• Derome, J., and Coauthors, 2001: Seasonal predictions based on two dynamical models. Atmos.–Ocean, in press.

  • Hasselmann, K., 1976: Stochastic climate models. Pt. 1: Theory. Tellus, 28 , 473485.

  • Houtekamer, P. L., , and J. Derome, 1994: Prediction experiments with two member ensembles. Mon. Wea. Rev, 122 , 21792191.

  • ——, and ——,. 1995: Methods for ensemble prediction. Mon. Wea. Rev, 123 , 21812196.

  • Kumar, A., , and M. P. Hoerling, 1995: Prospects and limitations of atmospheric GCM climate predictions. Bull. Amer. Meteor. Soc, 76 , 335345.

    • Search Google Scholar
    • Export Citation
  • Lau, N-C., 1981: A diagnostic study of recurrent meteorological anomalies appearing in a 15-year simulation with a GFDL general circulation model. Mon. Wea. Rev, 109 , 22872311.

    • Search Google Scholar
    • Export Citation
  • Lin, H., , and J. Derome, 1996: Changes in predictability associated with the PNA pattern. Tellus, 48A , 553571.

  • ——, and ——,. 1999: The genesis and predictability of persistent Pacific–North American anomalies in a model atmosphere. Tellus, 51A , 686697.

    • Search Google Scholar
    • Export Citation
  • Madden, R. A., 1976: Estimates of the natural variability of time averaged sea level pressure. Mon. Wea. Rev, 104 , 942952.

  • Mann, M. E., , and J. M. Lees, 1996: Robust estimation of background noise and signal detection in climate time series. Climatic Change, 33 , 409445.

    • Search Google Scholar
    • Export Citation
  • Marshall, J., , and F. Molteni, 1993: Toward a dynamical understanding of planetary-scale flow regimes. J. Atmos. Sci, 50 , 17921818.

  • Robinson, W. A., , and J. Qin, 1992: Predictability of the zonal index in a global model. Tellus, 44A , 331338.

  • Thompson, D. W. J., , and J. M. Wallace, 1998: The Arctic Oscillation signature in the wintertime geopotential height and temperature fields. Geophys. Res. Lett, 25 , 12971300.

    • 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.

  • Tung, K. K., , and A. J. Rosenthal, 1986: On the extended-range predictability of large-scale quasi-stationary patterns in the atmosphere. Tellus, 38A , 333365.

    • Search Google Scholar
    • Export Citation
  • 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
  • Zwiers, F., 1987: A potential predictability study conducted with an atmospheric general circulation model. Mon. Wea. Rev, 115 , 29572974.

    • Search Google Scholar
    • Export Citation
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Seasonal Predictability in a Model Atmosphere

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  • 1 Department of Atmospheric and Oceanic Sciences and Centre for Climate and Global Change Research, McGill University, Montreal, Quebec, Canada
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Abstract

The predictability of atmospheric mean-seasonal conditions in the absence of externally varying forcing is examined. A perfect-model approach is adopted, in which a global T21 three-level quasigeostrophic atmospheric model is integrated over 21 000 days to obtain a reference atmospheric orbit. The model is driven by a time-independent forcing, so that the only source of time variability is the internal dynamics. The forcing is set to perpetual winter conditions in the Northern Hemisphere (NH) and perpetual summer in the Southern Hemisphere.

A significant temporal variability in the NH 90-day mean states is observed. The component of that variability associated with the higher-frequency motions, or climate noise, is estimated using a method developed by Madden. In the polar region, and to a lesser extent in the midlatitudes, the temporal variance of the winter means is significantly greater than the climate noise, suggesting some potential predictability in those regions.

Forecast experiments are performed to see whether the presence of variance in the 90-day mean states that is in excess of the climate noise leads to some skill in the prediction of these states. Ensemble forecast experiments with nine members starting from slightly different initial conditions are performed for 200 different 90-day means along the reference atmospheric orbit. The serial correlation between the ensemble means and the reference orbit shows that there is skill in the 90-day mean predictions. The skill is concentrated in those regions of the NH that have the largest variance in excess of the climate noise. An EOF analysis shows that nearly all the predictive skill in the seasonal means is associated with one mode of variability with a strong axisymmetric component.

Corresponding author address: Dr. Hai Lin, Department of Atmospheric and Oceanic Sciences and Centre for Climate and Global Change Research, McGill University, 805 rue Sherbrooke ouest, Montreal, PQ H3A 2K6, Canada. Email: hlin@zephyr.meteo.mcgill.ca

Abstract

The predictability of atmospheric mean-seasonal conditions in the absence of externally varying forcing is examined. A perfect-model approach is adopted, in which a global T21 three-level quasigeostrophic atmospheric model is integrated over 21 000 days to obtain a reference atmospheric orbit. The model is driven by a time-independent forcing, so that the only source of time variability is the internal dynamics. The forcing is set to perpetual winter conditions in the Northern Hemisphere (NH) and perpetual summer in the Southern Hemisphere.

A significant temporal variability in the NH 90-day mean states is observed. The component of that variability associated with the higher-frequency motions, or climate noise, is estimated using a method developed by Madden. In the polar region, and to a lesser extent in the midlatitudes, the temporal variance of the winter means is significantly greater than the climate noise, suggesting some potential predictability in those regions.

Forecast experiments are performed to see whether the presence of variance in the 90-day mean states that is in excess of the climate noise leads to some skill in the prediction of these states. Ensemble forecast experiments with nine members starting from slightly different initial conditions are performed for 200 different 90-day means along the reference atmospheric orbit. The serial correlation between the ensemble means and the reference orbit shows that there is skill in the 90-day mean predictions. The skill is concentrated in those regions of the NH that have the largest variance in excess of the climate noise. An EOF analysis shows that nearly all the predictive skill in the seasonal means is associated with one mode of variability with a strong axisymmetric component.

Corresponding author address: Dr. Hai Lin, Department of Atmospheric and Oceanic Sciences and Centre for Climate and Global Change Research, McGill University, 805 rue Sherbrooke ouest, Montreal, PQ H3A 2K6, Canada. Email: hlin@zephyr.meteo.mcgill.ca

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