Articles | Volume 11, issue 3
https://doi.org/10.5194/essd-11-1239-2019
https://doi.org/10.5194/essd-11-1239-2019
Data description paper
 | 
21 Aug 2019
Data description paper |  | 21 Aug 2019

A machine-learning-based global sea-surface iodide distribution

Tomás Sherwen, Rosie J. Chance, Liselotte Tinel, Daniel Ellis, Mat J. Evans, and Lucy J. Carpenter

Viewed

Total article views: 6,687 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
4,822 1,718 147 6,687 169 193
  • HTML: 4,822
  • PDF: 1,718
  • XML: 147
  • Total: 6,687
  • BibTeX: 169
  • EndNote: 193
Views and downloads (calculated since 26 Mar 2019)
Cumulative views and downloads (calculated since 26 Mar 2019)

Viewed (geographical distribution)

Total article views: 6,687 (including HTML, PDF, and XML) Thereof 5,956 with geography defined and 731 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 24 Dec 2025
Download
Short summary
Iodine plays an important role in the Earth system, as a nutrient to the biosphere and by changing the concentrations of climate and air-quality species. However, there are uncertainties on the magnitude of iodine’s role, and a key uncertainty is our understanding of iodide in the global sea-surface. Here we take a data-driven approach using a machine learning algorithm to convert a sparse set of sea-surface iodide observations into a spatially and temporally resolved dataset for use in models.
Share
Altmetrics
Final-revised paper
Preprint