02 Mar 2023
 | 02 Mar 2023
Status: this preprint is currently under review for the journal ESSD.

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

Öykü Mete, Adam Subhas, Heather Kim, Ann Dunlea, Laura Whitmore, Alan Shiller, Melissa Gilbert, William Leavitt, and Tristan Horner

Abstract. Barium is widely used as a proxy for dissolved nutrients and particulate organic carbon fluxes in seawater. However, these proxy applications are limited by insufficient knowledge of the dissolved distribution of Ba ([Ba]). For example, there is significant spatial variability in the Ba–Si relationship, and ocean chemistry may influence sedimentary Ba preservation. To help address these issues, we developed 4,095 models for predicting [Ba] using Gaussian Progress Regression Machine Learning. These models were trained to predict [Ba] from standard oceanographic observations using GEOTRACES data from the Arctic, Atlantic, Pacific, and Southern Oceans. Trained models were then validated by comparing predictions against withheld [Ba] data from the Indian Ocean. We find that a model using depth, T, S, [O2], [PO4], and [NO3] as predictors can accurately predict [Ba] in the Indian Ocean with a mean absolute percentage deviation of 6.3 %. We use this model to simulate [Ba] on a global basis using these same six predictors in the World Ocean Atlas. The resulting [Ba] distribution constrains the total Ba budget of the ocean to 122±8 × 1012 mol and clarifies the global relationship between dissolved Ba and Si. We also calculate the saturation state of seawater with respect to barite, revealing that the ocean below 1,000 m is, on average, at or near saturation. We describe a number of possible applications for our model output, ranging from use in biogeochemical models to paleoproxy calibration. Our approach could be extended to other trace elements with relatively minor adjustments and demonstrates the utility of machine learning to accurately simulate the distributions of tracers in the sea.

Öykü Mete et al.

Status: open (until 27 Apr 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2023-67', Anonymous Referee #1, 14 Mar 2023 reply
  • RC2: 'Comment on essd-2023-67', Christophe Monnin, 21 Mar 2023 reply
  • RC3: 'Comment on essd-2023-67', Frank Pavia, 28 Mar 2023 reply
  • RC4: 'Comment on essd-2023-67', Anonymous Referee #4, 28 Mar 2023 reply

Öykü Mete et al.

Data sets

Distribution of dissolved barium in seawater determined using machine learning T. J. Horner and O. Z. Mete

Öykü Mete et al.


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Short summary
We present results from a machine learning model that accurately predicts dissolved barium concentrations in seawater. We use this model to simulate dissolved barium concentrations on a global basis, revealing that the whole-ocean barium inventory is significantly lower than previously thought. The model output can be used for a number of applications, including intercomparison, interpolation, and for identifying regions warranting additional investigation.