7-8aa1-38af566def97' 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>: 12</li><li><span class='xr-has-index'>tile</span>: 13</li><li><span class='xr-has-index'>j</span>: 90</li><li><span class='xr-has-index'>i</span>: 90</li><li><span>nb</span>: 4</li><li><span class='xr-has-index'>j_g</span>: 90</li><li><span class='xr-has-index'>i_g</span>: 90</li><li><span>nv</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-cc2154d9-4a87-40cc-a2cd-f53226bf3c31' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cc2154d9-4a87-40cc-a2cd-f53226bf3c31' class='xr-section-summary' >Coordinates: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>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'>2017-01-16T12:00:00 ... 2017-12-...</div><input id='attrs-8d4c4f37-381d-4c4b-971e-f3b6d58174fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d4c4f37-381d-4c4b-971e-f3b6d58174fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-596b0253-4d89-4843-b7c6-1c6fdba32cd8' class='xr-var-data-in' type='checkbox'><label for='data-596b0253-4d89-4843-b7c6-1c6fdba32cd8' 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>T</dd><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>center time of averaging period</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-01-16T12:00:00.000000000&#x27;, &#x27;2017-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-03-16T12:00:00.000000000&#x27;, &#x27;2017-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-05-16T12:00:00.000000000&#x27;, &#x27;2017-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-07-16T12:00:00.000000000&#x27;, &#x27;2017-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2017-09-16T00:00:00.000000000&#x27;, &#x27;2017-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2017-11-16T00:00:00.000000000&#x27;, &#x27;2017-12-16T06:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XC</span></div><div class='xr-var-dims'>(tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-435fda97-ca0a-468d-8af8-f65fc6aaf359' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-435fda97-ca0a-468d-8af8-f65fc6aaf359' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c27347f2-b21b-417f-8eea-c0f7df58902c' class='xr-var-data-in' type='checkbox'><label for='data-c27347f2-b21b-417f-8eea-c0f7df58902c' 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>coordinate :</span></dt><dd>YC XC</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></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> 411.33 kiB </td>\n",
       "                        <td> 411.33 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13, 90, 90) </td>\n",
       "                        <td> (13, 90, 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",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XC_bnds</span></div><div class='xr-var-dims'>(tile, j, i, nb)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;</div><input id='attrs-32fc10bd-1730-4529-87cf-659f4ef40e7f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32fc10bd-1730-4529-87cf-659f4ef40e7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91252fa0-b151-4666-a9cd-095470dc5623' class='xr-var-data-in' type='checkbox'><label for='data-91252fa0-b151-4666-a9cd-095470dc5623' 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 dcoumentation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>longitudes of tracer grid cell corners</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> 1.61 MiB </td>\n",
       "                        <td> 1.61 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13, 90, 90, 4) </td>\n",
       "                        <td> (13, 90, 90, 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",
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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> 411.33 kiB </td>\n",
       "                        <td> 411.33 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13, 90, 90) </td>\n",
       "                        <td> (13, 90, 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",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>YC</span></div><div class='xr-var-dims'>(tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-06151ba5-0dfb-4dfb-a722-92d6cfd52a3c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06151ba5-0dfb-4dfb-a722-92d6cfd52a3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-842ad37c-82d0-4536-af66-a3aba45a8a79' class='xr-var-data-in' type='checkbox'><label for='data-842ad37c-82d0-4536-af66-a3aba45a8a79' 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>coordinate :</span></dt><dd>YC XC</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></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> 411.33 kiB </td>\n",
       "                        <td> 411.33 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13, 90, 90) </td>\n",
       "                        <td> (13, 90, 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",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>YC_bnds</span></div><div class='xr-var-dims'>(tile, j, i, nb)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;</div><input id='attrs-84071ac7-77dc-480e-ab0f-d9b03535b68d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84071ac7-77dc-480e-ab0f-d9b03535b68d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b719cd2-1dad-42ee-bd18-8db96fe9d484' class='xr-var-data-in' type='checkbox'><label for='data-6b719cd2-1dad-42ee-bd18-8db96fe9d484' 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 dcoumentation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>latitudes of tracer grid cell corners</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> 1.61 MiB </td>\n",
       "                        <td> 1.61 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13, 90, 90, 4) </td>\n",
       "                        <td> (13, 90, 90, 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",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>YG</span></div><div class='xr-var-dims'>(tile, j_g, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-67c628fd-8111-4999-967b-4df0d4d4b42c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67c628fd-8111-4999-967b-4df0d4d4b42c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90eac306-9fa3-4a3c-9241-eb9a3f1808c4' class='xr-var-data-in' type='checkbox'><label for='data-90eac306-9fa3-4a3c-9241-eb9a3f1808c4' 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>Nonuniform grid spacing. Note: &#x27;southwest&#x27; does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm dcoumentation for details.</dd><dt><span>coordinate :</span></dt><dd>YG XG</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>latitude of &#x27;southwest&#x27; corner of tracer grid cell</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
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       "            <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> 411.33 kiB </td>\n",
       "                        <td> 411.33 kiB </td>\n",
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       "                        <th> Shape </th>\n",
       "                        <td> (13, 90, 90) </td>\n",
       "                        <td> (13, 90, 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",
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       "</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 5 6 ... 84 85 86 87 88 89</div><input id='attrs-a8d94b09-2723-4f8b-a57c-0d99cb7b673b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8d94b09-2723-4f8b-a57c-0d99cb7b673b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-268c309a-5ef4-4dd4-a129-69451a7469b2' class='xr-var-data-in' type='checkbox'><label for='data-268c309a-5ef4-4dd4-a129-69451a7469b2' 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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>grid index in x for variables at tracer and &#x27;v&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>XC</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'>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 5 6 ... 84 85 86 87 88 89</div><input id='attrs-8b8161da-677f-4944-9407-17e5b39df1df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b8161da-677f-4944-9407-17e5b39df1df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9125205-6333-408b-89ff-62cd0ca7f39d' class='xr-var-data-in' type='checkbox'><label for='data-a9125205-6333-408b-89ff-62cd0ca7f39d' 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>c_grid_axis_shift :</span></dt><dd>-0.5</dd><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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>grid index in x for variables at &#x27;u&#x27; and &#x27;g&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>XG</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'>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 5 6 ... 84 85 86 87 88 89</div><input id='attrs-c4c6c105-43c0-4aa4-9d6a-2c9d506c7201' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4c6c105-43c0-4aa4-9d6a-2c9d506c7201' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-191b40e4-1eca-49d5-b135-73b14bdc6396' class='xr-var-data-in' type='checkbox'><label for='data-191b40e4-1eca-49d5-b135-73b14bdc6396' 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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>grid index in y for variables at tracer and &#x27;u&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>YC</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'>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 5 6 ... 84 85 86 87 88 89</div><input id='attrs-31df7a0d-7a79-4571-98a0-333fb863426c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31df7a0d-7a79-4571-98a0-333fb863426c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b166307e-d20c-4fb0-acfa-f17fc7d3a308' class='xr-var-data-in' type='checkbox'><label for='data-b166307e-d20c-4fb0-acfa-f17fc7d3a308' 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>c_grid_axis_shift :</span></dt><dd>-0.5</dd><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>coverage_content_type :</span></dt><dd>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><dt><span>swap_dim :</span></dt><dd>YG</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'>tile</span></div><div class='xr-var-dims'>(tile)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-0e235fa9-bc01-465a-abbc-42954432db3b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e235fa9-bc01-465a-abbc-42954432db3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74fbf4db-85d7-427f-a6fc-4a9ea1246399' class='xr-var-data-in' type='checkbox'><label for='data-74fbf4db-85d7-427f-a6fc-4a9ea1246399' 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 ECCO V4 horizontal model grid is divided into 13 tiles of 90x90 cells for convenience.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>lat-lon-cap tile index</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12], 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=(12, 2), meta=np.ndarray&gt;</div><input id='attrs-93121048-bb98-4bf4-aedd-260d889b1eae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93121048-bb98-4bf4-aedd-260d889b1eae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c70516e6-4cdd-4007-8556-e6e6ca0c4481' class='xr-var-data-in' type='checkbox'><label for='data-c70516e6-4cdd-4007-8556-e6e6ca0c4481' 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><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>time bounds 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> 192 B </td>\n",
       "                        <td> 192 B </td>\n",
       "                    </tr>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-2db78f99-1d8f-4d6a-b952-d16e6cf225c6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2db78f99-1d8f-4d6a-b952-d16e6cf225c6' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>ETAN</span></div><div class='xr-var-dims'>(time, tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-14df1edd-c28e-466e-a8ca-c4dbc6a919db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-14df1edd-c28e-466e-a8ca-c4dbc6a919db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-25d505bd-83c5-4831-b4df-b5a6010a9141' class='xr-var-data-in' type='checkbox'><label for='data-25d505bd-83c5-4831-b4df-b5a6010a9141' 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>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). These corrections ARE made for the variables SSH and SSHNOIBC.</dd><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>valid_max :</span></dt><dd>1.460192084312439</dd><dt><span>valid_min :</span></dt><dd>-8.304216384887695</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                        <td> 4.82 MiB </td>\n",
       "                        <td> 4.82 MiB </td>\n",
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       "                        <td> (12, 13, 90, 90) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 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>SSH</span></div><div class='xr-var-dims'>(time, tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-9b847a00-7924-494b-a287-77a5f6d65782' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b847a00-7924-494b-a287-77a5f6d65782' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a9c0b05-b617-4ad2-9149-d0148f92d42d' class='xr-var-data-in' type='checkbox'><label for='data-6a9c0b05-b617-4ad2-9149-d0148f92d42d' 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>Dynamic sea surface height anomaly above the geoid, suitable for comparisons with altimetry sea surface height data products that apply the inverse barometer (IB) correction. Note: SSH is calculated by correcting model sea level anomaly ETAN for three effects: a) global mean steric sea level changes related to density changes in the Boussinesq volume-conserving model (Greatbatch correction, see sterGloH), b) the inverted barometer (IB) effect (see SSHIBC) and c) sea level displacement due to sea-ice and snow pressure loading (see sIceLoad). SSH can be compared with the similarly-named SSH variable in previous ECCO products that did not include atmospheric pressure loading (e.g., Version 4 Release 3). Use SSHNOIBC for comparisons with altimetry data products that do NOT apply the IB correction.</dd><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>valid_max :</span></dt><dd>1.4207719564437866</dd><dt><span>valid_min :</span></dt><dd>-1.8805772066116333</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 4.82 MiB </td>\n",
       "                        <td> 4.82 MiB </td>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SSHIBC</span></div><div class='xr-var-dims'>(time, tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-7dc482ee-74c0-423f-9c0e-c5b4058d37b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7dc482ee-74c0-423f-9c0e-c5b4058d37b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b54eeb4-45ea-48ad-876b-65998a37f297' class='xr-var-data-in' type='checkbox'><label for='data-0b54eeb4-45ea-48ad-876b-65998a37f297' 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>Not an SSH itself, but a correction to model sea level anomaly (ETAN) required to account for the static part of sea surface displacement by atmosphere pressure loading: SSH = SSHNOIBC - SSHIBC. Note: Use SSH for model-data comparisons with altimetry data products that DO apply the IB correction and SSHNOIBC for comparisons with altimetry data products that do NOT apply the IB correction.</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>The inverted barometer (IB) correction to sea surface height due to atmospheric pressure loading</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_max :</span></dt><dd>0.5248842239379883</dd><dt><span>valid_min :</span></dt><dd>-0.30144819617271423</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 4.82 MiB </td>\n",
       "                        <td> 4.82 MiB </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SSHNOIBC</span></div><div class='xr-var-dims'>(time, tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-b1935cc9-34fa-46bd-8cff-2d97831e7624' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1935cc9-34fa-46bd-8cff-2d97831e7624' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3158d62e-8272-4e89-850b-5a0ce33396ae' class='xr-var-data-in' type='checkbox'><label for='data-3158d62e-8272-4e89-850b-5a0ce33396ae' 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>Sea surface height anomaly above the geoid without the inverse barometer (IB) correction, suitable for comparisons with altimetry sea surface height data products that do NOT apply the inverse barometer (IB) correction. Note: SSHNOIBC is calculated by correcting model sea level anomaly ETAN for two effects: a) global mean steric sea level changes related to density changes in the Boussinesq volume-conserving model (Greatbatch correction, see sterGloH), b) sea level displacement due to sea-ice and snow pressure loading (see sIceLoad). In ECCO Version 4 Release 4 the model is forced with atmospheric pressure loading. SSHNOIBC does not correct for the static part of the effect of atmosphere pressure loading on sea surface height (the so-called inverse barometer (IB) correction). Use SSH for comparisons with altimetry data products that DO apply the IB correction.</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>long_name :</span></dt><dd>Sea surface height anomaly without the inverted barometer (IB) correction</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_max :</span></dt><dd>1.4550364017486572</dd><dt><span>valid_min :</span></dt><dd>-1.6654272079467773</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 4.82 MiB </td>\n",
       "                        <td> 4.82 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (12, 13, 90, 90) </td>\n",
       "                        <td> (12, 13, 90, 90) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-55d2df6c-6dab-455f-b600-f9b0a953947b' class='xr-section-summary-in' type='checkbox'  ><label for='section-55d2df6c-6dab-455f-b600-f9b0a953947b' class='xr-section-summary' >Indexes: <span>(6)</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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5feae393-9d18-4134-9e00-3b9c75119837' class='xr-index-data-in' type='checkbox'/><label for='index-5feae393-9d18-4134-9e00-3b9c75119837' 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;2017-01-16 12:00:00&#x27;, &#x27;2017-02-15 00:00:00&#x27;,\n",
       "               &#x27;2017-03-16 12:00:00&#x27;, &#x27;2017-04-16 00:00:00&#x27;,\n",
       "               &#x27;2017-05-16 12:00:00&#x27;, &#x27;2017-06-16 00:00:00&#x27;,\n",
       "               &#x27;2017-07-16 12:00:00&#x27;, &#x27;2017-08-16 12:00:00&#x27;,\n",
       "               &#x27;2017-09-16 00:00:00&#x27;, &#x27;2017-10-16 12:00:00&#x27;,\n",
       "               &#x27;2017-11-16 00:00:00&#x27;, &#x27;2017-12-16 06:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>i</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-daaa8f50-e360-4bbf-8669-7a4d335db3f5' class='xr-index-data-in' type='checkbox'/><label for='index-daaa8f50-e360-4bbf-8669-7a4d335db3f5' 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;i&#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><div></div><input id='index-7c99ec25-d81b-4ef2-af17-e73746d6bf75' class='xr-index-data-in' type='checkbox'/><label for='index-7c99ec25-d81b-4ef2-af17-e73746d6bf75' 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;i_g&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>j</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c33d6094-dc50-4a8f-bc31-904abaf0dfb7' class='xr-index-data-in' type='checkbox'/><label for='index-c33d6094-dc50-4a8f-bc31-904abaf0dfb7' 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;j&#x27;))</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><div></div><input id='index-fd23b408-b7a3-48e7-af63-d6b0b73de885' class='xr-index-data-in' type='checkbox'/><label for='index-fd23b408-b7a3-48e7-af63-d6b0b73de885' 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;j_g&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>tile</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7e030d20-7ba4-4678-a303-12ea2ad05034' class='xr-index-data-in' type='checkbox'/><label for='index-7e030d20-7ba4-4678-a303-12ea2ad05034' 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], dtype=&#x27;int32&#x27;, name=&#x27;tile&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fda442f4-c5b7-41ed-8987-d90d8c6e5dcd' class='xr-section-summary-in' type='checkbox'  ><label for='section-fda442f4-c5b7-41ed-8987-d90d8c6e5dcd' class='xr-section-summary' >Attributes: <span>(57)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.8, ACDD-1.3</dd><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>Ian Fenty and Ou Wang</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>comment :</span></dt><dd>Fields provided on the curvilinear lat-lon-cap 90 (llc90) native grid used in the ECCO model. SSH (dynamic sea surface height) = SSHNOIBC (dynamic sea surface without the inverse barometer correction) - SSHIBC (inverse barometer correction). The inverted barometer correction accounts for variations in sea surface height due to atmospheric pressure variations. Note: ETAN is model sea level anomaly and should not be compared with satellite altimetery products, see SSH and ETAN for more details.</dd><dt><span>coordinates_comment :</span></dt><dd>Note: the global &#x27;coordinates&#x27; attribute describes auxillary coordinates.</dd><dt><span>creator_email :</span></dt><dd>ecco-group@mit.edu</dd><dt><span>creator_institution :</span></dt><dd>NASA Jet Propulsion Laboratory (JPL)</dd><dt><span>creator_name :</span></dt><dd>ECCO Consortium</dd><dt><span>creator_type :</span></dt><dd>group</dd><dt><span>creator_url :</span></dt><dd>https://ecco-group.org</dd><dt><span>date_created :</span></dt><dd>2020-12-16T18:07:10</dd><dt><span>date_issued :</span></dt><dd>2020-12-16T18:07:10</dd><dt><span>date_metadata_modified :</span></dt><dd>2021-03-15T21:53:43</dd><dt><span>date_modified :</span></dt><dd>2021-03-15T21:53:43</dd><dt><span>geospatial_bounds_crs :</span></dt><dd>EPSG:4326</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-90.0</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>variable</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_resolution :</span></dt><dd>variable</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>history :</span></dt><dd>Inaugural release of an ECCO Central Estimate solution to PO.DAAC</dd><dt><span>id :</span></dt><dd>10.5067/ECL5M-SSH44</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 &gt; OCEANS &gt; SEA SURFACE TOPOGRAPHY &gt; SEA SURFACE HEIGHT, 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>metadata_link :</span></dt><dd>https://cmr.earthdata.nasa.gov/search/collections.umm_json?ShortName=ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4</dd><dt><span>naming_authority :</span></dt><dd>gov.nasa.jpl</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>platform_vocabulary :</span></dt><dd>GCMD platform keywords</dd><dt><span>processing_level :</span></dt><dd>L4</dd><dt><span>product_name :</span></dt><dd>SEA_SURFACE_HEIGHT_mon_mean_1992-01_ECCO_V4r4_native_llc0090.nc</dd><dt><span>product_time_coverage_end :</span></dt><dd>2018-01-01T00:00:00</dd><dt><span>product_time_coverage_start :</span></dt><dd>1992-01-01T12:00:00</dd><dt><span>product_version :</span></dt><dd>Version 4, Release 4</dd><dt><span>program :</span></dt><dd>NASA Physical Oceanography, Cryosphere, Modeling, Analysis, and Prediction (MAP)</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>references :</span></dt><dd>ECCO Consortium, Fukumori, I., Wang, O., Fenty, I., Forget, G., Heimbach, P., &amp; Ponte, R. M. 2020. Synopsis of the ECCO Central Production Global Ocean and Sea-Ice State Estimate (Version 4 Release 4). doi:10.5281/zenodo.3765928</dd><dt><span>source :</span></dt><dd>The ECCO V4r4 state estimate was produced by fitting a free-running solution of the MITgcm (checkpoint 66g) to satellite and in situ observational data in a least squares sense using the adjoint method</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention</dd><dt><span>summary :</span></dt><dd>This dataset provides monthly-averaged dynamic sea surface height and model sea level anomaly on the lat-lon-cap 90 (llc90) native model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea-ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.</dd><dt><span>time_coverage_duration :</span></dt><dd>P1M</dd><dt><span>time_coverage_end :</span></dt><dd>1992-02-01T00:00:00</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1M</dd><dt><span>time_coverage_start :</span></dt><dd>1992-01-01T12:00:00</dd><dt><span>title :</span></dt><dd>ECCO Sea Surface Height - Monthly Mean llc90 Grid (Version 4 Release 4)</dd><dt><span>uuid :</span></dt><dd>9302811e-400c-11eb-b69e-0cc47a3f49c3</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 25MB\n",
       "Dimensions:    (time: 12, tile: 13, j: 90, i: 90, nb: 4, j_g: 90, i_g: 90, nv: 2)\n",
       "Coordinates: (12/13)\n",
       "  * time       (time) datetime64[ns] 96B 2017-01-16T12:00:00 ... 2017-12-16T0...\n",
       "    XC         (tile, j, i) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>\n",
       "    XC_bnds    (tile, j, i, nb) float32 2MB dask.array<chunksize=(13, 90, 90, 4), meta=np.ndarray>\n",
       "    XG         (tile, j_g, i_g) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>\n",
       "    YC         (tile, j, i) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>\n",
       "    YC_bnds    (tile, j, i, nb) float32 2MB dask.array<chunksize=(13, 90, 90, 4), meta=np.ndarray>\n",
       "    ...         ...\n",
       "  * i          (i) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * i_g        (i_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j          (j) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j_g        (j_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
       "  * tile       (tile) int32 52B 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "    time_bnds  (time, nv) datetime64[ns] 192B dask.array<chunksize=(12, 2), meta=np.ndarray>\n",
       "Dimensions without coordinates: nb, nv\n",
       "Data variables:\n",
       "    ETAN       (time, tile, j, i) float32 5MB dask.array<chunksize=(12, 13, 90, 90), meta=np.ndarray>\n",
       "    SSH        (time, tile, j, i) float32 5MB dask.array<chunksize=(12, 13, 90, 90), meta=np.ndarray>\n",
       "    SSHIBC     (time, tile, j, i) float32 5MB dask.array<chunksize=(12, 13, 90, 90), meta=np.ndarray>\n",
       "    SSHNOIBC   (time, tile, j, i) float32 5MB dask.array<chunksize=(12, 13, 90, 90), meta=np.ndarray>\n",
       "Attributes: (12/57)\n",
       "    Conventions:                  CF-1.8, ACDD-1.3\n",
       "    acknowledgement:              This research was carried out by the Jet Pr...\n",
       "    author:                       Ian Fenty and Ou Wang\n",
       "    cdm_data_type:                Grid\n",
       "    comment:                      Fields provided on the curvilinear lat-lon-...\n",
       "    coordinates_comment:          Note: the global 'coordinates' attribute de...\n",
       "    ...                           ...\n",
       "    time_coverage_duration:       P1M\n",
       "    time_coverage_end:            1992-02-01T00:00:00\n",
       "    time_coverage_resolution:     P1M\n",
       "    time_coverage_start:          1992-01-01T12:00:00\n",
       "    title:                        ECCO Sea Surface Height - Monthly Mean llc9...\n",
       "    uuid:                         9302811e-400c-11eb-b69e-0cc47a3f49c3"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_SSH_s3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "968154c7-d122-4661-8273-c7e9c2023fbf",
   "metadata": {},
   "source": [
    "Now plot the SSH for Jan 2017 in tile 10 (Python numbering convention; 11 in Fortran/MATLAB numbering convention). Here we use the \"RdYlBu\" colormap, one of [many built-in colormaps](https://matplotlib.org/stable/gallery/color/colormap_reference.html) that the `matplotlib` package provides, or you can [create your own](https://matplotlib.org/stable/users/explain/colors/colormap-manipulation.html#colormap-manipulation). The \"_r\" at the end reverses the direction of the colormap, so red corresponds to the maximum values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2dcae0b6-15a0-424d-8ba5-931034986d0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f90819eef30>"
      ]
     },
     "execution_count": 7,
     "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": [
    "ds_SSH_s3.SSH.isel(time=0,tile=10).plot(cmap='RdYlBu_r')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e842d666-687d-4d54-b6de-5cf33d3dcb89",
   "metadata": {},
   "source": [
    "We can also use the `ecco_v4_py` package to plot a global map of Jan 2017 SSH, using the `plot_proj_to_latlon_grid` function which regrids from the native LLC grid to a lat/lon grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5533d10c-0db6-42b9-80d0-d5f0489e9f36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x540 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6), dpi= 90)\n",
    "ecco.plot_proj_to_latlon_grid(ds_SSH_s3.XC, ds_SSH_s3.YC, \\\n",
    "                              ds_SSH_s3.SSH.isel(time=0), \\\n",
    "                              user_lon_0=-160,\\\n",
    "                              projection_type='robin',\\\n",
    "                              plot_type='pcolormesh', \\\n",
    "                              cmap='RdBu_r',\\\n",
    "                              dx=1,dy=1,cmin=-1.2, cmax=1.2,show_colorbar=True)\n",
    "plt.title('Monthly mean SSH [m], Jan 2017')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bac5e1b-1a7a-489e-ae69-4c2659afc356",
   "metadata": {},
   "source": [
    "## What if you don't know the ShortName already?\n",
    "\n",
    "[NASA Earthdata](https://search.earthdata.nasa.gov/search) datasets are identified by ShortNames, but you might not know the ShortName of the variable or category of variables that you are seeking. One way to find the ShortName is to consult these [ECCOv4r4 variable lists](https://ecco-summer-school.github.io/ecco-2025/tutorials/ECCO_data_access/ECCO_data_structure.html#dataset-shortnames-and-variables-associated-with-them). But the \"query\" in `ecco_access` functions does not have to be a ShortName; it can also be a text string representing a variable name, or a word or phrase in the variable description.\n",
    "\n",
    "For example, perhaps you are looking to open the dataset that has native grid monthly sea ice concentration in 2007. If the query is not identified as a ShortName, then a text search of the [variable lists](https://ecco-summer-school.github.io/ecco-2025/tutorials/ECCO_data_access/ECCO_data_structure.html#dataset-shortnames-and-variables-associated-with-them) is conducted using *query*, *grid*, and *time_res*. Then of the identified matches, the user is asked to select one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8c4a2b26-34d4-4082-a7bc-7612d51e681d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ShortName Options for query \"ice\":\n",
      "                  Variable Name     Description (units)\n",
      "\n",
      "Option 1: ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4    *native grid,monthly means*\n",
      "                  SSH               Dynamic sea surface height anomaly. Suitable for\n",
      "                                    comparisons with altimetry sea surface height data\n",
      "                                    products that apply the inverse barometer\n",
      "                                    correction. (m)\n",
      "                  SSHIBC            The inverted barometer correction to sea surface\n",
      "                                    height due to atmospheric pressure loading. (m)\n",
      "                  SSHNOIBC          Sea surface height anomaly without the inverted\n",
