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.
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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17 citations as recorded by crossref.
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- Ozone deposition to a coastal sea: comparison of eddy covariance observations with reactive air–sea exchange models D. Loades et al. 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. 10.2151/sola.2020-037
- 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
- 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
- Iodine chemistry in the chemistry–climate model SOCOL-AERv2-I A. Karagodin-Doyennel et al. 10.5194/gmd-14-6623-2021
- Influences of oceanic ozone deposition on tropospheric photochemistry R. Pound et al. 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. 10.1073/pnas.1921914117
- 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
- The MILAN Campaign: Studying Diel Light Effects on the Air–Sea Interface C. Stolle et al. 10.1175/BAMS-D-17-0329.1
- 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
- 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
- Ocean Biogeochemistry Control on the Marine Emissions of Brominated Very Short‐Lived Ozone‐Depleting Substances: A Machine‐Learning Approach S. Wang et al. 10.1029/2019JD031288
- Global ocean methane emissions dominated by shallow coastal waters T. Weber et al. 10.1038/s41467-019-12541-7
15 citations as recorded by crossref.
- Machine learning calibration of low-cost NO<sub>2</sub> and PM<sub>10</sub> sensors: non-linear algorithms and their impact on site transferability P. Nowack et al. 10.5194/amt-14-5637-2021
- JlBox v1.1: a Julia-based multi-phase atmospheric chemistry box model L. Huang & D. Topping 10.5194/gmd-14-2187-2021
- Ozone deposition to a coastal sea: comparison of eddy covariance observations with reactive air–sea exchange models D. Loades et al. 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. 10.2151/sola.2020-037
- 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
- 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
- Iodine chemistry in the chemistry–climate model SOCOL-AERv2-I A. Karagodin-Doyennel et al. 10.5194/gmd-14-6623-2021
- Influences of oceanic ozone deposition on tropospheric photochemistry R. Pound et al. 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. 10.1073/pnas.1921914117
- 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
- The MILAN Campaign: Studying Diel Light Effects on the Air–Sea Interface C. Stolle et al. 10.1175/BAMS-D-17-0329.1
- 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
- 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
2 citations as recorded by crossref.
Latest update: 02 Jun 2023
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...