Although meteorological map typing using rotated principal component analysis (RPCA) typically has utilized one mode of decomposition (S-mode), there are several advantages to utilizing an alternative T-mode decomposition. Whereas S-mode representation analyzes a dispersion matrix of inter-station covariances/correlations, T-mode analysis transposes the S-mode input data matrix to analyze covariances/correlations among time periods. The advantages of T-mode analysis include the potential to retain several anomaly centers per principal component, the ability to capture phase relationships among persistent centers of action, and a simple structure criterion that renders the analysis more amenable to subclassification. A T-mode RPCA of Northern Hemisphere daily sea-level pressure is performed, and the results of the first two principal components are displayed. Several tests are conducted to show that the results effectively capture the structure of the input data. This analysis reveals large-scale patterns that may have a significant impact on the wintertime weather variability over the Arctic.