HR: 1330h
AN: A22C-1075    [PDF]
TI: Inverse modeling of biomass smoke emissions using the TOMS AI
AU: * Zhang, S Y
EM: yangz@umich.edu
AF: Department of Atmospheric, Oceanic and Space Sciences, University of Michigan, 2455 Haywood, Ann Arbor, MI 48109 United States
AU: Penner, J E
AF: Department of Atmospheric, Oceanic and Space Sciences, University of Michigan, 2455 Haywood, Ann Arbor, MI 48109 United States
AU: Torres, O
AF: JCET, University of Maryland Baltimore County, Baltimore, MD 20771 United States
AB: Results of inverse modeling of biomass smoke emissions using the TOMS AI and a three-dimensional transport model are presented. The IMPACT model with DAO meteorology data in 1997 are utilized to obtain aerosol spatial and temporal distributions. Two absorbing aerosol types are considered, including biomass smoke and mineral dust. First, a radiative transfer model is applied to generate the modeled AI. Then a Bayesian inverse technique is applied to optimize the difference between the modeled AI and the EP TOMS AI in the same period by regulating monthly a priori biomass smoke emissions, while the dust emissions are fixed. The modeled AI with a posteriori emissions generally is in better agreement with the EP TOMS AI. The annual global a posteriori source increases by about 13% for the year 1997 (6.31 Tg/yr BC) in the base scenario, with a larger adjustment of monthly regional emissions. Five sensitivity scenarios are carried out, including sensitivity to the a priori uncertainties, the height of the smoke layer, the cloud screening criteria of the daily EP TOMS AI, the adjustment of emissions in a lumped region outside of the major biomass burning regions, and the covariances between observations. Results suggest that a posteriori annual global emissions in the sensitivity scenarios are within 15% of that of the base scenario. However, the difference of annual a posteriori emissions between the sensitivity scenarios and the base scenario can be as large as 50% on regional scale. We are also applying the inverse model technique to the year 2000 to compare with biomass emissions deduced from an analysis based on burned areas.
DE: 0305 Aerosols and particles (0345, 4801)
DE: 0368 Troposphere--constituent transport and chemistry
SC: Atmospheric Sciences [A]
MN: 2003 Fall Meeting