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.

Data sets

Global predicted sea-surface iodide concentrations v0.0.1 T. Sherwen, R. J. Chance, L. Tinel, D. Ellis, M. J. Evans, and L. J. Carpenter https://doi.org/10.5285/6448e7c92d4e48188533432f6b26fe22

Model code and software

TreeSurgeon (Version v1.3): Wollemia D. Ellis and T. Sherwen https://doi.org/10.5281/zenodo.3346817

Sparse2Spatial (Version v0.1.1) T. Sherwen https://doi.org/10.5281/zenodo.3369212

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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.