Progress in ENSO prediction and predictability study

Author:

Tang Youmin12,Zhang Rong-Hua34,Liu Ting12ORCID,Duan Wansuo5,Yang Dejian6,Zheng Fei7,Ren Hongli8,Lian Tao1,Gao Chuan34,Chen Dake1,Mu Mu9

Affiliation:

1. State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Hangzhou 310012, China

2. Environmental Science and Engineering, University of Northern British Columbia, Prince George, British Columbia V2N 4Z9, Canada

3. Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071, China

4. Qingdao National Laboratory for Marine Science and Technology, Qingdao 266237, China

5. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

6. College of Oceanography, Hohai University, Nanjing 210098, China

7. International Center for Climate and Environment Science, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

8. Laboratory for Climate Studies & CMA—NJU Joint Laboratory for Climate Prediction Studies, National Climate Center, China Meteorological Administration, Beijing 100081, China

9. College of Atmospheric and Oceanic Science, Fudan University, Shanghai 200438, China

Abstract

AbstractENSO 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.

Funder

National Natural Science Foundation of China

National Key Research and Development Program

National Programe on Global Change and Air-Sea Interaction

Scientific Research Fund of the Second Institute of Oceanography

Publisher

Oxford University Press (OUP)

Subject

Multidisciplinary

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