Carbon monoxide source estimates: Multiple satellite datasets and high resolution adjoint inverse model.

摘要

This thesis uses a global 3-D chemical transport model (GEOS-Chem) and its adjoint, in conjunction with multiple global satellite datasets (from MOPITT, AIRS, SCIAMACHY and TES) to better understand and quantify the sources of carbon monoxide.;Adjoint inverse model dramatically improves the resolution of the CO source constraints and overcomes the aggregation error of the low resolution analytical estimates. The study aimed to estimate Asian CO sources using MOPITT satellite measurements obtained during Spring 2001 TRACE-P campaign. The two inverse methods, adjoint and analytical, generally give consistent source constraints when averaged over large regions. The adjoint solution reveals fine-scale variability (cities, political boundaries) that the analytical inversion cannot resolve, for example, in the Indian subcontinent, and some of that variability is of opposite sign which points to large aggregation errors in the analytical solution. Upward correction factors to Chinese emissions from the prior inventory are largest in central and eastern China, consistent with a recent bottom-up revision of that inventory.;MOPITT, AIRS, TES and SCIAMACHY CO satellite datasets all provide potentially complementary information about CO sources. MOPITT measurements have a long record of validation, AIRS provides unprecedented daily global dataset and SCIAMACHY instrument has unique vertical sensitivity extending all the way to the surface. Previous source inversion studies have mostly used individual datasets, while I investigated the benefit of using multiple measurements of varying vertical sensitivity, data density and data quality. Large uncertainties exist in the source estimates, and modeled concentrations show large disagreements with observations, particularly in matching the amplitude of the observed seasonal cycle. After establishing consistency among MOPITT, AIRS and SCIAMACHY Bremen datasets, I estimated monthly CO sources globally at 4° x 5° degree resolution over the whole year (May 2004--April 2005) in an adjoint inversion. CO source constraints benefit from the multiple datasets where the data are consistent (Northern Hemisphere and Australia) and remain difficult where the data is not consistent and where there are additional biases in the model (S. America, southern Africa). I find large northern hemispheric seasonal correction in the middle latitudes, with fall-winter-spring emissions much larger than in the summer. Annual global CO emission estimate is 1350 Tg.