globalchange  > 过去全球变化的重建
DOI: 10.1007/s00382-012-1481-2
Scopus记录号: 2-s2.0-84871928605
论文题名:
A verification framework for interannual-to-decadal predictions experiments
作者: Goddard L.; Kumar A.; Solomon A.; Smith D.; Boer G.; Gonzalez P.; Kharin V.; Merryfield W.; Deser C.; Mason S.J.; Kirtman B.P.; Msadek R.; Sutton R.; Hawkins E.; Fricker T.; Hegerl G.; Ferro C.A.T.; Stephenson D.B.; Meehl G.A.; Stockdale T.; Burgman R.; Greene A.M.; Kushnir Y.; Newman M.; Carton J.; Fukumori I.; Delworth T.
刊名: Climate Dynamics
ISSN: 9307575
出版年: 2013
卷: 40, 期:2017-01-02
起始页码: 245
结束页码: 272
语种: 英语
英文关键词: CMIP5 ; Decadal ; Prediction ; Uncertainty ; Verification
英文摘要: Decadal predictions have a high profile in the climate science community and beyond, yet very little is known about their skill. Nor is there any agreed protocol for estimating their skill. This paper proposes a sound and coordinated framework for verification of decadal hindcast experiments. The framework is illustrated for decadal hindcasts tailored to meet the requirements and specifications of CMIP5 (Coupled Model Intercomparison Project phase 5). The chosen metrics address key questions about the information content in initialized decadal hindcasts. These questions are: (1) Do the initial conditions in the hindcasts lead to more accurate predictions of the climate, compared to un-initialized climate change projections? and (2) Is the prediction model's ensemble spread an appropriate representation of forecast uncertainty on average? The first question is addressed through deterministic metrics that compare the initialized and uninitialized hindcasts. The second question is addressed through a probabilistic metric applied to the initialized hindcasts and comparing different ways to ascribe forecast uncertainty. Verification is advocated at smoothed regional scales that can illuminate broad areas of predictability, as well as at the grid scale, since many users of the decadal prediction experiments who feed the climate data into applications or decision models will use the data at grid scale, or downscale it to even higher resolution. An overall statement on skill of CMIP5 decadal hindcasts is not the aim of this paper. The results presented are only illustrative of the framework, which would enable such studies. However, broad conclusions that are beginning to emerge from the CMIP5 results include (1) Most predictability at the interannual-to-decadal scale, relative to climatological averages, comes from external forcing, particularly for temperature; (2) though moderate, additional skill is added by the initial conditions over what is imparted by external forcing alone; however, the impact of initialization may result in overall worse predictions in some regions than provided by uninitialized climate change projections; (3) limited hindcast records and the dearth of climate-quality observational data impede our ability to quantify expected skill as well as model biases; and (4) as is common to seasonal-to-interannual model predictions, the spread of the ensemble members is not necessarily a good representation of forecast uncertainty. The authors recommend that this framework be adopted to serve as a starting point to compare prediction quality across prediction systems. The framework can provide a baseline against which future improvements can be quantified. The framework also provides guidance on the use of these model predictions, which differ in fundamental ways from the climate change projections that much of the community has become familiar with, including adjustment of mean and conditional biases, and consideration of how to best approach forecast uncertainty. © 2012 The Author(s).
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/55064
Appears in Collections:过去全球变化的重建

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作者单位: International Research Institute for Climate and Society, The Earth Institute of Columbia University, Palisades, NY, United States; Climate Prediction Center, National Centers for Environmental Prediction, NOAA, Silver Spring, MD, United States; Earth System Research Laboratory, NOAA, University of Colorado, Boulder, CO, United States; UK Met Office, Hadley Centre, Exeter, United Kingdom; Canadian Centre for Climate Modelling and Analysis, Environment Canada, Victoria, BC, Canada; National Center for Atmospheric Research, Boulder, CO, United States; Rosentiel School for Marine and Atmospheric Science, University of Miami, Miami, FL, United States; NOAA's Geophysical Fluid Dynamics Laboratory, Princeton, NJ, United States; NCAS-Climate, Department of Meteorology, University of Reading, Reading, United Kingdom; University of Exeter, Exeter, United Kingdom; University of Edinburgh, Edinburgh, United Kingdom; European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom; Lamont-Doherty Earth Observatory, The Earth Institute of Columbia University, Palisades, NY, United States; University of Maryland, College Park, MD, United States; Jet Propulsion Laboratory, NASA, Pasadena, CA, United States; Florida International University, Miami, FL, United States

Recommended Citation:
Goddard L.,Kumar A.,Solomon A.,et al. A verification framework for interannual-to-decadal predictions experiments[J]. Climate Dynamics,2013-01-01,40(2017-01-02)
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