Spectra and correlations of climate data from days to decades

Abstract

The correlations of several daily surface meteorological parameters such as maximum, minimum, and mean temperature, diurnal temperature range, pressure, precipitation, and relative air humidity are analyzed by partly complementary methods being effective on different timescales: power spectral analysis, second‐ and higher‐degree detrended fluctuation analysis, Hurst analysis, and the direct estimation of the autocorrelation in the time domain. Data from American continental and maritime and European low‐elevation and mountain stations are used to see possible site dependencies. For all station types and locations, all meteorological parameters show correlations from the shortest to the longest statistically reliable timescales of about three decades. The correlations partly show a clear power law scaling with site‐dependent exponents. Mainly, the short‐time behavior of the correlations depends on the station type and differs considerably among the various meteorological parameters. In particular, the detrended fluctuation and the Hurst analyses reveal a possible power low behavior for long timescales which is less well resolved or even may remain unrecognized by the classical power spectral analysis and from the autocorrelation. The long‐time behavior of the American temperatures is governed by power laws. The corresponding exponents coincide for all temperatures except for the daily temperature range with different values for the maritime and the continental stations. From the European temperatures those from low‐elevation stations also scale quite well, whereas temperatures from mountain stations do not.