A review of progress in coupled ocean-atmosphere model developments for ENSO studies in China

ZHANG Rong-Hua; YU Yongqiang; SONG Zhenya; REN Hong-Li; TANG Youmin; QIAO Fangli; WU Tongwen; GAO Chuan; HU Junya; TIAN Feng; ZHU Yuchao; CHEN Lin; LIU Hailong; LIN Pengfei; WU Fanghua; WANG Lin

doi:10.1007/s00343-020-0157-8

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A review of progress in coupled ocean-atmosphere model developments for ENSO studies in China

Review | Updated：2023-04-27

- A review of progress in coupled ocean-atmosphere model developments for ENSO studies in China
- A review of progress in coupled ocean-atmosphere model developments for ENSO studies in China
- 海洋湖沼学报（英文） 2020年38卷第4期页码：930-961
- Affiliations：
  
  1.Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, and Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China
  2.Laboratory for Ocean and Climate Dynamics, Pilot National Laboratory for Marine Science and Technology, Qingdao 266237, China
  3.Center for Excellence in Quaternary Science and Global Change, Chinese Academy of Sciences, Xi'an 710061, China
  4.University of Chinese Academy of Sciences, Beijing 100049, China
  5.LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100049, China
  6.First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
  7.Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology, Qingdao 266237, China
  8.Key Laboratory of Marine Science and Numerical Modeling (MASNUM), Ministry of Natural Resources, Qingdao 266061, China
  9.National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Qingdao 266061, China
  10.State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
  11.Laboratory for Climate Studies and CMA-NJU Joint Laboratory for Climate Prediction Studies, National Climate Center, China Meteorological Administration, Beijing 100081, China
  12.Environmental Science and Engineering, University of Northern British Columbia, British Columbia V2N 4Z9, Canada
  13.State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
  14.Key Laboratory of Meteorological Disaster, Ministry of Education (KLME), and Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing 210044, China
  15.Beijing Climate Center, China Meteorological Administration, Beijing 100081, China
- Author bio：
  
  ZHANG Rong-Hua, rzhang@qdio.ac.cn
- Funds：
  
  the National Key Research and Development Program of China(2017YFC1404102);the National Key Research and Development Program of China(2017YFC1404100);the Strategic Priority Research Program of Chinese Academy of Sciences(XDB 40000000);the Strategic Priority Research Program of Chinese Academy of Sciences(XDB 42000000);the National Natural Science Foundation of China(41690122(41690120));the National Natural Science Foundation of China(41705082);the National Natural Science Foundation of China(41421005);the Shandong Taishan Scholarship, and the China Postdoctoral Science Foundation(2018M640659);the Shandong Taishan Scholarship, and the China Postdoctoral Science Foundation(2019M662453);YU Yongqiang is jointly supported by the Strategic Priority Research Program of Chinese Academy of Sciences(XDA 19060102);REN Hong-Li is jointly supported by the China National Science Foundation(41975094);the China National Key Research and Development Program on Monitoring, Early Warning and Prevention of Major Natural Disaster(2018YFC1506004);© Chinese Society for Oceanology and Limnology, Science Press and Springer-Verlag GmbH Germany, part of Springer Nature 2020
- DOI：10.1007/s00343-020-0157-8
  中图分类号：
- 收稿：2020-04-10，
  
  录用：2020-5-7，
  
  网络首发：2020-06-12，
  
  纸质出版：2020-07
- Accepted：
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A review of progress in coupled ocean-atmosphere model developments for ENSO studies in China[J]. 海洋湖沼学报（英文）, 2020,38(4):930-961.

ZHANG Rong-Hua, YU Yongqiang, SONG Zhenya, et al. A review of progress in coupled ocean-atmosphere model developments for ENSO studies in China[J]. Journal of Oceanology and Limnology, 2020, 38(4): 930-961.
A review of progress in coupled ocean-atmosphere model developments for ENSO studies in China[J]. 海洋湖沼学报（英文）, 2020,38(4):930-961. DOI： 10.1007/s00343-020-0157-8.

ZHANG Rong-Hua, YU Yongqiang, SONG Zhenya, et al. A review of progress in coupled ocean-atmosphere model developments for ENSO studies in China[J]. Journal of Oceanology and Limnology, 2020, 38(4): 930-961. DOI： 10.1007/s00343-020-0157-8.

摘要

Abstract

El Niño-Southern Oscillation (ENSO) is the strongest interannual signal that is produced by basinscale processes in the tropical Pacific

with significant effects on weather and climate worldwide. In the past

extensive and intensive international efforts have been devoted to coupled model developments for ENSO studies. A hierarchy of coupled ocean-atmosphere models has been formulated; in terms of their complexity

they can be categorized into intermediate coupled models (ICMs)

hybrid coupled models (HCMs)

and fully coupled general circulation models (CGCMs). ENSO modeling has made significant progress over the past decades

reaching a stage where coupled models can now be used to successfully predict ENSO events 6 months to one year in advance. Meanwhile

ENSO exhibits great diversity and complexity as observed in nature

which still cannot be adequately captured by current state-of-the-art coupled models

presenting a challenge to ENSO modeling. We primarily reviewed the long-term efforts in ENSO modeling continually and steadily made at different institutions in China; some selected representative examples are presented here to review the current status of ENSO model developments and applications

which have been actively pursued with noticeable progress being made recently. As ENSO simulations are very sensitive to model formulations and process representations etc.

dedicated efforts have been devoted to ENSO model developments and improvements. Now

different ocean-atmosphere coupled models have been available in China

which exhibit good model performances and have already had a variety of applications to climate modeling

including the Coupled Model Intercomparison Project Phase 6 (CMIP6). Nevertheless

large biases and uncertainties still exist in ENSO simulations and predictions

and there are clear rooms for their improvements

which are still an active area of researches and applications. Here

model performances of ENSO simulations are assessed in terms of advantages and disadvantages with these differently formulated coupled models

pinpointing to the areas where they need to be further improved for ENSO studies. These analyses provide valuable guidance for future improvements in ENSO simulations and predictions.

关键词

Keywords

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Y C Zhu , R H Zhang . An Argo-derived background diffusivity parameterization for improved ocean simulations in the tropical Pacific . Geophysical Research Letters , 2018 . 45 ( 3 ): 1 509 - 1 517 . DOI: 10.1002/2017gl076269 http://doi.org/10.1002/2017gl076269 .

Y C Zhu , R H Zhang , J C Sun . North Pacific upper-ocean cold temperature biases in CMIP6 simulations and the role of regional vertical mixing . Journal of Climate , 2020 . DOI: 10.1175/JCLI-D-19-0654.1 http://doi.org/10.1175/JCLI-D-19-0654.1 .

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