DOI: 10.1007/s00382-013-1965-8
Scopus记录号: 2-s2.0-84886676249
论文题名: Improved reliability of ENSO hindcasts with multi-ocean analyses ensemble initialization
作者: Zhu J. ; Huang B. ; Balmaseda M.A. ; Kinter III J.L. ; Peng P. ; Hu Z.-Z. ; Marx L.
刊名: Climate Dynamics
ISSN: 9307575
出版年: 2013
卷: 41, 期: 2017-09-10 起始页码: 2785
结束页码: 2795
语种: 英语
英文关键词: CFSv2
; ENSO prediction
; Multi-ocean analyses ensemble
; Reliability
英文摘要: Currently, ensemble seasonal forecasts using a single model with multiple perturbed initial conditions generally suffer from an "overconfidence" problem, i.e., the ensemble evolves such that the spread among members is small, compared to the magnitude of the mean error. This has motivated the use of a multi-model ensemble (MME), a technique that aims at sampling the structural uncertainty in the forecasting system. Here we investigate how the structural uncertainty in the ocean initial conditions impacts the reliability in seasonal forecasts, by using a new ensemble generation method to be referred to as the multiple-ocean analysis ensemble (MAE) initialization. In the MAE method, multiple ocean analyses are used to build an ensemble of ocean initial states, thus sampling structural uncertainties in oceanic initial conditions (OIC) originating from errors in the ocean model, the forcing flux, and the measurements, especially in areas and times of insufficient observations, as well as from the dependence on data assimilation methods. The merit of MAE initialization is demonstrated by the improved El Niño and the Southern Oscillation (ENSO) forecasting reliability. In particular, compared with the atmospheric perturbation or lagged ensemble approaches, the MAE initialization more effectively enhances ensemble dispersion in ENSO forecasting. A quantitative probabilistic measure of reliability also indicates that the MAE method performs better in forecasting all three (warm, neutral and cold) categories of ENSO events. In addition to improving seasonal forecasts, the MAE strategy may be used to identify the characteristics of the current structural uncertainty and as guidance for improving the observational network and assimilation strategy. Moreover, although the MAE method is not expected to totally correct the overconfidence of seasonal forecasts, our results demonstrate that OIC uncertainty is one of the major sources of forecast overconfidence, and suggest that the MAE is an essential component of an MME system. © 2013 Springer-Verlag Berlin Heidelberg. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/54753
Appears in Collections: 过去全球变化的重建
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作者单位: Center for Ocean-Land-Atmosphere Studies, 270 Research Hall, Mail Stop 6C5, George Mason University, 4400 University Drive, Fairfax, VA, 22030, United States; Department of Atmospheric, Oceanic, and Earth Sciences, College of Science, George Mason University, Fairfax, VA, United States; European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom; Climate Prediction Center, National Centers for Environmental Prediction/NOAA, College Park, MD, United States
Recommended Citation:
Zhu J.,Huang B.,Balmaseda M.A.,et al. Improved reliability of ENSO hindcasts with multi-ocean analyses ensemble initialization[J]. Climate Dynamics,2013-01-01,41(2017-09-10)