Articles | Volume 22, issue 14
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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2022-3', Anonymous Referee #1, 26 Apr 2022
    • AC1: 'Reply on RC1', Yan Zhang, 17 Jun 2022
  • RC2: 'Comment on acp-2022-3', Anonymous Referee #2, 06 May 2022
    • AC2: 'Reply on RC2', Yan Zhang, 17 Jun 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Yan Zhang on behalf of the Authors (17 Jun 2022)  Author's response
ED: Publish subject to technical corrections (12 Jul 2022) by Maria Kanakidou
AR by Yan Zhang on behalf of the Authors (13 Jul 2022)  Author's response    Manuscript
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.
Final-revised paper