Articles | Volume 16, issue 9
https://doi.org/10.5194/essd-16-4267-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-4267-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
19 Sep 2024
Data description paper |
| 19 Sep 2024
| 19 Sep 2024
A 20-year (1998–2017) global sea surface dimethyl sulfide gridded dataset with daily resolution
Shengqian Zhou
Shanghai Key Laboratory of Atmospheric Particle Pollution Prevention, Department of Environmental Science & Engineering, Fudan University, 200438 Shanghai, China
Ying Chen
CORRESPONDING AUTHOR
Shanghai Key Laboratory of Atmospheric Particle Pollution Prevention, Department of Environmental Science & Engineering, Fudan University, 200438 Shanghai, China
Institute of Eco-Chongming (IEC), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, 200062 Shanghai, China
Institute of Atmospheric Sciences, Fudan University, 200438 Shanghai, China
Shan Huang
Institute for Environmental and Climate Research, Jinan University, 511443 Guangzhou, China
Atmospheric Chemistry Department, Leibniz Institute for Tropospheric Research, 04318 Leipzig, Germany
Xianda Gong
Research Center for Industries of the Future, Westlake University, 310030 Hangzhou, China
Key Laboratory of Coastal Environment and Resources of Zhejiang Province, School of Engineering, Westlake University, 310030 Hangzhou, China
Guipeng Yang
Frontiers Science Center for Deep Ocean Multispheres and Earth System and Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education, Ocean University of China, 266100 Qingdao, China
Laboratory for Marine Ecology and Environmental Science, Qingdao National Laboratory for Marine Science and Technology, 266071 Qingdao, China
College of Chemistry and Chemical Engineering, Ocean University of China, 266100 Qingdao, China
Honghai Zhang
Frontiers Science Center for Deep Ocean Multispheres and Earth System and Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education, Ocean University of China, 266100 Qingdao, China
Laboratory for Marine Ecology and Environmental Science, Qingdao National Laboratory for Marine Science and Technology, 266071 Qingdao, China
College of Chemistry and Chemical Engineering, Ocean University of China, 266100 Qingdao, China
Hartmut Herrmann
Atmospheric Chemistry Department, Leibniz Institute for Tropospheric Research, 04318 Leipzig, Germany
Alfred Wiedensohler
Atmospheric Chemistry Department, Leibniz Institute for Tropospheric Research, 04318 Leipzig, Germany
Laurent Poulain
Atmospheric Chemistry Department, Leibniz Institute for Tropospheric Research, 04318 Leipzig, Germany
Yan Zhang
Shanghai Key Laboratory of Atmospheric Particle Pollution Prevention, Department of Environmental Science & Engineering, Fudan University, 200438 Shanghai, China
Institute of Eco-Chongming (IEC), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, 200062 Shanghai, China
Fanghui Wang
Shanghai Key Laboratory of Atmospheric Particle Pollution Prevention, Department of Environmental Science & Engineering, Fudan University, 200438 Shanghai, China
Zongjun Xu
Shanghai Key Laboratory of Atmospheric Particle Pollution Prevention, Department of Environmental Science & Engineering, Fudan University, 200438 Shanghai, China
Ke Yan
Shanghai Key Laboratory of Atmospheric Particle Pollution Prevention, Department of Environmental Science & Engineering, Fudan University, 200438 Shanghai, China
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
Model code and software
An artificial neural network ensemble model for sea surface DMS simulation Shengqian Zhou https://doi.org/10.5281/zenodo.12398985
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.
Dimethyl sulfide (DMS) is a crucial natural reactive gas in the global climate system due to its...
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