DOI: 10.1007/s00382-014-2461-5
Scopus记录号: 2-s2.0-84943820229
论文题名: Predictability and prediction skill of the boreal summer intraseasonal oscillation in the Intraseasonal Variability Hindcast Experiment
作者: Lee S.-S. ; Wang B. ; Waliser D.E. ; Neena J.M. ; Lee J.-Y.
刊名: Climate Dynamics
ISSN: 9307575
出版年: 2015
卷: 45, 期: 2017-07-08 起始页码: 2123
结束页码: 2135
语种: 英语
英文关键词: Boreal summer intraseasnal oscillation
; Intraseasonal Variability Hindcast Experiment (ISVHE)
; Predictability
; Prediction skill
英文摘要: Boreal summer intraseasonal oscillation (BSISO) is one of the dominant modes of intraseasonal variability of the tropical climate system, which has fundamental impacts on regional summer monsoons, tropical storms, and extra-tropical climate variations. Due to its distinctive characteristics, a specific metric for characterizing observed BSISO evolution and assessing numerical models’ simulations has previously been proposed (Lee et al. in Clim Dyn 40:493–509, 2013). However, the current dynamical model’s prediction skill and predictability have not been investigated in a multi-model framework. Using six coupled models in the Intraseasonal Variability Hindcast Experiment project, the predictability estimates and prediction skill of BSISO are examined. The BSISO predictability is estimated by the forecast lead day when mean forecast error becomes as large as the mean signal under the perfect model assumption. Applying the signal-to-error ratio method and using ensemble-mean approach, we found that the multi-model mean BSISO predictability estimate and prediction skill with strong initial amplitude (about 10 % higher than the mean initial amplitude) are about 45 and 22 days, respectively, which are comparable with the corresponding counterparts for Madden–Julian Oscillation during boreal winter (Neena et al. in J Clim 27:4531–4543, 2014a). The significantly lower BSISO prediction skill compared with its predictability indicates considerable room for improvement of the dynamical BSISO prediction. The estimated predictability limit is independent on its initial amplitude, but the models’ prediction skills for strong initial amplitude is 6 days higher than the corresponding skill with the weak initial condition (about 15 % less than mean initial amplitude), suggesting the importance of using accurate initial conditions. The BSISO predictability and prediction skill are phase and season-dependent, but the degree of dependency varies with the models. It is important to note that the estimation of prediction skill depends on the methods that generate initial ensembles. Our analysis indicates that a better dispersion of ensemble members can considerably improve the ensemble mean prediction skills. © 2015, The Author(s). 资助项目: MEST, Ministry of Education, Science and Technology
; NOAA, National Oceanic and Atmospheric Administration
; NRF, National Research Foundation of Korea
; NSF, National Science Foundation
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/53987
Appears in Collections: 过去全球变化的重建
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作者单位: Department of Meteorology, International Pacific Research Center (IPRC), University of Hawaii, Honolulu, HI, United States; Earth System Modeling Center/NIAMS, Nanjing University of Information Science and Technology, Nanjing, China; Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, CA, United States; Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States; Institute of Environmental Studies, Pusan National University, Busan, South Korea
Recommended Citation:
Lee S.-S.,Wang B.,Waliser D.E.,et al. Predictability and prediction skill of the boreal summer intraseasonal oscillation in the Intraseasonal Variability Hindcast Experiment[J]. Climate Dynamics,2015-01-01,45(2017-07-08)