Articles | Volume 22, issue 14
https://doi.org/10.5194/acp-22-9583-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-9583-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Simulating the radiative forcing of oceanic dimethylsulfide (DMS) in Asia based on machine learning estimates
Junri Zhao
Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
Shanghai Institute of Eco-Chongming (SIEC), Shanghai 200062, China
Weichun Ma
Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
Institute of Digitalized Sustainable Transformation, Big Data Institute, Fudan University, Shanghai 200433, China
Kelsey R. Bilsback
Department of Atmospheric Science, Colorado State University, Fort Collins, CO, United States of America
PSE Healthy Energy, Oakland, CA, United States of America
Jeffrey R. Pierce
Department of Atmospheric Science, Colorado State University, Fort Collins, CO, United States of America
Shengqian Zhou
Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
Shanghai Institute of Eco-Chongming (SIEC), Shanghai 200062, China
Ying Chen
Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
Shanghai Institute of Eco-Chongming (SIEC), Shanghai 200062, China
Guipeng Yang
Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education, College of Chemistry and Chemical Engineering, Ocean University of China, Qingdao 266100, China
Yan Zhang
CORRESPONDING AUTHOR
Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
Shanghai Institute of Eco-Chongming (SIEC), Shanghai 200062, China
Institute of Digitalized Sustainable Transformation, Big Data Institute, Fudan University, Shanghai 200433, China
Model code and software
geoschem/geos-chem: GEOS-Chem 12.9.3 (12.9.3) The International GEOS-Chem User Community https://doi.org/10.5281/zenodo.3974569
Short summary
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.
Marine dimethylsulfide (DMS) emissions play important roles in atmospheric sulfur cycle and...
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