• Behringer, D. W., M. Ji, and A. Leetma, 1998: An improved coupled model for ENSO prediction and implications for ocean initialization. Part I: The ocean data assimilation system. Mon. Wea. Rev, 126 , 10131021.

    • Search Google Scholar
    • Export Citation
  • Bretherton, C., C. Smith, and J. Wallace, 1992: An intercomparison of methods for finding coupled patterns in climate data. J. Climate, 5 , 541560.

    • Search Google Scholar
    • Export Citation
  • Chang, P., B. Wang, T. Li, and L. Ji, 1994: Interactions between the seasonal cycle and the southern oscillation—Frequency entrainment and chaos in a coupled ocean–atmosphere model. Geophys. Res. Lett, 21 , 28172820.

    • Search Google Scholar
    • Export Citation
  • Darnell, W., W. Staylor, N. Ritchey, S. Gupta, and A. Wilber, 1996: Surface radiation budget: A long-term global dataset of shortwave and longwave fluxes. Eos, Trans. Amer. Geophys. Union, Electronic Supplement. [Available online at http://www.agu.org/eos_elec/95206e.html].

    • Search Google Scholar
    • Export Citation
  • Deser, C., and J. Wallace, 1990: Large-scale atmospheric circulation features of warm and cold episodes in the tropical Pacific. J. Climate, 3 , 12541281.

    • Search Google Scholar
    • Export Citation
  • Esbensen, S., and V. Kushnir, 1981: The heat budget of the global ocean. An atlas based on estimates from surface marine observations. Tech. Rep. 29, Climate Research Institute, Oregon State University, Corvallis, OR, 27 pp.

    • Search Google Scholar
    • Export Citation
  • Galanti, E., and E. Tziperman, 2000: ENSO's phase locking to the seasonal cycle in the fast-SST, fast-wave, and mixed-mode regimes. J. Atmos. Sci, 57 , 29362950.

    • Search Google Scholar
    • Export Citation
  • Gibson, J., P. Kallberg, S. Uppala, A. Nomura, A. Hernandez, and E. Serrano, 1997: ERA Description. ECMWF Reanalysis Project Report Series, No. 1,. ECMWF, Reading, United Kingdom, 72 pp.

    • Search Google Scholar
    • Export Citation
  • Gordon, C., and W. Stern, 1982: A description of the GFDL global spectral model. Mon. Wea. Rev, 110 , 625644.

  • Hellerman, S., and M. Rosenstein, 1983: Normal monthly wind stress over the world ocean with error estimates. J. Phys. Oceanogr, 13 , 10931104.

    • Search Google Scholar
    • Export Citation
  • Ji, M., D. W. Behringer, and A. Leetma, 1998: An improved coupled model for ENSO prediction and implications for ocean initialization. Part II: The coupled model. Mon. Wea. Rev, 126 , 10221034.

    • Search Google Scholar
    • Export Citation
  • Jin, F., J. Neelin, and M. Ghil, 1994: ENSO on the devil's staircase: Annual sub-harmonic steps to chaos. Science, 264 , 7072.

  • Jost, V., J. Schulz, and S. Bakan, 1999: A new satellite-derived freshwater flux climatology. Proc. Conf. on the TOGA Coupled Ocean–Atmosphere Response Experiment (COARE) Boulder, CO, World Climate Research Program, 205–207.

    • Search Google Scholar
    • Export Citation
  • Kalnay, E., and and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc, 77 , 437471.

  • Levitus, S., and T. P. Boyer, 1994: Temperature. Vol. 4, World Ocean Atlas 1994,. NOAA Atlas NESDIS, 117 pp.

  • Levitus, S., R. Burgett, and T. P. Boyer, 1994: Salinity. Vol. 3, World Ocean Atlas 1994,. NOAA Atlas NESDIS, 99 pp.

  • Neelin, J. D., D. Battisti, A. Hirst, F. Jin, Y. Wakata, T. Yamagata, and S. Zebiak, 1998: ENSO theory. J. Geophys. Res, 103 , (C7),. 1426114290.

    • Search Google Scholar
    • Export Citation
  • Newman, M., and P. D. Sardeshmukh, 1995: A caveat concerning singular value decomposition. J. Climate, 8 , 352360.

  • Philander, S. G. H., and R. C. Pacanowski, 1981: Parameterization of vertical mixing in numerical models of tropical oceans. J. Geophys. Res, 86 , (C3),. 19031916.

    • Search Google Scholar
    • Export Citation
  • Reynolds, R., and T. 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
  • Smagorinsky, J., 1963: General circulation experiments with the primitive equations. Part I: The basic experiment. Mon. Wea. Rev, 91 , 99164.

    • Search Google Scholar
    • Export Citation
  • Stockdale, T. N., A. J. Busalacchi, D. Harrison, and R. Seager, 1998: Ocean modeling for ENSO. J. Geophys. Res, 103 , 1432514355.

