Climate Impact Lab Global Downscaled Projections for Climate Impacts Research¶
The Climate Impact Lab Downscaled Projections for Climate Impacts Research (CIL-GDPCR) collections contain bias corrected and downscaled 1/4° CMIP6 projections for temperature and precipitation.
See the project homepage for more information: github.com/ClimateImpactLab/downscaleCMIP6.
This tutorial covers accessing the STAC API to explore the collections and open a dataset. Additional tutorials are available at github.com/microsoft/PlanetaryComputerExamples.
In [1]:
import planetary_computer
import pystac_client
STAC Metadata¶
The CIL-GDPR datasets are grouped into two collections, depending on the license the data are provided under.
The data assets in this collection are a set of Zarr groups which can be opend by tools like xarray. Each Zarr group contains a single data variable (either pr, tasmax, or tasmin). The Planetary Computer provides a single STAC item per experiment, and each STAC item has one asset per data variable.
To access the data, we'll create a pystac_client.Client to access the Planetary Computer data catalog, including modifier=planetary_computer.sign_inplace to automatically sign the returned results. See https://planetarycomputer.microsoft.com/docs/quickstarts/reading-stac/ for more.
In [2]:
catalog = pystac_client.Client.open(
"https://planetarycomputer.microsoft.com/api/stac/v1/",
modifier=planetary_computer.sign_inplace,
)
collection = catalog.get_collection("cil-gdpcir-cc-by")
item = collection.get_item("cil-gdpcir-NUIST-NESM3-ssp585-r1i1p1f1-day")
item.assets
Out[2]:
{'pr': <Asset href=abfs://cil-gdpcir/ScenarioMIP/NUIST/NESM3/ssp585/r1i1p1f1/day/pr/v1.1.zarr>,
'tasmax': <Asset href=abfs://cil-gdpcir/ScenarioMIP/NUIST/NESM3/ssp585/r1i1p1f1/day/tasmax/v1.1.zarr>,
'tasmin': <Asset href=abfs://cil-gdpcir/ScenarioMIP/NUIST/NESM3/ssp585/r1i1p1f1/day/tasmin/v1.1.zarr>}
The STAC metadata includes all the required fields from the CMIP 6 controlled vocabularies.
In [3]:
item.properties["cmip6:source_id"]
Out[3]:
'NESM3'
Querying the STAC API¶
Use the Planetary Computer STAC API to find the exact data you want. You'll most likely want to query on the controlled vocabularies fields, under the cmip6: prefix.. See the collection summary for the set of allowed values for each of those.
In [4]:
collection.summaries.to_dict()
Out[4]:
{'cmip6:variable': ['pr', 'tasmax', 'tasmin'],
'cmip6:source_id': ['BCC-CSM2-MR',
'ACCESS-ESM1-5',
'ACCESS-CM2',
'MIROC-ES2L',
'MIROC6',
'NorESM2-LM',
'NorESM2-MM',
'GFDL-CM4',
'GFDL-ESM4',
'NESM3',
'MPI-ESM1-2-HR',
'HadGEM3-GC31-LL',
'UKESM1-0-LL',
'MPI-ESM1-2-LR',
'EC-Earth3',
'EC-Earth3-AerChem',
'EC-Earth3-CC',
'EC-Earth3-Veg',
'EC-Earth3-Veg-LR',
'CMCC-CM2-SR5',
'CMCC-ESM2'],
'cmip6:experiment_id': ['historical', 'ssp126', 'ssp245', 'ssp370', 'ssp585'],
'cmip6:institution_id': ['BCC',
'CAS',
'CCCma',
'CMCC',
'CSIRO',
'CSIRO-ARCCSS',
'DKRZ',
'EC-Earth-Consortium',
'INM',
'MIROC',
'MOHC',
'MPI-M',
'NCC',
'NOAA-GFDL',
'NUIST']}
In [5]:
search = catalog.search(
collections=["cil-gdpcir-cc-by"],
query={"cmip6:source_id": {"eq": "NESM3"}, "cmip6:experiment_id": {"eq": "ssp585"}},
)
items = search.get_all_items()
len(items)
