We re-analyze historical daily atmospheric temperature time series for investigating long-range correlation. Such a problem is attracting much attention due to the deep importance of assessing statistical dependence of atmospheric phenomena on climatic scales in the context of Climate modeling. In particular, we adopt Detrended Fluctuation Analysis (DFA), which is one of the most used techniques for detecting scale invariance in stationary signals contaminated by external non-stationary disturbances. A very standard application of this methodology seems to evidence persistence power-law exponents close to 0.65 on time scales greater than the meteorological one (>15 days). Nevertheless, more careful investigations put into evidence the local character of this exponent whose value decays progressively with scale. Our results show that the simple detection of approximately straight lines in a log-log plot cannot be considered as a signature of scale invariance and local scale features have to be explicitly investigated.
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