Articles | Volume 11, issue 3
Earth Syst. Sci. Data, 11, 1239–1262, 2019
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.
Earth Syst. Sci. Data, 11, 1239–1262, 2019
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
A machine-learning-based global sea-surface iodide distribution
Tomás Sherwen et al.
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Cited
17 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
- 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: 08 Aug 2022
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...
