Articles | Volume 11, issue 3
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

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

Model code and software

TreeSurgeon (Version v1.3): Wollemia D. Ellis and T. Sherwen

Sparse2Spatial (Version v0.1.1) T. Sherwen

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.