Naiming Yuan

Detrended Partial-Cross-Correlation Analysis: A New Method for Analyzing Correlations in Complex System

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.

Beyond Benford's Law: Distinguishing Noise from Chaos.

Determinism and randomness are two inherent aspects of all physical processes. Time series from chaotic systems share several features identical with those generated from stochastic processes, which makes them almost undistinguishable. In this paper, a new method based on Benfords law is designed in order to distinguish noise from chaos by only information from the first digit of considered series. By applying this method to discrete data, we confirm that chaotic data indeed can be distinguished from noise data, quantitatively and clearly.

Different multi-fractal behaviors of diurnal temperature range over the north and the south of China

Multi-fractal behaviors of diurnal temperature range (DTR for short) from 100 stations over China during 1956–2010 are analyzed by means of multi-fractal detrended fluctuation analysis. By making a Monte-Carlo simulation, we obtain two criterions which can be used to decide whether a DTR series is significantly multi-fractal or not. With these criterions, different multi-fractal behaviors are found over the north and the south of China, and Yangtze River is roughly the dividing line. Over the north region, nearly all the considered DTR series do not show multi-fractal behaviors, while the results are completely the opposite over the south. The findings are confirmed by the scaling behaviors of the corresponding DTR magnitude series and indicate that more scale-dependent structure differences may be hidden in DTR series over the north and the south of China. Therefore, an extensive analysis of the multi-fractal behaviors are essential for a better understanding of the complex structures of the climate changes.

On the Long-Term Climate Memory in the Surface Air Temperature Records over Antarctica: A Nonnegligible Factor for Trend Evaluation

AbstractIn this study, observed temperature records of 12 stations from Antarctica island, coastline, and continental areas are analyzed by means of detrended fluctuation analysis (DFA). After Monte Carlo significance tests, different long-term climate memory (LTM) behaviors are found: temperatures from coastal and island stations are characterized by significant long-term climate memory whereas temperatures over the Antarctic continent behave more like white noise, except for the Byrd station, which is located in the West Antarctica. It is argued that the emergence of LTM may be dominated by the interactions between local weather system and external slow-varying systems (ocean), and therefore the different LTM behaviors between temperatures over the Byrd station and that over other continental stations can be considered as a reflection of the different climatic environments between West and East Antarctica. By calculating the trend significance with the effect of LTM taken into account, and further compa...

Subarea characteristics of the long-range correlations and the index χ for daily temperature records over China

Daily temperature records including daily minimum, maximum, and average temperature from 190 meteorological stations over China during 1951–2000 are analyzed from two perspectives: (a) long-term persistence in direction of time varies, and (b) standard deviation in direction of amplitude varies. By employing the detrended fluctuation analysis (DFA), we find all the temperature records are long-term correlated, while the exponent α obtained from DFA varies from different districts of China due to different climate conditions, such as the southwest monsoon, subtropical high, northeast cold vortex, and the Tibetan plateau, etc. After we take the standard deviation into account, a new index χ = α × σ, which has been proposed recently, can be obtained. By further rescaling it as

Effect of extreme value loss on long-term correlated time series

\chi = \overline \chi - {{1} \left/ {5} \right.} \times {\sigma_{{\overline \chi }}}

Different scaling behaviors in daily temperature records over China

, we find an obvious change of χ for these three kinds of time series, from which the whole China can be divided into two groups, which are comparatively consistent with dry/wet distributions in the south–north areas over China.

Different spatial cross-correlation patterns of temperature records over China: A DCCA study on different time scales

In this study, relations between winter-time Pacific-Northern America pattern (PNA)/East Pacific wave-train (EPW) and winter-time drought in the west United States over the period of 1951–2010 are analyzed. Considering traditional Pearson’s Correlation Coefficient can be influenced by non-stationarity and nonlinearity, a recently proposed method, Detrended Partial-Cross-Correlation Analysis (DPCCA) is applied. With DPCCA, we analyzed the “intrinsic” correlations between PNA/EPW and the winter drought with possible effects of ENSO and PDO removed. We found, i) significant negative correlations between PNA/EPW and drought on time scales of 5–6 years after removing the effects of ENSO, ii) and significant negative correlations between PNA/EPW and drought on time scales of 15–25 years after removing the effects of PDO. By further studying the temporal evolutions of the “intrinsic” correlations, we found on time scales of 5–6 years, the “intrinsic” correlations between PNA/EPW and drought can vary severely with time, but for most time, the correlations are negative. While on interdecadal (15–25 years) time scales, after the effects of PDO removed, unlike the relations between PNA and drought, the “intrinsic” correlations between EPW and drought takes nearly homogeneous-sign over the whole period, indicating a better model can be designed by using EPW.