      "                                    barometer correction. Suitable for comparisons\n",
      "                                    with altimetry sea surface height data products\n",
      "                                    that do NOT apply the inverse barometer\n",
      "                                    correction. (m)\n",
      "                  ETAN              Model sea level anomaly, without corrections for\n",
      "                                    global mean density changes, inverted barometer\n",
      "                                    effect, or volume displacement due to submerged\n",
      "                                    sea-ice and snow. (m)\n",
      "\n",
      "Option 2: ECCO_L4_STRESS_LLC0090GRID_MONTHLY_V4R4    *native grid,monthly means*\n",
      "                  EXFtaux           Wind stress in the model +x direction (N/m^2)\n",
      "                  EXFtauy           Wind stress in the model +y direction (N/m^2)\n",
      "                  oceTAUX           Ocean surface stress in the model +x direction,\n",
      "                                    due to wind and sea-ice (N/m^2)\n",
      "                  oceTAUY           Ocean surface stress in the model +y direction,\n",
      "                                    due to wind and sea-ice (N/m^2)\n",
      "\n",
      "Option 3: ECCO_L4_HEAT_FLUX_LLC0090GRID_MONTHLY_V4R4    *native grid,monthly means*\n",
      "                  EXFhl             Open ocean air-sea latent heat flux (W/m^2)\n",
      "                  EXFhs             Open ocean air-sea sensible heat flux (W/m^2)\n",
      "                  EXFlwdn           Downward longwave radiative flux (W/m^2)\n",
      "                  EXFswdn           Downwelling shortwave radiative flux (W/m^2)\n",
      "                  EXFqnet           Open ocean net air-sea heat flux (W/m^2)\n",
      "                  oceQnet           Net heat flux into the ocean surface (W/m^2)\n",
      "                  SIatmQnt          Net upward heat flux to the atmosphere (W/m^2)\n",
      "                  TFLUX             Rate of change of ocean heat content per m^2\n",
      "                                    accounting for mass (e.g. freshwater) fluxes\n",
      "                                    (W/m^2)\n",
      "                  EXFswnet          Open ocean net shortwave radiative flux (W/m^2)\n",
      "                  EXFlwnet          Net open ocean longwave radiative flux (W/m^2)\n",
      "                  oceQsw            Net shortwave radiative flux across the ocean\n",
      "                                    surface (W/m^2)\n",
      "                  SIaaflux          Conservative ocean and sea-ice advective heat flux\n",
      "                                    adjustment, associated with temperature difference\n",
      "                                    between sea surface temperature and sea-ice,\n",
      "                                    excluding latent heat of fusion (W/m^2)\n",
      "\n",
      "Option 4: ECCO_L4_FRESH_FLUX_LLC0090GRID_MONTHLY_V4R4    *native grid,monthly means*\n",
      "                  EXFpreci          Precipitation rate (m/s)\n",
      "                  EXFevap           Open ocean evaporation rate (m/s)\n",
      "                  EXFroff           River runoff (m/s)\n",
      "                  SIsnPrcp          Snow precipitation on sea-ice (kg/(m^2 s))\n",
      "                  EXFempmr          Open ocean net surface freshwater flux from\n",
      "                                    precipitation, evaporation, and runoff (m/s)\n",
      "                  oceFWflx          Net freshwater flux into the ocean (kg/(m^2 s))\n",
      "                  SIatmFW           Net freshwater flux into the open ocean, sea-ice,\n",
      "                                    and snow (kg/(m^2 s))\n",
      "                  SFLUX             Rate of change of total ocean salinity per m^2\n",
      "                                    accounting for mass fluxes (g/(m^2 s))\n",
      "                  SIacSubl          Freshwater flux to the atmosphere due to\n",
      "                                    sublimation-deposition of snow or ice (kg/(m^2 s))\n",
      "                  SIrsSubl          Residual sublimation freshwater flux (kg/(m^2 s))\n",
      "                  SIfwThru          Precipitation through sea-ice (kg/(m^2 s))\n",
      "\n",
      "Option 5: ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_MONTHLY_V4R4    *native grid,monthly means*\n",
      "                  SIarea            Sea-ice concentration (fraction between 0 and 1)\n",
      "                  SIheff            Area-averaged sea-ice thickness (m)\n",
      "                  SIhsnow           Area-averaged snow thickness (m)\n",
      "                  sIceLoad          Average sea-ice and snow mass per unit area\n",
      "                                    (kg/m^2)\n",
      "\n",
      "Option 6: ECCO_L4_SEA_ICE_VELOCITY_LLC0090GRID_MONTHLY_V4R4    *native grid,monthly means*\n",
      "                  SIuice            Sea-ice velocity in the model +x direction (m/s)\n",
      "                  SIvice            Sea-ice velocity in the model +y direction (m/s)\n",
      "\n",
      "Option 7: ECCO_L4_SEA_ICE_HORIZ_VOLUME_FLUX_LLC0090GRID_MONTHLY_V4R4    *native grid,monthly means*\n",
      "                  ADVxHEFF          Lateral advective flux of sea-ice thickness in the\n",
      "                                    model +x direction (m^3/s)\n",
      "                  ADVyHEFF          Lateral advective flux of sea-ice thickness in the\n",
      "                                    model +y direction (m^3/s)\n",
      "                  DFxEHEFF          Lateral diffusive flux of sea-ice thickness in the\n",
      "                                    model +x direction (m^3/s)\n",
      "                  DFyEHEFF          Lateral diffusive flux of sea-ice thickness in the\n",
      "                                    model +y direction (m^3/s)\n",
      "                  ADVxSNOW          Lateral advective flux of snow thickness in the\n",
      "                                    model +x direction (m^3/s)\n",
      "                  ADVySNOW          Lateral advective flux of snow thickness in the\n",
      "                                    model +y direction (m^3/s)\n",
      "                  DFxESNOW          Lateral diffusive flux of snow thickness in the\n",
      "                                    model +x direction (m^3/s)\n",
      "                  DFyESNOW          Lateral diffusive flux of snow thickness in the\n",
      "                                    model +y direction (m^3/s)\n",
      "\n",
      "Option 8: ECCO_L4_SEA_ICE_SALT_PLUME_FLUX_LLC0090GRID_MONTHLY_V4R4    *native grid,monthly means*\n",
      "                  oceSPflx          Net salt flux into the ocean due to brine\n",
      "                                    rejection (g/(m^2 s))\n",
      "                  oceSPDep          Salt plume depth (m)\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Please select option [1-8]:  5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using dataset with ShortName: ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_MONTHLY_V4R4\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Files will be accessed from a requester pays S3 bucket.\n",
      "Requester is responsible for any data transfer fees.\n",
      "Do you want to proceed? [y/n]:  y\n"
     ]
    }
   ],
   "source": [
    "ds_seaice_conc = ea.ecco_podaac_to_xrdataset('ice',grid='native',time_res='monthly',\\\n",
    "                                               StartDate='2007-01',EndDate='2007-12',\\\n",
    "                                               mode='s3_open_fsspec')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ca2df4c-d97c-4c2e-90c5-0dbc3ebdd994",
   "metadata": {},
   "source": [
    "We select option 5, corresponding to ShortName ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_MONTHLY_V4R4. Let's look at the dataset contents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "09d8879a-92fb-4722-a8d0-b25e8e398c61",
   "metadata": {},
   "outputs": [
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       "    XG         (tile, j_g, i_g) float32 421kB dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;\n",
       "    YC         (tile, j, i) float32 421kB dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;\n",
       "    YC_bnds    (tile, j, i, nb) float32 2MB dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;\n",
       "    ...         ...\n",
       "  * i          (i) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * i_g        (i_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j          (j) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j_g        (j_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
       "  * tile       (tile) int32 52B 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "    time_bnds  (time, nv) datetime64[ns] 192B dask.array&lt;chunksize=(12, 2), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: nb, nv\n",
       "Data variables:\n",
       "    SIarea     (time, tile, j, i) float32 5MB dask.array&lt;chunksize=(12, 13, 90, 90), meta=np.ndarray&gt;\n",
       "    SIheff     (time, tile, j, i) float32 5MB dask.array&lt;chunksize=(12, 13, 90, 90), meta=np.ndarray&gt;\n",
       "    SIhsnow    (time, tile, j, i) float32 5MB dask.array&lt;chunksize=(12, 13, 90, 90), meta=np.ndarray&gt;\n",
       "    sIceLoad   (time, tile, j, i) float32 5MB dask.array&lt;chunksize=(12, 13, 90, 90), meta=np.ndarray&gt;\n",
       "Attributes: (12/57)\n",
       "    Conventions:                  CF-1.8, ACDD-1.3\n",
       "    acknowledgement:              This research was carried out by the Jet Pr...\n",
       "    author:                       Ian Fenty and Ou Wang\n",
       "    cdm_data_type:                Grid\n",
       "    comment:                      Fields provided on the curvilinear lat-lon-...\n",
       "    coordinates_comment:          Note: the global &#x27;coordinates&#x27; attribute de...\n",
       "    ...                           ...\n",
       "    time_coverage_duration:       P1M\n",
       "    time_coverage_end:            1992-02-01T00:00:00\n",
       "    time_coverage_resolution:     P1M\n",
       "    time_coverage_start:          1992-01-01T12:00:00\n",
       "    title:                        ECCO Sea-Ice and Snow Concentration and Thi...\n",
       "    uuid:                         cc62f1c2-400d-11eb-9f11-0cc47a3f49c3</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-59291b1a-d453-4bd0-8ca3-8b069081bfeb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-59291b1a-d453-4bd0-8ca3-8b069081bfeb' 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>: 12</li><li><span class='xr-has-index'>tile</span>: 13</li><li><span class='xr-has-index'>j</span>: 90</li><li><span class='xr-has-index'>i</span>: 90</li><li><span>nb</span>: 4</li><li><span class='xr-has-index'>j_g</span>: 90</li><li><span class='xr-has-index'>i_g</span>: 90</li><li><span>nv</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1c7c67a8-ac57-4944-b7d7-920c849ba6ac' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1c7c67a8-ac57-4944-b7d7-920c849ba6ac' class='xr-section-summary' >Coordinates: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>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'>2007-01-16T12:00:00 ... 2007-12-...</div><input id='attrs-fd7372dc-3b3c-48b0-a015-e8f87fd1498b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd7372dc-3b3c-48b0-a015-e8f87fd1498b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-caa68054-01ae-488c-9679-e509ece8f71e' class='xr-var-data-in' type='checkbox'><label for='data-caa68054-01ae-488c-9679-e509ece8f71e' 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>T</dd><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>center time of averaging period</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2007-01-16T12:00:00.000000000&#x27;, &#x27;2007-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2007-03-16T12:00:00.000000000&#x27;, &#x27;2007-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2007-05-16T12:00:00.000000000&#x27;, &#x27;2007-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2007-07-16T12:00:00.000000000&#x27;, &#x27;2007-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2007-09-16T00:00:00.000000000&#x27;, &#x27;2007-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2007-11-16T00:00:00.000000000&#x27;, &#x27;2007-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XC</span></div><div class='xr-var-dims'>(tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-2a465697-09dc-48aa-9540-9214db8a6a36' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a465697-09dc-48aa-9540-9214db8a6a36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2df5c184-7971-4664-8f17-420fb610dbe8' class='xr-var-data-in' type='checkbox'><label for='data-2df5c184-7971-4664-8f17-420fb610dbe8' 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>coordinate :</span></dt><dd>YC XC</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></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> 411.33 kiB </td>\n",
       "                        <td> 411.33 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13, 90, 90) </td>\n",
       "                        <td> (13, 90, 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",
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       "    </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'>(tile, j, i, nb)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;</div><input id='attrs-49b0341d-5ba7-41c2-a54f-85e7c2d2f3f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49b0341d-5ba7-41c2-a54f-85e7c2d2f3f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-027c26bf-44c6-4bce-ac2d-77f6cf1aade2' class='xr-var-data-in' type='checkbox'><label for='data-027c26bf-44c6-4bce-ac2d-77f6cf1aade2' 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 dcoumentation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>longitudes of tracer grid cell corners</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> 1.61 MiB </td>\n",
       "                        <td> 1.61 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13, 90, 90, 4) </td>\n",
       "                        <td> (13, 90, 90, 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",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XG</span></div><div class='xr-var-dims'>(tile, j_g, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-f6e4ac40-d148-483e-85cb-41a8ec9212b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6e4ac40-d148-483e-85cb-41a8ec9212b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bdbdae60-4684-4d48-a93b-dd18da626c1c' class='xr-var-data-in' type='checkbox'><label for='data-bdbdae60-4684-4d48-a93b-dd18da626c1c' 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>Nonuniform grid spacing. Note: &#x27;southwest&#x27; does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm dcoumentation for details.</dd><dt><span>coordinate :</span></dt><dd>YG XG</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>longitude of &#x27;southwest&#x27; corner of tracer grid cell</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</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> 411.33 kiB </td>\n",
       "                        <td> 411.33 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13, 90, 90) </td>\n",
       "                        <td> (13, 90, 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",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>YC</span></div><div class='xr-var-dims'>(tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-c2e89ffb-c856-46de-9874-6fa144928253' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2e89ffb-c856-46de-9874-6fa144928253' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20fcfed1-5a42-4846-b371-60554c1fd420' class='xr-var-data-in' type='checkbox'><label for='data-20fcfed1-5a42-4846-b371-60554c1fd420' 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>coordinate :</span></dt><dd>YC XC</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></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                    <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> 411.33 kiB </td>\n",
       "                        <td> 411.33 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13, 90, 90) </td>\n",
       "                        <td> (13, 90, 90) </td>\n",
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       "                    <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",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>YC_bnds</span></div><div class='xr-var-dims'>(tile, j, i, nb)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;</div><input id='attrs-9aa0f7a9-4c38-4b7d-8901-8e8671e2de49' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9aa0f7a9-4c38-4b7d-8901-8e8671e2de49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71531f1f-a672-4bcd-81cc-d0fd84ddcfcb' class='xr-var-data-in' type='checkbox'><label for='data-71531f1f-a672-4bcd-81cc-d0fd84ddcfcb' 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 dcoumentation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>latitudes of tracer grid cell corners</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                    <tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>YG</span></div><div class='xr-var-dims'>(tile, j_g, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-4f4d6751-6b97-4b19-bc1e-43861b0398e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f4d6751-6b97-4b19-bc1e-43861b0398e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-386079c8-b262-4427-8d64-ebef36e5843f' class='xr-var-data-in' type='checkbox'><label for='data-386079c8-b262-4427-8d64-ebef36e5843f' 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>Nonuniform grid spacing. Note: &#x27;southwest&#x27; does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm dcoumentation for details.</dd><dt><span>coordinate :</span></dt><dd>YG XG</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>latitude of &#x27;southwest&#x27; corner of tracer grid cell</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</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> 411.33 kiB </td>\n",
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       "                        <td> (13, 90, 90) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "  <text x=\"173.836907\" y=\"83.836907\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,173.836907,83.836907)\">90</text>\n",
       "  <text x=\"11.918454\" y=\"151.918454\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.918454,151.918454)\">13</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 5 6 ... 84 85 86 87 88 89</div><input id='attrs-8a387361-d143-466c-b367-f05e6015c015' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8a387361-d143-466c-b367-f05e6015c015' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f76f1ee-e631-4227-b9ff-3a2cafc2e4b4' class='xr-var-data-in' type='checkbox'><label for='data-3f76f1ee-e631-4227-b9ff-3a2cafc2e4b4' 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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>grid index in x for variables at tracer and &#x27;v&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>XC</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'>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 5 6 ... 84 85 86 87 88 89</div><input id='attrs-73cd8389-1e52-431a-9178-380fd50fd1a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-73cd8389-1e52-431a-9178-380fd50fd1a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d6000d9-2719-4911-a9a6-8aa6e90eda1c' class='xr-var-data-in' type='checkbox'><label for='data-4d6000d9-2719-4911-a9a6-8aa6e90eda1c' 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>c_grid_axis_shift :</span></dt><dd>-0.5</dd><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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>grid index in x for variables at &#x27;u&#x27; and &#x27;g&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>XG</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'>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 5 6 ... 84 85 86 87 88 89</div><input id='attrs-81330731-3165-4b38-a466-c09322242223' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81330731-3165-4b38-a466-c09322242223' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a74c0559-7be1-4107-baea-220acca55f8b' class='xr-var-data-in' type='checkbox'><label for='data-a74c0559-7be1-4107-baea-220acca55f8b' 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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>grid index in y for variables at tracer and &#x27;u&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>YC</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'>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 5 6 ... 84 85 86 87 88 89</div><input id='attrs-eee4de27-a561-4b65-8d81-ed01dbd9b7d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eee4de27-a561-4b65-8d81-ed01dbd9b7d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65e980d1-c62e-4efc-ae69-89f6251e1c19' class='xr-var-data-in' type='checkbox'><label for='data-65e980d1-c62e-4efc-ae69-89f6251e1c19' 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>c_grid_axis_shift :</span></dt><dd>-0.5</dd><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>coverage_content_type :</span></dt><dd>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><dt><span>swap_dim :</span></dt><dd>YG</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'>tile</span></div><div class='xr-var-dims'>(tile)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-81adb089-93f1-4681-a231-775fa5476923' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81adb089-93f1-4681-a231-775fa5476923' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2172114e-2bb6-4e1c-a7d2-3c9938e14a13' class='xr-var-data-in' type='checkbox'><label for='data-2172114e-2bb6-4e1c-a7d2-3c9938e14a13' 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 ECCO V4 horizontal model grid is divided into 13 tiles of 90x90 cells for convenience.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>lat-lon-cap tile index</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12], 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=(12, 2), meta=np.ndarray&gt;</div><input id='attrs-ef276b48-bda8-4cb5-8262-50c5d47e38f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef276b48-bda8-4cb5-8262-50c5d47e38f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4417721-d460-4b4c-b5a8-457e21501c46' class='xr-var-data-in' type='checkbox'><label for='data-a4417721-d460-4b4c-b5a8-457e21501c46' 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><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>time bounds 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> 192 B </td>\n",
       "                        <td> 192 B </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (12, 2) </td>\n",
       "                        <td> (12, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-0040f164-b415-4a08-a395-f034e41f91a6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0040f164-b415-4a08-a395-f034e41f91a6' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>SIarea</span></div><div class='xr-var-dims'>(time, tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-4fc2b15d-e8e6-4817-af1c-86fe01fa6ea9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4fc2b15d-e8e6-4817-af1c-86fe01fa6ea9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c858ab78-cdaa-4165-b9de-45bac0b657cf' class='xr-var-data-in' type='checkbox'><label for='data-c858ab78-cdaa-4165-b9de-45bac0b657cf' 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>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>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>valid_max :</span></dt><dd>0.9700000286102295</dd><dt><span>valid_min :</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",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 4.82 MiB </td>\n",
       "                        <td> 4.82 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (12, 13, 90, 90) </td>\n",
       "                        <td> (12, 13, 90, 90) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        <td>\n",
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       "  <text x=\"111.918454\" y=\"151.918454\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,111.918454,151.918454)\">13</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-dabee79c-adbf-4172-9958-c02fe41dc100' class='xr-section-summary-in' type='checkbox'  ><label for='section-dabee79c-adbf-4172-9958-c02fe41dc100' class='xr-section-summary' >Indexes: <span>(6)</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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a43b60ba-f58f-4f09-9fb7-e8d9a2cf931a' class='xr-index-data-in' type='checkbox'/><label for='index-a43b60ba-f58f-4f09-9fb7-e8d9a2cf931a' 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;2007-01-16 12:00:00&#x27;, &#x27;2007-02-15 00:00:00&#x27;,\n",
       "               &#x27;2007-03-16 12:00:00&#x27;, &#x27;2007-04-16 00:00:00&#x27;,\n",
       "               &#x27;2007-05-16 12:00:00&#x27;, &#x27;2007-06-16 00:00:00&#x27;,\n",
       "               &#x27;2007-07-16 12:00:00&#x27;, &#x27;2007-08-16 12:00:00&#x27;,\n",
       "               &#x27;2007-09-16 00:00:00&#x27;, &#x27;2007-10-16 12:00:00&#x27;,\n",
       "               &#x27;2007-11-16 00:00:00&#x27;, &#x27;2007-12-16 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>i</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ab784e1b-c072-490a-bdfe-00170839d3c5' class='xr-index-data-in' type='checkbox'/><label for='index-ab784e1b-c072-490a-bdfe-00170839d3c5' 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;i&#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><div></div><input id='index-fbf9ec44-bdb4-48fe-b819-bae58e1253cd' class='xr-index-data-in' type='checkbox'/><label for='index-fbf9ec44-bdb4-48fe-b819-bae58e1253cd' 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;i_g&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>j</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-062675b7-0421-42b9-afa9-4dd828c095a5' class='xr-index-data-in' type='checkbox'/><label for='index-062675b7-0421-42b9-afa9-4dd828c095a5' 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;j&#x27;))</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><div></div><input id='index-e1ef512b-a010-458d-99e3-f771c59f7290' class='xr-index-data-in' type='checkbox'/><label for='index-e1ef512b-a010-458d-99e3-f771c59f7290' 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;j_g&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>tile</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c452265c-a2f6-4d5b-92b8-a0b49f37a547' class='xr-index-data-in' type='checkbox'/><label for='index-c452265c-a2f6-4d5b-92b8-a0b49f37a547' 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], dtype=&#x27;int32&#x27;, name=&#x27;tile&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-81635d13-ca32-4308-8511-3e04a7ec8bf1' class='xr-section-summary-in' type='checkbox'  ><label for='section-81635d13-ca32-4308-8511-3e04a7ec8bf1' class='xr-section-summary' >Attributes: <span>(57)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.8, ACDD-1.3</dd><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>Ian Fenty and Ou Wang</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>comment :</span></dt><dd>Fields provided on the curvilinear lat-lon-cap 90 (llc90) native grid used in the ECCO model.</dd><dt><span>coordinates_comment :</span></dt><dd>Note: the global &#x27;coordinates&#x27; attribute describes auxillary coordinates.</dd><dt><span>creator_email :</span></dt><dd>ecco-group@mit.edu</dd><dt><span>creator_institution :</span></dt><dd>NASA Jet Propulsion Laboratory (JPL)</dd><dt><span>creator_name :</span></dt><dd>ECCO Consortium</dd><dt><span>creator_type :</span></dt><dd>group</dd><dt><span>creator_url :</span></dt><dd>https://ecco-group.org</dd><dt><span>date_created :</span></dt><dd>2020-12-16T18:15:57</dd><dt><span>date_issued :</span></dt><dd>2020-12-16T18:15:57</dd><dt><span>date_metadata_modified :</span></dt><dd>2021-03-15T21:53:43</dd><dt><span>date_modified :</span></dt><dd>2021-03-15T21:53:43</dd><dt><span>geospatial_bounds_crs :</span></dt><dd>EPSG:4326</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-90.0</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>variable</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_resolution :</span></dt><dd>variable</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>history :</span></dt><dd>Inaugural release of an ECCO Central Estimate solution to PO.DAAC</dd><dt><span>id :</span></dt><dd>10.5067/ECL5M-ICO44</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 &gt; CRYOSPHERE &gt; SEA ICE &gt; ICE DEPTH/THICKNESS, EARTH SCIENCE &gt; CRYOSPHERE &gt; SEA ICE &gt; SEA ICE CONCENTRATION, EARTH SCIENCE &gt; CRYOSPHERE &gt; SEA ICE &gt; SEA ICE CONCENTRATION &gt; ICE FRACTION, EARTH SCIENCE SERVICES &gt; MODELS &gt; EARTH SCIENCE REANALYSES/ASSIMILATION MODELS, EARTH SCIENCE &gt; CRYOSPHERE &gt; SEA ICE &gt; SNOW DEPTH</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>metadata_link :</span></dt><dd>https://cmr.earthdata.nasa.gov/search/collections.umm_json?ShortName=ECCO_L4_SEA_ICE_CONC_THICKNESS_LLC0090GRID_MONTHLY_V4R4</dd><dt><span>naming_authority :</span></dt><dd>gov.nasa.jpl</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>platform_vocabulary :</span></dt><dd>GCMD platform keywords</dd><dt><span>processing_level :</span></dt><dd>L4</dd><dt><span>product_name :</span></dt><dd>SEA_ICE_CONC_THICKNESS_mon_mean_1992-01_ECCO_V4r4_native_llc0090.nc</dd><dt><span>product_time_coverage_end :</span></dt><dd>2018-01-01T00:00:00</dd><dt><span>product_time_coverage_start :</span></dt><dd>1992-01-01T12:00:00</dd><dt><span>product_version :</span></dt><dd>Version 4, Release 4</dd><dt><span>program :</span></dt><dd>NASA Physical Oceanography, Cryosphere, Modeling, Analysis, and Prediction (MAP)</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>references :</span></dt><dd>ECCO Consortium, Fukumori, I., Wang, O., Fenty, I., Forget, G., Heimbach, P., &amp; Ponte, R. M. 2020. Synopsis of the ECCO Central Production Global Ocean and Sea-Ice State Estimate (Version 4 Release 4). doi:10.5281/zenodo.3765928</dd><dt><span>source :</span></dt><dd>The ECCO V4r4 state estimate was produced by fitting a free-running solution of the MITgcm (checkpoint 66g) to satellite and in situ observational data in a least squares sense using the adjoint method</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention</dd><dt><span>summary :</span></dt><dd>This dataset provides monthly-averaged sea-ice and snow concentration, thickness, and pressure loading on the lat-lon-cap 90 (llc90) native model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea-ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.</dd><dt><span>time_coverage_duration :</span></dt><dd>P1M</dd><dt><span>time_coverage_end :</span></dt><dd>1992-02-01T00:00:00</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1M</dd><dt><span>time_coverage_start :</span></dt><dd>1992-01-01T12:00:00</dd><dt><span>title :</span></dt><dd>ECCO Sea-Ice and Snow Concentration and Thickness - Monthly Mean llc90 Grid (Version 4 Release 4)</dd><dt><span>uuid :</span></dt><dd>cc62f1c2-400d-11eb-9f11-0cc47a3f49c3</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 25MB\n",
       "Dimensions:    (time: 12, tile: 13, j: 90, i: 90, nb: 4, j_g: 90, i_g: 90, nv: 2)\n",
       "Coordinates: (12/13)\n",
       "  * time       (time) datetime64[ns] 96B 2007-01-16T12:00:00 ... 2007-12-16T1...\n",
       "    XC         (tile, j, i) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>\n",
