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

• Published in: Journal of oceanology and limnology Vol. 38; no. 4; pp. 930 - 961
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: Heidelberg Science Press 01.07.2020; Springer Nature B.V; National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Qingdao 266061, China%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%Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, and Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China; National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Qingdao 266061, China; Laboratory for Ocean and Climate Dynamics, Pilot National Laboratory for Marine Science and Technology, Qingdao 266237,China%LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100049, China; LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100049, China%First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China; University of Chinese Academy of Sciences, Beijing 100049, China%University of Chinese Academy of Sciences, Beijing 100049, China; Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology,Qingdao 266237, China; Laboratory for Climate Studies and CMA-NJU Joint Laboratory for Climate Prediction Studies, National Climate Center,China Meteorological Administration, Beijing 100081, China%Environmental Science and Engineering, University of Northern British Columbia, British Columbia V2N 4Z9, Canada; Laboratory for Ocean and Climate Dynamics, Pilot National Laboratory for Marine Science and Technology, Qingdao 266237,China; Key Laboratory of Marine Science and Numerical Modeling (MASNUM), Ministry of Natural Resources, Qingdao 266061, China; State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China%First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China; Center for Excellence in Quaternary Science and Global Change, Chinese Academy of Sciences, Xi'an 710061, China; Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, and Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China; 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%LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100049, China%Beijing Climate Center, China Meteorological Administration, Beijing 100081, China; National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Qingdao 266061, China%State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
• Subjects: Atmosphere; Atmospheric models; China; Climate; Climate models; Complexity; Earth and Environmental Science; Earth Sciences; El Nino; El Nino phenomena; General Circulation Models; Intercomparison; model uncertainty; model validation; Modelling; Ocean models; Ocean-atmosphere interaction; Ocean-atmosphere system; Oceanography; Pacific Ocean; Review; Simulation; simulation models; Southern Oscillation; tropics; China; Pacific Ocean; model biases and uncertainties; coupled ocean-atmosphere models; simulations and predictions; El Niño-Southern Oscillation (ENSO); El Ni(n)o-Southern Oscillation (ENSO)
• ISSN: 2096-5508; 2523-3521
• DOI: 10.1007/s00343-020-0157-8

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 signifiscant 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.