/srv/conda/envs/notebook/lib/python3.10/site-packages/pystac_client/item_search.py:841: FutureWarning: get_all_items() is deprecated, use item_collection() instead. warnings.warn(
Out[5]:
1
In [6]:
item = items[0]
item
Out[6]:
Item: cil-gdpcir-NUIST-NESM3-ssp585-r1i1p1f1-day
| id: cil-gdpcir-NUIST-NESM3-ssp585-r1i1p1f1-day |
| bbox: [-180, -90, 180, 90] |
| datetime: None |
| cmip6:realm: atmos |
| cmip6:source: NESM v3 (2016): aerosol: none atmos: ECHAM v6.3 (T63; 192 x 96 longitude/latitude; 47 levels; top level 1 Pa) atmosChem: none land: JSBACH v3.1 landIce: none ocean: NEMO v3.4 (NEMO v3.4, tripolar primarily 1deg; 384 x 362 longitude/latitude; 46 levels; top grid cell 0-6 m) ocnBgchem: none seaIce: CICE4.1 |
| end_datetime: 2100-12-31T12:00:00Z |
| cmip6:license: https://github.com/ClimateImpactLab/downscaleCMIP6/tree/master/data_licenses/NESM3.txt |
| cmip6:mip_era: CMIP6 |
| cmip6:product: model-output |
| cmip6:table_id: day |
| cube:variables: {'pr': {'type': 'data', 'unit': 'mm day-1', 'attrs': {'units': 'mm day-1'}, 'shape': [31390, 720, 1440], 'dimensions': ['time', 'lat', 'lon']}, 'tasmax': {'type': 'data', 'unit': 'K', 'attrs': {'units': 'K', 'comment': "maximum near-surface (usually, 2 meter) air temperature (add cell_method attribute 'time: max')", 'history': "2019-08-11T09:50:24Z altered by CMOR: Treated scalar dimension: 'height'. 2019-08-11T09:50:24Z altered by CMOR: Inverted axis: lat.", 'long_name': 'Daily Maximum Near-Surface Air Temperature', 'cell_methods': 'area: mean time: maximum', 'cell_measures': 'area: areacella', 'standard_name': 'air_temperature'}, 'shape': [31390, 720, 1440], 'dimensions': ['time', 'lat', 'lon'], 'description': 'Daily Maximum Near-Surface Air Temperature'}, 'tasmin': {'type': 'data', 'unit': 'K', 'attrs': {'units': 'K', 'comment': "minimum near-surface (usually, 2 meter) air temperature (add cell_method attribute 'time: min')", 'history': "2019-08-11T09:48:45Z altered by CMOR: Treated scalar dimension: 'height'. 2019-08-11T09:48:45Z altered by CMOR: Inverted axis: lat.", 'long_name': 'Daily Minimum Near-Surface Air Temperature', 'cell_methods': 'area: mean time: minimum', 'cell_measures': 'area: areacella', 'standard_name': 'air_temperature'}, 'shape': [31390, 720, 1440], 'dimensions': ['time', 'lat', 'lon'], 'description': 'Daily Minimum Near-Surface Air Temperature'}} |
| start_datetime: 2015-01-01T12:00:00Z |
| cmip6:frequency: day |
| cmip6:source_id: NESM3 |
| cube:dimensions: {'lat': {'axis': 'y', 'step': 0.25, 'type': 'spatial', 'extent': [-89.875, 89.875], 'reference_system': 'epsg:4326'}, 'lon': {'axis': 'x', 'step': 0.25, 'type': 'spatial', 'extent': [-179.875, 179.875], 'reference_system': 'epsg:4326'}, 'time': {'step': 'P1DT0H0M0S', 'type': 'temporal', 'extent': ['2015-01-01T12:00:00Z', '2100-12-31T12:00:00Z'], 'description': 'time'}} |
| cmip6:experiment: update of RCP8.5 based on SSP5 |
| cmip6:Conventions: CF-1.7 CMIP-6.2 |
| cmip6:activity_id: ScenarioMIP |
| cmip6:institution: Nanjing University of Information Science and Technology, Nanjing, 210044, China |
| cmip6:source_type: AOGCM |
| cmip6:experiment_id: ssp585 |
| cmip6:forcing_index: 1 |
| cmip6:physics_index: 1 |
| cmip6:variant_label: r1i1p1f1 |
| cmip6:institution_id: NUIST |
| cmip6:sub_experiment: none |
