Quantifying the impact of model errors on top-down estimates of carbon monoxide emissions using satellite observations

Abstract

[1] We conduct inverse analyses of atmospheric CO, using the GEOS-Chem model and observations from the Measurement of Pollution in the Troposphere satellite instrument, to quantify the potential contribution of systematic model errors on top-down source estimates of CO. We assess how the specification of the source of CO from the oxidation of biogenic nonmethane volatile organic compounds (NMVOCs) in the inversion impacts the top-down estimates. Our results show that when the NMVOC source of CO is comparable to or larger than the combustion source, optimizing the CO from NMVOC emissions on larger spatial scales than the combustion emissions could result in significant overadjustment for the a posteriori CO emissions and could lead to negative sources of CO, such as we found for the top-down South American emissions in June. We quantify the impact of aggregation errors on the source estimates, associated with conducting the inversion at a lower resolution than the atmospheric model. We find that aggregating the emissions across spatial scales in which the a priori error in the emissions changes sign could introduce biases exceeding 20% in the flux estimates since the inversion cannot correct the a priori error by uniformly scaling the emissions across the region. We also use the GEOS-3 and GEOS-4 meteorological fields in GEOS-Chem to examine the impact of discrepancies in atmospheric transport and in the atmospheric OH distribution on the source estimates. We find that the differences in the OH distribution and transport fields associated with the GEOS-3 and GEOS-4 products introduce comparably large differences of as much as 20% in the source estimates. Our results indicate that mitigating systematic model error is critical for improving the accuracy of the inferred source estimates.