• 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
  • 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
  • Carton, J. A., , B. S. Giese, , X. Cao, , and L. Miller, 1996: Impact of altimeter, thermistor, and expendable bathythermograph data on retrospective analyses of the tropical Pacific Ocean. J. Geophys. Res, 101 , 1414714160.

    • 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: Implications for predictability. Science, 269 , 16991702.

    • Search Google Scholar
    • Export Citation
  • Chen, D., , M. A. Cane, , S. E. Zebiak, , and A. Kaplan, 1998: The impact of sea level data assimilation on the Lamont model prediction of the 1997/98 El Niño. Geophys. Res. Lett, 25 , 28372840.

    • Search Google Scholar
    • Export Citation
  • Chen, Y., , 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 ENSO cycle. Mon. Wea. Rev, 125 , 831845.

    • Search Google Scholar
    • Export Citation
  • Dabberdt, W. F., , and T. W. Schlatter, the Second Prospectus Development Team,. 1996: Research opportunities from emerging atmospheric observing and modeling capabilities. Bull. Amer. Meteor. Soc, 77 , 305323.

    • Search Google Scholar
    • Export Citation
  • Derber, J., , and A. Rosati, 1989: Global oceanic data assimilation system. J. Phys. Oceanogr, 19 , 13331348.

  • Fan, Y., , M. R. Allen, , D. L. T. Anderson, , and M. A. Balmaseda, 2000: How predictability depends on the nature of uncertainty in initial conditions in a coupled model of ENSO. J. Climate, 13 , 32983313.

    • Search Google Scholar
    • Export Citation
  • Federov, A. V., , and S. G. Philander, 2000: Is El Niño changing? Science, 288 , 19972002.

  • Fischer, M., , M. Latif, , M. Flügel, , and M. Ji, 1997: The impact of data assimilation on ENSO simulations and predictions. Mon. Wea. Rev, 125 , 819829.

    • Search Google Scholar
    • Export Citation
  • 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
  • Gill, A. E., 1980: Some simple solutions for heat-induced tropical circulation. Quart. J. Roy. Meteor. Soc, 106 , 447462.

  • Hao, Z., , and M. Ghil, 1994: Data assimilation in a simple tropical ocean model with wind stress errors. J. Phys. Oceanogr, 24 , 21112128.

    • Search Google Scholar
    • Export Citation
  • Hayes, S. P., , L. J. Mangum, , J. Picaut, , A. Sumi, , and K. Takeuchi, 1991: TOGA-TAO: A moored array for real-time measurements in the tropical Pacific Ocean. Bull. Amer. Meteor. Soc, 72 , 339347.

    • Search Google Scholar
    • Export Citation
  • Ji, M., , and A. Leetmaa, 1997: Impact of data assimilation on ocean initialization and El Niño prediction. Mon. Wea. Rev, 125 , 742753.

    • Search Google Scholar
    • Export Citation
  • Ji, M., , A. Leetmaa, , and J. Derber, 1995: An ocean analysis system for seasonal to interannual climate studies. Mon. Wea. Rev, 123 , 460481.

    • 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
  • Johnson, S. D., , D. S. Battisti, , and E. S. Sarachik, 2000: Empirically derived Markov models and prediction of tropical Pacific sea surface temperature anomalies. J. Climate, 13 , 317.

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

  • 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., , A. M. Moore, , and N. R. Smith, 1995: Assimilation of subsurface thermal data into a simple ocean model for the initialization of an intermediate tropical coupled ocean–atmosphere forecast model. Mon. Wea. Rev, 123 , 31033113.

    • Search Google Scholar
    • Export Citation
  • Langland, R. H., 1999: Workshop on targeted observations for extratropical and tropical forecasting. Bull. Amer. Meteor. Soc, 80 , 23312338.

    • Search Google Scholar
    • Export Citation
  • Langland, R. H., and Coauthors, 1999: The North Pacific Experiment (NORPEX-98): Targeted observations for improved North American weather forecasts. Bull. Amer. Meteor. Soc, 80 , 13631384.

    • Search Google Scholar
    • Export Citation
  • Latif, M., and Coauthors, 1998: A review of the predictability and prediction of ENSO. J. Geophys. Res, 103 , 1437514393.

