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bias maps
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@malmans2
malmans2 committed Feb 1, 2024
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{
"cells": [
{
"cell_type": "markdown",
"id": "767f05ef",
"metadata": {},
"source": [
"## CMIP6 sea ice thickness bias"
]
},
{
"cell_type": "markdown",
"id": "4dfe1c7f",
"metadata": {},
"source": [
"## Import libraries"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "51de2ed2-daed-4360-9ec1-b14eacad4608",
"metadata": {},
"outputs": [],
"source": [
"import warnings\n",
"\n",
"import cartopy.crs as ccrs\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import xarray as xr\n",
"from c3s_eqc_automatic_quality_control import diagnostics, download, plot\n",
"\n",
"plt.style.use(\"seaborn-v0_8-notebook\")\n",
"warnings.filterwarnings(\"ignore\", module=\"cf_xarray\")"
]
},
{
"cell_type": "markdown",
"id": "8bc4ee9d",
"metadata": {},
"source": [
"## Set parameters"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c2fc4017-d3b8-4daf-8f20-effa7e7bc2b2",
"metadata": {},
"outputs": [],
"source": [
"year_start = 2002\n",
"year_stop = 2014\n",
"assert year_start >= 2002 and year_stop <= 2014\n",
"\n",
"# Choose CMIP6 historical models\n",
"models = [\n",
" \"access_cm2\",\n",
" \"access_esm1_5\",\n",
" \"canesm5\",\n",
" \"cmcc_cm2_sr5\",\n",
" \"cmcc_esm2\",\n",
" \"cnrm_cm6_1\",\n",
" \"cnrm_cm6_1_hr\",\n",
" \"cnrm_esm2_1\",\n",
" \"e3sm_1_0\",\n",
" \"e3sm_1_1\",\n",
" \"e3sm_1_1_eca\",\n",
" \"ec_earth3_aerchem\",\n",
" \"ec_earth3_cc\",\n",
" \"ec_earth3_veg_lr\",\n",
" \"hadgem3_gc31_ll\",\n",
" \"ipsl_cm5a2_inca\",\n",
" \"ipsl_cm6a_lr\",\n",
" \"miroc6\",\n",
" \"miroc_es2l\",\n",
" \"mpi_esm1_2_hr\",\n",
" \"mpi_esm1_2_lr\",\n",
" \"nesm3\",\n",
" \"norcpm1\",\n",
" # \"taiesm1\", # very large values\n",
" \"ukesm1_0_ll\",\n",
"]"
]
},
{
"cell_type": "markdown",
"id": "15f1a9a1",
"metadata": {},
"source": [
"## Define request"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4fab90a8-b9ad-4e86-a4dc-be51e749b6b7",
"metadata": {},
"outputs": [],
"source": [
"months = [f\"{month:02d}\" for month in [1, 2, 3, 4, 10, 11, 12]]\n",
"year_ranges = {\n",
" \"envisat\": range(max(2002, year_start), min(2010, year_stop) + 1),\n",
" \"cryosat_2\": range(max(2010, year_start), min(2020, year_stop) + 1),\n",
"}\n",
"collection_id_satellite = \"satellite-sea-ice-thickness\"\n",
"request_satellite = {\n",
" \"version\": \"2_0\",\n",
" \"cdr_type\": \"cdr\",\n",
" \"variable\": \"all\",\n",
" \"month\": months,\n",
"}\n",
"\n",
"collection_id_cmip6 = \"projections-cmip6\"\n",
"request_cmip6 = {\n",
" \"format\": \"zip\",\n",
" \"temporal_resolution\": \"monthly\",\n",
" \"experiment\": \"historical\",\n",
" \"variable\": \"sea_ice_thickness\",\n",
" \"month\": months,\n",
"}\n",
"\n",
"chunks = {\"year\": 1}"
]
},
{
"cell_type": "markdown",
"id": "2b1b4a55",
"metadata": {},
"source": [
"## Define function to cache"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d1c76126-b244-48d1-86b7-21310a695647",
"metadata": {},
"outputs": [],
"source": [
"def regridded_time_weighted_mean(ds, times, **kwargs):\n",
" ds_sat = download.download_and_transform(\n",
" \"satellite-sea-ice-thickness\",\n",
" {\n",
" \"satellite\": \"envisat\",\n",
" \"version\": \"2_0\",\n",
" \"cdr_type\": \"cdr\",\n",
" \"variable\": \"all\",\n",
