{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3da16745-245c-47c2-9cfd-ef831df10a38",
   "metadata": {},
   "source": [
    "# Memory management in Python\n",
    "Andrew Delman, updated 2025-07-24\n",
    "\n",
    "## Objectives\n",
    "Demonstrate how Python objects (variables) are stored in and cleared from memory, and establish good practices for conserving memory in a Python workspace.\n",
    "\n",
    "## Introduction\n",
    "Python is a very useful computing language for doing numerical analysis of large datasets, largely because of packages that have been developed specifically for this purpose. [Xarray](https://docs.xarray.dev/en/stable/) allows us to open data from a number of files using a single line of code (with `open_mfdataset`), while [Dask](https://www.dask.org/) makes it easy to parallelize computations and stagger memory usage by \"chunking\" the computations. However, we still need to be mindful of the memory limitations of our workspace, and there are some quirks to memory management in Python.\n",
    "\n",
    "## Datasets\n",
    "These are the ECCO datasets used in the last part of this tutorial:\n",
    "\n",
    "- **ECCO_L4_GEOMETRY_LLC0090GRID_V4R4**\n",
    "- **ECCO_L4_TEMP_SALINITY_LLC0090GRID_MONTHLY_V4R4** (1992-2017)\n",
    "\n",
    "The `ecco_access` [Python package](https://ecco-access.readthedocs.io) will handle download or retrieval of the necessary data; it can be [installed](https://ecco-access.readthedocs.io/en/latest/Installation.html) using the `conda`, `mamba` or `pip` commands.\n",
    "\n",
    "\n",
    "## View vs. copy\n",
    "In some numerical computing languages such as Matlab, assigning a new variable instantly creates an independent block of memory in our workspace associated with that variable. In Python, when you create a variable by assigning data from another variable, the assignment does not create a copy of that data. Rather, a pointer called a *view* is created from the new variable to the data in the original. This is the default behavior for native Python objects such as lists, tuples, and dictionaries, as well as [NumPy arrays](https://numpy.org/doc/stable/user/basics.copies.html).\n",
    "\n",
    "### Native Python object (list)\n",
    "Consider a simple list of 3 numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "44e8f31c-2e4f-4816-992a-43c5e2727df1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_list = [1,2,3]\n",
    "view_list = test_list\n",
    "view_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a296388-8f57-4ad0-9b07-874ec212a2cc",
   "metadata": {},
   "source": [
    "We can see if `view_list` is read from the same object in memory as `test_list`, using the `is` operator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cbb28b19-cc69-407a-b138-df7bda5abe23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "view_list is test_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d0239a9-5686-4719-9a65-4c972b44577d",
   "metadata": {},
   "source": [
    "Not only that, because `view_list` is identical in memory space to `test_list`, when `view_list` is modified `test_list` is modified as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "db8da8a2-765a-40a3-8935-550e8ef5ebff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 4, 3]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "view_list[1] = 4\n",
    "test_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "867aa11d-9aad-4c38-978d-09f03de0035d",
   "metadata": {},
   "source": [
    "Compare this with creating a *copy* of `test_list`. There is a `copy` package that does this in Python, but you can also create a copy just by appending `.copy()`, or by using an operator on the right-hand side of the assignment. For example, we can \"append\" an empty list to create a copy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "614602d1-98d8-428c-8389-b1e3870dbfa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_list = [1,2,3]\n",
    "copy_list = test_list + []\n",
    "copy_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "556956c0-7644-4ef3-938a-a1b62bc3b97b",
   "metadata": {},
   "source": [
    "Note that if we use the `==` operator, which compares the *values* of the two objects, the result is True."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "feba8d0b-83a0-497d-bf95-30198f3a621d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copy_list == test_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85e53629-b3f5-45f6-9162-90227dc4579a",
   "metadata": {},
   "source": [
    "But if we compare using the `is` operator, we find that the two objects are independent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4e363950-d626-4441-b509-5064f97cbed5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copy_list is test_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5c04f54-ff5a-49b6-b3b8-e1a7a2b5bc56",
   "metadata": {},
   "source": [
    "And so if `copy_list` is modified, `test_list` is not changed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ff8fef45-8697-4a92-8696-78747e92ad8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copy_list[1] = 4\n",
    "test_list"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c0313f2-b87b-4ff8-a4bf-f2ac63dde90c",
   "metadata": {},
   "source": [
    "### NumPy arrays\n",
    "\n",
    "Views or copies of NumPy arrays can be created using the same general syntax used above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "85cefeb9-649f-45f9-8b42-0fe75ca391de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "test_array = np.array([1,2,3])\n",
    "view_array = test_array\n",
    "view_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ebc1150a-57c9-47e3-a30b-2f437607891e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "view_array is test_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0801b572-07ef-4640-8234-2823c84b4924",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4, 3])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "view_array[1] = 4\n",
    "test_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8397b050-474f-4cd1-9b47-08a3cdf29f64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_array = np.array([1,2,3])\n",
    "copy_array = test_array + 0\n",
    "copy_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1d880c4e-f5c6-4b79-95a3-f590644f0841",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copy_array == test_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "13c0a673-d67a-4cc2-aac8-c8b625e38c1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copy_array is test_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "82253592-b2f0-4606-9a1a-aa7f86d3ead0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copy_array[1] = 4\n",
    "test_array"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "579b8d77-0b86-4cb8-ba7c-e37134bc6774",
   "metadata": {},
   "source": [
    "For NumPy arrays, views or copies can also be created by appending `.view()` or `.copy()` to the variable name. For more examples of NumPy array views/copies and how they are handled by Xarray, see the [previous tutorial](https://ecco-v4-python-tutorial.readthedocs.io/ECCO_v4_Operating_on_Numpy_Arrays.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9115770f-97b8-4284-801d-c2fbe832b669",
   "metadata": {},
   "source": [
    "## Memory footprint\n",
    "\n",
    "As the examples above showed, understanding the difference between a view and a copy is important to ensure your code is working the way you think it should be. Python's capacity to reference an object without creating a separate copy of it is also a helpful memory-saver. But there are other ways to create a Python object without writing its data to memory. Let's consider the memory footprint of two types of arrays: NumPy arrays and [Dask arrays](https://docs.dask.org/en/stable/array.html).\n",
    "> **Note**: There are a number of tools that can be used to estimate the memory footprint of an object in Python, such as the object's `__sizeof__` method, `sys.getsizeof`, and `pympler.asizeof`. To get the most accurate estimate of actual memory usage in the workspace, in this tutorial we use `psutil.virtual_memory` from the [psutil]((https://psutil.readthedocs.io/en/latest/#memory) package. This function tells us the memory available, and then we can track how it changes.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "449a8b98-3d83-4ce5-a719-375644e816f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "svmem(total=7952175104, available=6066610176, percent=23.7, used=1612763136, free=2748981248, active=3048009728, inactive=1532313600, buffers=1011712, cached=3589419008, shared=8822784, slab=129548288)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import xarray as xr\n",
    "import psutil\n",
    "import os\n",
    "\n",
    "# memory stats (in bytes)\n",
    "psutil.virtual_memory()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d81d6d5d-3a2f-4c2a-89b8-c1f13a738a69",
   "metadata": {},
   "source": [
    "The above output gives us a variety of information about the memory in our system. We will be tracking the available memory (given as bytes in the tuple above).\n",
    "\n",
    "### NumPy arrays\n",
    "Consider the view/copy distinction in the previous section, but with larger arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e3cbc599-bd38-4ecd-b2ea-68b05dc7add9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Change in available memory: -8.155136  MB\n"
     ]
    }
   ],
   "source": [
    "# function to log memory \n",
    "# and print change in available memory since last log point\n",
    "def memory_log_print_avail_change(memory_log,\\\n",
    "                                   print_before_avail='Change in available memory:'):\n",
    "    memory_log.append(psutil.virtual_memory())\n",
    "    print(print_before_avail,np.diff(np.asarray([memory_log[ind].available\\\n",
    "                                         for ind in range(-2,0)]))[0]/(10**6),' MB')\n",
    "    \n",
    "\n",
    "\n",
    "memory_log = [psutil.virtual_memory()]\n",
    "test_array = np.reshape(np.arange(0,1000000),(1000,1000)).astype('float64')\n",
    "memory_log_print_avail_change(memory_log)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f41a2b5-c599-46d4-a075-53133693bec5",
   "metadata": {},
   "source": [
    "The change in our available memory is about 8 MB, which is what we would expect for an array with $10^6$ entries of type float64 (8 bytes each). How does our available memory change if we create a view of this array?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dafaa9d4-cf7b-42c6-8bf0-3dd945a45b17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Change in available memory: -0.012288  MB\n"
     ]
    }
   ],
   "source": [
    "view_array = test_array\n",
    "memory_log_print_avail_change(memory_log)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2ba24ae-1dcd-4c83-97ed-b36c1e4bc206",
   "metadata": {},
   "source": [
    "Effectively **zero** change! Note that we wouldn't expect the change to be identically zero, since a pointer has been created somewhere in memory. But the actual change in available memory depends not only on the memory footprint of new object(s) but on how Python allocates memory in response to those changes.\n",
    "\n",
    "Now what is the memory impact (footprint) from making a copy of this array?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cb681877-edbb-49fe-8ec9-77fc6a073387",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Change in available memory: -7.725056  MB\n"
     ]
    }
   ],
   "source": [
    "copy_array = test_array.copy()\n",
    "memory_log_print_avail_change(memory_log)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18c51083-61c3-4be4-858c-6888b5b90712",
   "metadata": {},
   "source": [
    "Very close to the size of the original array.\n",
    "\n",
    "### Dask arrays and delayed computations\n",
    "\n",
    "You may have noticed that NumPy creates (non-memory consuming) views of an array only if:\n",
    "\n",
    "- the view references the original array without any operations applied to it, OR\n",
