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Iodine intersects with the marine biogeochemical cycles of several major elements and can influence air quality through reactions with tropospheric ozone. Iodine is also an element of interest in paleoclimatology, whereby iodine-to-calcium ratios in marine carbonates are widely used as a proxy for past ocean redox state. While inorganic iodine in seawater is found predominantly in its reduced and oxidized anionic forms, iodide (I⁻) and iodate (IO₃⁻), the rates, mechanisms and intermediate species by which iodine cycles between these inorganic pools are poorly understood. Here, we address these issues by characterizing the speciation, composition and cycling of iodine in the upper 1,000 m of the water column at Station ALOHA in the subtropical North Pacific Ocean. We first obtained high-precision profiles of iodine speciation using isotope dilution and anion exchange chromatography, with measurements performed using inductively coupled plasma mass spectrometry (ICP-MS). These profiles indicate an apparent iodine deficit in surface waters approaching 8% of the predicted total, which we ascribe partly to the existence of dissolved organic iodine that is not resolved during chromatography. To test this, we passed large volumes of seawater through solid phase extraction columns and analyzed the eluent using high-performance liquid chromatography ICP-MS. These analyses reveal a significant pool of dissolved organic iodine in open ocean seawater, the concentration and complexity of which diminish with increasing water depth. Finally, we analyzed the rates of IO₃⁻formation using shipboard incubations of surface seawater amended with¹²⁹I⁻. These experiments suggest that intermediate iodine species oxidize to IO₃⁻much faster than I⁻does, and that rates of IO₃⁻formation are dependent on the presence of particles, but not light levels. Our study documents the dynamics of iodine cycling in the subtropical ocean, highlighting the critical role of intermediates in mediating redox transformations between the major inorganic iodine species.

 

The distribution of dissolved iodine in seawater is sensitive to multiple biogeochemical cycles, including those of nitrogen and oxygen. The iodine-to-calcium ratio (I/Ca) of marine carbonates, such as bulk carbonate or foraminifera, has emerged as a potential proxy for changes in past seawater oxygenation. However, the utility of the I/Ca proxy in deep-sea corals, natural archives of seawater chemistry with wide spatial coverage and radiometric dating potential, remains unexplored. Here, we present the first I/Ca data obtained from modern deep-sea corals, specifically scleractinian and bamboo corals, collected from the Atlantic, Eastern Pacific, and Southern Oceans, encompassing a wide range of seawater oxygen concentrations (10–280 μmol/kg). In contrast to thermodynamic predictions, we observe higher I/Ca ratios in aragonitic corals (scleractinian) compared to calcitic corals (bamboo). This observation suggests a strong biological control during iodate incorporation into deep-sea coral skeletons. For the majority of scleractinian corals, I/Ca exhibits a covariation with local seawater iodate concentrations, which is closely related to seawater oxygen content. Scleractinian corals also exhibit notably lower I/Ca below a seawater oxygen threshold of approximately 160 μmol/kg. In contrast, no significant differences in I/Ca are found among bamboo corals across the range of oxygen concentrations encountered (15–240 μmol/kg). In the North Atlantic, several hydrographic factors, such as temperature and/or salinity, may additionally affect coral I/Ca. Our results highlight the potential of I/Ca ratios in deep-sea scleractinian corals to serve as an indicator of past seawater iodate concentrations, providing valuable insights into historical seawater oxygen levels.

 

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). 

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. 

Abstract Groundwater-derived solute fluxes to the ocean have long been assumed static and subordinate to riverine fluxes, if not neglected entirely, in marine isotope budgets. Here we present concentration and isotope data for Li, Mg, Ca, Sr, and Ba in coastal groundwaters to constrain the importance of groundwater discharge in mediating the magnitude and isotopic composition of terrestrially derived solute fluxes to the ocean. Data were extrapolated globally using three independent volumetric estimates of groundwater discharge to coastal waters, from which we estimate that groundwater-derived solute fluxes represent, at a minimum, 5% of riverine fluxes for Li, Mg, Ca, Sr, and Ba. The isotopic compositions of the groundwater-derived Mg, Ca, and Sr fluxes are distinct from global riverine averages, while Li and Ba fluxes are isotopically indistinguishable from rivers. These differences reflect a strong dependence on coastal lithology that should be considered a priority for parameterization in Earth-system models.