Data · dataset · 2023
A spatially and vertically resolved global grid of dissolved barium concentrations in seawater determined using Gaussian Process Regression machine learning
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Dataset: Distribution of dissolved barium in seawater determined using machine learning We present a spatially and vertically resolved global grid of dissolved barium concentrations ([Ba]) in seawater determined using Gaussian Process Regression machine learning.
Description
This model was trained using 4,345 quality-controlled GEOTRACES data from the Arctic, Atlantic, Pacific, and Southern Oceans. Model output was validated by assessing the accuracy of [Ba] simulations in the Indian Ocean, noting that none of the Indian Ocean data were seen by the model during training.
We identify a model that can accurate predict [Ba] in the Indian Ocean using seven features: depth, temperature, salinity, as well as dissolved dioxygen, phosphate, nitrate, and silicate concentrations. This model achieves a mean absolute percentage error of 6.0 %, which we assume represents the generalization error. This model was used to simulate [Ba] on a global basis using predictor data from the World Ocean Atlas 2018.
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The global model of [Ba] is on a 1°x 1° grid with 102 depth levels from 0 to 5,500 m. The dissolved [Ba] output was then used to simulate dissolved Ba* (barium-star), which is the difference between 'observed' and [Ba] predicted from co-located [Si]. Lastly, [Ba] data were combined with temperature, salinity, and pressure data from the World Ocean Atlas to calculate the saturation state of seawater with respect to barite.
The model reveals that the volume-weighted mean oceanic [Ba] and and saturation state are 89 nmol/kg and 0.82, respectively. These results imply that the total marine Ba inventory is 122(±7) ×10¹² mol and that the ocean below 1,000 m is at barite equilibrium. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'.
The most current version of this dataset is available at: bco-dmo.org/dataset/885506 NSF Division of Ocean Sciences (NSF OCE) OCE-2023456, NSF Division of Ocean Sciences (NSF OCE) OCE-2048604
Links
Where it is published
- Dataverse dataset page dataverse.whoi.edu/dataset.xhtml?persistentId=hdl%3A1912%2F66425 ↗
landing page · from dataverse whoi edu
- Persistent identifier hdl.handle.net/1912/66425 ↗
DOI / persistent id · from dataverse whoi edu
Catalogue records · 1
- Dataverse API dataverse.whoi.edu/api/datasets/:persistentId/?persistentId=hdl%3A1912%2F66425 ↗
metadata API · from dataverse whoi edu
Topics
- From keywords
- Computer Science & AI · Machine learning
Related
Provenance · 1 source records, 7 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| MBLWHOI Library Dataverse | hdl:1912/66425 | 2 h ago | JSON v1 |
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| concepts[field].anzsrc:group:4611 | mapping · dataverse whoi edu | vocabulary-mapper@1.0.0 | keywords['machine learning'] |
| concepts[field].local:field:computer-science-ai | mapping · dataverse whoi edu | connector:dataverse_whoi_edu@1.0.0 | /subjects |
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