Bottom up isoprene emissions (open loop)

Produced by

NILU - 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

2018-2022

Model

MEGANv3 coupled with the SURFEX land surface model

File format

Zarr

Download the data

Time-chunked

https://data.seedsproject.eu/seeds_isop_cnrm-nilu_20180101-20221231_surfex-megan_ol_v1/slices.zarr

Geo-chunked

https://data.seedsproject.eu/seeds_isop_cnrm-nilu_20180101-20221231_surfex-megan_ol_v1/dices.zarr

How to download the data?

Variables

Click on a variable to see it on the viewer.

Isoprene emissions

emiss

Description

BVOC gas emissions from vegetation represent a hugely important contributor to the total emission of volatile organic compounds at the global scale. BVOCs in turn play a key role in tropospheric chemistry and impact both the formation of ozone photochemical smog and secondary organic aerosols, which both impact air quality. MEGAN is an open-sourced software package that has been adapted by researchers at NILU to operate with SURFEX model outputs and meteorological data from ECMWF. The MEGAN software calculates emissions of a wide range of BVOCs emitted by plants and aggregates these chemical components into representative chemical species defined in an external chemical mechanism. In our case, this done specifically is for the BVOCs defined (see Table 2 for full list) in the Regional Atmospheric Chemistry Mechanism (Stockwell et al., 1997) that is used in the MOCAGE (Guth et al., 2016) chemical transport model. MOCAGE is being used as the test model the SEEDS project since it is one of the CAMS regional atmospheric chemistry models.

Citation

NILU - CNRM. (2024). Bottom up isoprene emissions (open loop) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10731728

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_isop_cnrm-nilu_20180101-20221231_surfex-megan_ol_v1/slices.zarr")
)

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

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