ENSEMBLES: A new multi‐model ensemble for seasonal‐to‐annual predictions—Skill and progress beyond DEMETER in forecasting tropical Pacific SSTs
ENSEMBLES: A new multi‐model ensemble for seasonal‐to‐annual predictions—Skill and progress beyond DEMETER in forecasting tropical Pacific SSTs
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DOI：
10.1029/2009gl040896
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发表时间：
2009-11
影响因子：
5.2
通讯作者：
A. Weisheimer;F. Doblas-Reyes;T. Palmer;A. Alessandri;A. Arribas;M. Déqué;N. Keenlyside;M. Macvean-M
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文献类型：
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作者：
A. Weisheimer;F. Doblas-Reyes;T. Palmer;A. Alessandri;A. Arribas;M. Déqué;N. Keenlyside;M. Macvean-M
使用5个最先进的耦合大气 - 海洋环流模型的多模型集合，创建了一个新的用于季节到年度集合预测的46年回测数据集。在预测热带太平洋海表温度方面，多模型由于在所有预报时效上均降低了均方根误差并增强了集合离散度，其表现优于任何单一模型。与上一代（DEMETER）相比，系统误差显著降低。概率技巧评分显示，在4 - 6个月的预报范围内，新的多模型集合比DEMETER具有更高的技巧。然而，要实现具有强统计学意义的技巧显著提高，还需要大幅改进模型。将ENSEMBLES和DEMETER组合成一个大型多模型集合并不能进一步提高预报技巧。年度范围的回测显示，提前14个月的异常相关技巧约为0.5。多模型模拟的大量输出结果已公开，欢迎国际社会探索这些数据的全部科学潜力。
A new 46‐year hindcast dataset for seasonal‐to‐annual ensemble predictions has been created using a multi‐model ensemble of 5 state‐of‐the‐art coupled atmosphere‐ocean circulation models. The multi‐model outperforms any of the single‐models in forecasting tropical Pacific SSTs because of reduced RMS errors and enhanced ensemble dispersion at all lead‐times. Systematic errors are considerably reduced over the previous generation (DEMETER). Probabilistic skill scores show higher skill for the new multi‐model ensemble than for DEMETER in the 4–6 month forecast range. However, substantially improved models would be required to achieve strongly statistical significant skill increases. The combination of ENSEMBLES and DEMETER into a grand multi‐model ensemble does not improve the forecast skill further. Annual‐range hindcasts show anomaly correlation skill of ∼0.5 up to 14 months ahead. A wide range of output from the multi‐model simulations is becoming publicly available and the international community is invited to explore the full scientific potential of these data.