Subset EURO-CORDEX data for Ireland#

import os
import glob
from datetime import datetime, timezone
import geopandas as gpd
import intake
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import xarray as xr
from climag import climag
import seaborn as sns
from climag import climag_plot

DATA_DIR_BASE = os.path.join("/run/media/nms/MyPassport/EURO-CORDEX")

# directory to store outputs
DATA_DIR = os.path.join(DATA_DIR_BASE, "IE")
os.makedirs(DATA_DIR, exist_ok=True)

# Moorepark met station coords
LON, LAT = -8.26389, 52.16389

# 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")
ie_bbox = gpd.read_file(
    GPKG_BOUNDARY, layer="NUTS_RG_01M_2021_2157_IE_BBOX_DIFF"
)
ie_ne = gpd.read_file(GPKG_BOUNDARY, layer="ne_10m_land_2157_IE_BBOX_DIFF")

Reading the local catalogue#

JSON_FILE_PATH = os.path.join(
    "data", "EURO-CORDEX", "eurocordex_eur11_local_disk.json"
)

cordex_eur11_cat = intake.open_esm_datastore(JSON_FILE_PATH)

list(cordex_eur11_cat)

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

cordex_eur11_cat

eurocordex_eur11 catalog with 12 dataset(s) from 336 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 2
time_range 28
uri 336
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 336
format 1
opendap_url 336
driving_model 4
derived_variable_id 0 
cordex_eur11_cat.df.shape

(336, 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 (historical)#

cordex_eur11 = cordex_eur11_cat.search(
    experiment_id="historical", driving_model_id="ICHEC-EC-EARTH"
)

