In this study, Multifractal Detrended Fluctuation Analysis (MF-DFA) is applied to daily temperature time series (mean, maximum and minimum values) from 22 Greek meteorological stations with the purpose of examining firstly their scaling behavior and then checking if there are any differences in their multifractal characteristics. The results showed that the behavior is the same at almost all stations, i.e., time series are positive long-term correlated and their multifractal structure is insensitive to local fluctuations with large magnitude. Moreover, this study deals with the spatial distribution of the main characteristics of multifractal (singularity) spectrum: the dominant Hurst exponent, the width of the spectrum, the asymmetry and the truncation type of the spectrum. The spatial distributions are discussed in terms of possible effects from various climatic features. In general, local atmospheric circulation and weather conditions are found to affect the shape of the spectrum and the corresponding spatial distributions. Furthermore, the intercorrelation of the main multifractal spectrum parameters resulted in a well-defined group of stations sharing similar multifractal characteristics. The results indicate the usefulness of the non-linear analysis in climate research due to the complex interactions among the natural processes.

The Multifractal Detrended Fluctuation Analysis (MF-DFA) is used to examine the scaling behavior and the multifractal characteristics of the mean daily temperature time series of the ERA-Interim reanalysis data for a domain centered over Greece. The results showed that the time series from all grid points exhibit the same behavior: they have a positive long-term correlation and their multifractal structure is insensitive to local fluctuations with a large magnitude. Special emphasis was given to the spatial distribution of the main characteristics of the multifractal spectrum: the value of the Hölder exponent, the spectral width, the asymmetry, and the truncation type of the spectra. The most interesting finding is that the spatial distribution of almost all spectral parameters is decisively determined by the land–sea distribution. The results could be useful in climate research for examining the reproducibility of the nonlinear dynamics of reanalysis datasets and model outputs.