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• W4283013885 abstract "<strong class=journal-contentHeaderColor>Abstract.</strong> 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 <span class=inline-formula><math xmlns=http://www.w3.org/1998/Math/MathML id=M1 display=inline overflow=scroll dspmath=mathml><mrow class=chem><msup><msub><mi mathvariant=normal>SO</mi><mn mathvariant=normal>4</mn></msub><mrow><mn mathvariant=normal>2</mn><mo>-</mo></mrow></msup></mrow></math><span><svg:svg xmlns:svg=http://www.w3.org/2000/svg width=34pt height=16pt class=svg-formula dspmath=mathimg md5hash=1ff229b3a1dee9b612120d0c0866bf91><svg:image xmlns:xlink=http://www.w3.org/1999/xlink xlink:href=acp-22-9583-2022-ie00001.svg width=34pt height=16pt src=acp-22-9583-2022-ie00001.png/></svg:svg></span></span> 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 (<span class=inline-formula><math xmlns=http://www.w3.org/1998/Math/MathML id=M2 display=inline overflow=scroll dspmath=mathml><mrow><mn mathvariant=normal>0.5</mn><msup><mi/><mo>∘</mo></msup><mo>×</mo><mn mathvariant=normal>0.5</mn><msup><mi/><mo>∘</mo></msup></mrow></math><span><svg:svg xmlns:svg=http://www.w3.org/2000/svg width=52pt height=11pt class=svg-formula dspmath=mathimg md5hash=e3eab2aebde9b19bb51ba677b5b7fccf><svg:image xmlns:xlink=http://www.w3.org/1999/xlink xlink:href=acp-22-9583-2022-ie00002.svg width=52pt height=11pt src=acp-22-9583-2022-ie00002.png/></svg:svg></span></span>) 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 <span class=inline-formula><math xmlns=http://www.w3.org/1998/Math/MathML id=M3 display=inline overflow=scroll dspmath=mathml><mrow class=chem><msup><msub><mi mathvariant=normal>SO</mi><mn mathvariant=normal>4</mn></msub><mrow><mn mathvariant=normal>2</mn><mo>-</mo></mrow></msup></mrow></math><span><svg:svg xmlns:svg=http://www.w3.org/2000/svg width=34pt height=16pt class=svg-formula dspmath=mathimg md5hash=662559684694993a70e1282ef01f9183><svg:image xmlns:xlink=http://www.w3.org/1999/xlink xlink:href=acp-22-9583-2022-ie00003.svg width=34pt height=16pt src=acp-22-9583-2022-ie00003.png/></svg:svg></span></span> 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 <span class=inline-formula>−</span>200 to <span class=inline-formula>−</span>20 <span class=inline-formula>mW m^−2</span> and from <span class=inline-formula>−</span>900 to <span class=inline-formula>−</span>100 <span class=inline-formula>mW m^−2</span>, respectively, with levels varying by season. The strong negative IRF is mainly over remote ocean regions (<span class=inline-formula>−</span>900 to <span class=inline-formula>−</span>600 <span class=inline-formula>mW m^−2</span>). 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." @default.
• W4283013885 created "2022-06-18" @default.
• W4283013885 creator A5086664284 @default.
• W4283013885 date "2022-06-17" @default.
• W4283013885 modified "2023-10-18" @default.
• W4283013885 title "Reply on RC1" @default.
• W4283013885 doi "https://doi.org/10.5194/acp-2022-3-ac1" @default.
• W4283013885 hasPublicationYear "2022" @default.
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