Improved biomass burning emissions using the TOMS AI

we propose a scheme to model the absorbing aerosol index and improve the biomass carbon inventories by optimizing the difference between the TOMS aerosol index (AI) using an inverse model. Two absorbing aerosol types were considered, including biomass carbon and mineral dust. A priori monthly biomass carbon source was generated by Liousse et al. [1996]. Mineral dust emission was parameterized according to surface wind and soil moisture using the method developed by Ginoux [2000]. Biomass carbon and dust spatial and temporal distribution was generated by the LLNL/University of Michigan IMPACT model using the 1997 DAO meteorology data. With the modeled aerosol concentration, we first calculated the 340 and 380 nm radiance contrast with a radiative transfer model, and then modeled AI. Comparison of the modeled AI and observations suggests that the current biomass burning emission catches the burning season in Africa but has a earlier burning than the observations in South America. The burning in Sahelian region in January and southern Africa in July is weaker than the observations. To reduce the discrepancies between the observations and model results, we use an inverse model to generate a posteriori biomass burning source on monthly basis. The results of inverse modeling will be discussed.