1Department of Physics, University of Toronto, Toronto, Ontario, Canada
2Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA
3School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
4Center for Atmospheric Sciences, University of California, Berkeley, California, USA
Abstract. We conduct an inverse modeling analysis of measurements of atmospheric CO from the TES and MOPITT satellite instruments using the GEOS-Chem global chemical transport model. This is the first quantitative analysis of the consistency of the information provided by these two instruments on surface emissions of CO in an inverse modeling context. We focus on observations of CO for November 2004, when the climatological emission inventory in the GEOS-Chem model significantly underestimated the atmospheric abundance of CO as observed by TES and MOPITT. We find that both datasets suggest significantly greater emissions of CO from sub-equatorial Africa and the Indonesian/Australian region. The a posteriori emissions from sub-equatorial Africa based on TES and MOPITT data were 173 Tg CO/yr and 184 Tg CO/yr, respectively, compared to the a priori of 95 Tg CO/yr. In the Indonesian/Australian region, the a posteriori emissions inferred from TES and MOPITT data were 155 Tg CO/yr and 185 Tg CO/yr, respectively, whereas the a priori was 69 Tg CO/yr. The differences between the a posteriori emission estimates obtained from the two datasets are generally less than 20%, and are likely due to the different spatio-temporal sampling of the measurements. The a posteriori emissions significantly improve the simulated distribution of CO, however, large regional residuals remain, reflecting systematic errors in the analysis. For example, the a posteriori emissions obtained from both datasets do not completely reduce the underestimate in the model of CO column abundances over the southern tropical Atlantic, southern Africa, and over the Indian Ocean, where biases of 3–7% remain. Over eastern Asia the a posteriori emissions overestimate the CO column abundances by about 3–6%. These residuals reflect the sensitivity of the top-down source estimates to systematic errors in the analysis. Our results indicate that improving the accuracy of top-down emission estimates will require further characterization of model biases (chemical and transport) and the use of spatial-temporal inversion resolutions consistent with the information content of the observations.