  • Stricherz, J. N., D. M. Legler, and J. J. O'Brien, 1997: TOGA Pseudo-stress atlas 1985–1994, Vol. II. Tech. Rep. 2, Center for Ocean-Atmosphere Prediction Studies, The Florida State University, 158 pp.

    • Search Google Scholar
    • Export Citation
  • Syu, H., and J. Neelin, 1995: Variability in a hybrid coupled GCM. J. Climate, 8 , 21212142.

  • Tziperman, E., L. Stone, M. Cane, and H. Jarosh, 1994: El Niño chaos: Overlapping of resonances between the seasonal cycle and the Pacific ocean–atmosphere oscillator. Science, 264 , 7274.

    • Search Google Scholar
    • Export Citation
  • Tziperman, E., M. Cane, and S. Zebiak, 1995: Irregularity and locking to the seasonal cycle in an ENSO prediction model as explained by the quasi-periodicity route to chaos. J. Atmos. Sci, 52 , 293306.

    • Search Google Scholar
    • Export Citation
  • Tziperman, E., S. Zebiak, and M. Cane, 1997: Mechanisms of seasonal-ENSO interactions. J. Atmos. Sci, 54 , 6171.

  • Tziperman, E., M. Cane, S. Zebiak, Y. Zue, and B. Blumenthal, 1998: Locking of El Niño's peak time to the end of the calendar year in the delayed oscillator picture of ENSO. J. Climate, 11 , 21912199.

    • Search Google Scholar
    • Export Citation
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An Evaluation of Air–Sea Flux Products for ENSO Simulation and Prediction

M. J. HarrisonNational Oceanographic and Atmospheric Administration/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

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A. RosatiNational Oceanographic and Atmospheric Administration/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

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B. J. SodenNational Oceanographic and Atmospheric Administration/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey

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E. GalantiDepartment of Environmental Sciences, The Weizmann Institute of Science, Rehovot, Israel

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E. TzipermanDepartment of Environmental Sciences, The Weizmann Institute of Science, Rehovot, Israel

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Abstract

This paper presents a quantitative methodology for evaluating air–sea fluxes related to ENSO from different atmospheric products. A statistical model of the fluxes from each atmospheric product is coupled to an ocean general circulation model (GCM). Four different products are evaluated: reanalyses from the National Centers for Environmental Prediction (NCEP) and the European Centre for Medium-Range Weather Forecasts (ECMWF), satellite-derived data from the Special Sensor Microwave/Imaging (SSM/I) platform and the International Satellite Cloud Climatology Project (ISCCP), and an atmospheric GCM developed at the Geophysical Fluid Dynamics Laboratory (GFDL) as part of the Atmospheric Model Intercomparison Project (AMIP) II. For this study, comparisons between the datasets are restricted to the dominant air–sea mode.

The stability of a coupled model using only the dominant mode and the associated predictive skill of the model are strongly dependent on which atmospheric product is used. The model is unstable and oscillatory for the ECMWF product, damped and oscillatory for the NCEP and GFDL products, and unstable (nonoscillatory) for the satellite product. The ocean model is coupled with patterns of wind stress as well as heat fluxes. This distinguishes the present approach from the existing paradigm for ENSO models where surface heat fluxes are parameterized as a local damping term in the sea surface temperature (SST) equation.

Corresponding author address: M.J. Harrison, NOAA/GFDL, P.O. Box 308, Princeton, NJ 08542. Email: mjh@gfdl.noaa.gov

Abstract

This paper presents a quantitative methodology for evaluating air–sea fluxes related to ENSO from different atmospheric products. A statistical model of the fluxes from each atmospheric product is coupled to an ocean general circulation model (GCM). Four different products are evaluated: reanalyses from the National Centers for Environmental Prediction (NCEP) and the European Centre for Medium-Range Weather Forecasts (ECMWF), satellite-derived data from the Special Sensor Microwave/Imaging (SSM/I) platform and the International Satellite Cloud Climatology Project (ISCCP), and an atmospheric GCM developed at the Geophysical Fluid Dynamics Laboratory (GFDL) as part of the Atmospheric Model Intercomparison Project (AMIP) II. For this study, comparisons between the datasets are restricted to the dominant air–sea mode.

The stability of a coupled model using only the dominant mode and the associated predictive skill of the model are strongly dependent on which atmospheric product is used. The model is unstable and oscillatory for the ECMWF product, damped and oscillatory for the NCEP and GFDL products, and unstable (nonoscillatory) for the satellite product. The ocean model is coupled with patterns of wind stress as well as heat fluxes. This distinguishes the present approach from the existing paradigm for ENSO models where surface heat fluxes are parameterized as a local damping term in the sea surface temperature (SST) equation.

Corresponding author address: M.J. Harrison, NOAA/GFDL, P.O. Box 308, Princeton, NJ 08542. Email: mjh@gfdl.noaa.gov

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