Detrended fluctuation analysis of daily temperature records: Geographic dependence over Australia

Abstract

Daily temperature anomaly records are analyzed (61 for Australia, 18 for Hungary) by means of detrended fluctuation analysis. Positive long range asymptotic correlations extending up to 5-10 years are detected for each case. Contrary to earlier claims, the correlation exponent is not universal for continental stations. Interestingly, the dominant factor is geographic latitude over Australia: the general tendency is a decrease of correlation exponent with increasing distance from the equator. This tendency is in a complete agreement with the results found by Tsonis et al. (1999) for 500-hPa height anomalies in the northern hemisphere. The variance of fluctuations exhibits an opposite trend, the larger is the distance from the equator, the larger the amplitude of intrinsic fluctuations. The presence of Tropospheric Biennial Oscillation is clearly identified for three stations at the north-eastern edge of the Australian continent.

Extreme events: dynamics, statistics and prediction

“…It is affected, however, by jumps in the time series; such jumps are often present in temperature records (Rust et al, 2008). DFA has been applied to many climatic time series (e.g., Monetti et al, 2001;Eichner et al, 2003;Fraedrich and Blender, 2003;Bunde et al, 2004;Fraedrich and Blender, 2004;Király and Jánosi, 2004;Rybski et al, 2006;Fraedrich et al, 2009), and LRD behavior has been inferred to be present in them; the estimates obtained for the Hurst exponent have differed, however, fairly widely from study to study. Due to DFA's heuristic nature and the lack e marked by red shading.…”

Multifractal detrended fluctuation analysis in examining scaling properties of the spatial patterns of soil water storage

“…The DFA is a noble tool that handles these problems so that final output reflects the effect of the suite of processes on soil water distribution. The DFA technique has been successfully applied for scaling studies of nonstationary series in climatology (Kurnaz, 2004;Kiraly and Janosi, 2005), river runoff (Koscielny- Bunde et al, 2006), financial series (Grau-Carles, 2001), and biomedicine (Peng et al, 1992). However, to the best of our knowledge, this noble technique has not been applied in characterization of scaling properties of soil water storage.…”

Detecting the Temporal Scaling Behavior of the Normalized Difference Vegetation Index Time Series in China Using a Detrended Fluctuation Analysis

“…DFA stems from random walk theory and has been effectively used to explore the scaling behavior of natural process time series in diverse fields, such as weather records [25,35], tree-ring width [50], intervention time series of forest fires [31,46], groundwater systems [51], and features of atmosphere [52,53]. DFA provides a simple quantitative parameter to represent the properties of the scaling behavior of complex systems [54].…”

“…The DFA can be performed to explore intrinsic self-similar properties and avoid the spurious detection of apparent long-range correlations that may be caused by the non-stationary time series [34]. In addition, this method effectively filters out slow trends [35] and provides a simple exponent that represents the scaling behavior of a particular time series [36].…”