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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. 

Abstract Atmospheric deposition represents a major input for micronutrient trace elements (TEs) to the surface ocean and is often quantified indirectly through measurements of aerosol TE concentrations. Sea spray aerosol (SSA) dominates aerosol mass concentration over much of the global ocean, but few studies have assessed its contribution to aerosol TE loading, which could result in overestimates of “new” TE inputs. Low‐mineral aerosol concentrations measured during the U.S. GEOTRACES Pacific Meridional Transect (GP15; 152°W, 56°N to 20°S), along with concurrent towfish sampling of surface seawater, provided an opportunity to investigate this aspect of TE biogeochemical cycling. Central Pacific Ocean surface seawater Al, V, Mn, Fe, Co, Ni, Cu, Zn, and Pb concentrations were combined with aerosol Na data to calculate a “recycled” SSA contribution to aerosol TE loading. Only vanadium was calculated to have a SSA contribution averaging >1% along the transect (mean of 1.5%). We derive scaling factors from previous studies on TE enrichments in the sea surface microlayer and in freshly produced SSA to assess the broader potential for SSA contributions to aerosol TE loading. Maximum applied scaling factors suggest that SSA could contribute significantly to the aerosol loading of some elements (notably V, Cu, and Pb), while for others (e.g., Fe and Al), SSA contributions largely remained <1%. Our study highlights that a lack of focused measurements of TEs in SSA limits our ability to quantify this component of marine aerosol loading and the associated potential for overestimating new TE inputs from atmospheric deposition. 

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).