• Balmaseda, M. A., , D. Anderson, , and M. K. Davey, 1994: ENSO prediction using a dynamical model coupled to statistical atmospheres. Tellus, 46A , 497511.

    • Search Google Scholar
    • Export Citation
  • Balmaseda, M. A., , M. K. Davey, , and D. L. T. Anderson, 1995: Decadal and seasonal dependence of ENSO prediction skill. J. Climate, 8 , 27052715.

    • Search Google Scholar
    • Export Citation
  • Barnett, T. P., , M. Latif, , N. Graham, , M. Flügel, , S. Pazan, , and W. White, 1993: ENSO and ENSO-related predictability. Part I: Prediction of equatorial Pacific sea surface temperature with a hybrid coupled ocean–atmosphere model. J. Climate, 6 , 15451566.

    • Search Google Scholar
    • Export Citation
  • Battisti, D. S., 1988: Dynamics and thermodynamics of a warming event in a coupled tropical atmosphere–ocean model. J. Atmos. Sci, 45 , 28892919.

    • Search Google Scholar
    • Export Citation
  • Blanke, B., , J. D. Neelin, , and D. Gutzler, 1997: Estimating the effect of stochastic wind stress forcing on ENSO irregularity. J. Climate, 10 , 14731486.

    • Search Google Scholar
    • Export Citation
  • Chang, P., 1994: A study of the seasonal cycle of sea surface temperature in the tropical Pacific Ocean using reduced gravity models. J. Geophys. Res, 99 , 77257741.

    • Search Google Scholar
    • Export Citation
  • Chang, P., , L. Ji, , H. Li, , and M. Flügel, 1996: Chaotic dynamics versus stochastic processes in El Niño–Southern Oscillation in coupled ocean–atmosphere models. Physica D, 9 , 301320.

    • Search Google Scholar
    • Export Citation
  • Chang, P., , R. Saravanan, , T. DelSole, , and F. Wang, 2004: Predictability of linear coupled systems. Part I: Theoretical analyses. J. Climate, 17 , 14741486.

    • Search Google Scholar
    • Export Citation
  • Chen, D., , S. E. Zebiak, , A. J. Busalacchi, , and M. A. Cane, 1995: An improved procedure for El Niño forecasting. Science, 269 , 16991702.

    • Search Google Scholar
    • Export Citation
  • da Silva, A., , A. C. Young, , and S. Levitus, 1994: Algorithms and Procedures. Vol. 1, Atlas of Surface Marine Data 1994, NOAA Atlas NESDIS 6, 83 pp.

    • Search Google Scholar
    • Export Citation
  • Eckert, C., , and M. Latif, 1997: Predictability of a stochastically forced hybrid coupled model of El Niño. J. Climate, 10 , 14881504.

    • Search Google Scholar
    • Export Citation
  • Farrell, B. F., , and P. J. Ioannou, 1996: Generalized stability. Part I: Autonomous operators. J. Atmos. Sci, 53 , 20252040.

  • Flügel, M., , and P. Chang, 1998: Does the predictability of ENSO depend on the seasonal cycle? J. Atmos. Sci, 55 , 32303243.

  • Flügel, M., , and P. Chang, 1999: Stochastically induced climate shift of El Niño–Southern Oscillation. Geophys. Res. Lett, 26 , 24732476.

    • Search Google Scholar
    • Export Citation
  • Ji, M., , A. Leetmaa, , and V. E. Kousky, 1996: Coupled model predictions of ENSO during the 1980s and the 1990s at the National Centers for Environmental Prediction. J. Climate, 9 , 31053120.

    • Search Google Scholar
    • Export Citation
  • Kirtman, B., , and P. Schopf, 1998: Decadal variability in ENSO predictability and prediction. J. Climate, 11 , 28042822.

  • Latif, M., 1998: Dynamics of interdecadal variability in coupled ocean–atmosphere models. J. Climate, 11 , 602624.

  • Latif, M., , and M. Flügel, 1991: An investigation of short-range climate predictability in the tropical Pacific. J. Geophys. Res, 96 , 26612673.

    • Search Google Scholar
    • Export Citation
  • Penland, C., 1989: Random forcing and forecasting using principal oscillation pattern analysis. Mon. Wea. Rev, 117 , 21652185.

  • Penland, C., 1996: A stochastic model of IndoPacific sea surface temperature anomalies. Physica D, 98 , 534558.

