Articles | Volume 19, issue 6
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

Data sets

bjmcnabb/DMS_Climatology: DMS_Climatology publication (v1.0.0) Brandon McNabb

An updated climatology of surface dimethlysulfide concentrations and emission fluxes in the global ocean ( A. Lana, T. G. Bell, R. Simó, S. M. Vallina, J. Ballabrera-Poy, A. J. Kettle, J. Dachs, L. Bopp, E. S. Saltzman, J. Stefels, J. E. Johnson, and P. S. Liss 10/dbqjrm

Model code and software

bjmcnabb/DMS_Climatology: DMS_Climatology publication (v1.0.0) Brandon McNabb

Short summary
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.
Final-revised paper