intake-esm#

import json
import os
from datetime import datetime, timezone
import geopandas as gpd
import intake
import matplotlib.pyplot as plt
import numpy as np
import pooch
import climag.climag as cplt
import seaborn as sns

DATA_DIR_BASE = os.path.join("data", "EURO-CORDEX")

os.makedirs(DATA_DIR_BASE, exist_ok=True)

# Cork Airport met station coords
LON = -8.48611
LAT = 51.84722

# Ireland boundary
GPKG_BOUNDARY = os.path.join("data", "boundaries", "boundaries_all.gpkg")
ie = gpd.read_file(GPKG_BOUNDARY, layer="NUTS_RG_01M_2021_2157_IE")

timerange = [
    "19710101-19751231",
    "19760101-19801231",
    "19810101-19851231",
    "19860101-19901231",
    "19910101-19951231",
    "19960101-20001231",
    "20010101-20051231",
    "20360101-20401231",
    "20410101-20451231",
    "20460101-20501231",
    "20510101-20551231",
    "20560101-20601231",
    "20610101-20651231",
    "20660101-20701231",
]

# add additional time ranges for the MOHC datasets, which use a 360-day year
timerange = timerange + [t.replace("1231", "1230") for t in timerange]

variables = ["evspsblpot", "pr", "rsds", "tas"]

driving_model_id = [
    "CNRM-CERFACS-CNRM-CM5",
    "ICHEC-EC-EARTH",
    "MPI-M-MPI-ESM-LR",
    "MOHC-HadGEM2-ES",
]

Create local catalogue#

dkrz_cat = intake.open_catalog(["https://dkrz.de/s/intake"])

server = dkrz_cat._entries["dkrz_cordex_disk"]._open_args["esmcol_obj"]

server

'https://gitlab.dkrz.de/data-infrastructure-services/intake-esm/-/raw/master/esm-collections/cloud-access/dkrz_cordex_disk.json'

dkrz_cordex = intake.open_esm_datastore(
    server,
    read_csv_kwargs={"dtype": {"time_min": "string", "time_max": "string"}},
)

# download data if necessary
FILE_NAME = "dkrz_cordex_disk.json"
KNOWN_HASH = None
if not os.path.isfile(os.path.join(DATA_DIR_BASE, FILE_NAME)):
    pooch.retrieve(
        url=server, known_hash=KNOWN_HASH, fname=FILE_NAME, path=DATA_DIR_BASE
    )

    with open(
        os.path.join(DATA_DIR_BASE, f"{FILE_NAME[:-5]}.txt"),
        "w",
        encoding="utf-8",
    ) as outfile:
        outfile.write(
            f"Data downloaded on: {datetime.now(tz=timezone.utc)}\n"
            f"Download URL: {server}"
        )

# keep data for the relevant variables and time ranges
query = {
    "CORDEX_domain": "EUR-11",
    "experiment_id": ["historical", "rcp45", "rcp85"],
    "frequency": "day",
    "variable_id": variables,
    "time_range": timerange,
    "model_id": "SMHI-RCA4",
    "driving_model_id": driving_model_id,
    "member": ["r1i1p1", "r12i1p1"],
}

cordex_eur11 = dkrz_cordex.search(**query)

cordex_eur11

dkrz_cordex_disk catalog with 12 dataset(s) from 462 asset(s):

unique
project 1
product_id 1
CORDEX_domain 1
institute_id 1
driving_model_id 4
experiment_id 3
member 2
model_id 1
rcm_version_id 2
frequency 1
variable_id 4
version 4
time_range 28
uri 462
institution_id 1
source_id 1
simulation_id 2
grid_label 1
grid_id 1
time_reduction 1
realm 1
level_type 1
time_min 14
time_max 28
path 462
format 1
opendap_url 462
derived_variable_id 0 
cordex_eur11.df.shape

(462, 27)

list(cordex_eur11.df)

