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

Abstract. Atmospheric inversions are widely used to evaluate and improve inventories of methane (CH₄) emissions on scales ranging from global to national and beyond, 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.

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) 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, accounting 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 concentrations. 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 subregion 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 trends in Western and Southern Europe. Our results highlight the importance of improving details in the inversion setup, such as the treatment of lateral boundary conditions and the model representation of measurement sites, to narrow the uncertainty ranges further.