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List of Abstracts 28P-Sources.37 ID:4246 15:35Quantifying the impact of model errorson top-down estimates of carbon monoxide emissions using satellite observationsZhe Jiang 1 , Dylan Jones 1 , Monika Kopacz 2 , Jane Liu 1 , Daven Henze 31 Department of Physics, University of Toronto2 Woodrow Wilson School of Public and International Affairs, Princeton University3 3Department of Mechanical Engineering, University of Colorado at BoulderContact: zjiang@atmosp.physics.utoronto.caInverse modeling is a powerful tool for combining observations of atmospheric composition with knowledgeof atmospheric processes to estimate trace gas emissions. In this context, there has been much effort oninverse modeling of atmospheric carbon monoxide (CO). However, significant discrepancies exist in theinferred “top-down” emission estimates of CO, reflecting the sensitivity of the inversion analyses to theinversion configuration employed in the analyses and to biases in the atmospheric models. In this work, weexamine the potential impact of four different types of systematic model errors on the inferred CO sources.We assess the impact of aggregation errors on the source estimates, associated with conducting the inversionat a lower resolution than the atmospheric model. We also quantify the impact of errors in atmospherictransport and in the atmospheric OH abundance in the model on the source estimates. Inversion analyses ofCO typically linearize the CO chemistry by specifying a fixed distribution of OH to minimize thecomputation cost of the inversion. We show that if the imposed OH field is inconsistent with the atmosphericforward model, the linearization error in the chemistry can bias the source estimates. Atmospheric CO isemitted directly to the atmosphere as a by-product of combustion and it is produced from the oxidation ofmethane (CH4) and nonmethane volatile organic compounds (NMVOCs). We also show that the inferredtop-down source estimates are sensitivity to how these chemical sources of CO are specified in the inversion.P-Sources.38 ID:4251 15:35Evaluation of dynamic processes in the tropical troposphere in assimilated meteorological fields usingcarbon monoxide data from the Aura satellite and GEOS-Chem modelJunhua Liu, Jennifer LoganHarvard UniversityContact: jliu@seas.harvard.eduWe use the GEOS-Chem model to interpret the spatial and temporal variations of tropical tropospheric COobserved by the Microwave Limb Sounder (MLS) and the Tropospheric Emission Spectrometer (TES). Weapply these data to diagnose and evaluate transport in the GEOS-4 and GEOS-5 assimilated meteorologicalfields that drive the model. Our analysis focuses on the biomass burning seasons, and on the influence ofvertical mixing at the start of the wet season. Over South America, both models reproduce the timing of theobserved CO maximum in the lower troposphere (LT). In the upper troposphere (UT), the model’s seasonalmaximum of CO with GEOS-4 occurs about 1 month late, and with GEOS-5 it occurs even later; in both it istoo broad compared to MLS data. Our analysis suggests that these deficiencies are caused by two factors:deep convection decays at too low an altitude, and the source of CO from isoprene in the model is too largeearly in the wet season. The lag in GEOS-5 is greater in part because convection decays at a lower altitude,and in part because convection moves southward later than in GEOS-4. Over southern Africa, modelsimulations driven by GEOS-4 and GEOS-5 match the phase of the observed CO variation throughout thetroposphere fairly well, reflecting reasonable meteorological patterns. Isoprene does not contributesignificantly to the seasonality of CO in the UT in this region. Over northern Africa, both models match thephase and magnitude of the CO variation throughout the troposphere except at 215 hPa, where MLS does notshow a strong seasonal cycle, while it is pronounced in the models. The MLS data imply that the upwardiCACGP-IGAC 2010 12 July, 2010