• Bacmeister, J. T., P. J. Pegion, S. D. Schubert, and M. J. Suarez, 2000: An atlas of seasonal means simulated by the NSIPP 1 atmospheric GCM. NASA Tech. Memo. 104606, Goddard Space Flight Center, 194 pp.

    • Search Google Scholar
    • Export Citation
  • Baldwin, M. P., and T. J. Dunkerton, 2001: Stratospheric harbingers of anomalous weather regimes. Science, 294 , 581584.

  • Barnston, A. G., and R. E. Livezey, 1987: Classification, seasonality and persistence of low-frequency atmospheric circulation patterns. Mon. Wea. Rev., 115 , 10831126.

    • Search Google Scholar
    • Export Citation
  • Charney, J. G., and P. G. Drazin, 1961: Propagation of planetary-scale disturbances from the lower into the upper atmosphere. J. Geophys. Res., 66 , 83109.

    • Search Google Scholar
    • Export Citation
  • Gong, D. Y., and S. W. Wang, 1999: Definition of Antarctic Oscillation Index. Geophys. Res. Lett., 26 , 459462.

  • Kanamitsu, M., and Coauthors, 2002: NCEP dynamical seasonal forecast system 2000. Bull. Amer. Meteor. Soc., 83 , 10191037.

  • Karoly, D. J., 1989: Southern Hemisphere circulation features associated with El Niño–Southern Oscillation events. J. Climate, 2 , 12391252.

    • Search Google Scholar
    • Export Citation
  • Kumar, A., S. Schubert, and M. Suarez, 2003: Variability and predictability of 200-mb seasonal mean heights during summer and winter. J. Geophys. Res.,108, 4169, doi:10.102/2002JD002728.

    • Search Google Scholar
    • Export Citation
  • Lorenz, E., 1969: The predictability of a flow which possesses many scales of motion. Tellus, 21 , 289307.

  • Lorenz, E., 1982: Atmospheric predictability experiments with a large numerical model. Tellus, 34 , 505513.

  • Madden, R. A., and P. R. Julian, 1972: Description of global-scale circulation cells in the Tropics with a 40–50 day period. J. Atmos. Sci., 29 , 11091123.

    • Search Google Scholar
    • Export Citation
  • Mason, S. J., L. Goddard, N. E. Graham, E. Yulaeva, L. Q. Sun, and P. A. Arkin, 1999: The IRI seasonal climate prediction system and the 1997/98 El Niño event. Bull. Amer. Meteor. Soc., 80 , 18531873.

    • Search Google Scholar
    • Export Citation
  • Perlwitz, J., and H. F. Graf, 2001: The variability of the horizontal circulation in the troposphere and stratosphere—A comparison. Theor. Appl. Climatol., 69 , 149161.

    • Search Google Scholar
    • Export Citation
  • Reichler, T., and J. O. Roads, 2003: The role of boundary and initial conditions for dynamical seasonal predictability. Nonlinear Proc. Geophys., 10 , 211232.

    • Search Google Scholar
    • Export Citation
  • Reichler, T., M. Dameris, and R. Sausen, 2003: Determining the tropopause height from gridded data. Geophys. Res. Lett., 30 , 2042. doi:10.1029/2003GL018240.

    • Search Google Scholar
    • Export Citation
  • Roads, J. O., 1988: Lagged average predictions in a predictability experiment. J. Atmos. Sci., 45 , 147162.

  • Roads, J. O., S. C. Chen, and F. Fujioka, 2001: ECPC's weekly to seasonal global forecasts. Bull. Amer. Meteor. Soc., 82 , 639658.

  • Thompson, D. W. J., and J. M. Wallace, 2000: Annular modes in the extratropical circulation. Part I: Month-to-month variability. J. Climate, 13 , 10001016.

    • Search Google Scholar
    • Export Citation
  • Thompson, D. W. J., and S. Solomon, 2002: Interpretation of recent Southern Hemisphere climate change. Science, 296 , 895899.

  • Thompson, D. W. J., M. P. Baldwin, and J. M. Wallace, 2002: Stratospheric connection to Northern Hemisphere wintertime weather: Implications for prediction. J. Climate, 15 , 14211428.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., 1985: Potential predictability of daily geopotential heights over the Southern Hemisphere. Mon. Wea. Rev., 113 , 5464.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., G. W. Branstator, D. Karoly, A. Kumar, N. C. Lau, and C. Ropelewski, 1998: Progress during TOGA in understanding and modeling global teleconnections associated with tropical sea surface temperatures. J. Geophys. Res., 103 , 1429114324.

    • Search Google Scholar
    • Export Citation
  • von Storch, H., and F. W. Zwiers, 1999: Statistical Analysis in Climate Research. Vol. 12, Cambridge University Press, 484 pp.

  • Walker, G. T., 1924: World weather II. Mem. India Meteor. Dept., 24 , 275332.

  • Walker, G. T., and E. W. Bliss, 1932: World weather V. Mem. Roy. Meteor. Soc., 4 , 5384.

  • Wallace, J. M., and D. S. Gutzler, 1981: Teleconnections in the geopotential height field during the Northern Hemisphere winter. Mon. Wea. Rev., 109 , 784812.

    • Search Google Scholar
    • Export Citation
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Time–Space Distribution of Long-Range Atmospheric Predictability

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  • 1 Scripps Institution of Oceanography, University of California, San Diego, La Jolla, California
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Abstract

The global three-dimensional structure of long-range (one month to one season) atmospheric predictability was investigated with a general circulation model. The main focus was to ascertain the role of atmospheric initial conditions for such predictability as a function of lead time and space. Four types of predictability experiments with different types of initial and boundary conditions were conducted to this end. The experiments were verified against reanalysis and model data to determine real forecast skill, as well as skill under the perfect model assumption. Spatial maps and vertical cross sections of predictability at different lead times and for the two contrasting seasons were analyzed to document the varying influence of initial and boundary conditions on predictability. It was found that the atmosphere was remarkably sensitive to initial conditions on the week 3–6 forecast range. Particularly, the troposphere over Antarctica, the region over the tropical Indian Ocean, and the lower stratosphere were affected. It was shown that most of the initial condition memory was related to the persistent nature of the atmosphere in these regions, which in turn was linked to the major modes of atmospheric variability.

Corresponding author address: Dr. John O. Roads, Scripps Institution of Oceanography, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0224. Email: jroads@ucsd.edu

Abstract

The global three-dimensional structure of long-range (one month to one season) atmospheric predictability was investigated with a general circulation model. The main focus was to ascertain the role of atmospheric initial conditions for such predictability as a function of lead time and space. Four types of predictability experiments with different types of initial and boundary conditions were conducted to this end. The experiments were verified against reanalysis and model data to determine real forecast skill, as well as skill under the perfect model assumption. Spatial maps and vertical cross sections of predictability at different lead times and for the two contrasting seasons were analyzed to document the varying influence of initial and boundary conditions on predictability. It was found that the atmosphere was remarkably sensitive to initial conditions on the week 3–6 forecast range. Particularly, the troposphere over Antarctica, the region over the tropical Indian Ocean, and the lower stratosphere were affected. It was shown that most of the initial condition memory was related to the persistent nature of the atmosphere in these regions, which in turn was linked to the major modes of atmospheric variability.

Corresponding author address: Dr. John O. Roads, Scripps Institution of Oceanography, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093-0224. Email: jroads@ucsd.edu

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