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
11 citations as recorded by crossref.
- Tropospheric Ozone Assessment Report A. Archibald et al. 10.1525/elementa.2020.034
- 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
- 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
- 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
- A Global Model for Iodine Speciation in the Upper Ocean M. Wadley et al. 10.1029/2019GB006467
- 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
- Global ocean methane emissions dominated by shallow coastal waters T. Weber et al. 10.1038/s41467-019-12541-7
10 citations as recorded by crossref.
- Tropospheric Ozone Assessment Report A. Archibald et al. 10.1525/elementa.2020.034
- 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
- 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
- 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
- A Global Model for Iodine Speciation in the Upper Ocean M. Wadley et al. 10.1029/2019GB006467
- 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
1 citations as recorded by crossref.
Latest update: 28 Feb 2021
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...