Journal
QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY
Volume 134, Issue 630, Pages 241-260Publisher
WILEY
DOI: 10.1002/qj.210
Keywords
DEMETER; inflation; probabilistic verification; seasonal predictions; toy model; under-dispersion
Categories
Funding
- Swiss National Science Foundation
- ENSEMBLES [GOCE-CT-2003-505539]
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The success of multi-model ensemble combination has been demonstrated in many studies. Given that a multi-model contains information from all participating models, including the less skilful ones, the question remains as to why, and under what conditions, a multi-model can outperform the best participating single model. It is the aim of this paper to resolve this apparent paradox. The study is based on a synthetic forecast generator, allowing the generation of perfectly-calibrated single-model ensembles of any size and skill. Additionally, the degree of ensemble under-dispersion (or overconfidence) can be prescribed. Multi-model ensembles are then constructed from both weighted and unweighted averages of these single-model ensembles. Applying this toy model, we carry out systematic model-combination experiments. We evaluate how multi-model performance depends on the skill and overconfidence of the participating single models. It turns out that multi-model ensembles can indeed locally outperform a 'best-model' approach, but only if the single-model ensembles are overconfident. The reason is that multi-model combination reduces overconfidence, i.e. ensemble spread is widened while average ensemble-mean error is reduced. This implies a net gain in prediction skill, because probabilistic skill scores penalize overconfidence. Under these conditions, even the addition of an objectively-poor model can improve multi-model skill. It seems that simple ensemble inflation methods cannot yield the same skill improvement. Using seasonal near-surface temperature forecasts from the DEMETER dataset, we show that the conclusions drawn from the toy-model experiments hold equally in a real multi-model ensemble prediction system. Copyright (c) 2008 Royal Meteorological Society.
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