Variability in the El Niño‐Southern Oscillation (ENSO) has global impacts on seasonal temperatures and rainfall. Current detection methods for extreme phases, which occur with irregular periodicity, rely upon sea surface temperature anomalies within a strictly defined geographic region of the Pacific Ocean. However, under changing climate conditions and ocean warming, these historically motivated indicators may not be reliable into the future. In this work, we demonstrate the power of data clustering as a robust, automatic way to detect anomalies in climate patterns. Ocean temperature profiles from Argo floats are partitioned into similar groups utilizing unsupervised machine learning methods. The automatically identified groups of measurements represent spatially coherent, large‐scale water masses in the Pacific, despite no inclusion of geospatial information in the clustering task. Further, spatiotemporal dynamics of the clusters are strongly indicative of El Niño events, the east Pacific warming phase of ENSO. The fitting of a cluster model on a collection of ocean profiles identifies changes in the vertical structure of the temperature profiles through reassignment to a different group, concisely capturing physical changes to the water column during an El Niño event, such as thermocline tilting. Clustering proves to be an effective tool for analysis of the irregularly sampled (in space and time) data from Argo floats and may serve as a novel approach for detecting anomalies given the freedom from thresholding decisions. Unsupervised machine learning could be particularly valuable due to its ability to identify patterns in data sets without user‐imposed expectations, facilitating further discovery of anomaly indicators.

中文翻译：

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通过Argo温度曲线的无监督聚类检测厄尔尼诺现象

厄尔尼诺-南方涛动（ENSO）的变化对季节温度和降雨具有全球影响。当前用于极端阶段的检测方法（以不规则的周期性发生）依赖于严格定义的太平洋地理区域内的海面温度异常。但是，在不断变化的气候条件和海洋变暖的情况下，这些具有历史动机的指标在未来可能并不可靠。在这项工作中，我们证明了数据聚类的功能是一种强大的自动方法，可以检测气候模式中的异常。利用无人监督的机器学习方法，将来自Argo浮标的海洋温度剖面分为相似的组。自动识别的测量值组代表了太平洋中空间上连贯的大规模水团，尽管在聚类任务中没有包含地理空间信息。此外，这些星团的时空动态强烈指示了厄尔尼诺事件，这是ENSO的东太平洋变暖阶段。将群集模型拟合到海洋剖面的集合上，可以通过重新分配给不同的组来识别温度剖面的垂直结构的变化，从而在厄尔尼诺事件（例如温跃层倾斜）期间简明地捕获水柱的物理变化。事实证明，聚类是分析Argo浮标中不规则采样（时空）数据的有效工具，并且在没有阈值决策的情况下，聚类可以用作检测异常的新方法。