" \"year\": \"2002\",\n",
" \"month\": [f\"{month:02d}\" for month in [1, 2, 3, 4, 10, 11, 12]],\n",
" },\n",
" chunks={\"year\": 1},\n",
" )\n",
" ds[\"time\"] = pd.to_datetime(ds[\"time\"].dt.strftime(\"%Y-%m\"))\n",
" ds[\"time\"].attrs[\"standard_name\"] = \"time\"\n",
" ds = ds.sel(time=times)\n",
" ds = diagnostics.time_weighted_mean(ds)\n",
" return diagnostics.regrid(ds, ds_sat[[\"latitude\", \"longitude\"]], **kwargs)"
]
},
{
"cell_type": "markdown",
"id": "1591e857",
"metadata": {},
"source": [
"## Download and transform"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fe14c6bc-da5d-4cdf-864f-26acc1ceb4dd",
"metadata": {},
"outputs": [],
"source": [
"datasets_satellites = []\n",
"datasets_cmip6 = []\n",
"for satellite, year_range in year_ranges.items():\n",
" print(f\"{satellite=}\")\n",
" years = list(map(str, year_range))\n",
" ds = download.download_and_transform(\n",
" collection_id_satellite,\n",
" request_satellite | {\"satellite\": satellite, \"year\": years},\n",
" chunks=chunks,\n",
" )\n",
" times = ds[\"time\"].dt.strftime(\"%Y-%m\").values.tolist()\n",
" ds = diagnostics.time_weighted_mean(ds)\n",
" datasets_satellites.append(\n",
" ds.expand_dims(satellite=[satellite], product=[\"satellite\"])\n",
" )\n",
"\n",
" model_datasets = []\n",
" for model in models:\n",
" print(f\"{satellite=} {model=}\")\n",
" ds = download.download_and_transform(\n",
" collection_id_cmip6,\n",
" request_cmip6 | {\"model\": model, \"year\": years},\n",
" chunks=chunks,\n",
" transform_chunks=False,\n",
" transform_func=regridded_time_weighted_mean,\n",
" transform_func_kwargs={\n",
" \"times\": times,\n",
" \"method\": \"nearest_s2d\",\n",
" \"periodic\": True,\n",
" \"ignore_degenerate\": True,\n",
" },\n",
" )\n",
" model_datasets.append(\n",
" ds.expand_dims(model=[model], satellite=[satellite], product=[\"cmip6\"])\n",
" )\n",
" datasets_cmip6.append(\n",
" xr.concat(model_datasets, \"model\").mean(\"model\", keep_attrs=True)\n",
" )\n",
"ds_satellites = xr.concat(datasets_satellites, \"satellite\")\n",
"ds_cmip6 = xr.concat(datasets_cmip6, \"satellite\")"
]
},
{
"cell_type": "markdown",
"id": "cec1f783",
"metadata": {},
"source": [
"## Plot maps"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d97f2e93-a02e-4bad-b5ce-061f0e20f994",
"metadata": {},
"outputs": [],
"source": [
"projection = ccrs.Stereographic(central_latitude=90)\n",
"\n",
"biases = []\n",
"for satellite, ds_satellite in ds_satellites.groupby(\"satellite\"):\n",
" da_satellite = ds_satellite[\"sea_ice_thickness\"]\n",
" da_cmip6 = ds_cmip6.sel(satellite=[satellite])[\"sithick\"]\n",
" da = xr.concat([da_satellite, da_cmip6], \"product\")\n",
" facet = plot.projected_map(\n",
" da, col=\"product\", vmin=0, robust=True, projection=projection\n",
" )\n",
" facet.fig.suptitle(satellite, y=1.05)\n",
"\n",
" with xr.set_options(keep_attrs=True):\n",
" bias = da_cmip6.squeeze(\"product\") - da_satellite.squeeze(\"product\")\n",
" bias.attrs[\"long_name\"] = \"Bias of \" + bias.attrs[\"long_name\"]\n",
" biases.append(bias)\n",
"\n",
"bias = xr.concat(biases, \"satellite\")\n",
"plot.projected_map(bias, col=\"satellite\", projection=projection, robust=True)"
]
}
],
"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": "3.11.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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