Top down isoprene emissions

Produced by

BIRA-IASB

Data type

Gridded

Horizontal coverage

Europe (west=11.95° W, east=44.95° E, south=34.05° N, north=71.95° N)

Horizontal resolution

0.1° x 0.1°

Temporal resolution

Daily

Temporal coverage

2018-2022

Model

MAGRITTEv1.1

File format

Zarr

Download the data

Time-chunked

https://data.seedsproject.eu/seeds_top-down-isoprene-emissions_bira-iasb_20180101-20221231_magritte_v2/slices.zarr

Geo-chunked

https://data.seedsproject.eu/seeds_top-down-isoprene-emissions_bira-iasb_20180101-20221231_magritte_v2/dices.zarr

How to download the data?

Variables

Click on a variable to see it on the viewer.

daily isoprene emission fluxes

isoprene_flux

Description

The S5p-based top-down biogenic emission inventory is derived based on an inversion with the MAGRITTEv1.1 chemistry transport model (Müller et al. 2019) and its adjoint, and are constrained by TROPOMI HCHO vertical columns.

Citation

BIRA-IASB. (2024). Top down isoprene emissions [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10731785

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_top-down-isoprene-emissions_bira-iasb_20180101-20221231_magritte_v2/slices.zarr")
)

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

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