Inferring European Fossil Fuel CO₂ Emissions using TROPOMI NO₂ Data and Sector-Based NO_x:CO₂ Emission Ratios

Abstract. Accurate monitoring of fossil fuel CO₂ (ffCO₂) emissions is essential for tracking climate mitigation, yet natural carbon-cycle fluxes often obscure human-induced signals in atmospheric observations. This study presents a satellite-driven data assimilation framework that uses nitrogen oxides (NO_x = NO + NO₂) − short-lived trace gases co-emitted with CO₂ − to estimate ffCO₂ emissions. We estimate European NO_x emissions for 2021 by assimilating TROPOMI NO₂ observations into an Ensemble Kalman Filter (EnKF) framework, optimised within the GEOS-Chem atmospheric transport model. We use a computationally efficient offline treatment of NO_x chemistry, enabling large-ensemble inversions while retaining sensitivity to changes in photochemistry. Assimilating these data leads to a systematic reduction in the state vector uncertainty, with a mean ensemble-based error reduction of 4.2–5.7 %, and an overall improvement in model agreement with observations that corresponds to an annual correlation increase, ∆r =0.12. By leveraging sector-specific NO_x:CO₂ emission ratios, we translate our posterior NO_x flux estimates into improved ffCO₂ estimates that capture enhanced seasonal variability. Our inferred ffCO₂ emissions exhibit elevated values in autumn and winter, spatially concentrated over major source regions and consistent with surface temperature variability. Independent evaluation against in situ measurements confirms significant improvements in mean error statistics. While OCO-2 CO₂ column data remain dominated by biogenic signals, our NO₂-driven approach successfully isolates the fossil fuel component. This study demonstrates the potential of ensemble data assimilation and reduced-complexity chemistry to provide physically consistent constraints on European ffCO₂ estimates, establishing a vital foundation for future joint NO₂–CO₂ inversion systems.