['project',
 'product_id',
 'CORDEX_domain',
 'institute_id',
 'driving_model_id',
 'experiment_id',
 'member',
 'model_id',
 'rcm_version_id',
 'frequency',
 'variable_id',
 'version',
 'time_range',
 'uri',
 'institution_id',
 'source_id',
 'simulation_id',
 'grid_label',
 'grid_id',
 'time_reduction',
 'realm',
 'level_type',
 'time_min',
 'time_max',
 'path',
 'format',
 'opendap_url']

cordex_eur11.df.head()
project product_id CORDEX_domain institute_id driving_model_id experiment_id member model_id rcm_version_id frequency ... grid_label grid_id time_reduction realm level_type time_min time_max path format opendap_url
0 CORDEX output EUR-11 SMHI CNRM-CERFACS-CNRM-CM5 historical r1i1p1 SMHI-RCA4 v1 day ... None None None None None 19710101 19751231 /work/kd0956/CORDEX/data/cordex/output/EUR-11/... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-...
1 CORDEX output EUR-11 SMHI CNRM-CERFACS-CNRM-CM5 historical r1i1p1 SMHI-RCA4 v1 day ... None None None None None 19760101 19801231 /work/kd0956/CORDEX/data/cordex/output/EUR-11/... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-...
2 CORDEX output EUR-11 SMHI CNRM-CERFACS-CNRM-CM5 historical r1i1p1 SMHI-RCA4 v1 day ... None None None None None 19810101 19851231 /work/kd0956/CORDEX/data/cordex/output/EUR-11/... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-...
3 CORDEX output EUR-11 SMHI CNRM-CERFACS-CNRM-CM5 historical r1i1p1 SMHI-RCA4 v1 day ... None None None None None 19860101 19901231 /work/kd0956/CORDEX/data/cordex/output/EUR-11/... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-...
4 CORDEX output EUR-11 SMHI CNRM-CERFACS-CNRM-CM5 historical r1i1p1 SMHI-RCA4 v1 day ... None None None None None 19910101 19951231 /work/kd0956/CORDEX/data/cordex/output/EUR-11/... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-...

5 rows × 27 columns

# drop v1 of MPI-M-MPI-ESM-LR outputs
cordex_eur11_df = cordex_eur11.df.drop(
    cordex_eur11.df[
        (cordex_eur11.df["driving_model_id"] == "MPI-M-MPI-ESM-LR")
        & (cordex_eur11.df["rcm_version_id"] == "v1")
    ].index
)

# keep only r12i1p1 outputs of ICHEC-EC-EARTH
cordex_eur11_df = cordex_eur11_df.drop(
    cordex_eur11_df[
        (cordex_eur11_df["driving_model_id"] == "ICHEC-EC-EARTH")
        & (cordex_eur11_df["member"] == "r1i1p1")
    ].index
)

# extract driving model name, without the institution
cordex_eur11_df["driving_model"] = cordex_eur11_df["driving_model_id"].replace(
    to_replace={
        "CNRM-CERFACS-CNRM-CM5": "CNRM-CM5",
        "ICHEC-EC-EARTH": "EC-EARTH",
        "MOHC-HadGEM2-ES": "HadGEM2-ES",
        "MPI-M-MPI-ESM-LR": "MPI-ESM-LR",
    }
)

# replace URI to path to downloaded data
cordex_eur11_df["uri"] = (
    DATA_DIR_BASE
    + os.sep
    + "RCA4"
    + os.sep
    + cordex_eur11_df["experiment_id"]
    + os.sep
    + cordex_eur11_df["driving_model"]
    + os.sep
    + cordex_eur11_df["uri"].str.split("/").str[-1]
)

cordex_eur11_df["path"] = cordex_eur11_df["uri"]

