A Quantitative Analysis of Meteorological Anomaly Patterns Over the United States, 1900–1977

Abstract

Monthly meteorological data for the years 1900?77 are used in an eigenvector analysis of the anomaly patterns of surface temperature, precipitation and sea level pressure over the United States. Approximately 70% of the variance is contained in the first three of 61 temperature eigenvectors and in the first three of 25 pressure eigenvectors. Large-scale patterns of precipitation are also identified, although the compression of the data is somewhat less effective. The first eigenvector of each variable contains anomalies of the same sign over most of the United States; the second and third modes describe gradients in approximately perpendicular directions. Cross correlations between the amplitudes of eigenvectors of different variables are statistically significant, consistent with physical expectations, and, in some cases, are seasonally dependent. The first modes of both temperature and pressure are most persistent in the summer. Persistence on the seasonal time scale is generally largest for temperature and largest when summer is the antecedent season. The seasonal persistences of the amplitudes of the temperature eigenvectors are generally consistent with the persistences of station temperatures obtained recently by Namias (1978). The most prominent feature of the frequency spectra is a strong peak at 2.1 years in the amplitude of the third temperature eigenvector.