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Satellite estimates of burned area, associated carbon monoxide (CO) emission estimates, and CO column retrievals do not agree on the peak fire month in Africa, evident in both Northern and Southern Africa though distinct in the burning seasonality. Here we analyze this long-standing problem using (1) a top-down Bayesian inversion of Measurements Of Pollution In The Troposphere CO columns during 2005-2016 into surface CO emissions and (2) the bottom-up Global Fire Emissions Database 4.1 s. We show that Global Fire Emissions Database 4.1 s underestimates CO emissions by 12-62% in the late fire season and hypothesize that this is partly because it assumes seasonally static emission factors. However, the degree to which emission factors would have to vary through the season to bring top-down and bottom-up in agreement cannot be confirmed by past field-based measurements. Improved observational constraint on the seasonality of burned area, fuel combustion, and emission factors would further reduce the discrepancy between bottom-up and top-down emission estimates.

Plain Language Summary Fire is an integral component of African ecosystems, which emits large amounts of trace gases, pollutants, and aerosols into the atmosphere. The accurate assessment of this impact is currently hampered by the poor knowledge of fire emission estimates in Africa. One known issue is that the bottom-up estimates of carbon monoxide (CO) emissions using satellite-based burned area are generally underestimated and also describe a CO emission peak 1-2 months earlier than satellite CO column retrievals and their inversion into emissions. Here we used a Bayesian atmospheric inversion approach to analyze the seasonal variation in CO emissions, CO emission factors, and combustion efficiency of African savanna fires. The results lead us to hypothesize that under actual conditions, flaming- and smoldering-dominated combustions tend to appear successively when a moving fire front passes through a savanna. This mechanism is not sufficiently considered in current bottom-up fire emission models, which partly explains the emission underestimate and a phase shift to the early season in the seasonality of bottom-up emissions data.