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Abstract Radium‐226(²²⁶Ra) and barium (Ba) exhibit similar chemical behaviors and distributions in the marine environment, serving as valuable tracers of water masses, ocean mixing, and productivity. Despite their similar distributions, these elements originate from distinct sources and undergo disparate biogeochemical cycles, which might complicate the use of these tracers. In this study, we investigate these processes by analyzing a full‐depth ocean section of²²⁶Ra activities (T_1/2 = 1,600 years) and barium concentrations obtained from samples collected along the US GEOTRACES GP15 Pacific Meridional Transect during September–November 2018, spanning from Alaska to Tahiti. We find that surface waters possess low levels of²²⁶Ra and Ba due to export of sinking particulates, surpassing inputs from the continental margins. In contrast, deep waters have higher²²⁶Ra activities and Ba concentrations due to inputs from particle regeneration and sedimentary sources, with²²⁶Ra inputs primarily resulting from the decay of²³⁰Th in sediments. Further, dissolved²²⁶Ra and Ba exhibit a strong correlation along the GP15 section. To elucidate the drivers of the correlation, we used a water mass analysis, enabling us to quantify the influence of water mass mixing relative to non‐conservative processes. While a significant fraction of each element's distribution can be explained by conservative mixing, a considerable fraction cannot. The balance is driven using non‐conservative processes, such as sedimentary, rivers, or hydrothermal inputs, uptake and export by particles, and particle remineralization. Our study demonstrates the utility of²²⁶Ra and Ba as valuable biogeochemical tracers for understanding ocean processes, while shedding light on conservative and myriad non‐conservative processes that shape their respective distributions. 

This dataset includes measurements of dissolved barium (Ba) concentrations in the South Pacific and Southern Ocean from the US GEOTRACES GP17 section (GP17-OCE, Papeete, Tahiti to Punta Arenas, Chile) on R/V Roger Revelle from December 2022 to January 2023. 

This dataset includes measurements of the dissolved isotope radium-226 in the South Pacific and Southern Ocean. Samples were collected on the US GEOTRACES GP17-OCE cruise (Papeete, Tahiti to Punta Arenas, Chile) on R/V Roger Revelle from December 2022 to January 2023. Radium-223, radium-224, and radium-228 data will be made available in the future. 

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. 

Abstract. Barium is widely used as a proxy for dissolved silicon and particulateorganic carbon fluxes in seawater. However, these proxy applications arelimited by insufficient knowledge of the dissolved distribution of Ba([Ba]). For example, there is significant spatial variability in thebarium–silicon relationship, and ocean chemistry may influence sedimentaryBa preservation. To help address these issues, we developed 4095 models forpredicting [Ba] using Gaussian process regression machine learning. Thesemodels were trained to predict [Ba] from standard oceanographic observationsusing GEOTRACES data from the Arctic, Atlantic, Pacific, and Southernoceans. Trained models were then validated by comparing predictions againstwithheld [Ba] data from the Indian Ocean. We find that a model trained usingdepth, temperature, and salinity, as well as dissolved dioxygen, phosphate,nitrate, and silicate, can accurately predict [Ba] in the Indian Ocean with amean absolute percentage deviation of 6.0 %. We use this model tosimulate [Ba] on a global basis using these same seven predictors in theWorld Ocean Atlas. The resulting [Ba] distribution constrains the Ba budgetof the ocean to 122(±7) × 1012 mol and revealsoceanographically consistent variability in the barium–silicon relationship. We then calculate the saturation state of seawater with respect to barite. This calculation reveals systematic spatial and vertical variations in marine barite saturation and shows that the ocean below 1000 m is at equilibrium with respect tobarite. We describe a number of possible applications for our model outputs, ranging from use in mechanistic biogeochemical models to paleoproxy calibration. Ourapproach demonstrates the utility of machine learning in accurately simulatingthe distributions of tracers in the sea and provides a framework that couldbe extended to other trace elements. Our model, the data used in training and validation, and global outputs are available in Horner and Mete (2023, https://doi.org/10.26008/1912/bco-dmo.885506.2).