Yuan Naiming, Fu Zuntao, Zhang Huan, Piao Lin, Xoplaki Elena, Luterbacher Juerg
1] Chinese Academy of Meteorological Science, Beijing, 100081, China [2] Department of Geography, Climatology, Climate Dynamics and Climate Change, Justus Liebig University Giessen, Senckenbergstrasse 1, 35390 Giessen, Germany [3] Lab for Climate and Ocean-Atmosphere Studies, Dept. of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, 100871, China.
Lab for Climate and Ocean-Atmosphere Studies, Dept. of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, 100871, China.
Sci Rep. 2015 Jan 30;5:8143. doi: 10.1038/srep08143.
In this paper, a new method, detrended partial-cross-correlation analysis (DPCCA), is proposed. Based on detrended cross-correlation analysis (DCCA), this method is improved by including partial-correlation technique, which can be applied to quantify the relations of two non-stationary signals (with influences of other signals removed) on different time scales. We illustrate the advantages of this method by performing two numerical tests. Test I shows the advantages of DPCCA in handling non-stationary signals, while Test II reveals the "intrinsic" relations between two considered time series with potential influences of other unconsidered signals removed. To further show the utility of DPCCA in natural complex systems, we provide new evidence on the winter-time Pacific Decadal Oscillation (PDO) and the winter-time Nino3 Sea Surface Temperature Anomaly (Nino3-SSTA) affecting the Summer Rainfall over the middle-lower reaches of the Yangtze River (SRYR). By applying DPCCA, better significant correlations between SRYR and Nino3-SSTA on time scales of 6 ~ 8 years are found over the period 1951 ~ 2012, while significant correlations between SRYR and PDO on time scales of 35 years arise. With these physically explainable results, we have confidence that DPCCA is an useful method in addressing complex systems.
本文提出了一种新方法——去趋势化偏互相关分析（DPCCA）。该方法基于去趋势化互相关分析（DCCA），通过引入偏相关技术进行了改进，可用于量化两个非平稳信号（去除其他信号影响）在不同时间尺度上的关系。我们通过进行两个数值测试来说明该方法的优势。测试一展示了DPCCA在处理非平稳信号方面的优势，而测试二则揭示了在去除其他未考虑信号的潜在影响后，两个所考虑时间序列之间的“内在”关系。为了进一步展示DPCCA在自然复杂系统中的实用性，我们提供了关于冬季太平洋年代际振荡（PDO）和冬季尼诺3海表温度异常（Nino3 - SSTA）影响长江中下游夏季降水（SRYR）的新证据。通过应用DPCCA，在1951 - 2012年期间，发现SRYR与Nino3 - SSTA在6至8年时间尺度上存在更好的显著相关性，而SRYR与PDO在35年时间尺度上存在显著相关性。基于这些具有物理解释的结果，我们相信DPCCA是处理复杂系统的一种有用方法。
