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Scaling Analysis And Evolution Equation Of The North Atlantic Oscillation Index Fluctuations

Author

Listed:
  • C. COLLETTE

    (SUPRATECS, B5, Sart Tilman, B-4000 Liège, Belgium)

  • M. AUSLOOS

    (SUPRATECS, B5, Sart Tilman, B-4000 Liège, Belgium)

Abstract

The North Atlantic Oscillation (NAO) monthly index is studied from 1825 till 2002 in order to identify the scaling ranges of its fluctuations upon different delay times and to find out whether or not it can be regarded as a Markov process. A Hurst rescaled range analysis and a detrended fluctuation analysis both indicate the existence of weakly persistent long range time correlations for the whole scaling range and time span. Such correlations are similar to the Brownian fluctuations. The Fokker–Planck equation is derived and Kramers–Moyal coefficients are estimated from the data. They are interpreted in terms of a drift and a diffusion coefficient as in fluid mechanics. All partial distribution functions of the NAO monthly index fluctuations have a form close to a Gaussian, for all time lags, in agreement with the findings of the scaling analyses. This indicates the lack of predictive power of the present NAO monthly index. Yet there are some deviations for large (thus rare) events. Whence suggestions for other measurements are made if some improved predictability of the weather/climate in the North Atlantic is of interest. The subsequent Langevin equation of the NAO signal fluctuations is explicitly written in terms of the diffusion and drift parameters, and a characteristic time scale for these is given in Appendix A.

Suggested Citation

  • C. Collette & M. Ausloos, 2004. "Scaling Analysis And Evolution Equation Of The North Atlantic Oscillation Index Fluctuations," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 15(10), pages 1353-1366.
  • Handle: RePEc:wsi:ijmpcx:v:15:y:2004:i:10:n:s0129183104006789
    DOI: 10.1142/S0129183104006789
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    Cited by:

    1. Huang, Xu & Maçaira, Paula Medina & Hassani, Hossein & Cyrino Oliveira, Fernando Luiz & Dhesi, Gurjeet, 2019. "Hydrological natural inflow and climate variables: Time and frequency causality analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 480-495.
    2. Ausloos, Marcel & Cerqueti, Roy & Lupi, Claudio, 2017. "Long-range properties and data validity for hydrogeological time series: The case of the Paglia river," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 470(C), pages 39-50.

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