{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sea Ice Comparisons to Observations\n",
"\n",
"This script shows how to load and plot sea ice concentration from CICE output and compare it to the NSIDC CDR (National Snow and Ice Data Centre, Climate Data Record) dataset\n",
"\n",
"This notebook uses the _ACCESS-NRI Intake Catalog_ following the examples in [Tutorials/Using the Intake Catalog](https://cosima-recipes.readthedocs.io/en/latest/01-Cooking-Tutorials/01-Basics/02-ACCESS-NRI_Intake_Catalog.html). \n",
"\n",
"Requirements: The runs analysed here are only in access-nri-intake-catalog version 0.3.1 or newer, and xarray.DataTree introduced in xarray version v2024.10.0. This was tested using `conda/analysis3-25.05` from `/g/data/xp65/public/modules` and a _medium_ instance on _normalsr_ queue (although we recommend a larger instance if making iterative changes). "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**OM2 Experiments:**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These are the ACCESS-OM2 runs we are going to use, we can compare results from prototype runs forced with ERA5 against normal runs using JRA55do, as [described on the ACCESS_HIVE](https://forum.access-hive.org.au/t/era-5-forced-access-om2-simulations/1103/5). To compare against the observational datasets, we use IAF (Inter-Annual Forcing). _N.B._ The JRA55do runs used here a slightly different to the typical (e.g. _025deg_jra55_iaf_omip2_cycle6_) in the model version used and the timeframes evaluated."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"RUNS = {\n",
" \"025deg_era5\": [\"025deg_era5_iaf\"], # (our name: run name(s))\n",
" \"025deg_jra55\": [\"025deg_jra55_iaf_era5comparison\"],\n",
" \"1deg_era5\": [\"1deg_era5_iaf\"],\n",
" \"1deg_jra55\": [\"1deg_jra55_iaf_era5comparison\"],\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are going to look at Sea Ice Concentration and Sea Ice Volume"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"VARS = [\"aice_m\", \"hi_m\" ] # ice area fraction or sea ice concentration, ice thickness averaged by grid cell area\n",
"VARS_2D = [\"area_t\", \"geolat_t\", \"geolon_t\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Observational Data:**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sea Ice concentration is measured through passive microwave remote sensing. We are going to use the NSIDC CDR Dataset (described at [nsidc.org](https://nsidc.org/data/g02202/versions/4))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"OBS_TIME_SLICE = slice(\"1979\", \"2022\")\n",
"sh_obs_url = \"https://polarwatch.noaa.gov/erddap/griddap/nsidcG02202v4shmday\"\n",
"nh_obs_url = \"https://polarwatch.noaa.gov/erddap/griddap/nsidcG02202v4nhmday\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Load depenencies:**"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import intake\n",
"from xarray import DataTree, map_over_datasets\n",
"\n",
"from dask.distributed import Client\n",
"\n",
"import xarray as xr\n",
"import numpy as np\n",
"from datetime import timedelta\n",
"import cf_xarray as cfxr\n",
"import xesmf\n",
"\n",
"# plotting\n",
"import matplotlib.pyplot as plt\n",
"import cartopy.crs as ccrs\n",
"import cmocean.cm as cmo\n",
"import matplotlib.lines as mlines"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A standard way to calculate climatologies. (We start in 1991 as earlier decades are influenced by model spin-up for 0.25deg runs which only start in 1980.)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"CLIMAT_TIME_SLICE = slice(\"1991\", \"2020\")\n",
"\n",
"\n",
"def climatology(ds):\n",
" return ds.sel(time=CLIMAT_TIME_SLICE).groupby(\"time.month\").mean(\"time\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Start a dask client"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/distributed/node.py:187: UserWarning: Port 8787 is already in use.\n",
"Perhaps you already have a cluster running?\n",
"Hosting the HTTP server on port 45847 instead\n",
" warnings.warn(\n"
]
}
],
"source": [
"client = Client(threads_per_worker = 1)\n",
"\n",
"client.dashboard_link"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Open the _ACCESS-NRI Intake Catalog_"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"catalog = intake.cat.access_nri"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Load the ACCESS-OM2 results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For CICE data in OM2, we need to do some wrangling to make it easier to deal with. This is described in more detail in [DocumentedExamples/SeaIce_Plot_Example](https://cosima-recipes.readthedocs.io/en/latest/02-Easy-Recipes/Sea_Ice_Coordinates.html). Its included in this function:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def open_by_name(name, vars):\n",
" \"\"\"Return a dataset for the requested name and vars\"\"\"\n",
"\n",
" return (\n",
" catalog[name]\n",
" .search(variable=vars)\n",
" .to_dask(\n",
" xarray_open_kwargs={\n",
" \"chunks\": -1 ,\n",
" \"decode_coords\": False,\n",
" },\n",
" xarray_combine_by_coords_kwargs={\n",
" \"compat\": \"override\",\n",
" \"data_vars\": \"minimal\",\n",
" \"coords\": \"minimal\",\n",
" },\n",
" )\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"def open_by_experiment(exp_name, vars):\n",
" \"\"\"Concatenate any datasets provided for this experiment into one ds, and add area and geo coordinates\"\"\"\n",
"\n",
" # get the data for each run of this config\n",
