摘要：We compared and evaluated the performance of five methods for detecting abrupt climate changes using a time series with artificially generated abrupt characteristics. Next, we analyzed these methods using annual mean surface air temperature records from the Shenyang meteorological station. Our results show that the moving -test (MTT), Yamamoto (YAMA), and LePage (LP) methods can correctly and effectively detect abrupt changes in means, trends, and dynamic structure; however, they cannot detect changes in variability. We note that the sample size of the subseries used in these tests can affect their results. When the sample size of the subseries ranges from one-quarter to three-quarters of the jump scale, these methods can effectively detect abrupt changes; they perform best when the sample size is one-half of the jump scale. The Cramer method can detect abrupt changes in the mean and trend of a series but not changes in variability or dynamic structure. Finally, we found that the Mann-Kendall test could not detect any type of abrupt change. We found no difference in the results of any of the methods following removal of the mean, creation of an anomaly series, or normalization. However, detrending and study period selection affected the results of the Cramer and Mann-Kendall methods; in the latter case, they could lead to a completely different result.