cordex_eur11

eurocordex_eur11 catalog with 1 dataset(s) from 28 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 4
version 1
time_range 7
uri 28
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 28
format 1
opendap_url 28
driving_model 1
derived_variable_id 0 
cordex_eur11.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 historical r12i1p1 SMHI-RCA4 v1 day ... None None None None 19710101 19751231 data/EURO-CORDEX/RCA4/historical/EC-EARTH/evsp... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
1 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH historical r12i1p1 SMHI-RCA4 v1 day ... None None None None 19760101 19801231 data/EURO-CORDEX/RCA4/historical/EC-EARTH/evsp... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
2 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH historical r12i1p1 SMHI-RCA4 v1 day ... None None None None 19810101 19851231 data/EURO-CORDEX/RCA4/historical/EC-EARTH/evsp... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
3 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH historical r12i1p1 SMHI-RCA4 v1 day ... None None None None 19860101 19901231 data/EURO-CORDEX/RCA4/historical/EC-EARTH/evsp... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
4 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH historical r12i1p1 SMHI-RCA4 v1 day ... None None None None 19910101 19951231 data/EURO-CORDEX/RCA4/historical/EC-EARTH/evsp... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
5 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH historical r12i1p1 SMHI-RCA4 v1 day ... None None None None 19960101 20001231 data/EURO-CORDEX/RCA4/historical/EC-EARTH/evsp... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
6 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH historical r12i1p1 SMHI-RCA4 v1 day ... None None None None 20010101 20051231 data/EURO-CORDEX/RCA4/historical/EC-EARTH/evsp... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
7 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH historical r12i1p1 SMHI-RCA4 v1 day ... None None None None 19710101 19751231 data/EURO-CORDEX/RCA4/historical/EC-EARTH/pr_E... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
8 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH historical r12i1p1 SMHI-RCA4 v1 day ... None None None None 19760101 19801231 data/EURO-CORDEX/RCA4/historical/EC-EARTH/pr_E... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
9 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH historical r12i1p1 SMHI-RCA4 v1 day ... None None None None 19810101 19851231 data/EURO-CORDEX/RCA4/historical/EC-EARTH/pr_E... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
10 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH historical r12i1p1 SMHI-RCA4 v1 day ... None None None None 19860101 19901231 data/EURO-CORDEX/RCA4/historical/EC-EARTH/pr_E... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
11 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH historical r12i1p1 SMHI-RCA4 v1 day ... None None None None 19910101 19951231 data/EURO-CORDEX/RCA4/historical/EC-EARTH/pr_E... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
12 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH historical r12i1p1 SMHI-RCA4 v1 day ... None None None None 19960101 20001231 data/EURO-CORDEX/RCA4/historical/EC-EARTH/pr_E... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
13 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH historical r12i1p1 SMHI-RCA4 v1 day ... None None None None 20010101 20051231 data/EURO-CORDEX/RCA4/historical/EC-EARTH/pr_E... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
14 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH historical r12i1p1 SMHI-RCA4 v1 day ... None None None None 19710101 19751231 data/EURO-CORDEX/RCA4/historical/EC-EARTH/rsds... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
15 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH historical r12i1p1 SMHI-RCA4 v1 day ... None None None None 19760101 19801231 data/EURO-CORDEX/RCA4/historical/EC-EARTH/rsds... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
16 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH historical r12i1p1 SMHI-RCA4 v1 day ... None None None None 19810101 19851231 data/EURO-CORDEX/RCA4/historical/EC-EARTH/rsds... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
17 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH historical r12i1p1 SMHI-RCA4 v1 day ... None None None None 19860101 19901231 data/EURO-CORDEX/RCA4/historical/EC-EARTH/rsds... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
18 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH historical r12i1p1 SMHI-RCA4 v1 day ... None None None None 19910101 19951231 data/EURO-CORDEX/RCA4/historical/EC-EARTH/rsds... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
19 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH historical r12i1p1 SMHI-RCA4 v1 day ... None None None None 19960101 20001231 data/EURO-CORDEX/RCA4/historical/EC-EARTH/rsds... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
20 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH historical r12i1p1 SMHI-RCA4 v1 day ... None None None None 20010101 20051231 data/EURO-CORDEX/RCA4/historical/EC-EARTH/rsds... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
21 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH historical r12i1p1 SMHI-RCA4 v1 day ... None None None None 19710101 19751231 data/EURO-CORDEX/RCA4/historical/EC-EARTH/tas_... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
22 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH historical r12i1p1 SMHI-RCA4 v1 day ... None None None None 19760101 19801231 data/EURO-CORDEX/RCA4/historical/EC-EARTH/tas_... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
23 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH historical r12i1p1 SMHI-RCA4 v1 day ... None None None None 19810101 19851231 data/EURO-CORDEX/RCA4/historical/EC-EARTH/tas_... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
24 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH historical r12i1p1 SMHI-RCA4 v1 day ... None None None None 19860101 19901231 data/EURO-CORDEX/RCA4/historical/EC-EARTH/tas_... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
25 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH historical r12i1p1 SMHI-RCA4 v1 day ... None None None None 19910101 19951231 data/EURO-CORDEX/RCA4/historical/EC-EARTH/tas_... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
26 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH historical r12i1p1 SMHI-RCA4 v1 day ... None None None None 19960101 20001231 data/EURO-CORDEX/RCA4/historical/EC-EARTH/tas_... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH
27 CORDEX output EUR-11 SMHI ICHEC-EC-EARTH historical r12i1p1 SMHI-RCA4 v1 day ... None None None None 20010101 20051231 data/EURO-CORDEX/RCA4/historical/EC-EARTH/tas_... netcdf http://esgf1.dkrz.de/thredds/dodsC/cordex/EUR-... EC-EARTH

28 rows × 28 columns

data = xr.open_mfdataset(
    glob.glob(os.path.join(DATA_DIR_BASE, "RCA4/historical/EC-EARTH/*.nc")),
    chunks="auto",
    decode_coords="all",
)

# data = xr.open_mfdataset(
#     list(cordex_eur11.df["uri"]),
#     chunks="auto",
#     decode_coords="all"
# )

data

<xarray.Dataset>
Dimensions:       (time: 12784, rlat: 412, rlon: 424, 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] 1971-01-01T12:00:00 ... 2005-12-31T12...
    time_bnds     (time, bnds) datetime64[ns] dask.array<chunksize=(1826, 2), meta=np.ndarray>
    height        float64 2.0
Dimensions without coordinates: bnds
Data variables:
    evspsblpot    (time, rlat, rlon) float32 dask.array<chunksize=(862, 194, 200), meta=np.ndarray>
    pr            (time, rlat, rlon) float32 dask.array<chunksize=(862, 194, 200), meta=np.ndarray>
    rsds          (time, rlat, rlon) float32 dask.array<chunksize=(862, 194, 200), meta=np.ndarray>
    tas           (time, rlat, rlon) float32 dask.array<chunksize=(862, 194, 200), meta=np.ndarray>
Attributes: (12/22)
    Conventions:                    CF-1.4
    contact:                        rossby.cordex@smhi.se
    creation_date:                  2013-06-24-T01:07:21Z
    experiment:                     historical
    experiment_id:                  historical
    driving_experiment:             ICHEC-EC-EARTH, historical, r12i1p1
    ...                             ...
    product:                        output
    references:                     http://www.smhi.se/en/Research/Research-d...
    tracking_id:                    3315f9a7-cd0f-419c-89e5-42766ea8386d
    rossby_comment:                 201137: CORDEX Europe 0.11 deg | RCA4 v1 ...
    rossby_run_id:                  201137
    rossby_grib_path:               /nobackup/rossby16/rossby/joint_exp/corde...
# using Moorepark met station coordinates
cds = climag.rotated_pole_point(data=data, lon=LON, lat=LAT)

