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: 4,549 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,120 1,354 75 4,549 83 81
  • HTML: 3,120
  • PDF: 1,354
  • XML: 75
  • Total: 4,549
  • BibTeX: 83
  • EndNote: 81
Views and downloads (calculated since 26 Mar 2019)
Cumulative views and downloads (calculated since 26 Mar 2019)

Viewed (geographical distribution)

Total article views: 4,549 (including HTML, PDF, and XML) Thereof 3,852 with geography defined and 697 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 25 Apr 2024
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.
Altmetrics
Final-revised paper
Preprint