Progress in ENSO prediction and predictability study

IOCAS-IR

	Progress in ENSO prediction and predictability study
	Youmin Tang ; Rong-Hua Zhang; Ting Liu ; Wansuo Duan ; Dejian Yang ; Fei Zheng ; Hongli Ren ; Tao Lian ; Chuan Gao; Dake Chen ; Mu Mu
发表期刊	National Science Review
ISSN	2095-5138
	2018-11-15
出版年	2018
卷号	v.5 期号:06 页码:48-61
文献类型	CNKI期刊论文
摘要	ENSO is the strongest interannual signal in the global climate system with worldwide climatic, ecological and societal impacts. Over the past decades, the research about ENSO prediction and predictability has attracted broad attention. With the development of coupled models, the improvement in initialization schemes and the progress in theoretical studies, ENSO has become the most predictable climate mode at the time scales from months to seasons. This paper reviews in detail the progress in ENSO predictions and predictability studies achieved in recent years. An emphasis is placed on two fundamental issues: the improvement in practical prediction skills and progress in the theoretical study of the intrinsic predictability limit. The former includes progress in the couple models, data assimilations, ensemble predictions and so on, and the latter focuses on efforts in the study of the optimal error growth and in the estimate of the intrinsic predictability limit.
关键词	ENSO prediction and predictability coupled model ensemble prediction optimal error growth probabilistic prediction
CNKI专辑号	A;C;
CNKI专辑名称	基础科学;工程科技Ⅱ辑;
CNKI专题号	A009;A010;
CNKI专题名称	气象学;海洋学;
分类号	P732.4;P714.2
收录类别	中科院核心 ; 中科院扩展
语种	英文;
资助项目(基金）	supported by grants from the National Natural Science Foundation of China(41690124,41690121,41690120,41705049,41621064,41530961) ; the National Key Research and Development Program(2017YFA0604202) ; the National Programe on Global Change and Air-Sea Interaction(GASI-IPOVAI-06) ; the Scientific Research Fund of the Second Institute of Oceanography(JG1810)
文献类型	CNKI期刊论文
条目标识符	http://ir.qdio.ac.cn/handle/337002/188724
专题	中国科学院海洋研究所
作者单位	1.StateKeyLaboratoryofSatelliteOceanEnvironmentDynamics,SecondInstituteofOceanography 2.EnvironmentalScienceandEngineering,UniversityofNorthernBritishColumbia,PrinceGeorge 3.KeyLaboratoryofOceanCirculationandWaves,InstituteofOceanology,ChineseAcademyofSciences 4.QingdaoNationalLaboratoryforMarineScienceandTechnology 5.StateKeyLaboratoryofNumericalModelingforAtmosphericSciencesandGeophysicalFluidDynamics,InstituteofAtmosphericPhysics,ChineseAcademyofSciences 6.CollegeofOceanography,HohaiUniversity 7.InternationalCenterforClimateandEnvironmentScience,InstituteofAtmosphericPhysics,ChineseAcademyofSciences 8.LaboratoryforClimateStudies&CMA—NJUJointLaboratoryforClimatePredictionStudies,NationalClimateCenter,ChinaMeteorologicalAdministration 9.CollegeofAtmosphericandOceanicScience,FudanUniversity
推荐引用方式 GB/T 7714	Youmin Tang,Rong-Hua Zhang,Ting Liu,et al. Progress in ENSO prediction and predictability study. 2018.