Barium in seawater: dissolved distribution, relationship to silicon, and barite saturation state determined using machine learning
Barium in seawater: dissolved distribution, relationship to silicon, and barite saturation state determined using machine learning
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DOI：
10.5194/essd-15-4023-2023
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发表时间：
2023-09
影响因子：
11.4
通讯作者：
Öykü Z. Mete;A. Subhas;Heather H. Kim;A. Dunlea;L. Whitmore;A. Shiller;Melissa Gilbert;William D. Leavitt;Tristan J. Horner
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作者：
Öykü Z. Mete;A. Subhas;Heather H. Kim;A. Dunlea;L. Whitmore;A. Shiller;Melissa Gilbert;William D. Leavitt;Tristan J. Horner
抽象的。钡广泛用作海水中溶解硅和颗粒有机碳通量的代表。然而，这些代理应用由于对 Ba ([Ba]) 的溶解分布了解不足而受到限制。例如，钡-硅关系存在显着的空间变异性，海洋化学可能会影响沉积物的钡保存。为了帮助解决这些问题，我们开发了 4095 个模型，用于使用高斯过程回归机器学习来预测 [Ba]。这些模型经过训练，可以使用来自北冰洋、大西洋、太平洋和南大洋的 GEOTRACES 数据根据标准海洋学观测来预测 [Ba]。然后通过将预测与印度洋隐瞒的 [Ba] 数据进行比较来验证训练模型。我们发现，使用深度、温度和盐度以及溶解分子氧、磷酸盐、硝酸盐和硅酸盐训练的模型可以准确预测印度洋的 [Ba]，平均绝对百分比偏差为 6.0%。我们使用这个模型在全球范围内模拟 [Ba]，使用世界海洋地图集中的七个相同的预测变量。由此产生的 [Ba] 分布将海洋的 Ba 预算限制为 122(±7) × 1012 mol，并揭示了钡-硅关系中海洋学上一致的变化。然后我们计算海水相对于重晶石的饱和状态。该计算揭示了海洋重晶石饱和度的系统空间和垂直变化，并表明 1000 m 以下的海洋相对于重晶石处于平衡状态。我们描述了我们的模型输出的许多可能的应用，从机械生物地球化学模型的使用到古代理校准。我们的方法展示了机器学习在准确模拟海洋中示踪剂分布方面的实用性，并提供了一个可以扩展到其他痕量元素的框架。我们的模型、训练和验证中使用的数据以及全局输出可在 Horner 和 Mete 中找到（2023 年，https://doi.org/10.26008/1912/bco-dmo.885506.2）。
Abstract. 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).