Progress in ENSO prediction and predictability study

被引:1
|
作者
Youmin Tang [1 ,2 ]
Rong-Hua Zhang [3 ,4 ]
Ting Liu [1 ,2 ]
Wansuo Duan [5 ]
Dejian Yang [6 ]
Fei Zheng [7 ]
Hongli Ren [8 ]
Tao Lian [1 ]
Chuan Gao [3 ,4 ]
Dake Chen [1 ]
Mu Mu [9 ]
机构
[1] State Key Laboratory of Satellite Ocean Environment Dynamics,Second Institute of Oceanography
[2] Environmental Science and Engineering,University of Northern British Columbia, Prince George
[3] Key Laboratory of Ocean Circulation and Waves,Institute of Oceanology,Chinese Academy of Sciences
[4] Qingdao National Laboratory for Marine Science and Technology
[5] State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics,Chinese Academy of Sciences
[6] College of Oceanography, Hohai University
[7] International Center for Climate and Environment Science, Institute of Atmospheric Physics,Chinese Academy of Sciences
[8] Laboratory for Climate Studies & CMA—NJU Joint Laboratory for Climate Prediction Studies,National Climate Center,China Meteorological Administration
[9] College of Atmospheric and Oceanic Science,Fudan University
基金
中国国家自然科学基金;
关键词
ENSO prediction and predictability; coupled model; ensemble prediction; optimal error growth; probabilistic prediction;
D O I
暂无
中图分类号
P732.4 [海洋天气预报]; P714.2 [];
学科分类号
0706 ; 070601 ;
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.
引用
收藏
页码:826 / 839
页数:14
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