Six global biomass burning emission datasets: intercomparison and application in one global aerosol model
Authors: Pan Xiaohua , Ichoku Charles , Chin Mian , Bian Huisheng , Darmenov Anton , Colarco Peter , Ellison Luke , Kucsera Tom , da Silva Arlindo , Wang Jun , Oda Tomohiro , Cui Ge 
Published: January 2020

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.