document: Discovery of climate indices using clustering To analyze the effect of the oceans and atmosphere on land climate , Earth Scientists have developed climate indices , which are time series that summarize the behavior of selected regions of the Earth 's oceans and atmosphere . In the past , Earth scientists have used observation and , more recently , eigenvalue analysis techniques , such as principal components analysis ( PCA ) and singular value decomposition ( SVD ) , to discover climate indices . However , eigenvalue techniques are only useful for finding a few of the strongest signals . Furthermore , they impose a condition that all discovered signals must be orthogonal to each other , making it difficult to attach a physical interpretation to them . This paper presents an alternative clustering-based methodology for the discovery of climate indices that overcomes these limitiations and is based on clusters that represent regions with relatively homogeneous behavior . The centroids of these clusters are time series that summarize the behavior of the ocean or atmosphere in those regions . Some of these centroids correspond to known climate indices and provide a validation of our methodology ; other centroids are variants of known indices that may provide better predictive power for some land areas ; and still other indices may represent potentially new Earth science phenomena . Finally , we show that cluster based indices generally outperform SVD derived indices , both in terms of area weighted correlation and direct correlation with the known indices .
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