    "- the view references a subset of the original array using simple indexing\n",
    "\n",
    "So what if you want to create a \"view\" of an array that has been operated on in any variety of ways, while minimizing the memory impact until you are ready to actually \"execute\" the computations? Enter Dask arrays. Let's create a Dask array that is a view of our `test_array`, and then operate on it (take the square root)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7000d10d-0869-4707-aba8-36acf76a0b71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Change in available memory: 0.0  MB\n"
     ]
    }
   ],
   "source": [
    "import dask.array as da\n",
    "\n",
    "memory_log.append(psutil.virtual_memory())\n",
    "dask_test_array = da.from_array(test_array,chunks=(100,100))     # array has chunks of size 100x100\n",
    "dask_sqrt_array = dask_test_array**(1/2)\n",
    "memory_log_print_avail_change(memory_log)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b686fdd-8676-468e-b014-321bba9ab99f",
   "metadata": {},
   "source": [
    "No new memory usage from the two operations above. And now, we compute `dask_sqrt_array` so that it becomes a NumPy array in memory:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "43a87672-8e75-43eb-8833-dd343a787d61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Change in available memory: -26.894336  MB\n"
     ]
    }
   ],
   "source": [
    "sqrt_array_computed = dask_sqrt_array.compute()\n",
    "memory_log_print_avail_change(memory_log)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "033ac75e-8410-4065-bce4-6a76703b1e6c",
   "metadata": {},
   "source": [
    "Notice that the memory usage from calling `.compute` is larger than we would expect for the size of the array: (1000 x 1000 elements) x (8 bytes/element) $\\approx$ 8 MB. But there is some overhead associated with calling `.compute` for the first time in a notebook or Python script/function. What if we perform the same computation as above, using `copy_array` as our source since it is independent in memory?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5726c00b-1464-43c7-ac39-8a25cf632982",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Change in available memory: -8.8064  MB\n"
     ]
    }
   ],
   "source": [
    "dask_copy_array = da.from_array(copy_array,chunks=(100,100))     # array has chunks of size 100x100\n",
    "dask_copy_sqrt_array = dask_copy_array**(1/2)\n",
    "copy_sqrt_computed = dask_copy_sqrt_array.compute()\n",
    "memory_log_print_avail_change(memory_log)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e028a58-1b3c-487b-9cfc-a07b95e1f609",
   "metadata": {},
   "source": [
    "Much closer to our expected memory usage, since it is not the first time `.compute` has been called on a Dask array in this notebook.\n",
    "\n",
    "Let's try a \"reduction\" operation, where the computed result is smaller than the intermediate arrays involved. In this case we will take the square of `dask_test_array` and then sum over one of the dimensions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e4ab0345-024c-4807-9b6d-fed840764d25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Change in available memory: -2.105344  MB\n"
     ]
    }
   ],
   "source": [
    "dask_sq_array = dask_test_array**2\n",
    "sq_sum_computed = (dask_sq_array.sum(axis=-1)).compute()\n",
    "memory_log_print_avail_change(memory_log)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49b85896-7fb7-45ea-9edd-5ffa4a54db49",
   "metadata": {},
   "source": [
    "The memory usage is not as small as the expected size of `sq_sum_computed` (0.008 MB), but it is much smaller than the 8 MB that each of the intermediate arrays would have used if they were regular NumPy arrays. Calling `.compute` on a reduced array like this is where Dask really helps us save memory."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd869072-102c-492f-a3fa-05fe92771ec0",
   "metadata": {},
   "source": [
    "## Deleting variables to free up memory\n",
    "\n",
    "Now let's say we want (or need) to increase our available memory by deleting some of the arrays that we just computed. In Matlab for example you might just call `clear variable` to free up the memory used by `variable`. But in Python it is not that simple, especially when Dask is involved.\n",
    "\n",
    "### NumPy case (no Dask)\n",
    "\n",
    "Here we will compute (first using NumPy only) and then delete the square root of `test_array`, and compare the memory impacts to when we get Dask involved."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c0cf9d59-a1c5-410a-b93d-4dec9946945f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Change in available memory: -7.897088  MB\n",
      "Change in available memory: -8.00768  MB\n",
      "Change in available memory: 0.0  MB\n"
     ]
    }
   ],
   "source": [
    "# restart the memory log\n",
    "memory_log = [psutil.virtual_memory()]\n",
    "\n",
    "# copy test_array (to create a fresh \"source\" for this calculation)\n",
    "test_numpy = test_array.copy()\n",
    "memory_log_print_avail_change(memory_log)\n",
    "\n",
    "# take the square root\n",
    "test_numpy_sqrt = test_numpy**(1/2)\n",
    "memory_log_print_avail_change(memory_log)\n",
    "\n",
    "# delete the new square root array\n",
    "del test_numpy_sqrt\n",
    "memory_log_print_avail_change(memory_log)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3295af50-07fc-4c42-a146-9617a367da95",
   "metadata": {},
   "source": [
    "We created two new NumPy arrays in memory, and the footprint of each is ~8 MB, what we would expect. But when we deleted one of these arrays, we didn't recover its memory. Why?\n",
    "\n",
    "To understand this, let's consider how Python does [\"garbage collection\"](https://devguide.python.org/internals/garbage-collector/index.html). Each Python object has a certain number of references, which are created by equations/operational relationships to other objects). `del` decreases an object's reference count by 1, but the garbage collector will only free up memory associated with an object if the object **and** all objects it is associated with are zero.\n",
    "\n",
    "We can use `sys.getrefcount` to determine an object's number of references, but calling `sys.getrefcount` on an object also creates a temporary reference to that object. So the \"true\" number of references will be 1 less than the output of `sys.getrefcount`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cd3d4be9-d02e-416b-a669-93a26c789862",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Change in available memory: 0.0  MB\n",
      "Reference count of test_numpy_sqrt: 1\n",
      "Reference count of test_numpy: 1\n"
     ]
    }
   ],
   "source": [
    "# Recover test_numpy_sqrt (since we didn't really delete it, it is still cached in memory)\n",
    "test_numpy_sqrt = test_numpy**(1/2)\n",
    "memory_log_print_avail_change(memory_log)\n",
    "\n",
    "import sys\n",
    "\n",
    "print('Reference count of test_numpy_sqrt:',sys.getrefcount(test_numpy)-1)\n",
    "print('Reference count of test_numpy:',sys.getrefcount(test_numpy)-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "924b1828-7e28-46ef-97c6-7a00e171632b",
   "metadata": {},
   "source": [
    "These arrays each have 1 reference, which means if we use `del` on both of them, and they are not referencing any other object, then we should be able to clear both of them from memory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "2fa7a68c-61d5-4ec5-ae7a-a50cf2b8fc3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Change in available memory: 17.256448  MB\n"
     ]
    }
   ],
   "source": [
    "del test_numpy_sqrt\n",
    "del test_numpy\n",
    "memory_log_print_avail_change(memory_log)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef4d87c5-f5b1-4255-a9ee-b0b140c658db",
   "metadata": {},
   "source": [
    "And indeed, we did recover almost 16 MB of memory, the equivalent of the 2 arrays.\n",
    "\n",
    "### Complications of Dask\n",
    "\n",
    "We just demonstrated that in NumPy, when you execute a line of code that involves multiple objects, you usually establish a reference between them so that to delete the memory content of one, you have to delete all of them. With Dask, it gets still more complicated. Let's try to delete `copy_sqrt_computed`, which has the same values as `test_numpy_sqrt` did but was computed using Dask arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "025dc148-4ad1-4f80-83f0-53633a2523c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reference count of copy_sqrt_computed: 1\n",
      "Reference count of dask_copy_sqrt_array: 1\n",
      "Reference count of dask_copy_array: 1\n",
      "Reference count of copy_array: 102\n"
     ]
    }
   ],
   "source": [
    "print('Reference count of copy_sqrt_computed:',sys.getrefcount(copy_sqrt_computed)-1)\n",
    "print('Reference count of dask_copy_sqrt_array:',sys.getrefcount(dask_copy_sqrt_array)-1)\n",
    "print('Reference count of dask_copy_array:',sys.getrefcount(dask_copy_array)-1)\n",
    "print('Reference count of copy_array:',sys.getrefcount(copy_array)-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28f97e53-0c09-4dc7-bf14-53b44a49d985",
   "metadata": {},
   "source": [
    "All of the above arrays are linked by a chain of references, and notice that `copy_array` has 102 references! How did that happen? The Dask array `dask_copy_array` consists of 10x10 = 100 chunks, each sourced from `copy_array`. Each of those chunks adds a reference to `copy_array`.\n",
    "\n",
    "Let's delete all of the above arrays, as we did in the previous case with NumPy. As we do this, we will also track the number of references to `copy_array`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d8995672-d4ed-4c75-abff-ece680a83bb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reference count of copy_array:  1\n",
      "Change in available memory: 0.0  MB\n"
     ]
    }
   ],
   "source": [
    "del copy_sqrt_computed\n",
    "del dask_copy_sqrt_array\n",
    "del dask_copy_array\n",
    "print('Reference count of copy_array: ',sys.getrefcount(copy_array)-1)\n",
    "del copy_array\n",
    "memory_log_print_avail_change(memory_log)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9217b16e-bd28-4f8f-aeca-7a2e19209d6d",
   "metadata": {},
   "source": [
    "Now we deleted what would seem to be all the arrays that could be linked by references to `copy_sqrt_computed`, and still we haven't recovered memory!\n",
    "\n",
    "### Freeing up memory by saving to disk\n",
    "\n",
    "Python's memory management can be difficult to understand sometimes, especially when sophisticated packages like Dask are involved. But one way that an array's memory can be made available again is to save it to disk storage. Even if we don't need the array's data anymore, saving a NumPy array to a [.npz archive](https://numpy.org/doc/stable/reference/generated/numpy.savez.html) moves the array's reference away from the Dask array in the local workspace, to the newly created file.\n",
    "\n",
    "Let's try repeating the previous experiment by re-creating `copy_array` and make sure that new memory is being allocated for it. Don't ask me why we need to call `.copy` 3 times before new memory is allocated for the array; maybe you know the answer, but I don't!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c088e747-b282-4126-afb6-e935ce84ac81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Change in available memory: 0.0  MB\n"
     ]
    }
   ],
   "source": [
    "copy_array = test_array.copy()\n",
    "memory_log_print_avail_change(memory_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4c89148c-6e3f-4eb8-928c-52469aac99fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Change in available memory: 0.0  MB\n"
     ]
    }
   ],
   "source": [
    "copy_array_2 = copy_array.copy()\n",
