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Global predicted sea-surface iodide concentrations v0.0.0

Update Frequency: Not Planned
Status: Completed
Online Status: ONLINE
Publication State: Citable
Publication Date: 2019-02-08
DOI Publication Date: 2019-02-08
Download Stats: last 12 months

This dataset has been superseded. See Latest Version here

This dataset contains global spatially predicted sea-surface iodide concentrations at a monthly resolution. This dataset 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.

The main ensemble prediction ("Ensemble Monthly mean ") is provided in a NetCDF (1) file as a single variable. A second file (2) is provided which includes all of the predictions and the standard deviation on the prediction.



This is the output of the paper 'A machine learning based global sea-surface iodide distribution' (see related documentation). 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 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, we will update the global dataset through a "living data" model. The dataset versions archived here follow semantic versioning ( The pre-review dataset is achieved in the folder named v0.0.0, with the with publically released versions numbered starting from v1.0.0. Please refer to the referenced paper (see related documentation) for the current version number and information on this.

Citable as:  Sherwen, T.; Chance, R.J.; Tinel, L.; Ellis, D.; Evans, M.J.; Carpenter, L.J. (2019): Global predicted sea-surface iodide concentrations v0.0.0. Centre for Environmental Data Analysis, 08 February 2019. doi:10.5285/02c6f4eea9914e5c8a8390dd09e5709a.
Abbreviation: Not defined
Keywords: NE/N009983/1, Iodide, sea-surface, ozone deposition, iodine emission, Ocean, Model, NERC


Previous Info:
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Previously used record identifiers:
No related previous identifiers.
Access rules:
Access to these data is available to any registered CEDA user. Please Login or Register for an account to gain access.
Use of these data is covered by the following licence: When using these data you must cite them correctly using the citation given on the CEDA Data Catalogue record.
Data lineage:

Data sent to the Centre for Environmental Data Analysis for archiving from the project participants.

Data Quality:
Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)
File Format:
Data are NetCDF formatted.

Process overview

This dataset was generated by the computation detailed below.
Title Ensemble prediction from multiple Radom Forest Regressor models.
Abstract 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).
Input Description None
Output Description None
Software Reference None
  • long_name: Land/Water/Ice index
  • var_id: LWI
  • names: Land/Water/Ice index
  • var_id: lat
  • long_name: latitude
  • units: degrees_north
  • names: latitude
  • var_id: lon
  • units: degrees_east
  • long_name: longitude
  • names: longitude
  • units: nM
  • long_name: sea-surface iodide concentration
  • var_id: RFR(TEMP+SWrad+NO3+MLD+SAL)
  • names: sea-surface iodide concentration
  • var_id: time

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