El Nino Detection Via Unsupervised Clustering of Argo Temperature Profiles

Variability in the El Nino-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 Nino 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 Nino 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.