    "memory_log_print_avail_change(memory_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "de5145b0-9496-4bbc-9bea-5d0d44aa213c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Change in available memory: -7.839744  MB\n"
     ]
    }
   ],
   "source": [
    "copy_array_3 = copy_array_2.copy()\n",
    "memory_log_print_avail_change(memory_log)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "33c046fb-f5ef-440b-bfe0-da86adaf889b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Change in available memory: -8.105984  MB\n"
     ]
    }
   ],
   "source": [
    "# re-create the in-memory array copy_sqrt_computed\n",
    "dask_copy_array = da.from_array(copy_array_3,chunks=(100,100))     # array has chunks of size 100x100\n",
    "dask_copy_sqrt_array = dask_copy_array**(1/2)\n",
    "copy_sqrt_computed = dask_copy_sqrt_array.compute()\n",
    "memory_log_print_avail_change(memory_log)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "730c2a60-37a1-4578-bc4f-c61be2c442f2",
   "metadata": {},
   "source": [
    "Now that we finally re-created `copy_sqrt_computed`, let's remove its memory from our workspace by first saving it to `.npz`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "85d79a0e-89d0-451e-a2c6-30f5796c5de1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Change in available memory from saving copy_sqrt_computed to disk: -8.052736  MB\n",
      "Change in available memory from deleting copy_sqrt_computed: 15.716352  MB\n"
     ]
    }
   ],
   "source": [
    "# save copy_sqrt_computed to a .npz archive\n",
    "np.savez('copy_sqrt_array.npz',copy_sqrt_array=copy_sqrt_computed)\n",
    "memory_log_print_avail_change(memory_log,\\\n",
    "          'Change in available memory from saving copy_sqrt_computed to disk:')\n",
    "\n",
    "# delete the file that was just created (assuming it isn't needed later)\n",
    "os.remove('copy_sqrt_array.npz')\n",
    "\n",
    "# delete reference for copy_sqrt_computed\n",
    "del copy_sqrt_computed\n",
    "memory_log_print_avail_change(memory_log,\\\n",
    "          'Change in available memory from deleting copy_sqrt_computed:')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a28031bf-6443-4ceb-afd7-2fa9b3547707",
   "metadata": {},
   "source": [
    "We recovered (most of) the memory associated with `copy_sqrt_computed`, though it required saving the array to disk first (which initially used more memory), and then deleting its reference. Does this procedure work with the other in-memory array `copy_array_3`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "35ed16ba-c19f-4b62-bdc7-37c7a23dcec1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Change in available memory from saving copy_array_3 to disk: -7.716864  MB\n",
      "Change in available memory from deleting copy_array_3: 15.745024  MB\n"
     ]
    }
   ],
   "source": [
    "# # try the same thing with copy_array_3\n",
    "\n",
    "# save NumPy array to a .npz archive\n",
    "np.savez('copy_array.npz',copy_array=copy_array_3)\n",
    "memory_log_print_avail_change(memory_log,\\\n",
    "          'Change in available memory from saving copy_array_3 to disk:')\n",
    "\n",
    "# delete the file that was just created (assuming it isn't needed later)\n",
    "os.remove('copy_array.npz')\n",
    "\n",
    "# delete references involving copy_array_3\n",
    "del dask_copy_sqrt_array\n",
    "del dask_copy_array\n",
    "del copy_array_3\n",
    "memory_log_print_avail_change(memory_log,\\\n",
    "          'Change in available memory from deleting copy_array_3:')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a84fa35-3bcd-4a59-a2a7-b07988f0aadd",
   "metadata": {},
   "source": [
    "So we were able to recover the memory from `copy_array_3` as well.\n",
    "\n",
    "Let's take a look at the memory log and compare the case that did not involve Dask with the one that did."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9ecd2aeb-667e-470f-b22e-03da5d24312e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig,ax = plt.subplots(1,2,figsize=(12,7))\n",
    "ax[0].plot((np.asarray([memory_log[ind].used for ind in range(0,6)])\\\n",
    "            - memory_log[0].used)/(10**6),color='r',label='used')\n",
    "ax[0].plot((np.asarray([memory_log[ind].available for ind in range(0,6)])\\\n",
    "            - memory_log[0].available)/(10**6),color='b',label='available')\n",
    "ax[0].set_ylim([-27,27])\n",
    "ax[0].axhline(y=0,color='k',lw=0.5)\n",
    "ax[0].set_xlabel('Memory log entry')\n",
    "ax[0].set_ylabel('Memory change since start (MB)')\n",
    "ax[0].legend()\n",
    "ax[0].set_title('No Dask')\n",
    "ax[1].plot((np.asarray([memory_log[ind].used for ind in range(8,15)])\\\n",
    "            - memory_log[8].used)/(10**6),color='r',label='used')\n",
    "ax[1].plot((np.asarray([memory_log[ind].available for ind in range(8,15)])\\\n",
    "            - memory_log[8].available)/(10**6),color='b',label='available')\n",
    "ax[1].set_ylim([-27,27])\n",
    "ax[1].axhline(y=0,color='k',lw=0.5)\n",
    "ax[1].set_xlabel('Memory log entry')\n",
    "ax[1].set_ylabel('Memory change since start (MB)')\n",
    "ax[1].legend()\n",
    "ax[1].set_title('With Dask')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cc6978b-c31b-458b-a4e4-402588498f67",
   "metadata": {},
   "source": [
    "So in both cases we were able to complete the calculation and recover the memory used (i.e., make it available again). The procedure was just a little more complicated when using Dask arrays.\n",
    "\n",
    "It is worth remembering that Dask can also significantly *help* you limit the memory usage in your workspace (as we saw earlier with a [reduction operation](#Dask-arrays-and-delayed-computations))...even though you relinquish some control over memory management when using it."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92471d99-1fff-4508-aa7c-54846d82c7a0",
   "metadata": {},
   "source": [
    "## Xarray and memory management\n",
    "\n",
    "### Unsolicited data caching\n",
    "\n",
    "The [xarray](https://docs.xarray.dev/en/stable/) package can open data files as Dask arrays using `open_mfdataset`, which is very useful for limiting memory usage in your workspace. `xarray.open_mfdataset` opens individual variables in a data file as Dask arrays, so that you get a \"lazy\" view of a file (or many files concatenated together) without immediately loading data into memory. However, when data chunks *are* loaded into memory using `.compute`, this data is typically cached to speed up future loads of the same data. This results in unintended memory usage that might be problematic depending on the size of the loaded data, the data chunks, and the limitations of your computing environment. \n",
    "\n",
    "Consider a case where we load (compute) into memory the monthly sea surface temperature (SST) from ECCO, 1992-2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "200e8807-fe45-4c9d-866a-a02bdb812e59",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import xarray as xr\n",
    "from os.path import join,expanduser\n",
    "import glob\n",
    "import psutil\n",
    "\n",
    "import ecco_v4_py as ecco\n",
    "import ecco_access as ea\n",
    "\n",
    "\n",
    "# are you working in the AWS Cloud?\n",
    "incloud_access = False\n",
    "\n",
    "# indicate mode of access from PO.DAAC\n",
    "# options are:\n",
    "# 'download': direct download from internet to your local machine\n",
    "# 'download_ifspace': like download, but only proceeds \n",
    "#                     if your machine have sufficient storage\n",
    "# 's3_open': access datasets in-cloud from an AWS instance\n",
    "# 's3_open_fsspec': use jsons generated with fsspec and \n",
    "#                   kerchunk libraries to speed up in-cloud access\n",
    "# 's3_get': direct download from S3 in-cloud to an AWS instance\n",
    "# 's3_get_ifspace': like s3_get, but only proceeds if your instance \n",
    "#                   has sufficient storage\n",
    "user_home_dir = expanduser('~')\n",
    "download_dir = join(user_home_dir,'Downloads','ECCO_V4r4_PODAAC')\n",
    "if incloud_access:\n",
    "    access_mode = 's3_open_fsspec'\n",
    "    download_root_dir = None\n",
    "    jsons_root_dir = join(user_home_dir,'MZZ')\n",
    "else:\n",
    "    access_mode = 'download_ifspace'\n",
    "    download_root_dir = download_dir\n",
    "    jsons_root_dir = None\n",
    "\n",
    "\n",
    "\n",
    "ShortNames_list = [\"ECCO_L4_GEOMETRY_LLC0090GRID_V4R4\",\\\n",
    "                   \"ECCO_L4_TEMP_SALINITY_LLC0090GRID_MONTHLY_V4R4\"]\n",
    "\n",
    "# access datasets\n",
    "ds_dict = ea.ecco_podaac_to_xrdataset(ShortNames_list,\\\n",
    "                                              StartDate='1992-01',EndDate='2017-12',\\\n",
    "                                              mode=access_mode,\\\n",
    "                                              download_root_dir=download_root_dir,\\\n",
    "                                              jsons_root_dir=jsons_root_dir,\\\n",
    "                                              max_avail_frac=0.5)\n",
    "\n",
    "## Load the model grid\n",
    "ecco_grid = ds_dict[ShortNames_list[0]].compute()\n",
    "\n",
    "## Open ECCO monthly mean T/S files\n",
    "ecco_TS = ds_dict[ShortNames_list[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f39c9193-b35a-4759-bbcb-feef0bd9589e",
   "metadata": {},
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 7GB\n",
       "Dimensions:  (i: 90, i_g: 90, j: 90, j_g: 90, k: 50, k_u: 50, k_l: 50,\n",
       "              k_p1: 51, tile: 13, nb: 4, nv: 2, time: 312)\n",
       "Coordinates: (12/21)\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 9 ... 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 9 ... 81 82 83 84 85 86 87 88 89\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_u      (k_u) int32 200B 0 1 2 3 4 5 6 7 8 9 ... 41 42 43 44 45 46 47 48 49\n",
       "    ...       ...\n",
       "    Zu       (k_u) float32 200B ...\n",
       "    Zl       (k_l) float32 200B ...\n",
       "    XC_bnds  (tile, j, i, nb) float32 2MB ...\n",
       "    YC_bnds  (tile, j, i, nb) float32 2MB ...\n",
       "    Z_bnds   (k, nv) float32 400B ...\n",
       "  * time     (time) datetime64[ns] 2kB 1992-01-16T18:00:00 ... 2017-12-16T06:...\n",
       "Dimensions without coordinates: nb, nv\n",
       "Data variables: (12/22)\n",
       "    CS       (tile, j, i) float32 421kB ...\n",
       "    SN       (tile, j, i) float32 421kB ...\n",
       "    rA       (tile, j, i) float32 421kB ...\n",
       "    dxG      (tile, j_g, i) float32 421kB ...\n",
       "    dyG      (tile, j, i_g) float32 421kB ...\n",
       "    Depth    (tile, j, i) float32 421kB ...\n",
       "    ...       ...\n",
       "    hFacW    (k, tile, j, i_g) float32 21MB ...\n",
       "    hFacS    (k, tile, j_g, i) float32 21MB ...\n",
       "    maskC    (k, tile, j, i) bool 5MB ...\n",
       "    maskW    (k, tile, j, i_g) bool 5MB ...\n",
       "    maskS    (k, tile, j_g, i) bool 5MB ...\n",
       "    THETA    (time, k, tile, j, i) float32 7GB dask.array&lt;chunksize=(1, 25, 7, 45, 45), meta=np.ndarray&gt;\n",
       "Attributes: (12/58)\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",
       "    Conventions:                     CF-1.8, ACDD-1.3\n",
       "    coordinates_comment:             Note: the global &#x27;coordinates&#x27; attribute...\n",
       "    ...                              ...\n",
       "    references:                      ECCO Consortium, Fukumori, I., Wang, O.,...\n",
       "    source:                          The ECCO V4r4 state estimate was produce...\n",
       "    standard_name_vocabulary:        NetCDF Climate and Forecast (CF) Metadat...\n",
       "    summary:                         This dataset provides geometric paramete...\n",
       "    title:                           ECCO Geometry Parameters for the Lat-Lon...\n",
