Aspects of designing and evaluating seasonal‐to‐interannual Arctic sea‐ice prediction systems
Aspects of designing and evaluating seasonal‐to‐interannual Arctic sea‐ice prediction systems
复制标题
设计和评估季节性到年际北极海冰预测系统的方面
DOI：
10.1002/qj.2643
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发表时间：
2016
影响因子：
8.9
通讯作者：
S. Keeley
中科院分区：
文献类型：
--
作者：
E. Hawkins;S. Tietsche;J. Day;N. Melia;K. Haines;S. Keeley
利用理想化可预测性实验的经验教训，我们讨论了有关设计可操作的季节性到年际北极海冰预测系统的一些问题和观点。我们首先回顾了使用不同类型实验的层次结构来了解北极气候的可预测性的机会。我们还研究了集成系统设计的关键问题，例如测量技能、集成规模的作用和集成成员的生成。在评估一组预测实验的潜在技能时，使用多个指标至关重要，因为不同的选择可能会显着改变有关技能存在或缺乏的结论。我们发现，增加后报数量和整体规模对于可靠评估预测中的相关性和预期误差非常重要。对于其他指标，例如色散，增加整体规模是最重要的。技能的概率测量还可以提供有关预测可靠性的有用信息。此外，还测试了生成不同集合成员的各种方法。各种技术可以产生令人惊讶的不同的整体传播特性。汲取的经验教训应有助于为未来作战预测系统的设计提供信息。
Using lessons from idealised predictability experiments, we discuss some issues and perspectives on the design of operational seasonal to inter‐annual Arctic sea‐ice prediction systems. We first review the opportunities to use a hierarchy of different types of experiment to learn about the predictability of Arctic climate. We also examine key issues for ensemble system design, such as measuring skill, the role of ensemble size and generation of ensemble members. When assessing the potential skill of a set of prediction experiments, using more than one metric is essential as different choices can significantly alter conclusions about the presence or lack of skill. We find that increasing both the number of hindcasts and ensemble size is important for reliably assessing the correlation and expected error in forecasts. For other metrics, such as dispersion, increasing ensemble size is most important. Probabilistic measures of skill can also provide useful information about the reliability of forecasts. In addition, various methods for generating the different ensemble members are tested. The range of techniques can produce surprisingly different ensemble spread characteristics. The lessons learnt should help inform the design of future operational prediction systems.
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影响因子：
4.6
作者：
Goddard, L.;Kumar, A.;Delworth, T.
通讯作者：
Delworth, T.
影响因子：
4.8
作者：
F. Otto;C. Ferro;Thomas E. Fricker;E. Suckling
通讯作者：
F. Otto;C. Ferro;Thomas E. Fricker;E. Suckling
影响因子：
5.2
作者：
Day, J. J.;Hawkins, E.;Tietsche, S.
通讯作者：
Tietsche, S.
影响因子：
4.6
作者：
Doug M. Smith;Adam A. Scaife;G. Boer;M. Caian;F. Doblas-Reyes;V. Guemas;E. Hawkins;W. Hazeleger;L. Hermanson;Chun Kit Ho;M. Ishii;V. Kharin;M. Kimoto;B. Kirtman;J. Lean;D. Matei;W. Merryfield;W. Müller;H. Pohlmann;A. Rosati;B. Wouters;K. Wyser
通讯作者：
Doug M. Smith;Adam A. Scaife;G. Boer;M. Caian;F. Doblas-Reyes;V. Guemas;E. Hawkins;W. Hazeleger;L. Hermanson;Chun Kit Ho;M. Ishii;V. Kharin;M. Kimoto;B. Kirtman;J. Lean;D. Matei;W. Merryfield;W. Müller;H. Pohlmann;A. Rosati;B. Wouters;K. Wyser
影响因子：
5.2
作者：
Tietsche, S.;Day, J. J.;Hawkins, E.
通讯作者：
Hawkins, E.