Analysis of particulate emissions from tropical biomass burning using a global aerosol model and long-term surface observations
We use the GLOMAP global aerosol model evaluated against observations of surface particulate matter (PM 2.5) and aerosol optical depth (AOD) to better understand the impacts of biomass burning on tropical aerosol over the period 2003 to 2011. Previous studies report a large underestimation of AOD over regions impacted by tropical biomass burning, scaling particulate emissions from fire by up to a factor of 6 to enable the models to simulate observed AOD. To explore the uncertainty in emissions we use three satellite-derived fire emission datasets (GFED3, GFAS1 and FINN1). In these datasets the tropics account for 66–84 % of global particulate emissions from fire. With all emission datasets GLOMAP underestimates dry season PM 2.5 concentrations in regions of high fire activity in South America and underestimates AOD over South America, Africa and Southeast Asia. When we assume an upper estimate of aerosol hygroscopicity, underestimation of AOD over tropical regions impacted by biomass burning is reduced relative to previous studies. Where coincident observations of surface PM 2.5 and AOD are available we find a greater model underestimation of AOD than PM 2.5, even when we assume an upper estimate of aerosol hygroscopicity. Increasing particulate emissions to improve simulation of AOD can therefore lead to overestimation of surface PM 2.5 concentrations. We find that scaling FINN1 emissions by a factor of 1.5 prevents underestimation of AOD and surface PM 2.5 in most tropical locations except Africa. GFAS1 requires emission scaling factor of 3.4 in most locations with the exception of equatorial Asia where a scaling factor of 1.5 is adequate. Scaling GFED3 emissions by a factor of 1.5 is sufficient in active deforestation regions of South America and equatorial Asia, but a larger scaling factor is required elsewhere. The model with GFED3 emissions poorly simulates observed seasonal variability in surface PM 2.5 and AOD in regions where small fires dominate, providing independent evidence that GFED3 underestimates particulate emissions from small fires. Seasonal variability in both PM 2.5 and AOD is better simulated by the model using FINN1 emissions. Detailed observations of aerosol properties over biomass burning regions are required to better constrain particulate emissions from fires.