Articles | Volume 22, issue 14
https://doi.org/10.5194/acp-22-9583-2022
https://doi.org/10.5194/acp-22-9583-2022
Research article
 | 
27 Jul 2022
Research article |  | 27 Jul 2022

Simulating the radiative forcing of oceanic dimethylsulfide (DMS) in Asia based on machine learning estimates

Junri Zhao, Weichun Ma, Kelsey R. Bilsback, Jeffrey R. Pierce, Shengqian Zhou, Ying Chen, Guipeng Yang, and Yan Zhang

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Subject: Aerosols | Research Activity: Atmospheric Modelling and Data Analysis | Altitude Range: Troposphere | Science Focus: Chemistry (chemical composition and reactions)
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Cited articles

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Marine dimethylsulfide (DMS) emissions play important roles in atmospheric sulfur cycle and climate effects. In this study, DMS emissions were estimated by using the machine learning method and drove the global 3D chemical transport model to simulate their climate effects. To our knowledge, this is the first study in the Asian region that quantifies the combined impacts of DMS on sulfate, particle number concentration, and radiative forcings.
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