Top-down CO emission estimates using TROPOMI CO data in the TM5-4DVAR (r1258) inverse modeling suit

Abstract. Carbon monoxide in the atmosphere adversely affects air quality and climate, making knowledge about its sources crucial. However, current global bottom-up emission estimates retain significant uncertainties. In this study, we attempt to reduce these uncertainties by optimizing emission estimates for the second half of the year 2018 on a global scale with a focus on the Northern Hemisphere through the top-down approach of inverse modeling. Specifically, we introduce observations from the TROPOspheric Monitoring Instrument (TROPOMI) into the TM5-4DVAR model. The emissions are further constrained using NOAA surface flask measurements. We conducted six experiments to investigate the impact of data use in our inversions, varying the a priori emissions and observational datasets. Notably, the inversion driven by satellite observations alone agrees with flask measurements south of 55° N almost as well as the inversions that included those measurements. This indicates that our method could be suitable for inversions based purely on satellite observations. Compared to the bottom-up estimates, all experiments result in strong (up to 75 %) broad-scale emission reductions in China and India throughout the entire inversion period. Part of the reduction in China can be attributed to policy and technology changes (e.g., coal to gas). Additionally, the OH climatology used to simulate chemical loss appears to be underestimated in that region, which also skews the inversions towards lower emissions. In the experiments that include the surface flask measurements, we find strong localized emission increments over Europe and the Sahara, which are traced back to limitations of the model in reproducing point measurements on mountain tops.