Temporal–spatial diversities of long-range correlation for relative humidity over China

On climate prediction: how much can we expect from climate memory?

“…According to recent studies (Koscielny-Bunde et al 1998;Fraedrich and Blender 2003;Eichner et al 2003;Kantelhardt et al 2006;Lin et al 2007;Chen et al 2007;Vyushin and Kushner 2009;Yuan et al 2010;Dangendorf et al 2014;Jiang et al 2017), this research idea is possible, as for many climate variables it has been found that the variability on different time scales is not arbitrary, but follows a scaling manner as shown below, where s represents the time scale and H is the Hurst exponent (Hurst 1951). This scaling behavior indicates that the knowledge of high-frequency variability allows one to predict the low-frequency variability of a given process.…”

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].…”

“…Different exponents represent different types of scaling behavior in the original series. When α is less than 0.5, the time series exhibits short-range correlation or anti-correlation behavior [25,29,31,46] suggesting that the fluctuation of the series at different times are oppositely correlated and the opposite correlation is the strongest when α closes to 0; when α is around 0.5, the series corresponds to white noise (there is no correlation in the series), i.e., it is a random series; if a long-range correlation exists in the series, α is between 0.5 and 1 [29,31,34,36,46], and the series exhibits power-law behavior meaning that the fluctuation of the series at different times are positively correlated and the positive correlation is the strongest when α closes to 1; when α is equal to 1, the series exhibits characteristics of 1/f noise [31,46], thereby indicating that the series shows a self-organized criticality [55]; and when α is bigger than 1, long-range correlation exists in the series, although it does not obey a power-law relationship [31].…”

“…It is widely recognized that NDVI fluctuations are significantly related to climate variables [21] such as temperature [22,23], relative humidity [23] and solar radiation [24]. According to previous empirical studies, these factors are characterized by various temporal scaling behaviors [25][26][27], i.e., a long-range correlation behavior exists for these factors over time and exhibits diverse geographical distributions. However, few studies have formally identified and quantified the temporal scaling behaviors of the NDVI time series [28,29].…”

Correlation and Persistence in Global Solar Radiation

“…Temperature and precipitation have characteristic values of H (Koscielny- Bunde et al, 1998; although some claim that the scaling exponent is not universal for temperature data (Király & Jánosi, 2005;Rybski et al 2008). Relative humidity shows stronger persistence Lin et al, 2007), and wind speed also exhibit behaviour with long-range correlation (Govindan & Kantz, 2004;Kavasseri & Nagarajan, 2005;Koçak, 2009;Feng et al, 2009). One can be sure of the universality of the correlations in climatological time series but its exponents can be related to local patterns.…”