The North Atlantic ocean is key to climate through its role in heat transport and storage. Climate models suggest that the circulation is weakening but the physical drivers of this change are poorly constrained. Here, the root mechanisms are revealed with the explicitly transparent machine learning (ML) method Tracking global Heating with Ocean Regimes (THOR). Addressing the fundamental question of the existence of dynamical coherent regions, THOR identifies these and their link to distinct currents and mechanisms such as the formation regions of deep water masses, and the location of the Gulf Stream and North Atlantic Current. Beyond a black box approach, THOR is engineered to elucidate its source of predictive skill rooted in physical understanding. A labeled data set is engineered using an explicitly interpretable equation transform and k-means application to model data, allowing theoretical inference. A multilayer perceptron is then trained, explaining its skill using a combination of layerwise relevance propagation and theory. With abrupt CO₂ quadrupling, the circulation weakens due to a shift in deep water formation regions, a northward shift of the Gulf Stream and an eastward shift in the North Atlantic Current. If CO₂ is increased 1% yearly, similar but weaker patterns emerge influenced by natural variability. THOR is scalable and applicable to a range of models using only the ocean depth, dynamic sea level and wind stress, and could accelerate the analysis and dissemination of climate model data. THOR constitutes a step toward trustworthy ML called for within oceanography and beyond, as its predictions are physically tractable.

中文翻译：

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使用透明机器学习揭示全球变暖对北大西洋环流的影响

北大西洋通过其在热传输和储存方面的作用对气候至关重要。气候模型表明环流正在减弱，但这种变化的物理驱动因素受到的限制很差。在这里，通过明确透明的机器学习 (ML) 方法跟踪全球海洋供暖 (THOR) 揭示了根本机制。在解决动态相干区域存在的基本问题时，THOR 确定了这些及其与不同洋流和机制的联系，例如深水团的形成区域，以及墨西哥湾流和北大西洋洋流的位置。除了黑匣子方法之外，THOR 还旨在阐明其植根于物理理解的预测技能的来源。标记数据集是使用可明确解释的方程变换和 k 均值应用程序对模型数据进行设计的，从而允许进行理论推断。然后训练多层感知器，使用分层相关传播和理论的组合解释其技能。随着突然的 CO由于深水形成区的转移、墨西哥湾流向北移动和北大西洋洋流向东移动，环流在₂倍增后减弱。如果 CO ₂每年增加 1%，则会出现类似但较弱的模式，受自然变化的影响。THOR 具有可扩展性，适用于仅使用海洋深度、动态海平面和风应力的一系列模型，并且可以加速气候模型数据的分析和传播。THOR 是朝着值得信赖的 ML 迈出的一步，在海洋学内外都需要，因为它的预测在物理上是易于处理的。