Exploring Atmosphere–Ocean Coupling Using Principal Component and Redundancy Analysis

Abstract

Abstract Principal component analysis (PCA), which is designed to look at internal modes of variability, has often been applied beyond its intended design to study coupled modes of variability in combined datasets, also referred to as combined PCA. There are statistical techniques better suited for this purpose such as singular value decomposition (SVD) and canonical correlation analysis (CCA). In this paper, a different technique is examined that has not often been applied in climate science, that is, redundancy analysis (RA). Similar to multivariate regression, RA seeks to maximize the variance accounted for in one random vector that is linearly regressed against another random vector. RA can be used for forecasting and prediction studies of the climate system. This technique has the added advantage that the time-lagged redundancy index offers a robust method of identifying lead–lag relations among climate variables. In this study, combined PCA and RA of global sea surface temperatures (SSTs) and sea level pressures (SLPs) are carried out for the National Centers for Environmental Prediction (NCEP) reanalysis data and a simulation of the Canadian Centre for Climate Modeling and Analysis (CCCma) climate model. A simplified state-space model is also constructed to aid in the diagnosis and interpretation of the results. The relative advantages and disadvantages of combined PCA and RA are discussed. Overall, RA tends to provide a clearer and more consistent picture of the underlying physical processes than combined PCA.