Unsupervised Classification of Global Temperature Profiles Based on Gaussian Mixture Models

Abstract

Understanding ocean temperature distribution is vital for ocean stratification, currents, and marine ecosystems. This study analyzed the global 0.5-degree ocean temperature dataset from the Chinese Academy of Sciences Marine Data Center (July 2020) to identify regional temperature patterns. After standardizing the data, Principal Component Analysis (PCA) reduced the dimensionality from 32 to 7, preserving key temperature variations. A Gaussian Mixture Model (GMM) determined that 18 classifications were optimal by evaluating the variance and category weights. Applying GMM to the reduced data identified 18 distinct temperature distribution patterns across various marine environments, including polar currents, warm current mixing zones, ocean fronts, and enclosed basins, each with unique geographical and physical characteristics. Most classifications showed high posterior probabilities, indicating model accuracy, though lower probabilities were observed in complex regions like the Indian Ocean. The results highlight the significant roles of ocean currents, climatic phenomena, and ecological factors in temperature distribution, providing insights for ocean circulation studies, climate modeling, and marine biodiversity conservation. Future research should enhance the model accuracy by optimizing the parameters, expanding data coverage, integrating additional features, and combining marine observations with climate models to better understand ocean temperature patterns and their global climate impacts.