" cice_ds = xr.concat(\n",
" [open_by_name(iName, vars) for iName in RUNS[exp_name]], dim=\"time\"\n",
" )\n",
"\n",
" # We also want the area/lat/lon fields, these are separate datasets, so lets merge them.\n",
" area_ds = xr.merge(\n",
" [open_by_name(RUNS[exp_name][0], iVar) for iVar in VARS_2D]\n",
" ).load()\n",
"\n",
" # Label the lats and lons\n",
" cice_ds.coords[\"ni\"] = area_ds[\"xt_ocean\"].values\n",
" cice_ds.coords[\"nj\"] = area_ds[\"yt_ocean\"].values\n",
"\n",
" # Copy attributes for cf compliance\n",
" cice_ds.ni.attrs = area_ds.xt_ocean.attrs\n",
" cice_ds.nj.attrs = area_ds.yt_ocean.attrs\n",
"\n",
" cice_ds = cice_ds.rename(({\"ni\": \"xt_ocean\", \"nj\": \"yt_ocean\"}))\n",
"\n",
" # Add the geolon, geolat, and area as extra co-ordinates fields from area_t\n",
"\n",
" cice_ds = cice_ds.assign_coords(\n",
" {\n",
" \"geolat_t\": area_ds.geolat_t,\n",
" \"geolon_t\": area_ds.geolon_t,\n",
" \"area_t\": area_ds.area_t,\n",
" }\n",
" )\n",
"\n",
" # cice timestamps are also misleading:\n",
" cice_ds[\"time\"] = cice_ds.time.to_pandas() - timedelta(minutes=1)\n",
"\n",
" return cice_ds"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Because the dimensions are different for different experiments, they would not fit in a Dataset, a DataTree is required. The DataTree has a group for each experiment, which contains a xarray dataset with the data for that experiment. "
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/core.py:301: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" records = grouped.get_group(internal_key).to_dict(orient='records')\n",
"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/core.py:301: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" records = grouped.get_group(internal_key).to_dict(orient='records')\n",
"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/source.py:306: ConcatenationWarning: Attempting to concatenate datasets without valid dimension coordinates: retaining only first dataset. Request valid dimension coordinate to silence this warning.\n",
" warnings.warn(\n",
"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/core.py:301: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" records = grouped.get_group(internal_key).to_dict(orient='records')\n",
"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/source.py:306: ConcatenationWarning: Attempting to concatenate datasets without valid dimension coordinates: retaining only first dataset. Request valid dimension coordinate to silence this warning.\n",
" warnings.warn(\n",
"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/core.py:301: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" records = grouped.get_group(internal_key).to_dict(orient='records')\n",
"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/source.py:306: ConcatenationWarning: Attempting to concatenate datasets without valid dimension coordinates: retaining only first dataset. Request valid dimension coordinate to silence this warning.\n",
" warnings.warn(\n",
"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/core.py:301: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" records = grouped.get_group(internal_key).to_dict(orient='records')\n",
"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/core.py:301: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" records = grouped.get_group(internal_key).to_dict(orient='records')\n",
"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/source.py:306: ConcatenationWarning: Attempting to concatenate datasets without valid dimension coordinates: retaining only first dataset. Request valid dimension coordinate to silence this warning.\n",
" warnings.warn(\n",
"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/core.py:301: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" records = grouped.get_group(internal_key).to_dict(orient='records')\n",
"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/source.py:306: ConcatenationWarning: Attempting to concatenate datasets without valid dimension coordinates: retaining only first dataset. Request valid dimension coordinate to silence this warning.\n",
" warnings.warn(\n",
"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/core.py:301: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" records = grouped.get_group(internal_key).to_dict(orient='records')\n",
"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/source.py:306: ConcatenationWarning: Attempting to concatenate datasets without valid dimension coordinates: retaining only first dataset. Request valid dimension coordinate to silence this warning.\n",
" warnings.warn(\n",
"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/core.py:301: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" records = grouped.get_group(internal_key).to_dict(orient='records')\n",
"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/core.py:301: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" records = grouped.get_group(internal_key).to_dict(orient='records')\n",
"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/source.py:306: ConcatenationWarning: Attempting to concatenate datasets without valid dimension coordinates: retaining only first dataset. Request valid dimension coordinate to silence this warning.\n",
" warnings.warn(\n",
"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/core.py:301: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" records = grouped.get_group(internal_key).to_dict(orient='records')\n",
