🎉 NEW: We have released bias-corrected CORDEX-CORE simulations with the ISIMIP methodology for the AFR-22 and WAS-22 domains! 🌍 This allows non-expert users to directly use these datasets and avoid the need for custom bias-correction. 📊 Additional domains will be released throughout 2025 and 2026.
cavapy is a Python library designed to streamline the retrieval of CORDEX-CORE climate models hosted on THREDDS servers at the University of Cantabria. Using the Open-source Project for a Network Data Access Protocol (OPeNDAP), users can directly access and subset datasets without the need to download large NetCDF files. This capability is part of the Climate and Agriculture Risk Visualization and Assessment (CAVA) project, which focuses on providing high-resolution climate data for scientific, environmental, and agricultural applications.
With cavapy, users can efficiently integrate CORDEX-CORE data into their workflows, making it an ideal resource for hydrological and crop modeling, among other climate-sensitive analyses. Additionally, cavapy enables bias correction, potentially enhancing the precision and usability of the data for a wide range of applications.
The climate data provided by cavapy is hosted on the THREDDS data server of the University of Cantabria as part of the CAVA project. CAVA is a collaborative effort by FAO, the University of Cantabria, the University of Cape Town, and Predictia, aimed at democratising accessibility and usability of climate information.
- CORDEX-CORE Simulations: Dynamically downscaled high-resolution (25 km) climate models, used in the IPCC AR5 report, featuring simulations from:
- 3 Global Climate Models (GCMs)
- 2 Regional Climate Models (RCMs)
- Two Representative Concentration Pathways (RCPs: RCP2.6 and RCP8.5)
- Reanalyses Dataset:
- ERA5 (used for the optional bias correction of the CORDEX-CORE projections)
cavapy grants access to critical climate variables, enabling integration into diverse modeling frameworks. The variables currently available include:
- Daily Maximum Temperature (tasmax): °C
- Daily Minimum Temperature (tasmin): °C
- Daily Precipitation (pr): mm
- Daily Relative Humidity (hurs): %
- Daily Wind Speed (sfcWind): 2 m level, m/s
- Daily Solar Radiation (rsds): W/m²
cavapy can be installed with pip.
conda create -n test "python>=3.11"
conda activate test
pip install cavapy
The get_climate_data function performs automatically:
- Data retrieval in parallel
- Unit conversion
- Convert into a Gregorian calendar (CORDEX-CORE models do not have a full 365 days calendar) through linear interpolation
- Bias correction using the empirical quantile mapping (optional)
Depending on the interest, downloading climate data can be done in a few different ways. Note that GCM stands for General Circulation Model while RCM stands for Regional Climate Model. As the climate data comes from the CORDEX-CORE initiative, users can choose between 3 different GCMs downscaled with two RCMs. In total, there are six simulations for any given domain (except for CAS-22 where only three are available). Since bias-correction requires both the historical run of the CORDEX model and the observational dataset (in this case ERA5), even when the historical argument is set to False, the historical run will be used for learning the bias correction factor.
Option 1: Use pre-bias-corrected ISIMIP data (Recommended)
Example with AFR-22 domain:
import cavapy
# Get ISIMIP bias-corrected data (no additional bias correction needed)
Togo_climate_data = cavapy.get_climate_data(
country="Togo",
variables=["tasmax", "pr"],
cordex_domain="AFR-22",
rcp="rcp26",
gcm="MPI",
rcm="REMO",
years_up_to=2030,
dataset="CORDEX-CORE-BC" # Pre-bias-corrected with ISIMIP methodology
)
Example with WAS-22 domain:
import cavapy
# Get ISIMIP bias-corrected data for West Asia
Pakistan_climate_data = cavapy.get_climate_data(
country="Pakistan",
variables=["tasmax", "pr"],
cordex_domain="WAS-22",
rcp="rcp85",
gcm="MPI",
rcm="REMO",
years_up_to=2030,
dataset="CORDEX-CORE-BC" # Pre-bias-corrected with ISIMIP methodology
)
Option 2: Apply bias correction on-the-fly (Original method)
import cavapy
# Apply empirical quantile mapping bias correction
Togo_climate_data = cavapy.get_climate_data(
country="Togo",
variables=["tasmax", "pr"],
cordex_domain="AFR-22",
rcp="rcp26",
gcm="MPI",
rcm="REMO",
years_up_to=2030,
bias_correction=True,
dataset="CORDEX-CORE" # Original data with on-the-fly bias correction
)
import cavapy
# Get original CORDEX-CORE data without any bias correction
Togo_climate_data = cavapy.get_climate_data(
country="Togo",
variables=["tasmax", "pr"],
cordex_domain="AFR-22",
rcp="rcp26",
gcm="MPI",
rcm="REMO",
years_up_to=2030,
dataset="CORDEX-CORE" # Original data, no bias correction
)
This is useful when assessing changes from the historical period.
With ISIMIP bias-corrected data:
Example with AFR-22 domain:
import cavapy
# Get both historical and projection data (ISIMIP bias-corrected)
Togo_climate_data = cavapy.get_climate_data(
country="Togo",
variables=["tasmax", "pr"],
cordex_domain="AFR-22",
rcp="rcp26",
gcm="MPI",
rcm="REMO",
years_up_to=2030,
historical=True,
dataset="CORDEX-CORE-BC" # Pre-bias-corrected data
)
Example with WAS-22 domain:
import cavapy
# Get both historical and projection data for West Asia (ISIMIP bias-corrected)
Afghanistan_climate_data = cavapy.get_climate_data(
country="Afghanistan",
variables=["tasmax", "pr"],
cordex_domain="WAS-22",
rcp="rcp85",
gcm="NCC",
rcm="REMO",
years_up_to=2030,
historical=True,
dataset="CORDEX-CORE-BC" # Pre-bias-corrected data
)
With on-the-fly bias correction:
import cavapy
# Apply bias correction to both historical and projection data
Togo_climate_data = cavapy.get_climate_data(
country="Togo",
variables=["tasmax", "pr"],
cordex_domain="AFR-22",
rcp="rcp26",
gcm="MPI",
rcm="REMO",
years_up_to=2030,
bias_correction=True,
historical=True,
dataset="CORDEX-CORE"
)
import cavapy
Togo_climate_data = cavapy.get_climate_data(country="Togo", variables=["tasmax", "pr"], obs=True, years_obs=range(1980,2019))
cavapy now includes built-in plotting functions to easily visualize your climate data as maps and time series. The plotting functions work seamlessly with the data returned by get_climate_data(). However, if your main goal is visualisation, we strongly encourage you to check out CAVAanalytics, our R package.
plot_spatial_map(): Create spatial maps of climate variablesplot_time_series(): Generate time series plots with trend analysis
import cavapy
# Get climate data
data = cavapy.get_climate_data(country="Togo", obs=True, years_obs=range(1990, 2011))
# Plot mean temperature map for a specific period
fig = cavapy.plot_spatial_map(
data['tasmax'],
time_period=(2000, 2010),
title="Mean Max Temperature 2000-2010",
cmap="Reds"
)Example spatial map showing mean maximum temperature in Togo (2000-2010)
# Plot precipitation time series with trend analysis
fig_precip = cavapy.plot_time_series(
data['pr'],
title="Precipitation Time Series - Togo (1990-2000)",
trend_line=True,
ylabel="Annual Precipitation (mm)",
aggregation="sum",
figsize=(12, 6)
)Example time series plot showing precipitation trends in Togo (1990-2011) with trend line