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Quantifying tropospheric CO, O3 and OH using multi-species data assimilation

Atmospheric carbon monoxide (CO) emissions estimated from inverse modeling analyses exhibit large uncertainties, due, in part, to discrepancies in the tropospheric chemistry in atmospheric models. We attempt to reduce the uncertainties in CO emission estimates by constraining the modeled abundance of ozone (O3), nitrogen dioxide (NO2), nitric acid (HNO3), and formaldehyde (HCHO), which are constituents that play a key role in tropospheric chemistry. Using the GEOS-Chem four-dimensional variational (4D-Var) data assimilation system, we estimate CO emissions by assimilating observations of CO from the Measurement of Pollution In the Troposphere (MOPITT), together with observations of O3 from the Optical Spectrograph and InfraRed Imager System (OSIRIS) and the Infrared Atmospheric Sounding Interferometer (IASI), NO2 and HCHO from the Ozone Monitoring Instrument (OMI), and HNO3 from the Microwave Limb Sounder (MLS). Although our focus is on quantifying CO emission estimates, we also infer surface emissions of nitrogen oxides (NOx = NO + NO2) and isoprene. Our results reveal that this multiple species chemical data assimilation produces a chemical consistent state that effectively adjusts the CO-O3-OH coupling in the model. The O3-induced changes in OH are particularly large in the tropics. We show that the analysis results in a tropospheric chemical state that is better constrained. Our experiments also evaluate the inferred CO emission estimates from major anthropogenic, biomass burning and biogenic sources, and show CO improvements by 10-35% over the northern hemisphere after this chemical reanalysis.