| Global change science is a new research domain nowadays, and one of the most imp ortant studies of which is the climate change, to which great attention is paid by all governments in world. Abrupt climate change (ACC) is one of most important manifestation of climate change, is a integrative science of physics and climatology. Nowadays, studies on ACC are mainly based on proxy data using traditional statistical methods and have made some progresses, however, the non-linearity, multi-hierarchies and nonstationarity of climate system make it more difficult to analyze and detect ACC and put forward much higher requirement for the detecting methods. Based on the newest research production of nonlinear science, the main content of this thesis is as follows:1. This thesis introduced the main idea of the Hilbert transformation (HT), empirical mode decomposition (EMD), wavelet decomposition (WD) and wavelet transformation (WT), then, in the context of an ideal time series and the Guliya (located in Tibetan plateau) ice core (GIC)δ18O time series, merits and defects of EMD and WD as well as HT and WT in the nonlinear time series analysis are systematically analyzed, and aiming at the generation of the false components during their decomposing process, the author presented the method of correlation analysis based on their decomposing results, on which the false components might be wiped off and the remarkable signals of the original time series concentrate be picked out. Research results show that the combination of the EMD and the WD is able to more effectively identify the characteristic signals of the original series.2. This thesis introduced a new method-heuristic segmentation algorithm (briefly BG algorithm) which can divide a nonstationary time series into a series of self-stationary subsets, and appliedthis method to detect abrupt changes of nonstationary time series. Numerical test show that spike noise or random white noise had little... |
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