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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. 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). 

Dissolved Ba, Cd, Cu, Ga, Mn, Ni, and V concentration data from the US GEOTRACES Arctic Expedition (GN01, HLY1502) from August to October 2015. Clean seawater samples were collected using a GEOTRACES CTD referred to as GT-C/12L GoFlo. Additional near surface samples were collected using either a small boat or through the ice using Teflon coated Tygon tubing and a trace metal clean pump (IWAKI, model WMD-30LFY-115). This dataset also includes selected stable Ba isotope analyses. 

Abstract North African dust is known to be deposited in the Gulf of Mexico, but its deposition rate and associated supply of lithogenic dissolved metals, such as the abiotic metal thorium or the micronutrient metal iron, have not been well‐quantified.²³²Th is an isotope with similar sources as iron and its input can be quantified using radiogenic²³⁰Th. By comparing dissolved²³²Th fluxes at three sites in the northern Gulf of Mexico with upwind sites in the North Atlantic, we place an upper bound on North African dust contributions to²³²Th and Fe in the Gulf of Mexico, which is about 30% of the total input. Precision on this bound is hindered by uncertainty in the relative rates of dust deposition in the North Atlantic and the northern Gulf of Mexico. Based on available radium data, shelf sources, including rivers, submarine groundwater discharge, and benthic sedimentary releases are likely as important if not more important than dust in the budget of lithogenic metals in the Gulf of Mexico. In other words, it is likely there is no one dominant source of Th and Fe in the Gulf of Mexico. Finally, our estimated Fe input in the northern Gulf of Mexico implies an Fe residence time of less than 6 months, similar to that in the North Atlantic despite significantly higher supply rates in the Gulf of Mexico.