Tropospheric Aerosol Optical Thickness from the GOCART Model and Comparisons with Satellite and Sun Photometer Measurements

Mian Chin(Goddard Space Flight Center), Paul Ginoux(Goddard Space Flight Center), Stefan Kinne(Goddard Space Flight Center), Omar Torres(University of Maryland, Baltimore County), B. N. Holben(Goddard Space Flight Center), B. N. Duncan(Harvard University), Randall V. Martin(Harvard University), Jennifer A. Logan(Harvard University), Akiko Higurashi(National Institute for Environmental Studies), Teruyuki Nakajima(The University of Tokyo)
Journal of the Atmospheric Sciences
February 1, 2002
Cited by 1,700

Abstract

The Georgia Institute of Technology–Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model is used to simulate the aerosol optical thickness � for major types of tropospheric aerosols including sulfate, dust, organic carbon (OC), black carbon (BC), and sea salt. The GOCART model uses a dust emission algorithm that quantifies the dust source as a function of the degree of topographic depression, and a biomass burning emission source that includes seasonal and interannual variability based on satellite observations. Results presented here show that on global average, dust aerosol has the highest � at 500 nm (0.051), followed by sulfate (0.040), sea salt (0.027), OC (0.017), and BC (0.007). There are large geographical and seasonal variations of �, controlled mainly by emission, transport, and hygroscopic properties of aerosols. The model calculated total �s at 500 nm have been compared with the satellite retrieval products from the Total Ozone Mapping Spectrometer (TOMS) over both land and ocean and from the Advanced Very High Resolution Radiometer (AVHRR) over the ocean. The model reproduces most of the prominent features in the satellite data, with an overall agreement within a factor of 2 over the aerosol source areas and outflow regions. While there are clear differences among the satellite products, a major discrepancy between the model and the satellite data is that the model shows a stronger variation of � from source to remote regions. Quantitative comparison of


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