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Multi-model seasonal forecasting for the North Atlantic and Europe

机译：北大西洋和欧洲的多模式季节性预报

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摘要

The forecast quality of the DEMETER seasonal multi-model ensemble predictions has been assessed over the North Atlantic/Europe region. The analysis shows that single-model ensembles have positive though low skill in almost every season. The skill decreases with lead time, although the agreement in skill between the models increases. This may be due to a more relevant role of the decadal variability versus the initial conditions at longer lead times. The simple multi-model constructed from equally weighted single-model ensembles proves to have an average forecast quality superior to any of the single models and to persistence predictions, in particular in a probabilistic framework. In spite of the relatively low skill, a perfect model approach indicates that potential skill is much higher than actual skill. However, potential skill estimates depend strongly on the model used. It is suggested that a multi-model system may provide more realistic predictability estimates. The relevance of these results for operational seasonal forecasts and its users is discussed.
机译：已对北大西洋/欧洲地区的DEMETER季节多模型合奏预报的预报质量进行了评估。分析表明，几乎每个季节，单模乐团的演奏技巧都不错，但技巧却很低。尽管模型之间的技能一致性增加，但是技能会随着交付时间而降低。这可能是由于十年的可变性与更长的交付时间下的初始条件相比具有更重要的作用。由同等权重的单个模型集成构造而成的简单多模型被证明具有优于任何单个模型和持久性预测的平均预测质量，尤其是在概率框架中。尽管技能相对较低，但完善的模型方法表明潜在技能远高于实际技能。但是，潜在的技能估计很大程度上取决于所使用的模型。建议多模型系统可以提供更现实的可预测性估计。讨论了这些结果与运营季节预测及其用户的相关性。

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