Articles | Volume 17, issue 21
https://doi.org/10.5194/bg-17-5335-2020
https://doi.org/10.5194/bg-17-5335-2020
Research article
 | 
06 Nov 2020
Research article |  | 06 Nov 2020

Global ocean dimethyl sulfide climatology estimated from observations and an artificial neural network

Wei-Lei Wang, Guisheng Song, François Primeau, Eric S. Saltzman, Thomas G. Bell, and J. Keith Moore

Viewed

Total article views: 2,724 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,824 865 35 2,724 36 59
  • HTML: 1,824
  • PDF: 865
  • XML: 35
  • Total: 2,724
  • BibTeX: 36
  • EndNote: 59
Views and downloads (calculated since 11 Mar 2020)
Cumulative views and downloads (calculated since 11 Mar 2020)

Viewed (geographical distribution)

Total article views: 2,724 (including HTML, PDF, and XML) Thereof 2,350 with geography defined and 374 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 26 Apr 2024
Download
Short summary
Dimethyl sulfide, a volatile compound produced as a byproduct of marine phytoplankton activity, can be emitted to the atmosphere via gas exchange. In the atmosphere, DMS is oxidized to cloud condensation nuclei, thus contributing to cloud formation. Therefore, oceanic DMS plays an important role in regulating the planet's climate by influencing the radiation budget. In this study, we use an artificial neural network model to update the global DMS climatology and estimate the sea-to-air flux.
Altmetrics
Final-revised paper
Preprint