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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.
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Barium in seawater: dissolved distribution, relationship to silicon, and barite saturation state determined using machine learning
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).
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- PAR ID:
- 10462216
- Date Published:
- Journal Name:
- Earth System Science Data
- Volume:
- 15
- Issue:
- 9
- ISSN:
- 1866-3516
- Page Range / eLocation ID:
- 4023 to 4045
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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The isotopic composition of barium (δ138Ba) has emerged as a powerful tracer of deep-ocean circulation, water mass provenance, and the oceanic Ba cycle. Although the δ138Ba of water masses is primarily controlled by the balance between pelagic barite precipitation and Ba resupply from ocean circulation, questions remain regarding the isotopic offset associated with pelagic barite formation and how the resultant Ba isotope compositions are transmitted through the water column to marine sediments. To address these questions, we conducted a time series study of dissolved, particulate, and sedimentary Ba chemistry in the Gulf of Aqaba (GOA), in the northern Red Sea, from January 2015 to April 2016. These data span significant seasonal changes in hydrography, primary productivity, and aerosol deposition, revealing three principal findings. First, the dissolved Ba chemistry of the GOA is vertically uniform across the time series, largely reflecting water mass advection from the Red Sea, with mean dissolved Ba concentrations of 47.9 ± 4.7 nmol kg−1and mean δ138Ba = +0.55‰ ± 0.07‰ (±2 SD,n= 18). Second, despite significant variations in particulate matter composition and flux, the δ138Ba of sinking particulate Ba maintained a consistent isotope composition across different depths and over time at +0.09‰ ± 0.06‰ (n= 26). Consequently, these data imply a consistent Ba isotope offset of −0.46‰ ± 0.10‰ (±2 SD) between sinking particulates and seawater. This offset is similar to those determined in previous studies and indicates that it applies to particulates formed across diverse environmental conditions. Third, barite-containing sediment samples deposited in the GOA exhibit δ138Ba = +0.34‰ ± 0.03‰, which is offset by approximately +0.2‰ relative to sinking particles. While the specific mechanism driving this offset remains unresolved, our results highlight the importance of performing site-specific proxy validations and exercising careful site selection when applying novel paleoceanographic proxies.more » « less
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Lyons, Timothy W; Turchyn, Alexandra; Reinhard, Chris (Ed.)In the modern marine environment, barium isotope (δ138Ba) variations are primarily driven by barite cycling – barite incorporates “light” Ba isotopes from solution, rendering the residual Ba reservoir enriched in “heavy” Ba isotopes by a complementary amount. Since the processes of barite precipitation and dissolution are vertically segregated and spatially heterogeneous, barite cycling drives systematic variations in the barium isotope composition of seawater and sediments. This Element examines these variations; evaluates their global, regional, local, and geological controls; and, explores how δ138Ba can be exploited to constrain the origin of enigmatic sedimentary sulfates and to study marine biogeochemistry over Earth’s history.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/essd-15-4023-2023
