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Dimethylsulfide chemistry in the remote marine atmosphere: Evaluation and sensitivity analysis of available mechanisms

A box model of the marine boundary layer is used to simulate the oxidation products of dimethylsulfide, including non‐sea‐salt (nss) sulfate, sulfur dioxide (SO2), methane sulfonic acid (MSA), dimethylsulfoxide (DMSO), methane sulfinic acid (MSEA), and dimethylsulfone (DMSO2). The gas phase oxidatio...

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Bibliographic Details
Published in:Journal of Geophysical Research, Washington, DC Washington, DC, 1997-10, Vol.102 (D19), p.23251-23267
Main Authors: Capaldo, Kevin P., Pandis, Spyros N.
Format: Article
Language:English
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Summary:A box model of the marine boundary layer is used to simulate the oxidation products of dimethylsulfide, including non‐sea‐salt (nss) sulfate, sulfur dioxide (SO2), methane sulfonic acid (MSA), dimethylsulfoxide (DMSO), methane sulfinic acid (MSEA), and dimethylsulfone (DMSO2). The gas phase oxidation schemes of Yin et al. [1990], Koga and Tanaka [1993], Hertel et al. [1994], Pham et al. [1995], and Benkovitz et al. [1994] are compared with field measurements using nine scenarios. Heterogeneous oxidation of SO2 in cloud droplets and sea‐salt particles is also simulated. A sensitivity analysis is performed to evaluate which atmospheric parameters require the greatest attention in future field studies. Results indicate that the variations among the gas phase mechanisms are small with the parameterized mechanisms performing as accurately as the comprehensive ones. Among the nine scenarios tested, nss‐sulfate is predicted without bias. Predicted MSA and SO2 concentrations depend more on the gas phase mechanism, with the mechanisms tending to underpredict SO2 concentrations. Compared to differences in MSA and SO2 predictions, DMSO, MSEA, and DMSO2 predictions by the various mechanisms are similar. Sulfate predictions are sensitive to the uncertain parameterizations of heterogeneous processes. The interaction of the marine boundary layer with the free troposphere can explain much of the discrepancy between the model predictions and measurements.
ISSN:0148-0227
2156-2202
DOI:10.1029/97JD01807