Collaborative Research [electronic resource] : Separating Forced and Unforced Decadal Predictability in Models and Observations
- Published
- Washington, D.C. : United States. Dept. of Energy, 2014.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy. - Additional Creators
- Columbia University, United States. Department of Energy, United States. Department of Energy. Office of Science, and United States. Department of Energy. Office of Scientific and Technical Information
Access Online
- Restrictions on Access
- Free-to-read Unrestricted online access
- Summary
- 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.
- Report Numbers
- E 1.99:doe-cu-05109
doe-cu-05109 - Subject(s)
- Note
- Published through SciTech Connect.
04/09/2014.
"doe-cu-05109"
Tippett, Michael K. - Type of Report and Period Covered Note
- Final; 09/01/2010 - 08/31/2013
- Funding Information
- SC0005109
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