The rationale behind the success of multi-model ensembles in seasonal forecasting — I. Basic concept

The DEMETER multi-model ensemble system is used to investigate the rationale behind the multi-model concept. A comprehensive documentation of the differences in the single and multi-model performance in the DEMETER hindcast data set is given. Both deterministic and probabilistic diagnostics are used and a variety of analyses demonstrate the improvements achieved by using multi-model instead of single-model ensembles. In order to understand the reason behind the multi-model superiority, basic scenarios describing how the multi-model approach can improve over singlemodel skill are discussed. It is demonstrated that multi-model superiority is caused not only by error compensation but in particular by its greater consistency and reliability.

[1]  William R. Moninger,et al.  The Weather Information and Skill Experiment (WISE): The Effect of Varying Levels of Information on Forecast Skill , 1993 .

[2]  T. N. Krishnamurti,et al.  Improved Weather and Seasonal Climate Forecasts from Multimodel Superensemble. , 1999, Science.

[3]  Francisco J. Doblas-Reyes,et al.  Multi‐model spread and probabilistic seasonal forecasts in PROVOST , 2000 .

[4]  P. D. Thompson,et al.  How to Improve Accuracy by Combining Independent Forecasts , 1977 .

[5]  P. Todd,et al.  Simple Heuristics That Make Us Smart , 1999 .

[6]  A. H. Murphy,et al.  Objective and Subjective Precipitation Probability Forecasts: Some Methods for Improving Forecast Quality , 1986 .

[7]  David S. Richardson,et al.  A probability and decision‐model analysis of PROVOST seasonal multi‐model ensemble integrations , 2000 .

[8]  Arun Kumar,et al.  Seasonal Predictions, Probabilistic Verifications, and Ensemble Size , 2001 .

[9]  David L. T. Anderson,et al.  Decadal and Seasonal Dependence of ENSO Prediction Skill , 1995 .

[10]  F. Doblas-Reyes,et al.  Multi-model seasonal hindcasts over the Euro-Atlantic: skill scores and dynamic features , 2000 .

[11]  D. Richardson.  Skill and relative economic value of the ECMWF ensemble prediction system , 2000 .

[12]  Tim Palmer,et al.  The prospects for seasonal forecasting—A review paper , 1994 .

[13]  F. Molteni,et al.  The ECMWF Ensemble Prediction System: Methodology and validation , 1996 .

[14]  Balaji Rajagopalan,et al.  Categorical Climate Forecasts through Regularization and Optimal Combination of Multiple GCM Ensembles , 2002 .

[15]  Lance M. Leslie,et al.  Combining Predictive Schemes in Short-Term Forecasting , 1987 .

[16]  Rodica Branzei,et al.  Collecting Information to Improve Decision-Making , 2000, IGTR.

[17]  Eugenia Kalnay,et al.  Operational Ensemble Prediction at the National Meteorological Center: Practical Aspects , 1993 .

[18]  F. Sanders,et al.  Skill In Forecasting Daily Temperature and Precipitation: Some Experimental Results , 1973 .

[19]  Lance F. Bosart.  SUNYA Experimental Results in Forecasting Daily Temperature and Precipitation , 1975 .

[20]  F. Sanders.  On Subjective Probability Forecasting , 1963 .

[21]  John R. Gyakum.  Experiments in Temperature and Precipitation Forecasting for Illinois , 1986 .

[22]  Andrew P. Morse,et al.  DEVELOPMENT OF A EUROPEAN MULTIMODEL ENSEMBLE SYSTEM FOR SEASONAL-TO-INTERANNUAL PREDICTION (DEMETER) , 2004 .

[23]  T. Palmer,et al.  Development of a European Multi-Model Ensemble System for Seasonal to Inter-Annual Prediction (DEMETER) , 2004 .

[24]  F. Zwiers,et al.  Climate Predictions with Multimodel Ensembles , 2002 .

[25]  Huug van den Dool,et al.  An analysis of multimodel ensemble predictions for seasonal climate anomalies , 2002 .