cordex_eur11_df.head()
project product_id CORDEX_domain institute_id driving_model_id experiment_id member model_id rcm_version_id frequency ... grid_id time_reduction realm level_type time_min time_max path format opendap_url driving_model
0 CORDEX output EUR-11 SMHI CNRM-CERFACS-CNRM-CM5 historical r1i1p1 SMHI-RCA4 v1 day ... None None None None 19710101 19751231 data/EURO-CORDEX/RCA4/historical/CNRM-CM5/evsp... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... CNRM-CM5
1 CORDEX output EUR-11 SMHI CNRM-CERFACS-CNRM-CM5 historical r1i1p1 SMHI-RCA4 v1 day ... None None None None 19760101 19801231 data/EURO-CORDEX/RCA4/historical/CNRM-CM5/evsp... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... CNRM-CM5
2 CORDEX output EUR-11 SMHI CNRM-CERFACS-CNRM-CM5 historical r1i1p1 SMHI-RCA4 v1 day ... None None None None 19810101 19851231 data/EURO-CORDEX/RCA4/historical/CNRM-CM5/evsp... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... CNRM-CM5
3 CORDEX output EUR-11 SMHI CNRM-CERFACS-CNRM-CM5 historical r1i1p1 SMHI-RCA4 v1 day ... None None None None 19860101 19901231 data/EURO-CORDEX/RCA4/historical/CNRM-CM5/evsp... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... CNRM-CM5
4 CORDEX output EUR-11 SMHI CNRM-CERFACS-CNRM-CM5 historical r1i1p1 SMHI-RCA4 v1 day ... None None None None 19910101 19951231 data/EURO-CORDEX/RCA4/historical/CNRM-CM5/evsp... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... CNRM-CM5

5 rows × 28 columns

cordex_eur11_df.shape

(336, 28)

CSV_FILE_PATH = os.path.join(DATA_DIR_BASE, "eurocordex_eur11_catalogue.csv")

cordex_eur11_df.to_csv(CSV_FILE_PATH, index=False)

JSON_FILE_PATH = os.path.join(DATA_DIR_BASE, "dkrz_cordex_disk.json")

# modify the JSON catalogue
with open(JSON_FILE_PATH, encoding="utf-8") as json_file:
    cordex_eur11_cat = json.load(json_file)
    json_file.close()

GITHUB_CSV_LINK = (
    "https://raw.githubusercontent.com/ClimAg/data/main/eurocordex/"
    "eurocordex_eur11_catalogue.csv"
)

cordex_eur11_cat["catalog_file"] = GITHUB_CSV_LINK

cordex_eur11_cat["id"] = "eurocordex_eur11"

cordex_eur11_cat["description"] = (
    "This is an ESM collection for EURO-CORDEX data used in the ClimAg "
    "project. Data has been generated using the DKRZ intake-esm stores. "
    "Data is filtered for the EUR-11 CORDEX domain at the daily frequency, "
    "the historical (1976-2005) and future (rcp45, rcp85) (2041-2070) "
    "experiments, and the following variables: "
    + ", ".join(variables)
    + ". The SMHI-RCA4 RCM and four GCMs ("
    + ", ".join(driving_model_id)
    + ") are the models used to generate these data. Last updated: "
    + str(datetime.now(tz=timezone.utc))
    + "."
)

# save the modified JSON file
JSON_FILE_PATH = os.path.join(DATA_DIR_BASE, "eurocordex_eur11_local.json")

with open(JSON_FILE_PATH, "w", encoding="utf-8") as json_file:
    json.dump(cordex_eur11_cat, json_file, ensure_ascii=False, indent=4)

# create a copy that reads the CSV file from disk
cordex_eur11_cat["catalog_file"] = CSV_FILE_PATH
JSON_FILE_PATH = os.path.join(
    DATA_DIR_BASE, "eurocordex_eur11_local_disk.json"
)
with open(JSON_FILE_PATH, "w", encoding="utf-8") as json_file:
    json.dump(cordex_eur11_cat, json_file, ensure_ascii=False, indent=4)