       "    XC_bnds    (tile, j, i, nb) float32 2MB dask.array<chunksize=(13, 90, 90, 4), meta=np.ndarray>\n",
       "    XG         (tile, j_g, i_g) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>\n",
       "    YC         (tile, j, i) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>\n",
       "    YC_bnds    (tile, j, i, nb) float32 2MB dask.array<chunksize=(13, 90, 90, 4), meta=np.ndarray>\n",
       "    ...         ...\n",
       "  * i          (i) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * i_g        (i_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j          (j) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j_g        (j_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
       "  * tile       (tile) int32 52B 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "    time_bnds  (time, nv) datetime64[ns] 192B dask.array<chunksize=(12, 2), meta=np.ndarray>\n",
       "Dimensions without coordinates: nb, nv\n",
       "Data variables:\n",
       "    SIarea     (time, tile, j, i) float32 5MB dask.array<chunksize=(12, 13, 90, 90), meta=np.ndarray>\n",
       "    SIheff     (time, tile, j, i) float32 5MB dask.array<chunksize=(12, 13, 90, 90), meta=np.ndarray>\n",
       "    SIhsnow    (time, tile, j, i) float32 5MB dask.array<chunksize=(12, 13, 90, 90), meta=np.ndarray>\n",
       "    sIceLoad   (time, tile, j, i) float32 5MB dask.array<chunksize=(12, 13, 90, 90), meta=np.ndarray>\n",
       "Attributes: (12/57)\n",
       "    Conventions:                  CF-1.8, ACDD-1.3\n",
       "    acknowledgement:              This research was carried out by the Jet Pr...\n",
       "    author:                       Ian Fenty and Ou Wang\n",
       "    cdm_data_type:                Grid\n",
       "    comment:                      Fields provided on the curvilinear lat-lon-...\n",
       "    coordinates_comment:          Note: the global 'coordinates' attribute de...\n",
       "    ...                           ...\n",
       "    time_coverage_duration:       P1M\n",
       "    time_coverage_end:            1992-02-01T00:00:00\n",
       "    time_coverage_resolution:     P1M\n",
       "    time_coverage_start:          1992-01-01T12:00:00\n",
       "    title:                        ECCO Sea-Ice and Snow Concentration and Thi...\n",
       "    uuid:                         cc62f1c2-400d-11eb-9f11-0cc47a3f49c3"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_seaice_conc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67367f21-7503-47b2-b427-f503b53c7512",
   "metadata": {},
   "source": [
    "Now plot the sea ice concentration/fraction in tile 6 (which approximately covers the Arctic Ocean), during Sep 2007 which at the time was a record minimum for Arctic sea ice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "069d9c1a-05ae-4187-bcc1-d580d65e7560",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f90804fa690>"
      ]
     },
     "execution_count": 11,
     "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": [
    "ds_seaice_conc.SIarea.isel(time=8,tile=6).plot(cmap='cool')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f95980c-04bb-43a1-a240-14e7f4704911",
   "metadata": {},
   "source": [
    "## Using the `ecco_podaac_access` function\n",
    "\n",
    "### In-cloud direct access (mode = 's3_open')\n",
    "\n",
    "The `ecco_podaac_to_xrdataset` function that was previously used invokes `ecco_podaac_access` under the hood, and `ecco_podaac_access` can also be called directly. This can be useful if you want to obtain a list of file objects/paths or URLs that you can then process with your own code. Let's use this function with mode = `s3_open` (all `S3` modes only work from an AWS Cloud environment in region `us-west-2`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a4b69cc7-865e-45a5-920a-9b36afd8077b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'ShortName': 'ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4', 'temporal': '2015-01-02,2015-12-31'}\n",
      "\n",
      "Total number of matching granules: 12\n"
     ]
    }
   ],
   "source": [
    "curr_shortname = 'ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4'\n",
    "files_dict = ea.ecco_podaac_access(curr_shortname,\\\n",
    "                                    StartDate='2015-01',EndDate='2015-12',\\\n",
    "                                    mode='s3_open')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1b736345-c445-451a-b476-e4ac54832540",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<File-like object S3FileSystem, podaac-ops-cumulus-protected/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-01_ECCO_V4r4_native_llc0090.nc>,\n",
       " <File-like object S3FileSystem, podaac-ops-cumulus-protected/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-02_ECCO_V4r4_native_llc0090.nc>,\n",
       " <File-like object S3FileSystem, podaac-ops-cumulus-protected/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-03_ECCO_V4r4_native_llc0090.nc>,\n",
       " <File-like object S3FileSystem, podaac-ops-cumulus-protected/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-04_ECCO_V4r4_native_llc0090.nc>,\n",
       " <File-like object S3FileSystem, podaac-ops-cumulus-protected/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-05_ECCO_V4r4_native_llc0090.nc>,\n",
       " <File-like object S3FileSystem, podaac-ops-cumulus-protected/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-06_ECCO_V4r4_native_llc0090.nc>,\n",
       " <File-like object S3FileSystem, podaac-ops-cumulus-protected/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-07_ECCO_V4r4_native_llc0090.nc>,\n",
       " <File-like object S3FileSystem, podaac-ops-cumulus-protected/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-08_ECCO_V4r4_native_llc0090.nc>,\n",
       " <File-like object S3FileSystem, podaac-ops-cumulus-protected/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-09_ECCO_V4r4_native_llc0090.nc>,\n",
       " <File-like object S3FileSystem, podaac-ops-cumulus-protected/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-10_ECCO_V4r4_native_llc0090.nc>,\n",
       " <File-like object S3FileSystem, podaac-ops-cumulus-protected/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-11_ECCO_V4r4_native_llc0090.nc>,\n",
       " <File-like object S3FileSystem, podaac-ops-cumulus-protected/ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4/SEA_SURFACE_HEIGHT_mon_mean_2015-12_ECCO_V4r4_native_llc0090.nc>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "files_dict[curr_shortname]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff282e01-be1e-4b1c-87dc-fb7401016856",
   "metadata": {},
   "source": [
    "The output of `ecco_podaac_access` is in the form of a dictionary with ShortNames as keys. In this case, the value associated with this ShortName is a list of 12 file objects. These are files on `S3` (AWS's cloud storage system) that have been opened, which is a necessary step for the files' data to be accessed. The list of open files can be passed directly to `xarray.open_mfdataset`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "84c74d63-e1e7-4c20-97a0-0b63d52bdbe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_SSH_fromlist = xr.open_mfdataset(files_dict[curr_shortname],\\\n",
    "                                    compat='override',data_vars='minimal',coords='minimal',\\\n",
    "                                    parallel=True)"
   ]
  },
  {
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   "execution_count": 14,
   "id": "c6de508b-0327-41c5-9b63-f5e7e099a1f3",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 25MB\n",
       "Dimensions:    (time: 12, tile: 13, j: 90, i: 90, i_g: 90, j_g: 90, nv: 2, nb: 4)\n",
       "Coordinates: (12/13)\n",
       "  * i          (i) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * i_g        (i_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j          (j) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j_g        (j_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
       "  * tile       (tile) int32 52B 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "  * time       (time) datetime64[ns] 96B 2015-01-16T12:00:00 ... 2015-12-16T1...\n",
       "    ...         ...\n",
       "    YC         (tile, j, i) float32 421kB dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;\n",
       "    XG         (tile, j_g, i_g) float32 421kB dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;\n",
       "    YG         (tile, j_g, i_g) float32 421kB dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;\n",
       "    time_bnds  (time, nv) datetime64[ns] 192B dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\n",
       "    XC_bnds    (tile, j, i, nb) float32 2MB dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;\n",
       "    YC_bnds    (tile, j, i, nb) float32 2MB dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: nv, nb\n",
       "Data variables:\n",
       "    SSH        (time, tile, j, i) float32 5MB dask.array&lt;chunksize=(1, 13, 90, 90), meta=np.ndarray&gt;\n",
       "    SSHIBC     (time, tile, j, i) float32 5MB dask.array&lt;chunksize=(1, 13, 90, 90), meta=np.ndarray&gt;\n",
       "    SSHNOIBC   (time, tile, j, i) float32 5MB dask.array&lt;chunksize=(1, 13, 90, 90), meta=np.ndarray&gt;\n",
       "    ETAN       (time, tile, j, i) float32 5MB dask.array&lt;chunksize=(1, 13, 90, 90), meta=np.ndarray&gt;\n",
       "Attributes: (12/57)\n",
       "    acknowledgement:              This research was carried out by the Jet Pr...\n",
       "    author:                       Ian Fenty and Ou Wang\n",
       "    cdm_data_type:                Grid\n",
       "    comment:                      Fields provided on the curvilinear lat-lon-...\n",
       "    Conventions:                  CF-1.8, ACDD-1.3\n",
       "    coordinates_comment:          Note: the global &#x27;coordinates&#x27; attribute de...\n",
       "    ...                           ...\n",
       "    time_coverage_duration:       P1M\n",
       "    time_coverage_end:            2015-02-01T00:00:00\n",
       "    time_coverage_resolution:     P1M\n",
       "    time_coverage_start:          2015-01-01T00:00:00\n",
       "    title:                        ECCO Sea Surface Height - Monthly Mean llc9...\n",
       "    uuid:                         a4955186-400c-11eb-8c14-0cc47a3f49c3</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-72edc8d7-bc9b-4a73-9829-f970de060fbb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-72edc8d7-bc9b-4a73-9829-f970de060fbb' 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>: 12</li><li><span class='xr-has-index'>tile</span>: 13</li><li><span class='xr-has-index'>j</span>: 90</li><li><span class='xr-has-index'>i</span>: 90</li><li><span class='xr-has-index'>i_g</span>: 90</li><li><span class='xr-has-index'>j_g</span>: 90</li><li><span>nv</span>: 2</li><li><span>nb</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2cf3a56c-53c2-4caf-813f-b07c9fea2179' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2cf3a56c-53c2-4caf-813f-b07c9fea2179' class='xr-section-summary' >Coordinates: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>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 5 6 ... 84 85 86 87 88 89</div><input id='attrs-871569b7-bf50-4eaf-be8c-d1ea168757d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-871569b7-bf50-4eaf-be8c-d1ea168757d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b9b8679-c9dd-4d09-8165-053227751a12' class='xr-var-data-in' type='checkbox'><label for='data-0b9b8679-c9dd-4d09-8165-053227751a12' 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>long_name :</span></dt><dd>grid index in x for variables at tracer and &#x27;v&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>XC</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>coverage_content_type :</span></dt><dd>coordinate</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'>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 5 6 ... 84 85 86 87 88 89</div><input id='attrs-61f4795f-ad20-45d1-a095-1374d5f9d19d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-61f4795f-ad20-45d1-a095-1374d5f9d19d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be250def-6cd3-41a3-88a1-e6664a975bbd' class='xr-var-data-in' type='checkbox'><label for='data-be250def-6cd3-41a3-88a1-e6664a975bbd' 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>long_name :</span></dt><dd>grid index in x for variables at &#x27;u&#x27; and &#x27;g&#x27; locations</dd><dt><span>c_grid_axis_shift :</span></dt><dd>-0.5</dd><dt><span>swap_dim :</span></dt><dd>XG</dd><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>coverage_content_type :</span></dt><dd>coordinate</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'>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 5 6 ... 84 85 86 87 88 89</div><input id='attrs-66aa9962-4e0e-473c-bd79-a2044b803d80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66aa9962-4e0e-473c-bd79-a2044b803d80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73f30497-fca1-4995-b1de-77ee0e20a851' class='xr-var-data-in' type='checkbox'><label for='data-73f30497-fca1-4995-b1de-77ee0e20a851' 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>long_name :</span></dt><dd>grid index in y for variables at tracer and &#x27;u&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>YC</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>coverage_content_type :</span></dt><dd>coordinate</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'>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 5 6 ... 84 85 86 87 88 89</div><input id='attrs-a77e2bb3-90cc-41c2-9727-f9055fc486aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a77e2bb3-90cc-41c2-9727-f9055fc486aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbad17e6-52a3-4ec0-aec9-170ff5a5fe76' class='xr-var-data-in' type='checkbox'><label for='data-dbad17e6-52a3-4ec0-aec9-170ff5a5fe76' 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>long_name :</span></dt><dd>grid index in y for variables at &#x27;v&#x27; and &#x27;g&#x27; locations</dd><dt><span>c_grid_axis_shift :</span></dt><dd>-0.5</dd><dt><span>swap_dim :</span></dt><dd>YG</dd><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>coverage_content_type :</span></dt><dd>coordinate</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'>tile</span></div><div class='xr-var-dims'>(tile)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-eb91d2cd-e917-4803-998e-3c6d91908a4a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb91d2cd-e917-4803-998e-3c6d91908a4a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64af3d3a-936b-4a05-9fb4-c1cd42524e61' class='xr-var-data-in' type='checkbox'><label for='data-64af3d3a-936b-4a05-9fb4-c1cd42524e61' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>lat-lon-cap tile index</dd><dt><span>comment :</span></dt><dd>The ECCO V4 horizontal model grid is divided into 13 tiles of 90x90 cells for convenience.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12], 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'>2015-01-16T12:00:00 ... 2015-12-...</div><input id='attrs-186dca71-f1d0-4854-aaf2-ad0dafe7ccf6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-186dca71-f1d0-4854-aaf2-ad0dafe7ccf6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-142c70b1-0c52-4b15-b230-eeb6171fa2fc' class='xr-var-data-in' type='checkbox'><label for='data-142c70b1-0c52-4b15-b230-eeb6171fa2fc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>center time of averaging period</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-01-16T12:00:00.000000000&#x27;, &#x27;2015-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-03-16T12:00:00.000000000&#x27;, &#x27;2015-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-05-16T12:00:00.000000000&#x27;, &#x27;2015-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-07-16T12:00:00.000000000&#x27;, &#x27;2015-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2015-09-16T00:00:00.000000000&#x27;, &#x27;2015-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2015-11-16T00:00:00.000000000&#x27;, &#x27;2015-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XC</span></div><div class='xr-var-dims'>(tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-545b617c-1866-4302-bcac-99560bfab810' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-545b617c-1866-4302-bcac-99560bfab810' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8dc1802f-9bf8-4738-8273-afc4e393a948' class='xr-var-data-in' type='checkbox'><label for='data-8dc1802f-9bf8-4738-8273-afc4e393a948' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of tracer grid cell center</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>coordinate :</span></dt><dd>YC XC</dd><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>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                <tbody>\n",
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       "                        <td> 411.33 kiB </td>\n",
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       "                        <td> (13, 90, 90) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 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>YC</span></div><div class='xr-var-dims'>(tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-9377348a-cdc9-43f4-8c20-dec14ad4dbf5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9377348a-cdc9-43f4-8c20-dec14ad4dbf5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a92f020-d0cd-4379-b944-e45815f92592' class='xr-var-data-in' type='checkbox'><label for='data-3a92f020-d0cd-4379-b944-e45815f92592' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of tracer grid cell center</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>coordinate :</span></dt><dd>YC XC</dd><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>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XG</span></div><div class='xr-var-dims'>(tile, j_g, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-aba13ab8-cb21-4a76-b8ff-bfc8f08bae9f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aba13ab8-cb21-4a76-b8ff-bfc8f08bae9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43d295f3-ac5b-4c04-afe6-29cacb7cff1c' class='xr-var-data-in' type='checkbox'><label for='data-43d295f3-ac5b-4c04-afe6-29cacb7cff1c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of &#x27;southwest&#x27; corner of tracer grid cell</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>coordinate :</span></dt><dd>YG XG</dd><dt><span>comment :</span></dt><dd>Nonuniform grid spacing. Note: &#x27;southwest&#x27; does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm dcoumentation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>longitude</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> 411.33 kiB </td>\n",
       "                        <td> 411.33 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13, 90, 90) </td>\n",
       "                        <td> (13, 90, 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",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>YG</span></div><div class='xr-var-dims'>(tile, j_g, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-3c85e550-b27e-47c6-a02a-961815d4fa19' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c85e550-b27e-47c6-a02a-961815d4fa19' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2792780-270c-42b6-b3b6-2accf6f1324d' class='xr-var-data-in' type='checkbox'><label for='data-b2792780-270c-42b6-b3b6-2accf6f1324d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of &#x27;southwest&#x27; corner of tracer grid cell</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>coordinate :</span></dt><dd>YG XG</dd><dt><span>comment :</span></dt><dd>Nonuniform grid spacing. Note: &#x27;southwest&#x27; does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm dcoumentation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>standard_name :</span></dt><dd>latitude</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> 411.33 kiB </td>\n",
       "                        <td> 411.33 kiB </td>\n",
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       "                        <td> (13, 90, 90) </td>\n",
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       "                    <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",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, 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-24161b97-d812-4fd8-9aa6-afa37cf5fd37' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24161b97-d812-4fd8-9aa6-afa37cf5fd37' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8f71413-2c2c-4628-b723-fb0caba00bf6' class='xr-var-data-in' type='checkbox'><label for='data-c8f71413-2c2c-4628-b723-fb0caba00bf6' 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><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>time bounds of averaging period</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> 192 B </td>\n",
       "                        <td> 16 B </td>\n",
       "                    </tr>\n",
       "                    \n",
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       "                        <th> Shape </th>\n",
       "                        <td> (12, 2) </td>\n",
       "                        <td> (1, 2) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 25 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XC_bnds</span></div><div class='xr-var-dims'>(tile, j, i, nb)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;</div><input id='attrs-7b628ce3-e978-4f14-b3ab-d4c7f6428034' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b628ce3-e978-4f14-b3ab-d4c7f6428034' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88d83231-79f3-43ff-9117-405ff86f7ecb' class='xr-var-data-in' type='checkbox'><label for='data-88d83231-79f3-43ff-9117-405ff86f7ecb' 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 dcoumentation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>longitudes of tracer grid cell corners</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                    <tr>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 1.61 MiB </td>\n",
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       "                    <tr>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-41b96ecc-f435-407f-9881-0127cd047174' class='xr-section-summary-in' type='checkbox'  checked><label for='section-41b96ecc-f435-407f-9881-0127cd047174' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>SSH</span></div><div class='xr-var-dims'>(time, tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-21120fb0-1186-4ec2-9539-f847b1a6994e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21120fb0-1186-4ec2-9539-f847b1a6994e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2284f3e8-6dc5-455c-8ac5-a8701fa250ed' class='xr-var-data-in' type='checkbox'><label for='data-2284f3e8-6dc5-455c-8ac5-a8701fa250ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Dynamic sea surface height anomaly</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_geoid</dd><dt><span>comment :</span></dt><dd>Dynamic sea surface height anomaly above the geoid, suitable for comparisons with altimetry sea surface height data products that apply the inverse barometer (IB) correction. Note: SSH is calculated by correcting model sea level anomaly ETAN for three effects: a) global mean steric sea level changes related to density changes in the Boussinesq volume-conserving model (Greatbatch correction, see sterGloH), b) the inverted barometer (IB) effect (see SSHIBC) and c) sea level displacement due to sea-ice and snow pressure loading (see sIceLoad). SSH can be compared with the similarly-named SSH variable in previous ECCO products that did not include atmospheric pressure loading (e.g., Version 4 Release 3). Use SSHNOIBC for comparisons with altimetry data products that do NOT apply the IB correction.</dd><dt><span>valid_min :</span></dt><dd>-1.8805772066116333</dd><dt><span>valid_max :</span></dt><dd>1.4207719564437866</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                    <tr>\n",
       "                        <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> 4.82 MiB </td>\n",
       "                        <td> 411.33 kiB </td>\n",
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       "                        <th> Shape </th>\n",
       "                        <td> (12, 13, 90, 90) </td>\n",
       "                        <td> (1, 13, 90, 90) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 12 chunks in 25 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>SSHIBC</span></div><div class='xr-var-dims'>(time, tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-9784f1ae-4e4f-4028-a8a0-6cfec1ccda8c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9784f1ae-4e4f-4028-a8a0-6cfec1ccda8c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab423422-b1ff-4135-97bc-f3635327aca3' class='xr-var-data-in' type='checkbox'><label for='data-ab423422-b1ff-4135-97bc-f3635327aca3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>The inverted barometer (IB) correction to sea surface height due to atmospheric pressure loading</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>comment :</span></dt><dd>Not an SSH itself, but a correction to model sea level anomaly (ETAN) required to account for the static part of sea surface displacement by atmosphere pressure loading: SSH = SSHNOIBC - SSHIBC. Note: Use SSH for model-data comparisons with altimetry data products that DO apply the IB correction and SSHNOIBC for comparisons with altimetry data products that do NOT apply the IB correction.</dd><dt><span>valid_min :</span></dt><dd>-0.30144819617271423</dd><dt><span>valid_max :</span></dt><dd>0.5248842239379883</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 4.82 MiB </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SSHNOIBC</span></div><div class='xr-var-dims'>(time, tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-747e307d-8b97-4cbb-b0d6-b66010a57a6e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-747e307d-8b97-4cbb-b0d6-b66010a57a6e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-007a1400-6147-4e85-a7cd-02a4e6db7838' class='xr-var-data-in' type='checkbox'><label for='data-007a1400-6147-4e85-a7cd-02a4e6db7838' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Sea surface height anomaly without the inverted barometer (IB) correction</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>comment :</span></dt><dd>Sea surface height anomaly above the geoid without the inverse barometer (IB) correction, suitable for comparisons with altimetry sea surface height data products that do NOT apply the inverse barometer (IB) correction. Note: SSHNOIBC is calculated by correcting model sea level anomaly ETAN for two effects: a) global mean steric sea level changes related to density changes in the Boussinesq volume-conserving model (Greatbatch correction, see sterGloH), b) sea level displacement due to sea-ice and snow pressure loading (see sIceLoad). In ECCO Version 4 Release 4 the model is forced with atmospheric pressure loading. SSHNOIBC does not correct for the static part of the effect of atmosphere pressure loading on sea surface height (the so-called inverse barometer (IB) correction). Use SSH for comparisons with altimetry data products that DO apply the IB correction.</dd><dt><span>valid_min :</span></dt><dd>-1.6654272079467773</dd><dt><span>valid_max :</span></dt><dd>1.4550364017486572</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 4.82 MiB </td>\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, tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-0ade891f-0940-4001-91a1-95ceea9c0d01' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ade891f-0940-4001-91a1-95ceea9c0d01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f84f3a8e-2d1e-4553-aac5-fef88867fdaf' class='xr-var-data-in' type='checkbox'><label for='data-f84f3a8e-2d1e-4553-aac5-fef88867fdaf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Model sea level anomaly</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</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). These corrections ARE made for the variables SSH and SSHNOIBC.</dd><dt><span>valid_min :</span></dt><dd>-8.304216384887695</dd><dt><span>valid_max :</span></dt><dd>1.460192084312439</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-c8f289b5-b4df-4dda-addd-65fbc7432889' class='xr-section-summary-in' type='checkbox'  ><label for='section-c8f289b5-b4df-4dda-addd-65fbc7432889' class='xr-section-summary' >Indexes: <span>(6)</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><div></div><input id='index-803717ce-d2df-43a3-a23f-113be3a0de7d' class='xr-index-data-in' type='checkbox'/><label for='index-803717ce-d2df-43a3-a23f-113be3a0de7d' 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;i&#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><div></div><input id='index-eb4ba9b8-42a6-4f04-836d-290fc7e016b7' class='xr-index-data-in' type='checkbox'/><label for='index-eb4ba9b8-42a6-4f04-836d-290fc7e016b7' 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;i_g&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>j</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5dee3b26-97a6-4b4d-9ded-6069636c0375' class='xr-index-data-in' type='checkbox'/><label for='index-5dee3b26-97a6-4b4d-9ded-6069636c0375' 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;j&#x27;))</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><div></div><input id='index-76332346-8a3c-4719-a4b6-531234f27c5e' class='xr-index-data-in' type='checkbox'/><label for='index-76332346-8a3c-4719-a4b6-531234f27c5e' 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;j_g&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>tile</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fb10077d-d31f-420b-8fe7-0ec20c77b76f' class='xr-index-data-in' type='checkbox'/><label for='index-fb10077d-d31f-420b-8fe7-0ec20c77b76f' 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], dtype=&#x27;int32&#x27;, name=&#x27;tile&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e6ecfbd1-3feb-4e6c-9b93-29df8f7e8d32' class='xr-index-data-in' type='checkbox'/><label for='index-e6ecfbd1-3feb-4e6c-9b93-29df8f7e8d32' 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;2015-01-16 12:00:00&#x27;, &#x27;2015-02-15 00:00:00&#x27;,\n",
       "               &#x27;2015-03-16 12:00:00&#x27;, &#x27;2015-04-16 00:00:00&#x27;,\n",
       "               &#x27;2015-05-16 12:00:00&#x27;, &#x27;2015-06-16 00:00:00&#x27;,\n",
       "               &#x27;2015-07-16 12:00:00&#x27;, &#x27;2015-08-16 12:00:00&#x27;,\n",
       "               &#x27;2015-09-16 00:00:00&#x27;, &#x27;2015-10-16 12:00:00&#x27;,\n",
