Flux maps data v2025: Methane fluxes 2020-2024
Flux maps data v2024: Methane fluxes 2016-2023


for more information or in case of use in publications please contact Christine Groot Zwaaftink (cgz@nilu.no)

Observations

Methane observations shown in time series were retrieved from the ICOS data portal and EBAS. We thank the PIs for providing the data.

Prior fluxes v2025

Total prior fluxes were based on flux estimates from different sources:
Natural fluxes were simulated with the Community Land Model based on ERA5/ERA5-Land input data by I. Muzic, R. B. Skeie, J.I. Korsbakken and G. Myhre at CICERO.
Lawrence, D. M. et al. The community land model version 5: description of new features, benchmarking, and impact of forcing uncertainty. J. Adv. Model. Earth Syst. https://doi.org/10.1029/2018MS001583 (2019).

Anthropogenic fluxes were retrieved from EDGAR_2025_GHG.
Crippa, M., Guizzardi, D., Pagani, F., Banja, M., Muntean, M. et al., GHG emissions of all world countries - 2025 Report, Publications Office of the European Union, Luxembourg, 2025, doi:10.2760/9816914, JRC143227.

Ocean: Tsuruta et al. (2017): Global methane emission estimates for 2000-2012 from Carbon-Tracker Europe-CH4 v1.0, Geosci. Model Dev., 10, 1261-1289,https://doi.org/10.5194/gmd-10-1261-2017.

Fires: CAMS global biomass burning emissions based on fire radiative power, GFAS.

Geological: Etiope, G., Ciotoli, G., Schwietzke, S., and Schoell, M.(2019): Gridded maps of geological methane emissions and their isotopic signature, Earth Syst. Sci. Data, 11, 1-22, https://doi.org/10.5194/essd-11-1-2019.

Prior fluxes v2024

Total prior fluxes were based on flux estimates from different sources:
Natural fluxes were simulated with the Community Land Model by I. Muzic, R. B. Skeie, J.I. Korsbakken and G. Myhre at CICERO.
Lawrence, D. M. et al. The community land model version 5: description of new features, benchmarking, and impact of forcing uncertainty. J. Adv. Model. Earth Syst. https://doi.org/10.1029/2018MS001583 (2019).
Up to year 2016 we included fluxes from LPX-Bern DYPTOP.
Stocker, B. D., Spahni, R., and Joos, F. (2014): DYPTOP: a cost-efficient TOPMODEL implementation to simulate sub-grid spatio-temporal dynamics of global wetlands and peatlands, Geosci. Model Dev., 7, 3089-3110, https://doi.org/10.5194/gmd-7-3089-2014.

Anthropogenic fluxes were retrieved from EDGARv4.3, EDGARv5 and EDGARv7.
Janssens-Maenhout et al. (2019): EDGAR v4.3.2 Global Atlas of the three major greenhouse gas emissions for the period 1970-2012, Earth Syst. Sci. Data, 11, 959-1002, https://doi.org/10.5194/essd-11-959-2019.
Crippa et al. (2019): Fossil CO2 and GHG emissions of all world countries - 2019 Report, EUR 29849 EN, Publications Office of the European Union, Luxembourg, ISBN 978-92-76-11100-9, doi:10.2760/687800, JRC117610.
IEA-EDGAR CO2, a component of the EDGAR (Emissions Database for Global Atmospheric Research) Community GHG database version 7.0 (2022) including or based on data from IEA (2021) Greenhouse Gas Emissions from Energy, www.iea.org/data-and-statistics, as modified by the Joint Research Centre.

Ocean: Tsuruta et al. (2017): Global methane emission estimates for 2000-2012 from Carbon-Tracker Europe-CH4 v1.0, Geosci. Model Dev., 10, 1261-1289,https://doi.org/10.5194/gmd-10-1261-2017.

Fires: GFED4, Giglio, L., Randerson, J. T., and van der Werf, G. R. (2013):Analysis of daily, monthly, and annual burned area using the fourth-generation global fire emissions database (GFED4), J. Geophys. Res.-Biogeosci., 118, 317-328, https://doi.org/10.1002/jgrg.20042.

Geological: Etiope, G., Ciotoli, G., Schwietzke, S., and Schoell, M.(2019): Gridded maps of geological methane emissions and their isotopic signature, Earth Syst. Sci. Data, 11, 1-22, https://doi.org/10.5194/essd-11-1-2019.

Models

Atmospheric tansport model: Pisso et al. (2019).: The Lagrangian particle dispersion model FLEXPART version 10.4, Geosci. Model Dev., 12, 4955-4997,https://doi.org/10.5194/gmd-12-4955-2019.

Background fields: Groot Zwaaftink et al. (2018), Three-dimensional methane distribution simulated with FLEXPART 8-CTM-1.1 constrained with observation data, Geosci. Model Dev., 11, 4469-4487, doi.org/10.5194/gmd-2018-117.

Inverse modelling framework: Thompson, R. L. and Stohl, A.: FLEXINVERT: an atmospheric Bayesian inversion framework for determining surface fluxes of trace species using an optimized grid, Geosci. Model Dev., 7, 2223-2242, https://doi.org/10.5194/gmd-7-2223-2014, 2014.