Model parameter-related optimal perturbations and their contributions to El Nino prediction errors

doi:10.1007/s00382-018-4202-7

IOCAS-IR > 海洋环流与波动重点实验室

	Model parameter-related optimal perturbations and their contributions to El Nino prediction errors
	Tao, Ling-Jiang1,2 ; Gao, Chuan1,3 ; Zhang, Rong-Hua1,2,3
	2019-02-01
发表期刊	CLIMATE DYNAMICS
ISSN	0930-7575
卷号	52 期号:3-4 页码:1425-1441
通讯作者	Zhang, Rong-Hua(rzhang@qdio.ac.cn)
摘要	Errors in initial conditions and model parameters (MPs) are the main sources that limit the accuracy of ENSO predictions. In addition to exploring the initial error-induced prediction errors, model errors are equally important in determining prediction performance. In this paper, the MP-related optimal errors that can cause prominent error growth in ENSO predictions are investigated using an intermediate coupled model (ICM) and a conditional nonlinear optimal perturbation (CNOP) approach. Two MPs related to the Bjerknes feedback are considered in the CNOP analysis: one involves the SST-surface wind coupling (), and the other involves the thermocline effect on the SST (Te). The MP-related optimal perturbations (denoted as CNOP-P) are found uniformly positive and restrained in a small region: the component is mainly concentrated in the central equatorial Pacific, and the Te component is mainly located in the eastern cold tongue region. This kind of CNOP-P enhances the strength of the Bjerknes feedback and induces an El Nino- or La Nina-like error evolution, resulting in an El Nino-like systematic bias in this model. The CNOP-P is also found to play a role in the spring predictability barrier (SPB) for ENSO predictions. Evidently, such error growth is primarily attributed to MP errors in small areas based on the localized distribution of CNOP-P. Further sensitivity experiments firmly indicate that ENSO simulations are sensitive to the representation of SST-surface wind coupling in the central Pacific and tothe thermocline effect in the eastern Pacific in the ICM. These results provide guidance and theoretical support for the future improvement in numerical models to reduce the systematic bias and SPB phenomenon in ENSO predictions.
关键词	Intermediate coupled model CNOP approach Model parameters El Nino predictability
DOI	10.1007/s00382-018-4202-7
收录类别	SCI
语种	英语
资助项目	Taishan Scholarship ; National Programme on Global Change and Air-Sea Interaction[GASI-IPOVAI-01-01] ; National Programme on Global Change and Air-Sea Interaction[GASI-IPOVAI-06] ; NSFC-Shandong Joint Fund for Marine Science Research Centers[U1406402] ; Western Pacific Ocean System[XDA11020306] ; Western Pacific Ocean System[XDA11010105] ; CAS Strategic Priority Project ; National Natural Science Foundation of China[41690122 (41690120)] ; National Natural Science Foundation of China[41475101] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19060102] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19060102] ; National Natural Science Foundation of China[41475101] ; National Natural Science Foundation of China[41690122 (41690120)] ; CAS Strategic Priority Project ; Western Pacific Ocean System[XDA11010105] ; Western Pacific Ocean System[XDA11020306] ; NSFC-Shandong Joint Fund for Marine Science Research Centers[U1406402] ; National Programme on Global Change and Air-Sea Interaction[GASI-IPOVAI-06] ; National Programme on Global Change and Air-Sea Interaction[GASI-IPOVAI-01-01] ; Taishan Scholarship
WOS研究方向	Meteorology & Atmospheric Sciences
WOS类目	Meteorology & Atmospheric Sciences
WOS记录号	WOS:000460902200008
出版者	SPRINGER
引用统计	被引频次：17[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/155319
专题	海洋环流与波动重点实验室
通讯作者	Zhang, Rong-Hua
作者单位	1.Chinese Acad Sci, Inst Oceanol, Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 2.Univ Chinese Acad Sci, Beijing 10029, Peoples R China 3.Qingdao Natl Lab Marine Sci & Technol, Qingdao 266237, Peoples R China
第一作者单位	海洋环流与波动重点实验室
通讯作者单位	海洋环流与波动重点实验室
推荐引用方式 GB/T 7714	Tao, Ling-Jiang,Gao, Chuan,Zhang, Rong-Hua. Model parameter-related optimal perturbations and their contributions to El Nino prediction errors[J]. CLIMATE DYNAMICS,2019,52(3-4):1425-1441.
APA	Tao, Ling-Jiang,Gao, Chuan,&Zhang, Rong-Hua.(2019).Model parameter-related optimal perturbations and their contributions to El Nino prediction errors.CLIMATE DYNAMICS,52(3-4),1425-1441.
MLA	Tao, Ling-Jiang,et al."Model parameter-related optimal perturbations and their contributions to El Nino prediction errors".CLIMATE DYNAMICS 52.3-4(2019):1425-1441.

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