"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/source.py:306: ConcatenationWarning: Attempting to concatenate datasets without valid dimension coordinates: retaining only first dataset. Request valid dimension coordinate to silence this warning.\n",
" warnings.warn(\n",
"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/core.py:301: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" records = grouped.get_group(internal_key).to_dict(orient='records')\n",
"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/source.py:306: ConcatenationWarning: Attempting to concatenate datasets without valid dimension coordinates: retaining only first dataset. Request valid dimension coordinate to silence this warning.\n",
" warnings.warn(\n",
"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/core.py:301: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" records = grouped.get_group(internal_key).to_dict(orient='records')\n",
"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/core.py:301: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" records = grouped.get_group(internal_key).to_dict(orient='records')\n",
"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/source.py:306: ConcatenationWarning: Attempting to concatenate datasets without valid dimension coordinates: retaining only first dataset. Request valid dimension coordinate to silence this warning.\n",
" warnings.warn(\n",
"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/core.py:301: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" records = grouped.get_group(internal_key).to_dict(orient='records')\n",
"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/source.py:306: ConcatenationWarning: Attempting to concatenate datasets without valid dimension coordinates: retaining only first dataset. Request valid dimension coordinate to silence this warning.\n",
" warnings.warn(\n",
"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/core.py:301: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
" records = grouped.get_group(internal_key).to_dict(orient='records')\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 33.8 s, sys: 7.42 s, total: 41.2 s\n",
"Wall time: 37.3 s\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/g/data/xp65/public/apps/med_conda/envs/analysis3-25.08/lib/python3.11/site-packages/intake_esm/source.py:306: ConcatenationWarning: Attempting to concatenate datasets without valid dimension coordinates: retaining only first dataset. Request valid dimension coordinate to silence this warning.\n",
" warnings.warn(\n"
]
}
],
"source": [
"%%time\n",
"\n",
"si_name_ds_pairs = dict()\n",
"for run in RUNS.keys():\n",
" si_name_ds_pairs[run] = open_by_experiment(run, VARS)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"si_dt = DataTree.from_dict(si_name_ds_pairs)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"def match_timestamps_to_CDR(ds):\n",
"\n",
" # root dataset in datatree is not used\n",
" if ds is None or 'time' not in ds:\n",
" return ds\n",
"\n",
" # make a copy, datatree doesn't allow manipulating in place \n",
" cice_ds = ds.copy()\n",
"\n",
" # we are going to use the same timestamps as NSIDC\n",
" cice_ds[\"time\"] = [\n",
" np.datetime64(str(i)[0:7] + \"-01T00:00:00.000000000\")\n",
" for i in ds.time.values\n",
" ]\n",
"\n",
" return cice_ds"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"si_dt = si_dt.map_over_datasets(match_timestamps_to_CDR)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The result is a datatree, with a dataset for each experiment and timestamps which align with the observational timestamps"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
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intake_esm_attrs:realm :
seaIce
intake_esm_attrs:_data_format_ :
netcdf
intake_esm_dataset_key :
seaIce.1mon.d2:2.nc:5.ni:1440.nj:1080
\n",
"
<xarray.DatasetView> Size: 7GB\n",
"Dimensions: (time: 528, yt_ocean: 1080, xt_ocean: 1440)\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 4kB 1980-01-01 1980-02-01 ... 2023-12-01\n",
" * xt_ocean (xt_ocean) float64 12kB -279.9 -279.6 -279.4 ... 79.38 79.62 79.88\n",
" * yt_ocean (yt_ocean) float64 9kB -81.08 -80.97 -80.87 ... 89.74 89.84 89.95\n",
" geolat_t (yt_ocean, xt_ocean) float32 6MB nan nan nan nan ... nan nan nan\n",
" geolon_t (yt_ocean, xt_ocean) float32 6MB nan nan nan nan ... nan nan nan\n",
" area_t (yt_ocean, xt_ocean) float32 6MB nan nan nan nan ... nan nan nan\n",
"Data variables:\n",
" aice_m (time, yt_ocean, xt_ocean) float32 3GB dask.array<chunksize=(1, 1080, 1440), meta=np.ndarray>\n",
" hi_m (time, yt_ocean, xt_ocean) float32 3GB dask.array<chunksize=(1, 1080, 1440), meta=np.ndarray>\n",
"Attributes: (12/18)\n",
" title: sea ice model output for CICE\n",
" contents: Diagnostic and Prognostic Varia...\n",
" source: Los Alamos Sea Ice Model (CICE)...\n",
" time_period_freq: month_1\n",
" comment3: seconds elapsed into model date...\n",
" conventions: CF-1.0\n",
" ... ...\n",
" intake_esm_attrs:variable_standard_name: ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,...\n",
" intake_esm_attrs:variable_cell_methods: ,,,,,,,time: mean,time: mean,,,...\n",
" intake_esm_attrs:variable_units: radians,radians,m,m,m,degrees_n...\n",
" intake_esm_attrs:realm: seaIce\n",
" intake_esm_attrs:_data_format_: netcdf\n",
" intake_esm_dataset_key: seaIce.1mon.d2:2.nc:5.ni:1440.n...