       "               &#x27;2015-11-16 00:00:00&#x27;, &#x27;2015-12-16 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5abf669a-f1ee-412e-a0ce-c3572d76f2d4' class='xr-section-summary-in' type='checkbox'  ><label for='section-5abf669a-f1ee-412e-a0ce-c3572d76f2d4' class='xr-section-summary' >Attributes: <span>(57)</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>Ian Fenty and Ou Wang</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>comment :</span></dt><dd>Fields provided on the curvilinear lat-lon-cap 90 (llc90) native grid used in the ECCO model. SSH (dynamic sea surface height) = SSHNOIBC (dynamic sea surface without the inverse barometer correction) - SSHIBC (inverse barometer correction). The inverted barometer correction accounts for variations in sea surface height due to atmospheric pressure variations. Note: ETAN is model sea level anomaly and should not be compared with satellite altimetery products, see SSH and ETAN for more details.</dd><dt><span>Conventions :</span></dt><dd>CF-1.8, ACDD-1.3</dd><dt><span>coordinates_comment :</span></dt><dd>Note: the global &#x27;coordinates&#x27; attribute describes auxillary coordinates.</dd><dt><span>creator_email :</span></dt><dd>ecco-group@mit.edu</dd><dt><span>creator_institution :</span></dt><dd>NASA Jet Propulsion Laboratory (JPL)</dd><dt><span>creator_name :</span></dt><dd>ECCO Consortium</dd><dt><span>creator_type :</span></dt><dd>group</dd><dt><span>creator_url :</span></dt><dd>https://ecco-group.org</dd><dt><span>date_created :</span></dt><dd>2020-12-16T18:07:40</dd><dt><span>date_issued :</span></dt><dd>2020-12-16T18:07:40</dd><dt><span>date_metadata_modified :</span></dt><dd>2021-03-15T21:56:49</dd><dt><span>date_modified :</span></dt><dd>2021-03-15T21:56:49</dd><dt><span>geospatial_bounds_crs :</span></dt><dd>EPSG:4326</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-90.0</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>variable</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_resolution :</span></dt><dd>variable</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>history :</span></dt><dd>Inaugural release of an ECCO Central Estimate solution to PO.DAAC</dd><dt><span>id :</span></dt><dd>10.5067/ECL5M-SSH44</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, EARTH SCIENCE &gt; OCEANS &gt; SEA SURFACE TOPOGRAPHY &gt; SEA SURFACE HEIGHT</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>metadata_link :</span></dt><dd>https://cmr.earthdata.nasa.gov/search/collections.umm_json?ShortName=ECCO_L4_SSH_LLC0090GRID_MONTHLY_V4R4</dd><dt><span>naming_authority :</span></dt><dd>gov.nasa.jpl</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>platform_vocabulary :</span></dt><dd>GCMD platform keywords</dd><dt><span>processing_level :</span></dt><dd>L4</dd><dt><span>product_name :</span></dt><dd>SEA_SURFACE_HEIGHT_mon_mean_2015-01_ECCO_V4r4_native_llc0090.nc</dd><dt><span>product_time_coverage_end :</span></dt><dd>2018-01-01T00:00:00</dd><dt><span>product_time_coverage_start :</span></dt><dd>1992-01-01T12:00:00</dd><dt><span>product_version :</span></dt><dd>Version 4, Release 4</dd><dt><span>program :</span></dt><dd>NASA Physical Oceanography, Cryosphere, Modeling, Analysis, and Prediction (MAP)</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>references :</span></dt><dd>ECCO Consortium, Fukumori, I., Wang, O., Fenty, I., Forget, G., Heimbach, P., &amp; Ponte, R. M. 2020. Synopsis of the ECCO Central Production Global Ocean and Sea-Ice State Estimate (Version 4 Release 4). doi:10.5281/zenodo.3765928</dd><dt><span>source :</span></dt><dd>The ECCO V4r4 state estimate was produced by fitting a free-running solution of the MITgcm (checkpoint 66g) to satellite and in situ observational data in a least squares sense using the adjoint method</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention</dd><dt><span>summary :</span></dt><dd>This dataset provides monthly-averaged dynamic sea surface height and model sea level anomaly on the lat-lon-cap 90 (llc90) native model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea-ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.</dd><dt><span>time_coverage_duration :</span></dt><dd>P1M</dd><dt><span>time_coverage_end :</span></dt><dd>2015-02-01T00:00:00</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1M</dd><dt><span>time_coverage_start :</span></dt><dd>2015-01-01T00:00:00</dd><dt><span>title :</span></dt><dd>ECCO Sea Surface Height - Monthly Mean llc90 Grid (Version 4 Release 4)</dd><dt><span>uuid :</span></dt><dd>a4955186-400c-11eb-8c14-0cc47a3f49c3</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 25MB\n",
       "Dimensions:    (time: 12, tile: 13, j: 90, i: 90, i_g: 90, j_g: 90, nv: 2, nb: 4)\n",
       "Coordinates: (12/13)\n",
       "  * i          (i) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * i_g        (i_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j          (j) int32 360B 0 1 2 3 4 5 6 7 8 9 ... 81 82 83 84 85 86 87 88 89\n",
       "  * j_g        (j_g) int32 360B 0 1 2 3 4 5 6 7 8 ... 81 82 83 84 85 86 87 88 89\n",
       "  * tile       (tile) int32 52B 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "  * time       (time) datetime64[ns] 96B 2015-01-16T12:00:00 ... 2015-12-16T1...\n",
       "    ...         ...\n",
       "    YC         (tile, j, i) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>\n",
       "    XG         (tile, j_g, i_g) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>\n",
       "    YG         (tile, j_g, i_g) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>\n",
       "    time_bnds  (time, nv) datetime64[ns] 192B dask.array<chunksize=(1, 2), meta=np.ndarray>\n",
       "    XC_bnds    (tile, j, i, nb) float32 2MB dask.array<chunksize=(13, 90, 90, 4), meta=np.ndarray>\n",
       "    YC_bnds    (tile, j, i, nb) float32 2MB dask.array<chunksize=(13, 90, 90, 4), meta=np.ndarray>\n",
       "Dimensions without coordinates: nv, nb\n",
       "Data variables:\n",
       "    SSH        (time, tile, j, i) float32 5MB dask.array<chunksize=(1, 13, 90, 90), meta=np.ndarray>\n",
       "    SSHIBC     (time, tile, j, i) float32 5MB dask.array<chunksize=(1, 13, 90, 90), meta=np.ndarray>\n",
       "    SSHNOIBC   (time, tile, j, i) float32 5MB dask.array<chunksize=(1, 13, 90, 90), meta=np.ndarray>\n",
       "    ETAN       (time, tile, j, i) float32 5MB dask.array<chunksize=(1, 13, 90, 90), meta=np.ndarray>\n",
       "Attributes: (12/57)\n",
       "    acknowledgement:              This research was carried out by the Jet Pr...\n",
       "    author:                       Ian Fenty and Ou Wang\n",
       "    cdm_data_type:                Grid\n",
       "    comment:                      Fields provided on the curvilinear lat-lon-...\n",
       "    Conventions:                  CF-1.8, ACDD-1.3\n",
       "    coordinates_comment:          Note: the global 'coordinates' attribute de...\n",
       "    ...                           ...\n",
       "    time_coverage_duration:       P1M\n",
       "    time_coverage_end:            2015-02-01T00:00:00\n",
       "    time_coverage_resolution:     P1M\n",
       "    time_coverage_start:          2015-01-01T00:00:00\n",
       "    title:                        ECCO Sea Surface Height - Monthly Mean llc9...\n",
       "    uuid:                         a4955186-400c-11eb-8c14-0cc47a3f49c3"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_SSH_fromlist"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd4908cf-4702-4b4d-bee3-36dc0c9f6b94",
   "metadata": {},
   "source": [
    "## Accessing ECCOv4 release 5 output in the AWS Cloud\n",
    "\n",
    "In addition to accessing the ECCOv4r4 Central Estimate output released on PO.DAAC, `ecco_access` functions can also retrieve output from the newer release 5 (ECCOv4r5). Unlike v4r4, for which many fields can be accessed at monthly/daily/snapshot time resolutions and native/latlon grid configurations, only the v4r5 monthly mean native grid outputs are currently accessible, and `ecco_access` can only retrieve them within the AWS Cloud. However, a number of fields were archived in v4r5 that were not archived in v4r4, including the terms of momentum and sea-ice budgets.\n",
    "\n",
    "\n",
    "### SST comparison: v4r4 vs. v4r5\n",
    "\n",
    "First let's consider a field that is archived in both releases: potential temperature in the top layer, or SST. Accessing the monthly mean potential temperature for 1998 from v4r4 (and assuming we don't know the ShortName already):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ae4da393-b203-4160-9ffd-3f9c7431fa18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ShortName Options for query \"temperature\":\n",
      "                  Variable Name     Description (units)\n",
      "\n",
      "Option 1: ECCO_L4_ATM_STATE_LLC0090GRID_MONTHLY_V4R4    *native grid,monthly means*\n",
      "                  EXFatemp          Atmosphere surface (2 m) air temperature (degK)\n",
      "                  EXFaqh            Atmosphere surface (2 m) specific humidity (kg/kg)\n",
      "                  EXFuwind          Wind speed at 10m in the model +x direction (m/s)\n",
      "                  EXFvwind          Wind speed at 10m in the model +y direction (m/s)\n",
      "                  EXFwspee          Wind speed (m/s)\n",
      "                  EXFpress          Atmosphere surface pressure (N/m^2)\n",
      "\n",
      "Option 2: ECCO_L4_HEAT_FLUX_LLC0090GRID_MONTHLY_V4R4    *native grid,monthly means*\n",
      "                  EXFhl             Open ocean air-sea latent heat flux (W/m^2)\n",
      "                  EXFhs             Open ocean air-sea sensible heat flux (W/m^2)\n",
      "                  EXFlwdn           Downward longwave radiative flux (W/m^2)\n",
      "                  EXFswdn           Downwelling shortwave radiative flux (W/m^2)\n",
      "                  EXFqnet           Open ocean net air-sea heat flux (W/m^2)\n",
      "                  oceQnet           Net heat flux into the ocean surface (W/m^2)\n",
      "                  SIatmQnt          Net upward heat flux to the atmosphere (W/m^2)\n",
      "                  TFLUX             Rate of change of ocean heat content per m^2\n",
      "                                    accounting for mass (e.g. freshwater) fluxes\n",
      "                                    (W/m^2)\n",
      "                  EXFswnet          Open ocean net shortwave radiative flux (W/m^2)\n",
      "                  EXFlwnet          Net open ocean longwave radiative flux (W/m^2)\n",
      "                  oceQsw            Net shortwave radiative flux across the ocean\n",
      "                                    surface (W/m^2)\n",
      "                  SIaaflux          Conservative ocean and sea-ice advective heat flux\n",
      "                                    adjustment, associated with temperature difference\n",
      "                                    between sea surface temperature and sea-ice,\n",
      "                                    excluding latent heat of fusion (W/m^2)\n",
      "\n",
      "Option 3: ECCO_L4_MIXED_LAYER_DEPTH_LLC0090GRID_MONTHLY_V4R4    *native grid,monthly means*\n",
      "                  MXLDEPTH          Mixed-layer depth diagnosed using the temperature\n",
      "                                    difference criterion of Kara et al., 2000 (m)\n",
      "\n",
      "Option 4: ECCO_L4_TEMP_SALINITY_LLC0090GRID_MONTHLY_V4R4    *native grid,monthly means*\n",
      "                  THETA             Potential temperature, i.e., temperature of water\n",
      "                                    parcel at sea level pressure (degC)\n",
      "                  SALT              Salinity (1e-3, or parts per thousand)\n",
      "\n",
      "Option 5: ECCO_L4_OCEAN_3D_TEMPERATURE_FLUX_LLC0090GRID_MONTHLY_V4R4    *native grid,monthly means*\n",
      "                  ADVx_TH           Lateral advective flux of potential temperature in\n",
      "                                    the model +x direction (degC m^3/s)\n",
      "                  ADVy_TH           Lateral advective flux of potential temperature in\n",
      "                                    the model +y direction (degC m^3/s)\n",
      "                  ADVr_TH           Vertical advective flux of potential temperature\n",
      "                                    (degC m^3/s)\n",
      "                  DFxE_TH           Lateral diffusive flux of potential temperature in\n",
      "                                    the model +x direction (degC m^3/s)\n",
      "                  DFyE_TH           Lateral diffusive flux of potential temperature in\n",
      "                                    the model +y direction (degC m^3/s)\n",
      "                  DFrE_TH           Vertical diffusive flux of potential temperature,\n",
      "                                    explicit term (degC m^3/s)\n",
      "                  DFrI_TH           Vertical diffusive flux of potential temperature,\n",
      "                                    implicit term (degC m^3/s)\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Please select option [1-5]:  4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using dataset with ShortName: ECCO_L4_TEMP_SALINITY_LLC0090GRID_MONTHLY_V4R4\n"
     ]
    }
   ],
   "source": [
    "ds_T_v4r4 = ea.ecco_podaac_to_xrdataset('temperature',\\\n",
    "                                            version='v4r4',grid='native',time_res='monthly',\\\n",
    "                                            StartDate='1998-01',EndDate='1998-12',\\\n",
    "                                            mode='s3_open_fsspec')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "656ebb7c-6b83-4021-a8da-0f56d261f1bf",
   "metadata": {},
   "source": [
    "Select option 4 (the dataset that contains variables `THETA` and `SALT`), and the requested output is included in the `xarray` dataset `ds_T_v4r4`. Here is the contents of that dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "403b5422-3bc0-4bcb-a88d-bb28cc49e774",
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 510MB\n",
       "Dimensions:    (time: 12, k: 50, tile: 13, j: 90, i: 90, nb: 4, j_g: 90,\n",
       "                i_g: 90, nv: 2, k_l: 50, k_p1: 51, k_u: 50)\n",
       "Coordinates: (12/22)\n",
       "  * time       (time) datetime64[ns] 96B 1998-01-16T12:00:00 ... 1998-12-16T1...\n",
       "    XC         (tile, j, i) float32 421kB dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;\n",
       "    XC_bnds    (tile, j, i, nb) float32 2MB dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;\n",
       "    XG         (tile, j_g, i_g) float32 421kB dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;\n",
       "    YC         (tile, j, i) float32 421kB dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;\n",
       "    YC_bnds    (tile, j, i, nb) float32 2MB dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;\n",
       "    ...         ...\n",
       "  * k          (k) int32 200B 0 1 2 3 4 5 6 7 8 9 ... 41 42 43 44 45 46 47 48 49\n",
       "  * k_l        (k_l) int32 200B 0 1 2 3 4 5 6 7 8 ... 41 42 43 44 45 46 47 48 49\n",
       "  * k_p1       (k_p1) int32 204B 0 1 2 3 4 5 6 7 8 ... 43 44 45 46 47 48 49 50\n",
       "  * k_u        (k_u) int32 200B 0 1 2 3 4 5 6 7 8 ... 41 42 43 44 45 46 47 48 49\n",
       "  * tile       (tile) int32 52B 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "    time_bnds  (time, nv) datetime64[ns] 192B dask.array&lt;chunksize=(12, 2), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: nb, nv\n",
       "Data variables:\n",
       "    SALT       (time, k, tile, j, i) float32 253MB dask.array&lt;chunksize=(6, 50, 13, 90, 90), meta=np.ndarray&gt;\n",
       "    THETA      (time, k, tile, j, i) float32 253MB dask.array&lt;chunksize=(6, 50, 13, 90, 90), meta=np.ndarray&gt;\n",
       "Attributes: (12/62)\n",
       "    Conventions:                     CF-1.8, ACDD-1.3\n",
       "    acknowledgement:                 This research was carried out by the Jet...\n",
       "    author:                          Ian Fenty and Ou Wang\n",
       "    cdm_data_type:                   Grid\n",
       "    comment:                         Fields provided on the curvilinear lat-l...\n",
       "    coordinates_comment:             Note: the global &#x27;coordinates&#x27; attribute...\n",
       "    ...                              ...\n",
       "    time_coverage_duration:          P1M\n",
       "    time_coverage_end:               1992-02-01T00:00:00\n",
       "    time_coverage_resolution:        P1M\n",
       "    time_coverage_start:             1992-01-01T12:00:00\n",
       "    title:                           ECCO Ocean Temperature and Salinity - Mo...\n",
       "    uuid:                            f07693e6-4181-11eb-beb3-0cc47a3f44ff</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-6d192c10-6283-4d91-b308-55eddeb09ba3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6d192c10-6283-4d91-b308-55eddeb09ba3' 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>: 12</li><li><span class='xr-has-index'>k</span>: 50</li><li><span class='xr-has-index'>tile</span>: 13</li><li><span class='xr-has-index'>j</span>: 90</li><li><span class='xr-has-index'>i</span>: 90</li><li><span>nb</span>: 4</li><li><span class='xr-has-index'>j_g</span>: 90</li><li><span class='xr-has-index'>i_g</span>: 90</li><li><span>nv</span>: 2</li><li><span class='xr-has-index'>k_l</span>: 50</li><li><span class='xr-has-index'>k_p1</span>: 51</li><li><span class='xr-has-index'>k_u</span>: 50</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d523e51a-0fb0-4077-a8f6-e885484951f3' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d523e51a-0fb0-4077-a8f6-e885484951f3' class='xr-section-summary' >Coordinates: <span>(22)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>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'>1998-01-16T12:00:00 ... 1998-12-...</div><input id='attrs-4397a47f-59e2-478f-aadb-b0de3069f0ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4397a47f-59e2-478f-aadb-b0de3069f0ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-058c6efb-edc9-4459-bc1b-4452005c875b' class='xr-var-data-in' type='checkbox'><label for='data-058c6efb-edc9-4459-bc1b-4452005c875b' 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>T</dd><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>center time of averaging period</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1998-01-16T12:00:00.000000000&#x27;, &#x27;1998-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1998-03-16T12:00:00.000000000&#x27;, &#x27;1998-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1998-05-16T12:00:00.000000000&#x27;, &#x27;1998-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1998-07-16T12:00:00.000000000&#x27;, &#x27;1998-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1998-09-16T00:00:00.000000000&#x27;, &#x27;1998-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1998-11-16T00:00:00.000000000&#x27;, &#x27;1998-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XC</span></div><div class='xr-var-dims'>(tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-73988dad-db48-483a-be2a-ba7b797a1196' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-73988dad-db48-483a-be2a-ba7b797a1196' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd2e036d-49c1-4ab2-bb7c-9a8792ba1fe4' class='xr-var-data-in' type='checkbox'><label for='data-dd2e036d-49c1-4ab2-bb7c-9a8792ba1fe4' 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>coordinate :</span></dt><dd>YC XC</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></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> 411.33 kiB </td>\n",
       "                        <td> 411.33 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13, 90, 90) </td>\n",
       "                        <td> (13, 90, 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",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XC_bnds</span></div><div class='xr-var-dims'>(tile, j, i, nb)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;</div><input id='attrs-9e3fad55-7ae4-4abe-a561-ed2aac1ef2e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e3fad55-7ae4-4abe-a561-ed2aac1ef2e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34d68a78-dc95-48b5-b78c-754ba2c5b02c' class='xr-var-data-in' type='checkbox'><label for='data-34d68a78-dc95-48b5-b78c-754ba2c5b02c' 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 dcoumentation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>longitudes of tracer grid cell corners</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 1.61 MiB </td>\n",
       "                        <td> 1.61 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13, 90, 90, 4) </td>\n",
       "                        <td> (13, 90, 90, 4) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <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>XG</span></div><div class='xr-var-dims'>(tile, j_g, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-a0a332e6-ffc2-443b-a9ae-75e0dc7efe3a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0a332e6-ffc2-443b-a9ae-75e0dc7efe3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3f7f4c5d-76c2-4ddf-8556-f965ab280ba8' class='xr-var-data-in' type='checkbox'><label for='data-3f7f4c5d-76c2-4ddf-8556-f965ab280ba8' 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>Nonuniform grid spacing. Note: &#x27;southwest&#x27; does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm dcoumentation for details.</dd><dt><span>coordinate :</span></dt><dd>YG XG</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>longitude of &#x27;southwest&#x27; corner of tracer grid cell</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 411.33 kiB </td>\n",
       "                        <td> 411.33 kiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13, 90, 90) </td>\n",
       "                        <td> (13, 90, 90) </td>\n",
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       "                    <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",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>YC</span></div><div class='xr-var-dims'>(tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-03547324-20fe-4fb9-b253-a9f3e1b88693' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03547324-20fe-4fb9-b253-a9f3e1b88693' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df44af64-d67b-420f-8ebf-c52f4b8178c3' class='xr-var-data-in' type='checkbox'><label for='data-df44af64-d67b-420f-8ebf-c52f4b8178c3' 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>coordinate :</span></dt><dd>YC XC</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></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> 411.33 kiB </td>\n",
       "                        <td> 411.33 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13, 90, 90) </td>\n",
       "                        <td> (13, 90, 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",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>YC_bnds</span></div><div class='xr-var-dims'>(tile, j, i, nb)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;</div><input id='attrs-c8978faf-6447-492a-9a1a-d5b960adc4b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8978faf-6447-492a-9a1a-d5b960adc4b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-124360fa-60e1-457e-9ef8-e214dfa5f5ac' class='xr-var-data-in' type='checkbox'><label for='data-124360fa-60e1-457e-9ef8-e214dfa5f5ac' 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 dcoumentation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>latitudes of tracer grid cell corners</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> 1.61 MiB </td>\n",
       "                        <td> 1.61 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13, 90, 90, 4) </td>\n",
       "                        <td> (13, 90, 90, 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",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>YG</span></div><div class='xr-var-dims'>(tile, j_g, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-1217b80a-d144-4a58-a194-ac503aa7e8f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1217b80a-d144-4a58-a194-ac503aa7e8f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a2c8632-23ce-4342-8d13-8c9e7790ec7d' class='xr-var-data-in' type='checkbox'><label for='data-1a2c8632-23ce-4342-8d13-8c9e7790ec7d' 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>Nonuniform grid spacing. Note: &#x27;southwest&#x27; does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm dcoumentation for details.</dd><dt><span>coordinate :</span></dt><dd>YG XG</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>latitude of &#x27;southwest&#x27; corner of tracer grid cell</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</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> 411.33 kiB </td>\n",
       "                        <td> 411.33 kiB </td>\n",
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       "                        <td> (13, 90, 90) </td>\n",
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       "                    <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",
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       "</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=(50,), meta=np.ndarray&gt;</div><input id='attrs-80c665aa-5455-4f0c-b6e6-4d91a8827534' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-80c665aa-5455-4f0c-b6e6-4d91a8827534' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d93e4e9-053d-438a-8097-46e83865714d' class='xr-var-data-in' type='checkbox'><label for='data-7d93e4e9-053d-438a-8097-46e83865714d' 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.</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></dl></div><div class='xr-var-data'><table>\n",
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       "            <table style=\"border-collapse: collapse;\">\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
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       "                        <td> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
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       "                    <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>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=(50, 2), meta=np.ndarray&gt;</div><input id='attrs-020a7f4c-1547-4629-88a2-598a73a8b48a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-020a7f4c-1547-4629-88a2-598a73a8b48a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e109038-4e93-4742-8603-47a5860f4a3d' class='xr-var-data-in' type='checkbox'><label for='data-7e109038-4e93-4742-8603-47a5860f4a3d' 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>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>depths of tracer grid cell upper and lower interfaces</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> 400 B </td>\n",
       "                        <td> 400 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50, 2) </td>\n",
       "                        <td> (50, 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=\"83\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"33\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"33\" 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=\"33\" y1=\"0\" x2=\"33\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <polygon points=\"0.0,0.0 33.13387930424821,0.0 33.13387930424821,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"16.566940\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2</text>\n",
       "  <text x=\"53.133879\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,53.133879,60.000000)\">50</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=(50,), meta=np.ndarray&gt;</div><input id='attrs-072c4087-061a-47dd-9db9-5d32733d0627' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-072c4087-061a-47dd-9db9-5d32733d0627' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-745d97fe-47dc-403b-980a-9110904c4a75' class='xr-var-data-in' type='checkbox'><label for='data-745d97fe-47dc-403b-980a-9110904c4a75' 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 first tracer grid cell (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 ocean variables in which the lower (l) face of a tracer grid cell on the logical grid corresponds to the top face of the grid cell on the physical grid. In other words, the logical vertical grid of MITgcm ocean variables is inverted relative to the physical vertical grid.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>depth of the top face of tracer grid cells</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></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> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </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=\"79\" 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=\"29\" x2=\"120\" y2=\"29\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"29\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"29\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,29.030629010473877 0.0,29.030629010473877\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"49.030629\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >50</text>\n",
       "  <text x=\"140.000000\" y=\"14.515315\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,14.515315)\">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=(51,), meta=np.ndarray&gt;</div><input id='attrs-fd437891-fde6-4052-b076-bf0c04b1f8b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd437891-fde6-4052-b076-bf0c04b1f8b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f93d8c2-cc4c-4212-9cbb-3d0eafdb96ca' class='xr-var-data-in' type='checkbox'><label for='data-2f93d8c2-cc4c-4212-9cbb-3d0eafdb96ca' 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 upper interface of the surface grid cell. Last element is the depth of the lower interface 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 tracer grid cell interface</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></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> 204 B </td>\n",
       "                        <td> 204 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (51,) </td>\n",
       "                        <td> (51,) </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=\"78\" 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=\"28\" x2=\"120\" y2=\"28\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"28\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"28\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,28.91939874812263 0.0,28.91939874812263\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"48.919399\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >51</text>\n",
       "  <text x=\"140.000000\" y=\"14.459699\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,14.459699)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></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=(50,), meta=np.ndarray&gt;</div><input id='attrs-af67fd7c-bfda-4dfc-85b3-9e47183942cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af67fd7c-bfda-4dfc-85b3-9e47183942cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd1dce27-69a3-49b0-9b5a-977726374d80' class='xr-var-data-in' type='checkbox'><label for='data-fd1dce27-69a3-49b0-9b5a-977726374d80' 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 -10m, the depth of the bottom face of the first 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 ocean variables in which the upper (u) face of a tracer grid cell on the logical grid corresponds to the bottom face of the grid cell on the physical grid. In other words, the logical vertical grid of MITgcm ocean variables is inverted relative to the physical vertical grid.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>depth of the bottom face of tracer grid cells</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></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> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </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=\"79\" 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=\"29\" x2=\"120\" y2=\"29\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"29\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"29\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,29.030629010473877 0.0,29.030629010473877\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"49.030629\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >50</text>\n",
