This dataset contains global spatially predicted sea-surface iodide concentrations at a monthly resolution for the year 1970. It was developed as part of the NERC project Iodide in the ocean:distribution and impact on iodine flux and ozone loss (NE/N009983/1), which aimed to quantify the dominant controls on the sea surface iodide distribution and improve parameterisation of the sea-to-air iodine flux and of ozone deposition.
This dataset is the output used in the published paper 'A machine learning based global sea-surface iodide distribution' ( https://doi.org/10.5194/essd-2019-40).
The main ensemble prediction ("Ensemble_Monthly_mean ") is provided in a NetCDF file as a single variable (1). A second file (2) is provided which includes all of the predictions and the standard deviation on the prediction.
For ease of use, this output has been re-gridded to various commonly used atmosphere and ocean model resolutions (see table SI table A5 in paper). These re-gridded files are included in the folder titled "regridded_data".
Additionally, a further file (3) is provided including the prediction made included data from the Skagerak dataset. As stated in the paper referenced above, it is recommended to use the use the core files (1,2) or their re-gridded equivalents.
As new observations are made, this global data product will be updated through a "living data" model. The dataset versions follow semantic versioning (https://semver.org/). This dataset contains the first publicly released version v0.0.1 and supersedes the pre-review dataset named v0.0.0, Please refer to the paper referenced above for the current version number and information on this.
Updates for v0.0.1 vs. v0.0.0
- Additional files included of the core data re-gridded for 0.5x0.5 degree and 0.25x0.25 degree horizontal resolution.
- Minor updates were applied to all metadata in NetCDF files.
- Updates were made to coordinate grids used for regriding files from 1x1 degree to 4x5 degree.
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|Previously used record identifiers:||
No related previous identifiers.
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Use of these data is covered by the following licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/. When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record.
Data sent to the Centre for Environmental Data Analysis for archiving from the project participants.
Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)
The following citations have been automatically harvested from external sources associated with this resource where DOI tracking is possible. As such some citations may be missing from this list whilst others may not be accurate. Please contact the helpdesk to raise any issues to help refine these citation trackings.
|Hepach, H., Wadley, M. R., Stevens, D. P., Jickells, T., Hughes, C., Chance, R., Tinel, L., & Carpenter, L. J. (2020). A Global Model for Iodine Speciation in the Upper Ocean. https://doi.org/10.1002/essoar.10502078.2|
|Sherwen, T., Chance, R. J., Tinel, L., Ellis, D., Evans, M. J., & Carpenter, L. J. (2019). A machine-learning-based global sea-surface iodide distribution. Earth System Science Data, 11(3), 1239â1262. https://doi.org/10.5194/essd-11-1239-2019|
|Wadley, M. R., Stevens, D. P., Jickells, T. D., Hughes, C., Chance, R., Hepach, H., Tinel, L., & Carpenter, L. J. (2020). A Global Model for Iodine Speciation in the Upper Ocean. Global Biogeochemical Cycles, 34(9). Portico. https://doi.org/10.1029/2019gb006467|
|Wadley, M. R., Stevens, D. P., Jickells, T., Hughes, C., Chance, R., Hepach, H., & Carpenter, L. J. (2020). Modelling iodine in the ocean. https://doi.org/10.1002/essoar.10502078.1|
Ensemble prediction from multiple Radom Forest Regressor models.
The Radom Forest Regressor is a machine learning algorithm, that builds non-parameteric predictions of a target variable based on other input data or "features". Here, multiple Radom Forest Regressor models have been combined to make an ensemble prediction. See related documents for more information.
Mutiple open-source Python packages were used to built this dataset and its output, including: Pandas (Wes McKinney, 2010), Xarray (Hoyer and Hamman, 2017) and Scikit-learn (Pedregosa et al., 2011), and the xESMF package (Zhuang, 2018)
Inputs used were sea-surface iodide observations and existing datasets of ancillary chemical and physical variables described. Iodide observations are described by Chance et al. (2019b) and made available by the British Oceanographic Data Centre 30 (BODC, Chance et al. (2019); DOI:10/czhx). Ancillary data extracted for Chance et al. (2019) observation locations and globally to predict spatial fields as available from sources stated in Table 1 in the accompanying paper (Sherwen et al 2019).
- units: unitless
- var_id: LWI
- long_name: Land/Water/Ice index
- units: degrees_north
- long_name: latitude
- var_id: lat
- long_name: longitude
- units: degrees_east
- var_id: lon
- long_name: sea-surface iodide concentration
- units: kg/m3
- var_id: Ensemble_Monthly_mean
- var_id: time
- standard_name: time
- long_name: time