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 article
 | 
21 Aug 2019
Data description article |  | 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: 7,654 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
5,603 1,884 167 7,654 189 235
  • HTML: 5,603
  • PDF: 1,884
  • XML: 167
  • Total: 7,654
  • BibTeX: 189
  • EndNote: 235
Views and downloads (calculated since 26 Mar 2019)
Cumulative views and downloads (calculated since 26 Mar 2019)

Viewed (geographical distribution)

Total article views: 7,654 (including HTML, PDF, and XML) Thereof 6,906 with geography defined and 748 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 25 Mar 2026
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