       "    uuid:                            87ff7d24-86e5-11eb-9c5f-f8f21e2ee3e0</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-1547a65b-d10c-4397-9ced-bbbc3909c078' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1547a65b-d10c-4397-9ced-bbbc3909c078' 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'>i</span>: 90</li><li><span class='xr-has-index'>i_g</span>: 90</li><li><span class='xr-has-index'>j</span>: 90</li><li><span class='xr-has-index'>j_g</span>: 90</li><li><span class='xr-has-index'>k</span>: 50</li><li><span class='xr-has-index'>k_u</span>: 50</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'>tile</span>: 13</li><li><span>nb</span>: 4</li><li><span>nv</span>: 2</li><li><span class='xr-has-index'>time</span>: 312</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d26988e7-42a1-4b1a-bb2f-87eff5669c08' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d26988e7-42a1-4b1a-bb2f-87eff5669c08' class='xr-section-summary' >Coordinates: <span>(21)</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-f6b54003-60cb-439e-a4e4-16ac01e7e2f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6b54003-60cb-439e-a4e4-16ac01e7e2f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94bb2442-28c8-47f1-8ad5-99830b8aa578' class='xr-var-data-in' type='checkbox'><label for='data-94bb2442-28c8-47f1-8ad5-99830b8aa578' title='Show/Hide data 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-158bf066-5636-4a02-bce5-e4ce78b14169' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-158bf066-5636-4a02-bce5-e4ce78b14169' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c70c4edb-b58f-4859-859d-5d9f4360d45f' class='xr-var-data-in' type='checkbox'><label for='data-c70c4edb-b58f-4859-859d-5d9f4360d45f' title='Show/Hide data 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-8adc020e-b747-4f2f-8f53-aa8f1a9e69a0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8adc020e-b747-4f2f-8f53-aa8f1a9e69a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15b54828-763f-4db7-a560-177d0c8acd15' class='xr-var-data-in' type='checkbox'><label for='data-15b54828-763f-4db7-a560-177d0c8acd15' title='Show/Hide data 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-b48099e1-363a-4475-a116-55d157bbaf62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b48099e1-363a-4475-a116-55d157bbaf62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48580e66-49e0-4412-80f3-34a87adb3d7e' class='xr-var-data-in' type='checkbox'><label for='data-48580e66-49e0-4412-80f3-34a87adb3d7e' title='Show/Hide data 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'>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-0907ff1e-d22c-439f-a10d-7beaebeb9ad9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0907ff1e-d22c-439f-a10d-7beaebeb9ad9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11812903-bcdf-4307-91f2-906377527365' class='xr-var-data-in' type='checkbox'><label for='data-11812903-bcdf-4307-91f2-906377527365' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>grid index in z for tracer variables</dd><dt><span>swap_dim :</span></dt><dd>Z</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], 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-59254605-244f-4a23-9a30-fa9346f8f0da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-59254605-244f-4a23-9a30-fa9346f8f0da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7b5f3b5-4807-4f1e-a2de-6d765054bd54' class='xr-var-data-in' type='checkbox'><label for='data-a7b5f3b5-4807-4f1e-a2de-6d765054bd54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>grid index in z corresponding to the bottom face of tracer grid cells (&#x27;w&#x27; locations)</dd><dt><span>c_grid_axis_shift :</span></dt><dd>0.5</dd><dt><span>swap_dim :</span></dt><dd>Zu</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></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-bb6babcd-cdc2-4c41-9ff2-606a2cf9f770' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb6babcd-cdc2-4c41-9ff2-606a2cf9f770' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d79aed5-1f43-4cd3-8881-2ae24591af30' class='xr-var-data-in' type='checkbox'><label for='data-5d79aed5-1f43-4cd3-8881-2ae24591af30' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>grid index in z corresponding to the top face of tracer grid cells (&#x27;w&#x27; locations)</dd><dt><span>c_grid_axis_shift :</span></dt><dd>-0.5</dd><dt><span>swap_dim :</span></dt><dd>Zl</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></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-58d03538-ecd6-4ef9-b7aa-96b53be751f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58d03538-ecd6-4ef9-b7aa-96b53be751f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d07364d-1607-4dea-9124-6efc4f3fa927' class='xr-var-data-in' type='checkbox'><label for='data-2d07364d-1607-4dea-9124-6efc4f3fa927' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Z</dd><dt><span>long_name :</span></dt><dd>grid index in z for variables at &#x27;w&#x27; locations</dd><dt><span>c_grid_axis_shift :</span></dt><dd>[-0.5  0.5]</dd><dt><span>swap_dim :</span></dt><dd>Zp1</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></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'>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-1f2fab6d-c15b-491b-aaae-86210e167318' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1f2fab6d-c15b-491b-aaae-86210e167318' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11946631-4726-4b47-908a-3b93d285fb5b' class='xr-var-data-in' type='checkbox'><label for='data-11946631-4726-4b47-908a-3b93d285fb5b' title='Show/Hide data 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>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-d8679a39-b97d-447e-a1fd-14f6a7065e4e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8679a39-b97d-447e-a1fd-14f6a7065e4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-532c6bd0-fa46-43f6-bb93-ca69a5717621' class='xr-var-data-in' type='checkbox'><label for='data-532c6bd0-fa46-43f6-bb93-ca69a5717621' title='Show/Hide data 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",
       "    <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 5 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-dcc52007-c82b-4a51-90a4-b5626e4d7de1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dcc52007-c82b-4a51-90a4-b5626e4d7de1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ec6a9ff-6ef9-40d4-b70b-c492d38c8198' class='xr-var-data-in' type='checkbox'><label for='data-5ec6a9ff-6ef9-40d4-b70b-c492d38c8198' title='Show/Hide data 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",
       "    <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 5 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>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'>...</div><input id='attrs-7935cebb-d806-4ce5-a644-462262a3b71c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7935cebb-d806-4ce5-a644-462262a3b71c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4430d5e6-ccb6-412b-bcf4-b47560456def' class='xr-var-data-in' type='checkbox'><label for='data-4430d5e6-ccb6-412b-bcf4-b47560456def' title='Show/Hide data 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'><pre>[105300 values with dtype=float32]</pre></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'>...</div><input id='attrs-66e139b1-3294-49ae-a327-d8c179b18dcc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66e139b1-3294-49ae-a327-d8c179b18dcc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d3895f3-620e-4ffb-b840-ae9affcc7347' class='xr-var-data-in' type='checkbox'><label for='data-0d3895f3-620e-4ffb-b840-ae9affcc7347' title='Show/Hide data 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>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'><pre>[105300 values with dtype=float32]</pre></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-634800d4-9636-430d-8825-84d5d26a71bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-634800d4-9636-430d-8825-84d5d26a71bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee2c2649-c7f0-4b2e-ae2b-7c40485f1478' class='xr-var-data-in' type='checkbox'><label for='data-ee2c2649-c7f0-4b2e-ae2b-7c40485f1478' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>depth of tracer grid cell center</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>up</dd><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>standard_name :</span></dt><dd>depth</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 5 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",
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       "        </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'>...</div><input id='attrs-38990a50-84b1-4821-a371-814863062ffa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38990a50-84b1-4821-a371-814863062ffa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2875909e-91f8-4c31-b8a8-fe553fcf3fdd' class='xr-var-data-in' type='checkbox'><label for='data-2875909e-91f8-4c31-b8a8-fe553fcf3fdd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>depth of top/bottom face of tracer grid cell</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>up</dd><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>standard_name :</span></dt><dd>depth</dd></dl></div><div class='xr-var-data'><pre>[51 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Zu</span></div><div class='xr-var-dims'>(k_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-780ac171-c35d-4713-9929-8186169ca938' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-780ac171-c35d-4713-9929-8186169ca938' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c8c3082-cf8c-4ce0-8f97-047970f23376' class='xr-var-data-in' type='checkbox'><label for='data-1c8c3082-cf8c-4ce0-8f97-047970f23376' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>depth of bottom face of tracer grid cell</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>up</dd><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>standard_name :</span></dt><dd>depth</dd></dl></div><div class='xr-var-data'><pre>[50 values with dtype=float32]</pre></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'>...</div><input id='attrs-c3b6aa7c-eb35-4cb9-bf7f-fc60aaf0e734' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c3b6aa7c-eb35-4cb9-bf7f-fc60aaf0e734' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60a60ae6-91ad-4001-b731-f1c509052548' class='xr-var-data-in' type='checkbox'><label for='data-60a60ae6-91ad-4001-b731-f1c509052548' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>depth of top face of tracer grid cell</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>up</dd><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>standard_name :</span></dt><dd>depth</dd></dl></div><div class='xr-var-data'><pre>[50 values with dtype=float32]</pre></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'>...</div><input id='attrs-f41b988f-8dd7-45cb-bd5f-e19862a6f9fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f41b988f-8dd7-45cb-bd5f-e19862a6f9fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00f25b93-2c51-4409-ac68-4ad456bb8d04' class='xr-var-data-in' type='checkbox'><label for='data-00f25b93-2c51-4409-ac68-4ad456bb8d04' title='Show/Hide data 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'><pre>[421200 values with dtype=float32]</pre></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'>...</div><input id='attrs-79b08bfc-522c-41a2-9398-5f6665d7a13f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-79b08bfc-522c-41a2-9398-5f6665d7a13f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfbe4537-27c4-4481-a8ba-87e33de9b5a0' class='xr-var-data-in' type='checkbox'><label for='data-dfbe4537-27c4-4481-a8ba-87e33de9b5a0' title='Show/Hide data 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'><pre>[421200 values with dtype=float32]</pre></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'>...</div><input id='attrs-42f097d2-30e4-41a3-984e-d56c67171a59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42f097d2-30e4-41a3-984e-d56c67171a59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-110dd093-fad4-44ad-90d8-820a4a920436' class='xr-var-data-in' type='checkbox'><label for='data-110dd093-fad4-44ad-90d8-820a4a920436' title='Show/Hide data 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'><pre>[100 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1992-01-16T18:00:00 ... 