Reliability of ENSO Dynamical Predictions

Youmin Tang Courant Institute of Mathematical Sciences, New York University, New York, New York

Search for other papers by Youmin Tang in
Current site
Google Scholar
PubMed
Close
,
Richard Kleeman Courant Institute of Mathematical Sciences, New York University, New York, New York

Search for other papers by Richard Kleeman in
Current site
Google Scholar
PubMed
Close
, and
Andrew M. Moore Program in Atmospheric and Oceanic Sciences, University of Colorado, Boulder, Colorado

Search for other papers by Andrew M. Moore in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

In this study, ensemble predictions were constructed using two realistic ENSO prediction models and stochastic optimals. By applying a recently developed theoretical framework, the authors have explored several important issues relating to ENSO predictability including reliability measures of ENSO dynamical predictions and the dominant precursors that control reliability. It was found that prediction utility (R), defined by relative entropy, is a useful measure for the reliability of ENSO dynamical predictions, such that the larger the value of R, the more reliable the prediction. The prediction utility R consists of two components, a dispersion component (DC) associated with the ensemble spread and a signal component (SC) determined by the predictive mean signals. Results show that the prediction utility R is dominated by SC.

Using a linear stochastic dynamical system, SC was examined further and found to be intrinsically related to the leading eigenmode amplitude of the initial conditions. This finding was validated by actual model prediction results and is also consistent with other recent work. The relationship between R and SC has particular practical significance for ENSO predictability studies, since it provides an inexpensive and robust method for exploring forecast uncertainties without the need for costly ensemble runs.

Corresponding author address: Dr. Youmin Tang, Environmental Science and Engineering, 3333 University Way, Prince George, University of Northern British Columbia, V2N 4Z9, Canada. Email: ytang@CIMS.nyu.edu

Abstract

In this study, ensemble predictions were constructed using two realistic ENSO prediction models and stochastic optimals. By applying a recently developed theoretical framework, the authors have explored several important issues relating to ENSO predictability including reliability measures of ENSO dynamical predictions and the dominant precursors that control reliability. It was found that prediction utility (R), defined by relative entropy, is a useful measure for the reliability of ENSO dynamical predictions, such that the larger the value of R, the more reliable the prediction. The prediction utility R consists of two components, a dispersion component (DC) associated with the ensemble spread and a signal component (SC) determined by the predictive mean signals. Results show that the prediction utility R is dominated by SC.

Using a linear stochastic dynamical system, SC was examined further and found to be intrinsically related to the leading eigenmode amplitude of the initial conditions. This finding was validated by actual model prediction results and is also consistent with other recent work. The relationship between R and SC has particular practical significance for ENSO predictability studies, since it provides an inexpensive and robust method for exploring forecast uncertainties without the need for costly ensemble runs.

Corresponding author address: Dr. Youmin Tang, Environmental Science and Engineering, 3333 University Way, Prince George, University of Northern British Columbia, V2N 4Z9, Canada. Email: ytang@CIMS.nyu.edu

Save
  • 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., and P. Delecluse, 1993: Variability of the tropical Atlantic Ocean simulated by a general circulation model with two different mixed layer physics. J. Phys. Oceanogr., 23 , 13631388.

    • Search Google Scholar
    • Export Citation
  • Buizza, R., and T. N. Palmer, 1998: Impact of ensemble size on ensemble prediction. Mon. Wea. Rev., 126 , 25032518.

  • Chen, Y-Q., D. S. Battisti, T. N. Palmer, J. Barsugli, and E. S. Sarachik, 1997: A study of the predictability of tropical Pacific SST in a coupled atmosphere–ocean model using singular vector analysis: The role of the annual cycle and the ENSO cycle. Mon. Wea. Rev., 125 , 831845.

    • Search Google Scholar
    • Export Citation
  • Cover, T. M., and J. A. Thomas, 1991: Elements of Information Theory. Wiley, 576 pp.

  • DelSole, T., 2001: Optimally persistent patterns in time-varying fields. J. Atmos. Sci., 58 , 13411356.

  • Epstein, E. S., 1969: Stochastic dynamic predictions. Tellus, 21 , 388407.

  • Farrell, B. F., P. J. Ioannou, and J. Petros, 1993: Stochastic dynamics of baroclinic waves. J. Atmos. Sci., 50 , 40444057.

  • Hasselmann, K., 1988: PIPs and POPs: The reduction of complex dynamical systems using principal interaction and oscillation patterns. J. Geophys. Res., 93 , 1101511021.

    • Search Google Scholar
    • Export Citation
  • Ji, M., R. W. Reynolds, and D. W. Behringer, 2000: Use of TOPEX/Poseidon sea level data for ocean analyses and ENSO prediction: Some early results. J. Climate, 13 , 216231.

    • Search Google Scholar
    • Export Citation
  • Jin, F-F., 1997: An equatorial ocean recharge paradigm for ENSO. Part I: Conceptual model. J. Atmos. Sci., 54 , 811829.

  • Kirtman, B. P., and J. Shukla, 1998: Current status of ENSO forecast skill. Climate Variability and Predictability (CLIVAR) Numerical Experimental Group Rep. [Available online at http://www.clivar.org/publications/wg_reports/wgsip/nino3/report.htm.].

  • Kleeman, R., 1991: A simple model of the atmospheric response to ENSO sea surface temperature anomalies. J. Atmos. Sci., 48 , 318.

  • Kleeman, R., 2002: Measuring dynamical prediction utility using relative entropy. J. Atmos. Sci., 59 , 20572072.

