GSTDTAP  > 地球科学
DOI10.2172/1127130
报告编号DOE-CU-05109
来源IDOSTI ID: 1127130
Collaborative Research: Separating Forced and Unforced Decadal Predictability in Models and Observations
Tippett, Michael K.
2014-04-09
出版年2014
语种英语
国家美国
领域地球科学
英文摘要This report is a progress report of the accomplishments of the research grant “Collaborative Research: Separating Forced and Unforced Decadal Predictability in Models and Observa- tions” during the period 1 May 2011- 31 August 2013. This project is a collaborative one between Columbia University and George Mason University. George Mason University will submit a final technical report at the conclusion of their no-cost extension. The purpose of the proposed research is to identify unforced predictable components on decadal time scales, distinguish these components from forced predictable components, and to assess the reliability of model predictions of these components. Components of unforced decadal predictability will be isolated by maximizing the Average Predictability Time (APT) in long, multimodel control runs from state-of-the-art climate models. Components with decadal predictability have large APT, so maximizing APT ensures that components with decadal predictability will be detected. Optimal fingerprinting techniques, as used in detection and attribution analysis, will be used to separate variations due to natural and anthropogenic forcing from those due to unforced decadal predictability. This methodology will be applied to the decadal hindcasts generated by the CMIP5 project to assess the reliability of model projections. The question of whether anthropogenic forcing changes decadal predictability, or gives rise to new forms of decadal predictability, also will be investigated.
URL查看原文
来源平台US Department of Energy (DOE)
引用统计
文献类型科技报告
条目标识符http://119.78.100.173/C666/handle/2XK7JSWQ/6468
专题地球科学
推荐引用方式
GB/T 7714
Tippett, Michael K.. Collaborative Research: Separating Forced and Unforced Decadal Predictability in Models and Observations,2014.
条目包含的文件
条目无相关文件。
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Tippett, Michael K.]的文章
百度学术
百度学术中相似的文章
[Tippett, Michael K.]的文章
必应学术
必应学术中相似的文章
[Tippett, Michael K.]的文章
相关权益政策
暂无数据
收藏/分享
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。