Development of a parametrised atmospheric NO_x chemistry scheme to help quantify fossil fuel CO₂ emission estimates

Abstract. Success of the Paris Agreement relies on rapid reductions in fossil fuel CO₂ (ffCO₂) emissions. Atmospheric data can verify the ffCO₂ reductions pledged by nations in their nationally determined contributions. However, estimating ffCO₂ from atmospheric CO₂ is challenging due to natural fluxes and varying backgrounds. One approach is to combine with nitrogen oxides (NO_x = NO + NO₂), which are co-emitted with CO₂ during combustion. A key challenge in using NO_x to estimate ffCO₂ is the computational cost of modelling atmospheric photochemistry. Additionally, the NO₂:NO column ratio must be well understood to convert model NO_x columns to NO₂ columns for comparison with satellite data. We use random forest regression to parameterise NO_x chemistry, relying only on meteorological parameters and NO_x concentration. The regression is trained on outputs from a nested GEOS (Goddard Earth Observing System)-Chem model simulation for mainland Europe in 2019. We develop a monthly NO_x chemistry parameterisation that performs well when tested on perturbed emission runs (R² > 0.95) and on unseen meteorology for 2021 (R² > 0.79). We also parameterise the NO₂:NO ratio (R² > 0.99 on perturbed outputs, R² > 0.92 on unseen meteorology). Additionally, we present an alternative method to predict NO_x rates by scaling baseline NO_x rates with changes in NO_x concentration (R² = 1.0 on perturbed outputs). Our models reproduce NO₂ columns with minimal deviation from full-chemistry models, with reconstruction error smaller than the TROPOspheric Monitoring Instrument (TROPOMI) precision in over 99.9 % of cases, supporting robust ffCO₂ inversion efforts. These results provide a robust framework for accurately estimating fossil fuel CO₂ emissions from atmospheric data, enabling more reliable monitoring and verification of global emissions reductions.