Analytical regional inversion system for CO2 fluxes in Poland – first results from CoCO2 project

In order to accurately and precisely estimate anthropogenic greenhouse gas (GHG) emissions at different spatial and temporal scales, independent tools based on atmospheric observations are required as a necessary source of information for mitigation climate change efforts to be successful. Bayesian inversion systems utilizing state-of-the-art atmospheric transport models constitute a key element of anthropogenic emissions monitoring and verification systems, allowing for mathematically-grounded method of assessing emissions based on observed mole fractions. Poland, the fifth largest economy in the EU, is simultaneously the fourth largest emitter of GHGs in terms of CO2 equivalent, owing primarily to only slowly decreasing reliance on coal for power generation.  Here, we present first results of the developmental inversion framework consisting of the WRF-GHG model run at 5 km spatial resolution over Central Europe, coupled with an analytical system in order to explain total emissions of CO2 for selected months (February and July) over Poland and Germany, the largest emitter of CO2 in Europe, for comparison. We also compare results for both 2018 and 2021 in an attempt to capture changes in emission patterns following the implementation of the various policies both before and after Paris Agreement. We also focus on the ability of the inversion system to capture changes in biogenic and anthropogenic emissions and address challenges stemming from the limited ground-based observation network in Poland. Furthermore, we also discuss the ability of the system to distinguish emissions on the national, voivodeship (admin level 1) and city scale, thanks to the additional high-resolution simulations and in-situ observations in the city of Kraków.   The presented work was funded by the CoCO2 project, which has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 958927 and the "Excellence Initiative - Research University" programme at AGH University of Kraków. We also gratefully acknowledge Polish high-performance computing infrastructure PLGrid (HPC Centres: ACK Cyfronet AGH) for providing computer facilities and support within computational grant no. PLG/2022/015860.