| cmip6:further_info_url: https://furtherinfo.es-doc.org/CMIP6.NUIST.NESM3.ssp585.none.r1i1p1f1 |
| cmip6:realization_index: 1 |
| cmip6:sub_experiment_id: none |
| cmip6:data_specs_version: 01.00.30 |
| cmip6:nominal_resolution: 250 km |
| cmip6:initialization_index: 1 |
STAC Extensions
| https://stac-extensions.github.io/datacube/v2.0.0/schema.json |
Assets
Asset:
| href: abfs://cil-gdpcir/ScenarioMIP/NUIST/NESM3/ssp585/r1i1p1f1/day/pr/v1.1.zarr |
| type: application/vnd+zarr |
| owner: cil-gdpcir-NUIST-NESM3-ssp585-r1i1p1f1-day |
| cmip6:grid: T63 |
| msft:https-url: https://rhgeuwest.blob.core.windows.net/cil-gdpcir/ScenarioMIP/NUIST/NESM3/ssp585/r1i1p1f1/day/pr/v1.1.zarr |
| cmip6:grid_label: gn |
| cmip6:tracking_id: hdl:21.14100/ed432434-922e-4cea-8400-c321598fd7cf hdl:21.14100/abea2bb8-09d0-4114-9051-fe232efe108e hdl:21.14100/205a607f-75f4-4919-b495-45fa8fdb77b8 |
| cmip6:variable_id: pr |
| xarray:open_kwargs: {'chunks': {}, 'engine': 'zarr', 'consolidated': True, 'storage_options': {'account_name': 'rhgeuwest', 'credential': 'st=2023-06-22T15%3A36%3A45Z&se=2023-06-30T15%3A36%3A45Z&sp=rl&sv=2021-06-08&sr=c&skoid=c85c15d6-d1ae-42d4-af60-e2ca0f81359b&sktid=72f988bf-86f1-41af-91ab-2d7cd011db47&skt=2023-06-23T15%3A36%3A44Z&ske=2023-06-30T15%3A36%3A44Z&sks=b&skv=2021-06-08&sig=C8zGe%2B5GRYp/uYA%2B4yhjz8Sk5ZztiuRwRmLEj5fMQM0%3D'}} |
| cmip6:creation_date: 2019-08-11T09:53:50Z |
Asset:
| href: abfs://cil-gdpcir/ScenarioMIP/NUIST/NESM3/ssp585/r1i1p1f1/day/tasmax/v1.1.zarr |
| type: application/vnd+zarr |
| owner: cil-gdpcir-NUIST-NESM3-ssp585-r1i1p1f1-day |
| cmip6:grid: T63 |
| msft:https-url: https://rhgeuwest.blob.core.windows.net/cil-gdpcir/ScenarioMIP/NUIST/NESM3/ssp585/r1i1p1f1/day/tasmax/v1.1.zarr |
| cmip6:grid_label: gn |
| cmip6:tracking_id: hdl:21.14100/01f58d9c-1317-467e-813d-a2358cdf7954 hdl:21.14100/e027cda7-ac28-4e00-b03f-53ad2e6d30b4 hdl:21.14100/3898bc79-5174-4fcc-9637-4573914ad548 |
| cmip6:variable_id: tasmax |
| xarray:open_kwargs: {'chunks': {}, 'engine': 'zarr', 'consolidated': True, 'storage_options': {'account_name': 'rhgeuwest', 'credential': 'st=2023-06-22T15%3A36%3A45Z&se=2023-06-30T15%3A36%3A45Z&sp=rl&sv=2021-06-08&sr=c&skoid=c85c15d6-d1ae-42d4-af60-e2ca0f81359b&sktid=72f988bf-86f1-41af-91ab-2d7cd011db47&skt=2023-06-23T15%3A36%3A44Z&ske=2023-06-30T15%3A36%3A44Z&sks=b&skv=2021-06-08&sig=C8zGe%2B5GRYp/uYA%2B4yhjz8Sk5ZztiuRwRmLEj5fMQM0%3D'}} |
| cmip6:creation_date: 2019-08-11T09:50:24Z |
Asset:
| href: abfs://cil-gdpcir/ScenarioMIP/NUIST/NESM3/ssp585/r1i1p1f1/day/tasmin/v1.1.zarr |
| type: application/vnd+zarr |
| owner: cil-gdpcir-NUIST-NESM3-ssp585-r1i1p1f1-day |
| cmip6:grid: T63 |
| msft:https-url: https://rhgeuwest.blob.core.windows.net/cil-gdpcir/ScenarioMIP/NUIST/NESM3/ssp585/r1i1p1f1/day/tasmin/v1.1.zarr |
| cmip6:grid_label: gn |
| cmip6:tracking_id: hdl:21.14100/1998e7f0-ad6a-4720-8150-9085db6f96b8 hdl:21.14100/ca181871-4103-481d-8764-b74c2448427f hdl:21.14100/fceb22c4-0454-4d68-bb48-1d903df19e63 |
| cmip6:variable_id: tasmin |
| xarray:open_kwargs: {'chunks': {}, 'engine': 'zarr', 'consolidated': True, 'storage_options': {'account_name': 'rhgeuwest', 'credential': 'st=2023-06-22T15%3A36%3A45Z&se=2023-06-30T15%3A36%3A45Z&sp=rl&sv=2021-06-08&sr=c&skoid=c85c15d6-d1ae-42d4-af60-e2ca0f81359b&sktid=72f988bf-86f1-41af-91ab-2d7cd011db47&skt=2023-06-23T15%3A36%3A44Z&ske=2023-06-30T15%3A36%3A44Z&sks=b&skv=2021-06-08&sig=C8zGe%2B5GRYp/uYA%2B4yhjz8Sk5ZztiuRwRmLEj5fMQM0%3D'}} |