  • Kleeman, R., and A. M. Moore, 1997: A theory for the limitation of ENSO predictability due to stochastic atmospheric transients. J. Atmos. Sci., 54 , 753767.

    • Search Google Scholar
    • Export Citation
  • Kleeman, R., and A. M. Moore, 1999: A new method for determining the reliability of dynamical ENSO predictions. Mon. Wea. Rev., 127 , 694705.

    • Search Google Scholar
    • Export Citation
  • Kleeman, R., and A. J. Majda, 2005: Predictability in a model of geophysical turbulence. J. Atmos. Sci., in press.

  • Kleeman, R., Y. Tang, and A. Moore, 2003: The calculation of climatically relevant singular vectors in the presence of weather noise. J. Atmos. Sci., 60 , 28562867.

    • Search Google Scholar
    • Export Citation
  • Madec, G., P. Delecluse, M. Imbard, and C. Levy, 1998: OPA 8.1 ocean general circulation model reference manual. Institut Pierre Simon Laplace (IPSL), 91 pp.

  • Molteni, R., and T. N. Palmer, 1993: Predictability and finite-time instability of the northern winter circulation. Quart. J. Roy. Meteor. Soc., 119 , 269298.

    • Search Google Scholar
    • Export Citation
  • Moore, A. M., and R. Kleeman, 1998: Skill assessment for ENSO using ensemble prediction. Quart. J. Roy. Meteor. Soc., 124 , 557584.

  • Moore, A. M., and R. Kleeman, 1999: Stochastic forcing of ENSO by the intraseasonal oscillation. J. Climate, 12 , 11991220.

  • Palmer, T. N., 1999: Predicting uncertainty in forecast of weather and climate. ECMWF Tech. Memo. 294, 93 pp.

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

  • Segschneider, J., D. L. T. Anderson, J. Vialard, M. Balmaseda, T. N. Stockdale, A. Troccoli, and K. Haines, 2001: Initialization of seasonal forecasts assimilating sea level and temperature observations. J. Climate, 14 , 42924307.

    • Search Google Scholar
    • Export Citation
  • Smith, T. M., R. W. Reynolds, R. E. Livezey, and D. C. Stokes, 1996: Reconstruction of historical sea surface temperatures using empirical orthogonal functions. J. Climate, 9 , 14031420.

    • Search Google Scholar
    • Export Citation
  • Tang, Y., 2002: Hybrid coupled models of the tropical Pacific: I. Interannual variability. Climate Dyn., 19 , 331342.

  • Tang, Y., and W. W. Hsieh, 2003: ENSO simulation and prediction in a hybrid coupled model with data assimilation. J. Meteor. Soc. Japan, 81 , 119.

    • Search Google Scholar
    • Export Citation
  • Tang, Y., R. Kleeman, A. M. Moore, A. Weaver, and J. Vialard, 2003: The use of ocean reanalysis products to initialize ENSO predictions. Geophys. Res. Lett., 30 .1694, doi:10.1029/2003GL017664.

    • Search Google Scholar
    • Export Citation
  • Tang, Y., R. Kleeman, A. M. Moore, J. Vialard, and A. Weaver, 2004: An off-line, numerically efficient initialization scheme in an oceanic general circulation model for El Niño–Southern Oscillation prediction. J. Geophys. Res., 109 .C05014, doi:10.1029/2003JC002159.

    • Search Google Scholar
    • Export Citation
  • Toth, Z., and E. Kalnay, 1993: Ensemble forecasting at NMC: The generation of perturbations. Bull. Amer. Meteor. Soc., 74 , 23172330.

  • Trevisan, A., F. Pancotti, and F. Molteni, 2001: Ensemble prediction in a model with flow regimes. Quart. J. Roy. Meteor. Soc., 127 , 343358.

    • Search Google Scholar
    • Export Citation
  • Vialard, J., P. Delecluse, and C. Menkes, 2002: A modeling study of salinity variability and its effects in the tropical Pacific Ocean during the 1993–1999 period. J. Geophys. Res., 107 .8005, doi:10.1029/2000JC000758.

    • Search Google Scholar
    • Export Citation
  • von Storch, H., and F. W. Zwiers, 1999: Statistical Analysis in Climate Research. Cambridge University Press, 484 pp.

  • Wyrtki, K., 1975: Fluctuations of the dynamic topography in the Pacific Ocean. J. Phys. Oceanogr., 5 , 450459.

  • Xu, J. S., and H. von Storch, 1990: Predicting the state of the Southern Oscillation using principal oscillation pattern analysis. J. Climate, 3 , 13161329.

    • Search Google Scholar
    • Export Citation
  • Xue, Y., M. A. Cane, S. E. Zebiak, and M. B. Blumenthal, 1994: On the prediction of ENSO: A study with a low-order Markov model. Tellus, 46A , 512528.

    • Search Google Scholar
    • Export Citation
  • Xue, Y., M. A. Cane, S. E. Zebiak, and T. N. Palmer, 1997: Predictability of a coupled model of ENSO using singular vector analysis. Part II: Optimal growth and forecast skill. Mon. Wea. Rev., 125 , 20572073.

    • Search Google Scholar
    • Export Citation
  • Zavala-Garay, J., A. M. Moore, C. L. Perez, and R. Kleeman, 2003: The response of a coupled model of ENSO to observed estimates of stochastic forcing. J. Climate, 16 , 28272842.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 231 85 11
PDF Downloads 142 55 9