Journal cover Journal topic
Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
Atmos. Chem. Phys., 16, 11083-11106, 2016
https://doi.org/10.5194/acp-16-11083-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
07 Sep 2016
Analysis of particulate emissions from tropical biomass burning using a global aerosol model and long-term surface observations
Carly L. Reddington1, Dominick V. Spracklen1, Paulo Artaxo2, David A. Ridley1,a, Luciana V. Rizzo3, and Andrea Arana2 1School of Earth and Environment, University of Leeds, Leeds, UK
2Department of Applied Physics, Institute of Physics, University of Sao Paulo, Sao Paulo, Brazil
3Institute of Environmental, Chemical and Pharmaceutical Sciences, Federal University of Sao Paulo, Diadema, Brazil
anow at: Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, USA
Abstract. We use the GLOMAP global aerosol model evaluated against observations of surface particulate matter (PM2.5) 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 PM2.5 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 PM2.5 and AOD are available we find a greater model underestimation of AOD than PM2.5, 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 PM2.5 concentrations. We find that scaling FINN1 emissions by a factor of 1.5 prevents underestimation of AOD and surface PM2.5 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 PM2.5 and AOD in regions where small fires dominate, providing independent evidence that GFED3 underestimates particulate emissions from small fires. Seasonal variability in both PM2.5 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.

Citation: Reddington, C. L., Spracklen, D. V., Artaxo, P., Ridley, D. A., Rizzo, L. V., and Arana, A.: Analysis of particulate emissions from tropical biomass burning using a global aerosol model and long-term surface observations, Atmos. Chem. Phys., 16, 11083-11106, https://doi.org/10.5194/acp-16-11083-2016, 2016.
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Short summary
We use a global aerosol model evaluated against long-term observations of surface aerosol and aerosol optical depth (AOD) to better understand the impacts of biomass burning on tropical aerosol. We use three satellite-derived fire emission datasets in the model, identifying regions where these datasets capture observations and where emissions are likely to be underestimated. For coincident observations of surface aerosol and AOD, model underestimation of AOD is greater than of surface aerosol.
We use a global aerosol model evaluated against long-term observations of surface aerosol and...
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