2017-12-...</div><input id='attrs-35a39b46-98ee-4dd3-ab8d-70c84648921a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-35a39b46-98ee-4dd3-ab8d-70c84648921a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dea2990a-7bf4-4cdc-969b-824fbbd1dcf1' class='xr-var-data-in' type='checkbox'><label for='data-dea2990a-7bf4-4cdc-969b-824fbbd1dcf1' title='Show/Hide data 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;1992-01-16T18:00:00.000000000&#x27;, &#x27;1992-02-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;1992-03-16T12:00:00.000000000&#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></ul></div></li><li class='xr-section-item'><input id='section-604e9f9d-c321-4cb3-9661-e79c456b3d1a' class='xr-section-summary-in' type='checkbox'  ><label for='section-604e9f9d-c321-4cb3-9661-e79c456b3d1a' class='xr-section-summary' >Data variables: <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>CS</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'>...</div><input id='attrs-ae8f6f8d-aae8-4e05-8250-ec4498c1490c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae8f6f8d-aae8-4e05-8250-ec4498c1490c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-338150d3-56ce-4828-babb-4b1980c3b8e0' class='xr-var-data-in' type='checkbox'><label for='data-338150d3-56ce-4828-babb-4b1980c3b8e0' title='Show/Hide data 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>cosine of tracer grid cell orientation vs geographical north</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>coordinate :</span></dt><dd>YC XC</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>comment :</span></dt><dd>CS and SN are required to calculate the geographic (meridional, zonal) components of vectors on the curvilinear model grid. Note: for vector R with components R_x and R_y: R_{east} = CS R_x - SN R_y.  R_{north} = SN R_x + CS R_y</dd></dl></div><div class='xr-var-data'><pre>[105300 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SN</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'>...</div><input id='attrs-1528ae1c-cb2a-4697-b087-bb31ec7d422b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1528ae1c-cb2a-4697-b087-bb31ec7d422b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f208e4c9-471b-4893-83e2-78bf7de36d1b' class='xr-var-data-in' type='checkbox'><label for='data-f208e4c9-471b-4893-83e2-78bf7de36d1b' title='Show/Hide data 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>sine of tracer grid cell orientation vs geographical north</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>coordinate :</span></dt><dd>YC XC</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>comment :</span></dt><dd>CS and SN are required to calculate the geographic (meridional, zonal) components of vectors on the curvilinear model grid. Note: for vector R with components R_x and R_y in local grid directions x and y, the geographical eastward component R_{east} = CS R_x - SN R_y. The geographical northward component R_{north} = SN R_x + CS R_y.</dd></dl></div><div class='xr-var-data'><pre>[105300 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rA</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'>...</div><input id='attrs-bfe6fd24-f85d-4fc1-baf6-ac9dbba39dcd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bfe6fd24-f85d-4fc1-baf6-ac9dbba39dcd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eed4437f-1a99-4c07-94c3-ac67f77d7962' class='xr-var-data-in' type='checkbox'><label for='data-eed4437f-1a99-4c07-94c3-ac67f77d7962' title='Show/Hide data 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>area of tracer grid cell</dd><dt><span>units :</span></dt><dd>m2</dd><dt><span>coordinate :</span></dt><dd>YC XC</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>standard_name :</span></dt><dd>cell_area</dd></dl></div><div class='xr-var-data'><pre>[105300 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dxG</span></div><div class='xr-var-dims'>(tile, j_g, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8ff42e6f-d55a-408a-af00-5a925931b1ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8ff42e6f-d55a-408a-af00-5a925931b1ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f518b269-5fbe-415b-86ff-32f26384382c' class='xr-var-data-in' type='checkbox'><label for='data-f518b269-5fbe-415b-86ff-32f26384382c' title='Show/Hide data 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>distance between &#x27;southwest&#x27; and &#x27;southeast&#x27; corners of the tracer grid cell</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>coordinate :</span></dt><dd>YG XC</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>comment :</span></dt><dd>Alternatively, the length of &#x27;south&#x27; side of tracer grid cell. Note: &#x27;south&#x27;, &#x27;southwest&#x27;, and &#x27;southeast&#x27; do not correspond to geographic orientation but are used for convenience to describe the computational grid. See MITgcm documentation for details.</dd></dl></div><div class='xr-var-data'><pre>[105300 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dyG</span></div><div class='xr-var-dims'>(tile, j, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-635cd338-801d-463b-a8eb-1450f377e08e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-635cd338-801d-463b-a8eb-1450f377e08e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68907c9e-e1cb-4779-80f0-c568c0a1e6b1' class='xr-var-data-in' type='checkbox'><label for='data-68907c9e-e1cb-4779-80f0-c568c0a1e6b1' title='Show/Hide data 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>distance between &#x27;southwest&#x27; and &#x27;northwest&#x27; corners of the tracer grid cell</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>coordinate :</span></dt><dd>YC XG</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>comment :</span></dt><dd>Alternatively, the length of &#x27;west&#x27; side of tracer grid cell. Note: &#x27;west, &#x27;southwest&#x27;, and &#x27;northwest&#x27; do not correspond to geographic orientation but are used for convenience to describe the computational grid. See MITgcm documentation for details.</dd></dl></div><div class='xr-var-data'><pre>[105300 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Depth</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'>...</div><input id='attrs-b48a141b-3404-4051-83f1-111ccb0024b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b48a141b-3404-4051-83f1-111ccb0024b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51e76900-0b18-4fcb-9d1c-ba4b4c8ca987' class='xr-var-data-in' type='checkbox'><label for='data-51e76900-0b18-4fcb-9d1c-ba4b4c8ca987' title='Show/Hide data 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 seafloor depth below ocean surface at rest</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>coordinate :</span></dt><dd>XC YC</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>standard_name :</span></dt><dd>sea_floor_depth_below_geoid</dd><dt><span>comment :</span></dt><dd>Model sea surface height (SSH) of 0m corresponds to an ocean surface at rest relative to the geoid. Depth corresponds to seafloor depth below geoid. Note: the MITgcm used by ECCO V4r4 implements &#x27;partial cells&#x27; so the actual model seafloor depth may differ from the seafloor depth provided by the input bathymetry file.</dd></dl></div><div class='xr-var-data'><pre>[105300 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rAz</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'>...</div><input id='attrs-26098c50-a8a9-43f5-890b-35845262ff7c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-26098c50-a8a9-43f5-890b-35845262ff7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef9e2c33-bf19-4ab0-b77f-cf61f4946af4' class='xr-var-data-in' type='checkbox'><label for='data-ef9e2c33-bf19-4ab0-b77f-cf61f4946af4' title='Show/Hide data 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>area of vorticity &#x27;g&#x27; grid cell</dd><dt><span>units :</span></dt><dd>m2</dd><dt><span>coordinate :</span></dt><dd>YG XG</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>standard_name :</span></dt><dd>cell_area</dd><dt><span>comment :</span></dt><dd>Vorticity cells are staggered in space relative to tracer cells, nominally situated on tracer cell corners. Vorticity cell (i,j) is located at the &#x27;southwest&#x27; corner of tracer grid cell (i, j). Note: &#x27;southwest&#x27; does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm documentation for details.</dd></dl></div><div class='xr-var-data'><pre>[105300 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dxC</span></div><div class='xr-var-dims'>(tile, j, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8f808bb1-313a-4b8b-9b41-36f72048e471' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8f808bb1-313a-4b8b-9b41-36f72048e471' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-783d6e92-7ca8-4a92-9f1b-6d68651a72f2' class='xr-var-data-in' type='checkbox'><label for='data-783d6e92-7ca8-4a92-9f1b-6d68651a72f2' title='Show/Hide data 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>distance between centers of adjacent tracer grid cells in the &#x27;x&#x27; direction</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>coordinate :</span></dt><dd>YC XG</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>comment :</span></dt><dd>Alternatively, the length of &#x27;north&#x27; side of vorticity grid cells. Note: &#x27;north&#x27; does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm documentation for details.</dd></dl></div><div class='xr-var-data'><pre>[105300 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dyC</span></div><div class='xr-var-dims'>(tile, j_g, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d09d6109-7a78-4d36-8614-ce01afca6bd1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d09d6109-7a78-4d36-8614-ce01afca6bd1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81a0f7d8-4ff2-4710-900f-969c19894aef' class='xr-var-data-in' type='checkbox'><label for='data-81a0f7d8-4ff2-4710-900f-969c19894aef' title='Show/Hide data 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>distance between centers of adjacent tracer grid cells in the &#x27;y&#x27; direction</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>coordinate :</span></dt><dd>YG XC</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>comment :</span></dt><dd>Alternatively, the length of &#x27;east&#x27; side of vorticity grid cells. Note: &#x27;east&#x27; does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm documentation for details.</dd></dl></div><div class='xr-var-data'><pre>[105300 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rAw</span></div><div class='xr-var-dims'>(tile, j, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-231b9ea9-8d61-46f1-9923-97571bbb1c41' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-231b9ea9-8d61-46f1-9923-97571bbb1c41' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b622f59-2b71-4bf0-9b35-a5d3283c30ef' class='xr-var-data-in' type='checkbox'><label for='data-3b622f59-2b71-4bf0-9b35-a5d3283c30ef' title='Show/Hide data 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>area of &#x27;v&#x27; grid cell</dd><dt><span>units :</span></dt><dd>m2</dd><dt><span>coordinate :</span></dt><dd>YG XC</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>standard_name :</span></dt><dd>cell_area</dd><dt><span>comment :</span></dt><dd>Model &#x27;v&#x27; grid cells are staggered in space between adjacent tracer grid cells in the &#x27;x&#x27; direction. &#x27;v&#x27; grid cell (i,j) is situated at the &#x27;west&#x27; edge of tracer grid cell (i, j). Note: &#x27;west&#x27; does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm documentation for details.