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Annales Geophysicae An interactive open-access journal of the European Geosciences Union
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Volume 27, issue 9 | Copyright

Special issue: From Deserts to Monsoons – First International Aegean...

Ann. Geophys., 27, 3439-3464, 2009
https://doi.org/10.5194/angeo-27-3439-2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.

  02 Sep 2009

02 Sep 2009

Light absorption by pollution, dust, and biomass burning aerosols: a global model study and evaluation with AERONET measurements

Mian Chin1, T. Diehl1,2, O. Dubovik3, T. F. Eck1,2, B. N. Holben1, A. Sinyuk1,4, and D. G. Streets5 Mian Chin et al.
  • 1Laboratory for Atmospheres, NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 2University of Maryland at Baltimore County, Baltimore, MD, USA
  • 3Laboratoire d'Optique Atmospherique, Universite de Lille 1/CNRS, Villeneuve d'Ascq, Lille, France
  • 4Science Systems and Applications, Inc., Lanham, MD, USA
  • 5Argonne National Laboratory, Argonne, IL, USA

Abstract. Atmospheric aerosol distributions from 2000 to 2007 are simulated with the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model to attribute light absorption by aerosol to its composition and sources from pollution, dust, and biomass burning. The 8-year, global averaged total aerosol optical depth (τ), absorption optical depth (τa), and single scattering albedo (ω) at 550 nm are estimated at 0.14, 0.0086, and 0.95, respectively, with sulfate making the largest fraction of τ (37%), followed by dust (30%), sea salt (16%), organic matter (OM) (13%), and black carbon (BC) (4%). BC and dust account for 43% and 53% of τa, respectively. From a model experiment with "tagged" sources, natural aerosols are estimated to be 58% of τ and 53% of τa, with pollution and biomass burning aerosols to share the rest. Comparing with data from the surface sunphotometer network AERONET, the model tends to reproduce much better the AERONET direct measured data of τ and the Ångström exponent (α) than its retrieved quantities of ω and τa. Relatively small in its systematic bias of τ for pollution and dust regions, the model tends to underestimate τ for biomass burning aerosols by 30–40%. The modeled α is 0.2–0.3 too low (particle too large) for pollution and dust aerosols but 0.2–0.3 too high (particle too small) for the biomass burning aerosols, indicating errors in particle size distributions in the model. Still, the model estimated ω is lower in dust regions and shows a much stronger wavelength dependence for biomass burning aerosols but a weaker one for pollution aerosols than those quantities from AERONET. These comparisons necessitate model improvements on aerosol size distributions, the refractive indices of dust and black carbon aerosols, and biomass burning emissions in order to better quantify the aerosol absorption in the atmosphere.

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