Climate Predictions with Multimodel Ensembles

Viatcheslav V. Kharin Canadian Centre for Climate Modelling and Analysis, Meteorological Service of Canada, Victoria, British Columbia, Canada

Search for other papers by Viatcheslav V. Kharin in
Current site
Google Scholar
PubMed
Close
and
Francis W. Zwiers Canadian Centre for Climate Modelling and Analysis, Meteorological Service of Canada, Victoria, British Columbia, Canada

Search for other papers by Francis W. Zwiers in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Several methods of combining individual forecasts from a group of climate models to produce an ensemble forecast are considered. These methods are applied to an ensemble of 500-hPa geopotential height forecasts derived from the Atmospheric Model Intercomparison Project (AMIP) integrations performed by 10 different modeling groups. Forecasts are verified against reanalyses from the European Centre for Medium-Range Weather Forecasts. Forecast skill is measured by means of error variance. In the Tropics, the simple ensemble mean produces the most skillful forecasts. In the extratropics, the regression-improved ensemble mean performs best. The “superensemble” forecast that is obtained by optimally weighting the individual ensemble members does not perform as well as either the simple ensemble mean or the regression-improved ensemble mean. The sample size evidently is too small to estimate reliably the relatively large number of optimal weights required for the superensemble approach.

Corresponding author address: Francis W. Zwiers, Canadian Centre for Climate Modelling and Analysis, University of Victoria, P.O. Box 1700, Stn CSC, Victoria, BC V8W 2Y2, Canada. Email: francis.zwiers@ec.gc.ca

Abstract

Several methods of combining individual forecasts from a group of climate models to produce an ensemble forecast are considered. These methods are applied to an ensemble of 500-hPa geopotential height forecasts derived from the Atmospheric Model Intercomparison Project (AMIP) integrations performed by 10 different modeling groups. Forecasts are verified against reanalyses from the European Centre for Medium-Range Weather Forecasts. Forecast skill is measured by means of error variance. In the Tropics, the simple ensemble mean produces the most skillful forecasts. In the extratropics, the regression-improved ensemble mean performs best. The “superensemble” forecast that is obtained by optimally weighting the individual ensemble members does not perform as well as either the simple ensemble mean or the regression-improved ensemble mean. The sample size evidently is too small to estimate reliably the relatively large number of optimal weights required for the superensemble approach.

Corresponding author address: Francis W. Zwiers, Canadian Centre for Climate Modelling and Analysis, University of Victoria, P.O. Box 1700, Stn CSC, Victoria, BC V8W 2Y2, Canada. Email: francis.zwiers@ec.gc.ca

Save
  • Akaike, H., 1974: A new look at the statistical model identification. IEEE Trans. Auto. Control, 19 , 716723.

  • Bengtsson, L., U. Schlese, E. Roeckner, M. Latif, T. P. Barnett, and N. Graham, 1993: A two-tiered approach to long-range climate forecasting. Science, 261 , 10261029.

    • Search Google Scholar
    • Export Citation
  • Danard, M. B., M. M. Holl, and J. R. Clark, 1968: Fields by correlation assembly—A numerical analysis technique. Mon. Wea. Rev., 96 , 141149.

    • Search Google Scholar
    • Export Citation
  • Davis, R. E., 1976: Predictability of sea surface temperature and sea level pressure anomalies over the North Pacific Ocean. J. Phys. Oceanogr., 6 , 249266.

    • Search Google Scholar
    • Export Citation
  • Derome, J., G. Brunet, A. Plante, N. Gagnon, G. J. Boer, F. W. Zwiers, S. Lambert, and H. Ritchie, 2001: Seasonal predictions based on two dynamical models. Atmos.–Ocean, in press.

    • Search Google Scholar
    • Export Citation
  • Doblas-Reyes, F. J., M. Déqué, and J-P. Piedelievre, 2000: Multi-model spread and probabilistic forecasts in PROVOST. Quart. J. Roy. Meteor. Soc., 126 , 20692087.

    • Search Google Scholar
    • Export Citation
  • Fraedrich, K., and N. R. Smith, 1989: Combining predictive schemes in long-range forecasting. J. Climate, 2 , 291294.

  • Gates, W. L., 1992: AMIP: The Atmospheric Model Intercomparison Project. Bull. Amer. Meteor. Soc., 73 , 19621970.

  • Gibson, J. K., P. Kalberg, S. Uppala, A. Hernandes, A. Nomura, and E. Serrano, 1997: ECMWF Reanalysis Report Series 1—ERA Description. ECMWF, Reading, United Kingdom, 72 pp.

    • Search Google Scholar
    • Export Citation
  • Hasselmann, K. F., 1979: On the signal-to-noise problem in atmospheric response studies. Meteorology of the Tropical Ocean, D. B. Shaw, Ed., Royal Meteorological Society, 251–259.

    • Search Google Scholar
    • Export Citation
  • Hasselmann, K. F., . 1997: Multi-pattern fingerprint method for detection and attribution of climate change. Climate Dyn., 13 , 601612.

    • Search Google Scholar
    • Export Citation
  • Kharin, V. V., F. W. Zwiers, and N. Gagnon, 2001: Skill of seasonal hindcasts as a function of the ensemble size. Climate Dyn., 17 , 835843.

    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., C. M. Kishtawal, T. E. LaRow, D. R. Bachiochi, Z. Zhang, C. E. Willifor, S. Gadgil, and S. Surendran, 1999: Improved weather and seasonal climate forecasts from multimodel superensemble. Science, 285 , 15481550.

    • Search Google Scholar
    • Export Citation
  • Krishnamurti, T. N., C. M. Kishtawal, Z. Zhang, T. E. LaRow, D. R. Bachiochi, C. E. Willifor, S. Gadgil, and S. Surendran, 2000: Multimodel ensemble forecasts for weather and seasonal climate. J. Climate, 13 , 41964216.

    • Search Google Scholar
    • Export Citation
  • Lorenz, E. N., 1982: Atmospheric predictability with a large numerical model. Tellus, 34 , 505513.

  • Michaelson, J., 1987: Cross-validation in statistical climate forecast models. J. Climate Appl. Meteor., 26 , 15891600.

  • Phillips, T. J., 1994: A summary documentation of the AMIP models. Tech. Report PCMDI Rep. 18, PCMDI, Lawrence Livermore National Laboratory, Livermore, CA, 343 pp.

    • Search Google Scholar
    • Export Citation
  • Rowell, D. P., and F. W. Zwiers, 1999: The global distribution of the sources of decadal variability and mechanisms over the tropical Pacific and southern North America. Climate Dyn., 15 , 751772.

    • Search Google Scholar
    • Export Citation
  • Thompson, P. D., 1977: How to improve accuracy by combining independent forecasts. Mon. Wea. Rev., 105 , 228229.

  • Zwiers, F. W., 1996: Interannual variability and predictability in an ensemble of AMIP climate simulations conducted with the CCC GCM2. Climate Dyn., 12 , 825848.

    • Search Google Scholar
    • Export Citation
  • Zwiers, F. W., . 1999: The detection of climate change. Anthropogenic Climate Change, H. von Storch and G. Flöser, Eds., Springer-Verlag, 161–206.

    • Search Google Scholar
    • Export Citation
  • Zwiers, F. W., X. L. Wang, and J. Sheng, 2000: The effects of specifying bottom boundary conditions in an ensemble of GCM simulations. J. Geophys. Res., 105 , 72957315.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 1023 352 50
PDF Downloads 623 205 38