eCLM atmospheric forcing based on ERA5

eCLM atmospheric forcing based on ERA5#

Basis: Mainly CDO commands.

By sourcing the provided environment file

source jsc.2024_Intel.sh

Creation of forcing data from ERA5#

A possible source of atmospheric forcing for CLM (eCLM, CLM5, CLM3.5) is ERA5. It is safer to extract the lowermost level of temperature, humidity and wind of ERA5 instead of taking mixed 2m-values and 10m values. This internal issue provides some details. The download_ERA5_input.py can be adapted to download another set of quantities.

The folder mkforcing/ contains three scripts that assist the ERA5 retrieval.

Note: This worfklow is not fully tested.

Download of ERA5 data#

download_ERA5_input.py contains a prepared retrieval for the cdsapi python module. The script requires that cdsapi is installed with a user specific key (API access token).

More information about the installation and access can be found here or alternatively here.

Usage: Either directly: python download_ERA5_input.py <year> <month> <output_directory> Or using the wrapper script: ./download_ERA5_input_wrapper.sh after changing dates and output directory in the Settings section inside this wrapper script.

Non-JSC users should adapt the download script to include temperature, specific humidity and horizontal wind speed.

Preparation of ERA5 data I: Lowermost model level variables (10m altitude)#

extract_ERA5_meteocloud.sh prepares ERA5 variables form the lowermost model level (relies on JSC-local files).

Uses level 137, for more information see https://confluence.ecmwf.int/display/UDOC/L137+model+level+definitions

extract_ERA5_meteocloud.sh uses JSC-local grib-input files in /p/data1/slmet/met_data/ecmwf/era5/grib/. Further information: /p/data1/slmet/met_data/ecmwf/README.md.

extract_ERA5_meteocloud.sh provides NetCDF files meteocloud_YYYY_MM.nc with lowermost model level atmospheric variables. The variables from these NetCDF files are used by prepare_ERA5_input.sh in the following ERA5 preparation step.

Usage: Running the wrapper job sbatch extract_ERA5_meteocloud_wrapper.job after adapting year and month loops according to needed dates.

Preparation of ERA5 data II: Remapping, Data merging, CLM3.5#

The prepare_ERA5_input.sh script prepares ERA5 data by remapping, changing variable names, and modifying units. The script performs three main steps:

  1. Remapping - Regrid ERA5 data to the target domain

  2. Merging - Combine different ERA5 datasets

  3. CLM3.5 specific preparation - Apply CLM3.5-specific formatting

Remapping Setup#

When using remapping, the script requires remapping weights and a grid definition file for the target domain. These can be created either automatically by the script or manually beforehand.

Option 1: Automatic creation (recommended)

Specify the domain file and enable automatic generation:

sh prepare_ERA5_input.sh iyear=<year> imonth=<month> lwgtdis=true lgriddes=true domainfile=<eclm_domainfile.nc>

Option 2: Manual creation

Create the remapping weights and grid definition file manually:

cdo gendis,<eclm_domainfile.nc> <era5caf_yyyy_mm.nc> <wgtdis_era5caf_to_domain.nc>
cdo gendis,<eclm_domainfile.nc> <era5meteo_yyyy_mm.nc> <wgtdis_era5meteo_to_domain.nc>
cdo griddes <eclm_domainfile.nc> > <domain_griddef.txt>

Then specify the created files as options:

sh prepare_ERA5_input.sh iyear=<year> imonth=<month> wgtcaf=<wgtcaf> wgtmeteo=<wgtmeteo> griddesfile=<griddesfile>

For additional options, see the script documentation.

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