# copy CRS
data_crs = data.rio.crs

data_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]]]]')

# subset for reference period and spin-up year
data = data.sel(time=slice("1975", "2005"))

# copy time_bnds coordinates
data_time_bnds = data.coords["time_bnds"]

Ireland subset#

# clip to Ireland's boundary
data = data.rio.clip(ie.buffer(6500).to_crs(data_crs), all_touched=True)

# number of grid cells with data
len(
    data.groupby("time.season")
    .mean(dim="time")["tas"][0]
    .values.flatten()[
        np.isfinite(
            data.groupby("time.season")
            .mean(dim="time")["tas"][0]
            .values.flatten()
        )
    ]
)

765

Visualise fields#

for var in data.data_vars:
    climag_plot.plot_averages(
        data=data.sel(time=slice("1976", "2005")),
        var=var,
        averages="season",
        boundary_data=ie_ne,
        cbar_levels=15,
    )
../_images/587fb739dde24a90cb5f9d61021bdc2f4c1f5ab9f237031ae40d9269ae630cd6.png ../_images/8000b5004d38dbc0c414d61c5f08cf525edc9561bd095d430efa6d728c6027a8.png ../_images/40c03b3f307486e53f8e68018ff7676f956db51effc39d5f21d495231f4d88da.png ../_images/7b654e2a687a6862f64a92bc02ac038e4e51a4ad65f855815d712ba40c67ee84.png

Time series#

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

data_ie_df = pd.DataFrame({"time": data_ie["time"]})
# configure plot title
plot_title = []
for var in data_ie.data_vars:
    data_ie_df[var] = data_ie[var]
    plot_title.append(
        f"{data_ie[var].attrs['long_name']} [{data_ie[var].attrs['units']}]"
    )

data_ie_df.set_index("time", inplace=True)

data_ie_df.plot(
    subplots=True,
    layout=(4, 1),
    figsize=(12, 11),
    legend=False,
    xlabel="",
    title=plot_title,
    linewidth=0.5,
)

sns.despine()
plt.tight_layout()
plt.show()
../_images/88b5458013d7aa3d646a62d3081e155f99164aaa9648cafcc33cd5ebd53ba7cc.png

Box plots#

data_ie = data_ie.sel(time=slice("1976", "2005"))

data_ie_df = pd.DataFrame({"time": data_ie["time"]})
for var in data_ie.data_vars:
    data_ie_df[var] = data_ie[var]
data_ie_df.set_index("time", inplace=True)

fig, axs = plt.subplots(2, 2, figsize=(12, 4))
for ax, var in zip(axs.flat, data_ie.data_vars):
    data_ie_df.plot.box(
        column=var,
        vert=False,
        showmeans=True,
        ax=ax,
        patch_artist=True,
        color={
            "medians": "Crimson",
            "whiskers": "DarkSlateGrey",
            "caps": "DarkSlateGrey",
        },
        boxprops={"facecolor": "Lavender", "color": "DarkSlateGrey"},
        meanprops={
            "markeredgecolor": "DarkSlateGrey",
            "marker": "d",
            "markerfacecolor": (1, 1, 0, 0),  # transparent
        },
        flierprops={
            "alpha": 0.5,
            "markeredgecolor": "LightSteelBlue",
            "zorder": 1,
        },
    )
    ax.set_title(
        f"{data_ie[var].attrs['long_name']} [{data_ie[var].attrs['units']}]"
    )
    ax.set(yticklabels=[])
plt.tight_layout()
plt.show()
../_images/fb3e8978026cb8a4d050128d282c8b7c578ddd5aba2c727299ed39780d2289ad.png