       "  <text x=\"140.000000\" y=\"14.515315\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,14.515315)\">1</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 5 6 ... 84 85 86 87 88 89</div><input id='attrs-877d5836-fd6f-4eca-ba7c-45e8a2c18fb3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-877d5836-fd6f-4eca-ba7c-45e8a2c18fb3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ee2a6a0-8c51-4827-ac90-e2f4cb1be82f' class='xr-var-data-in' type='checkbox'><label for='data-1ee2a6a0-8c51-4827-ac90-e2f4cb1be82f' 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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>grid index in x for variables at tracer and &#x27;v&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>XC</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'>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 5 6 ... 84 85 86 87 88 89</div><input id='attrs-0c91d9e9-e083-46bd-8123-80ba13cfce15' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c91d9e9-e083-46bd-8123-80ba13cfce15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1c52b59-7726-488a-a1c0-a91032d7de53' class='xr-var-data-in' type='checkbox'><label for='data-b1c52b59-7726-488a-a1c0-a91032d7de53' 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>c_grid_axis_shift :</span></dt><dd>-0.5</dd><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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>grid index in x for variables at &#x27;u&#x27; and &#x27;g&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>XG</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'>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 5 6 ... 84 85 86 87 88 89</div><input id='attrs-a8c99b2e-b1f0-4b36-bbfa-42091c58ab17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a8c99b2e-b1f0-4b36-bbfa-42091c58ab17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59d44484-bca2-4c9f-a101-0bc1a447e589' class='xr-var-data-in' type='checkbox'><label for='data-59d44484-bca2-4c9f-a101-0bc1a447e589' 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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>grid index in y for variables at tracer and &#x27;u&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>YC</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'>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 5 6 ... 84 85 86 87 88 89</div><input id='attrs-2f9b329f-0511-458f-888d-989279d7b27d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2f9b329f-0511-458f-888d-989279d7b27d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5eaba681-c360-485b-946f-f10867442bce' class='xr-var-data-in' type='checkbox'><label for='data-5eaba681-c360-485b-946f-f10867442bce' 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>c_grid_axis_shift :</span></dt><dd>-0.5</dd><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>coverage_content_type :</span></dt><dd>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><dt><span>swap_dim :</span></dt><dd>YG</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'>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 ... 44 45 46 47 48 49</div><input id='attrs-5e9668cc-45ab-4903-a78f-b5e378408743' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e9668cc-45ab-4903-a78f-b5e378408743' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f5c26e9-38bf-4da6-860a-91ffc2496a80' class='xr-var-data-in' type='checkbox'><label for='data-9f5c26e9-38bf-4da6-860a-91ffc2496a80' 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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>grid index in z for tracer variables</dd><dt><span>swap_dim :</span></dt><dd>Z</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], 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 ... 44 45 46 47 48 49</div><input id='attrs-cd84b87a-1418-4722-80d9-426e8d1e30c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd84b87a-1418-4722-80d9-426e8d1e30c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-501e3c72-8538-423b-8a0f-cbc36da96ea9' class='xr-var-data-in' type='checkbox'><label for='data-501e3c72-8538-423b-8a0f-cbc36da96ea9' 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>c_grid_axis_shift :</span></dt><dd>-0.5</dd><dt><span>comment :</span></dt><dd>First index corresponds to the top surface of the uppermost tracer grid cell. The use of &#x27;l&#x27; in the variable name follows the MITgcm convention for ocean variables in which the lower (l) face of a tracer grid cell on the logical grid corresponds to the top face of the grid cell on the physical grid.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</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><dt><span>swap_dim :</span></dt><dd>Zl</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], 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 ... 45 46 47 48 49 50</div><input id='attrs-c420d42e-7c16-4661-ad85-871088631b54' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c420d42e-7c16-4661-ad85-871088631b54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13be2e06-ee5a-44ee-bec4-49c0a5195fac' class='xr-var-data-in' type='checkbox'><label for='data-13be2e06-ee5a-44ee-bec4-49c0a5195fac' 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>c_grid_axis_shift :</span></dt><dd>[-0.5, 0.5]</dd><dt><span>comment :</span></dt><dd>Includes top of uppermost model tracer cell (k_p1=0) and bottom of lowermost tracer cell (k_p1=51).</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>grid index in z for variables at &#x27;w&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>Zp1</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], dtype=int32)</pre></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 ... 44 45 46 47 48 49</div><input id='attrs-4d83eff3-127b-403a-a22c-9bf52a034902' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d83eff3-127b-403a-a22c-9bf52a034902' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e5f04a8-1ccf-4ef5-a96c-d8a0b5fd8ae9' class='xr-var-data-in' type='checkbox'><label for='data-0e5f04a8-1ccf-4ef5-a96c-d8a0b5fd8ae9' 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>c_grid_axis_shift :</span></dt><dd>0.5</dd><dt><span>comment :</span></dt><dd>First index corresponds to the bottom surface of the uppermost tracer grid cell. The use of &#x27;u&#x27; in the variable name follows the MITgcm convention for ocean variables in which the upper (u) face of a tracer grid cell on the logical grid corresponds to the bottom face of the grid cell on the physical grid.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</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><dt><span>swap_dim :</span></dt><dd>Zu</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], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>tile</span></div><div class='xr-var-dims'>(tile)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-bd0cb6bb-e26b-40a8-b190-53ceaadee03a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd0cb6bb-e26b-40a8-b190-53ceaadee03a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a306e2e-f544-4cf3-bb02-ade68cee45f9' class='xr-var-data-in' type='checkbox'><label for='data-1a306e2e-f544-4cf3-bb02-ade68cee45f9' 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 ECCO V4 horizontal model grid is divided into 13 tiles of 90x90 cells for convenience.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>lat-lon-cap tile index</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12], 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=(12, 2), meta=np.ndarray&gt;</div><input id='attrs-89e9546b-056f-43a2-8a39-4a60ed725456' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89e9546b-056f-43a2-8a39-4a60ed725456' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d352284-ef5e-4dcc-b994-7e95943ff092' class='xr-var-data-in' type='checkbox'><label for='data-3d352284-ef5e-4dcc-b994-7e95943ff092' 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><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>time bounds 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> 192 B </td>\n",
       "                        <td> 192 B </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (12, 2) </td>\n",
       "                        <td> (12, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
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       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"91\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-50662e86-34ab-4930-9a1a-49f93b94cb55' class='xr-section-summary-in' type='checkbox'  checked><label for='section-50662e86-34ab-4930-9a1a-49f93b94cb55' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>SALT</span></div><div class='xr-var-dims'>(time, k, tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6, 50, 13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-4ce39d4a-a87c-48a8-b75f-c4399ee7be0d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ce39d4a-a87c-48a8-b75f-c4399ee7be0d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-add569c9-7016-4121-912d-2269042e1013' class='xr-var-data-in' type='checkbox'><label for='data-add569c9-7016-4121-912d-2269042e1013' 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>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>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>valid_max :</span></dt><dd>41.26802444458008</dd><dt><span>valid_min :</span></dt><dd>17.106637954711914</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> 241.01 MiB </td>\n",
       "                        <td> 120.51 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (12, 50, 13, 90, 90) </td>\n",
       "                        <td> (6, 50, 13, 90, 90) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 2 chunks in 3 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>\n",
       "        </td>\n",
       "        <td>\n",
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       "        </td>\n",
       "    </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, tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6, 50, 13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-c99c3603-8075-4ec1-a5de-97235754d73f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c99c3603-8075-4ec1-a5de-97235754d73f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea2d9fc0-d690-4719-b9b7-2ffd078e1ae3' class='xr-var-data-in' type='checkbox'><label for='data-ea2d9fc0-d690-4719-b9b7-2ffd078e1ae3' 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>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 $p_0=-g \\rho_{0} z$.</dd><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>valid_max :</span></dt><dd>36.032955169677734</dd><dt><span>valid_min :</span></dt><dd>-2.2909388542175293</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> 241.01 MiB </td>\n",
       "                        <td> 120.51 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (12, 50, 13, 90, 90) </td>\n",
       "                        <td> (6, 50, 13, 90, 90) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-d417c4f4-3f88-47f5-93f5-fcbe22454f81' class='xr-section-summary-in' type='checkbox'  ><label for='section-d417c4f4-3f88-47f5-93f5-fcbe22454f81' class='xr-section-summary' >Indexes: <span>(10)</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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f304374a-d983-437b-82c5-019b426197ec' class='xr-index-data-in' type='checkbox'/><label for='index-f304374a-d983-437b-82c5-019b426197ec' 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;1998-01-16 12:00:00&#x27;, &#x27;1998-02-15 00:00:00&#x27;,\n",
       "               &#x27;1998-03-16 12:00:00&#x27;, &#x27;1998-04-16 00:00:00&#x27;,\n",
       "               &#x27;1998-05-16 12:00:00&#x27;, &#x27;1998-06-16 00:00:00&#x27;,\n",
       "               &#x27;1998-07-16 12:00:00&#x27;, &#x27;1998-08-16 12:00:00&#x27;,\n",
       "               &#x27;1998-09-16 00:00:00&#x27;, &#x27;1998-10-16 12:00:00&#x27;,\n",
       "               &#x27;1998-11-16 00:00:00&#x27;, &#x27;1998-12-16 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>i</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9e51f1da-0924-46a8-a34c-38f5b9024a50' class='xr-index-data-in' type='checkbox'/><label for='index-9e51f1da-0924-46a8-a34c-38f5b9024a50' 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;i&#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><div></div><input id='index-6c314fef-bf4a-445b-b9d9-f7bbf6920284' class='xr-index-data-in' type='checkbox'/><label for='index-6c314fef-bf4a-445b-b9d9-f7bbf6920284' 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;i_g&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>j</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2587040b-1b8f-439a-8673-c684d4388ce6' class='xr-index-data-in' type='checkbox'/><label for='index-2587040b-1b8f-439a-8673-c684d4388ce6' 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;j&#x27;))</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><div></div><input id='index-63b61226-0b22-425f-99b1-0714edc65b3f' class='xr-index-data-in' type='checkbox'/><label for='index-63b61226-0b22-425f-99b1-0714edc65b3f' 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;j_g&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>k</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f7c1cc70-9498-4246-9c1c-3b15be6b5c87' class='xr-index-data-in' type='checkbox'/><label for='index-f7c1cc70-9498-4246-9c1c-3b15be6b5c87' 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],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;k&#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><div></div><input id='index-45ec6d08-ff6d-4956-8c1a-0107785928c3' class='xr-index-data-in' type='checkbox'/><label for='index-45ec6d08-ff6d-4956-8c1a-0107785928c3' 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],\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_p1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-891ccf19-c091-42d9-9abd-68c1d7fdd52b' class='xr-index-data-in' type='checkbox'/><label for='index-891ccf19-c091-42d9-9abd-68c1d7fdd52b' 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],\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_u</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9cf1fe27-1228-4ea9-8dea-4dd915c9ba0a' class='xr-index-data-in' type='checkbox'/><label for='index-9cf1fe27-1228-4ea9-8dea-4dd915c9ba0a' 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],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;k_u&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>tile</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-85d64db0-27bc-4e70-9056-534cd236152d' class='xr-index-data-in' type='checkbox'/><label for='index-85d64db0-27bc-4e70-9056-534cd236152d' 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], dtype=&#x27;int32&#x27;, name=&#x27;tile&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bee4372f-bfca-4d72-9790-878135f3f393' class='xr-section-summary-in' type='checkbox'  ><label for='section-bee4372f-bfca-4d72-9790-878135f3f393' class='xr-section-summary' >Attributes: <span>(62)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.8, ACDD-1.3</dd><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>Ian Fenty and Ou Wang</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>comment :</span></dt><dd>Fields provided on the curvilinear lat-lon-cap 90 (llc90) native grid used in the ECCO model.</dd><dt><span>coordinates_comment :</span></dt><dd>Note: the global &#x27;coordinates&#x27; attribute describes auxillary coordinates.</dd><dt><span>creator_email :</span></dt><dd>ecco-group@mit.edu</dd><dt><span>creator_institution :</span></dt><dd>NASA Jet Propulsion Laboratory (JPL)</dd><dt><span>creator_name :</span></dt><dd>ECCO Consortium</dd><dt><span>creator_type :</span></dt><dd>group</dd><dt><span>creator_url :</span></dt><dd>https://ecco-group.org</dd><dt><span>date_created :</span></dt><dd>2020-12-18T14:39:50</dd><dt><span>date_issued :</span></dt><dd>2020-12-18T14:39:50</dd><dt><span>date_metadata_modified :</span></dt><dd>2021-03-15T21:53:43</dd><dt><span>date_modified :</span></dt><dd>2021-03-15T21:53:43</dd><dt><span>geospatial_bounds_crs :</span></dt><dd>EPSG:4326</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-90.0</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>variable</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_resolution :</span></dt><dd>variable</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_vertical_max :</span></dt><dd>0.0</dd><dt><span>geospatial_vertical_min :</span></dt><dd>-6134.5</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>history :</span></dt><dd>Inaugural release of an ECCO Central Estimate solution to PO.DAAC</dd><dt><span>id :</span></dt><dd>10.5067/ECL5M-OTS44</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 &gt; OCEANS &gt; OCEAN TEMPERATURE &gt; POTENTIAL TEMPERATURE, EARTH SCIENCE &gt; OCEANS &gt; SALINITY/DENSITY &gt; SALINITY, 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>metadata_link :</span></dt><dd>https://cmr.earthdata.nasa.gov/search/collections.umm_json?ShortName=ECCO_L4_TEMP_SALINITY_LLC0090GRID_MONTHLY_V4R4</dd><dt><span>naming_authority :</span></dt><dd>gov.nasa.jpl</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>platform_vocabulary :</span></dt><dd>GCMD platform keywords</dd><dt><span>processing_level :</span></dt><dd>L4</dd><dt><span>product_name :</span></dt><dd>OCEAN_TEMPERATURE_SALINITY_mon_mean_1992-01_ECCO_V4r4_native_llc0090.nc</dd><dt><span>product_time_coverage_end :</span></dt><dd>2018-01-01T00:00:00</dd><dt><span>product_time_coverage_start :</span></dt><dd>1992-01-01T12:00:00</dd><dt><span>product_version :</span></dt><dd>Version 4, Release 4</dd><dt><span>program :</span></dt><dd>NASA Physical Oceanography, Cryosphere, Modeling, Analysis, and Prediction (MAP)</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>references :</span></dt><dd>ECCO Consortium, Fukumori, I., Wang, O., Fenty, I., Forget, G., Heimbach, P., &amp; Ponte, R. M. 2020. Synopsis of the ECCO Central Production Global Ocean and Sea-Ice State Estimate (Version 4 Release 4). doi:10.5281/zenodo.3765928</dd><dt><span>source :</span></dt><dd>The ECCO V4r4 state estimate was produced by fitting a free-running solution of the MITgcm (checkpoint 66g) to satellite and in situ observational data in a least squares sense using the adjoint method</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention</dd><dt><span>summary :</span></dt><dd>This dataset provides monthly-averaged ocean potential temperature and salinity on the lat-lon-cap 90 (llc90) native model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r4 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r4 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea-ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.</dd><dt><span>time_coverage_duration :</span></dt><dd>P1M</dd><dt><span>time_coverage_end :</span></dt><dd>1992-02-01T00:00:00</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1M</dd><dt><span>time_coverage_start :</span></dt><dd>1992-01-01T12:00:00</dd><dt><span>title :</span></dt><dd>ECCO Ocean Temperature and Salinity - Monthly Mean llc90 Grid (Version 4 Release 4)</dd><dt><span>uuid :</span></dt><dd>f07693e6-4181-11eb-beb3-0cc47a3f44ff</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 510MB\n",
       "Dimensions:    (time: 12, k: 50, tile: 13, j: 90, i: 90, nb: 4, j_g: 90,\n",
       "                i_g: 90, nv: 2, k_l: 50, k_p1: 51, k_u: 50)\n",
       "Coordinates: (12/22)\n",
       "  * time       (time) datetime64[ns] 96B 1998-01-16T12:00:00 ... 1998-12-16T1...\n",
       "    XC         (tile, j, i) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>\n",
       "    XC_bnds    (tile, j, i, nb) float32 2MB dask.array<chunksize=(13, 90, 90, 4), meta=np.ndarray>\n",
       "    XG         (tile, j_g, i_g) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>\n",
       "    YC         (tile, j, i) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>\n",
       "    YC_bnds    (tile, j, i, nb) float32 2MB dask.array<chunksize=(13, 90, 90, 4), meta=np.ndarray>\n",
       "    ...         ...\n",
       "  * k          (k) int32 200B 0 1 2 3 4 5 6 7 8 9 ... 41 42 43 44 45 46 47 48 49\n",
       "  * k_l        (k_l) int32 200B 0 1 2 3 4 5 6 7 8 ... 41 42 43 44 45 46 47 48 49\n",
       "  * k_p1       (k_p1) int32 204B 0 1 2 3 4 5 6 7 8 ... 43 44 45 46 47 48 49 50\n",
       "  * k_u        (k_u) int32 200B 0 1 2 3 4 5 6 7 8 ... 41 42 43 44 45 46 47 48 49\n",
       "  * tile       (tile) int32 52B 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "    time_bnds  (time, nv) datetime64[ns] 192B dask.array<chunksize=(12, 2), meta=np.ndarray>\n",
       "Dimensions without coordinates: nb, nv\n",
       "Data variables:\n",
       "    SALT       (time, k, tile, j, i) float32 253MB dask.array<chunksize=(6, 50, 13, 90, 90), meta=np.ndarray>\n",
       "    THETA      (time, k, tile, j, i) float32 253MB dask.array<chunksize=(6, 50, 13, 90, 90), meta=np.ndarray>\n",
       "Attributes: (12/62)\n",
       "    Conventions:                     CF-1.8, ACDD-1.3\n",
       "    acknowledgement:                 This research was carried out by the Jet...\n",
       "    author:                          Ian Fenty and Ou Wang\n",
       "    cdm_data_type:                   Grid\n",
       "    comment:                         Fields provided on the curvilinear lat-l...\n",
       "    coordinates_comment:             Note: the global 'coordinates' attribute...\n",
       "    ...                              ...\n",
       "    time_coverage_duration:          P1M\n",
       "    time_coverage_end:               1992-02-01T00:00:00\n",
       "    time_coverage_resolution:        P1M\n",
       "    time_coverage_start:             1992-01-01T12:00:00\n",
       "    title:                           ECCO Ocean Temperature and Salinity - Mo...\n",
       "    uuid:                            f07693e6-4181-11eb-beb3-0cc47a3f44ff"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_T_v4r4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11e954bc-dfad-4216-bf64-355f4d40129a",
   "metadata": {},
   "source": [
    "Now try the same thing, but with v4r5.\n",
    "\n",
    "> **Attention:**\n",
    "> The v4r5 outputs are currently accessed not through PO.DAAC's S3 bucket, but on the `s3://ecco-model-granules/` bucket. This bucket is a [\"requester pays\"](https://docs.aws.amazon.com/AmazonS3/latest/userguide/RequesterPaysBuckets.html) bucket, which means that the entity requesting the data (i.e., you) would pay the data transfer fees. However, no need to get alarmed! When transferring data within the same AWS region as the bucket, the requester does not have to pay any data transfer fees. If your AWS EC2 instance is set up in the same region *us-west-2 (Oregon)* as the `ecco-model-granules` bucket, there will be no cost associated with the data transfer.\n",
    "> \n",
    "> When accessing data from the `ecco-model-granules` bucket, a prompt appears by default to remind the user that the data access is \"requester pays\". This prompt can be suppressed by setting `prompt_request_payer=False`.\n",
    "\n",
    "For mode = `s3_open_fsspec`, the `json` files will be stored under `~/MZZ/v4r5` by default, unless a different path is specified using the `jsons_root_dir` option."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d90dd913-06df-4f7f-aecd-e6395770de6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ShortName Options for query \"temperature\":\n",
      "                  Variable Name     Description (units)\n",
      "\n",
      "Option 1: ECCO_L4_ATM_SURFACE_TEMP_HUM_WIND_PRES_LLC0090GRID_MONTHLY_V4R5    *native grid,monthly means*\n",
      "                  EXFatemp          Surface (2 m) air temperature over open water.\n",
      "                                    (degK)\n",
      "                  EXFaqh            Surface (2 m) specific humidity over open water.\n",
      "                                    (kg/kg)\n",
      "                  EXFpress          Atmospheric pressure field at sea level. (N/m^2)\n",
      "                  EXFuwind          Wind speed at 10m in the +x direction at the\n",
      "                                    tracer cell on the native model grid. (m/s)\n",
      "                  EXFvwind          Wind speed at 10m in the +y direction at the\n",
      "                                    tracer cell on the native model grid. (m/s)\n",
      "                  EXFwspee          10-m wind speed magnitude (>= 0 ) over open water.\n",
      "                                    (m/s)\n",
      "\n",
      "Option 2: ECCO_L4_OCEAN_AND_ICE_SURFACE_HEAT_FLUX_LLC0090GRID_MONTHLY_V4R5    *native grid,monthly means*\n",
      "                  EXFhl             Air-sea latent heat flux per unit area of open\n",
      "                                    water not covered by sea-ice. (W/m^2)\n",
      "                  EXFhs             Air-sea sensible heat flux per unit area of open\n",
      "                                    water not covered by sea-ice. (W/m^2)\n",
      "                  EXFlwdn           Downward longwave radiative flux. (W/m^2)\n",
      "                  EXFlwnet          Net longwave radiative flux per unit area of open\n",
      "                                    water not covered by sea-ice. (W/m^2)\n",
      "                  EXFswdn           Downward shortwave radiative flux. (W/m^2)\n",
      "                  EXFswnet          Net shortwave radiative flux per unit area of open\n",
      "                                    water not covered by sea-ice. (W/m^2)\n",
      "                  EXFqnet           Net air-sea heat flux (turbulent and radiative)\n",
      "                                    per unit area of open water not covered by\n",
      "                                    sea-ice. (W/m^2)\n",
      "                  SIatmQnt          Net upward heat flux to the atmosphere across open\n",
      "                                    water and sea-ice or snow surfaces. (W/m^2)\n",
      "                  SIaaflux          Heat flux associated with the temperature\n",
      "                                    difference between sea surface temperature and\n",
      "                                    sea-ice (assume 0 degree C in the model). (W/m^2)\n",
      "                  oceQnet           Net heat flux into the ocean surface from all\n",
      "                                    processes: air-sea turbulent and radiative fluxes\n",
      "                                    and turbulent and conductive fluxes between the\n",
      "                                    ocean and sea-ice and snow. oceQnet does not\n",
      "                                    include the change in ocean heat content due to\n",
      "                                    changing ocean ocean mass, oceFWflx. (W/m^2)\n",
      "                  oceQsw            Net shortwave radiative flux across the ocean\n",
      "                                    surface. (W/m^2)\n",
      "                  TFLUX             The rate of change of ocean heat content due to\n",
      "                                    heat fluxes across the liquid surface and the\n",
      "                                    addition or removal of mass. Unlike oceQnet, TFLUX\n",
      "                                    includes the contribution to the ocean heat\n",
      "                                    content from changing ocean mass, oceFWflx.\n",
      "                                    (W/m^2)\n",
      "\n",
      "Option 3: ECCO_L4_OCEAN_TEMPERATURE_FLUX_LLC0090GRID_MONTHLY_V4R5    *native grid,monthly means*\n",
      "                  ADVx_TH           Lateral advective flux of potential temperature\n",
      "                                    (THETA) in the +x direction through the 'u' face\n",
      "                                    of the tracer cell on the native model grid. (degC\n",
      "                                    m^3/s)\n",
      "                  DFxE_TH           Lateral diffusive flux of potential temperature\n",
      "                                    (THETA) in the +x direction through the 'u' face\n",
      "                                    of the tracer cell on the native model grid. (degC\n",
      "                                    m^3/s)\n",
      "                  ADVy_TH           Lateral advective flux of potential temperature\n",
      "                                    (THETA) in the +y direction through the 'v' face\n",
      "                                    of the tracer cell on the native model grid. (degC\n",
      "                                    m^3/s)\n",
      "                  DFyE_TH           Lateral diffusive flux of potential temperature\n",
      "                                    (THETA) in the +y direction through the 'v' face\n",
      "                                    of the tracer cell on the native model grid. (degC\n",
      "                                    m^3/s)\n",
      "                  ADVr_TH           Vertical advective flux of potential temperature\n",
      "                                    (THETA) in the +z direction through the top 'w'\n",
      "                                    face of the tracer cell on the native model grid.\n",
      "                                    (degC m^3/s)\n",
      "                  DFrE_TH           The explicit term of the vertical diffusive flux\n",
      "                                    of potential temperature (THETA) in the +z\n",
      "                                    direction through the top 'w' face of the tracer\n",
      "                                    cell on the native model grid. (degC m^3/s)\n",
      "                  DFrI_TH           The implicit term of the vertical diffusive flux\n",
      "                                    of potential temperature (THETA) in the +z\n",
      "                                    direction through the top 'w' face of the tracer\n",
      "                                    cell on the native model grid. (degC m^3/s)\n",
      "\n",
      "Option 4: ECCO_L4_OCEAN_TEMPERATURE_SALINITY_LLC0090GRID_MONTHLY_V4R5    *native grid,monthly means*\n",
      "                  THETA             Sea water potential temperature, i.e., the\n",
      "                                    temperature a parcel of sea water would have if\n",
      "                                    moved adiabatically to sea level pressure. (degC)\n",
      "                  SALT              Sea water salinity. (1e-3, or parts per thousand)\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Please select option [1-4]:  4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using dataset with ShortName: ECCO_L4_OCEAN_TEMPERATURE_SALINITY_LLC0090GRID_MONTHLY_V4R5\n"
     ]
    }
   ],
   "source": [
    "ds_T_v4r5 = ea.ecco_podaac_to_xrdataset('temperature',\\\n",
    "                                            version='v4r5',grid='native',time_res='monthly',\\\n",
    "                                            StartDate='1998-01',EndDate='1998-12',\\\n",
    "                                            mode='s3_open_fsspec',\\\n",
    "                                            prompt_request_payer=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3271a603-9737-45be-9a95-e685a796db12",
   "metadata": {},
   "source": [
    "Select option 4 again (the dataset with `THETA` and `SALT`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "68ac21ac-7d00-4df0-9c5f-c570761057ea",
   "metadata": {},
   "outputs": [
    {
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       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 510MB\n",
       "Dimensions:    (time: 12, k: 50, tile: 13, j: 90, i: 90, nb: 4, j_g: 90,\n",
       "                i_g: 90, nv: 2, k_l: 50, k_p1: 51, k_u: 50)\n",
       "Coordinates: (12/22)\n",
       "  * time       (time) datetime64[ns] 96B 1998-01-16T12:00:00 ... 1998-12-16T1...\n",
       "    XC         (tile, j, i) float32 421kB dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;\n",
       "    XC_bnds    (tile, j, i, nb) float32 2MB dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;\n",
       "    XG         (tile, j_g, i_g) float32 421kB dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;\n",
       "    YC         (tile, j, i) float32 421kB dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;\n",
       "    YC_bnds    (tile, j, i, nb) float32 2MB dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;\n",
       "    ...         ...\n",
       "  * k          (k) int32 200B 0 1 2 3 4 5 6 7 8 9 ... 41 42 43 44 45 46 47 48 49\n",
