Climate and Dynamics
An analysis of multimodel ensemble predictions for seasonal climate anomalies
Article first published online: 11 DEC 2002
Copyright 2002 by the American Geophysical Union.
Journal of Geophysical Research: Atmospheres (1984–2012)
Volume 107, Issue D23, pages ACL 18-1–ACL 18-12, 16 December 2002
How to Cite
An analysis of multimodel ensemble predictions for seasonal climate anomalies, J. Geophys. Res., 107(D23), 4710, doi:10.1029/2002JD002712, 2002., , , and ,
- Issue published online: 11 DEC 2002
- Article first published online: 11 DEC 2002
- Manuscript Accepted: 8 SEP 2002
- Manuscript Revised: 30 AUG 2002
- Manuscript Received: 2 JUL 2002
 In this paper the potential advantages and relative performances of different techniques for constructing multimodel ensemble seasonal predictions are examined. Two commonly used methods of constructing multimodel ensemble predictions are analyzed. Particular emphasis is placed on the analysis of the schemes themselves. In the first technique—simple multimodel ensemble (SME) predictions—equal weights are assigned to the ensemble mean predictions of each of the atmospheric general circulation models (AGCM). In the second approach—optimal multimodel ensemble (OME) predictions—the weights are obtained using a multiple linear regression. A theoretical analysis of these techniques is complemented by the analyses based on seasonal climate simulations for 45 January–February–March seasons over the 1950–1994 period. A comparison of seasonal simulation skill scores between SME and OME indicates that for the bias corrected data, i.e., when the seasonal anomalies of each of the AGCMs are computed with respect to its own mean state, the performance of seasonal predictions based on the simpler SME approach is comparable to that of the more complex OME approach. A major reason for this result is that the data record of historical predictions may not be long enough for a stable estimate of weights at individual geographical locations to be obtained. This problem can be reduced by extending the multiple linear regression approach to include the spatial domain. However, even with this algorithm change, the performance of OME in seasonal predictions does not improve over that using the SME approach. Results, therefore, indicate that the use of more sophisticated techniques for constructing multimodel ensembles may not be any more advantageous than the use of simpler approaches. Results also show that on average the skill scores for the predictions based on multimodel ensemble prediction techniques are only marginally better than those of the best AGCM. However, an advantage of multimodel ensemble prediction techniques may be that they retain the best performance of each AGCM on a regional basis in the merged forecasts.