An inter-comparison of inverse models for estimating European CH4 emissions

Atmospheric inversions are widely used to evaluate and improve inventories of methane (CH4) emis-
sions across scales from global to local, combining observations with atmospheric transport models. This study
uses the dense network of in situ stations of the Integrated Carbon Observation System (ICOS) to explore how
well in situ data can constrain European CH4 emissions. Following the concept of inter-comparison studies of
the atmospheric tracer transport model inter-comparison Project (TransCom), a CH4 inverse inter-comparison
modeling study has been performed, focusing on Europe for the period 2006–2018. The aim is to investigate
the capability of inverse models to deliver consistent flux estimates at the national scale and evaluate trends in
emission inventories, using a detailed dataset of CH4 emissions described and presented here for first time.
Study participants were asked to perform inverse modelling computations using a common database of a priori
CH4 emissions and in-situ observations as specified in a protocol. The participants submitted their best estimates
of CH4 emissions for the 27 European Union (EU-27) member states, the United Kingdom (UK), Switzerland,
and Norway. Results were collected from 9 different inverse modelling systems, using 7 different global and
regional transport models. The range of outcomes allows us to assess posterior emission uncertainty, account-
ing for transport model uncertainty and inversion design decisions, including a priori emission and model-data
mismatch uncertainty.
This paper presents inversion results covering 15 years, that are used to investigate the seasonality and trends
of CH4 emissions. The different inversion systems show a range of a posteriori emission adjustments, pointing to factors that should receive further attention in the design of inversions such as optimising background mole
fractions. Most inverse models increase the seasonal cycle amplitude, by up to 400 Gg month−1, with the largest
adjustments to the a priori emissions in Western and Eastern Europe. This might be due to underestimation
of emissions from wetlands during summer or the importance of seasonality in other microbial sources, such
as landfills and waste water treatment plants. In Northern Europe, absolute flux adjustments are comparatively
small, which could imply that the emission magnitude is relatively well captured by the a priori, though the lower
station density could contribute also.
Across Europe, the inverse models yield a similar decreasing trend in CH4 emissions compared to the a
priori emissions (−12.3 % instead of −9.1 %) from 2006 to 2018. While both the a priori and the a posteriori
trend for the EU-27 are statistically significant from zero, their difference is not. On a subregional scale, the
differences between a posteriori and a priori trends are more statistically significant over regions with more
in-situ measurement sites, such as over Western and Southern Europe.
Uncertainties in the a priori anthropogenic emissions, such as in the agriculture sector (cows, manure), or waste
sector (microbial CH4 emissions), but also in the a priori natural emissions, e.g. wetlands, might be responsible
for the discrepancies between the a priori and a posteriori emission shift in the trends in Western, Eastern and
Southern Europe.
Our results highlight the importance of improving the inversion setup, such as the treatment of lateral boundary
conditions and the model representation of measurement sites, to narrow the uncertainty ranges further. The refer-
enced dataset related to the analysis and figures are available at the ICOS portal: https://doi.org/10.18160/KZ63-
2NDJ (Ioannidis et al., 2025).