Climate Dynamics

, Volume 27, Issue 5, pp 441–458 | Cite as

A new method to detect transitory signatures and local time/space variability structures in the climate system: the scale-dependent correlation analysis

  • Xavier Rodó
  • Miquel-Àngel Rodríguez-Arias


The study of transitory signals and local variability structures in both/either time and space and their role as sources of climatic memory, is an important but often neglected topic in climate research despite its obvious importance and extensive coverage in the literature. Transitory signals arise either from non-linearities, in the climate system, transitory atmosphere–ocean couplings, and other processes in the climate system evolving after a critical threshold is crossed. These temporary interactions that, though intense, may not last long, can be responsible for a large amount of unexplained variability but are normally considered of limited relevance and often, discarded. With most of the current techniques at hand these typology of signatures are difficult to isolate because the low signal-to-noise ratio in midlatitudes, the limited recurrence of the transitory signals during a customary interval of data considered. Also, there is often a serious problem arising from the smoothing of local or transitory processes if statistical techniques are applied, that consider all the length of data available, rather than taking into account the size of the specific variability structure under investigation. Scale-dependent correlation (SDC) analysis is a new statistical method capable of highlighting the presence of transitory processes, these former being understood as temporary significant lag-dependent autocovariance in a single series, or covariance structures between two series. This approach, therefore, complements other approaches such as those resulting from the families of wavelet analysis, singular-spectrum analysis and recurrence plots. A main feature of SDC is its high-performance for short time series, its ability to characterize phase-relationships and thresholds in the bivariate domain. Ultimately, SDC helps tracking short-lagged relationships among processes that locally or temporarily couple and uncouple. The use of SDC is illustrated in the present paper by means of some synthetic time-series examples of increasing complexity, and it is compared with wavelet analysis in order to provide a well-known reference of its capabilities. A comparison between SDC and companion techniques is also addressed and results are exemplified for the specific case of some relevant El Niño-Southern Oscillation teleconnections.


Indian Ocean Mother Wavelet ENSO Event Singular Spectrum Analysis Recurrence Plot 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Authors wish to thank P. Yiou for comments on SDC methodology, C. Torrence and G. Compo for wavelet software and the comments of two anonymous referees. This work received partial funding by a NOAA/OGP research grant. X. Rodó was in receipt of a Ramon y Cajal contract of the MCYT and acknowledges support from ICREA institution. M.A. Rodríguez-Arias received funding from the PCB-UB.


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Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  1. 1.Laboratori de Recerca del Clima, Parc Científic de BarcelonaUniversity of BarcelonaBarcelona CataloniaSpain

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