Abstract
Principal components analysis (PCA) and singular value decomposition (SVD) are used to identify the primary modes of decadal and multidecadal variability in annual global Palmer Drought Severity Index (PDSI) values and seaβsurface temperatures (SSTs). The PDSI and SST data for 1925β2003 were detrended and smoothed (with a 10βyear moving average) to isolate the decadal and multidecadal variability. The first two principal components (PCs) of the PDSI PCA explained almost 38% of the decadal and multidecadal variance in the detrended and smoothed global annual PDSI data. The first two PCs of detrended and smoothed global annual SSTs explained nearly 56% of the decadal variability in global SSTs. The PDSI PCs and the SST PCs are directly correlated in a pairwise fashion. The first PDSI and SST PCs reflect variability of the detrended and smoothed annual Pacific Decadal Oscillation (PDO), as well as detrended and smoothed annual Indian Ocean SSTs. The second set of PCs is strongly associated with the Atlantic Multidecadal Oscillation (AMO). The SVD analysis of the crossβcovariance of the PDSI and SST data confirmed the close link between the PDSI and SST modes of decadal and multidecadal variation and provided a verification of the PCA results. These findings indicate that the major modes of multidecadal variations in SSTs and landβsurface climate conditions are highly interrelated through a small number of spatially complex but slowly varying teleconnections. Therefore, these relations may be adaptable to providing improved baseline conditions for seasonal climate forecasting. Copyright Β© 2006 John Wiley & Sons, Ltd.