radians,radians,m,m,m,degrees_north,degrees_east,C,C,degrees_north,degrees_east,1,1,%,%,%,,cm/day,%/day,%/day,%/day,cm/day,cm/day,m,m,m,m,cm/day,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,cm/day,cm/day,W/m^2,kg/m^2/s,kg/m^2/s,W/m^2,W/m^2,W/m^2,W/m^2,1,W/m^2,W/m^2,m,m,1,cm/day,cm/day,cm/day,cm/day,%/day,cm/day,%/day,ppt,cm/day,cm/day,N/m^2,N/m^2,N/m^2,N/m^2,N/m,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,m^2,days since 1980-01-01 00:00:00,days since 1980-01-01 00:00:00,,N/m^2,m^2,m/s,m,m/s
angle grid makes with latitude line on U grid,angle grid makes with latitude line on T grid,T cell width on East side,T cell width on North side,category maximum thickness,T grid center latitude,T grid center longitude,air temperature,snow/ice surface temperature,U grid center latitude,U grid center longitude,ice area (aggregate),ice area, categories,bare ice albedo,snow/ice broad band albedo,snow albedo,near IR diffuse albedo,near IR direct albedo,visible diffuse albedo,visible direct albedo,ice grid block mask,congelation ice growth,area tendency dynamics,area tendency thermo,strain rate (divergence),volume tendency dynamics,volume tendency thermo,T cell width through middle,U cell width through middle,T cell height through middle,U cell height through middle,evaporative water flux,top surface conductive heat flux,top sfc conductive heat flux, cat,heat flux ice to ocean,latent heat flux,latent heat flux, category,down longwave flux,upward longwave flux,net surface heat flux causing melt,net sfc heat flux causing melt, cat,frazil ice growth,freshwtr flx ice to ocn,freeze/melt potential,salt flux ice to ocean,salt flux ice to ocn (cpl),sensible heat flux,net surface heat flux, categories,snow/ice/ocn absorbed solar flux,down solar flux,shortwave scaling factor,SW flux thru ice to ocean,upward solar flux,grid cell mean ice thickness,grid cell mean snow thickness,fraction of time-avg interval that ice is present,basal ice melt,lateral ice melt,top snow melt,top ice melt,lead area opening rate,rainfall rate,strain rate (shear),bulk ice salinity,snow-ice formation,snowfall rate,atm/ice stress (x),atm/ice stress (y),coriolis stress (x),coriolis stress (y),compressive ice strength,internal ice stress (x),internal ice stress (y),ocean/ice stress (x),ocean/ice stress (y),sea sfc tilt stress (x),sea sfc tilt stress (y),area of T grid cells,model time,boundaries for time-averaging interval,ocean grid mask,internal stress tensor trace,area of U grid cells,atm velocity (x),ice velocity (x),atm velocity (y),ice volume, categories,ice velocity (y)
radians,radians,m,m,m,degrees_north,degrees_east,C,C,degrees_north,degrees_east,1,1,%,%,%,%,%,%,%,,cm/day,%/day,%/day,%/day,cm/day,cm/day,m,m,m,m,cm/day,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,W/m^2,cm/day,cm/day,W/m^2,kg/m^2/s,kg/m^2/s,W/m^2,W/m^2,W/m^2,W/m^2,1,W/m^2,W/m^2,m,m,1,cm/day,cm/day,cm/day,cm/day,%/day,cm/day,%/day,ppt,cm/day,cm/day,N/m^2,N/m^2,N/m^2,N/m^2,N/m,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,N/m^2,m^2,days since 1960-01-01 00:00:00,days since 1960-01-01 00:00:00,,N/m^2,m^2,m/s,m/s,m/s,m,m/s
intake_esm_attrs:realm :
seaIce
intake_esm_attrs:_data_format_ :
netcdf
intake_esm_dataset_key :
seaIce.1mon.d2:2.nc:5.ni:360.nj:300
\n",
"
<xarray.DatasetView> Size: 623MB\n",
"Dimensions: (time: 720, yt_ocean: 300, xt_ocean: 360)\n",
"Coordinates:\n",
" * time (time) datetime64[ns] 6kB 1959-01-01 1959-02-01 ... 2019-12-01\n",
