• Barnett, T. P., 1995: Monte Carlo climate forecasts. J. Climate,8, 1005–1022.

  • ——, and Coauthors, 1994: Forecasting global ENSO-related climate anomalies. Tellus,46A, 381–397.

  • 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, 1026–1029.

  • ——, K. Arpe, E. Roeckner, and U. Schulzweida, 1996: Climate predictability experiments with a general circulation model. Climate Dyn.,12, 261–278.

  • Brankovic, C., T. N. Palmer, and L. Ferranti, 1994: Predictability of seasonal atmospheric variations. J. Climate,7, 217–237.

  • 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, 1699–1702.

  • Gates, W. L., 1992: AMIP: The Atmospheric Model Intercomparison Project. Bull. Amer. Meteor. Soc.,73, 1962–1970.

  • Horel, J., and J. Wallace, 1981: Planetary-scale atmospheric phenomena associated with the Southern Oscillation. Mon. Wea. Rev.,109, 813–829.

  • Ji, M., A. Kumar, and A. Leetmaa, 1994a: A multiseason climate forecast system at the National Meteorological Center. Bull. Amer. Meteor. Soc.,75, 569–577.

  • ——, ——, and ——, 1994b: An experimental coupled forecast system at the National Meteorological Center: Some early results. Tellus,46A, 398–418.

  • Kumar, A., and M. P. Hoerling, 1995: Prospects and limitations of seasonal atmospheric GCM predictions. Bull. Amer. Meteor. Soc.,76, 335–345.

  • ——, and ——, 1997: Interpretation and implications of the observed Inter–El Niño variability. J. Climate,10, 83–91.

  • ——, ——, A. Leetmaa, and P. D. Sardeshmukh, 1996: Assessing a GCM’s suitability for making seasonal prediction. J. Climate,9, 115–129.

  • Namias, J., 1978: Multiple causes of the North American abnormal winter of 1976–77. Mon. Wea. Rev.,106, 279–295.

  • North, G. R., T. L. Bell, R. F. Cahalan, and F. J. Moeng, 1982: Sampling errors in the estimation of empirical orthogonal functions. Mon. Wea. Rev.,110, 699–706.

  • Roeckner, E., and Coauthors, 1992: Simulation of the present-day climate with the ECHAM model: Impact of model physics and resolution. Max-Planck-Institut für Meteorologie Rep. 93, 171 pp. [Available from Max-Planck Institute for Meteorology, Bundesstrasse 55, Hamburg, Germany.].

  • Tribbia, J. J., and D. P. Baumhefner, 1988: Estimates of the predictability of low-Frequency variability with a spectral general circulation model. J. Atmos. Sci.,45, 2306–2317.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 14 14 2
PDF Downloads 3 3 0

Potential Predictability and AMIP Implications of Midlatitude Climate Variability in Two General Circulation Models

View More View Less
  • 1 Climate Research Division, Scripps Institute of Oceanography, University of California, San Diego, La Jolla, California
  • | 2 Max-Planck Institute for Meteorology, Hamburg, Germany
  • | 3 Climate Modeling Branch, NOAA/NCEP, Washington, D.C.
© Get Permissions
Restricted access

Abstract

Ensembles of extended Atmospheric Model Intercomparison Project (AMIP) runs from the general circulation models of the National Centers for Environmental Prediction (formerly the National Meteorological Center) and the Max-Planck Institute (Hamburg, Germany) are used to estimate the potential predictability (PP) of an index of the Pacific–North America (PNA) mode of climate change. The PP of this pattern in “perfect” prediction experiments is 20%–25% of the index’s variance. The models, particularly that from MPI, capture virtually all of this variance in their hindcasts of the winter PNA for the period 1970–93.

The high levels of internally generated model noise in the PNA simulations reconfirm the need for an ensemble averaging approach to climate prediction. This means that the forecasts ought to be expressed in a probabilistic manner. It is shown that the models’ skills are higher by about 50% during strong SST events in the tropical Pacific, so the probabilistic forecasts need to be conditional on the tropical SST.

Taken together with earlier studies, the present results suggest that the original set of AMIP integrations (single 10-yr runs) is not adequate to reliably test the participating models’ simulations of interannual climate variability in the midlatitudes.

Corresponding author address: Dr. Tim P. Barnett, Climate Research Division, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA 92093-0224.

Email: tbarnett@ucsd.edu

Abstract

Ensembles of extended Atmospheric Model Intercomparison Project (AMIP) runs from the general circulation models of the National Centers for Environmental Prediction (formerly the National Meteorological Center) and the Max-Planck Institute (Hamburg, Germany) are used to estimate the potential predictability (PP) of an index of the Pacific–North America (PNA) mode of climate change. The PP of this pattern in “perfect” prediction experiments is 20%–25% of the index’s variance. The models, particularly that from MPI, capture virtually all of this variance in their hindcasts of the winter PNA for the period 1970–93.

The high levels of internally generated model noise in the PNA simulations reconfirm the need for an ensemble averaging approach to climate prediction. This means that the forecasts ought to be expressed in a probabilistic manner. It is shown that the models’ skills are higher by about 50% during strong SST events in the tropical Pacific, so the probabilistic forecasts need to be conditional on the tropical SST.

Taken together with earlier studies, the present results suggest that the original set of AMIP integrations (single 10-yr runs) is not adequate to reliably test the participating models’ simulations of interannual climate variability in the midlatitudes.

Corresponding author address: Dr. Tim P. Barnett, Climate Research Division, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA 92093-0224.

Email: tbarnett@ucsd.edu

Save