Calculate photosynthetically active radiation#

# Papaioannou et al. (1993) - irradiance ratio
data = data.assign(PAR=data["rsds"] * 0.473)

# compare radiation vals
data_ie = data.sel({"rlon": cds[0], "rlat": cds[1]}, method="nearest").sel(
    time=slice("1997", "1999")
)

data_ie_df = pd.DataFrame({"time": data_ie["time"]})
for var in ["rsds", "PAR"]:
    data_ie_df[var] = data_ie[var]

data_ie_df.set_index("time", inplace=True)

data_ie = data.sel({"rlon": cds[0], "rlat": cds[1]}, method="nearest").sel(
    time=slice("1976", "2005")
)

fig = data_ie_df.plot.box(
    vert=False,
    showmeans=True,
    figsize=(10, 3),
    patch_artist=True,
    color={
        "medians": "Crimson",
        "whiskers": "DarkSlateGrey",
        "caps": "DarkSlateGrey",
    },
    boxprops={"facecolor": "Lavender", "color": "DarkSlateGrey"},
    meanprops={
        "markeredgecolor": "DarkSlateGrey",
        "marker": "d",
        "markerfacecolor": (1, 1, 0, 0),  # transparent
    },
    flierprops={
        "alpha": 0.5,
        "markeredgecolor": "LightSteelBlue",
        "zorder": 1,
    },
)
plt.tight_layout()
plt.show()
../_images/994950c10b0b859bd5b6e539e28adf1ed904eec36a7eb193db343d2e8770e518.png

Convert units and rename variables#

for v in data.data_vars:
    var_attrs = data[v].attrs  # extract attributes
    if v == "tas":
        var_attrs["units"] = "°C"
        data[v] = data[v] - 273.15
        var_attrs["note"] = "Converted from K to °C by subtracting 273.15"
    elif v == "PAR":
        var_attrs["units"] = "MJ m⁻² day⁻¹"
        data[v] = data[v] * (60 * 60 * 24 / 1e6)
        var_attrs["long_name"] = "Surface Photosynthetically Active Radiation"
        var_attrs["note"] = (
            "Calculated by multiplying surface downwelling shortwave "
            "radiation with an irradiance ratio of 0.473 based on Papaioannou "
            "et al. (1993); converted from W m⁻² to MJ m⁻² day⁻¹ by "
            "multiplying 0.0864 based on the FAO Irrigation and Drainage "
            "Paper No. 56 (Allen et al., 1998, p. 45)"
        )
    elif v in ("pr", "evspsblpot"):
        var_attrs["units"] = "mm day⁻¹"
        data[v] = data[v] * 60 * 60 * 24
        var_attrs["note"] = (
            "Converted from kg m⁻² s⁻¹ to mm day⁻¹ by multiplying 86,400,"
            " assuming a water density of 1,000 kg m⁻³"
        )
    data[v].attrs = var_attrs  # reassign attributes

# rename variables
data = data.rename({"tas": "T", "pr": "PP", "evspsblpot": "PET"})

# assign dataset name
for x in ["CNRM-CM5", "EC-EARTH", "HadGEM2-ES", "MPI-ESM-LR"]:
    if x in data.attrs["driving_model_id"]:
        data.attrs["dataset"] = (
            f"IE_EURO-CORDEX_RCA4_{x}_{data.attrs['experiment_id']}"
        )

# keep only required variables
data = data.drop_vars(["rsds"])

# assign attributes to the data
data.attrs["comment"] = (
    "This dataset has been clipped with the Island of Ireland's boundary and "
    "units have been converted. "
    "Last updated: "
    + str(datetime.now(tz=timezone.utc))
    + " by nstreethran@ucc.ie."
)