  • Lau, K-M., 1985: Elements of a stochastic-dynamical theory of the long-term variability of the El Niño/Southern Oscillation. J. Atmos. Sci, 42 , 15521558.

    • Search Google Scholar
    • Export Citation
  • McPhaden, M. J., and Coauthors, 1998: The Tropical Ocean–Global Atmosphere observing system: A decade of progress. J. Geophys. Res, 103 , 1416914240.

    • Search Google Scholar
    • Export Citation
  • Miller, R. N., 1990: Tropical data assimilation experiments with simulated data: The impact of the tropical ocean and global atmosphere thermal array for the ocean. J. Geophys. Res, 95 , 1146111482.

    • Search Google Scholar
    • Export Citation
  • Moore, A. M., , and R. Kleeman, 1996: The dynamics of error growth and predictability in a coupled model of ENSO. Quart. J. Roy. Meteor. Soc, 122 , 14051446.

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

  • Moore, A. M., , and R. Kleeman, 2001: The differences between the optimal perturbations of coupled models of ENSO. J. Climate, 14 , 138163.

    • Search Google Scholar
    • Export Citation
  • Moore, A. M., , J. Vialard, , A. T. Weaver, , D. L. T. Anderson, , R. Kleeman, , and J. R. Johnson, 2003: The role of air–sea interaction in controlling the optimal perturbations of low-frequency tropical coupled ocean–atmosphere modes. J. Climate, 16 , 951968.

    • Search Google Scholar
    • Export Citation
  • Morss, R. E., , and D. S. Battisti, 2004: Evaluating observing requirements for ENSO prediction: Experiments with an intermediate coupled model. J. Climate, 17 , 30573073.

    • Search Google Scholar
    • Export Citation
  • Palmer, T. N., , R. Gelaro, , J. Barkmeijer, , and R. Buizza, 1998: Singular vectors, metrics, and adaptive observations. J. Atmos. Sci, 55 , 633653.

    • 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.

  • Perigaud, C. M., , C. Cassou, , B. Dewitte, , L-L. Fu, , and J. D. Neelin, 2000: Using data and intermediate coupled models for seasonal-to-interannual forecasts. Mon. Wea. Rev, 128 , 30253049.

    • Search Google Scholar
    • Export Citation
  • Philander, S. G. H., , W. J. Hurlin, , and R. C. Pacanowski, 1987: Initial conditions for a general circulation model of tropical oceans. J. Phys. Oceanogr, 17 , 147157.

    • Search Google Scholar
    • Export Citation
  • Reynolds, R. W., , and T. M. Smith, 1994: Improved global sea surface temperature analyses using optimum interpolation. J. Climate, 7 , 929948.

    • Search Google Scholar
    • Export Citation
  • Rosati, A., , K. Miyakoda, , and R. Gudgel, 1997: The impact of ocean initial conditions on ENSO forecasting with a coupled model. Mon. Wea. Rev, 125 , 754772.

    • Search Google Scholar
    • Export Citation
  • Sheinbaum, J., , and D. L. T. Anderson, 1990a: Variational assimilation of XBT data. Part I. J. Phys. Oceanogr, 20 , 672688.

  • Sheinbaum, J., , and D. L. T. Anderson, 1990b: Variational assimilation of XBT data. Part II: Sensitivity studies and use of smoothing constraints. J. Phys. Oceanogr, 20 , 689704.

    • Search Google Scholar
    • Export Citation
  • Snyder, C., 1996: Summary of an informal workshop on adaptive observations and FASTEX. Bull. Amer. Meteor. Soc, 77 , 953961.

  • Sun, C., , Z. Hao, , M. Ghil, , and J. D. Neelin, 2002: Data assimilation for a coupled ocean–atmosphere model. Part I: Sequential state estimation. Mon. Wea. Rev, 130 , 10731099.

    • Search Google Scholar
    • Export Citation
  • Thompson, C. J., 1998: Initial conditions for optimal growth in a coupled ocean–atmosphere model of ENSO. J. Atmos. Sci, 55 , 537557.

    • Search Google Scholar
    • Export Citation
  • Thompson, C. J., , and D. S. Battisti, 2000: A linear stochastic dynamical model of ENSO. Part I: Model development. J. Climate, 13 , 28182832.

