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 paper
| 
21 Aug 2019
Data description paper |
| 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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- Understanding the variability of ground-level ozone and fine particulate matter over the Tibetan plateau with data-driven approach H. Zhong et al. 10.1016/j.jhazmat.2024.135341
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- Senescence as the main driver of iodide release from a diverse range of marine phytoplankton H. Hepach et al. 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. 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. 10.5194/amt-14-5637-2021
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- Machine learning algorithm reveals surface deoxygenation in the Agulhas Current due to warming T. Mashifane et al. 10.1016/j.pocean.2024.103407
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- Estimation of reactive inorganic iodine fluxes in the Indian and Southern Ocean marine boundary layer S. Inamdar et al. 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. 10.1021/acsestair.4c00357
- Marine iodine emissions in a changing world L. Carpenter et al. 10.1098/rspa.2020.0824
- Tropospheric Ozone Assessment Report A. Archibald et al. 10.1525/elementa.2020.034
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- Role of oceanic ozone deposition in explaining temporal variability in surface ozone at High Arctic sites J. Barten et al. 10.5194/acp-21-10229-2021
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- An adaptive HMM method to simulate and forecast ocean chemistry data in aquaculture Y. Sun & D. Li 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. 10.3389/fmars.2020.00621
- A Global Model for Iodine Speciation in the Upper Ocean M. Wadley et al. 10.1029/2019GB006467
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Latest update: 30 May 2025
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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