Testing the local catalogue#

# JSON_FILE_PATH = (
#     "https://raw.githubusercontent.com/ClimAg/data/main/eurocordex/"
#     "eurocordex_eur11_local.json"
# )
JSON_FILE_PATH = os.path.join(
    DATA_DIR_BASE, "eurocordex_eur11_local_disk.json"
)

cordex_eur11_cat = intake.open_esm_datastore(JSON_FILE_PATH)

list(cordex_eur11_cat)

['EUR-11.MPI-M-MPI-ESM-LR.SMHI.SMHI-RCA4.historical.day',
 'EUR-11.MPI-M-MPI-ESM-LR.SMHI.SMHI-RCA4.rcp45.day',
 'EUR-11.ICHEC-EC-EARTH.SMHI.SMHI-RCA4.rcp45.day',
 'EUR-11.CNRM-CERFACS-CNRM-CM5.SMHI.SMHI-RCA4.rcp45.day',
 'EUR-11.MOHC-HadGEM2-ES.SMHI.SMHI-RCA4.rcp85.day',
 'EUR-11.MOHC-HadGEM2-ES.SMHI.SMHI-RCA4.historical.day',
 'EUR-11.MPI-M-MPI-ESM-LR.SMHI.SMHI-RCA4.rcp85.day',
 'EUR-11.MOHC-HadGEM2-ES.SMHI.SMHI-RCA4.rcp45.day',
 'EUR-11.CNRM-CERFACS-CNRM-CM5.SMHI.SMHI-RCA4.historical.day',
 'EUR-11.ICHEC-EC-EARTH.SMHI.SMHI-RCA4.rcp85.day',
 'EUR-11.CNRM-CERFACS-CNRM-CM5.SMHI.SMHI-RCA4.rcp85.day',
 'EUR-11.ICHEC-EC-EARTH.SMHI.SMHI-RCA4.historical.day']

cordex_eur11_cat

eurocordex_eur11 catalog with 12 dataset(s) from 420 asset(s):

unique
project 1
product_id 1
CORDEX_domain 1
institute_id 1
driving_model_id 4
experiment_id 3
member 2
model_id 1
rcm_version_id 2
frequency 1
variable_id 5
version 2
time_range 28
uri 420
institution_id 1
source_id 1
simulation_id 2
grid_label 1
grid_id 1
time_reduction 1
realm 1
level_type 1
time_min 14
time_max 28
path 420
format 1
opendap_url 420
driving_model 4
derived_variable_id 0 
cordex_eur11_cat.df.shape

(420, 28)

cordex_eur11_cat.df.head()
project product_id CORDEX_domain institute_id driving_model_id experiment_id member model_id rcm_version_id frequency ... grid_id time_reduction realm level_type time_min time_max path format opendap_url driving_model
0 CORDEX output EUR-11 SMHI CNRM-CERFACS-CNRM-CM5 historical r1i1p1 SMHI-RCA4 v1 day ... None None None None 19710101 19751231 data/EURO-CORDEX/RCA4/historical/CNRM-CM5/evsp... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... CNRM-CM5
1 CORDEX output EUR-11 SMHI CNRM-CERFACS-CNRM-CM5 historical r1i1p1 SMHI-RCA4 v1 day ... None None None None 19760101 19801231 data/EURO-CORDEX/RCA4/historical/CNRM-CM5/evsp... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... CNRM-CM5
2 CORDEX output EUR-11 SMHI CNRM-CERFACS-CNRM-CM5 historical r1i1p1 SMHI-RCA4 v1 day ... None None None None 19810101 19851231 data/EURO-CORDEX/RCA4/historical/CNRM-CM5/evsp... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... CNRM-CM5
3 CORDEX output EUR-11 SMHI CNRM-CERFACS-CNRM-CM5 historical r1i1p1 SMHI-RCA4 v1 day ... None None None None 19860101 19901231 data/EURO-CORDEX/RCA4/historical/CNRM-CM5/evsp... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... CNRM-CM5
4 CORDEX output EUR-11 SMHI CNRM-CERFACS-CNRM-CM5 historical r1i1p1 SMHI-RCA4 v1 day ... None None None None 19910101 19951231 data/EURO-CORDEX/RCA4/historical/CNRM-CM5/evsp... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... CNRM-CM5

