globalchange  > 过去全球变化的重建
DOI: 10.1007/s00382-015-2608-z
Scopus记录号: 2-s2.0-84957430665
论文题名:
A study of the impact of parameter optimization on ENSO predictability with an intermediate coupled model
作者: Wu X.; Han G.; Zhang S.; Liu Z.
刊名: Climate Dynamics
ISSN: 9307575
出版年: 2016
卷: 46, 期:2017-03-04
起始页码: 711
结束页码: 727
语种: 英语
英文关键词: Ensemble Kalman filter ; ENSO predictability ; Intermediate coupled model ; Parameter optimization
英文摘要: Model error is a major obstacle for enhancing the forecast skill of El Niño-Southern Oscillation (ENSO). Among three kinds of model error sources—dynamical core misfitting, physical scheme approximation and model parameter errors, the model parameter errors are treatable by observations. Based on the Zebiak-Cane model, an ensemble coupled data assimilation system is established to study the impact of parameter optimization (PO) on ENSO predictions within a biased twin experiment framework. “Observations” of sea surface temperature anomalies drawn from the “truth” model are assimilated into a biased prediction model in which model parameters are erroneously set from the “truth” values. The degree by which the assimilation and prediction with or without PO recover the “truth” is a measure of the impact of PO. Results show that PO improves ENSO predictability—enhancing the seasonal-interannual forecast skill by about 18 %, extending the valid lead time up to 33 % and ameliorating the spring predictability barrier. Although derived from idealized twin experiments, results here provide some insights when a coupled general circulation model is initialized from the observing system. © 2015, Springer-Verlag Berlin Heidelberg.
资助项目: NSFC, National Natural Science Foundation of China ; NSFC, National Natural Science Foundation of China ; NSFC, National Natural Science Foundation of China ; NSFC, National Natural Science Foundation of China ; NSFC, National Natural Science Foundation of China
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/53841
Appears in Collections:过去全球变化的重建

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作者单位: Key Laboratory of Marine Environmental Information Technology, State Oceanic Administration, National Marine Data and Information Service, Tianjin, China; GFDL/NOAA, Princeton University, Princeton, NJ, United States; Center for Climate Research and Department of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison, Madison, WS, United States; Lab. of Ocean-Atmos. Studies, Peking University, Beijing, China

Recommended Citation:
Wu X.,Han G.,Zhang S.,et al. A study of the impact of parameter optimization on ENSO predictability with an intermediate coupled model[J]. Climate Dynamics,2016-01-01,46(2017-03-04)
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