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

Data sets

A 20-year (1998-2017) global sea surface dimethyl sulfide gridded dataset with daily resolution Shengqian Zhou et al. https://doi.org/10.5281/zenodo.11879900

A 20-year (1998-2017) global sea surface dimethyl sulfide gridded dataset with daily resolution, v5.1 S. Zhou et al. https://doi.org/10.5281/zenodo.15717448

Model code and software

An artificial neural network ensemble model for sea surface DMS simulation Shengqian Zhou https://doi.org/10.5281/zenodo.12398985

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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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