5 rows × 28 columns

Read a subset (precipitation)#

# filter data subset
query = dict(
    experiment_id="rcp85", variable_id="pr", driving_model_id="ICHEC-EC-EARTH"
)

cordex_eur11_pr = cordex_eur11_cat.search(**query)

cordex_eur11_pr

eurocordex_eur11 catalog with 1 dataset(s) from 7 asset(s):

unique
project 1
product_id 1
CORDEX_domain 1
institute_id 1
driving_model_id 1
experiment_id 1
member 1
model_id 1
rcm_version_id 1
frequency 1
variable_id 1
version 1
time_range 7
uri 7
institution_id 1
source_id 1
simulation_id 1
grid_label 1
grid_id 1
time_reduction 1
realm 1
level_type 1
time_min 7
time_max 7
path 7
format 1
opendap_url 7
driving_model 1
derived_variable_id 0 
cordex_eur11_pr.df
project product_id CORDEX_domain institute_id driving_model_id experiment_id member model_id rcm_version_id frequency ... grid_id time_reduction realm level_type time_min time_max path format opendap_url driving_model
0 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH rcp85 r12i1p1 SMHI-RCA4 v1 day ... None None None None 20360101 20401231 data/EURO-CORDEX/RCA4/rcp85/EC-EARTH/pr_EUR-11... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
1 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH rcp85 r12i1p1 SMHI-RCA4 v1 day ... None None None None 20410101 20451231 data/EURO-CORDEX/RCA4/rcp85/EC-EARTH/pr_EUR-11... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
2 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH rcp85 r12i1p1 SMHI-RCA4 v1 day ... None None None None 20460101 20501231 data/EURO-CORDEX/RCA4/rcp85/EC-EARTH/pr_EUR-11... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
3 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH rcp85 r12i1p1 SMHI-RCA4 v1 day ... None None None None 20510101 20551231 data/EURO-CORDEX/RCA4/rcp85/EC-EARTH/pr_EUR-11... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
4 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH rcp85 r12i1p1 SMHI-RCA4 v1 day ... None None None None 20560101 20601231 data/EURO-CORDEX/RCA4/rcp85/EC-EARTH/pr_EUR-11... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
5 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH rcp85 r12i1p1 SMHI-RCA4 v1 day ... None None None None 20610101 20651231 data/EURO-CORDEX/RCA4/rcp85/EC-EARTH/pr_EUR-11... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
6 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH rcp85 r12i1p1 SMHI-RCA4 v1 day ... None None None None 20660101 20701231 data/EURO-CORDEX/RCA4/rcp85/EC-EARTH/pr_EUR-11... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH

7 rows × 28 columns

pr = cordex_eur11_pr.to_dataset_dict()

--> The keys in the returned dictionary of datasets are constructed as follows:
	'CORDEX_domain.driving_model_id.institute_id.model_id.experiment_id.frequency'
100.00% [1/1 00:01<00:00] 
pr = pr.popitem()[1]

