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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 (NOx = NO + NO2), which are co-emitted with CO2 during combustion. A key challenge in using NOx to estimate ffCO2 is the computational cost of modelling atmospheric photochemistry. Additionally, the NO2 : NO column ratio must be well understood to convert model NOx columns to NO2 columns for comparison with satellite data. We use random forest regression to parameterise NOx chemistry, relying only on meteorological parameters and NOx 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 NOx 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 NOx rates by scaling baseline NOx rates with changes in NOx 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.
Schooling et al. (Mon,) studied this question.