Abstract
In this study, the performance of moving cut data-approximate entropy (MC-ApEn) to detect abrupt dynamic changes was investigated. Numerical tests in a time series model indicate that the MC-ApEn method is suitable for the detection of abrupt dynamic changes for three types of meteorological data: daily maximum temperature, daily minimum temperature, and daily precipitation. Additionally, the MC-ApEn method was used to detect abrupt climate changes in daily precipitation data from Northwest China and the Pacific Decadal Oscillation (PDO) index. The results show an abrupt dynamic change in precipitation in 1980 and in the PDO index in 1976. The times indicated for the abrupt changes are identical to those from previous results. Application of the analysis to observational data further confirmed the performance of the MC-ApEn method. Moreover, MC-ApEn outperformed the moving t test (MTT) and the moving detrended fluctuation analysis (MDFA) methods for the detection of abrupt dynamic changes in a simulated 1000-point daily precipitation dataset.
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