Eigenvector Analysis of Monthly Mean Surface Data

Abstract

Eigenvector or principal component analysis has been carried out on monthly means of surface pressure, temperature, and rainfall defined on grids extending over both hemispheres and the tropical belt. In each case, 10 out of a possible 120 components were sufficient to account for more than 80% of the observed variance regardless of whether the annual cycle was first removed. The major pressure components can be interpreted in terms of meridional and land-sea temperature gradients, and reflect a basic difference in the seasonal cycle in the Northern and Southern Hemispheres. While both pressure and temperature show coherent departures on a hemispheric scale, rainfall components are regional in character, and the variance reduction is more evenly distributed over the major components. The use of principal components as climatological indices merits greater attention than has so far been given.