Long range correlations in tree ring chronologies of the USA: Variation within and across species

Abstract

Tree ring width data are among the best proxies for reconstructing past temperature and precipitation records. The discovery of fractal scaling and long‐memory in meteorological and hydrological signals motivates us to investigate such properties in tree ring chronologies. Detrended fluctuation analysis and adaptive fractal analysis are utilized to estimate the Hurst parameter values of 697 tree ring chronologies from the continental United States. We find significant differences in the Hurst parameter values across the 10 species studied in the work. The long‐range scaling relations found here suggest that the behavior of tree ring growth observed in a short calibration period may be similar to the general behavior of tree ring growth in a much longer period, and therefore, the limited calibration period may be more useful than originally thought. The variations of the long‐range correlations within and across species may be further explored in future to better reconstruct paleoclimatic records.

Declining tree growth resilience mediates subsequent forest mortality in the US Mountain West

“…However, growth autocorrelation and variability might integrate other signals on top of tree physiology. Notably, autocorrelation and variability of tree growth are partly driven by that of climate and hydrology (Bowers et al, 2013; Coulthard et al, 2020). For example, PDSI is a strong determinant of tree growth in the region and was substantially more autocorrelated at colder sites ( r = −.45, p < .001), hence potentially explaining the strong negative correlation observed between MAT and growth autocorrelation.…”

Complex Systems, Emergence, and Multiscale Analysis: A Tutorial and Brief Survey

“…Its dimensionality cannot be reduced by popular methods such as principle component analysis [124]. Interesting examples of such processes include genetics [125][126][127][128][129], human cognition [130] and coordination [131], posture [132], vision [133,134], physiological signals [80,[135][136][137][138][139][140][141][142][143], neuronal firing [144,145], urban traffic [146], tree rings [147], global terrorism [148], human response to natural and social phenomena [149], foreign exchange rate [76], and the distribution of prime numbers [150].…”

“…In this section, we discuss an adaptive algorithm, which has a number of interesting properties: (1) it can accurately determine a trend in the signal; depending on the purpose of applications, one may treat the trend and associated nonstationarities as noise, and remove them, or retain them, as the signals one wishes to further study (such as the global warming trend); (2) it is more superior in reducing noise in the signals than linear filters, wavelet methods, and chaos-based methods; (3) it can conveniently decompose a complex signal into many functions of different frequency; (4) it is excellent in obtaining fractal properties from the data, especially when the data contain a strong and nonlinear trend. The method has been successfully applied to study traffic flow [146,174], various kinds of geophysical data including soil temperature, soil moisture, air temperature, and wind speed [175][176][177][178], tree rings [147], variation of electricity consumption with time [179], single neuron firing [145], clinical scalp EEG [180], ngram usage [149], quantum modeling of exciton diffusion in light harvesting systems [181], sentiments in novels [182,183], newspaper advertisements [184], textual cultural heritage [185], and global terrorism [148]. The method will be very useful for analyzing various kinds of geophysical time series that have been rapidly accumulating in recent years.…”

Analyses of geographical observations in the Heihe River Basin: Perspectives from complexity theory

“…Not only so, the method performs the best among all known methods when these function are concerned (Hu et al, 2009b;Gao et al, 2010;Tung et al, 2011;Gao et al, 2012a). Among the many applications, AFA method has also been used to analyze complex time series data in geophysics, including tree-ring Chronologies (Bowers et al, 2013), soil moisture (Shen et al, 2018;Zhang et al, 2018), and air temperature (Yang et al, 2019).…”