RAPID-RAPIT

快速

• 批准号: NE/G015384/1
• 负责人: Michael Goldstein
• 金额: $ 32.26万
• 依托单位: Durham University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2009
• 资助国家: 英国
• 起止时间: 2009 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FG015384%2F1
• 关键词: RAPID; RAPIT

North west Europe has a relatively mild climate in part because of heat pulled north through the Atlantic by the overturning. There is a risk that global warming will cause this circulation to rapidly decrease with consequences involving not only colder winters for Europe but also changes in sea level and precipitation. This project will carry out a risk assessment of rapid changes of the Atlantic overturning. We will use two models of the climate system, HADCM3, the Hadley Centre model used in the IPCC AR4, and CHIME, a global climate model developed at the National Oceanography Centre, Southampton. This has the same atmospheric model as HADCM3 but has a very different structure to the ocean component. Making use of the resources of climateprediction.net we will run a large ensemble of both models to assess the uncertainties in the system. We will then use modern Bayesian statistical techniques to combine model output, data and expert opinion in our risk assessment. An assessment of the utility of the data from the RAPID-WATCH arrays is an important aim of the project.

欧洲西北部的气候相对温和，部分原因是由于倾覆导致的热量通过大西洋向北移动。全球变暖有可能导致这一环流迅速减少，其后果不仅涉及欧洲冬季的寒冷，而且还涉及海平面和降水量的变化。该项目将对大西洋倾覆的快速变化进行风险评估。我们将使用两个气候系统模型，HADCM 3，IPCC AR 4中使用的Hadley中心模型，以及CHIME，南安普顿国家海洋学中心开发的全球气候模型。这与HADCM 3具有相同的大气模型，但与海洋部分具有非常不同的结构。利用climateprediction.net的资源，我们将运行两个模型的大型集合，以评估系统中的不确定性。然后，我们将使用现代贝叶斯统计技术，在风险评估中结合联合收割机模型输出、数据和专家意见。对快速监测阵列数据的效用进行评估是该项目的一个重要目标。

项目成果

Bayesian policy support for adaptive strategies using computer models for complex physical systems

使用复杂物理系统的计算机模型对自适应策略提供贝叶斯政策支持

• DOI: 10.1057/jors.2011.110
• 发表时间: 2017
• 期刊: Journal of the Operational Research Society
• 影响因子: 3.6
• 作者: Williamson D

Climate Simulators and Climate Projections

• DOI: 10.1146/annurev-statistics-022513-115652
• 发表时间: 2014-01
• 作者: J. Rougier;M. Goldstein

History matching for exploring and reducing climate model parameter space using observations and a large perturbed physics ensemble

• DOI: 10.1007/s00382-013-1896-4
• 发表时间: 2013-08
• 期刊: Climate Dynamics
• 影响因子: 4.6
• 作者: D. Williamson;M. Goldstein;L. Allison;A. Blaker;P. Challenor;L. Jackson;K. Yamazaki

Fast Linked Analyses for Scenario-Based Hierarchies

基于场景的层次结构的快速链接分析

• DOI: 10.1111/j.1467-9876.2012.01042.x
• 发表时间: 2012
• 期刊: Applied Statistics
• 作者: Williamson D