Barium in seawater: dissolved distribution, relationship to silicon, and barite saturation state determined using machine learning

Mete, Öykü Z.; Subhas, Adam V.; Kim, Heather H.; Dunlea, Ann G.; Whitmore, Laura M.; Shiller, Alan M.; Gilbert, Melissa; Leavitt, William D.; Horner, Tristan J.

doi:https://doi.org/10.5194/essd-15-4023-2023

Articles | Volume 15, issue 9

https://doi.org/10.5194/essd-15-4023-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/essd-15-4023-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 9

Data description paper

|

13 Sep 2023

Data description paper |

| 13 Sep 2023

Barium in seawater: dissolved distribution, relationship to silicon, and barite saturation state determined using machine learning

Öykü Z. Mete, Adam V. Subhas, Heather H. Kim, Ann G. Dunlea, Laura M. Whitmore, Alan M. Shiller, Melissa Gilbert, William D. Leavitt, and Tristan J. Horner

Supplement

https://doi.org/10.5194/essd-15-4023-2023-supplement

Data sets

Distribution of dissolved barium in seawater determined using machine learning T. J. Horner and O. Z. Mete https://doi.org/10.26008/1912/bco-dmo.885506.2

Model code and software

Machine Learning Model 3080 O. Z. Mete and T. J. Horner https://doi.org/10.26008/1912/bco-dmo.885506.2

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Short summary

We present results from a machine learning model that accurately predicts dissolved barium concentrations for the global ocean. Our results reveal that the whole-ocean barium inventory is significantly lower than previously thought and that the deep ocean below 1000 m is at equilibrium with respect to barite. The model output can be used for a number of applications, including intercomparison, interpolation, and identification of regions warranting additional investigation.