       "  * k_l        (k_l) int32 200B 0 1 2 3 4 5 6 7 8 ... 41 42 43 44 45 46 47 48 49\n",
       "  * k_p1       (k_p1) int32 204B 0 1 2 3 4 5 6 7 8 ... 43 44 45 46 47 48 49 50\n",
       "  * k_u        (k_u) int32 200B 0 1 2 3 4 5 6 7 8 ... 41 42 43 44 45 46 47 48 49\n",
       "  * tile       (tile) int32 52B 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "    time_bnds  (time, nv) datetime64[ns] 192B dask.array&lt;chunksize=(12, 2), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: nb, nv\n",
       "Data variables:\n",
       "    SALT       (time, k, tile, j, i) float32 253MB dask.array&lt;chunksize=(6, 50, 13, 90, 90), meta=np.ndarray&gt;\n",
       "    THETA      (time, k, tile, j, i) float32 253MB dask.array&lt;chunksize=(6, 50, 13, 90, 90), meta=np.ndarray&gt;\n",
       "Attributes: (12/63)\n",
       "    Conventions:                     CF-1.8, ACDD-1.3\n",
       "    acknowledgement:                 This research was carried out by the Jet...\n",
       "    author:                          Ian Fenty, Ou Wang, Ichiro Fukumori\n",
       "    cdm_data_type:                   Grid\n",
       "    comment:                         Fields provided on the curvilinear lat-l...\n",
       "    coordinates_comment:             Note: the global &#x27;coordinates&#x27; attribute...\n",
       "    ...                              ...\n",
       "    time_coverage_duration:          P1M\n",
       "    time_coverage_end:               1992-02-01T00:00:00\n",
       "    time_coverage_resolution:        P1M\n",
       "    time_coverage_start:             1992-01-01T12:00:00\n",
       "    title:                           ECCO Ocean Temperature and Salinity - Mo...\n",
       "    uuid:                            8a5175e0-2719-11f0-9a28-0a58a9feac02</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-5abdf8a1-7133-4975-886b-67fd89832596' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5abdf8a1-7133-4975-886b-67fd89832596' 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>: 12</li><li><span class='xr-has-index'>k</span>: 50</li><li><span class='xr-has-index'>tile</span>: 13</li><li><span class='xr-has-index'>j</span>: 90</li><li><span class='xr-has-index'>i</span>: 90</li><li><span>nb</span>: 4</li><li><span class='xr-has-index'>j_g</span>: 90</li><li><span class='xr-has-index'>i_g</span>: 90</li><li><span>nv</span>: 2</li><li><span class='xr-has-index'>k_l</span>: 50</li><li><span class='xr-has-index'>k_p1</span>: 51</li><li><span class='xr-has-index'>k_u</span>: 50</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5171c921-cce2-4320-8577-ebdc728617a0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5171c921-cce2-4320-8577-ebdc728617a0' class='xr-section-summary' >Coordinates: <span>(22)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>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'>1998-01-16T12:00:00 ... 1998-12-...</div><input id='attrs-a6b6d1f7-632d-4019-a1c6-34277cc18691' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6b6d1f7-632d-4019-a1c6-34277cc18691' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb04271c-9f09-43ac-b46e-6f4ff71e2fdd' class='xr-var-data-in' type='checkbox'><label for='data-eb04271c-9f09-43ac-b46e-6f4ff71e2fdd' 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>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>comment :</span></dt><dd> </dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>center time of averaging period</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1998-01-16T12:00:00.000000000&#x27;, &#x27;1998-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1998-03-16T12:00:00.000000000&#x27;, &#x27;1998-04-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1998-05-16T12:00:00.000000000&#x27;, &#x27;1998-06-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;1998-07-16T12:00:00.000000000&#x27;, &#x27;1998-08-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1998-09-16T00:00:00.000000000&#x27;, &#x27;1998-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1998-11-16T00:00:00.000000000&#x27;, &#x27;1998-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>XC</span></div><div class='xr-var-dims'>(tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-9e69d4a7-2630-4342-b247-9c2de2dc49a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e69d4a7-2630-4342-b247-9c2de2dc49a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9109ab4a-9b21-4d24-8775-b63e87a20ac8' class='xr-var-data-in' type='checkbox'><label for='data-9109ab4a-9b21-4d24-8775-b63e87a20ac8' 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>coordinate :</span></dt><dd>YC XC</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></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> 411.33 kiB </td>\n",
       "                        <td> 411.33 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13, 90, 90) </td>\n",
       "                        <td> (13, 90, 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",
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       "    </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'>(tile, j, i, nb)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;</div><input id='attrs-57dcc904-e7d7-44fe-a0de-8b6f3c31d261' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57dcc904-e7d7-44fe-a0de-8b6f3c31d261' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acf81111-d29d-4874-80a9-32966d1325f0' class='xr-var-data-in' type='checkbox'><label for='data-acf81111-d29d-4874-80a9-32966d1325f0' 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 dcoumentation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>longitudes of tracer grid cell corners</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> 1.61 MiB </td>\n",
       "                        <td> 1.61 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (13, 90, 90, 4) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <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>XG</span></div><div class='xr-var-dims'>(tile, j_g, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-90015e0d-3728-42f9-ab06-4da0fcec13ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90015e0d-3728-42f9-ab06-4da0fcec13ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1af51e00-d1b6-4a12-954e-4c308f317ed8' class='xr-var-data-in' type='checkbox'><label for='data-1af51e00-d1b6-4a12-954e-4c308f317ed8' 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>Nonuniform grid spacing. Note: &#x27;southwest&#x27; does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm dcoumentation for details.</dd><dt><span>coordinate :</span></dt><dd>YG XG</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>longitude of &#x27;southwest&#x27; corner of tracer grid cell</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</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> 411.33 kiB </td>\n",
       "                        <td> 411.33 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13, 90, 90) </td>\n",
       "                        <td> (13, 90, 90) </td>\n",
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       "                    <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",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>YC</span></div><div class='xr-var-dims'>(tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-0f7c6371-cfe7-44ab-af23-fa8aef464b09' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0f7c6371-cfe7-44ab-af23-fa8aef464b09' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6137c135-f1e6-4991-bfca-9c18866deecd' class='xr-var-data-in' type='checkbox'><label for='data-6137c135-f1e6-4991-bfca-9c18866deecd' 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>coordinate :</span></dt><dd>YC XC</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></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                    <tr>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 411.33 kiB </td>\n",
       "                        <td> 411.33 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (13, 90, 90) </td>\n",
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       "                    <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",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>YC_bnds</span></div><div class='xr-var-dims'>(tile, j, i, nb)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90, 4), meta=np.ndarray&gt;</div><input id='attrs-1b542f0d-bd99-4761-aef6-0599f56f1eb0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b542f0d-bd99-4761-aef6-0599f56f1eb0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bee545bc-b1df-434a-91d3-cc777892a3f3' class='xr-var-data-in' type='checkbox'><label for='data-bee545bc-b1df-434a-91d3-cc777892a3f3' 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 dcoumentation for details.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>latitudes of tracer grid cell corners</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",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 1.61 MiB </td>\n",
       "                        <td> 1.61 MiB </td>\n",
       "                    </tr>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (13, 90, 90, 4) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                    <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>YG</span></div><div class='xr-var-dims'>(tile, j_g, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-7bcf1c7d-27c9-4bc2-9e99-bda22d41b4cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7bcf1c7d-27c9-4bc2-9e99-bda22d41b4cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-af4ca17a-4aa8-4fc1-b1ae-bc69fd95057b' class='xr-var-data-in' type='checkbox'><label for='data-af4ca17a-4aa8-4fc1-b1ae-bc69fd95057b' 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>Nonuniform grid spacing. Note: &#x27;southwest&#x27; does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm dcoumentation for details.</dd><dt><span>coordinate :</span></dt><dd>YG XG</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>latitude of &#x27;southwest&#x27; corner of tracer grid cell</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                        <th> Chunk </th>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
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       "                        <td> 411.33 kiB </td>\n",
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       "                        <td> (13, 90, 90) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                        <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>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=(50,), meta=np.ndarray&gt;</div><input id='attrs-acf8b426-3a96-4c0a-b59f-e7ed665ebb1a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-acf8b426-3a96-4c0a-b59f-e7ed665ebb1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5670e4d-6a7b-4b1e-833d-2344d5694fca' class='xr-var-data-in' type='checkbox'><label for='data-f5670e4d-6a7b-4b1e-833d-2344d5694fca' 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.</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></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                    <tr>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (50,) </td>\n",
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       "                        <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",
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       "</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=(50, 2), meta=np.ndarray&gt;</div><input id='attrs-be8f8c62-4300-4450-b153-f4669fc01583' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be8f8c62-4300-4450-b153-f4669fc01583' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ddfe26e-9117-464a-9916-9a5e75613c8b' class='xr-var-data-in' type='checkbox'><label for='data-3ddfe26e-9117-464a-9916-9a5e75613c8b' 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>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>depths of top and bottom faces of tracer grid cell</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> Array </th>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 400 B </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (50, 2) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "        </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=(50,), meta=np.ndarray&gt;</div><input id='attrs-18cfb998-f60f-49f8-b32e-49479a3ac385' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18cfb998-f60f-49f8-b32e-49479a3ac385' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e82ff5f7-7890-4c8a-9f17-a704988d97fd' class='xr-var-data-in' type='checkbox'><label for='data-e82ff5f7-7890-4c8a-9f17-a704988d97fd' 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></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> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </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=\"79\" 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=\"29\" x2=\"120\" y2=\"29\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"29\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"29\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,29.030629010473877 0.0,29.030629010473877\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"49.030629\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >50</text>\n",
       "  <text x=\"140.000000\" y=\"14.515315\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,14.515315)\">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=(51,), meta=np.ndarray&gt;</div><input id='attrs-10e64a4e-746f-4007-b508-f819d3ec9719' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10e64a4e-746f-4007-b508-f819d3ec9719' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7906af12-c8a0-47df-98aa-2a2ed75d7927' class='xr-var-data-in' type='checkbox'><label for='data-7906af12-c8a0-47df-98aa-2a2ed75d7927' 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></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> 204 B </td>\n",
       "                        <td> 204 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (51,) </td>\n",
       "                        <td> (51,) </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=\"78\" 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=\"28\" x2=\"120\" y2=\"28\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"28\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"28\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,28.91939874812263 0.0,28.91939874812263\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"48.919399\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >51</text>\n",
       "  <text x=\"140.000000\" y=\"14.459699\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,14.459699)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></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=(50,), meta=np.ndarray&gt;</div><input id='attrs-4ceb93ac-6fe7-413a-af01-8fcbc17f970f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ceb93ac-6fe7-413a-af01-8fcbc17f970f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-515c6123-1ece-4e71-8fbd-99db0a537d39' class='xr-var-data-in' type='checkbox'><label for='data-515c6123-1ece-4e71-8fbd-99db0a537d39' 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 -10m, 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></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> 200 B </td>\n",
       "                        <td> 200 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (50,) </td>\n",
       "                        <td> (50,) </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=\"79\" 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=\"29\" x2=\"120\" y2=\"29\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"29\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"29\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,29.030629010473877 0.0,29.030629010473877\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"49.030629\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >50</text>\n",
       "  <text x=\"140.000000\" y=\"14.515315\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,14.515315)\">1</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 5 6 ... 84 85 86 87 88 89</div><input id='attrs-df0d0896-2eea-465c-9c5b-3e8799319f72' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df0d0896-2eea-465c-9c5b-3e8799319f72' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e40bf16-0eea-4d76-b168-3adfed3050a1' class='xr-var-data-in' type='checkbox'><label for='data-1e40bf16-0eea-4d76-b168-3adfed3050a1' 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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>grid index in x for variables at tracer and &#x27;v&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>XC</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'>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 5 6 ... 84 85 86 87 88 89</div><input id='attrs-1b3e8509-421c-4e42-aa5f-efe9dd843e42' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b3e8509-421c-4e42-aa5f-efe9dd843e42' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e79a8bf6-6ebe-471b-9b85-f1009486618f' class='xr-var-data-in' type='checkbox'><label for='data-e79a8bf6-6ebe-471b-9b85-f1009486618f' 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>c_grid_axis_shift :</span></dt><dd>-0.5</dd><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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>grid index in x for variables at &#x27;u&#x27; and &#x27;g&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>XG</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'>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 5 6 ... 84 85 86 87 88 89</div><input id='attrs-f2c0085c-eff5-4999-8a04-56714d7eea9a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f2c0085c-eff5-4999-8a04-56714d7eea9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43f5a685-7777-4373-884d-e35e45802c8f' class='xr-var-data-in' type='checkbox'><label for='data-43f5a685-7777-4373-884d-e35e45802c8f' 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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>grid index in y for variables at tracer and &#x27;u&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>YC</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'>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 5 6 ... 84 85 86 87 88 89</div><input id='attrs-f44bf45c-d4e4-4096-a87b-e165b5791bfa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f44bf45c-d4e4-4096-a87b-e165b5791bfa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-916cd4c7-e860-4e79-907e-c58acd05b244' class='xr-var-data-in' type='checkbox'><label for='data-916cd4c7-e860-4e79-907e-c58acd05b244' 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>c_grid_axis_shift :</span></dt><dd>-0.5</dd><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>coverage_content_type :</span></dt><dd>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><dt><span>swap_dim :</span></dt><dd>YG</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'>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 ... 44 45 46 47 48 49</div><input id='attrs-ba5f4e1f-31f4-44a6-8a85-bacb511fe0b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba5f4e1f-31f4-44a6-8a85-bacb511fe0b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32c8716b-466a-4cae-bc26-d4a2ea0ea63d' class='xr-var-data-in' type='checkbox'><label for='data-32c8716b-466a-4cae-bc26-d4a2ea0ea63d' 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>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>grid index in z for tracer variables</dd><dt><span>swap_dim :</span></dt><dd>Z</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], 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 ... 44 45 46 47 48 49</div><input id='attrs-b44af277-3e0c-4509-a38d-f6172577f07c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b44af277-3e0c-4509-a38d-f6172577f07c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bdb8010-a90e-4652-9789-6600e4ac2f57' class='xr-var-data-in' type='checkbox'><label for='data-4bdb8010-a90e-4652-9789-6600e4ac2f57' 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>c_grid_axis_shift :</span></dt><dd>-0.5</dd><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>coverage_content_type :</span></dt><dd>coordinate</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><dt><span>swap_dim :</span></dt><dd>Zl</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], 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 ... 45 46 47 48 49 50</div><input id='attrs-e2308cc0-474f-45d4-b22a-469044625e08' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2308cc0-474f-45d4-b22a-469044625e08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6977da84-9c63-47b7-8359-a9eb7a364583' class='xr-var-data-in' type='checkbox'><label for='data-6977da84-9c63-47b7-8359-a9eb7a364583' 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>c_grid_axis_shift :</span></dt><dd>[-0.5, 0.5]</dd><dt><span>comment :</span></dt><dd>Includes top of uppermost model tracer cell (k_p1=0) and bottom of lowermost tracer cell (k_p1=51).</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>grid index in z for variables at &#x27;w&#x27; locations</dd><dt><span>swap_dim :</span></dt><dd>Zp1</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], dtype=int32)</pre></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 ... 44 45 46 47 48 49</div><input id='attrs-94ac3f24-0bd8-4bec-857c-137c38c307a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94ac3f24-0bd8-4bec-857c-137c38c307a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b6b8fae-c5a4-414f-8253-1a668a99ccb0' class='xr-var-data-in' type='checkbox'><label for='data-8b6b8fae-c5a4-414f-8253-1a668a99ccb0' 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>c_grid_axis_shift :</span></dt><dd>0.5</dd><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>coverage_content_type :</span></dt><dd>coordinate</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><dt><span>swap_dim :</span></dt><dd>Zu</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], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>tile</span></div><div class='xr-var-dims'>(tile)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-f9819e6d-20c7-4078-a2de-df53cd4c3e78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f9819e6d-20c7-4078-a2de-df53cd4c3e78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ff15142-8581-4900-b7f7-ef90a783c637' class='xr-var-data-in' type='checkbox'><label for='data-1ff15142-8581-4900-b7f7-ef90a783c637' 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 ECCO V4 horizontal model grid is divided into 13 tiles of 90x90 cells for convenience.</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>lat-lon-cap tile index</dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12], 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=(12, 2), meta=np.ndarray&gt;</div><input id='attrs-1cddaac2-ffc6-4d62-8dc1-54d67ab85206' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1cddaac2-ffc6-4d62-8dc1-54d67ab85206' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-109e9a28-d970-4408-9c05-5272a1f71f27' class='xr-var-data-in' type='checkbox'><label for='data-109e9a28-d970-4408-9c05-5272a1f71f27' 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><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>long_name :</span></dt><dd>time bounds of averaging period</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                        <td> 192 B </td>\n",
       "                        <td> 192 B </td>\n",
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       "                        <td> (12, 2) </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-7e079844-43f4-4b70-bdd7-410b284f61b1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7e079844-43f4-4b70-bdd7-410b284f61b1' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>SALT</span></div><div class='xr-var-dims'>(time, k, tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6, 50, 13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-e4b499bf-b43e-4252-afc4-8bf4686d87a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4b499bf-b43e-4252-afc4-8bf4686d87a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c68ff156-75e1-45a3-b83d-d71812ae6ef3' class='xr-var-data-in' type='checkbox'><label for='data-c68ff156-75e1-45a3-b83d-d71812ae6ef3' 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>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>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>valid_max :</span></dt><dd>40.863277435302734</dd><dt><span>valid_min :</span></dt><dd>20.68772315979004</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 2 chunks in 3 graph layers </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>THETA</span></div><div class='xr-var-dims'>(time, k, tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(6, 50, 13, 90, 90), meta=np.ndarray&gt;</div><input id='attrs-e1242897-3fa8-49fa-8d41-760976fcb095' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1242897-3fa8-49fa-8d41-760976fcb095' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e5904a1-d818-4225-881b-fda8b34aa620' class='xr-var-data-in' type='checkbox'><label for='data-7e5904a1-d818-4225-881b-fda8b34aa620' 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>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 $p_0=-g \\rho_{0} z$.</dd><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>valid_max :</span></dt><dd>36.032955169677734</dd><dt><span>valid_min :</span></dt><dd>-2.2909388542175293</dd></dl></div><div class='xr-var-data'><table>\n",
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       "               &#x27;1998-03-16 12:00:00&#x27;, &#x27;1998-04-16 00:00:00&#x27;,\n",
       "               &#x27;1998-05-16 12:00:00&#x27;, &#x27;1998-06-16 00:00:00&#x27;,\n",
       "               &#x27;1998-07-16 12:00:00&#x27;, &#x27;1998-08-16 12:00:00&#x27;,\n",
       "               &#x27;1998-09-16 00:00:00&#x27;, &#x27;1998-10-16 12:00:00&#x27;,\n",
       "               &#x27;1998-11-16 00:00:00&#x27;, &#x27;1998-12-16 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>i</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-54d1c669-12f6-4610-86bc-0e669b189ce5' class='xr-index-data-in' type='checkbox'/><label for='index-54d1c669-12f6-4610-86bc-0e669b189ce5' 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;i&#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><div></div><input id='index-8a451c93-eb77-4d55-b449-a9d2c81dc454' class='xr-index-data-in' type='checkbox'/><label for='index-8a451c93-eb77-4d55-b449-a9d2c81dc454' 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;i_g&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>j</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bc145df9-cedf-4487-a6fd-5b60b1fdb953' class='xr-index-data-in' type='checkbox'/><label for='index-bc145df9-cedf-4487-a6fd-5b60b1fdb953' 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;j&#x27;))</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><div></div><input id='index-4bdbb3c5-b65f-40a9-9f02-019840d7af12' class='xr-index-data-in' type='checkbox'/><label for='index-4bdbb3c5-b65f-40a9-9f02-019840d7af12' 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;j_g&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>k</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d2535970-9404-4e2f-8180-caddf6ac95db' class='xr-index-data-in' type='checkbox'/><label for='index-d2535970-9404-4e2f-8180-caddf6ac95db' 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],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;k&#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><div></div><input id='index-bcd4fa19-bfde-4d42-b90d-65e6076e5736' class='xr-index-data-in' type='checkbox'/><label for='index-bcd4fa19-bfde-4d42-b90d-65e6076e5736' 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],\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_p1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-85b92fed-0a12-4b94-9c8c-161e38ba43f5' class='xr-index-data-in' type='checkbox'/><label for='index-85b92fed-0a12-4b94-9c8c-161e38ba43f5' 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],\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_u</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5466e38c-8296-41b2-b3d2-6de46e07f929' class='xr-index-data-in' type='checkbox'/><label for='index-5466e38c-8296-41b2-b3d2-6de46e07f929' 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],\n",
       "      dtype=&#x27;int32&#x27;, name=&#x27;k_u&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>tile</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-87852feb-e8ed-487b-bb88-460446f68d7c' class='xr-index-data-in' type='checkbox'/><label for='index-87852feb-e8ed-487b-bb88-460446f68d7c' 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], dtype=&#x27;int32&#x27;, name=&#x27;tile&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4103d228-b109-4599-af5d-63e58f62f6e1' class='xr-section-summary-in' type='checkbox'  ><label for='section-4103d228-b109-4599-af5d-63e58f62f6e1' class='xr-section-summary' >Attributes: <span>(63)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.8, ACDD-1.3</dd><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>Ian Fenty, Ou Wang, Ichiro Fukumori</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>comment :</span></dt><dd>Fields provided on the curvilinear lat-lon-cap 90 (llc90) native grid used in the ECCO model.</dd><dt><span>coordinates_comment :</span></dt><dd>Note: the global &#x27;coordinates&#x27; attribute describes auxillary coordinates.</dd><dt><span>creator_email :</span></dt><dd>ecco-group@mit.edu</dd><dt><span>creator_institution :</span></dt><dd>NASA Jet Propulsion Laboratory (JPL)</dd><dt><span>creator_name :</span></dt><dd>ECCO Consortium</dd><dt><span>creator_type :</span></dt><dd>group</dd><dt><span>creator_url :</span></dt><dd>https://ecco-group.org</dd><dt><span>date_created :</span></dt><dd>2025-05-02T05:51:49</dd><dt><span>date_issued :</span></dt><dd>2025-05-02T05:51:49</dd><dt><span>date_metadata_modified :</span></dt><dd>2025-05-02T05:51:49</dd><dt><span>date_modified :</span></dt><dd>2025-05-02T05:51:49</dd><dt><span>geospatial_bounds_crs :</span></dt><dd>EPSG:4326</dd><dt><span>geospatial_lat_max :</span></dt><dd>90.0</dd><dt><span>geospatial_lat_min :</span></dt><dd>-90.0</dd><dt><span>geospatial_lat_resolution :</span></dt><dd>variable</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_resolution :</span></dt><dd>variable</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>geospatial_vertical_max :</span></dt><dd>0.0</dd><dt><span>geospatial_vertical_min :</span></dt><dd>-6134.5</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>history :</span></dt><dd>Inaugural release of an ECCO Central Estimate solution to PO.DAAC</dd><dt><span>id :</span></dt><dd>10.5067/ECL5M-OTS45</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 &gt; OCEANS &gt; OCEAN TEMPERATURE &gt; POTENTIAL TEMPERATURE, EARTH SCIENCE &gt; OCEANS &gt; SALINITY/DENSITY &gt; SALINITY, 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>CC BY 4.0</dd><dt><span>metadata_link :</span></dt><dd>https://cmr.earthdata.nasa.gov/search/collections.umm_json?ShortName=ECCO_L4_OCEAN_TEMPERATURE_SALINITY_LLC0090GRID_MONTHLY_V4R5</dd><dt><span>naming_authority :</span></dt><dd>gov.nasa.jpl</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>platform_vocabulary :</span></dt><dd>GCMD platform keywords</dd><dt><span>processing_level :</span></dt><dd>L4</dd><dt><span>product_name :</span></dt><dd>OCEAN_TEMPERATURE_SALINITY_mon_mean_1992-01_ECCO_V4r5_native_llc0090.nc</dd><dt><span>product_time_coverage_end :</span></dt><dd>2017-12-31T12:00:00</dd><dt><span>product_time_coverage_start :</span></dt><dd>1992-01-01T12:00:00</dd><dt><span>product_version :</span></dt><dd>Version 4, Release 5</dd><dt><span>program :</span></dt><dd>NASA Physical Oceanography; Cryosphere; and Modeling, Analysis, and Prediction (MAP)</dd><dt><span>project :</span></dt><dd>Estimating the Circulation and Climate of the Ocean (ECCO)</dd><dt><span>project_summary :</span></dt><dd>Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r5 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r5 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea-ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r5 covers the period 1992-01-01T12:00:00 to 2020-01-01T00:00:00.</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>references :</span></dt><dd>ECCO Consortium, Fukumori, I., Wang, O., Fenty, I., Forget, G., Heimbach, P., &amp; Ponte, R. M. 2020. Synopsis of the ECCO Central Production Global Ocean and Sea-Ice State Estimate (Version 4 Release 5). doi:10.5281/zenodo.3765928</dd><dt><span>source :</span></dt><dd>The ECCO V4r5 state estimate was produced by fitting a free-running solution of the MITgcm (checkpoint 66g) to satellite and in situ observational data in a least squares sense using the adjoint method.</dd><dt><span>standard_name_vocabulary :</span></dt><dd>NetCDF Climate and Forecast (CF) Metadata Convention</dd><dt><span>summary :</span></dt><dd>This dataset contains monthly-averaged ocean temperature and salinity on the native Lat-Lon-Cap 90 (LLC90) model grid from the ECCO Version 4 revision 5 (V4r5) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates are dynamically and kinematically-consistent reconstructions of the three-dimensional, time-evolving ocean, sea-ice, and surface atmospheric states. ECCO V4r5 is a free-running solution of a global, nominally 1-degree configuration of the MIT general circulation model (MITgcm) that has been fit to observations in a least-squares sense. Observational data constraints used in V4r5 include sea surface height (SSH) from satellite altimeters [ERS-1/2, TOPEX/Poseidon, GFO, ENVISAT, Jason-1,2,3, CryoSat-2, and SARAL/AltiKa]; sea surface temperature (SST) from satellite radiometers [AVHRR], sea surface salinity (SSS) from the Aquarius satellite radiometer/scatterometer, ocean bottom pressure (OBP) from the GRACE satellite gravimeter; sea-ice concentration from satellite radiometers [SSM/I and SSMIS], and in-situ ocean temperature and salinity measured with conductivity-temperature-depth (CTD) sensors and expendable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r5 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00.</dd><dt><span>time_coverage_duration :</span></dt><dd>P1M</dd><dt><span>time_coverage_end :</span></dt><dd>1992-02-01T00:00:00</dd><dt><span>time_coverage_resolution :</span></dt><dd>P1M</dd><dt><span>time_coverage_start :</span></dt><dd>1992-01-01T12:00:00</dd><dt><span>title :</span></dt><dd>ECCO Ocean Temperature and Salinity - Monthly Mean llc90 Grid (Version 4 Release 5)</dd><dt><span>uuid :</span></dt><dd>8a5175e0-2719-11f0-9a28-0a58a9feac02</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 510MB\n",