# reassign time_bnds
data.coords["time_bnds"] = data_time_bnds

# reassign CRS
data.rio.write_crs(data_crs, inplace=True)

<xarray.Dataset>
Dimensions:       (rlat: 33, rlon: 37, time: 11323, bnds: 2)
Coordinates:
    lat           (rlat, rlon) float64 dask.array<chunksize=(33, 37), meta=np.ndarray>
    lon           (rlat, rlon) float64 dask.array<chunksize=(33, 37), meta=np.ndarray>
  * rlat          (rlat) float64 3.685 3.795 3.905 4.015 ... 6.985 7.095 7.205
  * rlon          (rlon) float64 -17.27 -17.16 -17.05 ... -13.53 -13.41 -13.3
  * time          (time) datetime64[ns] 1975-01-01T12:00:00 ... 2005-12-31T12...
    height        float64 2.0
    rotated_pole  |S1 b''
    time_bnds     (time, bnds) datetime64[ns] dask.array<chunksize=(365, 2), meta=np.ndarray>
    spatial_ref   int64 0
Dimensions without coordinates: bnds
Data variables:
    PET           (time, rlat, rlon) float32 dask.array<chunksize=(263, 33, 37), meta=np.ndarray>
    PP            (time, rlat, rlon) float32 dask.array<chunksize=(263, 33, 37), meta=np.ndarray>
    T             (time, rlat, rlon) float32 dask.array<chunksize=(263, 33, 37), meta=np.ndarray>
    PAR           (time, rlat, rlon) float32 dask.array<chunksize=(263, 33, 37), meta=np.ndarray>
Attributes: (12/24)
    Conventions:                    CF-1.4
    contact:                        rossby.cordex@smhi.se
    creation_date:                  2013-06-24-T01:07:21Z
    experiment:                     historical
    experiment_id:                  historical
    driving_experiment:             ICHEC-EC-EARTH, historical, r12i1p1
    ...                             ...
    tracking_id:                    3315f9a7-cd0f-419c-89e5-42766ea8386d
    rossby_comment:                 201137: CORDEX Europe 0.11 deg | RCA4 v1 ...
    rossby_run_id:                  201137
    rossby_grib_path:               /nobackup/rossby16/rossby/joint_exp/corde...
    dataset:                        IE_EURO-CORDEX_RCA4_EC-EARTH_historical
    comment:                        This dataset has been clipped with the Is...
data.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]]]]')

Visualise#

Seasonal averages#

for var in data.data_vars:
    climag_plot.plot_averages(
        data=data.sel(time=slice("1976", "2005")),
        var=var,
        averages="season",
        boundary_data=ie_ne,
        cbar_levels=15,
    )
../_images/c81498cff1cf4dafe4574bc4a44b7690c3f07f49d489a13b2ba584508001f0f9.png ../_images/7d5624ade4d5da396a12638cc6dcc75292b92b00cdc81da626a937ae03aeba70.png ../_images/ab818d331b42584a908f9b3de877ae93c76ba20e3790f297fed6a988f6a6877b.png ../_images/46e5d870d6ac5c610dd3b3af2a6a25f11c21bfba7f62bbc5ee2ca4cc674ff8ca.png

Time series#

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

data_ie_df = pd.DataFrame({"time": data_ie["time"]})
# configure plot title
plot_title = []
for var in data_ie.data_vars:
    data_ie_df[var] = data_ie[var]
    plot_title.append(
        f"{data_ie[var].attrs['long_name']} [{data_ie[var].attrs['units']}]"
    )

data_ie_df.set_index("time", inplace=True)

data_ie_df.plot(
    subplots=True,
    layout=(4, 1),
    figsize=(12, 11),
    legend=False,
    xlabel="",
    title=plot_title,
    linewidth=0.5,
)
sns.despine()
plt.tight_layout()
plt.show()
../_images/90c295d5d7e1d2c9e810ad90c7d2cb32b0ec10450aba9fae30d0102fb0758b79.png

Box plots#

data_ie = data.sel({"rlon": cds[0], "rlat": cds[1]}, method="nearest").sel(
    time=slice("1976", "2005")
)

data_ie_df = pd.DataFrame({"time": data_ie["time"]})
for var in data_ie.data_vars:
    data_ie_df[var] = data_ie[var]
data_ie_df.set_index("time", inplace=True)

fig, axs = plt.subplots(2, 2, figsize=(12, 4))
for ax, var in zip(axs.flat, data_ie.data_vars):
    data_ie_df.plot.box(
        column=var,
        vert=False,
        showmeans=True,
        ax=ax,
        patch_artist=True,
        color={
            "medians": "Crimson",
            "whiskers": "DarkSlateGrey",
            "caps": "DarkSlateGrey",
        },
        boxprops={"facecolor": "Lavender", "color": "DarkSlateGrey"},
        meanprops={
            "markeredgecolor": "DarkSlateGrey",
            "marker": "d",
            "markerfacecolor": (1, 1, 0, 0),  # transparent
        },
        flierprops={
            "alpha": 0.5,
            "markeredgecolor": "LightSteelBlue",
            "zorder": 1,
        },
    )
    ax.set_title(
        f"{data_ie[var].attrs['long_name']} [{data_ie[var].attrs['units']}]"
    )
    ax.set(yticklabels=[])
plt.tight_layout()
plt.show()
../_images/b34d1ca11c05dad80944ea985046ad7b9b304d799937ae087cb70ab57a5923fe.png