Abstract:
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.
