Climate Dynamics

, Volume 26, Issue 2–3, pp 285–294 | Cite as

Examination of multi-model ensemble seasonal prediction methods using a simple climate system



A simple climate model was designed as a proxy for the real climate system, and a number of prediction models were generated by slightly perturbing the physical parameters of the simple model. A set of long (240 years) historical hindcast predictions were performed with various prediction models, which are used to examine various issues of multi-model ensemble seasonal prediction, such as the best ways of blending multi-models and the selection of models. Based on these results, we suggest a feasible way of maximizing the benefit of using multi models in seasonal prediction. In particular, three types of multi-model ensemble prediction systems, i.e., the simple composite, superensemble, and the composite after statistically correcting individual predictions (corrected composite), are examined and compared to each other. The superensemble has more of an overfitting problem than the others, especially for the case of small training samples and/or weak external forcing, and the corrected composite produces the best prediction skill among the multi-model systems.


Observation Model Prediction Skill Seasonal Prediction Overfitting Problem Correlation Skill 



The present study was supported by the Climate Environment System Research Center sponsored by the Korean Science and Engineering Foundation and the Korea Meteorological Administration.


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Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  1. 1.School of Earth and Environmental SciencesSeoul National UniversityGwanak-gu, SeoulKorea

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