气候变化领域集成服务门户(CCPortal): Simulating the radiative forcing of oceanic dimetnylsulfide (DMS) in Asia based on machine learning estimates

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DOI	10.5194/acp-22-9583-2022
	Simulating the radiative forcing of oceanic dimetnylsulfide (DMS) in Asia based on machine learning estimates
	Zhao, Junri; Ma, Weichun; Bilsback, Kelsey R.; Pierce, Jeffrey R.; Zhou, Shengqian; Chen, Ying; Yang, Guipeng; Zhang, Yan
发表日期	2022
ISSN	1680-7316
EISSN	1680-7324
起始页码	9583
结束页码	9600
卷号	22 期号:14 页码:18
英文摘要	Dimethylsulfide (DMS) emitted from seawater is a key precursor to new particle formation and acts 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 the radiative effects over the Asian region were evaluated for the first time. First, we constructed novel monthly resolved DMS emissions (0.5 degrees x 0.5 degrees) for the year 2017 using a machine learning model; 4351 seawater DMS measurements (including the recent measurements made over the Chinese seas) and 12 relevant environment parameters were selected for model training. We found that the model could predict the observed DMS concentrations with a correlation coefficient of 0.75 and fill the values in regions lacking observations. Across the Asian seas, the highest seasonal mean DMS concentration occurred in March-April-May (MAM), and we estimate the annual DMS emission flux of 1.25 Tg (S), which is equivalent to 15.4 % of anthropogenic sulfur emissions over the entire simulation domain (which covered most of Asia) in 2017. The model estimates of DMS and methane sulfonic acid (MSA), using updated DMS emissions, were evaluated by comparing them with cruise survey experiments and long-term online measurement site data. The improvement in model performance can be observed compared with simulation results derived from the global-database DMS emissions. The relative contributions of DMS to SO42- and CCN were higher in remote oceanic areas, contributing 88 % and 42 % of all sources, respectively. Correspondingly, the sulfate direct radiative forcing (DRF) and indirect radiative forcing (IRF) contributed by DMS ranged from -200 to -20 mWm(-2) and from -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 mWm(-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 the impact of DMS on climate and addresses knowledge gaps related to factors controlling aerosols in the marine boundary layer and their climate impacts.
学科领域	Environmental Sciences; Meteorology & Atmospheric Sciences
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:000830443100001
来源期刊	ATMOSPHERIC CHEMISTRY AND PHYSICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/273108
作者单位	Fudan University; Fudan University; Colorado State University; Ocean University of China
推荐引用方式 GB/T 7714	Zhao, Junri,Ma, Weichun,Bilsback, Kelsey R.,et al. Simulating the radiative forcing of oceanic dimetnylsulfide (DMS) in Asia based on machine learning estimates[J],2022,22(14):18.
APA	Zhao, Junri.,Ma, Weichun.,Bilsback, Kelsey R..,Pierce, Jeffrey R..,Zhou, Shengqian.,...&Zhang, Yan.(2022).Simulating the radiative forcing of oceanic dimetnylsulfide (DMS) in Asia based on machine learning estimates.ATMOSPHERIC CHEMISTRY AND PHYSICS,22(14),18.
MLA	Zhao, Junri,et al."Simulating the radiative forcing of oceanic dimetnylsulfide (DMS) in Asia based on machine learning estimates".ATMOSPHERIC CHEMISTRY AND PHYSICS 22.14(2022):18.