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El Nino detection via unsupervised clustering of Argo temperature profiles
  • Isabel A Houghton,
  • James Wilson
Isabel A Houghton
University of San Francisco

Corresponding Author:ihoughton@usfca.edu

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James Wilson
University of San Francisco
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Abstract

Variability in the El Nino-Southern Oscillation 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, temporal dynamics of the clusters are strongly indicative of El Nino events, the Pacific warming phase of the El Nino-Southern Oscillation. The unsupervised clustering task successfully 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 tilting of the thermocline. Clustering proves to be an effective tool for analysis of the irregularly sampled (in space and time) data from ocean floats and may serve as a novel approach for detecting future anomalies given the freedom from thresholding decisions. Unsupervised machine learning approaches could be particularly valuable due to their ability to identify patterns in datasets without user-imposed expectations, facilitating further discovery of anomaly indicators.