Atmospheric carbon monoxide (CO) is a product of incomplete combustion and a byproduct of the oxidation of hydrocarbons. It plays a key role in controlling the oxidative capacity of the atmosphere since it is the main sink for the hydroxyl radical (OH), the primary tropospheric oxidant. As a result of its lifetime, CO is a useful tracer of long-range transport in models. However, estimates of the regional sources of CO are uncertain. Inverse modeling has become a widely used approach for better quantifying the sources, but a fundamental assumption in these inversions, which is typically not valid, is that the observations and models are unbiased. In this thesis, the GEOS-Chem model and observations of CO from the Measurement Of Pollution In The Troposphere (MOPITT) instrument are employed to study the impact of systematic model errors on inversion analyses of CO. The impact of the treatment of biogenic non-methane volatile organic compounds (NMVOCs), aggregation errors, and discrepancies in the meteorological fields and OH distribution on the CO source estimates are examined. The influence of vertical transport errors on the source estimates is assessed using newly available MOPITT version 5 (V5) retrievals in a comparative inversion analysis employing surface level, profile, and column data. To quantify the potential impact of discrepancies in long-range transport on the source estimates, a high-resolution, regional inversion over North America, with optimized lateral boundary conditions, was conducted and compared with the results of a global inversion. The influence of the spatial-temporal distribution of the observations on the source estimates was also assessed through a comparison of the inversion analyses of MOPITT data and aircraft data from the Intercontinental Transport Experiment – North America, Phase A (INTEX-A) aircraft campaign. The results presented in the thesis provide a more comprehensive understanding of the potential impact of system model errors on inversion analyses of CO. This work also represents the first inverse modeling analysis of the MOPITT v5 retrievals. The results demonstrate the potential utility of these new data for characterizing vertical transport errors in models and they reveal that the new data can provide reliable constraints in regional CO source estimates.

Atmospheric carbon monoxide (CO) is a product of incomplete combustion and a byproduct of the oxidation of hydrocarbons. It plays a key role in controlling the oxidative capacity of the atmosphere since it is the main sink for the hydroxyl radical (OH), the primary tropospheric oxidant. As a result of its lifetime, CO is a useful tracer of long-range transport in models. However, estimates of the regional sources of CO are uncertain. Inverse modeling has become a widely used approach for better quantifying the sources, but a fundamental assumption in these inversions, which is typically not valid, is that the observations and models are unbiased. In this thesis, the GEOS-Chem model and observations of CO from the Measurement Of Pollution In The Troposphere (MOPITT) instrument are employed to study the impact of systematic model errors on inversion analyses of CO. The impact of the treatment of biogenic non-methane volatile organic compounds (NMVOCs), aggregation errors, and discrepancies in the meteorological fields and OH distribution on the CO source estimates are examined. The influence of vertical transport errors on the source estimates is assessed using newly available MOPITT version 5 (V5) retrievals in a comparative inversion analysis employing surface level, profile, and column data. To quantify the potential impact of discrepancies in long-range transport on the source estimates, a high-resolution, regional inversion over North America, with optimized lateral boundary conditions, was conducted and compared with the results of a global inversion. The influence of the spatial-temporal distribution of the observations on the source estimates was also assessed through a comparison of the inversion analyses of MOPITT data and aircraft data from the Intercontinental Transport Experiment – North America, Phase A (INTEX-A) aircraft campaign. The results presented in the thesis provide a more comprehensive understanding of the potential impact of system model errors on inversion analyses of CO. This work also represents the first inverse modeling analysis of the MOPITT v5 retrievals. The results demonstrate the potential utility of these new data for characterizing vertical transport errors in models and they reveal that the new data can provide reliable constraints in regional CO source estimates.