EMPIRICAL ORTHOGONAL FUNCTIONS OF MONTHLY PRECIPITATION AND TEMPERATURE OVER THE UNITED-STATES AND HOMOGENEOUS STOCHASTIC-MODELS

The monthly mean precipitation and temperature at p = 62 stations over the United States and Canada for N = 91 years (1900-1990) are analyze d in terms of empirical orthogonal functions (EOFs) and their variance s. The eigenvalues and eigenfunctions are compared with a succession o f stochastic noise models: (1) uncorrelated noise, having eigenvalues depending on the ratio pin, with n = N - 1; (2) homogeneous noise havi ng spatial correlations which are fit to the observations; and (3) hom ogeneous noise having both spatial and temporal correlations fit to th e observations. Individual monthly data for January and July were anal yzed as well as a combined data set of all months. The eigenvalue spec tra of the homogeneous noise models are found to be in close agreement with the observed spectra even when time correlation is excluded from the model. Time correlations only slightly affect the results for tem perature and have less impact for precipitation. The EOF patterns of t he noise models contain inhomogeneities due only to the distribution o f stations, the common correlation length, and the limited sample but are nevertheless in good agreement with the observed patterns, whose i nhomogeneities may also be affected by secular trends and physical inh omogeneities such as orography. The observed EOF eigenvectors also sho w identifiable deviations from the homogeneous EOFs. Further work will be needed to see if these deviations can be convincingly associated w ith true physical inhomogeneities.