CH₄ emissions from Northern Europe wetlands: compared data assimilation approaches

Abstract. Atmospheric inverse modelling and ecosystem data assimilation are two complementary approaches to estimate CH₄ emissions. The inverse approach infers emission estimates from observed atmospheric CH₄ mixing ratio, which provide robust large scale constraints on total methane emissions, but with poor spatial and process resolution. On the other hand, in the ecosystem data assimilation approach, the fit of an ecosystem model (e.g. a Dynamic Global Vegetation Model, DGVM) to eddy-covariance (EC) flux measurements is used to optimize model parameters, leading to more realistic emission estimates.

Coupled data assimilation frameworks capable of assimilating both atmospheric and ecosystem observations have been shown to work for estimating CO₂ emissions (e.g. Rayner et al. (2005)), however ecosystem data assimilation for estimation CH₄ emissions is relatively new. Kallingal et al. (2024) developed the GRaB-AM data assimilation system, which performs a parameter optimization of the LPJ-GUESS against eddy-covariance estimation of CH₄ emissions. The optimization improves the fit to EC data, but the validity of the estimate at large scale remained to be tested.

In this study, we used the LUMIA regional atmospheric inversion system (Monteil and Scholze, 2021) to confront wetland emissions from the GRaB-AM approach to atmospheric CH₄ measurements in Europe. We then perform inversions using the information from GRaB-AM as prior. This let us infer a refined estimate for wetland emissions in Nordic Europe, and to explore the potential for a fully coupled data assimilation framework.