• Ausloos, M., , and Ivanova K. , 2001: Power-law correlations in the southern-oscillation-index fluctuations characterizing El Niño. Phys. Rev. E, 63 .047201, doi:10.1103/PhysRevE. 63.047201.

    • Search Google Scholar
    • Export Citation
  • Barnston, G., , and Livezey R. E. , 1987: Classification, seasonality and low-frequency atmospheric circulation patterns. Mon. Wea. Rev., 115 , 10831126.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chu, P. C., , and Ivanov L. M. , 2005: Statistical characteristics of irreversable predictability time in regional ocean models. Nonlinear Processes Geophys., 12 , 110.

    • Search Google Scholar
    • Export Citation
  • Chu, P. C., , Ivanov L. M. , , and Fan C. W. , 2002a: Backward Fokker-Planck equation for determining model valid prediction period. J. Geophys. Res., 107 .3058, doi:10.1029/2001JC000879.

    • Search Google Scholar
    • Export Citation
  • Chu, P. C., , Ivanov L. M. , , Margolina T. M. , , and Melnichenko O. V. , 2002b: On probabilistic stability of an atmospheric model to various amplitude perturbations. J. Atmos. Sci., 59 , 28602873.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chu, P. C., , Ivanov L. M. , , Kantha L. H. , , Melnichenko O. V. , , and Poberezhny Y. A. , 2002c: Power law decay in model predictability skill. Geophys. Res. Lett., 29 .1748, doi:10.1029/2002GL014891.

    • Search Google Scholar
    • Export Citation
  • Chu, P. C., , Ivanov L. M. , , Kantha L. H. , , Margolina T. M. , , Melnichenko O. V. , , and Poberenzhny Y. A. , 2004: Lagrangian predictabilty of high-resolution regional ocean models. Nonlinear Processes Geophys., 11 , 4766.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Collette, C., , and Ausloos M. , 2004: Scaling analysis and evolution equation of the North Atlantic oscillation index fluctuations. Int. J. Mod. Phys. C, 15 , 13531366.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ding, M., , and Yang W. , 1995: Distribution of the first return time in fractional Brownian motion and its application to the study of on-off intermittency. Phys. Rev. E, 52 , 207213.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Drosdowsky, W., 1994: Analog (nonlinear) forecasts of the Southern Oscillation index time series. Wea. Forcasting, 9 , 7884.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Greatbatch, R. J., 2000: The North Atlantic Oscillation. Stochastic Environ. Res. Risk Assess., 14 , 213242.

  • Ivanov, L. M., , and Chu P. C. , 2007: On stochastic stability of regional ocean models to finite-amplitude pertubations of initial conditions. Dyn. Atmos. Oceans, 43 , 199225.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Keppenne, C. L., , and Ghil M. , 1992: Adaptive spectral analysis and prediction of the Southern Oscillation index. J. Geophys. Res., 97 , 2044920454.

  • Lind, P. G., , Mora A. , , Gallas J. A. C. , , and Haase M. , 2005: Reducing stochasticity in the North Atlantic Oscillation index with coupled Langevin equations. Phys. Rev. E, 72 .056706, doi:10.1103/PhysRevE.72.056706.

    • Search Google Scholar
    • Export Citation
  • Maharaj, E. A., , and Wheeler M. J. , 2005: Forecasting an index of the Madden-Julian Oscillation. Int. J. Climatol., 25 , 16111618.

  • Nicolis, C., 1992: Probabilistic aspects of error growth in atmospheric dynamics. Quart. J. Roy. Meteor. Soc., 118 , 553568.

  • Palmer, T. N., 2000: Predicting uncertainty in forecasts of weather and climate. Rep. Prog. Phys., 63 , 71116.

  • Rangarajan, G., , and Ding M. , 2000: First passage time distribution for anomalous diffusion. Phys. Lett. A, 273 , 322330.

  • Rimmington, G. M., , and Nicholls N. , 1993: Forecasting wheat yields in Australia with the Southern Oscillation index. Aust. J. Agric. Res., 44 , 625632.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ropelewski, C. F., , and Halpert M. S. , 1987: Global and regional scale precipitation patterns associated with the El Niño/Southern Oscillation. Mon. Wea. Rev., 115 , 16061626.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, S. R., , and Sterns C. R. , 1993: Antarctic pressure and temperature anomalies surrounding the minimum in the Southern Oscillation index. J. Geophys. Res., 98 , 6178.

    • Search Google Scholar
    • Export Citation
  • Torrence, C., , and Campo G. P. , 1998: A practical guide to wavelet analysis. Bull. Amer. Meteor. Soc., 79 , 6278.

  • Walker, G. T., , and Bliss E. W. , 1937: World weather VI. Mem. Roy. Meteor. Soc., 4 , 119139.

  • Wallace, J. M., 2000: North Atlantic oscillation/annular mode: Two paradigms—One phenomenon. Quart. J. Roy. Meteor. Soc., 126 , 791805.

    • Search Google Scholar
    • Export Citation
  • Wanner, H., , Bronnimann S. , , Casty C. , , Gyalistras D. , , Luterbacher J. , , Schmutz C. , , Stephenson D. B. , , and Xoplaki E. , 2001: North Atlantic oscillation—Concepts and studies. Surv. Geophys., 22 , 321382.

    • Crossref
    • Search Google Scholar
    • Export Citation
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First Passage Time Analysis on Climate Indices

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  • 1 Naval Ocean Analysis and Prediction Laboratory, Naval Postgraduate School, Monterey, California
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Abstract

Climate variability is simply represented by teleconnection patterns such as the Arctic Oscillation (AO), Antarctic Oscillation (AAO), North Atlantic Oscillation (NAO), Pacific–North American pattern (PNA), and Southern Oscillation (SO) with associated indices. Two approaches can be used to predict the indices: forward and backward methods. The forward method is commonly used to predict the index fluctuation ρ at time t with a given temporal increment τ. Using this method, it was found that the index (such as for NAO) has the Brownian fluctuations. On the basis of the first passage time (FPT) concept, the backward method is introduced in this study to predict the typical time span (τ) needed to generate a fluctuation in the index of a given increment ρ. After the five monthly indices (AO, AAO, NAO, PNA, and SO) run through the past history, the FPT density functions are obtained. FPT presents a new way to detect the temporal variability of the climate indices. The basic features for the index prediction are also discussed.

Corresponding author address: Peter C. Chu, Naval Ocean Analysis and Prediction Laboratory, Naval Postgraduate School, Monterey, CA 93943. Email: pcchu@nps.edu

Abstract

Climate variability is simply represented by teleconnection patterns such as the Arctic Oscillation (AO), Antarctic Oscillation (AAO), North Atlantic Oscillation (NAO), Pacific–North American pattern (PNA), and Southern Oscillation (SO) with associated indices. Two approaches can be used to predict the indices: forward and backward methods. The forward method is commonly used to predict the index fluctuation ρ at time t with a given temporal increment τ. Using this method, it was found that the index (such as for NAO) has the Brownian fluctuations. On the basis of the first passage time (FPT) concept, the backward method is introduced in this study to predict the typical time span (τ) needed to generate a fluctuation in the index of a given increment ρ. After the five monthly indices (AO, AAO, NAO, PNA, and SO) run through the past history, the FPT density functions are obtained. FPT presents a new way to detect the temporal variability of the climate indices. The basic features for the index prediction are also discussed.

Corresponding author address: Peter C. Chu, Naval Ocean Analysis and Prediction Laboratory, Naval Postgraduate School, Monterey, CA 93943. Email: pcchu@nps.edu

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