pr

<xarray.Dataset>
Dimensions:       (rlat: 412, rlon: 424, member: 1, time: 12784, bnds: 2)
Coordinates:
    lat           (rlat, rlon) float64 dask.array<chunksize=(412, 424), meta=np.ndarray>
    lon           (rlat, rlon) float64 dask.array<chunksize=(412, 424), meta=np.ndarray>
  * rlat          (rlat) float64 -23.38 -23.27 -23.16 ... 21.62 21.73 21.84
  * rlon          (rlon) float64 -28.38 -28.27 -28.16 ... 17.94 18.05 18.16
    rotated_pole  |S1 b''
  * time          (time) datetime64[ns] 2036-01-01T12:00:00 ... 2070-12-31T12...
    time_bnds     (time, bnds) datetime64[ns] dask.array<chunksize=(1827, 2), meta=np.ndarray>
  * member        (member) object 'r12i1p1'
Dimensions without coordinates: bnds
Data variables:
    pr            (member, time, rlat, rlon) float32 dask.array<chunksize=(1, 1827, 412, 424), meta=np.ndarray>
Attributes: (12/44)
    Conventions:                        CF-1.4
    contact:                            rossby.cordex@smhi.se
    experiment:                         RCP8.5
    experiment_id:                      rcp85
    driving_experiment:                 ICHEC-EC-EARTH, rcp85, r12i1p1
    driving_model_id:                   ICHEC-EC-EARTH
    ...                                 ...
    intake_esm_attrs:time_reduction:    None
    intake_esm_attrs:realm:             None
    intake_esm_attrs:level_type:        None
    intake_esm_attrs:_data_format_:     netcdf
    intake_esm_attrs:driving_model:     EC-EARTH
    intake_esm_dataset_key:             EUR-11.ICHEC-EC-EARTH.SMHI.SMHI-RCA4....
pr.rio.crs

CRS.from_wkt('GEOGCRS["undefined",BASEGEOGCRS["undefined",DATUM["World Geodetic System 1984",ELLIPSOID["WGS 84",6378137,298.257223563,LENGTHUNIT["metre",1]],ID["EPSG",6326]],PRIMEM["Greenwich",0,ANGLEUNIT["degree",0.0174532925199433],ID["EPSG",8901]]],DERIVINGCONVERSION["Pole rotation (netCDF CF convention)",METHOD["Pole rotation (netCDF CF convention)"],PARAMETER["Grid north pole latitude (netCDF CF convention)",39.25,ANGLEUNIT["degree",0.0174532925199433,ID["EPSG",9122]]],PARAMETER["Grid north pole longitude (netCDF CF convention)",-162,ANGLEUNIT["degree",0.0174532925199433,ID["EPSG",9122]]],PARAMETER["North pole grid longitude (netCDF CF convention)",0,ANGLEUNIT["degree",0.0174532925199433,ID["EPSG",9122]]]],CS[ellipsoidal,2],AXIS["longitude",east,ORDER[1],ANGLEUNIT["degree",0.0174532925199433,ID["EPSG",9122]]],AXIS["latitude",north,ORDER[2],ANGLEUNIT["degree",0.0174532925199433,ID["EPSG",9122]]]]')

Time subset#

pr_50 = pr.sel(time="2055-06-21")

pr_50

<xarray.Dataset>
Dimensions:       (rlat: 412, rlon: 424, member: 1, time: 1, bnds: 2)
Coordinates:
    lat           (rlat, rlon) float64 dask.array<chunksize=(412, 424), meta=np.ndarray>
    lon           (rlat, rlon) float64 dask.array<chunksize=(412, 424), meta=np.ndarray>
  * rlat          (rlat) float64 -23.38 -23.27 -23.16 ... 21.62 21.73 21.84
  * rlon          (rlon) float64 -28.38 -28.27 -28.16 ... 17.94 18.05 18.16
    rotated_pole  |S1 b''
  * time          (time) datetime64[ns] 2055-06-21T12:00:00
    time_bnds     (time, bnds) datetime64[ns] dask.array<chunksize=(1, 2), meta=np.ndarray>
  * member        (member) object 'r12i1p1'
Dimensions without coordinates: bnds
Data variables:
    pr            (member, time, rlat, rlon) float32 dask.array<chunksize=(1, 1, 412, 424), meta=np.ndarray>
Attributes: (12/44)
    Conventions:                        CF-1.4
    contact:                            rossby.cordex@smhi.se
    experiment:                         RCP8.5
    experiment_id:                      rcp85
    driving_experiment:                 ICHEC-EC-EARTH, rcp85, r12i1p1
    driving_model_id:                   ICHEC-EC-EARTH
    ...                                 ...
    intake_esm_attrs:time_reduction:    None
    intake_esm_attrs:realm:             None
    intake_esm_attrs:level_type:        None
    intake_esm_attrs:_data_format_:     netcdf
    intake_esm_attrs:driving_model:     EC-EARTH
    intake_esm_dataset_key:             EUR-11.ICHEC-EC-EARTH.SMHI.SMHI-RCA4....
plot_transform = cplt.rotated_pole_transform(pr_50)
data_var = pr_50["pr"]  # variable name
plot_data = data_var * 60 * 60 * 24  # convert to mm day⁻¹
cbar_label = data_var.attrs["long_name"] + " [mm day⁻¹]"  # colorbar label

