Preprints
https://doi.org/10.5194/essd-2023-249
https://doi.org/10.5194/essd-2023-249
12 Dec 2023
 | 12 Dec 2023
Status: this preprint is currently under review for the journal ESSD.

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

Abstract. The oceanic emission of dimethyl sulfide (DMS) plays a vital role in the Earth's climate system and is a significant source of uncertainty in aerosol radiative forcing. Currently, the widely used monthly climatology of sea surface DMS concentration cannot meet the requirement for accurately simulating DMS-derived aerosols by chemical transport models. Thus, there is an urgent need to construct a global sea surface DMS dataset with high time resolution spanning multiple years. Here we develop an artificial neural network ensemble model based on 9 environmental factors, which demonstrate high accuracy and generalization in predicting DMS concentrations. Subsequently, a global sea surface DMS concentration and flux dataset (1°×1°) with daily resolution covering the period from 1998 to 2017 is established. According to this dataset, the global annual average concentration was ~1.82 nM, and the annual total emission was ~17.9 TgS yr–1, with ~60 % originating from the southern hemisphere. While overall seasonal variations are consistent with previous DMS climatologies, notable differences exist in regional-scale spatial distributions. The new dataset enables further investigation of daily and decadal variations. During 1998–2017, the global annual average concentration exhibited a slight decrease, while total emissions showed no significant trend. Benefiting from the incorporation of daily and interannual variation information, the DMS flux from our dataset showed a much stronger correlation with observed atmospheric methanesulfonic acid concentration compared to those from previous monthly climatologies. As a result, it can serve as an improved emission inventory of oceanic DMS and has the potential to enhance the simulation of DMS-derived aerosols and associated radiative effects. The new DMS gridded products are available at https://zenodo.org/record/7898187 (Zhou et al., 2023).

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
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

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-249', Murat Aydin, 27 Jan 2024
    • AC2: 'Reply on RC1', Shengqian Zhou, 07 Apr 2024
  • RC2: 'Comment on essd-2023-249', Anonymous Referee #2, 29 Jan 2024
    • AC1: 'Reply on RC2', Shengqian Zhou, 07 Apr 2024
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, 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 https://doi.org/10.5281/zenodo.10279659

Model code and software

An artificial neural network ensemble model for sea surface DMS simulation Shengqian Zhou https://doi.org/10.5281/zenodo.8077751

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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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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