Stomatal and in-canopy resistances (open loop)

Produced by

CNRM

Data type

Gridded

Horizontal coverage

Europe (west=25.0° W, east=45.0° E, south=30.0° N, north=70.0° N)

Horizontal resolution

0.1° x 0.1°

Temporal resolution

Hourly

Temporal coverage

2018-2020

Model

SURFEX land surface model

File format

Zarr

Download the data

Time-chunked

https://data.seedsproject.eu/seeds_r_cnrm_20180101-20201231_surfex_ol_v1/slices.zarr

Geo-chunked

https://data.seedsproject.eu/seeds_r_cnrm_20180101-20201231_surfex_ol_v1/dices.zarr

How to download the data?

Variables

Click on a variable to see it on the viewer.

Stomatal resistance (spatially averaged)

r_stom_isba

In-canopy resistance (spatially averaged)

r_inc_isba

Description

Dry deposition of gases to the land surface is an important sink for reactive trace gases in the troposphere. Understanding dry deposition is of importance for understanding air quality and its impacts on human health and vegetation. SURFEX calculates dry deposition using a software scheme that was recently developed in the SEEDS project (deliverable D3.4). The dry deposition software scheme calculates dry deposition velocities, which can be used as an input to calculate dry deposition fluxes in an air quality model. The dry deposition velocities are provided for ozone, sulphur dioxide, and reactive nitrogen compounds (NOx, ammonia, nitric acid, organic nitrates, and per-nitric acid). In addition to the dry deposition velocities, SURFEX calculates various diagnostics to help understand the deposition of pollutants into the vegetation leaf canopy (in-canopy resistance) and into their stomata (stomatal resistance). All of the dry deposition data are output on an hourly time resolution.

Download instructions

This sample Python code shows how to download this dataset, create subsets of it and save them as NetCDF, using the Xarray library.

import xarray as xr
import fsspec

# Open remote dataset
ds = xr.open_zarr(
  store=fsspec.get_mapper("https://data.seedsproject.eu/seeds_r_cnrm_20180101-20201231_surfex_ol_v1/slices.zarr")
)

# Subset a time range, save as NetCDF
time_subset = ds.r_stom_isba.sel(time=slice('2019-06-01', '2019-07-01'))
time_subset.to_netcdf('./r_stom_isba_time_subset.nc')

# Subset a time and geographical range, save as NetCDF
time_geo_subset = ds.r_stom_isba.sel(time=slice('2019-06-01', '2019-07-01'), latitude=slice(40, 43), longitude=slice(0,3.5))
time_geo_subset.to_netcdf('./r_stom_isba_time_geo_subset.nc')