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Atmospheric sulfur cycle simulated in the global model GOCART: Model description and global properties

Author(s): Chin M, Rood RB, Lin SJ, Müller JF, Thompson AM

Published: October, 2000

Publisher: Journal of Geophysical Research

DOI: 10.1029/2000JD900384

Tags: Stratospheric Aerosols, Climate Science, Tropospheric Aerosols

URL: http://www.agu.org/pubs/crossref/2000/2000JD900384.shtml

Abstract: The Georgia Tech/Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model is used to simulate the atmospheric sulfur cycle. The model uses the assimilated meteorological data from the Goddard Earth Observing System Data Assimilation System (GEOS DAS). Global sulfur budgets from a 6-year simulation for SO2, sulfate, dimethylsulfide (DMS), and methanesulfonic acid (MSA) are presented in this paper. In a normal year without major volcanic perturbations, about 20% of the sulfate precursor emission is from natural sources (biogenic and volcanic), and 80% is anthropogenic; the same sources contribute 33% and 67%, respectively, to the total sulfate burden. A sulfate production efficiency of 0.41-0.42 is estimated in the model, and efficiency which is defined as a ratio of the amount of sulfate produced to the total amount of SO2 emitted and produced in the atmosphere. This value indicates that less than half of the SO2 entering the atmosphere contributes to the sulfate production, the rest being removed by dry and wet depositions. In a simulation for 1990 we estimate a total sulfate production of 39 Tg S yr-1, with 36% and 64% from in-air and in-cloud oxidation, respectively, of SO2. We also demonstrate that major volcanic eruptions, such as the Mount Pinatubo eruption in 1991, can significantly change the sulfate formation pathways, distributions, abundance, and lifetime. Comparison with other models shows that the parameterizations for wet removal or wet production of sulfate are the most critical factors in determining the burdens of SO2 and sulfate. Therefore a priority for future research should be to reduce the large uncertainties associated with the wet physical and chemical processes.


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