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Methodology

The data were decomposed via principal component analysis in the T- and S-mode. The T-mode results will be presented. A standard approach used by the majority of investigators was taken, namely begin by correlating the data into a 228 by 228 matrix and then diagonalizing that matrix into 228 eigenvectors and corresponding eigenvalues. Since the principal component model was used, the eigenvectors were scaled by the square root of the corresponding eigenvalues to arrive at principal component loadings (time series) which are in the same units as the parent similarity matrix (i.e., correlations) and portray the time variability of the data. Principal component scores were then calculated and plotted on a polar stereographic map centered on Antarctica. The resulting spatial patterns are thought to be dominant modes of the relative variability of the MSU channel 4 data. The question remains: Are they? By truncating the number of eigenmodes from 228 to 6 and application of various targets and rotations to these data, a fuller picture of lower stratospheric temperature anomalies emerges. The rotations applied include two orthogonal transformations (Varimax, Quartimax), one oblique transformation (Covarimin), and two Procrustes transformations (targeted trends of temperature and a series of random targets).