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In recent years, methane (CH4) has attracted increasing scientific attention as the second most abundant anthropogenic greenhouse gas (GHG) in the atmosphere. Due to the high reduction potential and the relatively short atmospheric lifetime of around 9 years, mitigation measures can become effective within a relatively short period of time. However, the current estimates of CH4 fluxes from emission inventories are still subject to uncertainties at both global and regional scale.An effort to reduce uncertainties from those bottom-up flux estimates is given by inverse modelling, which provides a robust tool to verify GHG emissions by combining GHG observations as well as atmospheric transport modelling and statistical optimization.In this study, we use an inverse modelling approach to estimate CH4 fluxes at European scale for the year 2022. Additionally, we use the European in-situ observation network to explore the feasibility of reducing uncertainties in CH4 fluxes in Austria, a European country with a limited availability of stationary observations. This work is part of the Austrian ASAP18 flagship project GHG-KIT: Keep it traceable.Hereby, the inverse modelling tool FLEXINVERT is used, which is based on the backward simulations of the Lagrangian particle dispersion model FLEXPART (FLEXible PARTicle). In particular, we investigate to what extent prolonged backward trajectories of 50 to 100 days contribute to better constrain the CH4 fluxes. In an attempt to estimate background concentrations as accurately as possible, we use global CH4 concentration fields obtained with the chemical transport model FLEXPART (CTM).
Wittig et al. (Fri,) studied this question.