A spatially and vertically resolved global grid of dissolved barium concentrations in seawater determined using Gaussian Process Regression machine learning ...

• Main Authors: Horner, Tristan J., Mete, Oyku Z.
• Format: Dataset
• Language: English
• Published: Biological and Chemical Oceanography Data Management Office (BCO-DMO) 2023
• Subjects: barium; barite; machine learning; Arctic; Pacific; Indian
• Online Access: https://dx.doi.org/10.26008/1912/bco-dmo.885506.1; https://hdl.handle.net/1912/29704

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 1,157 Indian Ocean data were seen by the model during training. We identify a model that can accurate predict [Ba] in the Indian Ocean using six features: depth, temperature, salinity, dissolved oxygen, dissolved phosphate, and dissolved nitrate. This model achieves a mean absolute percentage deviation of 6.3 %. 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 ...