Articles | Volume 19, issue 6
https://doi.org/10.5194/bg-19-1705-2022
https://doi.org/10.5194/bg-19-1705-2022
Research article
 | 
24 Mar 2022
Research article |  | 24 Mar 2022

Improved prediction of dimethyl sulfide (DMS) distributions in the northeast subarctic Pacific using machine-learning algorithms

Brandon J. McNabb and Philippe D. Tortell

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Cited articles

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Asher, E. C., Merzouk, A., and Tortell, P. D.: Fine-scale spatial and temporal variability of surface water dimethylsufide (DMS) concentrations and sea–air fluxes in the NE Subarctic Pacific, Mar. Chem., 126, 63–75, https://doi.org/10/chmbhk, 2011. 
Asher, E. C., Dacey, J. W., Ianson, D., Peña, A., and Tortell, P. D.: Concentrations and cycling of DMS, DMSP, and DMSO in coastal and offshore waters of the Subarctic Pacific during summer, 2010-2011, J. Geophys. Res.-Oceans, 122, 3269–3286, https://doi.org/10.1002/2016JC012465, 2017. 
Ayers, G. P. and Cainey, J. M.: The CLAW hypothesis: a review of the major developments, Environ. Chem., 4, 366–374, https://doi.org/10/b7p54b, 2007. 
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The trace gas dimethyl sulfide (DMS) plays an important role in the ocean sulfur cycle and can also influence Earth’s climate. Our study used two statistical methods to predict surface ocean concentrations and rates of sea–air exchange of DMS in the northeast subarctic Pacific. Our results show improved predictive power over previous approaches and suggest that nutrient availability, light-dependent processes, and physical mixing may be important controls on DMS in this region.
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