  • Penland, C., , and T. Magorian, 1993: Prediction of Niño 3 sea surface temperatures using linear inverse modeling. J. Climate, 6 , 10671076.

    • Search Google Scholar
    • Export Citation
  • Penland, C., , and P. D. Sardeshmukh, 1995: The optimal growth of tropical sea surface temperature anomalies. J. Climate, 8 , 19992024.

  • Penland, C., , M. Flügel, , and P. Chang, 2000: Identification of dynamical regimes in an intermediate coupled ocean–atmosphere model. J. Climate, 13 , 21052115.

    • Search Google Scholar
    • Export Citation
  • Schopf, P. S., , and M. J. Suarez, 1988: Vacillations in a coupled ocean– atmosphere model. J. Atmos. Sci, 45 , 549566.

  • Slutz, R. J., , S. J. Lubker, , J. D. Hiscox, , S. D. Woodruff, , R. L. Jenne, , D. H. Joseph, , P. M. Steurer, , and J. D. Elms, 1985: Comprehensive Ocean–Atmosphere Data Set; Release 1. NOAA/Environmental Research Laboratory, Boulder, CO, 269 pp.

    • Search Google Scholar
    • Export Citation
  • Thompson, C. J., , and D. S. Battisti, 2001: A linear stochastic dynamical model of ENSO. Part II: Analysis. J. Climate, 14 , 445466.

  • von Storch, H., , G. Bürger, , R. Schnur, , and J-S. von Storch, 1995: Principal oscillation patterns. A review. J. Climate, 8 , 377400.

  • Zebiak, S. E., , and M. A. Cane, 1987: A model ENSO. Mon. Wea. Rev, 115 , 22622278.

  • Zhang, L., , M. Flügel, , and P. Chang, 2003: Testing the stochastic mechanism for low-frequency variations in ENSO predictability. Geophys. Res. Lett.,30, 1630, doi:10.1029/2003GL017505.

    • Search Google Scholar
    • Export Citation
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The Role of Stochastic Forcing in Modulating ENSO Predictability

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  • 1 Department of Oceanography, Texas A&M University, College Station, Texas
  • | 2 NOAA–CIRES Climate Diagnostics Center, Boulder, Colorado
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Abstract

Predictability analysis of a 1000-yr simulated zonal wind stress anomaly in an intermediate coupled model reveals that low-frequency variations in ENSO prediction are closely linked to changes in spatial structures of the uncoupled atmospheric noise. Enhanced predictability well beyond 1 yr is attained during those decades in which the structures of the stochastic component resemble a certain optimal noise structure, while during other periods the system quickly loses its predictability when the noise has less resemblance to the optimals. The optimal noise forcing can maximize the system's predictability up to 1 yr in advance. Its spatial characteristics are such that maximum variability is located in the western Pacific at about 8°N. Within the limitations of the authors' simple model, the results suggest that changes in ENSO predictability can be explained in terms of changes in the characteristics of the noise forcing, without invoking changes in mean state and coupled dynamics. Therefore, the results offer a null hypothesis for low-frequency variations of ENSO predictability.

Corresponding author address: Dr. Moritz Flügel, Department of Oceanography, Texas A&M University, College Station, TX 77843-3146. Email: mfluegel@ocean.tamu.edu

Abstract

Predictability analysis of a 1000-yr simulated zonal wind stress anomaly in an intermediate coupled model reveals that low-frequency variations in ENSO prediction are closely linked to changes in spatial structures of the uncoupled atmospheric noise. Enhanced predictability well beyond 1 yr is attained during those decades in which the structures of the stochastic component resemble a certain optimal noise structure, while during other periods the system quickly loses its predictability when the noise has less resemblance to the optimals. The optimal noise forcing can maximize the system's predictability up to 1 yr in advance. Its spatial characteristics are such that maximum variability is located in the western Pacific at about 8°N. Within the limitations of the authors' simple model, the results suggest that changes in ENSO predictability can be explained in terms of changes in the characteristics of the noise forcing, without invoking changes in mean state and coupled dynamics. Therefore, the results offer a null hypothesis for low-frequency variations of ENSO predictability.

Corresponding author address: Dr. Moritz Flügel, Department of Oceanography, Texas A&M University, College Station, TX 77843-3146. Email: mfluegel@ocean.tamu.edu

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