Mete
Oyku Z.
Mete
Oyku Z.
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DatasetA spatially and vertically resolved global grid of dissolved barium concentrations in seawater determined using Gaussian Process Regression machine learning(Biological and Chemical Oceanography Data Management Office (BCO-DMO). Contact: bco-dmo-data@whoi.edu, 2023-02-22) Horner, Tristan J. ; Mete, Oyku Z.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 1,157 Indian Ocean data were seen by the model during training. We identify a model that can accurate predict [Ba] in the Indian Ocean using six features: depth, temperature, salinity, dissolved oxygen, dissolved phosphate, and dissolved nitrate. This model achieves a mean absolute percentage deviation of 6.3 %. 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–1 and 0.82, respectively. These results imply that the total marine Ba inventory is 122±8 ×10¹² mol. For a complete list of measurements, refer to the full dataset description in the supplemental file 'Dataset_description.pdf'. The most current version of this dataset is available at: https://www.bco-dmo.org/dataset/885506
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ArticleBarium in seawater: dissolved distribution, relationship to silicon, and barite saturation state determined using machine learning(Copernicus Publications, 2023-09-13) Mete, Oyku Z. ; Subhas, Adam V. ; Kim, Heather H. ; Dunlea, Ann G. ; Whitmore, Laura M. ; Shiller, Alan M. ; Gilbert, Melissa ; Leavitt, William D. ; Horner, Tristan J.Barium is widely used as a proxy for dissolved silicon and particulate organic carbon fluxes in seawater. However, these proxy applications are limited by insufficient knowledge of the dissolved distribution of Ba ([Ba]). For example, there is significant spatial variability in the barium–silicon relationship, and ocean chemistry may influence sedimentary Ba preservation. To help address these issues, we developed 4095 models for predicting [Ba] using Gaussian process regression machine learning. These models were trained to predict [Ba] from standard oceanographic observations using GEOTRACES data from the Arctic, Atlantic, Pacific, and Southern oceans. Trained models were then validated by comparing predictions against withheld [Ba] data from the Indian Ocean. We find that a model trained using depth, temperature, and salinity, as well as dissolved dioxygen, phosphate, nitrate, and silicate, can accurately predict [Ba] in the Indian Ocean with a mean absolute percentage deviation of 6.0 %. We use this model to simulate [Ba] on a global basis using these same seven predictors in the World Ocean Atlas. The resulting [Ba] distribution constrains the Ba budget of the ocean to 122(±7) × 1012 mol and reveals oceanographically 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 to barite. We describe a number of possible applications for our model outputs, ranging from use in mechanistic biogeochemical models to paleoproxy calibration. Our approach demonstrates the utility of machine learning in accurately simulating the distributions of tracers in the sea and provides a framework that could be 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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ArticleDistribution and drivers of organic carbon sedimentation along the continental margins(American Geophysical Union, 2024-08-17) Tegler, Logan A. ; Horner, Tristan J. ; Galy, Valier ; Bent, Shavonna M. ; Wang, Yi ; Kim, Heather H. ; Mete, Oyku Z. ; Nielsen, Sune G.Organic carbon (OC) sedimentation in marine sediments is the largest long-term sink of atmospheric CO2 after silicate weathering. Understanding the mechanistic and quantitative aspects of OC delivery and preservation in marine sediments is critical for predicting the role of the oceans in modulating global climate. Yet, estimates of the global OC sedimentation in marginal settings span an order of magnitude, and the primary controls of OC preservation remain highly debated. Here, we provide the first global bottom-up estimate of OC sedimentation along the margins using a synthesis of literature data. We quantify both terrestrial- and marine-sourced OC fluxes and perform a statistical analysis to discern the key factors influencing their magnitude. We find that the margins host 23.2 ± 3.5 Tmol of OC sedimentation annually, with approximately 84% of marine origin. Accordingly, we calculate that only 2%–3% of OC exported from the euphotic zone escapes remineralization before sedimentation. Surprisingly, over half of all global OC sedimentation occurs below bottom waters with oxygen concentrations greater than 180 μM, while less than 4% occurs in settings with <50 μM oxygen. This challenges the prevailing paradigm that bottom-water oxygen (BWO) is the primary control on OC preservation. Instead, our statistical analysis reveals that water depth is the most significant predictor of OC sedimentation, surpassing all other factors investigated, including BWO levels and sea-surface chlorophyll concentrations. This finding suggests that the primary control on OC sedimentation is not production, but the ability of OC to resist remineralization during transit through the water column and while settling on the seafloor.