An Intercomparison of Methods for Finding Coupled Patterns in Climate Data

Abstract

This paper introduces a conceptual framework for comparing methods that isolate important coupled modes of variability between time series of two fields. Four specific methods are compared: principal component analysis with the fields combined (CPCA), canonical correlation analysis (CCA) and a variant of CCA proposed by Barnett and Preisendorfer (BP), principal component analysis of one single field followed by correlation of its component amplitudes with the second field (SFPCA), and singular value decomposition of the covariance matrix between the two fields (SVD). SVD and CPCA are easier to implement than BP, and do not involve user-chosen parameters. All methods are applied to a simple but geophysically relevant model system and their ability to detect a coupled signal is compared as parameters such as the number of points in each field, the number of samples in the time domain, and the signal-to-noise ratio are varied. In datasets involving geophysical fields, the number of sampling times ma... Abstract This paper introduces a conceptual framework for comparing methods that isolate important coupled modes of variability between time series of two fields. Four specific methods are compared: principal component analysis with the fields combined (CPCA), canonical correlation analysis (CCA) and a variant of CCA proposed by Barnett and Preisendorfer (BP), principal component analysis of one single field followed by correlation of its component amplitudes with the second field (SFPCA), and singular value decomposition of the covariance matrix between the two fields (SVD). SVD and CPCA are easier to implement than BP, and do not involve user-chosen parameters. All methods are applied to a simple but geophysically relevant model system and their ability to detect a coupled signal is compared as parameters such as the number of points in each field, the number of samples in the time domain, and the signal-to-noise ratio are varied. In datasets involving geophysical fields, the number of sampling times ma...