Six global biomass burning emission datasets: intercomparison and application in one global aerosol model

Abstract

Abstract. Aerosols from biomass burning (BB) emissions are poorly constrained in global and regional models, resulting in a high level of uncertainty in understanding their impacts. In this study, we compared six BB aerosol emission datasets for 2008 globally as well as in 14 regions. The six BB emission datasets are (1) GFED3.1 (Global Fire Emissions Database version 3.1), (2) GFED4s (GFED version 4 with small fires), (3) FINN1.5 (FIre INventory from NCAR version 1.5), (4) GFAS1.2 (Global Fire Assimilation System version 1.2), (5) FEER1.0 (Fire Energetics and Emissions Research version 1.0), and (6) QFED2.4 (Quick Fire Emissions Dataset version 2.4). The global total emission amounts from these six BB emission datasets differed by a factor of 3.8, ranging from 13.76 to 51.93 Tg for organic carbon and from 1.65 to 5.54 Tg for black carbon. In most of the regions, QFED2.4 and FEER1.0, which are based on satellite observations of fire radiative power (FRP) and constrained by aerosol optical depth (AOD) data from the Moderate Resolution Imaging Spectroradiometer (MODIS), yielded higher BB aerosol emissions than the rest by a factor of 2–4. By comparison, the BB aerosol emissions estimated from GFED4s and GFED3.1, which are based on satellite burned-area data, without AOD constraints, were at the low end of the range. In order to examine the sensitivity of model-simulated AOD to the different BB emission datasets, we ingested these six BB emission datasets separately into the same global model, the NASA Goddard Earth Observing System (GEOS) model, and compared the simulated AOD with observed AOD from the AErosol RObotic NETwork (AERONET) and the Multiangle Imaging SpectroRadiometer (MISR) in the 14 regions during 2008. In Southern Hemisphere Africa (SHAF) and South America (SHSA), where aerosols tend to be clearly dominated by smoke in September, the simulated AOD values were underestimated in almost all experiments compared to MISR, except for the QFED2.4 run in SHSA. The model-simulated AOD values based on FEER1.0 and QFED2.4 were the closest to the corresponding AERONET data, being, respectively, about 73 % and 100 % of the AERONET observed AOD at Alta Floresta in SHSA and about 49 % and 46 % at Mongu in SHAF. The simulated AOD based on the other four BB emission datasets accounted for only ∼50 % of the AERONET AOD at Alta Floresta and ∼20 % at Mongu. Overall, during the biomass burning peak seasons, at most of the selected AERONET sites in each region, the AOD values simulated with QFED2.4 were the highest and closest to AERONET and MISR observations, followed closely by FEER1.0. However, the QFED2.4 run tends to overestimate AOD in the region of SHSA, and the QFED2.4 BB emission dataset is tuned with the GEOS model. In contrast, the FEER1.0 BB emission dataset is derived in a more model-independent fashion and is more physically based since its emission coefficients are independently derived at each grid box. Therefore, we recommend the FEER1.0 BB emission dataset for aerosol-focused hindcast experiments in the two biomass-burning-dominated regions in the Southern Hemisphere, SHAF, and SHSA (as well as in other regions but with lower confidence). The differences between these six BB emission datasets are attributable to the approaches and input data used to derive BB emissions, such as whether AOD from satellite observations is used as a constraint, whether the approaches to parameterize the fire activities are based on burned area, FRP, or active fire count, and which set of emission factors is chosen.