Progress in ENSO prediction and predictability study
文献类型：CNKI期刊论文
| 作者 | Youmin Tang; Rong-Hua Zhang ; Ting Liu; Wansuo Duan; Dejian Yang; Fei Zheng; Hongli Ren; Tao Lian; Chuan Gao; Dake Chen
|
| 发表日期 | 2018-11-15 |
| 出处 | National Science Review
 |
| 关键词 | ENSO prediction and predictability coupled model ensemble prediction optimal error growth probabilistic prediction |
| 英文摘要 | 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. |
| 文献子类 | CNKI期刊论文 |
| 资助机构 | 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) |
| 卷 | v.5期:06页:48-61 |
| 语种 | 英文; |
| 分类号 | P732.4;P714.2 |
| ISSN号 | 2095-5138 |
| 源URL | [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. |
入库方式： OAI收割
来源：海洋研究所
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