</dd></dl></div><div class='xr-var-data'><pre>[105300 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rAs</span></div><div class='xr-var-dims'>(tile, j_g, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e54e63eb-2797-4cbb-a403-06d6ed6bef97' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e54e63eb-2797-4cbb-a403-06d6ed6bef97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f87ea61-b24c-4e65-b495-7c6f04d67cd4' class='xr-var-data-in' type='checkbox'><label for='data-2f87ea61-b24c-4e65-b495-7c6f04d67cd4' title='Show/Hide data 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>area of &#x27;u&#x27; grid cell</dd><dt><span>units :</span></dt><dd>m2</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>standard_name :</span></dt><dd>cell_area</dd><dt><span>comment :</span></dt><dd>Model &#x27;u&#x27; grid cells are staggered in space between adjacent tracer grid cells in the &#x27;y&#x27; direction. &#x27;u&#x27; grid cell (i,j) is situated at the &#x27;south&#x27; edge of tracer grid cell (i, j). Note: &#x27;south&#x27; does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm documentation for details.</dd></dl></div><div class='xr-var-data'><pre>[105300 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>drC</span></div><div class='xr-var-dims'>(k_p1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e2b8055e-00a3-416c-9915-ed7e9d40b76e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2b8055e-00a3-416c-9915-ed7e9d40b76e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ae41d4b-918d-4588-a282-396ccf27e7d6' class='xr-var-data-in' type='checkbox'><label for='data-2ae41d4b-918d-4588-a282-396ccf27e7d6' title='Show/Hide data 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>distance between the centers of adjacent tracer grid cells in the &#x27;z&#x27; direction</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>The first element corresponds to the distance between the depth of the center of the uppermost model grid cell and the surface.</dd></dl></div><div class='xr-var-data'><pre>[51 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>drF</span></div><div class='xr-var-dims'>(k)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1ea9eed7-2595-4c70-a1f4-93979f84b6e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ea9eed7-2595-4c70-a1f4-93979f84b6e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe0d1479-073f-43b6-9571-7be1a6ed0863' class='xr-var-data-in' type='checkbox'><label for='data-fe0d1479-073f-43b6-9571-7be1a6ed0863' title='Show/Hide data 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>distance between the upper and lower interfaces of the model grid cell</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>cell_thickness</dd><dt><span>comment :</span></dt><dd>Nominal grid cell thickness. Note: in the z* coordinate system used in ECCO V4, actual tracer grid cell thickness, h, varies through time as h(i,j,k,t)= drF(k) hfacC(i,j,k,t).</dd></dl></div><div class='xr-var-data'><pre>[50 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PHrefC</span></div><div class='xr-var-dims'>(k)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d6f646a5-b4ff-40e2-8c39-f98c56041537' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6f646a5-b4ff-40e2-8c39-f98c56041537' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4dcd0be-5e46-48ae-9d5b-e48261a8f27f' class='xr-var-data-in' type='checkbox'><label for='data-c4dcd0be-5e46-48ae-9d5b-e48261a8f27f' title='Show/Hide data 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>reference ocean hydrostatic pressure at tracer grid cell center</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>comment :</span></dt><dd>PHrefC = p_ref (k) / rhoConst = rhoConst g z(k) / rhoConst = g z(k), where p_ref(k) is reference hydrostatic ocean pressure at center of tracer grid cell k, rhoConst is reference density (1029 kg m-3), g is acceleration due to gravity (9.81 m s-2), and z(k) is depth at center of tracer grid cell k. Units: p:[kg m-1 s-2], rhoConst:[kg m-3], g:[m s-2], z_m(t):[m]. Note: does not include atmospheric pressure loading. Quantity referred to in some contexts as hydrostatic pressure potential. PHIHYDcR is anomaly of PHrefC.</dd></dl></div><div class='xr-var-data'><pre>[50 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>PHrefF</span></div><div class='xr-var-dims'>(k_p1)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-52351b28-7464-49cc-97d3-d5732610b409' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52351b28-7464-49cc-97d3-d5732610b409' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f852bd85-fce9-4ce9-97e0-b37f3118b36f' class='xr-var-data-in' type='checkbox'><label for='data-f852bd85-fce9-4ce9-97e0-b37f3118b36f' title='Show/Hide data 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>reference ocean hydrostatic pressure at tracer grid cell top/bottom interface</dd><dt><span>units :</span></dt><dd>m2 s-2</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>comment :</span></dt><dd>PHrefF = p_ref (k_l) / rhoConst = rhoConst g z(k_l) / rhoConst = g z(k_l), where p_ref(k_l) is reference hydrostatic ocean pressure at lower interface of tracer grid cell k, rhoConst is reference density (1029 kg m-3), g is acceleration due to gravity (9.81 m s-2), and z(k) is depth at center of tracer grid cell k. Units: p:[kg m-1 s-2], rhoConst:[kg m-3], g:[m s-2], z_m(t):[m]. Note: does not include atmospheric pressure loading. Quantity referred to in some contexts as hydrostatic pressure potential. See PHrefC</dd></dl></div><div class='xr-var-data'><pre>[51 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hFacC</span></div><div class='xr-var-dims'>(k, tile, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-521f2605-b628-43a4-9a0c-9f8b987bf98f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-521f2605-b628-43a4-9a0c-9f8b987bf98f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5924298-f9f1-4bc7-a4e8-a6993dff98ea' class='xr-var-data-in' type='checkbox'><label for='data-b5924298-f9f1-4bc7-a4e8-a6993dff98ea' title='Show/Hide data 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>vertical open fraction of tracer grid cell</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>comment :</span></dt><dd>Tracer grid cells may be fractionally closed in the vertical. The open vertical fraction is hFacC. The model allows for partially-filled cells to represent topographic variations more smoothly (hFacC &lt; 1). Completely closed (dry) tracer grid cells have hFacC = 0. Note: the model z* coordinate system allows hFacC to vary through time. A time-invariant hFacC field is provided for reference.</dd></dl></div><div class='xr-var-data'><pre>[5265000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hFacW</span></div><div class='xr-var-dims'>(k, tile, j, i_g)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-24e39b13-2cc9-4de8-b73b-43998f0fe513' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24e39b13-2cc9-4de8-b73b-43998f0fe513' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1f65527-ed97-4529-baca-74ac934e6230' class='xr-var-data-in' type='checkbox'><label for='data-c1f65527-ed97-4529-baca-74ac934e6230' title='Show/Hide data 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>vertical open fraction of tracer grid cell &#x27;west&#x27; face</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>comment :</span></dt><dd>The &#x27;west&#x27; face of tracer grid cells may be fractionally closed in the vertical. The open vertical fraction is hFacW. The model allows for partially-filled cells for smoother representation of seafloor topography. Tracer grid cells adjacent in the &#x27;x&#x27; direction that are partially closed in the vertical have hFacW &lt; 1. The model z* coordinate system used by the model permits hFacC, and therefore hFacW, to vary through time. A time-invariant hFacW field is provided for reference. Note: The term &#x27;west&#x27; does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm documentation for details.</dd></dl></div><div class='xr-var-data'><pre>[5265000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hFacS</span></div><div class='xr-var-dims'>(k, tile, j_g, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9ad8cd3e-e7cd-4e2b-9807-c88dfe97d315' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ad8cd3e-e7cd-4e2b-9807-c88dfe97d315' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7dabcf36-82b5-44b8-87e1-af1edceb15c2' class='xr-var-data-in' type='checkbox'><label for='data-7dabcf36-82b5-44b8-87e1-af1edceb15c2' title='Show/Hide data 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>vertical open fraction of tracer grid cell &#x27;south&#x27; face</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>comment :</span></dt><dd>The &#x27;south&#x27; face of tracer grid cells may be fractionally closed in the vertical. The open vertical fraction is hFacS. The model allows for partially-filled cells for smoother representation of seafloor topography. Tracer grid cells adjacent in the &#x27;y&#x27; direction that are partially closed in the vertical have hFacS &lt; 1. The model z* coordinate system used by the model permits hFacC, and therefore hFacS, to vary through time. A time-invariant hFacS field is provided for reference. Note:  The term &#x27;south&#x27; does not correspond to geographic orientation but is used for convenience to describe the computational grid. See MITgcm documentation for details.</dd></dl></div><div class='xr-var-data'><pre>[5265000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>maskC</span></div><div class='xr-var-dims'>(k, tile, j, i)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-39df3bfa-8acd-49b5-9340-782c470c980d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39df3bfa-8acd-49b5-9340-782c470c980d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67369ac3-a5c6-4aeb-8d5c-bc64e81ea475' class='xr-var-data-in' type='checkbox'><label for='data-67369ac3-a5c6-4aeb-8d5c-bc64e81ea475' title='Show/Hide data 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>wet/dry boolean mask for tracer grid cell</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>comment :</span></dt><dd>True for tracer grid cells with nonzero open vertical fraction (hFacC &gt; 0), otherwise False. Although hFacC can vary though time, cells will never close if starting open and will never open if starting closed: hFacC(i,j,k,t) &gt; 0 for all t, if hFacC(i,j,k,t=0) and hFacC(i,j,k,t) = 0 for all t, if hFacC(i,j,k,t=0) = 0. Therefore, maskC is time invariant.</dd></dl></div><div class='xr-var-data'><pre>[5265000 values with dtype=bool]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>maskW</span></div><div class='xr-var-dims'>(k, tile, j, i_g)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d6da37a1-05d1-41ba-ba4d-3d14785e5571' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6da37a1-05d1-41ba-ba4d-3d14785e5571' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d53593b-5922-48a8-8f13-e3397f28147b' class='xr-var-data-in' type='checkbox'><label for='data-8d53593b-5922-48a8-8f13-e3397f28147b' title='Show/Hide data 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>wet/dry boolean mask for &#x27;west&#x27; face of tracer grid cell</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>comment :</span></dt><dd>True for grid cells with nonzero open vertical fraction along their &#x27;west&#x27; face (hFacW &gt; 0), otherwise False. Although hFacW can vary though time, cells will never close if starting open and will never open if starting closed: hFacW(i,j,k,t) &gt; 0 for all t, if hFacW(i,j,k,t=0) and hFacW(i,j,k,t) = 0 for all t, if hFacW(i,j,k,t=0) = 0. Therefore, maskW is time invariant. Note: </dd></dl></div><div class='xr-var-data'><pre>[5265000 values with dtype=bool]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>maskS</span></div><div class='xr-var-dims'>(k, tile, j_g, i)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f78bf0ba-854c-442b-86c8-afe14645d759' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f78bf0ba-854c-442b-86c8-afe14645d759' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f554f1e-42b1-45d3-b085-c6ca0512c110' class='xr-var-data-in' type='checkbox'><label for='data-1f554f1e-42b1-45d3-b085-c6ca0512c110' title='Show/Hide data 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>wet/dry boolean mask for &#x27;south&#x27; face of tracer grid cell</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>comment :</span></dt><dd>True for grid cells with nonzero open vertical fraction along their &#x27;south&#x27; face (hFacS &gt; 0), otherwise False. Although hFacS can vary though time, cells will never close if starting open and will never open if starting closed: hFacS(i,j,k,t) &gt; 0 for all t, if hFacS(i,j,k,t=0) and hFacS(i,j,k,t) = 0 for all t, if hFacS(i,j,k,t=0) = 0. Therefore, maskS is time invariant. Note: </dd></dl></div><div class='xr-var-data'><pre>[5265000 values with dtype=bool]</pre></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=(1, 25, 7, 45, 45), meta=np.ndarray&gt;</div><input id='attrs-cf3f3e74-2442-4b26-9e19-7d70b9662009' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf3f3e74-2442-4b26-9e19-7d70b9662009' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4d75b0c-c07f-4e01-9329-823a47731080' class='xr-var-data-in' type='checkbox'><label for='data-c4d75b0c-c07f-4e01-9329-823a47731080' title='Show/Hide data 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>Potential temperature </dd><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>coverage_content_type :</span></dt><dd>modelResult</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>comment :</span></dt><dd>Sea water potential temperature is the temperature a parcel of sea water would have if moved adiabatically to sea level pressure. Note: the equation of state is a modified UNESCO formula by Jackett and McDougall (1995), which uses the model variable potential temperature as input assuming a horizontally and temporally constant pressure of $p_0=-g \\rho_{0} z$.</dd><dt><span>valid_min :</span></dt><dd>-2.2909388542175293</dd><dt><span>valid_max :</span></dt><dd>36.032955169677734</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> 6.12 GiB </td>\n",
