Articles | Volume 11, issue 3
https://doi.org/10.5194/essd-11-1239-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-11-1239-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
21 Aug 2019
Data description article |
| 21 Aug 2019
| 21 Aug 2019
A machine-learning-based global sea-surface iodide distribution
National Centre for Atmospheric Science, University of York, York, YO10 5DD, UK
Wolfson Atmospheric Chemistry Laboratories, University of York, York, YO10 5DD, UK
Rosie J. Chance
Wolfson Atmospheric Chemistry Laboratories, University of York, York, YO10 5DD, UK
Liselotte Tinel
Wolfson Atmospheric Chemistry Laboratories, University of York, York, YO10 5DD, UK
Daniel Ellis
Wolfson Atmospheric Chemistry Laboratories, University of York, York, YO10 5DD, UK
Mat J. Evans
National Centre for Atmospheric Science, University of York, York, YO10 5DD, UK
Wolfson Atmospheric Chemistry Laboratories, University of York, York, YO10 5DD, UK
Lucy J. Carpenter
Wolfson Atmospheric Chemistry Laboratories, University of York, York, YO10 5DD, UK
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Total article views: 6,283 (including HTML, PDF, and XML)
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- An improved estimate of inorganic iodine emissions from the ocean using a coupled surface microlayer box model R. Pound et al. https://doi.org/10.5194/acp-24-9899-2024
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- Surface Inorganic Iodine Speciation in the Indian and Southern Oceans From 12°N to 70°S R. Chance et al. https://doi.org/10.3389/fmars.2020.00621
- A Global Model for Iodine Speciation in the Upper Ocean M. Wadley et al. https://doi.org/10.1029/2019GB006467
34 citations as recorded by crossref.
- A machine learning methodology for the generation of a parameterization of the hydroxyl radical D. Anderson et al. https://doi.org/10.5194/gmd-15-6341-2022
- Aerosol iodine recycling is a major control on tropospheric reactive iodine abundance A. Moon et al. https://doi.org/10.5194/acp-26-2353-2026
- Mapping the global distribution of lead and its isotopes in seawater with explainable machine learning A. Olivelli et al. https://doi.org/10.5194/essd-17-3679-2025
- JlBox v1.1: a Julia-based multi-phase atmospheric chemistry box model L. Huang & D. Topping https://doi.org/10.5194/gmd-14-2187-2021
- Simultaneous observations of atmospheric IO radical, O3, and sea surface iodide over the tropical western Pacific warm pool: strong correlation of IO levels with estimated inorganic iodine fluxes H. Takashima et al. https://doi.org/10.1186/s40645-025-00775-7
- An improved estimate of inorganic iodine emissions from the ocean using a coupled surface microlayer box model R. Pound et al. https://doi.org/10.5194/acp-24-9899-2024
- Iodide, iodate & dissolved organic iodine in the temperate coastal ocean M. Jones et al. https://doi.org/10.3389/fmars.2024.1277595
- Impacts of ocean biogeochemistry on atmospheric chemistry L. Tinel et al. https://doi.org/10.1525/elementa.2023.00032
- Barium in seawater: dissolved distribution, relationship to silicon, and barite saturation state determined using machine learning Ö. Mete et al. https://doi.org/10.5194/essd-15-4023-2023
- Iodine chemistry in the chemistry–climate model SOCOL-AERv2-I A. Karagodin-Doyennel et al. https://doi.org/10.5194/gmd-14-6623-2021
- Understanding the variability of ground-level ozone and fine particulate matter over the Tibetan plateau with data-driven approach H. Zhong et al. https://doi.org/10.1016/j.jhazmat.2024.135341
- The impacts of marine-emitted halogens on OH radicals in East Asia during summer S. Fan & Y. Li https://doi.org/10.5194/acp-22-7331-2022
- Negligible temperature dependence of the ozone–iodide reaction and implications for oceanic emissions of iodine L. Brown et al. https://doi.org/10.5194/acp-24-3905-2024
- Senescence as the main driver of iodide release from a diverse range of marine phytoplankton H. Hepach et al. https://doi.org/10.5194/bg-17-2453-2020
- Speciation of dissolved inorganic iodine in a coastal fjord: a time-series study from Bedford Basin, Nova Scotia, Canada Q. Shi et al. https://doi.org/10.3389/fmars.2023.1171999
- Machine learning calibration of low-cost NO2 and PM10 sensors: non-linear algorithms and their impact on site transferability P. Nowack et al. https://doi.org/10.5194/amt-14-5637-2021
- Low-pressure storms drive nitrous oxide emissions in the Southern Ocean C. Kelly et al. https://doi.org/10.1038/s41467-026-68744-2
- Speciation and cycling of iodine in the subtropical North Pacific Ocean I. Ştreangă et al. https://doi.org/10.3389/fmars.2023.1272968
- Machine learning algorithm reveals surface deoxygenation in the Agulhas Current due to warming T. Mashifane et al. https://doi.org/10.1016/j.pocean.2024.103407
- Ozone deposition to a coastal sea: comparison of eddy covariance observations with reactive air–sea exchange models D. Loades et al. https://doi.org/10.5194/amt-13-6915-2020
- Global Bromine- and Iodine-Mediated Tropospheric Ozone Loss Estimated Using the CHASER Chemical Transport Model T. Sekiya et al. https://doi.org/10.2151/sola.2020-037
- Estimation of reactive inorganic iodine fluxes in the Indian and Southern Ocean marine boundary layer S. Inamdar et al. https://doi.org/10.5194/acp-20-12093-2020
- Machine-Learning-Based Estimation of Marine CHBr3 Emissions around Asia and the Implication on Bromine Radicals and Ozone Depletion J. Chen et al. https://doi.org/10.1021/acsestair.4c00357
- Marine iodine emissions in a changing world L. Carpenter et al. https://doi.org/10.1098/rspa.2020.0824
- Tropospheric Ozone Assessment Report A. Archibald et al. https://doi.org/10.1525/elementa.2020.034
- Influences of oceanic ozone deposition on tropospheric photochemistry R. Pound et al. https://doi.org/10.5194/acp-20-4227-2020
- Global reconstruction reduces the uncertainty of oceanic nitrous oxide emissions and reveals a vigorous seasonal cycle S. Yang et al. https://doi.org/10.1073/pnas.1921914117
- Role of oceanic ozone deposition in explaining temporal variability in surface ozone at High Arctic sites J. Barten et al. https://doi.org/10.5194/acp-21-10229-2021
- Evaluation of processes driving iodine monoxide and ozone variability over the Western Pacific warm pool T. Sekiya et al. https://doi.org/10.1186/s40645-025-00712-8
- Analysis of surface deoxygenation trends in the Benguela upwelling system: An interpretable machine learning approach T. Mashifane et al. https://doi.org/10.1002/lol2.70090
- The MILAN Campaign: Studying Diel Light Effects on the Air–Sea Interface C. Stolle et al. https://doi.org/10.1175/BAMS-D-17-0329.1
- An adaptive HMM method to simulate and forecast ocean chemistry data in aquaculture Y. Sun & D. Li https://doi.org/10.1016/j.compag.2023.107767
- Surface Inorganic Iodine Speciation in the Indian and Southern Oceans From 12°N to 70°S R. Chance et al. https://doi.org/10.3389/fmars.2020.00621
- A Global Model for Iodine Speciation in the Upper Ocean M. Wadley et al. https://doi.org/10.1029/2019GB006467
Saved (final revised paper)
Latest update: 11 Jun 2026
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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