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Cao, The impact of global warming on ENSO variability in climate records, COLA Technical Report (2001) CTR 110. W.В Katz and N.В Nicholls (eds.), Teleconnections Linking Worldwide Climate Anomalies (Cambridge University Press, 1991)В p. National Research Council, Learning to Predict Climate Variations Associated with El NiГ±o and the Southern Oscillation (National Academy Press, 1996)В p.G.В Philander, El NiГ±o, La NiГ±a, and the Southern Oscillations (Academic Press, 1990)В p. L.В De Lathauwer, B.В De Moor and J.В Vandewalle, IEEE Trans.A.В Cichocki and S.В Amari, Adaptive Blind Signal and Image Processing (John Wiley, Chichester, UK, 2002)В p.C.В Douglas, A.В Cichocki and S.В Amari, IEEE Trans.

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B.В Priestley, Spectral Analysis and Time Series (Academic Press, London, 1991)В p. Tukey, Power spectral methods of analysis and their application to problems in airplane dynamics, Bell System Monograph (1956) #2606. P.В Shen (World Scientific, Singapore, 2005)В pp. E.В Huang, HilbertвЂ“Huang Transform: Introduction and Applications, eds. Gledhill, Methods for investigating conformational change in biomolecular simulations, A dissertation for the degree of Doctor of Philosophy at the Department of Chemistry, the University of Southampton (2003), p. P.В Flandrin, P.В GonГ§alvГЁs and G.В Rilling, HilbertвЂ“Huang Transform: Introduction and Applications, eds.P.В Flandrin, G.В Rilling and P.В GonГ§alvГЁs, IEEE Signal Process.O.В Attoh-Okine (eds.), HilbertвЂ“Huang Transform in Engineering (CRC Press, 2005)В p. P.В Shen (eds.), HilbertвЂ“Huang Transform and Its Applications (World Scientific, Singapore, 2005)В p.

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This new approach utilizes the full advantage of the statistical characteristics of white noise to perturb the signal in its true solution neighborhood, and to cancel itself out after serving its purpose therefore, it represents a substantial improvement over the original EMD and is a truly noise-assisted data analysis (NADA) method. With this ensemble mean, one can separate scales naturally without any a priori subjective criterion selection as in the intermittence test for the original EMD algorithm. The effect of the added white noise is to provide a uniform reference frame in the timeвЂ“frequency space therefore, the added noise collates the portion of the signal of comparable scale in one IMF. As EEMD is a timeвЂ“space analysis method, the added white noise is averaged out with sufficient number of trials the only persistent part that survives the averaging process is the component of the signal (original data), which is then treated as the true and more physical meaningful answer. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the different scale signals to collate in the proper intrinsic mode functions (IMF) dictated by the dyadic filter banks.

This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. A new Ensemble Empirical Mode Decomposition (EEMD) is presented.