       "                        <td> 1.35 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (312, 50, 13, 90, 90) </td>\n",
       "                        <td> (1, 25, 7, 45, 45) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 4992 chunks in 625 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></ul></div></li><li class='xr-section-item'><input id='section-d635bf85-0a41-4405-aeb2-27130690dbdc' class='xr-section-summary-in' type='checkbox'  ><label for='section-d635bf85-0a41-4405-aeb2-27130690dbdc' 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>i</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3aca925b-bbdb-4f8c-af30-099100a8346b' class='xr-index-data-in' type='checkbox'/><label for='index-3aca925b-bbdb-4f8c-af30-099100a8346b' 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-3da40337-0d06-45ae-8933-aa63d6623917' class='xr-index-data-in' type='checkbox'/><label for='index-3da40337-0d06-45ae-8933-aa63d6623917' 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-6f3c760e-4679-4c42-9a69-afdd1282add1' class='xr-index-data-in' type='checkbox'/><label for='index-6f3c760e-4679-4c42-9a69-afdd1282add1' 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-3c3b1d30-d672-4796-aca9-ca708b40e105' class='xr-index-data-in' type='checkbox'/><label for='index-3c3b1d30-d672-4796-aca9-ca708b40e105' 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-77980459-5752-4062-90c1-854b5fff91c4' class='xr-index-data-in' type='checkbox'/><label for='index-77980459-5752-4062-90c1-854b5fff91c4' 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_u</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cf3013c6-0a49-45b2-8ff8-9c32caa48359' class='xr-index-data-in' type='checkbox'/><label for='index-cf3013c6-0a49-45b2-8ff8-9c32caa48359' 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>k_l</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-89973603-80ba-4284-95d5-ea24dbf11abd' class='xr-index-data-in' type='checkbox'/><label for='index-89973603-80ba-4284-95d5-ea24dbf11abd' 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-5a9ace55-ef1f-42ca-b5ad-057e5b506477' class='xr-index-data-in' type='checkbox'/><label for='index-5a9ace55-ef1f-42ca-b5ad-057e5b506477' 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>tile</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6b3b71ee-c34b-439d-8a30-cc219cf22b8b' class='xr-index-data-in' type='checkbox'/><label for='index-6b3b71ee-c34b-439d-8a30-cc219cf22b8b' 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-68f0b982-1a2d-4f90-9bec-6dca6fd7b646' class='xr-index-data-in' type='checkbox'/><label for='index-68f0b982-1a2d-4f90-9bec-6dca6fd7b646' 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;1992-01-16 18:00:00&#x27;, &#x27;1992-02-15 12:00:00&#x27;,\n",
       "               &#x27;1992-03-16 12:00:00&#x27;, &#x27;1992-04-16 00:00:00&#x27;,\n",
       "               &#x27;1992-05-16 12:00:00&#x27;, &#x27;1992-06-16 00:00:00&#x27;,\n",
       "               &#x27;1992-07-16 12:00:00&#x27;, &#x27;1992-08-16 12:00:00&#x27;,\n",
       "               &#x27;1992-09-16 00:00:00&#x27;, &#x27;1992-10-16 12:00:00&#x27;,\n",
       "               ...\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;, length=312, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b1524573-85cb-47fe-a3af-9a247150ffd1' class='xr-section-summary-in' type='checkbox'  ><label for='section-b1524573-85cb-47fe-a3af-9a247150ffd1' class='xr-section-summary' >Attributes: <span>(58)</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.</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>2021-03-16T22:56:35</dd><dt><span>date_issued :</span></dt><dd>2021-03-16T22:56:35</dd><dt><span>date_metadata_modified :</span></dt><dd>2021-03-16T22:56:35</dd><dt><span>date_modified :</span></dt><dd>2021-03-16T22:56:35</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/ECL5A-GRD44</dd><dt><span>institution :</span></dt><dd>NASA Jet Propulsion Laboratory (JPL)</dd><dt><span>instrument_vocabulary :</span></dt><dd>GCMD instrument keywords</dd><dt><span>keywords :</span></dt><dd>EARTH SCIENCE SERVICES &gt; MODELS &gt; EARTH SCIENCE REANALYSES/ASSIMILATION MODELS</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>license :</span></dt><dd>Public Domain</dd><dt><span>metadata_link :</span></dt><dd>https://cmr.earthdata.nasa.gov/search/collections.umm_json?ShortName=ECCO_L4_GEOMETRY_LLC0090GRID_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>GRID_GEOMETRY_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 geometric parameters for the lat-lon-cap 90 (llc90) native model grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Parameters include areas and lengths of grid cell sides, horizontal and vertical coordinates of grid cell centers and corners, grid rotation angles, and global domain geometry including bathymetry and land/ocean masks. 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>title :</span></dt><dd>ECCO Geometry Parameters for the Lat-Lon-Cap 90 (llc90) Native Model Grid (Version 4 Release 4)</dd><dt><span>uuid :</span></dt><dd>87ff7d24-86e5-11eb-9c5f-f8f21e2ee3e0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 7GB\n",
       "Dimensions:  (i: 90, i_g: 90, j: 90, j_g: 90, k: 50, k_u: 50, k_l: 50,\n",
       "              k_p1: 51, tile: 13, nb: 4, nv: 2, time: 312)\n",
       "Coordinates: (12/21)\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 9 ... 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 9 ... 81 82 83 84 85 86 87 88 89\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_u      (k_u) int32 200B 0 1 2 3 4 5 6 7 8 9 ... 41 42 43 44 45 46 47 48 49\n",
       "    ...       ...\n",
       "    Zu       (k_u) float32 200B ...\n",
       "    Zl       (k_l) float32 200B ...\n",
       "    XC_bnds  (tile, j, i, nb) float32 2MB ...\n",
       "    YC_bnds  (tile, j, i, nb) float32 2MB ...\n",
       "    Z_bnds   (k, nv) float32 400B ...\n",
       "  * time     (time) datetime64[ns] 2kB 1992-01-16T18:00:00 ... 2017-12-16T06:...\n",
       "Dimensions without coordinates: nb, nv\n",
       "Data variables: (12/22)\n",
       "    CS       (tile, j, i) float32 421kB ...\n",
       "    SN       (tile, j, i) float32 421kB ...\n",
       "    rA       (tile, j, i) float32 421kB ...\n",
       "    dxG      (tile, j_g, i) float32 421kB ...\n",
       "    dyG      (tile, j, i_g) float32 421kB ...\n",
       "    Depth    (tile, j, i) float32 421kB ...\n",
       "    ...       ...\n",
       "    hFacW    (k, tile, j, i_g) float32 21MB ...\n",
       "    hFacS    (k, tile, j_g, i) float32 21MB ...\n",
       "    maskC    (k, tile, j, i) bool 5MB ...\n",
       "    maskW    (k, tile, j, i_g) bool 5MB ...\n",
       "    maskS    (k, tile, j_g, i) bool 5MB ...\n",
       "    THETA    (time, k, tile, j, i) float32 7GB dask.array<chunksize=(1, 25, 7, 45, 45), meta=np.ndarray>\n",
       "Attributes: (12/58)\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",
       "    Conventions:                     CF-1.8, ACDD-1.3\n",
       "    coordinates_comment:             Note: the global 'coordinates' attribute...\n",
       "    ...                              ...\n",
       "    references:                      ECCO Consortium, Fukumori, I., Wang, O.,...\n",
       "    source:                          The ECCO V4r4 state estimate was produce...\n",
       "    standard_name_vocabulary:        NetCDF Climate and Forecast (CF) Metadat...\n",
       "    summary:                         This dataset provides geometric paramete...\n",
       "    title:                           ECCO Geometry Parameters for the Lat-Lon...\n",
       "    uuid:                            87ff7d24-86e5-11eb-9c5f-f8f21e2ee3e0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## merge grid and temperature datasets\n",
    "ds = xr.merge((ecco_grid,ecco_TS[['THETA']]))\n",
    "\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cfe38710-f1ac-40b8-a8ef-c0fcbe104881",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Change in available memory: -2155.773952  MB\n"
     ]
    }
   ],
   "source": [
    "# compute temperature for top level\n",
    "memory_log = [psutil.virtual_memory()]\n",
    "SST_computed = ds.THETA.isel(k=0).compute()\n",
    "memory_log_print_avail_change(memory_log)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5266b2c2-aaac-4e13-8d67-4dc7752b3a6c",
   "metadata": {},
   "source": [
    "But wait...should the temperature at 1 depth level take up ~2 GB of memory?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9df78042-d3b7-4457-b539-74716cbfb15d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SST_computed data type: float32\n",
      "SST_computed number of elements: 32853600\n",
      "Estimated memory footprint of SST_computed: 131.4144 MB\n"
     ]
    }
   ],
   "source": [
    "print('SST_computed data type:',SST_computed.dtype)\n",
    "print('SST_computed number of elements:',SST_computed.size)\n",
    "print('Estimated memory footprint of SST_computed:',(4*SST_computed.size)/(10**6),'MB')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6ab2360-7518-47d2-8124-c2c8c86a61ed",
   "metadata": {},
   "source": [
    "So why was so much more memory used than the size of the array itself? When `.compute` is invoked on a dataset sourced from file(s) using `open_mfdataset`, any chunk that is accessed is also cached (presumably to make subsequent loads of that data faster). So what are the chunk sizes in the `k` dimension?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "82792480-37e0-4df9-b4b9-7e4bbc6229b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 25)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k_dimnum = list(ds.THETA.dims).index('k')\n",
    "ds.THETA.chunks[k_dimnum]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20e5bd2b-19d9-43cd-821a-22a0d724a783",
   "metadata": {},
   "source": [
    "The array is divided in 2 chunks in the `k` dimension each of size 25. So even if we are only using `.compute` on a single depth level, 25 depth levels are being cached (in a somewhat compressed format) and using up memory. Note that even if we re-chunk the dataset or data array after accessing the file(s), caching happens according to the native chunk sizes of the file. And at least in my experience, passing the option `cache=False` to `open_mfdataset` does not change the memory usage.\n",
    "\n",
