A machine-learning-based global sea-surface iodide distribution

Sherwen, Tomás; Chance, Rosie J.; Tinel, Liselotte; Ellis, Daniel; Evans, Mat J.; Carpenter, Lucy J.

Iodide in the sea-surface plays an important role in the Earth system. It modulates the oxidising capacity of the troposphere and provides iodine to terrestrial ecosystems. However, our understanding of its distribution is limited due to a paucity of observations. Previous efforts to generate global distributions have generally fitted sea-surface iodide observations to relatively simple functions using proxies for iodide such as nitrate and sea-surface temperature. This approach fails to account for coastal influences and variation in the bio-geochemical environment. Here we use a machine learning regression approach (random forest regression) to generate a high-resolution (0.125×0.125, 12.5km×12.5km), monthly dataset of present-day global sea-surface iodide. We use a compilation of iodide observations (1967–2018) that has a 45 % larger sample size than has been used previously as the dependent variable and co-located ancillary parameters (temperature, nitrate, phosphate, salinity, shortwave radiation, topographic depth, mixed layer depth, and chlorophyll a) from global climatologies as the independent variables. We investigate the regression models generated using different combinations of ancillary parameters and select the 10 best-performing models to be included in an ensemble prediction. We then use this ensemble of models, combined with global fields of the ancillary parameters, to predict new high-resolution monthly global sea-surface iodide fields representing the present day. Sea-surface temperature is the most important variable in all 10 models. We estimate a global average sea-surface iodide concentration of 106 nM (with an uncertainty of ∼20 %), which is within the range of previous estimates (60–130 nM). Similar to previous work, higher concentrations are predicted for the tropics than for the extra-tropics. Unlike the previous parameterisations, higher concentrations are also predicted for shallow areas such as coastal regions and the South China Sea. Compared to previous work, the new parameterisation better captures observed variability. The iodide concentrations calculated here are significantly higher (40 % on a global basis) than the commonly used MacDonald et al. (2014) parameterisation, with implications for our understanding of iodine in the atmosphere. We envisage these fields could be used to represent present-day sea-surface iodide concentrations, in applications such as climate and air-quality modelling. The global iodide dataset is made freely available to the community (https://doi.org/10/gfv5v3, Sherwen et al.2019), and as new observations are made, we will update the global dataset through a “living data” model.

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Sherwen, Tomás / Chance, Rosie J. / Tinel, Liselotte / et al: A machine-learning-based global sea-surface iodide distribution. 2019. Copernicus Publications.

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Rechteinhaber: Tomás Sherwen et al.

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