Articles | Volume 11, issue 3
Earth Syst. Sci. Data, 11, 1239–1262, 2019
https://doi.org/10.5194/essd-11-1239-2019
Earth Syst. Sci. Data, 11, 1239–1262, 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 et al.

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AR: Author's response | RR: Referee report | ED: Editor decision
AR by Tomás Sherwen on behalf of the Authors (24 Jul 2019)  Author's response    Manuscript
ED: Publish as is (25 Jul 2019) by Scott Stevens
AR by Tomás Sherwen on behalf of the Authors (29 Jul 2019)  Author's response    Manuscript
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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.