Dataset: Distribution of dissolved barium in seawater determined using machine learning
Data Citation:
Horner, T. J., Mete, O. Z. (2023) A spatially and vertically resolved global grid of dissolved barium concentrations in seawater determined using Gaussian Process Regression machine learning. Biological and Chemical Oceanography Data Management Office (BCO-DMO). (Version 2) Version Date 2023-07-11 [if applicable, indicate subset used]. doi:10.26008/1912/bco-dmo.885506.2 [access date]
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This dataset is licensed under Creative Commons Attribution 4.0.
If you wish to use this dataset, it is highly recommended that you contact the original principal investigators (PI). Should the relevant PI be unavailable, please contact BCO-DMO (info@bco-dmo.org) for additional guidance. For general guidance please see the BCO-DMO Terms of Use document.
DOI:10.26008/1912/bco-dmo.885506.2
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Spatial Extent: N:89.5 E:179.5 S:-77.5 W:-179.5
Temporal Extent: 2007 - 2018
Project:
Principal Investigator:
Tristan J. Horner (Woods Hole Oceanographic Institution, WHOI)
Student:
Oyku Z. Mete (Woods Hole Oceanographic Institution, WHOI)
BCO-DMO Data Manager:
Shannon Rauch (Woods Hole Oceanographic Institution, WHOI BCO-DMO)
Version:
2
Version Date:
2023-07-11
Restricted:
No
Validated:
Yes
Current State:
Final no updates expected
A spatially and vertically resolved global grid of dissolved barium concentrations in seawater determined using Gaussian Process Regression machine learning
Abstract:
We present a spatially and vertically resolved global grid of dissolved barium concentrations ([Ba]) in seawater determined using Gaussian Process Regression machine learning. 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. 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.