Collaborative Research: Type 1 - LOIL02170097: Decadal Predictability of Extreme Events: Impact of a Model Error Representation and Numerical Resolution

协作研究：类型 1 - LOIL02170097：极端事件的十年可预测性：模型误差表示和数值分辨率的影响

• 批准号: 1048915
• 负责人: Gennady Samorodnitsky
• 金额: $ 50.99万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-07-01 至 2015-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1048915&HistoricalAwards=false
• 关键词: Collaborative; Research; Type; LOIL02170097; Decadal

Type 1 - LOIL02170097: Decadal predictability of extreme events:Impact of a model error representation and numerical resolution(collaborative research)The investigators will implement a stochastic backscatter scheme into the CommunityAtmosphere Model and explore how to improve the internal variability and, in particular,the prediction of extreme events on decadal and regional scales. Only a small number ofpublications apply Extreme Value Theory to climate models and many questions remainopen. While some work has been conducted comparing model and reanalysis with constantgreenhouse forcing, the major body of published work in this area focuses on the occurrenceof extreme events in a changing climate and on the robustness of climate trends of extremeevents across di^erent low-resolution models.The emphasis of the investigators is very di^erent: they will look at the internal variabilityof models under a constant greenhouse forcing. The investigators focus both on the ability oflow-resolution climate models to realistically predict extreme events on decadal time-scalesand global spatial scales, and on the feasibility of replacing the missing variability due tolow-resolution with a stochastic model error scheme and if such a scheme can improve thedecadal prediction of extreme events. Model integrations with and without a stochasticbackscatter scheme would be conducted and extreme value statistics be used to determinethe impact of the scheme onto the occurrence of extreme events. For comparison, the samestatistic would be computed using the the ERA40, the ERA-Interim analysis and/or theNCEP/NCAR reanalysis as best proxy for multi-decadal observations.

类型1-LOIL02170097：极端事件的十年可预测性：模式误差表示和数值解决的影响(合作研究)研究人员将在社区大气模式中实施随机后向散射方案，并探索如何改善内部可变性，特别是在十年和区域尺度上对极端事件的预测。只有少数出版物将极值理论应用于气候模型，许多问题仍然悬而未决。虽然已经有一些工作将模式和再分析与持续的温室强迫进行了比较，但这一领域已发表的主要工作集中在气候变化中极端事件的发生以及不同低分辨率模式中极端事件的气候趋势的稳健性。研究人员的重点是非常不同的：他们将研究持续温室强迫下模式的内部变化。研究人员既关注低分辨率气候模式在十年时间尺度和全球空间尺度上真实预测极端事件的能力，也关注用随机模式误差方案取代由于低分辨率而丢失的可变性的可行性，以及这种方案是否可以改善对极端事件的十年预测。将进行具有和不具有随机后向散射方案的模型集成，并使用极值统计来确定该方案对极端事件发生的影响。为了进行比较，将使用ERA40、ERA中期分析和/或NCEP/NCAR再分析作为多年代观测的最佳替代方法来计算相同的统计量。