Articles | Volume 16, issue 9
https://doi.org/10.5194/essd-16-4267-2024
https://doi.org/10.5194/essd-16-4267-2024
Data description paper
 | 
19 Sep 2024
Data description paper |  | 19 Sep 2024

A 20-year (1998–2017) global sea surface dimethyl sulfide gridded dataset with daily resolution

Shengqian Zhou, Ying Chen, Shan Huang, Xianda Gong, Guipeng Yang, Honghai Zhang, Hartmut Herrmann, Alfred Wiedensohler, Laurent Poulain, Yan Zhang, Fanghui Wang, Zongjun Xu, and Ke Yan

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Cited articles

Alcolombri, U., Ben-Dor, S., Feldmesser, E., Levin, Y., Tawfik, D. S., and Vardi, A.: Identification of the algal dimethyl sulfide–releasing enzyme: a missing link in the marine sulfur cycle, Science, 348, 1466–1469, 2015. 
Andreae, M. O.: Ocean-Atmosphere Interactions in the Global Biogeochemical Sulfur Cycle, Mar. Chem., 30, 1–29, https://doi.org/10.1016/0304-4203(90)90059-L, 1990. 
Arnold, S. R., Spracklen, D. V., Gebhardt, S., Custer, T., Williams, J., Peeken, I., and Alvain, S.: Relationships between atmospheric organic compounds and air-mass exposure to marine biology, Environ. Chem., 7, 232–241, https://doi.org/10.1071/en09144, 2010. 
Aurin, D. A. and Dierssen, H. M.: Advantages and limitations of ocean color remote sensing in CDOM-dominated, mineral-rich coastal and estuarine waters, Remote Sens. Environ., 125, 181–197, https://doi.org/10.1016/j.rse.2012.07.001, 2012. 
Barnes, I., Hjorth, J., and Mihalopoulos, N.: Dimethyl sulfide and dimethyl sulfoxide and their oxidation in the atmosphere, Chem. Rev., 106, 940–975, https://doi.org/10.1021/cr020529+, 2006. 
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Short summary
Dimethyl sulfide (DMS) is a crucial natural reactive gas in the global climate system due to its great contribution to aerosols and subsequent impact on clouds over remote oceans. Leveraging machine learning techniques, we constructed a long-term global sea surface DMS gridded dataset with daily resolution. Compared to previous datasets, our new dataset holds promise for improving atmospheric chemistry modeling and advancing our comprehension of the climate effects associated with oceanic DMS.
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