| cmip6:creation_date: 2019-08-11T09:48:45Z |
Links
Link:
| rel: collection |
| href: https://planetarycomputer.microsoft.com/api/stac/v1/collections/cil-gdpcir-cc-by |
| type: application/json |
Link:
| rel: parent |
| href: https://planetarycomputer.microsoft.com/api/stac/v1/collections/cil-gdpcir-cc-by |
| type: application/json |
Link: Microsoft Planetary Computer STAC API
| rel: root |
| href: https://planetarycomputer.microsoft.com/api/stac/v1/ |
| type: application/json |
| title: Microsoft Planetary Computer STAC API |
Link:
| rel: self |
| href: https://planetarycomputer.microsoft.com/api/stac/v1/collections/cil-gdpcir-cc-by/items/cil-gdpcir-NUIST-NESM3-ssp585-r1i1p1f1-day |
| type: application/geo+json |
We can now load the assets with xarray:
In [7]:
import xarray as xr
asset = item.assets["tasmax"]
ds = xr.open_dataset(asset.href, **asset.extra_fields["xarray:open_kwargs"])
ds
Out[7]:
<xarray.Dataset>
Dimensions: (lat: 720, lon: 1440, time: 31390)
Coordinates:
* lat (lat) float64 -89.88 -89.62 -89.38 -89.12 ... 89.38 89.62 89.88
* lon (lon) float64 -179.9 -179.6 -179.4 -179.1 ... 179.4 179.6 179.9
* time (time) object 2015-01-01 12:00:00 ... 2100-12-31 12:00:00
Data variables:
tasmax (time, lat, lon) float32 dask.array<chunksize=(365, 360, 360), meta=np.ndarray>
Attributes: (12/47)
Conventions: CF-1.7 CMIP-6.2
activity_id: ScenarioMIP
contact: climatesci@rhg.com
creation_date: 2019-08-11T09:50:24Z
data_specs_version: 01.00.30
dc6_bias_correction_method: Quantile Delta Method (QDM)
... ...
sub_experiment_id: none
table_id: day
tracking_id: hdl:21.14100/01f58d9c-1317-467e-813d-a2358c...
variable_id: tasmax
variant_label: r1i1p1f1
version_id: v20190811And form a datacube with xarray.combine_by_coords.
In [8]:
import xarray as xr
asset = item.assets["tasmax"]
ds = xr.combine_by_coords(
[
xr.open_dataset(asset.href, **asset.extra_fields["xarray:open_kwargs"])
for asset in item.assets.values()
],
combine_attrs="drop_conflicts",
)
ds
Out[8]:
<xarray.Dataset>
Dimensions: (lat: 720, lon: 1440, time: 31390)
Coordinates:
* lat (lat) float64 -89.88 -89.62 -89.38 -89.12 ... 89.38 89.62 89.88
* lon (lon) float64 -179.9 -179.6 -179.4 -179.1 ... 179.4 179.6 179.9
* time (time) object 2015-01-01 12:00:00 ... 2100-12-31 12:00:00
Data variables:
pr (time, lat, lon) float64 dask.array<chunksize=(365, 360, 360), meta=np.ndarray>
tasmax (time, lat, lon) float32 dask.array<chunksize=(365, 360, 360), meta=np.ndarray>
tasmin (time, lat, lon) float32 dask.array<chunksize=(365, 360, 360), meta=np.ndarray>
Attributes: (12/40)
Conventions: CF-1.7 CMIP-6.2
activity_id: ScenarioMIP
contact: climatesci@rhg.com
data_specs_version: 01.00.30
dc6_bias_correction_method: Quantile Delta Method (QDM)
dc6_citation: Please refer to https://github.com/ClimateI...
... ...
source_type: AOGCM
sub_experiment: none
sub_experiment_id: none
table_id: day
variant_label: r1i1p1f1
version_id: v20190811We'll load the data for a specific date and plot the result.
In [9]:
tmax = ds.tasmax.isel(time=0).load()
tmax.plot(size=10);
