Abstract
This paper describes the application of orthogonal transformations to detect multivariate change in the monthly mean sea surface temperature (SST) as given by the NOAA/NASA Oceans Pathfinder data. The transforms applied include multivariate alteration detection (MAD) variates based on canonical correlation analysis, and maximum autocorrelation factors (MAFs). The method described can be considered as an extension to empirical orthogonal function analysis that is specially tailored for change detection in spatial data since it first maximises differences in the data between two points in time and then maximises autocorrelation between neighbouring observations. The results show that the large scale ocean events associated with the El Niño/Southern Oscillation related changes are concentrated in the first SST MAF/MAD mode and the two first sea surface height MAF/MAD modes. The MAD/MAF analysis also revealed a spatially correlated structure in the Western Mediterranean Sea that turned out to be related to a strong semi-annual variation in the SST for 1997 which was difficult to resolve from a traditional principal component analysis.
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