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

Viewed

Total article views: 3,134 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,802 1,305 27 3,134 167 15 31
  • HTML: 1,802
  • PDF: 1,305
  • XML: 27
  • Total: 3,134
  • Supplement: 167
  • BibTeX: 15
  • EndNote: 31
Views and downloads (calculated since 22 Mar 2022)
Cumulative views and downloads (calculated since 22 Mar 2022)

Viewed (geographical distribution)

Total article views: 3,134 (including HTML, PDF, and XML) Thereof 3,185 with geography defined and -51 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 28 Mar 2024
Download
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.
Altmetrics
Final-revised paper
Preprint