Acrylic acid and related dimethylated sulfur compounds in the Bohai and Yellow Seas during summer and winter

Wu, Xi; Li, Peifeng; Zhang, Honghai; Zhu, Mao‐Xu; Liu, Chunying; Yang, Gui‐Peng

doi:10.5194/bg-2019-172

Cited by 2 publications

(1 citation statement)

References 31 publications

(38 reference statements)

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“…The red points (1022 valid measurements) represent the local DMS observation dataset (2011, 2013, 2015, 2016, and 2017) across several Chinese seas from China Ocean University. Details can be found in our previous studies (G.-P. Yang et al, 2014Xu et al, 2021;Zhai et al, 2020;Wu et al, 2020;Jian et al, 2019;Yu et al, 2019;Mao et al, 2021). The blue points (3329 valid measurements) represent the observations from the Global Surface Seawater DMS Database (http://saga.pmel.noaa.gov/dms/, last access: 1 May 2021).…”

Section: Machine Learning Estimates Of Sea-surface Dms Concentration ...mentioning

confidence: 99%

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

Zhao

Bilsback

et al. 2022

Atmos. Chem. Phys.

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Abstract. 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∘×0.5∘) 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 mW m−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 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 the impact of DMS on climate and addresses knowledge gaps related to factors controlling aerosols in the marine boundary layer and their climate impacts.

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Section: Machine Learning Estimates Of Sea-surface Dms Concentration ...mentioning

confidence: 99%

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

Zhao

Bilsback

et al. 2022

Atmos. Chem. Phys.

View full text Add to dashboard Cite

show abstract

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

Zhao

Bilsback

et al. 2022

Preprint

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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.

show abstract

Acrylic acid and related dimethylated sulfur compounds in the Bohai and Yellow Seas during summer and winter

Cited by 2 publications

References 31 publications

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

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

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

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