Addressing model uncertainty in seasonal and annual dynamical ensemble forecasts
Addressing model uncertainty in seasonal and annual dynamical ensemble forecasts
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DOI：
10.1002/qj.464
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发表时间：
2009-07
影响因子：
8.9
通讯作者：
F. Doblas-Reyes;A. Weisheimer;M. Déqué;N. Keenlyside;M. McVean;J. Murphy;P. Rogel;Doug M. Smith;T. Palmer
中科院分区：
文献类型：
--
作者：
F. Doblas-Reyes;A. Weisheimer;M. Déqué;N. Keenlyside;M. McVean;J. Murphy;P. Rogel;Doug M. Smith;T. Palmer
描述了解决模式不确定性对季节/年度预报影响的三种预报系统的相对优点。一个系统由多个模型组成，而另外两个系统通过摄动参数和随机物理技术扰动参考模型的参数化来采样不确定性。1991年至2001年的集合重新预报是用从实际初始条件开始的耦合气候模式进行的。由于对抽样不确定性的不同策略，以及初始化方法和参考预报系统的不同，预报质量也有所不同。与参考预报系统相比，随机物理集合和扰动参数集合都提高了可靠性，但不能提高识别能力。尽管多模型实验的整体规模大于其他两个实验，但大多数评估是使用同等规模的整体来完成的。这三个乐团表现出相似的技能水平：表演上的显著差异通常在5%到20%之间。然而，对于提前时间小于5个月的季节性预测，一个由9个成员组成的多模型显示出更好的结果，其次是随机物理和扰动参数集合。相反，对于提前时间超过4个月的季节性预测，扰动参数组合往往会给出更好的结果。所有的系统都表明，扩散不能被认为是技能的有用预测指标。年度平均预测显示，预测质量低于季节性预测。两个系统之间只发现了很小的差异。与所有其他系统相比，完整的多型号组合的质量有所提高，主要是因为交货期超过四个月的更大的组合规模和年度预测。版权所有(C)2009皇家气象学会和皇冠版权所有
The relative merits of three forecast systems addressing the impact of model uncertainty on seasonal/annual forecasts are described. One system consists of a multi‐model, whereas two other systems sample uncertainties by perturbing the parametrization of reference models through perturbed parameter and stochastic physics techniques. Ensemble re‐forecasts over 1991 to 2001 were performed with coupled climate models started from realistic initial conditions. Forecast quality varies due to the different strategies for sampling uncertainties, but also to differences in initialisation methods and in the reference forecast system. Both the stochastic‐physics and perturbed‐parameter ensembles improve the reliability with respect to their reference forecast systems, but not the discrimination ability. Although the multi‐model experiment has an ensemble size larger than the other two experiments, most of the assessment was done using equally‐sized ensembles. The three ensembles show similar levels of skill: significant differences in performance typically range between 5 and 20%. However, a nine‐member multi‐model shows better results for seasonal predictions with lead times shorter than five months, followed by the stochastic‐physics and perturbed‐parameter ensembles. Conversely, for seasonal predictions with lead times longer than four months, the perturbed‐parameter ensemble gives more often better results. All systems suggest that spread cannot be considered a useful predictor of skill. Annual‐mean predictions showed lower forecast quality than seasonal predictions. Only small differences between the systems were found. The full multi‐model ensemble has improved quality with respect to all other systems, mainly from the larger ensemble size for lead times longer than four months and annual predictions. Copyright © 2009 Royal Meteorological Society and Crown Copyright