Abrupt climate change detection based on heuristic segmentation algorithm

Climate system is nonlinear, non-stationary and hierarchical, which makes even harder to detect and analyze abrupt climate changes. Based on Student's t-test, Bernaola Galvan recently proposed a heuristic segmentation algorithm to segment the time series into several subsets with different scales, which is more effective in detecting the abrupt changes of nonlinear time series. In this paper, we try to verify the effectiveness of heuristic segmentation algorithm in dealing with nonlinear time series by an ideal time series. Through detecting and analyzing the information of abrupt climate changes contained in recent 2000a' s tree annual growth ring, we succeeded in distinguishing abrupt changes with different scales. The research based on the newly defined parameter of abrupt change density shows that human activities might have lead to the recent 1000a' s unbalanced distribution of serial and spares segments of abrupt climate changes, which may be one of the manifestations of global temperature change.