" * xt_ocean (xt_ocean) float64 3kB -279.5 -278.5 -277.5 ... 77.5 78.5 79.5\n",
" * yt_ocean (yt_ocean) float64 2kB -77.88 -77.63 -77.38 ... 88.87 89.32 89.77\n",
" geolat_t (yt_ocean, xt_ocean) float32 432kB nan nan nan ... 65.63 65.21\n",
" geolon_t (yt_ocean, xt_ocean) float32 432kB nan nan nan ... 79.97 79.99\n",
" area_t (yt_ocean, xt_ocean) float32 432kB nan nan ... 1.121e+08 3.857e+07\n",
"Data variables:\n",
" aice_m (time, yt_ocean, xt_ocean) float32 311MB dask.array<chunksize=(1, 300, 360), meta=np.ndarray>\n",
" hi_m (time, yt_ocean, xt_ocean) float32 311MB dask.array<chunksize=(1, 300, 360), meta=np.ndarray>\n",
"Attributes: (12/17)\n",
" title: sea ice model output for CICE\n",
" contents: Diagnostic and Prognostic Varia...\n",
" source: Los Alamos Sea Ice Model (CICE)...\n",
" time_period_freq: month_1\n",
" comment3: seconds elapsed into model date...\n",
" conventions: CF-1.0\n",
" ... ...\n",
" intake_esm_attrs:variable_long_name: angle grid makes with latitude ...\n",
" intake_esm_attrs:variable_standard_name: ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,...\n",
" intake_esm_attrs:variable_cell_methods: ,,,,,,,time: mean,time: mean,,,...\n",
" intake_esm_attrs:realm: seaIce\n",
" intake_esm_attrs:_data_format_: netcdf\n",
" intake_esm_dataset_key: seaIce.1mon.d2:2.nc:5.ni:360.nj...
angle grid makes with latitude line on U grid,angle grid makes with latitude line on T grid,T cell width on East side,T cell width on North side,category maximum thickness,T grid center latitude,T grid center longitude,air temperature,snow/ice surface temperature,U grid center latitude,U grid center longitude,ice area (aggregate),ice area, categories,bare ice albedo,snow/ice broad band albedo,snow albedo,near IR diffuse albedo,near IR direct albedo,visible diffuse albedo,visible direct albedo,ice grid block mask,congelation ice growth,area tendency dynamics,area tendency thermo,strain rate (divergence),volume tendency dynamics,volume tendency thermo,T cell width through middle,U cell width through middle,T cell height through middle,U cell height through middle,evaporative water flux,top surface conductive heat flux,top sfc conductive heat flux, cat,heat flux ice to ocean,latent heat flux,latent heat flux, category,down longwave flux,upward longwave flux,net surface heat flux causing melt,net sfc heat flux causing melt, cat,frazil ice growth,freshwtr flx ice to ocn,freeze/melt potential,salt flux ice to ocean,salt flux ice to ocn (cpl),sensible heat flux,net surface heat flux, categories,snow/ice/ocn absorbed solar flux,down solar flux,shortwave scaling factor,SW flux thru ice to ocean,upward solar flux,grid cell mean ice thickness,grid cell mean snow thickness,fraction of time-avg interval that ice is present,basal ice melt,lateral ice melt,top snow melt,top ice melt,lead area opening rate,rainfall rate,strain rate (shear),bulk ice salinity,snow-ice formation,snowfall rate,atm/ice stress (x),atm/ice stress (y),coriolis stress (x),coriolis stress (y),compressive ice strength,internal ice stress (x),internal ice stress (y),ocean/ice stress (x),ocean/ice stress (y),sea sfc tilt stress (x),sea sfc tilt stress (y),area of T grid cells,model time,boundaries for time-averaging interval,ocean grid mask,internal stress tensor trace,area of U grid cells,atm velocity (x),ice velocity (x),atm velocity (y),ice volume, categories,ice velocity (y)