Estimation of CO₂ fluxes in the cities of Zurich and Paris using the ICON-ART CTDAS inverse modelling framework

Abstract. Observation-based estimation of urban CO₂ emissions can help cities track their pathway to net zero emissions, a goal many cities worldwide have adopted. While mesoscale atmospheric transport models are an effective component in inversion systems estimating country-level emissions, their use in urban-scale inversions presents a significant challenge. Here, we present one-year flux inversion results with the mesoscale ICON-ART atmospheric transport model for two cities with contrasting size and topographic complexity: Zurich and Paris. Inversions were performed with an ensemble square root filter, assimilating observations from a dense rooftop CO₂ sensor network in Zurich and from a tall tower network in Paris. The inversion framework optimized gridded anthropogenic and biospheric fluxes, along with background mole fractions from eight inflow regions. Prior anthropogenic emissions were based on detailed inventories provided by local authorities. In Zurich, the inversion resulted in a posterior annual anthropogenic emission of 1012.3 ± 38.8 kt yr^-1 representing approximately a 30 % reduction compared to the prior, with the most significant decreases during winter periods of elevated ambient temperatures. In contrast, the posterior fluxes in Paris remained close to the prior, with an annual emission of 3580.0 ± 101.9 kt yr^-1, 7 % higher than the prior. This comparison highlights the influence of city-specific factors—such as topography, city size, and observational network—on the inversion system performance. Furthermore, our findings demonstrate the potential of mesoscale models to refine urban emission estimates, offering valuable insights for policymakers and researchers working to improve emission inventories and advance urban climate strategies.