       "Dimensions:    (time: 12, k: 50, tile: 13, j: 90, i: 90, nb: 4, j_g: 90,\n",
       "                i_g: 90, nv: 2, k_l: 50, k_p1: 51, k_u: 50)\n",
       "Coordinates: (12/22)\n",
       "  * time       (time) datetime64[ns] 96B 1998-01-16T12:00:00 ... 1998-12-16T1...\n",
       "    XC         (tile, j, i) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>\n",
       "    XC_bnds    (tile, j, i, nb) float32 2MB dask.array<chunksize=(13, 90, 90, 4), meta=np.ndarray>\n",
       "    XG         (tile, j_g, i_g) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>\n",
       "    YC         (tile, j, i) float32 421kB dask.array<chunksize=(13, 90, 90), meta=np.ndarray>\n",
       "    YC_bnds    (tile, j, i, nb) float32 2MB dask.array<chunksize=(13, 90, 90, 4), meta=np.ndarray>\n",
       "    ...         ...\n",
       "  * k          (k) int32 200B 0 1 2 3 4 5 6 7 8 9 ... 41 42 43 44 45 46 47 48 49\n",
       "  * k_l        (k_l) int32 200B 0 1 2 3 4 5 6 7 8 ... 41 42 43 44 45 46 47 48 49\n",
       "  * k_p1       (k_p1) int32 204B 0 1 2 3 4 5 6 7 8 ... 43 44 45 46 47 48 49 50\n",
       "  * k_u        (k_u) int32 200B 0 1 2 3 4 5 6 7 8 ... 41 42 43 44 45 46 47 48 49\n",
       "  * tile       (tile) int32 52B 0 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "    time_bnds  (time, nv) datetime64[ns] 192B dask.array<chunksize=(12, 2), meta=np.ndarray>\n",
       "Dimensions without coordinates: nb, nv\n",
       "Data variables:\n",
       "    SALT       (time, k, tile, j, i) float32 253MB dask.array<chunksize=(6, 50, 13, 90, 90), meta=np.ndarray>\n",
       "    THETA      (time, k, tile, j, i) float32 253MB dask.array<chunksize=(6, 50, 13, 90, 90), meta=np.ndarray>\n",
       "Attributes: (12/63)\n",
       "    Conventions:                     CF-1.8, ACDD-1.3\n",
       "    acknowledgement:                 This research was carried out by the Jet...\n",
       "    author:                          Ian Fenty, Ou Wang, Ichiro Fukumori\n",
       "    cdm_data_type:                   Grid\n",
       "    comment:                         Fields provided on the curvilinear lat-l...\n",
       "    coordinates_comment:             Note: the global 'coordinates' attribute...\n",
       "    ...                              ...\n",
       "    time_coverage_duration:          P1M\n",
       "    time_coverage_end:               1992-02-01T00:00:00\n",
       "    time_coverage_resolution:        P1M\n",
       "    time_coverage_start:             1992-01-01T12:00:00\n",
       "    title:                           ECCO Ocean Temperature and Salinity - Mo...\n",
       "    uuid:                            8a5175e0-2719-11f0-9a28-0a58a9feac02"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_T_v4r5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae6a6b21-429d-4139-9d79-8878d2a86fa0",
   "metadata": {},
   "source": [
    "Now plot a map of the difference in surface potential temperature (i.e., SST) between the two releases, on tile 10 in May 1998."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "113f588b-dc79-4e19-9f11-160901d38644",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x540 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "T_diff = ds_T_v4r5.THETA - ds_T_v4r4.THETA\n",
    "plt.figure(figsize=(12,6), dpi= 90)\n",
    "ecco.plot_proj_to_latlon_grid(ds_T_v4r4.XC, ds_T_v4r4.YC, \\\n",
    "                              T_diff.isel(time=4,k=0), \\\n",
    "                              user_lon_0=-160,\\\n",
    "                              projection_type='robin',\\\n",
    "                              plot_type='pcolormesh', \\\n",
    "                              cmap='RdBu_r',\\\n",
    "                              dx=1,dy=1,cmin=-1, cmax=1,show_colorbar=True)\n",
    "plt.title('SST difference v4r5 - v4r4 [C], May 1998\\n')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bde63fca-1110-4b1d-9302-430d05d7b21b",
   "metadata": {},
   "source": [
    "### Momentum budget terms in v4r5\n",
    "\n",
    "Now, let's consider a dataset that was *only* archived in v4r5, not v4r4: the terms of the momentum budget. We will access and view the terms of the $u$-momentum budget, i.e., the momentum along the $x$-axis of the llc90 native grid.\n",
    "\n",
    "> **Note:** ECCOv4r5 has two different sets of momentum budget terms archived. The first set (options 1 and 2) below quantifies the effect of (lateral) vorticity advection vs. other advection terms that contribute to the momentum budget (vertical and kinetic energy gradient). The second set (options 5 and 6) consists of the primitive equation terms that describe $u$- and $v$-momentum tendency, which you probably learned in introductory fluid dynamics classes. We consider the second set below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "94a09ce3-36b2-4d40-9b4f-1c7777886148",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ShortName Options for query \"momentum\":\n",
      "                  Variable Name     Description (units)\n",
      "\n",
      "Option 1: ECCO_L4_OCEAN_3D_MOMENTUM_ADV_TEND_X_LLC0090GRID_MONTHLY_V4R5    *native grid,monthly means*\n",
      "                  Um_AdvZ3          U momentum tendency from vorticity advection.\n",
      "                                    (m/s^2)\n",
      "                  Um_AdvRe          U momentum tendency from vertical advection,\n",
      "                                    explicit part. (m/s^2)\n",
      "                  Um_dKEdx          U momentum tendency from grad KE. (m/s^2)\n",
      "\n",
      "Option 2: ECCO_L4_OCEAN_3D_MOMENTUM_ADV_TEND_Y_LLC0090GRID_MONTHLY_V4R5    *native grid,monthly means*\n",
      "                  Vm_AdvZ3          V momentum tendency from vorticity advection.\n",
      "                                    (m/s^2)\n",
      "                  Vm_AdvRe          V momentum tendency from vertical advection,\n",
      "                                    explicit part. (m/s^2)\n",
      "                  Vm_dKEdy          V momentum tendency from grad KE. (m/s^2)\n",
      "\n",
      "Option 3: ECCO_L4_OCEAN_3D_MOMENTUM_DISS_TEND_X_LLC0090GRID_MONTHLY_V4R5    *native grid,monthly means*\n",
      "                  Um_Diss2          U momentum tendency from harmonic visc alone.\n",
      "                                    (m/s^2)\n",
      "                  Um_Diss4          U momentum tendency from biharmonic visc alone.\n",
      "                                    (m/s^2)\n",
      "                  UBotDrag          U momentum tendency from bottom drag. (m/s^2)\n",
      "                  USidDrag          U momentum tendency from side drag. (m/s^2)\n",
      "                  UShIDrag          U momentum tendency from friction/no-slip at base\n",
      "                                    of shelf ice. (m/s^2)\n",
      "\n",
      "Option 4: ECCO_L4_OCEAN_3D_MOMENTUM_DISS_TEND_Y_LLC0090GRID_MONTHLY_V4R5    *native grid,monthly means*\n",
      "                  Vm_Diss2          V momentum tendency from harmonic visc alone.\n",
      "                                    (m/s^2)\n",
      "                  Vm_Diss4          V momentum tendency from biharmonic visc alone.\n",
      "                                    (m/s^2)\n",
      "                  VBotDrag          V momentum tendency from bottom drag. (m/s^2)\n",
      "                  VSidDrag          V momentum tendency from side drag. (m/s^2)\n",
      "                  VShIDrag          V momentum tendency from friction/no-slip at base\n",
      "                                    of shelf ice. (m/s^2)\n",
      "\n",
      "Option 5: ECCO_L4_OCEAN_3D_MOMENTUM_TEND_X_LLC0090GRID_MONTHLY_V4R5    *native grid,monthly means*\n",
      "                  TOTUTEND          Tendency of zonal component of velocity. (m/s/day)\n",
      "                  Um_Cori           U momentum tendency from Coriolis term. (m/s^2)\n",
      "                  Um_Advec          U momentum tendency from advection terms. (m/s^2)\n",
      "                  Um_Diss           U momentum tendency from dissipation, explicit\n",
      "                                    part. (m/s^2)\n",
      "                  Um_Ext            U momentum tendency from external forcing. (m/s^2)\n",
      "                  Um_dPhiX          U momentum tendency from pressure/potential grad.\n",
      "                                    (m/s^2)\n",
      "                  AB_gU             U momentum tendency from Adams-Bashforth. (m/s^2)\n",
      "                  Um_ImplD          U momentum tendency from dissipation, implicit\n",
      "                                    part. (m/s^2)\n",
      "\n",
      "Option 6: ECCO_L4_OCEAN_3D_MOMENTUM_TEND_Y_LLC0090GRID_MONTHLY_V4R5    *native grid,monthly means*\n",
      "                  TOTVTEND          Tendency of meridional component of velocity.\n",
      "                                    (m/s/day)\n",
      "                  Vm_Cori           V momentum tendency from Coriolis term. (m/s^2)\n",
      "                  Vm_Advec          V momentum tendency from advection terms. (m/s^2)\n",
      "                  Vm_Diss           V momentum tendency from dissipation, explicit\n",
      "                                    part. (m/s^2)\n",
      "                  Vm_Ext            V momentum tendency from external forcing. (m/s^2)\n",
      "                  Vm_dPhiY          V momentum tendency from pressure/potential grad.\n",
      "                                    (m/s^2)\n",
      "                  AB_gV             V momentum tendency from Adams-Bashforth. (m/s^2)\n",
      "                  Vm_ImplD          V momentum tendency from dissipation, implicit\n",
      "                                    part. (m/s^2)\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Please select option [1-6]:  5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using dataset with ShortName: ECCO_L4_OCEAN_3D_MOMENTUM_TEND_X_LLC0090GRID_MONTHLY_V4R5\n"
     ]
    }
   ],
   "source": [
    "# u-momentum budget terms\n",
    "ds_u_mom = ea.ecco_podaac_to_xrdataset('momentum',\\\n",
    "                                            version='v4r5',grid='native',time_res='monthly',\\\n",
    "                                            StartDate='1998-01',EndDate='1998-12',\\\n",
    "                                            mode='s3_open_fsspec',\\\n",
    "                                            prompt_request_payer=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3d391f4-3d18-4eb8-b0d3-bff9884cb426",
   "metadata": {},
   "source": [
    "Select option 5 to look at the primitive equation $u$-momentum terms.\n",
    "\n",
    "Let's plot the Coriolis and pressure gradient contributions to the model $u$-momentum budget on tile 7 (North Pacific). This is a rotated tile, which means the positive $x$ direction is *southward*. Hence the $u$-momentum budget corresponds to approximately the meridional momentum budget, with the sign reversed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "60691316-dd01-4107-9f2a-fb6d46c4af51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x630 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "seismic_nanmasked = mpl.colormaps['seismic'].copy()\n",
    "seismic_nanmasked.set_bad('gray')\n",
    "\n",
    "plt.figure(figsize=(10,7), dpi= 90)\n",
    "ds_u_mom.Um_Cori.isel(time=4,k=0,tile=7).plot(cmap=seismic_nanmasked)\n",
    "plt.title('$u$-momentum Coriolis term [m s$^{-2}$], surface, May 1998\\n')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5b27d866-7510-4462-ab66-0c31d2b1ded6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x630 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7),dpi=90)\n",
    "ds_u_mom.Um_dPhiX.isel(time=4,k=0,tile=7).plot(cmap=seismic_nanmasked)\n",
    "plt.title('$u$-momentum pressure gradient term [m s$^{-2}$], surface, May 1998\\n')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39fb41f1-112c-42b1-8408-71762424fbc6",
   "metadata": {},
   "source": [
    "The fact that these two terms are nearly equal and opposite reflects the dominance of geostrophic balance. The sum of the Coriolis and pressure gradient terms (1st plot below) is balanced mostly by two frictional terms (2nd plot below): the sum of the surface (Ekman) stress included in the external forcing term `Um_Ext`, and the implicit dissipation of momentum `Um_ImplD`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "825ee6e2-2e0d-451c-a4e4-34121df88f89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ro6Uu1tZH7Zxae09WVS9L22XuyevrqPU9U6tWLXh5eeHMmTNm6df/Liq3qKgIn376KZYuXYqxY8ea0itOu1VexvXnvqqRR3t8DlhLpi7W/BdLhtZ73dactV7W1M3a95q119Ka90GNGjXg4eEhHGyw5f0eGBiInj17YvHixejfvz8CAwM1taHc6NGjMWbMGDz33HP4+OOPdXsG39PTE0uXLsXcuXORk5OD+vXrm2aEqTjjjjX7/f777xg8eLBZ/jfffDP8/f1x9OhRXepL1nGbEe6zZ89W+tZ89uxZ06wUwLUOdrt27Uyvm2++uVplenp6om3btli/fr1Z+rp16+Dh4YEOHTpUK//AwEC0b98ehw4dMqt3+Uvmg8fHx0d1pCY6Otr0Bi331VdfVaveVXnqqafwww8/VAo4Aa59qH3xxRcA9D2/trhW3t7euOuuuzBp0iScPHkSFy5cEO77999/Y/fu3abfs7OzceDAAdx2223CY2SujUxdtNYHkL8nq6qX2vbq3JNa3zOenp6Ij4+vNOJjabaBiq5cuYLS0lJTwBIAFBQUmB0vKqOqVQXt8Tlg7b561kUr2XvdXpy1XoC4bnp//lvzPggMDMTtt9+OlStXqgak2voee+KJJ9CrVy+zzrRsG8r169cPPj4+GDRoEMrKynSf671GjRpo0aIFjEYjXn31Vdxxxx1o0qSJ1H6xsbE4cOCA2f6//fYbLl++jLi4OF3rS5a5zQh3ZGQkjEYjVq9ejebNm+PIkSOYMmUKCgsLq5yhpDpmz56N++67DyNHjsSgQYPw888/Y+bMmRgzZgyio6Ornf/ChQtx9913w8PDAw899BCCgoKQnZ2NTz/9FPPnzxdOnH+9Jk2aYNOmTdi4cSOio6MRFRWFqKgo9O3bF8uXL8fEiRPRo0cPbNu2DV9++WW1621Jr169MGnSJIwaNQrffvstHnjgARiNRvz+++94/fXXERcXZ5rFRM/zq0deBw8exJQpUzBw4EA0aNAAubm5eP7559GqVSuLo+Th4eEYNmwY5s6dC39/f8yaNQu1a9e2uDJbVddGa1201qdcVfdkUVGRxXpVVe/q3pNa3zPTpk1Dv379MG7cOPTu3RvffvstPv30UwBVT+UVEhKCW2+9FXPmzEFwcDA8PDywYMEChISEID8/v1IZTzzxBPr27YtvvvnG9AXTFm26nuhzQGZfveoiozr3ui05sl7bt29H165dsW3bNtXVCq2pm96f/9a+DxYsWIBu3bohISEBjz32GAIDA7Fnzx60a9cOPXv2tPoeq+ocqOnSpYvFfa1tA3Dtv5BDhgxBWloaBg8eXOlRJTWXLl3CZ599BuBafEB+fj4+/PBDAMD999+PgIAAfPfdd9i1axfi4+ORn5+PNWvW4MsvvzTNsFLOmv3Gjh2LiRMnIioqCgkJCTh9+jTmzJmDuLi4as+mQpIcGrKpsw8++ECJjo5WAgIClLvvvlv55ptvFG9vb2Xt2rUWj7t+RgBFUY++zszMVAAomzdvrlTuLbfconh7eyt169ZVpk2bphQXF+uW/3fffafcd999SlBQkBIQEKA0bdpUmThxonLhwgVh/tfndebMGaVPnz5KjRo1FABmEdmpqalKdHS0YjQalSFDhiibNm1SnRGiOm1Q8+GHHypdunRRgoODFW9vb6Vx48bK5MmTlZMnT1b7/IrSZfO6/vfTp08rQ4cOVerXr6/4+voqderUUQYNGqRkZWUJ21mex0cffaQ0btxY8fHxUe64445K0fZq9bV0bbTUxdr6iM5pOUv3ZFX1sqbeWu9Ja+pnySuvvKLUrVtX8ff3VxISEpR169ZVmqFBVO4ff/yhdO3aVQkICFBiYmKU559/3myGhHL/+c9/zMr48ssvq5ylxJo2Vfdz4HqW9tVSF1G6NZ8jWu910Swlen2Waa2XFte3pXwWlYyMjGrVrar3mqX3mdp5s/Z9sH37dqVTp06Kv7+/EhISonTp0sXsfWbNe7iqc6AoimrZ17t+lhJr26AoivLVV18pAJSvvvrKYhnlyu8rtVdmZqaiKIryww8/KO3atVMCAwOVoKAg5f7771edbcaa/crKypRXX31VadGihRIQEKBERUUpAwYMUI4ePWpVfUk/BkXhzOdEtjRixAj88ssvlZYVdhRnq48zmzdvHubPn4/z58/D39/f0dUhSV26dEHNmjWxdu1aeHp66v5cuT2UlZWhrKwMd999N2rVqmUaDU1OTsaOHTuwbds2B9fQcZzhHPz73//G2rVrTYveEInw7iAiwrUAyUmTJmHz5s3YunUrUlJSMH/+fIwaNYqdbRf28ccfw9vbGy+//LKjq6LJnDlz4O3tjR07dpil7969G5MmTXJQrZyDI8/BoUOHsGHDBrz22mtISkpiZ5uq5DbPcBMRVYePjw9+//13rFy5Enl5eYiMjMSECRMwd+5cR1eNNHrjjTdMKyvWq1fPwbXR5rHHHkPPnj0BmM+mZOvgdlfgyHPw+OOP4/vvv0fv3r11mTed3B8fKSEiIiIisiH+D4SIiIiIyIbY4SYiIiIisiF2uImIiIiIbIgdbiIiIiIiG2KHm4iIiIjIhtjhJiIiIiKyIXa4iYiIiIhsiB1uIiIiIiIbYoebiIiIiMiG2OEmIiIiIrIhdriJiIiIiGyIHW4iIiIiElq6dCnatWsHX19f9OnTxyZlvPPOO/D09ITRaDS9Fi5caJOyHMHL0RUgIiIiIucVFRWFGTNm4Ouvv0ZOTo7NymnRogXS09Ntlr8jcYSbiIiIiIT69euHPn36IDw8vNK2wsJCjBs3DvXq1UPt2rUxfPhw5OXlOaCWzo0dbiIiIiLS5NFHH8X58+dx8OBBZGZmori4GOPGjdOU16FDh1C7dm3Ur18fiYmJuHDhgr6VdSCDoiiKoytBRERERM4tJSUF6enp2LhxIwDgzJkziIiIwJkzZxAWFgYA+OOPP9C8eXNcvnwZnp6euHLlCkRdTYPBAF9fXwDAn3/+ibKyMjRo0ABZWVkYM2YMAgMDsWnTJru0zdb4DDcRERERSTt27Jipk1yRh4cHTp06hbp166JmzZq4ePGi6vGxsbE4duwYAJjlUb9+fbzyyito0aIFLl26hICAAJu1wV7Y4SYiIiIiaTExMfDw8MCJEyeEneLCwkJNeXt4XHvq2V0exOAz3EREREQkVFJSgqKiIpSUlKCsrAxFRUW4evUqIiIi0KdPH4wbNw5nz54FAJw6dQobNmyQLuOzzz7DyZMnAQA5OTmYMGECunfvjsDAQF3b4ijscBMRERGR0Lx58+Dv74/58+dj8+bN8Pf3x7333gvg2vzZoaGhuPXWWxEcHIxOnTph//790mVs27YNrVu3RkBAADp06IAGDRpg1apVejfFYRg0SURERERkQxzhJiIiIiKyIXa4iYiIiIhsiLOUVGAwGBxdBSIiIiKrOcuTwaEGA2y5vmTFKQRdEZ/hrsBgMCAlJcXR1SAiG0ieOVM1/fwFuX/0WZrhqqREPd1LMLQhSpfNHwCKiuSOsZSXHvtbIns+LJ0n0TY/P332t0Svc6IlH9nrKro/ZNO1lK1Xut556VW2LD3fS6NGGZymw20wGJBiw/xT4DxfLrTgCDcRERERVRufUxbjuSEiIiIisiGOcBMRERFRtXEUV4znhoiIiIjIhjjCTUQuJ3niRPFGo1E1uUxyfMFScKSIXgF8IpaCrUR5yQbF6RnA50ii9omuhaUgQUfRM3jQkWU74/3hSnV1JRzFFeO5ISIiIiKyIY5wExEREVG1cRRXjOeGiIiIiMiGOMJNRERERNXGUVwxdriJyGklT52qvsFCtOGlIvWPfNngQdmARkvH6LXSpCW2DpbTsvKgiD1WmpQ9t/a4RrL0DODTM0jQGVd7dMZgR2esEzkOO9xEREREVG0c4RZjh5uIiIiIqo0dbjGeGyIiIiIiG+IINxERERFVG0dxxdjhJiK7SW7RQjX9as9+qul/X1DPp3Z4mbCMQsExeq32aIlegXdaAsP0CmTTUrbsKo3OGDQpuj/0DHzTc2VRR15v2TrJ7q+l3Xrtb4+8tOTjyOBd0gcvIRERERFVG0e4xXhuiIiIiIhsiCPcRERERFRtBkdXwIlxhJuIiIiIyIY4wk1ERERE1ebp6Ao4MXa4ichuZv/8s/oGUbrAxInJwm16Lemt1/6OptfsFFqWdhelO3KWElG6qH16Xm9HLtXuyKXdbZ2PnmXriUu7U0Uu9qeDiIiIiJwRn1MW47khIiIiIrIhjnATERERUbVxFFeM54aIiIiIyIY4wk1Eukt+9ln1DaIoIsG62mWCMQHZpcQt0TOAT8QeS0I7Y8CaXnXSEjQpWqpdNjhSS9n24Mil3W19rzlyOXZHBmxaup9cJQCTo7hiPDdERERERDbEEW4iIiIiqjaO4oqxw01ERERE1cYOtxjPDRERERGRDXGEm4gsSh45Un2D0Sg+qLBQPV0yks1DsL+Xl3isQK+VIz1QJpcRxEGeInoGQrnSCpiODIoTEd2azsrWgYhagiYdufqlLGcsw9L+rvL+5iiuGM8NEREREZENuch3JiIiIiJyZgZHV8CJcYSbiIiIiMiGOMJNRERERNXm6egKODGOcBMRERER2RBHuIkIAJA8bJj6Bi1TCYhC6nVak91Hz5B9HWcrEI1giOsrN+ahZ7P1XNJetl6OXG7bkTNjyHJkux05S4kljrxOzniPOBuO4orx3BARERER2RBHuImIiIio2jiKK8ZzQ0RERERkQxzhJiIiIqJq4yiuGDvcRG4quVcv9Q2hoerpspFvOgVAApBe8t1p1zmWrJdewZSWaAmClN1fr8uhJSjNkWU7YxCdMwY0ulKgqiWie80Zl4kn58MvI0RERERUbR42fFlr6dKlaNeuHXx9fdGnTx+L+44YMQI+Pj4wGo2m1549eyRKsx473ERERETkFqKiojBjxgyMGTPGqv0TExNRWFhoenXo0MEm9XLS/8sSERERkStxhlHcfv36AQDS09ORk5Pj4Nr8P2c4N0REREREFhkMBtMrJSVFlzxXrlyJsLAwNG/eHC+99BLKysp0yfd6LtfhPn78OPr06YOaNWsiPDwc/fv3x+nTpwEAxcXFGDduHMLCwhAWFoakpCSUMNKAiIiIyOZs/Qy3oiimlx4d7vHjx+PQoUM4c+YMli9fjpdffhkvv/xytfNV43KPlCQmJsJgMCArKwuKomDIkCGYMGECPvjgA8ybNw+7du1CRkYGACAhIQGpqamYNWuWg2tNZBvJ/fuLN164oJ4u+hebaKYQo1GqTgDkp8aQnY3EHlNmaCH6gi+qk+Cce3n5SGVjaZues5HoeYxMPpbGTWy9rLyenHE2C1db2l0vWu5Z2fo6clYTRzE4ugKS2rRpY/q5ffv2eOaZZ7By5UpMnDhR97JcboQ7MzMTAwYMgNFoRFBQEAYOHIhffvkFALBixQrMmDEDkZGRiIyMxPTp07F8+XIH15iIiIiInJ2Hh+26xS7X4Z40aRLWr1+PvLw8XLhwAWvWrEGPHj2Qm5uLnJwcxMfHm/aNj49HdnY28vLyVPNKSUkxex6IiIiIiLTxtOHLWiUlJSgqKkJJSQnKyspQVFSEq1evqu67bt065OfnQ1EU/PDDD1iwYAEefPBB2WZbxeU63B07dsTff/+NGjVqICwsDOfPn8eMGTNQWFgIAAitsKhH+c8FBQWqeaWkpJg9D0RERERErmvevHnw9/fH/PnzsXnzZvj7++Pee+8FAIwdOxZjx4417bt06VLUq1cPQUFBGDJkCBITEzF58mSb1MulnuEuKyvDPffcgwEDBuCrr74CcK3TfN999+HTTz8FAOTl5SE8PNz0MwAEBQU5psJERERENwhnGMVNSUkRBlS+/vrrZr/v2LHDDjW6xqU63OfPn0dWVhbGjx+PgIAAAEBSUhJeeOEFlJaWIjo6Gunp6WjYsCGAa3MwxsTEICQkxJHVJqo2YXDkP//ZUSUbqSQKXBTtLwqytEQyeFB6f0A+cFGWnhF8gnPuIWien596MCUgf7llL5/o9gAcG6eqFy2BfXoFxTkymFLPoElH7Q+4VvCsiDPWifTjDF9GrBYeHo5GjRohLS0NRUVFKCoqQlpaGqKjoxEeHo6RI0di/vz5OHXqFE6dOoXU1FSMHj3a0dUmIiIicnvOsLS7s3K5cYlNmzZh4sSJqFu3LsrKytC6dWt88sknAICZM2fi3LlzaNq0KQBgyJAhmDZtmiOrS0REREQ3OJfrcDdr1gxffvml6jZvb2+kpaUhLS3NzrUiIiIiurG5w0i0rfDcEBERERHZkMuNcBO5M2Fw5Nmz6umWItn0isDRc/k/2bz0DMyUpeX8yQZsSp4PHwurfooCKmWD4hy5uKeWeFdnXDnSHkGCerXbHitN2jofS9whoNeVcBRXjOeGiIiIiMiG+N2PiIiIiKqNo7hiPDdERERERDbEEW4iIiIiqjaO4orx3BARERER2RBHuIkcIHnqVPUNv/+unm5pCXcR0QwmlmY2USOaKUTPWUq0zEaiV9ki9pj+Qq+l7gEEGNW3lZSoj6voNXtJVdv0oOelkG2fPWYQscdsHXrOnKLXMvGObJ+rzV7iKsu+GxxdASfmYrccERERETkjT0dXwInxkRIiIiIiIhviCDcRERERVRtHccV4boiIiIiIbIgj3Nfp3z+5Utr69bMdUBNyZ2V+AarpHrIRXZaCKUXb7BHRJRsMqGe0lYaAQ+kyZI/RK5DTEkFeRqP6vWaPQDbZ+Fw9OXJ5dXu8xWy9tLuWYxwZNGmPt7deb1c9A3SdDUdxxXhuiIiIiIhsiCPcRERERFRtHMUV47khIiIiIrIhjnATERERUbVxFFeM54aIiIiIyIY4wn0dd4gSJufnUZivT0ZapjGQnTpCS0i97DF6ztYhKkOv5eP1nDpCz+kKBLPSeAhnL/GRL0MnsremPWaUsMdnvz1m8XDk7CyOZI+l3e3x8eXqOIorxnNDRERERGRD/F5GRERERNXGUVwxnhsiIiIiIhviCDcRERERVZvB0RVwYuxwX2fDBi7jTnYgig6TDezTM4BPNnLLUqSQbDCgnu3WKzhSS9n2iNySzUsQTOkTGqqa7uen/o9Peyz5LmqCngFuIq623Latl0u3x0eLFu4cuOgObSAxXl4iIiIiqjZPR1fAifEZbiIiIiIiG+IINxERERFVG0dxxdjhJiIiIqJqY4dbjB1uIkcQBfbpGQml12qPto7OAuRXv7RE1A7ZSD0t7XZkpJ5s+wT7+/kFyJftQHpdVi2cNaBSjSMDHWU563llUCNVB28fIiIiIqo2jnCL8dwQEREREdkQR7iJiIiIqNo4iivGc0NEREREZEMc4SYiIiKiauMorhg73EQ2lPzss+obHBmGb+vZS6rapsf+lsiuDy479YA92i2axcbSbC46zVIi+oOpZfYSPZdql6Xn7SyiVztcbUYQeywfT+Ru2OEmIiIiomrjCLcYzw0RERERkQ1xhJuIiIiIqs3g6Ao4MY5wExERERHZEEe4iWwov8hHNd1oVE/3EAXLidItRW3pFR3myLWwtSyvLgoeFJ1DWXoGTcrmo6XdovugsFCqSpZGZwIE59bLS/0o2WBDLbe56HTYIz5XRDaQU0teWm4dPfYnAgBPR1fAiXGEm4iIiIjIhjjCTURERETVxlFcMZ4bIiIiIiIb4gg3EREREVUbR3HFXOrcGI1Gs5e3tzdatmxp2l5cXIxx48YhLCwMYWFhSEpKQgkjP4iIiIhszsOGL1fnUiPchddF1bds2RKDBg0y/T5v3jzs2rULGRkZAICEhASkpqZi1qxZdq0nUTnpiTH0nKVERM+1rW1Ny5QLek3T4Eiiulpa2t0Jl7T3MRrV9/dT//Npj0uk5dSK6DnbihpHng+9j9GLXrezbP5E1eWyXxr27t2LX3/9FSNGjDClrVixAjNmzEBkZCQiIyMxffp0LF++3HGVJCIiIrpBcIRbzGXbsHz5ciQkJCAqKgoAkJubi5ycHMTHx5v2iY+PR3Z2NvLy8lTzSElJgcFgML2IiIiIiPTmkh3uS5cu4YMPPsDo0aNNaeWPm4SGhprSyn8uKChQzSclJQWKopheRERERKQNR7jFXLIN69atQ0BAAHr06GFKM/7zrGDF0ezyn4OCguxbQSIiIiKif7hkeMBbb72FRx55BF4Vohtq1KiB6OhopKeno2HDhgCA9PR0xMTEICQkxOq8u3ZNrpS2bdvs6leabkjPPad+7zz7bOX7DAB8IiLUM7pwQT3dUlSmXmtbO2OQpZ5Bk64UJaXn0u4O5CO4b41GH5uXLXuaLJ1y2WP02r+qbXpwxsBIPfPSM95cRM9z6IRvY1UuOYprJy53bg4dOoTdu3fj0UcfrbRt5MiRmD9/Pk6dOoVTp04hNTXV7LETIiIiInJfS5cuRbt27eDr64s+ffpY3Nee00m7XId7+fLl6NSpE2666aZK22bOnIkOHTqgadOmaNq0Ke644w5MmzbNAbUkIiIiurE4wzPcUVFRmDFjBsaMGVPlvhWnk87IyMDOnTuRmpoqUZr1XOSfFP9v4cKFwm3e3t5IS0tDWlqaHWtERERERM6gX79+AK49VpyTk2Nx3xUrVmDx4sWIjIwEAEyfPh1TpkyxyfotLjfCTURERETOx9Yj3BWnck5JSalWXbVMJ10dLjfCbWtaVhsjkuXz+iuq6dl9xqum16sw3aWZ61ZftWqbM67EqFcgp6W8ZJcYtPWSdpZoiaLTq30O/BD0MarXyWjUb2xI9tTaYzFXPeOS7RHjrOfbVZat3372qKsrLXrrbPScwrmq6aRlJtywBke4iYiIiKjanOEZbmvZezppdriJiIiI6IZScTrpclqmk7YWHykhIiIiompzhlHckpIS06usrAxFRUXw8PCAj0/lef7Lp5Pu2LEjANh0Oml2uImIiIjILcybNw+zZ///onP+/v7o3Lkztm/fjrFjxwIAXn/9dQDXppM+d+4cmjZtCgAYMmSIzaaTZoebiIiIiKrNGUa4U1JShDOYlHe0y9lzOml2uK/jKsunkmubnZurmv6sYGV3XDCqp4tmLwGAs2fV02WnY3DkrCayM28A4uXuRceI9petkyV6fbBoKVv2+mm53nrNbCI4T6Kl4AHAz0/9T7youqKstEwMI6LXW8yRS7vrSc9zK+LISYWIrMFbkYiIiIiqzRlGuJ0VO9xEREREVG0Gg8F2mes4B7cj8MsIEREREZENcYSbiIiIiKrPlg/NFxfbLm87YIf7OgywIEd67rnZqunJU6eqHxAXJ84sPFw9fdcuuUqJAuIsBdE5cu1i2aXMHbnWtz3y0RJ4Kks2UFVHfn4BqumysZ+ysbOA/K2mVzClJfYowx3wbz3ZG285IiIiIqo+jnAL8RluIiIiIiIb4gg3EREREVUfn9UR4gg3EREREZEN8asIEREREVUfR7iFeGauw3uFnNHsF17QLa/k1q3VN5w6pZ6u58wY9pgSQVRf2Wko7DFLiWxelvaXXbNcNl3LjCOyU2NouD9E/6bVa/YSLcur2zq9qm2OYo8l3F3JjToDDKm7Qd8GRERERKSrG/XblRX4DDcRERERkQ3xqwgRERERVR9HuIU4wk1EREREZEP8KkJ0g5n944+q6cnnzqkfEBGhnm4pCFGvQD09I9lkI5jsERypZxSdLD3X+pYNqNQzWFSwzSdU/f7081MfZ9ISx+mMl9uRy8rrxVK7Q0PljiksVE8X3bIcoK0mnkAhjnATEREREdkQv4oQERERUfVxhFuIZ4aIiIiIqo8dbiE+UkJEREREZEP8KnIdrgxFN6zu3dXTt29XTxdFLwHiiCTZN5ieqxvK7q/n8n/OGEUne261BMnaIyJPsn16rUBpD/YI2JTNH5A/J7K3h6Vb7cIF9XRRcKRs2UajuGzZxWpvyP4ER7iFOMJNRERERGRD/CpCRERERNXHEW4hjnATEREREdkQv4oQERERUfVxhFuII9xERERERDbEryLXkY1CJnIbcXFy6WfPivPSa8oAPaeOsMf0DbLTLjhylhI9OXJ9cMlz5SFI9/LyUU3Xc3IWPS+3qF6yp9yRl040UY6lCXS++049/eabZ0uWnixdtggHdSvgyRDiCDcRERERkQ3xqwgRERERVR9HuIU4wk1EREREZEP8KkJERERE1ccRbiGemetYigMjcmuiaCRRAKSeH6yywZHOuOQ7II5kE9XX3YMmZffXcl1l70/BNQowitog/kew7G2r5+W2dWCmlrL1CtjMzRUHQN58s3q6waAeBNmli/r+v/yini66nYiqix1uIiIiIqo+jnAL8RluIiIiIiIb4lcRIiIiIqo+jnALcYSbiIiIiMiGXPKryCeffIJZs2bhjz/+QEhICGbNmoWxY8eiuLgYEydOxOrVqwEAQ4YMweLFi+El8Y3LaLRVrYmc2+y//lJNT65RQ/2ACxfEmclGjcmuTGmJXkvkybbBEtnINC1L3soGZsqydF7tEaknS6dVP/38AoRFyAZN6hVMaYkjT7mIqN2it31RkXoAJACEh6unt2unni76mx4aqp5uj48ctx4EduvGVY/LnZkvvvgCiYmJeO+999CpUyfk5+fj9OnTAIB58+Zh165dyMjIAAAkJCQgNTUVs2bNcmSViYiIiNwfO9xCUo+UFBUVoaTCV7kNGzZg8+bNulfKkpkzZ2LWrFno0qULPD09UaNGDTRp0gQAsGLFCsyYMQORkZGIjIzE9OnTsXz5crvWj4iIiIioIqkO9z333IMffvgBADB79mwkJiYiMTERs2eL58zU08WLF7F//37k5+ejSZMmiIiIwMCBA3Hq1Cnk5uYiJycH8fHxpv3j4+ORnZ2NvLw81fxSUlJgMBhMLyIiIiLSyMvLdi8XJ9Xh/vXXX3HrrbcCAN59911s3boVu3fvttsocm5uLhRFwapVq/Dll1/iyJEj8Pb2xrBhw1D4z8NgoRUezCr/uaCgQDW/lJQUKIpiehERERER6U3qK0NpaSkMBgOOHDmCsrIyNGvWDMC1jrA9GP+Jfhg/fjxiY2MBXBtpb9y4MTw8rn13yMvLQ/g/URXlI9tBQUF2qR8RERHRDcsNRqJtRerM3HrrrRg3bhxOnjyJHj16AAD++usvs1FlWwoNDUW9evVUH/8ICQlBdHQ00tPT0bBhQwBAeno6YmJiEBISYnUZXNqdXElyaal4Y3S0avLskyelypgt+EKdfOWK+KCcHPX0Ro3U00UzR+i5zrJes5dYIjtTiF5LweuZl57TNOg1ZYaWOsku+S5It/RvYKNRPIOJGr1mL7G0Ta/ZSPScIUVEdMsGBoofUxX9M7qwUH1mE9FtIDlZjSb2+Mgh1yH1SMmyZcuQl5eHkJAQ03Pb3333HYYMGWKTyql57LHH8Morr+D48eO4fPky5syZg7vvvhtGoxEjR47E/PnzcerUKZw6dQqpqakYPXq03epGREREdMPiM9xCUi2Ii4vD+++/b5bWv39/9O/fv8pji4qK4OXlZZoTe8OGDfDy8kKvXr1kqoBnnnkG58+fR6tWrQAAXbt2xapVqwBcm8Hk3LlzaNq0KYBr83BPmzZNKn8iIiIiIj1JrzT59ttv45577kHLli0BAN988w3WrVtX5XF6zXDi6emJl156CWfPnsXZs2exfv16REREAAC8vb2RlpaG3Nxc5ObmYunSpVKL3hARERGRRhzhFpLqcM+fPx+LFy/GwIEDkZ2dDQCIjIzECy+8UOWxjp7hhIiIiIjIEaS+Mrz11lvYuXMnoqOjMXXqVABAo0aNcPTo0SqPdfQMJ9Y6dszRNSCSYGkJcEG0UPLFi6rpswMD5co+dUq8rcJ8+GZEUcmi9ZdFAX+idMC1IpX0rKtsRJ6lc6hGy3Lz9iAbWKvjGueiESvRcvB6xsjqNeCnZ3Ck7K0mvv3ll3a3x20um5cbDMrKuyEbbR2pM3Px4kVERkYCgGmmkOLiYvj6+lZ5rKNnOCEiIiIicgSpR0rat2+PtLQ0s7QVK1agY8eOVR7rDDOcEBEREZGN8BluIakWLFmyBHfffTfeffddFBYW4o477sDp06fx9ddfV3lsdWY4ISIiIiJyVVId7gYNGuC3337Df//7Xxw7dgwxMTHo2bMnAq189nPHjh3Yv39/paXWZ82aJVMNIiIiInI2bjASbSvSZ8bPzw8PPfSQdEFPP/000tLScMcdd5h10NVWjSQiIiIichdVdrgnTZpkVUaLFi2yuH3FihX44Ycf0KRJE+tq5iCvv145rVEj9Yjp22+Xm0OcSHei2T0sESy7niyYWWT25cvq6bGxwiKSjx9X3yCa2US05LvszBtajnHkiIwjZynRc1YTW59DS+dJduoPnZZ8t7TNx6iebjT6qKbLz+IhP7OJI29z0SVSFPm/oV5e4hlM1Mi+xbTMDONKEyPZHEe4haoMmixfRKaqV1X8/f3RsGFDXSpNRERERE7GSYImi4uLMW7cOISFhSEsLAxJSUkoEXwzGjFiBHx8fGA0Gk2vPXv26HE2zFTZgrfffluXgpKTk/Hvf/8bCxYssGoaQSIiIiIiWfPmzcOuXbuQkZEBAEhISEBqaqowZjAxMRFLliyxaZ2kx/4zMzOxZs0aHD9+HHXr1sWgQYPQoEGDKo+bMmUKCgoK8J///AfBwcFm286fPy9bDSIiIiJyJk7ySMmKFSuwePFi09ox06dPx5QpUxw6SYfUPNwbN25Es2bNsGvXLpSVleHbb7/FLbfcgg0bNlh17NatW7F161Zs2LDB7EVEREREZInBYDC9UlJSVPfJzc1FTk4O4ivEJcXHxyM7Oxt5eXmqx6xcuRJhYWFo3rw5XnrpJZSVleled6mvIk8//TTWr1+Pnj17mtI+/fRTTJw4EX379rV4bOfOnbXV0M7UYrdE04zffrtt60JUpeho8TbRMuqiAMVdu9TT27aVqxOAq0vfVE336fYv9QNEgWz2CJq0R1SVKGpMz7L14moRYLJBnqJzKBtMaWmbIN1PEDQpin3WEqcqe4voGCsqXXZRkXoApJ5L2tvj7a1X2W7Bxp9RiqJUuU/hP+/liiuZl/9cUFCAkJAQs/3Hjx+PF154AWFhYdi3bx8GDBgADw8PTJw4Ubd6A5Ij3KdOncL9999vlta9e3ecPn26ymPLysqwYMECNGnSBEajEU2aNMGCBQtQWloqV2MiIiIiIhXGf77BVhzNLv85KCio0v5t2rRBrVq14Onpifbt2+OZZ57B2rVrda+X1FeR/v37Y8WKFRg9erQp7Z133sGAAQOqPHbWrFnYuHEjkpOT0aBBA/z555947rnnUFBQgPnz58vXnIiIiIichxM8w12jRg1ER0cjPT3dNDteeno6YmJiKo1uq/HwkBqLtlqVZ6Zv376mxWmKi4vxxBNPYMmSJYiNjUVWVhYOHz6M7t27V1nQe++9h507dyImJgYAcPvtt6Njx47o1KkTO9xEREREpIuRI0di/vz56NixIwAgNTXVbLC4onXr1qF79+4ICgrC/v37sWDBAjz55JO616nKDnf8dYthtGvXzvTzbbfdZnVBly5dQnh4uFlaeHg4Ll26ZHUeREREROSknGCEGwBmzpyJc+fOoWnTpgCAIUOGYNq0aQCAsWPHAgBe/2elw6VLl+Kxxx5DSUkJ6tati8TEREyePFn3OhkUa55A18HAgQPh4+ODRYsWoVatWjhz5gymTJmCS5cuYf369faoQpUMBgOmTq18OtLT1fc/ckQ9/ZFHuAKlrSRPnaqaPvuFF+xcE9tI7tpVfYMo6E4UGAkAFy7IpYtu6AqBJxXN1vDRkSyIEBeuQCliKZpMNojOHoGLousnShdF0Yn21zMvLe2TjaLTKx2Qb4coXXSeLK3mKnhvyKZfQoBqup5vb1G67IKclo6RTRexdLlFl0N0yi29ZdTo+dEim48WTz5psCqQ0B4MBgOU2bbr/xiSk52mrVpo+ipy8uRJnD171qzhLVu2tHjMf/7zHwwePBh16tSBv78/ioqK0LVrV6xevVpLFYiIiIjImTjJCLczkjozBw8exIABA3D48OFr32QUxfR8d1WzjdSuXRtbt27F8ePHcfz4cURHRyMqKkp7zYmIiIiIXIBUhzspKQkJCQn47rvvUL9+fRw7dgzPPvus6aF0a9StWxd169aVrigREREROTGOcAtJj3B/9dVX8PHxgaIoCAkJwcKFC9GyZUsMGTKk0v533nkndv2zmEbr1q1No+HXO3DggIaqExERERE5P6kOt4+Pj2m5yxo1auDkyZMICQnBmTNnVPdPTEw0/fzUU09pryUREREROTeOcAtJnZkOHTpg8+bN6N+/PxISEtCvXz/4+fkJpwd8+OGHTT83atRI9dGT3bt3S1bZttRWvRZFa4uin4OC1JerBYCCAs5gUh2iaP5nnxWf8+eec6FzLrsctZbZG0Rh/hER6umiN4Cnp7hsEXt8GMvORiI7ZYA91mvWcr31WtJer3W7LdFzlhK96qvl/pCd4kPwnvQLVf9cszTDhmyzRXnJ3gZaytZruXlL9Hp76zlJj+y5tVRX9mNdn9QlfP/9900j3IsWLcKLL76IgoICTJo0qcpjExISkJ+fXym9Z8+eOH/+vEw1iIiIiMjZ8JuBkNSZCQwMNP3s5+eHGTNmWH2s2tyJZ8+ehaeWUTIiIiIiIhdRZYf79ddfN63K88orrwj3Gz9+vGp6jRo1YDAYcOnSJYSFhZltKygowKhRo2TqS0RERETOiCPcQlWemU8++cTU4d6wYYPqPgaDQdjh3rhxIxRFwf333292vIeHB+rUqYObbrpJS72JiIiIyJmwwy1U5Zn57LPPAABlZWVYunQpbrrpJnh7e1tdQOfOnQEAOTk5lUa4nZFa3JhaIKUlllap7tJFPbhv3z7nC+wbM0a9rsuWOa6uskvouhxRZFN4uNz+gPwHn2htZEEkT7JorWgAsys8fmZNerLoTeOMwYOW6LX2s57Bg84Y5KlXxJ/WY2TysXT+ZO8pwX3gUXJVNd3Pz0dYtF4r2tsjTlXPoEln7M/pFZ/rjG0j/Vh9eT08PHDbbbehUBR9XYWwsDBkZWVh9+7dlZaFF42OExEREZGL4LcGIakz07JlSxw9ehSNZId8AXz44YcYNmwYmjRpgoyMDDRv3hy//PIL7rzzTna4iYiIiMhtSXW4H3jgAfTq1QuJiYmIiYmBh4eHaVvv3r0tHpuSkoJ3330XAwYMQI0aNfDjjz9ixYoV+O2337TVnIiIiIicB0e4haTOzBtvvAHg2hzcFRkMhio73NnZ2ejfv79Z2vDhwxEdHY0XXnhBphpERERERC5DqsOdmZmpuaCwsDCcP38eNWvWRN26dZGeno6aNWuiyO2j4IiIiIhuABzhFrLbmRk4cCC2bNmCwYMHY9SoUejSpQu8vb0xaNAge1XBKmrRw6IJIkSRxpbiSs+eVU9v3Vp9RhDRKtw7d+o3U8jUqeplHzmiWxG6kV35HACGDVNv36pVtp9tZfBg9bJFq6ij8IR6upaZJnSaQUHLjZ6cl6e+oV079XTRzEcbN6qni2ZUAVxrCXfZsrUsM27rKSUs0Ws2Ekt1Eh2jJS+96DRTjpeXeJYSW89GYo/bQK/9tXDGfqEjP3LI9qRuuStXrmDRokXYvn17pZlGDhw4YPHY559/3vTzxIkTcdtttyE/Px/du3eXrDIREREROR1n/CbjJDyq3uX/TZo0Ce+99x7uv/9+HDp0CI888gguXbqEBx54oMpjr1y5guLiYtPvHTt2RLdu3XD1qvr8o0RERERE7kCqw71x40Z8+umnmDBhAry8vDBhwgRs2LAB27dvr/LY++67D/v27TNL27t3LxISEqQqTEREREROyMvLdi8XJ9XhvnjxIuLi4gAAfn5+KCoqQtOmTbF///4qjz148CDat29vlta+fXukp6fLVIGIiIiIyKVIfWVo3LgxfvrpJ7Rq1QotWrTA4sWLERoainBRVGEF/v7+KCwsRHBwsCmtsLAQPj7ioBBHkAlaEAU0WiJaDVtUrpYyZImCI0V1atxYPRDwjz9sH4QoCjq1dN2io9XTRQGNsoErolXJAWDNGrlzkjxypPoGLTeCbDCZqAwtN6cgoHL2H3+Ij1GRLLp4WoIHReyxzrJey83rucy4bPCs7FLpWsoW0TNoUpSuV10tkQ2a1HFFe71iarUeo8f+epZNNsILISR1ZlJTU01Luz/33HMYPHgwCgoKTPNzW3LXXXdh4sSJeO211+Dj44MrV65g8uTJ6Nq1q7aaExERERG5AKkO9z333GP6uV27dvhDYrTqxRdfRK9evRAeHo66devi+PHjuPnmm7F582aZKhARERGRM+IIt5DUmbn//vsxfPhw9OnTB36S/16sU6cOvv/+e+zduxfZ2dmIjY3FrbfeCoPBIJUPEREREZErkQqa7Ny5M+bPn4+IiAiMGjXKqtlJKjIYDIiNjUX9+vVRr149draJiIiI3AVnKREyKBVXr7HSjz/+iFWrVmHNmjXw9fXFsGHDMHfuXIvHnD17FkOGDMFXX30FX19fXL16Fffccw9WrVqFWrVqWVXuiBEjsHr1arNAy6+++godOnQAABQXF2PixIlYvXo1AGDIkCFYvHgxvKy8UAaDAW+8Ufl0iA4XBaGIAvsA8aqVojJE+4uCL3//XVy2bAyRaDE/URzbjz/KB01u3aoeuBgfr75/o0bq6U2aiMsQruooIGq3KEYwLLRMmFeZ4DutB8THqBLdbJaWNRXdJKJ00Y0r2t9StOixY1JlzL75ZnFeKpK/+Ua8Uc9ARDWWPk9k85INYLX0n0XZY2T3t1S27KqOorL1bLdsuoiWQFXZdgs+6MtCw4RFi96uonTRR4VsuqVtsvHKWui1eKmeHBkT/fjjBmjoxtmEwWCAsnWr7fK/+26naasWUiPc5Vq3bo1FixZh//79aNq0KVJTU6s8JikpCWFhYTh+/DguX76M48ePIywsDOPGjZMqOzExEYWFhaZXeWcbAObNm4ddu3YhIyMDGRkZ2Llzp1V1IyIiIiKyFekO95UrV7Bu3Tr07NkTDRs2RHFxMd5+++0qj/vf//6H5cuXIzIyEgAQERGBN998E9u2bZOvtcCKFSswY8YMREZGIjIyEtOnT8fy5ct1y5+IiIiIBPhIiZBUh3vUqFGoU6cO5s6di06dOuHIkSP4+uuvMXz48CqP9fPzQ25urlnahQsX4OvrK1XhlStXIiwsDM2bN8dLL72EsrJr/5rPzc1FTk4O4is8ixAfH4/s7Gzk5eWp5pWSkgKDwWB6ERERERHpTeorg9FoxNatW9G2bVvpgh5++GHcf//9mDlzJmJjY3Hs2DGkpqZiyJAhVucxfvx4vPDCCwgLC8O+ffswYMAAeHh4YOLEiab5wUMrPIBb/nNBQQFCQkIq5ZeSkoKUlBTT7+x0ExEREWnkBiPRtiJ1Zl5++WXNBc2dOxf+/v6YNm0acnJyEB0djaFDh+LZZ5+1Oo82bdqYfm7fvj2eeeYZrFy5EhMnToTxn2CUvLw808qX5SPbQUFBmutNRERERFQddvsq4uXlhVmzZmHWrFm65enh8f9PxNSoUQPR0dFIT09Hw4YNAQDp6emIiYlRHd2WIZqkQcRSVLQo0lg2kloU7C6a3QMQf/HMyVFP//pr9fT//lc9vVs39RlHAODzz3sLtsxXTfXymq6aHhennotoZhFA3D7ZlcxF5+/8BfGTWaIyvLx0mr3EEtmwfdnwf0sjGaKGW5rCR4alKRRcaYTFHsuJ61W2likUnLFsES3TWYim5dDpfFj+PFD/DNHr9rfH0u5aOHJZeXu8LV2eK33+2plNz0x+fr5V+wUHB1u137p169C9e3cEBQVh//79WLBgAZ588knT9pEjR2L+/Pno2LEjgGtL0Y8ePVq+4kREREREOrFphzs0NNTic9GKosBgMKC0tNSq/JYuXYrHHnsMJSUlqFu3LhITEzF58mTT9pkzZ+LcuXNo2rQpgGvzcE+bNq16jSAiIiKiqnGEW8jqM1NaWoro6GhkZmZavax7Zmam5oqp2bFjh8Xt3t7eSEtLQ1pamq7lEhERERFpZXWH29PTE0ajEVevXrW6wx0bG6u5YkRERETkQjjCLSR1ZqZPn46RI0ciJSUFMTExZkGL1j6H7ezUYr1E948ogMLS8raipdpFQX+WggHVWArqEAV/ysbQFRcXq6Z//vmH4sIxVJB+q2qq7NLBn38uv6x8ixbqQZ6iskXn1tI1EtVXdIyfnyiY0g70Wp4bkL6hk8+dE+elJjpavE32Auq5HrXsh4W7kI00ls1HC70CUi21QfSescNa315ePuJ6SdCzf+TIoElHulHf9iRH6tZ99NFHAQAbNmwwPZst+xw2EREREbkhZ/xG5CSkzozez2QTEREREbk7qQ53xWeyz549a1pghoiIiIhucBzhFpJ6NPTSpUt4/PHHERAQgDp16iAgIABjx47FxYsXhcfMmTPH9CIiIiIiutFIdbgnT56Mw4cPY+vWrThx4gS2bt2KP/74A1OmTBEeoyiK6UVEREREbsrLy3YvFyfVgk8++QQ///wzwsLCAAB16tTBunXr0KJFC7z22muqxyQni5f7dkZqT8mIJjEQpVuaNVG0TTSpg+xTO5YmXJCdhKJPH/X0bt28VdNPnjwsLlzg+ecHqKb//rv6/qIJKHx9pYvGqVPq6bITD2iZlUZ2eWIfUaUsFa7XUu2isi3d6KIZTCIi1NNFF1aUbmmGFBFRXlqW9BbRa7YOPem1FralKRdkp2lwpaXr7TFLiYiGWUocuey6LFebIUWWM9aJHEfqdlAUxWwqQADw8PCwavQ6OztbuK1evXoy1SAiIiIiZ8NvGUJSZ6Znz5546KGHsGDBAsTGxuLYsWOYPn06evXqVeWxcXFxMBgMps55xSXfOaUgERERkYtjh1tI6hnuRYsWoV69eujUqRMiIiLwr3/9C9HR0XjppZeqPDY3Nxfnz59Hbm4ucnNz8csvv2DAgAFYuXKl5soTERERETm7Kr+KfPLJJ+jduzcAwNfXFytWrMDy5ctx5swZ1KpVy2yk2pKQkJBKv7/55pto06YNhgwZoqHqREREROQ0OMItVOWZGTp0KPLz8wEANWvWRH5+PgwGA2rXrl3twi9evIgzZ85UOx89qQW5yS4zbul+E8V6ycbeiPKxFEsmW3aTJurpq1bJL6MuIgrM/Ppr3YoQyslRTxcFsGr5HNErgMloVP9nVICldeVlC5GNAtYSNCkqQ8/gOh2Xz5beXzZ4UK/A1qq22ZozBovK0nJ/6Hn9JMm+LZ0xQPFGXQqebkxV3oo1a9bE+++/jxYtWqC0tBQ///yzapBky5YtLeYzadIks98vXbqEL7/8En1EPS4iIiIich38hiNU5Zl55ZVXMHnyZGRmZqKsrAytWrWqtI/BYKgy8DE3N9fsd6PRiFmzZmHo0KGSVSYiIiIich1Vdrh79eplmoUkKCgIBQUFmgp6++23NR1HRERERC6AI9xCUmcmKytLuoCK829zvm0iIiIiutFIdbjLV5iU0blzZwDXHjv5888/pY+3N7VYLz0XRdMrPktLjJIojk0Ud7d4sX7BkSI336xexs03q+//7bfqK5d++KF4RdNnnlFPv3BBPb1RI7n9tSxEp9sif6HimT0DRBdcr6BCSzezbLSvbD6WTqCtAzP1DOR0YNCdND3Ltse9ptf+WvKSXb1Ux3Or1y2lZ5C4Xvs7ml4x0W7thmy0dWx+ZjIzM21dBBERERGR05Ja+IaIiIiISJWXl+1eEoqLizFu3DiEhYUhLCwMSUlJKBH8K0Jm3+qQ6nCfOnVK9woQEREREell3rx52LVrFzIyMpCRkYGdO3ciNTW12vtWh1SHOy4uDr169cKGDRts0vsnIiIiIhflJCPcK1aswIwZMxAZGYnIyEhMnz4dy5cvr/a+1SHV4T569Cg6duyIadOmISoqChMnTsTBgwd1rxQRERERkazc3Fzk5OQgPj7elBYfH4/s7Gzk5eVp3re6pL4y1K1bF8888wyeeeYZ7NmzB++++y66du2KuLg4jBw5EsOHD0dwcLCuFbQ3mS9RoiXftcwUIppYQXZSB0tEgfMBfmX6FWJjHTuqz2oybpx4lpKICPV00bkVXT8t11uWnpM3hApmMAm29ewlVW2TITjps1VWuzUJDFRNTj56VH1/PWe5sPWsFVqWdnel9bMd+Z9TPZd2l91fw/mzddFabrUbdbYO2fMhm48rKbNxaKDBYDD9nJycjJSUlEr7FP7zdyO0whRs5T8XFBQgJCRE077VpfnyRkVFITIyEsHBwcjLy8OGDRswY8YMvPLKKxg+fDgAoGvXrmYnR+R///uf1moQERER0Q1AsTTY8g/jP4NKeXl5CA8PN/0MXFvAUeu+1SX1VeTSpUtYuXIlunbtiltuuQVHjx7F22+/jSNHjmDr1q34/PPP8dRTT5n279OnDx544AE88MADuPXWW/HTTz+hSZMmuO+++9C0aVMcPHgQt956q64NIiIiIiL7Kymx3ctaNWrUQHR0NNLT001p6enpiImJqTRiLbNvdUmNcEdERKBJkyZ49NFHsWnTpkqPj3To0AG333676fcJEyaYfr777rvx6aefon379qa04cOHY9q0aVrrTkREREROwlnm0xg5ciTmz5+Pjh07AgBSU1MxevToau9bHVId7l27dqFly5YW9/n8889V03/44Qe0a9fOLK1NmzbYt2+fTBWIiIiIiIRmzpyJc+fOoWnTpgCAIUOGmAZ4x44dCwB4/fXXq9xXT1Id7qo625Y0a9YMc+bMQXJyMjw9PVFaWor58+ejWbNmmvO0BbXYJj1jkUSxU6Ll1UVlyMZgAeKgv6Ii11n/qHFj9eBIUTwcIL/KuGh/2RXDLR0jShcFeMrmD1gIYIrwUU2XXgreUuEieq1pf+aMfNmybxo9g0XtEVxn64grPYMH9SzbkfSql4st7a5Xde0RJOjIW0evjztX4ixt8Pb2RlpaGtLS0iptK+9oW7OvnuwWE7t8+XL07t0b//nPfxAVFYUTJ06gZs2a2LRpk72qQERERERkd3brcDdr1gyHDh3C7t27cfLkSURFRaF9+/bwcod5cIiIiIhucM4ywu2M7Nrb9fT0RKdOnexZJBERERGRQ0l3uC9evIjDhw+joKDALP1f//qXxeOOHz+OmTNnYv/+/ZWO/fPPP2WrQUREREROhCPcYlId7nXr1mH06NG4evUq/P39TekGgwHnz5+3eOzQoUMREBCAp59+GoGCleCcgVrc2D9zoVei5cYSBUeKytAz0GXuXPVVGp2RwaAeHPnDD+r7R0eL85KNlZMNjhQFo1o6RlSG7BNWWoJnRcf4RQSopnv42WEZVMkAxWRLb74LF9TTHbnCpl5Bk1qOcWRkmp4fYLJl2+Mvv62XXLTDtdPzVrNHPnqt3miPVTFv1JU3SZ3UZf/3v/+NtLQ0DBs2TLqg/fv34+zZs/DxUZ8hgYiIiIhcF0e4xaTmg8vLy8PQoUM1FdS8eXOcPHlS07FERERERK5KaoR76NChWL9+PQYMGCBdUL9+/dCrVy8kJSWhTp06Ztt69+4tnR8REREROQ+OcItJdbizsrKwbNkyLFmyBBHXrdDx8ccfWzz21VdfBXBtycyKDAYDO9xERERE5LakOtxt27ZF27ZtNRWUmZmp6TgiIiIicn4c4RaT6nAnJ6vPHOFO1KKHRctti2Z7sLTitegYvYL5PVAmLtwJpaWp31PduqnvHxennm5pwgy9JqHQ8kEiO0vJqVNy+WiZKER0D4ryCpad3UNPGpaVn52bq74hJEQ1Ofn0afX97TF9g57TFdi6bD3fALJ5aZkZxh5vfCechkKvCWD0XNpdz48K2VlHZPPRk151tZQXuY4qL2FGRgaaN28OADh48KBwv5YtW1rM58qVK1i0aBG2b9+Os2fPQlEU07YDBw5YW18iIiIickIc4RarssPdvn1700I18fHxqvsYDAaUlpZazGfSpEnYvn07HnvsMUyfPh3z58/Ha6+9hsGDB8vXmoiIiIjIRVQ5LWDFVSHLyspUX1V1tgFg48aN+PTTTzFhwgR4eXlhwoQJ2LBhA7Zv316tBhARERGR45WU2O7l6qTm4a6OixcvIu6fB3D9/PxQVFSEpk2bYv/+/Zryu3z5Mho1aoTQCks3FhcXY9y4cQgLC0NYWBiSkpJQ4g5XiYiIiIhclt0ew2/cuDF++ukntGrVCi1atMDixYsRGhqKcNGa5lWYNWsWoqOjcfbsWVPavHnzsGvXLmRkZAAAEhISkJqailmzZlWr7s89J7ckelKSOLhUr4AWUczYCy/MlcvIwSwtyS7DUqCqbNydnt/RbB2YaandontKNmjSL0J9dVgfeyztLiqjsFCYVXKtWuobKnxWWFWGPSK97BF9xmgrskDLrenIFe1ly7DH21hEz5haVxk7dJV6OoLdPolTU1NR+M8fyeeeew6DBw9GQUEB3njjDem8Dhw4gM8++wyLFi3CwIEDTekrVqzA4sWLERkZCQCYPn06pkyZUu0ONxERERFZxg63mN063Pfcc4/p53bt2uGPP/7QlE9JSQnGjBmDtLQ0s/Tc3Fzk5OSYBXbGx8cjOzsbeXl5CBFMCUZEREREZEtSz3CfFfxL9tChQ7pUxhovvfQSWrZsiS5dupill4+eV3ymu/znioGfFaWkpMBgMJheRERERKQNgybFpDrcLVu2xNdff22WtmzZMnTo0EHXSokcPXoUaWlpePHFFyttM/7zHGZeXp4prfznoKAg1fxSUlKgKIrpRURERESkN6lHSl555RUMGjQIo0ePxuTJk/HEE0/gp59+wpdffmmr+pnZuXMnzpw5Y1qI5+rVq8jPz0dERAQ++eQTREdHIz09HQ0bNgQApKenIyYmho+TEBEREdmYO4xE24pUh/uhhx7Cbbfdhvvvvx+LFy9Gjx49cODAAeEIcrnS0lJ88cUX6NatG3x9fTVXduDAgejevbvp9927d2PkyJFIT09HzZo1MXLkSMyfPx8dO3YEcC1Qc/To0ZrL08rSDSeaIUL2Jn3hBbmZU5xVu3bq6cIZMwTnr8KTRJWIJrTIyREfI1O2pYhzS7OIyOR14YJcPoC43aK8ROdclB4WKjghgPw0LLIzhViaIUV00mVnVRHlo2VKHL04cil4LfSapkf05nM1slOC6HiN9JwpRPYYPWfrcCVc2p0qkr6EK1euxIkTJ9CjRw98//332Lt3L+6++26Lx3h6emLQoEHCZ6mt5e/vD39/f9PvYWFhMBgMiIiIAADMnDkT586dQ9OmTQEAQ4YMwbRp06pVJhERERFVjSPcYlId7i5duuDSpUvYu3cvGjVqhI8++ggDBw7EqFGj8Pzzz1s89o477sD+/fvRtm3balX4+vpcqDBc5+3tjbS0tEozmBAREREROYpUh/vWW2/Fc889B69//rfx4IMP4tZbb8XQoUOrPDY+Ph49evTAww8/jHr16sHD4//jNcePHy9ZbSIiIiJyJhzhFpPqcL/wwgsAAEVRcOrUKURGRqJevXrYvn17lcfu3bsXTZs2xY8//ogff/zRlG4wGNjhJiIiIiK3JdXhLigowJNPPom1a9fCy8sLFy9exMaNG3HgwAHMmTPH4rHbtm2rVkUdaeJE9aXag/2uqqafEKwgbUmAX5lq+uy5rrVUu6y6ddWDPyMj1c+5KO7tn8f4VYmCI48cUU8/dUo9Xcsy9LLBQnoG2cjG/MkGWfr5iWcVDdArOlhLAKToGNmy9Yz00isoTsua13pxZLu1lOEq+WssQ6/Lredl1YueS5+7UiCnI9/eenGVejqC1DzcSUlJKC0txS+//AIfHx8AQPv27bF27Vqrjs/NzcX7779vGik/ceIEcmSniiAiIiIiciFS3+W++OILZGZmwt/f37QyY0REBE6fPl3lsXv27EGvXr3QpEkT/PTTT5g6dSp+++03vPLKK9i0aZO22hMRERGRU+AIt5jUCLevry9Krjub586dQ1hYWJXHPvXUU3jrrbewa9cuU9Blhw4dsHfvXpkqEBERERG5FKkOd69evZCYmGiaiu/KlSv497//jb59+1Z57OHDh9GnTx8AMI2OBwQE4MqVK3I1JiIiIiKnU1Jiu5erk3qk5Pnnn8fIkSMRHh6OsrIyGI1G9OnTB6+88kqVx9arVw8//fQTWrVqZUo7cOAA6tevL19rOxNd6L8v+KimWwp8ED2yHh4u9d3H7Z08qR5M2bWrejBlo0bivHbuVM8rNlZ9/8JC9TJEcYBa4ve0rFqpF9H9rFeQJQD4hau/NzxkVwwUnVxLqz3KBk3qubqh7NKiIlqCDfWMuJWl1wqKzhhMqWXpYJ3KLpMbE9OVPW41e1xuR97+sizVlStNuj6pSxgYGIh169bhzJkzyMrKQkxMDOrUqWPVsc8++yx69eqFqVOnori4GG+++SZeeOEFLFiwQFPFiYiIiMh5uMNItK1o+s5Uq1Yt1KpVS+qYQYMGITg4GK+++ipiY2OxYcMGLFmyBD169NBSBSIiIiJyIuxwi1XZ4a5fv77pmWtL/vzzzyr3uf/++3H//fdbVzMiIiIiIjdQZYd7yZIlpp9//fVXLFu2DGPHjkVsbCyysrLw5ptvYtSoUVUWtHLlSrRq1crsGe709HT88ssvVi0NT0RERETOiyPcYlV2uB944AHTz/PmzcMXX3yBm266yWz70KFD8eyzz1rMJzk5Gfv27TNLq1u3Lvr27csONxERERG5LalnuA8fPozY66Z2iI2NxeHDh6s89vz58wgPDzdLCw8Px7lz52SqYHNqkcCyq1RbiiZ+7TX1GTPIOtu2qZ+/bdv0KyM+Xj1ddsYRQHwvWJrZRIaWsmWj3WVnL7G0TXrJdy2zQIiO0Stdy6wVsh8W9lhGXXbWFi3TVthj9hLZc2Xr/e3EGUcSnfRUqXLGJe3dgTPel85Cas6h9u3b44knnjB1ks+ePYtx48bh9ttvr/LY+vXr45tvvjFL27lzZ6UOPBERERGRO5H6vrZixQoMHjwYtWvXhp+fH4qKitCxY0esXr26ymOffvppPPjgg3jqqafQuHFjHDlyBC+//DIWLVqkufJERERE5Bw4wi0m1eGuW7cuduzYgb/++gsnT55EVFQUoqOjrTp28ODBMBqNeO2117BmzRrExsZi2bJlZs+IExERERG5G01PJMXExCAmJkb6uF69eqFXr15aiiQiIiIiJ8YRbjGpDndGRgbGjRuHAwcOoPCfdZ0VRYHBYEBpaalVeZw8eRJnz56FoiimtJYtW8pUw6bUgiJkAyU8UKZPZcghQkPV00X3wYUL4rxEwZGidNl4NUtBk7ZePt7SB6soaNLLSz1sxEdUWT0jVfVKt1S2uOHiY/Sok6VtjvwLqNe51TNgUzYfN+fIW01LPs54mxNZQ+oT5pFHHkHbtm0xf/58BAYGShV08OBBDBgwAIcPH4bBYDB11AFY3VknIiIiIufELz5iUh3uQ4cOYe/evfDwkJrcBACQlJSEhIQEfPfdd6hfvz6OHTuGZ599Fh07dpTOi4iIiIjIVUh1uDt27IhffvlF0yMgBw8exFdffQUfHx8oioKQkBAsXLgQLVu2xJAhQ6TzIyIiIiLnwRFuMakOd5s2bdCjRw8MGzYMERERZtvGjx9v8VgfHx+UlV17trlGjRo4efIkQkJCcObMGckqExERERG5DqkO9549e9CoUSPs2bPHLN1gMFTZ4e7QoQM2b96M/v37IyEhAf369YOfnx9uu+02+VoTERERkVPhCLeYVId7WzXWz37//fdNI9yLFi3CSy+9hPz8fEyaNElznrbAGUZINNFEXJzc/oC2iS5k8tFzhXPZSRosfbDKrhru4yd5ovScnkX2ImmZQsEe++u1tLulG9pR7DErjT04sGx7FK1XGZbyscdMKES2YJd3f2lpKUaMGIFVq1YBAPz8/DB9+nR7FE1EREREdsAvMmJVdrjr1auH7OxsANeevS6fyu9658+fF+bh6emJXbt2wesGneeUiIiIiG5cVfaAV69ebfp548aNmgt6/PHHsXDhQkybNk1zHkRERETknDjCLVZlh/vOO+80/dy5c2fNBW3atAm//PILFi9ejLp165rN5X3gwAHN+RIRERGR47HDLWa3ZzyeeuopexVlN2VQXwCIgZeuraBgtmr6hQvJqumipeAB8YePbNCkiD1iB7U8CSYbNCl+LwnoGTyo5wmxddSYPYImRZz1L6mtl3bX81FIJzyHjrzN9Twdsrc5n3Ale7PbLdeoUSPVVSV3795tryoQERERkY044XdKpyG/RrtGCQkJquk9e/a0VxWIiIiIiPDtt9+iVatWCAgIQHx8fKU1Zio6duwYDAYDjEaj6dWrVy+p8uw2wq0oSqW0s2fPwtPT015VICIiIiIbcZUR7vPnz6Nnz55YuHAhhg8fjpUrV6Jnz544evQoQi08J5qTk2NxuyVVdrjnzJljVUazZs1STS+fSvDSpUsICwsz21ZQUIBRo0ZZlT8RERERUXVt2LABdevWxZgxYwAAY8aMwZIlS7BhwwaMHDnSJmVW2eH+8ccfTT+XlJTgiy++QOPGjREbG4vs7GwcPnxY+LgIcG0qQUVRcP/992PDhg2mdA8PD9SpUwc33XRTNZtge6KALr32J9dw4YJ6enS0+BjZRfv0WgXS0jG2jjGzRH4FSg3RpbIrR4rSRRfPkUGTWlZc1Gt/S0NXzrg6pYg9Vvd0IY4MmhTRspirq4ysujtXuQ4HDx5EfHy8WVp8fDwOHjxo8bhbbrkFJSUluO2227Bw4UI0adLE6jKrfNtU7CQ/9thjWLp0KR5//HFT2rJly7Bv3z7h8eVTCebk5FQa4SYiIiIiskbFxReTk5ORkpJSaZ/i4mKUlpYK8/D19UVhYWGlR0NCQ0NRUFCgekx4eDi+//57tG7dGhcvXsTcuXNxzz33ICMjA8HBwVbVXWoodv369abh93KPPvoo1q9fX+Wx7GwTERERua+SEtu9gGvxgOUvtc42APTt2xf+/v7CV1ZWFoxGI/Ly8syOy8vLQ1BQkGqeRqMRt912G7y9vREaGooXX3wRxcXFUjPtSXW4w8PD8cUXX5ilbdmyBTVr1pTJhoiIiIhId//973/NOubXv+Li4tCyZUukp6ebHZeeno4WLVpYVYbBYDAbbbeG1JNYCxYsQL9+/dCtWzfExsYiKysLW7duxapVq6QKJSIiIiL34irPcPft2xdTpkzB8uXLMWzYMKxatQonT55E3759Vff//vvvERwcjJtuugmXL1/G3LlzYTAY0KFDB6vLlBrhfvDBB/Hzzz+jffv2UBQF7du3x08//YSHHnqoymPXrFmjmv7BBx/IVIGIiIiISLOwsDBs3rwZL7/8MkJCQvDKK69g8+bNqFGjBgAgOzsbRqMR2dnZAIA///wTPXv2RHBwMOrXr4+MjAxs2bIFISEhVpdpUNQmyLaB4OBg5OfnV0oPCwvD+fPn7VGFKhkMBigWHrS/nmg2krlz1ZcGJ/dUv776ku8AYDTK5aXXcuxaytArXcsxomlNtbyXkvv3V99QWCiXLpqWRpRuKS/Z9aXtMUuJiKiulmYikR3W0vNmE908ojefPW502esqqGuZl4+waNHlEKXLzhCk5XLbOl3rMTK05CM7c4qebejRw6C6zokjGAwGPPGE7ery2mvO01YtqvyEfv311zF27FgAwCuvvCLcb/z48arp5Z1sRVFQUFBgdrKOHj0Kb29vqQoTEREREbmSKjvcn3zyianDXXGKwIoMBoOwwx0aGmp6sPz6KVg8PDyQnCweHSQiIiIi1+Aqz3A7QpUd7s8++wwAUFZWhqVLl+Kmm26SGpXOzMyEoii4/fbbsXfvXlO6h4cHatWqBT8tC1sQEREREbkIq4MmPTw8cNttt8FL8jnB2NhYxMXF4fTp04iNjTW9YmJiNHW2k5KSEBMTg+DgYNStWxdPPfUUrl69CuDaZOfjxo1DWFgYwsLCkJSUhBJ+3SIiIiKyOVvPw+3KpHrPLVu2xNGjR9GoUSNNhe3Zswfbt2/H2bNnzZ7lXrRokdV5JCYmYsGCBQgMDMSZM2cwYMAALFy4EDNmzMC8efOwa9cuZGRkAAASEhKQmpqKWbNmWZ2/WiCkR8lV1X093GBZX6q+zExxYF+nTvo8MqVn0KSeeelF9GGaPHOm+oZTp8SZiQIXRV/wRYWLgu7sEdGl58XQKy9LAyR6Le3ujFF0WtY4lw2mFNDSbL24+5832Thmso47dIxtRerWeuCBB9CrVy8kJiYiJiYGHh7/3znt3bu3xWPT0tIwdepU3Hffffj888+RkJCALVu24IEHHpCqcNOmTc1+9/DwwB9//AEAWLFiBRYvXozIyEgAwPTp0zFlyhSpDjcRERERkZ6kOtxvvPEGgMoj0gaDocoO95IlS/D555+jc+fOqFGjBjZs2IDPPvvMqmXhr7dgwQLMnz8fhYWFqFmzJp5//nnk5uYiJycH8fHxpv3i4+ORnZ2NvLw8qbkSiYiIiEgOR7jFpBa+yczMVH39+eefVR57+vRpdO7cGcA/810rChISEvDJJ59IV/qZZ55BQUEBfv31V4wdOxYREREo/OffyBVnQin/uaCgQDWflJQU0/Kcskt0EhERERFZQ6rDXR0RERE4ceIEAKB+/frYvn07MjIyzB5LkdW0aVO0atUKI0aMgPGf5y3z8vJM28t/DgoKUj0+JSUFiqKYXkRERESkDYMmxaQeKalfv77qSLCvry9iYmIwYMAAPProo6qd6CeeeALff/89+vbti0mTJuHee+8FcO056+ooLi7GH3/8gRo1aiA6Ohrp6elo2LAhACA9PR0xMTF8nISIiIiIHEaqw/3YY49h+fLlGDt2LOrVq4esrCwsW7YMDz/8MLy8vJCcnIycnBykpKRUOnbixImmn4cMGYJ//etfKCwsrBQEaUlhYSHWr1+Pvn37IiQkBL/88gvmzZuH++67DwAwcuRIzJ8/Hx07dgQApKamYvTo0TJNVP0W5SVYXtcDZVJ5E5UTrUYtYo+IenvMXiK7jLSPYKIQTTNHSC6rLWRpqEV2vW0RLRdDrwulJR/ZGWBky9BzlhI9h8pkr5NsupM2W8QZZ/iQvQWd9Ty5ygivq9TTEaQu+8cff4wvvvjCbFrAPn364OGHH8a+fftw77334qGHHlLtcF8vJiZGurIGgwGrV6/GlClTcOXKFdSuXRsPPvggZs++Ni3bzJkzce7cOVMnfsiQIZg2bZp0OUREREREepHqcB8+fBj16tUzS4uJiTFNy9e2bVucOXNGv9pdJzAwEF999ZVwu7e3N9LS0pCWlmazOhARERFRZRzhFpOKWLz99tuRlJSE3NxcAMD58+fx1FNP4dZbbwUAHDp0CLVr19a/lkRERERELkpqhPvtt9/G4MGDER4eDj8/PxQVFaFjx45YvXo1AODSpUt4/fXXbVJRIiIiInJeHOEWk+pw161bFzt27EBOTg5OnDiBqKgoREdHm7a3bt1a9wo6A3HQhd1mVSQXtXOn+rLvw4apL/nuSgFP9gimLBP8E85DyzLjshUWlWGpbD0DMPXiyEg2ewRy6nUOHXgtrpao3+eWYm0ZNFk9jjwfWsp2pXNL6jRdwujoaLOONhERERHd2DjCLcYhWiIiIiIiG+I/KYiIiIio2jjCLcYRbiIiIiIiG+II93UYmED2ILrPRPF4ouApLaMJssc4MvBH1O4AS4GLsqseyuajZ9myK1Bq4chlSu2Rj+wNKnsfuFiEmzOOMDrj31UtbwtnPLfOhudIjCPcREREREQ25ITfO4mIiIjI1XCEW4wdbiIiIiKqNna4xfhICRERERGRDXGEm4iIiIiqjSPcYuxwV4MHyhxdBXJRlia6UKMlyl+vySlE+ci2wVJe0u2zdIBeM4VoqaxeM6RomWFDtr56/mWUvYCi/fWcpcQeU2NIXqcyLx+pbCyRbbY9Lrds2Xp+rsnScp5sfW45Q4p7Y4ebiIiIiKqNXwzE+Aw3EREREZENcYSbiIiIiKqNI9xiHOEmIiIiIrIhjnBfh4GQZA+i4BhnXOlbz1XJ9YopLLMwVuBh6yA6ewRNajm5ei1lruX8yR4jG3GrJZLtBqXX6XBkTK09Lrc9lna/EUd7b8Q2W4sj3ERERERENsShASIiIiKqNo5wi3GEm4iIiIjIhjjCTURERETVxhFuMY5wExERERHZEEe4iRxANAmF7OQUWkYT9JrMYv362dJlDxuWrEvZmug1e4mW2Tr0nJXDlfB8VIueS77rWYZsXnq+xWTLli3jBr3VdMPzJ8YRbiIiIiIiG+IINxERERFVG0e4xTjCTURERERkQxzhJiIiIqJq4wi3GDvc11O7W7h0MOlMNmjSkUu+i9K7dlUPgAQAo1EuL7vQK1rUEtnATNl0e5xAeyztLhtMaand9nhzOCFHvpf0Co7Uk7sHQbpKN8Rdzrct8JESIiIiIiIbcpHvTERERETkzDjCLcYRbiIiIiIiG+IINxERERFVG0e4xdjhrobZc+c6ugrkos6eVU8XBRtGRKinHzkiLkOUl16rvlnKR1S2KCZOduFBD5SJN8oGRzoymFKv/bVwlSgswHJdRTePPZZt1YmWW1CvlRX1pFedLNXVke12xkBVch0u9IlLRERERM6KXwzE+Aw3EREREZENcYSbiIiIiKpNUSw87neD4wg3EREREZENcYSbiIiIiHRQ6ugKOC12uK8z+7nnHF0FugF8/vls1fROndSXS2/WRPRvOvE/qQoL1dNlJ28IDdUnH0A8e4mIrjMr6DUbibvPXuIu9DpXThoFZuvbVs8ZUhzJlepK7o2f3kRERESkA45wi/AZbiIiIiIiG2KHm4iIiIh0UGrDl35OnjyJ3r17IyoqCgaDAenp6VUe8+2336JVq1YICAhAfHw89uzZI1WmS3W4r1y5gjFjxqB+/foICgpCkyZNsGLFCtP24uJijBs3DmFhYQgLC0NSUhJK+AAXEREREf3Dw8MD3bt3x8aNG63a//z58+jZsyfGjRuH3NxcPPnkk+jZsycuXLhgdZku9Qx3SUkJIiMj8fXXX6NBgwb4/vvvkZCQgOjoaNx7772YN28edu3ahYyMDABAQkICUlNTMWvWLAfXnMg6O3eqB1Pu3GnnilQwcqR6IKel5dhFS9eLvv+Gh6unC+Pe7LHmtT2+rMuuba0lENAZ16OWTXfGdcntQEuV9IoN1lKGXm7Qy+0mXGMe7jp16iAxMdHq/Tds2IC6detizJgxAIAxY8ZgyZIl2LBhA0aOHGlVHi41wh0YGIg5c+agYcOGMBgMaN++Pbp27Ypdu3YBAFasWIEZM2YgMjISkZGRmD59OpYvX+7gWhMRERHdCGz7SInBYDC9UlJS7NUoHDx4EPHx8WZp8fHxOHjwoNV5uNQI9/WKioqwd+9ePPzww8jNzUVOTo7ZCYmPj0d2djby8vIQEhLiuIoSERERUbUoilLlPsXFxSgtFT/z7evrC4PBIFVuYWEhQq+bIzc0NBQFBQVW5+FSI9wVKYqC0aNHo3HjxujXrx8K/5l0uOIJKf9ZdEJSUlLMvi0RERERkVaOD5rs27cv/P39ha+srCzpVhmNRuTl5Zml5eXlISgoyOo8XLLDrSgKnnjiCRw6dAgbN26Eh4cHjP+sqFHxhJT/LDohKSkpUBTF9CIiIiIi1/Xf//7XrG93/SsuLk46z5YtW1aaySQ9PR0tWrSwOg+Xe6REURQ8+eST2Lt3L7Zu3Wp6VKRGjRqIjo5Geno6GjZsCODayYiJieHjJETV8Pbb6oGcepo6VT0w00MUgOMuy+CJaAmatHW79cxfdplSSxG6tqbjvSa8nwVjX+5yufUsw9ZBofZYSNYdPqLEXGfhm6IKn0NXr15FUVERfHx84OFR+f3Yt29fTJkyBcuXL8ewYcOwatUqnDx5En379rW6PJcb4R43bhy+/fZbfPXVV6hRo4bZtpEjR2L+/Pk4deoUTp06hdTUVIwePdpBNSUiIiIiZ1T+iAkA3H777fD398eOHTsAANnZ2TAajcjOzgYAhIWFYfPmzXj55ZcREhKCV155BZs3b67UD7XEpUa4s7Ky8Oqrr8LX1xexsbGm9KFDh+L111/HzJkzce7cOTRt2hQAMGTIEEybNs1R1SUiIiK6gbjOCLelR4nr1atnig0sd+edd0rNSnI9l+pwx8bGWjxB3t7eSEtLQ1pamh1rRUREREQk5lIdbiIiIiJyVq6x8I0juNwz3EREREREroQj3EQ2NGaM+uwby5bZfuYPVyKK2r9UpD4mEGBp1grZGTBkaZk6Qna6Ai2zlOi1HraeU0fI7q/nFBT2mLZCp3Z4efkI0sVF6zUDhiNPrbvM4uEu7dCH6zzDbW8c4SYiIiIisiGOcBMRERGRDjjCLcIRbiIiIiIiG+IINxERERHpgCPcIuxwE12nVy/1QEejUT4vUdDMsGHqZYhiAd09yHLxYrn2Pfus+vkDAB8tAYd60Ss4UkTLEud6BSLquMS5XYImZcvWsr9O7fDzUw+atEfQnSMD/rS8LRx56xBVBzvcRERERKQDjnCL8BluIiIiIiIb4gg3EREREemAK02KsMNNRERERDrgIyUifKSEiIiIiMiGOMJNdB3ZSS4sRcHLrjIu2l80q8mqVe49e4nIc8+J2z11qvq5CogQTDOTk6Oeruea1/aYIUU0jY6oTqKbTXZ/S8fYY4YUW7PDLCUegn/De3mJx8T0/JySdaMuZX6jtlsOR7hFOMJNRERERGRDHOEmIiIiIh1whFuEI9xERERERDbEEW4iIiIi0gFHuEXY4Sa6zoYN6gF5osBFS/FwskGTouAbLSt636heeEEukDR55kz1DceOyRcue8FlaQm+lA3k1LMNjgyO1CtQ1Q5Bk6J0Ly/1Jd+vbRNXS2Z/PZdXl93fGZeVJ7IVdriJiIiISAdc+EaEz3ATEREREdkQR7iJiIiISAd8hluEI9xERERERDZkUBRFcXQlnIXBYEBKSoqjq0EuZvBg9WBKQBzsKIpLk138T0tcmCgolMwljxmjvsHSSb9wQa4Q2eA6S2XLRqDJrihpKZiysFCuDBHZ5RO1kC3DUtmi1T11Si/zCxAWLTrlonTZzxZL9FpAVETP2GB7xO3aeqFVS8fcd58BztKNMxgMAHbYsIR/OU1bteAINxERERGRDfEZbiIiIiLSAZ/hFuEINxERERGRDXGEm4iIiIh0wBFuEXa4iYiIiEgHXPhGhB1uompas0Y868eYMXLLwYsmudAStS+aIaVXL7k6ifLRsuJ1aKh6up4TR4SFyn3gz547Vz192TLV9ORnnxVnJrtcumhKCdn8AfGFEpGdCUXLTajXMvF6TluhJxsv7e5hofPi5aX+RKiW96utcRl1Ina4iYiIiEgXfKREhEGTREREREQ2xBFuIiIiItIBR7hFOMJNRERERGRDHOEmsqFly/RZRj0hQbx8vIhsvJrsMvRaVhkXxQjKBmyKVsgGgKsl6uMIPriqmp48dapUpWY/95y4cIHkYcOkj1Gl5aTLBhzK3giWypBdT1zPNa/12l9LXrZOB+Dn5yN1iGwwpSNjTrWQvc1lY4YtHaMX9wgu5Qi3CEe4iYiIiIhsiCPcRERERKQDzsMtwhFuIiIiIiIb4gg3EREREemAz3CLcISbiIiIiMiGOMJN5AJES6JbmjhCrwkitETzi+g1k4Cu0fyizARTqiQnJcnndeGCerrs9CyWlm/Xa5YSLRdcdloae8xeImKPGVJE6aJ2a1iP3UNwzkVLvmt5v8qSPYX2qJNs2e4xU4gjcYRbhCPcREREREQ2xBFuIiIiItIBR7hFOMJNRERERGRDLtXhXrp0Kdq1awdfX1/06dPHbFtxcTHGjRuHsLAwhIWFISkpCSV8GIuIiIjITkpt+HJtLvVISVRUFGbMmIGvv/4aOTk5ZtvmzZuHXbt2ISMjAwCQkJCA1NRUzJo1yxFVJdKVaClzLUFHen0PdWTAk0NZOoGWolhVzF61Smr/5JEjxRtFgXeywZT2iIaVDbLUwh5LvssGR4raLbu/hW2iJd9FJG9Zi1wpMNMey8pzzI8qcqkR7n79+qFPnz4IDw+vtG3FihWYMWMGIiMjERkZienTp2P58uUOqCURERHRjajMhi/X5hZjVLm5ucjJyUF8fLwpLT4+HtnZ2cjLy0NISIjjKkdERER0Q3D9Rz9sxaVGuEUK//k3ZGiFyYrLfy4oKBAel5KSAoPBYHoREREREenNLTrcxn8ecM3LyzOllf8cFBQkPC4lJQWKopheRERERKQVgyZF3OKRkho1aiA6Ohrp6elo2LAhACA9PR0xMTF8nITcgqUFBmXJLvJnD/ZYSNDHT6ePO0uVlVx5MLlvX/X9RRdJtGIlAEREqKfrFTRpqd163aCyQYiWjpHd35ErTcquOGohLw/hIqVywZRaYoP1WtTUEluXwUB0shWXurwlJSWmV1lZGYqKiuDh4QEfHx+MHDkS8+fPR8eOHQEAqampGD16tINrTERERHSjcP2RaFtxqQ73vHnzMHv2bNPv/v7+6Ny5M7Zv346ZM2fi3LlzaNq0KQBgyJAhmDZtmqOqSkREREQEwMU63CkpKUhJSVHd5u3tjbS0NKSlpdm3UkREREQEjnCLuUXQJBERERGRNU6ePInevXsjKioKBoMB6enpFvc/duwYDAYDjEaj6dWrVy+pMl1qhJuIiIiInJVrLFDj4eGB7t27Y8aMGbj99tutPi4nJ8dsCmoZ7HATOZGpU5Ol9nfGpZS1HiNDw4rX8jNKaJkxQ7RNdmoYQSNmb9ggLlsgOSlJfcM/06laTc8l3/VcVl6v66pl9hLZGV30vNckyc5eYqloPS+fLFvPCMIl328MderUQWJiol3L5CMlRERERKQD287DXXGxQlFMny3dcsstiIiIQO/evfH7779LHcsRbiIiIiJyetYsUlhcXIzSUnHwpq+vr/Tq4uHh4fj+++/RunVrXLx4EXPnzsU999yDjIwMBAcHW5UHR7iJiIiISAeOX2myb9++8Pf3F76ysrKkW2U0GnHbbbfB29sboaGhePHFF1FcXIzdu3dbnQdHuImIiIjILfz3v/+1eRnlj7XIYIebyIkE+KlHeF8tkf9nlF6rbWuJ25INFpJd2dpScJaPlyBKvsgOQXQ2DpZL7tpVXLYocl50AWVPrpZ268WB59zizebIsnWi11Lwlsh+hrh74KKWOrnOsu+uMw93UYUb8+rVqygqKoKPjw88PCr/vf3+++8RHByMm266CZcvX8bcuXNhMBjQoUMHq8vjIyVEREREdEMpf8QEAG6//Xb4+/tjx44dAIDs7GwYjUZkZ2cDAP7880/07NkTwcHBqF+/PjIyMrBlyxaEhIRYXZ7LfGciIiIiImfmOiPclgIw69Wrh8LCQtPvgwcPxuDBg6tVHjvcRERERKQD1+lw2xsfKSEiIiIisiGOcBMRERGRDlxjaXdHYIebyAXouaq2aHIKUYS8ltlOZGcAkK2rxTrZeklveyztLjudC+SXfU8eM0Z9g2jJdz1n67AHe9TJkfeajYlmLwHEM5houG1140rLpbvOjCOkJ152IiIiItIBn+EW4TPcREREREQ2xBFuIiIiItIBR7hFOMJNRERERGRDHOEmclN6BebouQyxPZZ2Fy7hLhvQWGHRA6vStZShY6RX8q23qm8ID1dPd+By4rpypWg5vYIpAf0CcTWU7SEIrJVdDt4eMaGODOQUccY66Ycj3CIc4SYiIiIisiG3+D5FRERERI7GebhFOMJNRERERGRDHOEmIiIiIh3wGW4RdriJ3JRsAI6W4EgRWwdHelj6t6Vs4KJe6YA4oNIeqx6KVogUkQ2a1BIl60r0bIPsdbXH/aFnhKLgXPkY1dNLvNT/me7IW03LqXWH25wch7cPEREREemAI9wifIabiIiIiMiGOMJNRERERDrgCLcIR7iJiIiIiGyII9xEREREpAOOcIuww03kRMoE/3TSshSwXssH6zmBguwsJT4ll9Q3XLCwvLpophC9ZmnQMkuJXku7i06UpW2COs3+/HOpopN79RJvlJ0hRUTLTSvaJnuzOSM9pwgStVvLG1zyGC8vuSXftXyu6UXPsh058Y3jcOEbET5SQkRERERkQxzhJiIiIiId8JESEY5wExERERHZEEe4iYiIiEgHHOEWYYebyInotSI0IL9ssmy8mqU4OVF8lsUl2dVLly9ctE0UuJiTI1clUWAkIH9y7XHBL1yQK0NL2aJzKxtMqSUyTDYI0h7RZ3rdB3pG8Ol5b8oGTfqpB01qWdpdxB5LvnNpd6oO3j5EREREpAOOcIvwGW4iIiIiIhviCDcRERER6YAj3CIc4SYiIiIisiGOcBM5EVFQjl6LJOrJUqyaKPDo1Cn17/iidkdFaPiIEgQJzn7tNfm8JCW3aKG+wVKgpZofflBPj4gQHyMK/gwNlStbwNLKlMkJCeobRPXVM4BPNqpX9GaSXbHSUl6yK4vq2W4RBwZNioKlvbzkPg8s0SsWVs/Ve/Xav6p6OReOcItwhJuIiIiIyIZc5jsTERERETkz2alfbxwc4SYiIiIisiGOcBMRERGRDvgMt4jbjXAXFxdj3LhxCAsLQ1hYGJKSklBij5XFiIiIiIhUuN0I97x587Br1y5kZGQAABISEpCamopZs2Y5uGZE+tNz9hLZKHgtK5xHR6unz507W65wJzX755/1ySg2VjU5eetW8TF33qmaPDs3V48aWRYXp54umjnlyBH1dNGMKmfPypcdHq6ermVWDhFXmlZIRMv50GmmFy+vAKlstHDGmUJk62TpGOfDEW4RtxvhXrFiBWbMmIHIyEhERkZi+vTpWL58uaOrRUREROTmSm34cm1u1eHOzc1FTk4O4uPjTWnx8fHIzs5GXl6e4ypGRERERDcst+pwF/7zP+7QCv+WLP+5oKCg0v4pKSkwGAymFxERERFpVWbDl2tzqw638Z+VxSqOZpf/HBQUVGn/lJQUKIpiehERERER6c2guFlPMyYmBkuWLMGDDz4IAPjwww8xadIkZGdnV3ksR7mJiIjIlThLNy4uLg5ZWVk2yz82NhbHjh2zWf625nazlIwcORLz589Hx44dAQCpqakYPXq0VcdWvGkNBoPT3MT2xHbfWNjuGwvbfWNhu8meXLkzbA9u1+GeOXMmzp07h6ZNmwIAhgwZgmnTpjm4VkRERER0o3K7R0r0cqN+Q2a7byxs942F7b6xsN1EzsOtgib1lJyc7OgqOATbfWNhu28sbPeNhe0mch4c4SYiIiIisiGOcBMRERER2RA73ERERERENsQONxERERGRDbHDTURERERkQ+xwX6e4uBjjxo1DWFgYwsLCkJSUhJKSEkdXS1dLly5Fu3bt4Ovriz59+phtc+f2X7lyBWPGjEH9+vURFBSEJk2aYMWKFabt7tz2pKQkxMTEIDg4GHXr1sVTTz2Fq1evAnDvdgPA5cuX0ahRI4SGhprS3LnNI0aMgI+PD4xGo+m1Z88e03Z3bjsAfPLJJ4iPj0dgYCCioqLw+uuvA3Dfdle8zkajEd7e3mjZsqVpu7u2GwCOHz+OPn36oGbNmggPD0f//v1x+vRpAO7dbnJN7HBfZ968edi1axcyMjKQkZGBnTt3IjU11dHV0lVUVBRmzJiBMWPGVNrmzu0vKSlBZGQkvv76a+Tn5+Odd97B5MmTsWXLFgDu3fbExET8/vvvyM/PR3p6On766ScsXLgQgHu3GwBmzZqF6OhoszR3b3NiYiIKCwtNrw4dOpi2uXPbv/jiCyQmJmLJkiXIz89HRkYGunTpAsB9213xOhcWFqJp06YYNGiQabu7thu4dp8DQFZWFjIzM3HlyhVMmDABgHu3m1yUQmaio6OV9evXm35ft26dUq9ePQfWyHaSk5OVBx54wCztRmq/oihK3759lZkzZyqKcuO0/e+//1buuusuZfjw4YqiuHe79+/frzRr1kz54osvlJCQEFO6O7f5kUceUSZMmCDc7s5tb9eunfLGG2+obnPndpf7/vvvFU9PT+X48eOmNHdud4sWLZT333/f9Pt7772nNG/eXFEU9243uSaOcFeQm5uLnJwcxMfHm9Li4+ORnZ2NvLw8x1XMTm609hcVFWHv3r1o2bLlDdH2BQsWICgoCLVr18ZPP/2EpKQkt253SUkJxowZg7S0NPj6+prS3bnN5VauXImwsDA0b94cL730EsrKygC4d9svXryI/fv3Iz8/H02aNEFERAQGDhyIU6dOuXW7K1q+fDkSEhIQFRUFwL2vNwBMmjQJ69evR15eHi5cuIA1a9agR48ebt9uck3scFdQWFgIAGbPepb/XFBQ4IAa2deN1H5FUTB69Gg0btwY/fr1uyHa/swzz6CgoAC//vorxo4di4iICLdu90svvYSWLVuaHiko585tBoDx48fj0KFDOHPmDJYvX46XX34ZL7/8MgD3bntubi4URcGqVavw5Zdf4siRI/D29sawYcPcut3lLl26hA8++ACjR482pbl7uzt27Ii///4bNWrUQFhYGM6fP48ZM2a4fbvJNbHDXYHRaAQAs2/A5T8HBQU5pE72dKO0X1EUPPHEEzh06BA2btwIDw+PG6btANC0aVO0atUKI0aMcNt2Hz16FGlpaXjxxRcrbXPXNpdr06YNatWqBU9PT7Rv3x7PPPMM1q5dC8C9217etvHjxyM2NhZGoxGzZ8/G1q1b4eFx7U+dO7a73Lp16xAQEIAePXqY0tz5epeVleGee+5Bx44dTc+v33nnnbjvvvvcut3kutjhrqBGjRqIjo5Genq6KS09PR0xMTEICQlxXMXs5EZov6IoePLJJ7F3715s2bLF1K4boe0VFRcX448//nDbdu/cuRNnzpxB8+bNERERgX79+iE/Px8RERH4448/3LLNIuWdTcC97/PQ0FDUq1cPBoOh0raQkBC3bXe5t956C4888gi8vLxMae58vc+fP4+srCyMHz8eAQEBCAgIQFJSEvbs2YPS0lK3bTe5MMc+Qu58Zs6cqbRu3Vo5efKkcvLkSaV169bK7NmzHV0tXRUXFyuXL19Wpk+frvTq1Uu5fPmycuXKFUVR3L/9iYmJSsuWLZWzZ89W2uaubS8oKFBWrFih5ObmKmVlZcrBgweVpk2bKmPGjFEUxT3bfenSJVN7Tp48qXz00UdKcHCwcvLkSeXq1atu2eZya9euVfLy8pSysjJl3759SmxsrLJw4ULTdndu+7x585RWrVopOTk5yqVLl5Thw4cr3bp1UxTFvdv9+++/KwaDQTl06FClbe7c7kaNGinPPPOMcvnyZeXy5cvK008/rURHRyuK4t7tJtfEDvd1rl69qiQmJiqhoaFKaGio8uSTTyrFxcWOrpaukpOTFQBmr86dOyuK4t7tP3bsmAJA8fX1VQIDA02vxx9/XFEU9217YWGh0q1bNyUsLEwJDAxU6tevr0yZMkW5ePGioiju2+6Ktm3bZjZLiTu3uVOnTkpISIgSGBio3HTTTcrzzz+vlJaWmra7c9tLSkqUSZMmKTVr1lRq1qypPPTQQ8rJkycVRXHvdk+dOlX517/+pbrNndudkZGh3HvvvUpYWJgSGhqqdO3aVTlw4ICiKO7dbnJNBkVRFIcOsRMRERERuTE+w01EREREZEPscBMRERER2RA73ERERERENsQONxERERGRDbHDTURERERkQ+xwExERERHZEDvcREREREQ2xA43EVVbQkICXn31VeH2Pn36ICUlRXXbzp07ER0drbnsjRs3Ii4uTvPxlhiNRvz88882yft6mzdvRlxcHIxGIzZu3GiXMmVVdZ0rSklJQZ8+fYTbR4wYgaeeekqfihEROTkvR1eAiFzf559/rvnYTp06IScnR8fa6KewsNBuZU2aNAlz5szB8OHD7VamrOpcZyKiGxlHuIlIM0VRUFpa6uhquIXMzEy0bNlS07ElJSU618YcrzMRUfWww01EZnJycnDPPfcgODgYbdu2RWpqqtkjG3FxcXjuuefQvn17BAQE4Ndff0WXLl2wZMkS0z4fffQRGjVqhJCQEIwZM8Zih3D79u0IDQ01/d6lSxc8++yzuO+++2A0GtGmTRuzxzpycnJw7733mur366+/muVXWFiIcePGoV69eqhduzaGDx+OvLw8AMCUKVPQuXNnlJWVAQA+/PBDRERE4O+//1atm8FgQHp6OoBrj0j06tUL48aNQ2hoKOrVq4e1a9eqHldSUoIOHTpgxowZprQnn3wSd999t6nscufOnYPRaERpaSnuuOMOGI1GXLlyBQUFBXjssccQGRmJyMhIjB07FhcvXgQAHDt2DAaDAW+//TYaNWqEunXrVqrD+PHj8eijj5qlPf/88+jevTsAYMuWLWjXrh1CQkIQGRmJxMREXL582bRvVde5sLAQDzzwAGrXro2QkBD861//wk8//VTpPIwaNQrBwcFo3LgxNmzYoHq+AODo0aPo1asXatWqhdjYWMybN890rjIzM9GtWzeEhIQgLCwMHTt2xKVLl4R5ERE5G3a4icjMww8/jNjYWJw+fRpr1qzB8uXLK+3zzjvv4N1330VhYSFuvvlms21//PEHHn74YSxevBjnzp1D27Zt8cUXX0jVYeXKlViwYAEuXLiAdu3aISkpyax+kZGROHXqFN5//30sW7bM7NhHH30U58+fx8GDB5GZmYni4mKMGzcOAPDcc8/h4sWLmDdvHrKysvD444/j3XffRe3ata2q15dffomOHTvi3LlzmDdvHkaPHo2CgoJK+3l5eWH16tV49dVXsX37dmzatAnr16/HqlWr4OFh/rFbs2ZN06Mru3fvRmFhIXx9fTFhwgQcOXIEv/zyC37++Wf8/vvvmDhxotmxn3zyCX744QdkZmZWqsPw4cPx4YcfmnWiV61ahWHDhgEA/P39sWzZMpw/fx7ffvsttm3bhkWLFpnlYek6l5WV4eGHH0ZmZiZOnz6N1q1bY8CAAVAUxbTPF198gdtuuw3nz5/HokWLMHjwYBw9erRSXS9fvoy7774bd911F44fP46dO3figw8+wNtvvw0AmD59Oho1aoSzZ8/i9OnTeOGFF+DlxSciiciFKERE/8jOzlYAKGfOnDGlLVy4UImNjTX9HhsbqyxevNjsuM6dO5vS5syZoyQkJJhtb9KkiZKcnKxa5rZt25SQkBCzvJ5++mnT77t27VKMRqNZ/U6fPm3avmDBAlP9/v77b8XDw0M5d+6cafvhw4cVb29vpaSkxPR7aGio0qxZM2XKlCkWzwcA5ccff1QURVGSk5OV22+/3bStrKxM8fHxUX744Qfh8atXr1bq1q2r1KxZU9m8ebPVZZWWliq+vr7Kd999Z9r+7bffKr6+vkppaamSmZlptr9Is2bNlDVr1iiKoigHDhxQgoKClIsXL6ruu3jxYqVbt26m36u6ztfLzc1VACg5OTmKolw7X02bNjXbp3v37srcuXMVRVGURx55RJkwYYKiKIqybt06JT4+3mzfN998U7nrrrsURVGU4cOHK71791YOHz5ssb1ERM6KI9xEZHLixAn4+fkhPDzclFavXr1K+6mlVcwjNjbWLO3636sSERFh+jkwMNA0Alxev4oj0hXzPnbsGMrKytCgQQOEhoYiNDQUt956Kzw8PHDq1CkAQOPGjdGlSxccPnwYU6ZM0Vwvg8EAf39/1RHucv369UNpaSmioqLQs2dPq8s5c+YMrly5YvYoT4MGDXDlyhWcPXvWlGbpOgDAsGHDsHLlSgDX/mvw4IMPIiAgAACwb98+dOvWDXXq1EFwcDCmTZtmlndV+V++fBmJiYmIi4tDcHCwqa4V81C7D44fP14pr2PHjuGXX34xXbPQ0FBMnjzZdM1eeOEF1K1bF926dUNcXBxSUlIqPZpDROTM2OEmIpOoqCgUFRWZdZqys7Mr7Xf9YxHX55GVlWWWppZHdepX8ZnrinnHxMTAw8MDJ06cwIULF0yvoqIi03POH330Eb777jv06NEDiYmJutRLZPLkyWjWrBmuXLmC//znP1YfV6tWLfj4+ODYsWOmtMzMTPj6+pp9GbJ0HQBg6NCh2Lp1K44fP441a9aYHicBgMGDB6Nr1674888/kZ+fj9TUVLPHQarK/6WXXsL+/fuxa9cu5Ofnm+paMQ+1+0DtefOYmBi0bdvW7Jrl5+cjIyMDAFC7dm28+uqryMrKwn//+1+8/vrrFp8HJyJyNuxwE5FJTEwMOnbsiGnTpuHy5cv4448/8Oabb0rlMWDAAGzduhWffvopSkpKsGzZMhw+fFjX+j3zzDO4fPkyDh06hDfeeMO0PSIiAn369MG4ceNMXxpOnTpl6pxlZ2fjsccew7vvvouVK1ciPT1dun3W+uSTT7Bu3Tq89957WLNmDWbMmIGDBw9adayHhwcefvhhTJ8+HefPn8e5c+cwffp0DBs2rMpOdkXR0dG48847MWrUKPj4+KBLly6mbfn5+QgNDUVgYCB+++03vPbaa1Lty8/Ph5+fH2rUqIHCwkJMmzat0j6HDx/GsmXLUFJSgk8//RT/+9//MHDgwEr79ezZE6dPn8arr76KoqIilJaW4tChQ9i+fTsAYN26dcjOzoaiKAgJCYGnpyef4SYil8IONxGZWb16Nf7880/UqVMHgwYNwtChQ+Hr62v18TfffDNWrVqF8ePHo2bNmvj+++9NM2PoVb+//voLtWvXxsMPP1xpJo533nnH9ChJcHAwOnXqhP3796O0tBRDhgzByJEjTbOcrFmzBv/+97/x22+/6VY/4NqjL6NGjcKKFSsQGRmJNm3aICUlBYMHDzYLYrTk5ZdfRlxcHJo1a4bmzZujUaNGlYIarTF8+HB8+eWXGDp0qFln/Y033sCLL74Io9GIsWPHYtCgQVL5Tpo0CZ6enqhTpw5uueUWdOjQodI+3bt3x3fffYewsDBMmDAB7733Hho3blxpP6PRiK+//hpbt25FXFwcatasiYcfftj0SMn+/ftNM7h06NABo0aNQu/evSXPBBGR4xiU6/+HSERUQWpqKv73v//h66+/dnRViIiIXBJHuInIzIEDB/D7779DURTs378fS5cuRf/+/R1dLSIiIpfFh+CIyMyZM2cwduxYnD59GrVq1cKoUaMwatQoR1eLiIjIZfGREiIiIiIiG+IjJURERERENsQONxGRhPT0dBgMBruU1aVLFyxZssQuZV3PaDTi559/dkjZRETuhh1uIrKrY8eOwWAw4MKFCxb3qbjKYlW6dOlimrPZ0UaMGIF33nnHqn1l22krcXFx2Lhxo1laYWEhWrRo4ZgKERG5GXa4iYiIiIhsiB1uItLdokWL0LhxYwQFBaFhw4ZYunSpadttt90G4NoqiEajEe+//36V+V0/Artx40arR4azs7Nxzz33oFatWqhRowZ69OhhtmT6iBEjMGbMGAwaNAhBQUG4+eabzUbLL1y4gAEDBiA0NBRNmjTBjh07rCq33IcffohGjRohJCQEY8aMQc+ePZGSkiKVR7ktW7agdevWCAkJQZs2bczmRi8rK8Mrr7yCJk2aICgoCI0bN8YXX3xhOq5du3YICQlBZGQkEhMTTQvw9O/fH9nZ2Rg8eLBpERwAMBgMSE9PB3BtufaXXnoJDRs2RFhYGLp3744///zTVHZcXBwWLlyI9u3bIygoCJ07d8Zff/1lOvbpp59GREQEgoODcdNNN+G///2vpvYTEbkshYhIZx9++KGSnZ2tlJWVKf/73/8UPz8/ZdeuXYqiKEpmZqYCQMnNzbU6v9jYWGXDhg2m3zds2KDExsZadWxmZqby2WefKZcvX1by8vKUhx56SOnWrZtp+yOPPKIYjUZl69atSklJiTJ37lyzvIcNG6bcc889Sm5urnL8+HGlbdu2irUfnYcOHVL8/PyUzz//XCkuLlbefPNNxcvLS0lOTrbq+M6dOyuLFy9WFEVRjhw5ovj5+SkfffSRUlxcrKxfv17x9/dX/vzzT0VRFOXll19W6tevr/zwww9KWVmZkpWVpfz666+KoijKjh07lAMHDiglJSXK0aNHlSZNmijz5s0zlXP9+VUURQGg/Pjjj4qiKMq7776rREVFKQcPHlQuX76sTJo0SWnatKlSXFxsOr558+bK0aNHlcuXLysJCQnKI488oiiKonz55ZdKdHS0cvz4cUVRFCUrK0s5dOiQVe0nInIXHOEmIt09+OCDiImJgcFgQNeuXXHfffc57BnruLg4JCQkwM/PD8HBwZg+fTp27NiBsrIy0z49evTAXXfdBU9PT4wcORJZWVk4d+4cSktLsXbtWsybNw+hoaGIiorC1KlTrS577dq1uPvuu9G9e3d4eXlhzJgxuOmmmzS144MPPkCXLl3Qr18/eHl54aGHHsKdd96JNWvWAABee+01pKSkoG3btjAYDKhXrx6aNm0KAOjUqRNat24NT09PNGjQAI8//rjU9Vi1ahXGjx+PFi1awM/PD6mpqcjJycHevXtN+4wbNw4NGjSAn58fhgwZgv379wMAvL29UVRUhIyMDBQXF6NevXqazwERkatih5uIdPf++++jTZs2qFGjBkJDQ/HZZ5/h7NmzDqnLmTNn8PDDDyMmJgbBwcH417/+hatXr6KgoMC0T0REhOnnwMBAAEBBQQHOnj2Lq1evIjY21rS94s9VOXHiBGJiYszS6tWrp6kdOTk5lR6jadCgAXJycgAAWVlZaNy4seqx+/btQ7du3VCnTh0EBwdj2rRpUtfj+rJ9fX0RFRVlKhuofA7Lz2/Xrl0xe/ZszJw5E+Hh4XjwwQeRmZlpddlERO6AHW4i0lV2djYeeeQRLFy4EGfOnMGFCxdw//33Q/lnjS0PD/mPHaPRiEuXLpl+P3nypNXHPvvss7h06RIOHDiA/Px80zPYihVrfoWHh8Pb2xtZWVmmtOzsbKvLjoqKMj3LrOX4iqKjo82ePQeAzMxMREdHA7j2ReDIkSOqxw4ePBhdu3bFn3/+ifz8fKSmppq1v6prcn3ZV69exYkTJ0xlVyUxMRHfffcdsrOz4evri/Hjx1t1HBGRu2CHm4h0VVhYCEVRULt2bXh4eOCzzz7Dli1bTNtr1aoFDw8PHD161Oo827RpgzVr1qCoqAh//vkn0tLSrD42Pz8fAQEBCA0Nxblz5zB79myrj/X09MSAAQMwa9YsXLhwASdOnMALL7xg9fEDBgzA1q1bsWXLFpSUlGDFihU4fPiw1cdXNHDgQGzfvh2bNm1CaWkpPv74Y+zcuRODBg0CADz++OOYPXs20tPToSgKsrOz8dtvvwG4dg5CQ0MRGBiI3377Da+99ppZ3nXq1LF4PYYOHYqlS5fi119/xZUrVzBjxgzUrVvXFABryb59+7B7925cvXoV/v7+CAwMhJeXl6ZzQETkqtjhJiJdNWvWDNOnT8ddd92FmjVrYu3atejdu7dpu7+/P5KTk5GQkIDQ0FCsXr26yjznzZuHCxcuoFatWnj44YcxfPhwq+sze/ZsHDlyBDVq1EDHjh2RkJAg1Z7//Oc/MBqNiI2NxV133YVhw4ZZfezNN9+Md955B0888QRq1qyJPXv24K677oKvr69UHQCgUaNG+Pjjj5GcnIwaNWpgzpw52LBhAxo0aAAAGD9+PJ544gkMGDAAQUFB6Natm2k0/Y033sCLL75omoWkvJNebtq0aVi6dClq1KiBxMTESmUPHz4cSUlJ6NmzJyIiIvDTTz9h8+bNVnWc8/PzkZiYiJo1ayIiIgInTpzAyy+/LN1+IiJXZlCs+b8qERHp4uabb8bMmTMxdOhQR1eFiIjshCPcREQ2tHnzZhQUFODKlSt46aWXcOLECXTv3t3R1SIiIjvig3RERDb05Zdf4pFHHkFxcTFuvvlmbNq0CeHh4Y6uFhER2REfKSEiIiIisiE+UkJEREREZEPscBMRERER2RA73ERERERENsQONxERERGRDbHDTURERERkQ+xwExERERHZEDvcREREREQ2xA43EREREZEN/R+tk4DK44tH3QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 900x630 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Um_Cori_plus_pgrad = ds_u_mom.Um_Cori + ds_u_mom.Um_dPhiX\n",
    "\n",
    "plt.figure(figsize=(10,7),dpi=90)\n",
    "Um_Cori_plus_pgrad.isel(time=4,k=0,tile=7).plot(cmap=seismic_nanmasked)\n",
    "plt.title('$u$-momentum Coriolis plus pressure gradient terms [m s$^{-2}$], surface, May 1998\\n')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9117ac3f-4785-455c-b719-27844aa75e9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x630 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Um_Ext_plus_ImplD = ds_u_mom.Um_Ext + ds_u_mom.Um_ImplD\n",
    "\n",
    "plt.figure(figsize=(10,7),dpi=90)\n",
    "Um_Ext_plus_ImplD.isel(time=4,k=0,tile=7).plot(cmap=seismic_nanmasked)\n",
    "plt.title('$u$-momentum external forcing + implicit dissipation terms [m s$^{-2}$], surface, May 1998\\n')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5f4a7f56-ae75-4511-9a9b-368cfb3edfdf",
   "metadata": {},
   "source": [
    "Notice that the two term combinations above are nearly equal in magnitude and opposite in sign.\n",
    "\n",
    "If we look at a level well below the surface (~200 m), the residual after summing the Coriolis and pressure gradient terms is even smaller."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "75e209f2-d764-4628-9485-64169c151a98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x630 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k_plot = 16\n",
    "\n",
    "plt.figure(figsize=(10,7),dpi=90)\n",
    "Um_Cori_plus_pgrad.isel(time=4,k=k_plot,tile=7).plot(cmap=seismic_nanmasked)\n",
    "# the syntax \"%.0f\" % X\" rounds X to the nearest integer, with 0 digits after the decimal point\n",
    "plt.title('$u$-momentum Coriolis plus pressure gradient terms [m s$^{-2}$], '\\\n",
    "            +f'{\"%.0f\" % -Um_Cori_plus_pgrad.Z[k_plot].values} m depth, May 1998\\n')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ab62668-44f1-40d6-a4b0-c310fb550c28",
   "metadata": {},
   "source": [
    "> **Exercise:** Try plotting the terms of the meridional momentum budget globally. To do this successfully you will need both the $u$- and $v$-momentum terms, and to interpolate and rotate them using the $CS$ and $SN$ parameters in the grid file (you can use the v4r4 grid file for this). [This tutorial](https://ecco-summer-school.github.io/ecco-2025/tutorials/ECCO_v4_computations/ECCO_v4_Gradient_calc_on_native_grid.html) is a good resource for how to do the interpolation/rotation on the llc90 grid."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}