plt.figure(figsize=(20, 10))
ax = plt.axes(projection=plot_transform)

# specify gridline spacing and labels
ax.gridlines(
    draw_labels=True,
    xlocs=range(-180, 180, 10),
    ylocs=range(-90, 90, 5),
    color="lightslategrey",
    linewidth=0.5,
)

# plot data for the variable
plot_data.plot(
    ax=ax,
    cmap="GnBu",
    transform=plot_transform,
    x="rlon",
    y="rlat",
    cbar_kwargs=dict(label=cbar_label),
    levels=15,
    robust=True,
)

# add boundaries
ax.coastlines(resolution="50m", color="darkslategrey", linewidth=0.5)

plt.show()
../_images/ce485c44cbfb130b6f7e9bf61368389dddcd41a5d0c158153fce07d2be3e5939.png

Point subset#

cds = cplt.rotated_pole_point(data=pr, lon=LON, lat=LAT)

pr_ca = pr.sel({"rlon": cds[0], "rlat": cds[1]}, method="nearest")

plt.figure(figsize=(12, 4))
plt.plot(pr_ca["time"], pr_ca["pr"].values[0] * 60 * 60 * 24)
plt.xlabel(pr_ca["time"].attrs["standard_name"])
plt.ylabel(pr_ca["pr"].attrs["long_name"] + " [mm day⁻¹]")
plt.tight_layout()
plt.show()
../_images/b7b90a71cba211c5b885bcae0d3b3111b6fae7350305e74b3c6d3f63f0d85622.png

Ireland subset#

# clip to Ireland's boundary
pr_ie = pr_50.rio.clip(ie.buffer(500).to_crs(pr_50.rio.crs))

# find number of grid cells with data
len(pr_ie["pr"].values.flatten()[np.isfinite(pr_ie["pr"].values.flatten())])

575

plot_transform = cplt.rotated_pole_transform(pr_ie)
data_var = pr_ie["pr"]  # extract variable name
plot_data = data_var * 60 * 60 * 24  # convert to mm day⁻¹
cbar_label = data_var.attrs["long_name"] + " [mm day⁻¹]"  # colorbar label

plt.figure(figsize=(7, 7))
ax = plt.axes(projection=cplt.projection_hiresireland)

# plot data for the variable
plot_data.plot(
    ax=ax,
    cmap="mako",
    transform=plot_transform,
    x="rlon",
    y="rlat",
    cbar_kwargs=dict(label=cbar_label),
    levels=10,
    robust=True,
)

# add boundaries
ax.coastlines(resolution="10m", color="darkslategrey", linewidth=0.75)

plt.axis("equal")
plt.tight_layout()
plt.xlim(-1.5, 1.33)
plt.ylim(-2.05, 2.05)

ax.gridlines(
    draw_labels=dict(bottom="x", left="y"),
    xlocs=range(-180, 180, 2),
    ylocs=range(-90, 90, 1),
    color="lightslategrey",
    linewidth=0.5,
    x_inline=False,
    y_inline=False,
)

plt.show()
../_images/c8a8330df32510951e053e15a771d50036600a77c1dfff969abad8aa88f520d0.png