Climate data download from the COPERNICUS Climate data store and CDS toolbox
The atmospheric variables from regional climate models or observations are typically provided in spatial and temporal resolutions that are different from the requirements of the Virtual Ecosystem. This document describes how to download climate data from the Copernicus Climate Data Store (CDS) and basic pre-processing options using the CDS toolbox. You need to create a user account to access all data and functionalities.
Note
At present, the pre-processing does not include scaling or topographic adjustment.
Climate input variables
The abiotic module of the Virtual Ecosystem requires the following climate input variables (or derivatives) at each time step (default: monthly means):
Air temperature (typically 2m; mean, minimum, and maximum)
Air humidity (typically 2m; relative or specific humidity)
Air pressure (typically mean sea level or surface pressure)
Wind speed (typically 10m)
Precipitation
and optionally:
atmospheric \(\ce{CO_{2}}\) concentration
soil temperature
soil moisture
Recommended data sets
We recommend the following data sets to force the Virtual Ecosystem microclimate simulations:
ERA5 / ERA5-Land
ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 4 to 7 decades. This reanalysis dataset combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. The data is available in hourly and monthly averaged time steps at a spatial resolution is in 0.25 x 0.25 deg resolution. The data set starts in 1950 and is updated regularely.
The full documentation and download link can be accessed here for hourly data and here for monthly data
ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5 (0.1 x 0.1 deg).
The full documentation and download link can be accessed here for hourly data and here for monthly data
Example code to manipulate downloaded ERA5-Land data as used in the
ve_run
example is available here.WFDE5
This global dataset provides bias-corrected reconstruction of near-surface meteorological variables derived from the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalyses (ERA5). The output is available in hourly and daily time steps for the period 1979-2019 in 0.5 x 0.5 deg resolution.
The full documentation and download link can be accessed here .
CORDEX-SEA
This data set was created with regional climate models (RCM) as part of the Coordinated Regional Climate Downscaling Experiment (CORDEX). The spatial resolution is 0.22 x 0.22 deg, the spatial extent is 15°S to 27°N and 89 to 146°E, the temporal resolution depends on the selected period:
historical data (1950-2005) is available in hourly time step
scenario data (2006-2100; RCP 2.6, 4.5 and 8.5) is available in daily time step
The full documentation and download link can be accessed here.
Atmospheric \(\ce{CO_{2}}\)
Observed global \(\ce{CO_{2}}\) levels (Mauna Loa, NOAA/GML) are available in monthly or annual resolution (1958 - present) here . Monthly data derived from satellite observation (2002 - present) is available here . Alternatively, reconstructed gridded monthly \(\ce{CO_{2}}\) data for the historical period (1953 - 2013) and future CMIP6 scenarios (2015 - 2150) can be downloaded here (Cheng, Wei et al., 2021).
Step-by-step example
Follow one of the links above to access overview information about the data set. You find a detailed documentation of the data set in the ‘Documentation’ section. To select data, navigate to the tab ‘Download Data’.
Selection
This is an example of a selection of tabs to download historical ‘2m air temperature’ from the CORDEX-SEA (you can download multiple variables and years in one request):
Domain (South-East Asia),
Experiment (here: ‘historical’, RCPs available)
Horizontal resolution (‘0.22 degree x 0.22 degree’)
Temporal resolution (‘daily mean’)
Variables (here: ‘2m_air_temperature’)
Global climate model (here: ‘mohc_hadgem2_es’)
Regional climate model (here: ‘gerics_remo2015’)
Ensemble member (r1i1p1)
Start year and End year (here: 2001-2005)
Once you selected the data, you can either download the dataset for further processing or click on ‘show Toolbox request’ at the bottom of the page, copy the code, and open the CDS toolbox editor.
The code to manipulate climate data as used in the ve_run
example is available
here.
Toolbox template CORDEX-SEA
The template below describes how to request a data set, reproject the data on a regular grid (note that the projection name is not changed!), select the area of interest, calculate the monthly means, and download the product. For illustration, the routine also plots the mean value. Adjust the ‘data’ lines to match your data request. You find the full documentation of the CDS toolbox here.
# EXAMPLE CODE to preprocess CORDEX-SEA with CDS toolbox
import cdstoolbox as ct
@ct.application(title='Download data')
@ct.output.download()
@ct.output.figure()
def download_application():
data =ct.catalogue.retrieve(
'projections-cordex-domains-single-levels',
{
'domain': 'south_east_asia',
'experiment': 'historical',
'horizontal_resolution': '0_22_degree_x_0_22_degree',
'temporal_resolution': 'daily_mean',
'variable': '2m_air_temperature',
'gcm_model': 'mohc_hadgem2_es',
'rcm_model': 'gerics_remo2015',
'ensemble_member': 'r1i1p1',
'start_year': '2001',
'end_year': '2005',
}
)
regular = ct.geo.make_regular(data, xref='rlon', yref='rlat')
sel_extent = ct.cube.select(regular, extent=[116., 118, 4., 6.])
monthly_mean = ct.climate.monthly_mean(sel_extent)
average = ct.cube.average(monthly_mean, dim='time')
fig = ct.cdsplot.geomap(average)
return monthly_mean, fig
The data handling for simulations is managed by the data
module and the Data
class, which provides the
data loading and storage functions for the Virtual Ecosystem. The data system is
extendable to provide support for different file formats and axis validation but that is
beyond the scope of this document.