Nonuniversal atmospheric persistence: Different scaling of daily minimum and maximum temperatures

Abstract

An extensive investigation of 61 daily temperature records by means of detrended fluctuation analysis has revealed that the value of correlation exponent is not universal, contrary to earlier claims. Furthermore, statistically significant differences are found for daily minimum and maximum temperatures measured at the same station, suggesting different degrees of long-range correlations for the two extremes. Numerical tests on synthetic time series demonstrate that a correlated signal interrupted by uncorrelated segments exhibits an apparently lower exponent value over the usual time window of empirical data analysis. In order to find statistical differences between the two daily extreme temperatures, high frequency (10 min) records were evaluated for two distant locations. The results show that daily maxima characterize better the dynamic equilibrium state of the atmosphere than daily minima, for both stations. This provides a conceptual explanation why scaling analysis can yield different exponent values for minima and maxima.

Role of global warming on the statistics of record-breaking temperatures

“…For the last point, Krug [32] has shown that for an exponential daily temperature distribution whose width is increasing linearly with time, the number of record events after t years grows as (ln t) 2 , intermediate to the ln t growth of a stationary distribution and linear growth when the average temperature systematically increases. (iv) Day/night or high/low asymmetry [33]. That is, as a function of time there are more days whose highs exceeds a given threshold and fewer days whose high is less than a threshold.…”

Comparative Spectral Analysis and Correlation Properties of Observed and Simulated Total Column Ozone Records

“…Long range correlations are present in the signal when the fluctuation function follows a power law F (n) ∼ n α (straight line in a log-log plot) with exponent values α > 1/2. Clean power law behavior is rarely obtained for empirical data, especially for such complex systems as the atmosphere [22,23,28,30]. The reason is that many different processes of different characteristic time scales affect the instantaneous value of any atmospheric parameter, therefore nonstationarities, various trends, cycles etc.…”

Applicatıon of detrended fluctuation analysis to temperature data from Turkey

“…In the literature, there exist many studies that investigate the long-term correlation of temperature data. Some of these studies (Koscielny- Bunde et al, 1998;Bunde et al, 2001;Govindan et al, 2003) claim that the scaling exponent is universal for the temperature data while the others Kurnaz, 2004;Pattanyús-Ábrahám et al, 2004;Kiràly and Jánosi, 2005;Bartos and Jánosi, 2006;Kiràly et al, 2006;Rybski et al, 2008) claim the opposite. Apart from temperature data, the DFA analysis has also been applied to some meteorological and climatological variables such as wind speed (Govindan and Kantz, 2004;Kavasseri and Nagarajan, 2004), relative humidity (Chen et al, 2007), cloud breaking (Ivanova and Ausloos, 1999) and NAO (North Atlantic Oscillation) index (Caldeira et al, 2007).…”