    "So what can we do? One option is to align our workflow more closely with the native chunking of the source file(s). **At this point I recommend restarting the kernel of this notebook so we can get a fresh open of the data files.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "583d9d44-57a9-4359-a75e-c8e1c7cef929",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Change in available memory: -1969.475584  MB\n",
      "CPU times: user 40.3 s, sys: 4.21 s, total: 44.5 s\n",
      "Wall time: 47.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "from os.path import join,expanduser\n",
    "import glob\n",
    "import psutil\n",
    "\n",
    "import ecco_v4_py as ecco\n",
    "import ecco_access as ea\n",
    "\n",
    "\n",
    "# are you working in the AWS Cloud?\n",
    "incloud_access = False\n",
    "\n",
    "# indicate mode of access from PO.DAAC\n",
    "# options are:\n",
    "# 'download': direct download from internet to your local machine\n",
    "# 'download_ifspace': like download, but only proceeds \n",
    "#                     if your machine have sufficient storage\n",
    "# 's3_open': access datasets in-cloud from an AWS instance\n",
    "# 's3_open_fsspec': use jsons generated with fsspec and \n",
    "#                   kerchunk libraries to speed up in-cloud access\n",
    "# 's3_get': direct download from S3 in-cloud to an AWS instance\n",
    "# 's3_get_ifspace': like s3_get, but only proceeds if your instance \n",
    "#                   has sufficient storage\n",
    "user_home_dir = expanduser('~')\n",
    "download_dir = join(user_home_dir,'Downloads','ECCO_V4r4_PODAAC')\n",
    "if incloud_access:\n",
    "    access_mode = 's3_open_fsspec'\n",
    "    download_root_dir = None\n",
    "    jsons_root_dir = join(user_home_dir,'MZZ')\n",
    "else:\n",
    "    access_mode = 'download_ifspace'\n",
    "    download_root_dir = download_dir\n",
    "    jsons_root_dir = None\n",
    "\n",
    "\n",
    "\n",
    "ShortNames_list = [\"ECCO_L4_GEOMETRY_LLC0090GRID_V4R4\",\\\n",
    "                   \"ECCO_L4_TEMP_SALINITY_LLC0090GRID_MONTHLY_V4R4\"]\n",
    "\n",
    "# access datasets\n",
    "ds_dict = ea.ecco_podaac_to_xrdataset(ShortNames_list,\\\n",
    "                                              StartDate='1992-01',EndDate='2017-12',\\\n",
    "                                              mode=access_mode,\\\n",
    "                                              download_root_dir=download_root_dir,\\\n",
    "                                              jsons_root_dir=jsons_root_dir,\\\n",
    "                                              max_avail_frac=0.5)\n",
    "\n",
    "## Load the model grid\n",
    "ecco_grid = ds_dict[ShortNames_list[0]].compute()\n",
    "\n",
    "## Open ECCO monthly mean T/S files\n",
    "ecco_TS = ds_dict[ShortNames_list[1]]\n",
    "\n",
    "## merge grid and temperature datasets\n",
    "ds = xr.merge((ecco_grid,ecco_TS[['THETA']]))\n",
    "\n",
    "# compute temperature for top level, by looping through time chunks\n",
    "memory_log = [psutil.virtual_memory()]\n",
    "SST_computed = ds.THETA.isel(k=0)\n",
    "SST_computed_array = np.empty(SST_computed.shape).astype('float32')\n",
    "for time_ind in range(ds.THETA.sizes['time']):\n",
    "    SST_computed_array[time_ind,...] = ds.THETA.isel(time=time_ind,k=0).compute()\n",
    "SST_computed.data = SST_computed_array\n",
    "\n",
    "memory_log.append(psutil.virtual_memory())\n",
    "print('Change in available memory:',np.diff(np.asarray([memory_log[ind].available\\\n",
    "                                                    for ind in range(-2,0)]))[0]/(10**6),' MB')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af74327d-83e3-4e3b-9ffe-6544c8134de7",
   "metadata": {},
   "source": [
    "In the code above we did explicitly something that Dask does behind the scenes...looped through chunks so that our data loads are done in smaller pieces. But the memory used is still a lot more than we should need for 1 depth level, because chunks were cached that include the top **25** depth levels of `THETA`.\n",
    "\n",
    "### A workaround to save workspace memory\n",
    "\n",
    "There is one more trick up our sleeves to try to flush out the cache...re-open the data files on each loop iteration using `open_mfdataset`. This should have the effect of replacing our cache-linked dataset object with a clean \"lazy\" dataset (no data loaded or cached).\n",
    "\n",
    "And since calls to `open_mfdataset` involving hundreds of files take time, and this would be repeated hundreds of times, we will [pickle](https://docs.python.org/3/library/pickle.html) the dataset object so that it can be re-used quickly. Essentially what we are doing is saving the \"pointers\" to the file contents, while flushing out the cache on each loop iteration.\n",
    "\n",
    "Let's **restart the kernel one more time** before running the following code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "70a67d6c-e449-44f3-8dbd-a795bd1a8e48",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Change in available memory: -128.9216  MB\n",
      "CPU times: user 1min 16s, sys: 4.84 s, total: 1min 21s\n",
      "Wall time: 1min 28s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "from os.path import join,expanduser\n",
    "import glob\n",
    "import psutil\n",
    "\n",
    "\n",
    "import ecco_v4_py as ecco\n",
    "import ecco_access as ea\n",
    "\n",
    "\n",
    "# are you working in the AWS Cloud?\n",
    "incloud_access = False\n",
    "\n",
    "# indicate mode of access from PO.DAAC\n",
    "# options are:\n",
    "# 'download': direct download from internet to your local machine\n",
    "# 'download_ifspace': like download, but only proceeds \n",
    "#                     if your machine have sufficient storage\n",
    "# 's3_open': access datasets in-cloud from an AWS instance\n",
    "# 's3_open_fsspec': use jsons generated with fsspec and \n",
    "#                   kerchunk libraries to speed up in-cloud access\n",
    "# 's3_get': direct download from S3 in-cloud to an AWS instance\n",
    "# 's3_get_ifspace': like s3_get, but only proceeds if your instance \n",
    "#                   has sufficient storage\n",
    "user_home_dir = expanduser('~')\n",
    "download_dir = join(user_home_dir,'Downloads','ECCO_V4r4_PODAAC')\n",
    "if incloud_access:\n",
    "    access_mode = 's3_open_fsspec'\n",
    "    download_root_dir = None\n",
    "    jsons_root_dir = join(user_home_dir,'MZZ')\n",
    "else:\n",
    "    access_mode = 'download_ifspace'\n",
    "    download_root_dir = download_dir\n",
    "    jsons_root_dir = None\n",
    "\n",
    "\n",
    "\n",
    "ShortNames_list = [\"ECCO_L4_GEOMETRY_LLC0090GRID_V4R4\",\\\n",
    "                   \"ECCO_L4_TEMP_SALINITY_LLC0090GRID_MONTHLY_V4R4\"]\n",
    "\n",
    "# access datasets\n",
    "ds_dict = ea.ecco_podaac_to_xrdataset(ShortNames_list,\\\n",
    "                                              StartDate='1992-01',EndDate='2017-12',\\\n",
    "                                              mode=access_mode,\\\n",
    "                                              download_root_dir=download_root_dir,\\\n",
    "                                              jsons_root_dir=jsons_root_dir,\\\n",
    "                                              max_avail_frac=0.5)\n",
    "\n",
    "## Load the model grid\n",
    "ecco_grid = ds_dict[ShortNames_list[0]].compute()\n",
    "\n",
    "## Open ECCO monthly mean T/S files\n",
    "ecco_TS = ds_dict[ShortNames_list[1]]\n",
    "\n",
    "# pickle ecco_TS dataset object\n",
    "import pickle\n",
    "ecco_TS_pickled = pickle.dumps(ecco_TS)\n",
    "\n",
    "## merge grid and temperature datasets\n",
    "ds = xr.merge((ecco_grid,ecco_TS[['THETA']]))\n",
    "\n",
    "# compute temperature for top level, by looping through time chunks\n",
    "# while re-opening and closing ecco_TS dataset on each loop iteration\n",
    "memory_log = [psutil.virtual_memory()]\n",
    "SST_computed = ds.THETA.isel(k=0)\n",
    "SST_computed_array = np.empty(SST_computed.shape).astype('float32')\n",
    "for time_ind in range(ds.THETA.sizes['time']):\n",
    "    # re-load pickled dataset object (equivalent to calling open_mfdataset again, but much quicker)\n",
    "    ecco_TS = pickle.loads(ecco_TS_pickled)\n",
    "    # compute SST for current time chunk\n",
    "    SST_computed_array[time_ind,...] = ds.THETA.isel(time=time_ind,k=0).compute()\n",
    "    # close dataset object\n",
    "    ecco_TS.close()\n",
    "SST_computed.data = SST_computed_array\n",
    "\n",
    "memory_log.append(psutil.virtual_memory())\n",
    "print('Change in available memory:',np.diff(np.asarray([memory_log[ind].available\\\n",
    "                                                     for ind in range(-2,0)]))[0]/(10**6),' MB')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac0c121e-2093-4e70-979f-c61e661f22f7",
   "metadata": {},
   "source": [
    "Our memory usage now is very close to the expected footprint of the SST array itself, rather than ~15 times larger! The trade-off is that it took a little longer to load the data into memory with the \"opening\" and closing of the dataset object on each iteration.\n",
    "\n",
    "Note that it is `ecco_TS` that we have flushed the cache from, **not** `ds`. This procedure needs to be applied to the dataset object associated with the initial `open_mfdataset` call (in this case, the `open_mfdataset` call is hidden in the `ecco_podaac_to_xrdataset` function), rather than any subsequent datasets that it might be merged or concatenated into.\n",
    "\n",
    "To review, the procedure to flush the cache associated with `open_mfdataset` is:\n",
    "\n",
    "- When loading data explicitly in smaller \"chunks\", be sure to loop through the dataset in ways that are well aligned with the pre-existing chunking of the source files.\n",
    "\n",
    "- Before calling `.compute`, \"re-open\" the dataset by calling `open_mfdataset` (or a pickled equivalent).\n",
    "\n",
    "- Load your chunk into memory using `.compute`.\n",
    "\n",
    "- Close the dataset object (this may not be strictly necessary, but is a good practice for clean coding).\n",
    "\n",
    "\n",
    "\n",
    "And now, just to be sure that it is SST that we have loaded, and because it is fun to end a tutorial by plotting data, let's look at the ECCO climatological SST for January and July."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "50fd4645-d656-44bc-a8ce-d6ae6acb40d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import ecco_v4_py as ecco\n",
    "\n",
    "dx = .25\n",
    "dy = .25\n",
    "plt.figure(figsize=(12,6))\n",
    "ecco.plot_proj_to_latlon_grid(ds.XC,ds.YC,\\\n",
    "                              SST_computed.isel(time=slice(0,None,12)).mean('time'),\\\n",
    "                              dx=dx,dy=dy,user_lon_0=200,\\\n",
    "                              cmin=-2,cmax=32,show_colorbar=True,cmap='RdYlBu_r')\n",
    "plt.title('ECCO Jan mean SST [C]')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "ecco.plot_proj_to_latlon_grid(ds.XC,ds.YC,\\\n",
    "                              SST_computed.isel(time=slice(6,None,12)).mean('time'),\\\n",
    "                              dx=dx,dy=dy,user_lon_0=200,\\\n",
    "                              cmin=-2,cmax=32,show_colorbar=True,cmap='RdYlBu_r')\n",
    "plt.title('ECCO Jul mean SST [C]')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "ecco.plot_proj_to_latlon_grid(ds.XC,ds.YC,\\\n",
    "                              SST_computed.isel(time=slice(6,None,12)).mean('time')\\\n",
    "                              - SST_computed.isel(time=slice(0,None,12)).mean('time'),\\\n",
    "                              dx=dx,dy=dy,user_lon_0=200,\\\n",
    "                              cmin=-15,cmax=15,show_colorbar=True,cmap='seismic')\n",
    "plt.title('Difference Jul minus Jan SST [C]')\n",
    "plt.show()"
   ]
  }
 ],
 "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
}