中文摘要：

In this paper, the early warning signals of abrupt temperature change in different regions of China are investigated.Seven regions are divided on the basis of different climate temperature patterns, obtained through the rotated empirical orthogonal function, and the signal-to-noise temperature ratios for each region are then calculated. Based on the concept of critical slowing down, the temperature data that contain noise in the different regions of China are preprocessed to study the early warning signals of abrupt climate change. First, the Mann–Kendall method is used to identify the instant of abrupt climate change in the temperature data. Second, autocorrelation coefficients that can identify critical slowing down are calculated. The results show that the critical slowing down phenomenon appeared in temperature data about 5–10 years before abrupt climate change occurred, which indicates that the critical slowing down phenomenon is a possible early warning signal for abrupt climate change, and that noise has less influence on the detection results of the early warning signals. Accordingly, this demonstrates that the model is reliable in identifying the early warning signals of abrupt climate change based on detecting the critical slowing down phenomenon, which provides an experimental basis for the actual application of the method.

英文摘要：

In this paper, the early warning signals of abrupt temperature change in different regions of China are investigated. Seven regions are divided on the basis of different climate temperature patterns, obtained through the rotated empirical orthogonal function, and the signal-to-noise temperature ratios for each region are then calculated. Based on the concept of critical slowing down, the temperature data that contain noise in the different regions of China are preprocessed to study the early warning signals of abrupt climate change. First, the Mann-Kendall method is used to identify the instant of abrupt climate change in the temperature data. Second, autocorrelation coefficients that can identify critical slowing down are calculated. The results show that the critical slowing down phenomenon appeared in temperature data about 5-10 years before abrupt climate change occurred, which indicates that the critical slowing down phenomenon is a possible early warning signal for abrupt climate change, and that noise has less influence on the detection results of the early warning signals. Accordingly, this demonstrates that the model is reliable in identifying the early warning signals of abrupt climate change based on detecting the critical slowing down phenomenon, which provides an experimental basis for the actual application of the method.