Abstract
AbstractEvolution of Earth’s climate system over the past 800,000 years represents a complex process with successions of uneven glacial and interglacial periods. The length, amplitudes, and development of each climate cycle depend on a number of different factors, including the orbital parameters attributed to insolation and the complex responses of the Earth system to solar radiation primarily through the amplification by Earth’s albedo and greenhouse gas and secondarily through a system of heat reservoirs, such as ice sheet and deep ocean, distributed throughout our planet. The purpose of this study is to analyze the transitions related to climate cycles in Antarctic ice core data (EPICA Dome C) of deuterium composition and dust concentration recorded for the past 800,000 years [1] using Flicker-Noise Spectroscopy (FNS), an analytical toolset for the extraction and analysis of information in stochastic time and space series, containing both regular and chaotic components, by using power spectra and difference moments (structural functions) of various orders [2].The FNS nonstationarity factors for the deuterium composition and dust (logarithm) concentration, which represent the normalized discrete derivative of the second-order structural function of the source signal with respect to a given shifted “window” interval, were built for different intervals of averaging to identify the major changes in the dynamics of both time series and their precursors. It is shown that when displayed together with the source signals, the positive peaks in the nonstationarity factors provide more reliable estimates of the transition of the climate system from one sub-period to another within a specific climate cycle as compared to predefined thresholds in dust or deuterium values. For climatic transitions, the power spectral estimates of the nonstationarity factors contain several periodicities in addition to the orbital ones. These frequencies may be attributed to specific heat accumulation and discharge processes in the climate system. The results of this study demonstrate the potential of FNS in the analysis of climate data series and may be used in refining climate transition models.This study was supported by the Russian Foundation for Basic Research, project no. 08-02-00230a.[1] Lambert F., et al. (2008) Dust-climate couplings over the past 800,000 years from the EPICA Dome C ice core, Nature 452, 616-619.[2] Timashev, S. F., Polyakov Yu. S. (2007) Review of flicker noise spectroscopy in electrochemistry, Fluctuations and Noise Letters 7(2), R15-R47.
Highlights
Introduction toFlickerNoise Spectroscopy (FNS)FlickerNoise Spectroscopy (FNS) is a signal analysis toolset for information extraction from stochastic time or space series, which include both regular and chaotic components, based on the analysis of the correlation links for signal irregularities and regular components
FNS can be applied to three types of problems: 2. Determination of parameters that characterize the dynamics or structural features of complex systems; 3
The FNS nonstationarity factor was successfully used for finding precursors of earthquakes [1, 45] and electric breakdowns in semiconductor systems [6]
Summary
Consider the time series of a dynamic variable V with values recorded at spaced time intervals. K∆T; ∆T is the increment by which the averaging “window” is shifted; k is the index of the current averaging “window”; N1 is the fraction of all points N in the averaging interval T for which reliable estimates of the difference moments can be calculated. The nonstationarity factor virtually represents a discrete derivative of the total sum of difference moments within the current “window” at all time lag values, with respect to the increment by which the analysis “window” is shifted. In other words, it gives an aggregate estimate of the rate of changes taking place at “all” time scales from one averaging “window” to another. The FNS nonstationarity factor was successfully used for finding precursors of earthquakes [1, 45] and electric breakdowns in semiconductor systems [6]
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