Using machine learning to accurately simulate the oceanic barium distribution

Science Highlights

17 October 2023

Mete and colleagues (2023, see reference below) used Machine Learning (ML) to predict the global distribution of oceanic barium (Ba). Models were first trained to predict [Ba] from standard oceanographic observations using GEOTRACES data from the Arctic, Atlantic, Pacific, and Southern oceans. Model predictions of [Ba] were then compared with actual [Ba] data from the Indian Ocean, with the best models achieving a mean absolute percentage error of just 6.0 %. This successful comparison allowed the authors to calculate the global distribution of [Ba], Ba*, and marine barite saturation using data from the World Ocean Atlas. This approach revealed four significant findings: 1) the ocean contains 122±7 Tmol of dissolved Ba; 2) the variability in the barium–silicon relationship is consistent with the biogeochemical characteristics of both elements; 3) marine barite saturation exhibits systematic spatial and vertical variations; 4) taken as a whole, the ocean below 1000 m is at equilibrium with respect to barite. These results have broad implications, both for the modern ocean and for interpreting paleo-records of barite. A data product, which includes a global grid of predictions and the ML model itself, is freely available from BCO-DMO: https://www.bco-dmo.org/dataset/885506.

Figure: Model output showing the dissolved distribution of [Ba], Ba*, and barite saturation state (Ωbarite) in the surface of the Southern Ocean. Barium-star represents the difference between ‘in situ’ (i.e., ML model predicted) and silicate-predicted [Ba], defined as Ba* = [Ba]in situ – (0.54 × [Si] + 39.3). Barite saturation state, Ωbarite, is the ratio between the Ba and sulfate ion product and the in situ barite solubility product. The dashed and dotted lines show the locations of the southern Antarctic Circumpolar Current Front and the Subantarctic Front, respectively.

Reference:

Mete, Ö. Z., Subhas, A. V., Kim, H. H., Dunlea, A. G., Whitmore, L. M., Shiller, A. M., Gilbert, M., Leavitt, W. D., & Horner, T. J. (2023). Barium in seawater: dissolved distribution, relationship to silicon, and barite saturation state determined using machine learning. Earth System Science Data, 15, 4023–4045. Access the paper: 10.5194/essd-15-4023-2023

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