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Discussion papers
https://doi.org/10.5194/essd-2019-40
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2019-40
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Data description paper 26 Mar 2019

Data description paper | 26 Mar 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Earth System Science Data (ESSD).

A machine learning based global sea-surface iodide distribution

Tomás Sherwen1,2, Rosie J. Chance2, Liselotte Tinel2, Daniel Ellis2, Mat J. Evans1,2, and Lucy J. Carpenter2 Tomás Sherwen et al.
  • 1National Centre for Atmospheric Science, University of York, York, YO10 5DD, UK
  • 2Wolfson Atmospheric Chemistry Laboratories, University of York, York, YO10 5DD, UK

Abstract. 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 of sea-surface temperature (Chance et al., 2014; MacDonald et al., 2014). 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° x 0.125°, ∼ 12.5 km), monthly dataset of present-day global sea-surface iodide. We use a compilation of iodide observations (Chance et al., 2019b) that is 45 % larger than has been used previously (Chance et al., 2014) 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 ten 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 a new high resolution global sea-surface iodide field. Sea-surface temperature is the most important variable in all of the top ten 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. The global iodide dataset is made freely available to the community (DOI: https://doi.org/10/gfv5v3) and as new observations are made, we will update the global dataset through a "living data" model.

Tomás Sherwen et al.
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Status: open (extended)
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Tomás Sherwen et al.
Data sets

Global predicted sea-surface iodide concentrations v0.0.0. T. Sherwen, R. Chance, L. Tinel, D. Ellis, M. Evans, and L. J. Carpenter https://doi.org/10.5285/02c6f4eea9914e5c8a8390dd09e5709a

Model code and software

TreeSurgeon: Epiphyte D. Ellis and T. Sherwen https://doi.org/10.5281/ZENODO.2579240

Tomás Sherwen et al.
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Latest update: 19 Jun 2019
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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.
Iodine plays an important role in the Earth system, as a nutrient to the biosphere and by...
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