    • 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.

  • Wallace, J. M., , E. M. Rasmusson, , T. P. Mitchell, , V. E. Kousky, , E. S. Sarachik, , and H. von Storch, 1998: On the structure and evolution of ENSO-related climate variability in the tropical Pacific: Lessons from TOGA. J. Geophys. Res, 103 , 1424114259.

    • 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, , and S. E. Zebiak, 1997a: Predictability of a coupled model of ENSO using singular vector analysis. Part I: Optimal growth in seasonal background and ENSO cycles. Mon. Wea. Rev, 125 , 20432056.

    • Search Google Scholar
    • Export Citation
  • Xue, Y., , M. A. Cane, , S. E. Zebiak, , and T. N. Palmer, 1997b: 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
  • Xue, Y., , A. Leetmaa, , and M. Ji, 2000: ENSO prediction with Markov models: The impact of sea level. J. Climate, 13 , 849871.

  • Zebiak, S. E., , and M. A. Cane, 1987: A model El Niño–Southern Oscillation. Mon. Wea. Rev, 115 , 22622278.

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Designing Efficient Observing Networks for ENSO Prediction

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  • 1 National Center for Atmospheric Research,* Boulder, Colorado
  • | 2 Joint Institute for the Study of the Atmosphere and Ocean, University of Washington, Seattle, Washington
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Abstract

The Tropical Atmosphere Ocean (TAO) array of moored buoys in the tropical Pacific Ocean is a major source of data for understanding and predicting El Niño–Southern Oscillation (ENSO). Despite the importance of the TAO array, limited work has been performed where observations are most important for predicting ENSO effectively. To address this issue, this study performs a series of observing system simulation experiments (OSSEs) with a linearized intermediate coupled ENSO model, stochastically forced. ENSO forecasts are simulated for a variety of observing network configurations, and forecast skill averaged over many simulated ENSO events is compared.

The first part of this study examined the relative importance of sea surface temperature (SST) and subsurface ocean observations, requirements for spacing and meridional extent of observations, and important regions for observations in this system. Using these results as a starting point, this paper develops efficient observing networks for forecasting ENSO in this system, where efficient is defined as providing reasonably skillful forecasts for relatively few observations. First, efficient networks that provide SST and thermocline depth data at the same locations are developed and discussed. Second, efficient networks of only thermocline depth observations are addressed, assuming that many SST observations are available from another source (e.g., satellites). The dependence of the OSSE results on the duration of the simulated data record is also explored. The results suggest that several decades of data may be sufficient for evaluating the effects of observing networks on ENSO forecast skill, despite being insufficient for evaluating the long-term potential predictability of ENSO.

Corresponding author address: Dr. Rebecca E. Morss, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307. Email: morss@ucar.edu

Abstract

The Tropical Atmosphere Ocean (TAO) array of moored buoys in the tropical Pacific Ocean is a major source of data for understanding and predicting El Niño–Southern Oscillation (ENSO). Despite the importance of the TAO array, limited work has been performed where observations are most important for predicting ENSO effectively. To address this issue, this study performs a series of observing system simulation experiments (OSSEs) with a linearized intermediate coupled ENSO model, stochastically forced. ENSO forecasts are simulated for a variety of observing network configurations, and forecast skill averaged over many simulated ENSO events is compared.

The first part of this study examined the relative importance of sea surface temperature (SST) and subsurface ocean observations, requirements for spacing and meridional extent of observations, and important regions for observations in this system. Using these results as a starting point, this paper develops efficient observing networks for forecasting ENSO in this system, where efficient is defined as providing reasonably skillful forecasts for relatively few observations. First, efficient networks that provide SST and thermocline depth data at the same locations are developed and discussed. Second, efficient networks of only thermocline depth observations are addressed, assuming that many SST observations are available from another source (e.g., satellites). The dependence of the OSSE results on the duration of the simulated data record is also explored. The results suggest that several decades of data may be sufficient for evaluating the effects of observing networks on ENSO forecast skill, despite being insufficient for evaluating the long-term potential predictability of ENSO.

Corresponding author address: Dr. Rebecca E. Morss, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307. Email: morss@ucar.edu

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