Biogeosciences
Biogeosciences
BG
Articles | Volume 19, issue 6
Articles | Volume 19, issue 6
https://doi.org/10.5194/bg-19-1705-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-19-1705-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
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

Viewed

Total article views: 2,748 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,967 702 79 2,748 71 69
  • HTML: 1,967
  • PDF: 702
  • XML: 79
  • Total: 2,748
  • BibTeX: 71
  • EndNote: 69
Views and downloads (calculated since 13 Aug 2021)
Cumulative views and downloads (calculated since 13 Aug 2021)

Viewed (geographical distribution)

Total article views: 2,748 (including HTML, PDF, and XML) Thereof 2,648 with geography defined and 100 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 28 Jun 2025
Download
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.
Read more
Share
Altmetrics
Final-revised paper
Preprint