Preprints
https://doi.org/10.5194/acp-2022-3
https://doi.org/10.5194/acp-2022-3
 
22 Mar 2022
22 Mar 2022
Status: this preprint is currently under review for the journal ACP.

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

Junri Zhao1,2, Weichun Ma1,3, Kelsey R. Bilsback4, Jeffrey R. Pierce4, Shengqian Zhou1,2, Ying Chen1,2, Guipeng Yang5, and Yan Zhang1,2,3,6 Junri Zhao et al.
  • 1Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China
  • 2Shanghai Institute of Eco-Chongming (SIEC), Shanghai 200062, China
  • 3Institute of Digitalized Sustainable Transformation, Big Data Institute, Fudan University, Shanghai 200433, China
  • 4Department of Atmospheric Science, Colorado State University, Fort Collins, CO, United States of America
  • 5Key Laboratory of Marine Chemistry Theory and Technology, Ministry of Education, College of Chemistry and Chemical Engineering, Ocean University of China, Qingdao 266100, China
  • 6National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Shanghai, China

Abstract. DMS emitted from the sea water is a key precursor to new particle formation, acting as a regulator in Earth’s warming climate system. However, DMS’s effects are not well understood in various ocean regions. In this study, we estimated DMS emissions based on a machine learning method and used the GEOS-Chem global 3D chemical transport model coupled with the TwO Moment Aerosol Sectional (TOMAS) microphysics scheme to simulate the atmospheric chemistry and radiative effects of DMS. The contributions of DMS to atmospheric SO42- aerosol and cloud condensation nuclei (CCN) concentrations along with their radiative effect over the Asian region were evaluated for the first time. Firstly, we constructed novel monthly-resolved DMS emissions (0.5° × 0.5°) for the year 2017 using a machine learning model. 4351 seawater DMS measurements (including the recent ones over the Chinese Sea) and 12 relevant environment parameters were selected for training. We found the model could predict the observed DMS concentrations with a correlation coefficient of 0.75 and fill the values in regions lack of observations. Across Asian Seas, the highest seasonal mean DMS concentration occurred in Mar-Apr-May (MAM), and we estimate annual DMS emission flux of 1.25 Tg (S), which accounts for 15.4 % of anthropogenic sulfur emissions over the entire simulation domain (covers most of Asia) in 2017. The model estimates of DMS and methane sulfonic acid (MSA), using updated DMS emissions, were evaluated by comparing with cruise survey experiments and long-term online measurement site data. The improvement in model performance can be observed compared with the global-database DMS emissions. The relative contributions of DMS to SO42- and CCN were higher in remote oceanic areas, which reached up to 88 % and 42 % of all sources. Correspondingly, the sulfate direct radiative forcing (DRF) and indirect radiative forcing (IRF) contributed by DMS ranged from -200 to -20 mW m-2 and -900 to -100 mW m-2, respectively, with levels varying by season. The strong negative IRF is mainly over remote ocean regions ( -900 to -600 mW m-2). Generally, the magnitude of IRF derived by DMS was twice as large as its DRF. This work provides insights into the source strength of DMS and its impact on climate, addressing knowledge gaps related to factors controlling aerosols in the marine boundary layer and their climate impacts.

Junri Zhao et al.

Status: final response (author comments only)

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
  • RC2: 'Comment on acp-2022-3', Anonymous Referee #2, 06 May 2022

Junri Zhao et al.

Junri Zhao et al.

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Latest update: 27 May 2022
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Short summary
The marine DMS emissions play important roles in atmospheric sulfur cycle and climate effects. In this study, DMS emissions were estimated by using 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 Asia region that quantifies the combined impacts of DMS on sulfate, particle number concentration, and radiative forcings.
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