Global carbon monoxide emissions constrained by TROPOMI

Since carbon monoxide (CO) in the atmosphere adversely affects air quality and climate, knowledge about its sources is 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 data from the TROPOspheric Monitoring Instrument (TROPOMI) into the TM5-4DVAR model. For this purpose, we developed novel methods to handle the unprecedented amount of data provided by TROPOMI. We compare the results from three inversion experiments that optimize CO emissions based on different observational data. In one experiment we only assimilate TROPOMI data, in a second experiment we only assimilate NOAA surface flask measurements, and in a third experiment we assimilate both datasets. We show that the inversion that assimilates only satellite observations reproduces flask measurements south of 55° N almost as well as the inversions that assimilated these measurements. These results show that the assimilation of TROPOMI data alone may provide reliable CO source estimates globally.