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Volume 18 Issue 4

Jul.  2001

Article Contents
Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2001 >  18(4): 587-603

Predictions of ENSO with a Coupled Atmosphere-Ocean General Circulation Model

  • 1.

    LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029


doi: 10.1007/s00376-001-0047-8

  • Predictions of ENSO are described by using a coupled atmosphere-ocean general circulation model. The initial conditions are created by forcing the coupled system using SST anomalies in the tropical Pacific at the background of the coupled model climatology. A series of 24-month hindcasts for the period from November 1981 to December 1997 are carried out to validate the performance of the coupled system. Correlations of SST anomalies in the Nino3 region exceed 0.54 up to 15 months in advance and the rms errors are less than 0.9℃. The system is more skillful in predicting SST anomalies in the 1980s and less in the 1990s. The model skills are also seasonal-dependent, which are lower for the predictions starting from late autumn to winter and higher for those from spring to autumn in a year-time forecast length. The prediction, beginning from March, persists 8 months long with the correlation skill exceeding 0.6, which is important in predictions of summer rainfall in China. The predictions are succesful in many aspects for the 1997-2000 ENSO events.
  • [1] LI Shan, RONG Xingyao, LIU Yun, LIU Zhengyu, Klaus FRAEDRICH, 2013: Dynamic Analogue Initialization for Ensemble Forecasting, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1406-1420.  doi: 10.1007/s00376-012-2244-z
    [2] Wang Huijun, Zhou Guangqing, Lin Zhaohui, Zhao Yan, Guo Yufu, Ma Zhuguo, 2001: Recent Researches on the Short-Term Climate Prediction at IAP－A Brief Review, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 929-936.
    [3] Wu Aiming, Ni Yunqi, 1999: A Hybrid Coupled Ocean-Atmosphere Model and ENSO Prediction Study, ADVANCES IN ATMOSPHERIC SCIENCES, 16, 405-418.  doi: 10.1007/s00376-999-0019-y
    [4] Chuan GAO, Xinrong WU, Rong-Hua ZHANG, 2016: Testing a Four-Dimensional Variational Data Assimilation Method Using an Improved Intermediate Coupled Model for ENSO Analysis and Prediction, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 875-888.  doi: 10.1007/s00376-016-5249-1
    [5] Ben TIAN, Hong-Li REN, 2022: Diagnosing SST Error Growth during ENSO Developing Phase in the BCC_CSM1.1(m) Prediction System, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 427-442.  doi: 10.1007/s00376-021-1189-5
    [6] Lu ZHOU, Rong-Hua ZHANG, 2022: A Hybrid Neural Network Model for ENSO Prediction in Combination with Principal Oscillation Pattern Analyses, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 889-902.  doi: 10.1007/s00376-021-1368-4
    [7] Tingyu WANG, Ping HUANG, 2024: Superiority of a Convolutional Neural Network Model over Dynamical Models in Predicting Central Pacific ENSO, ADVANCES IN ATMOSPHERIC SCIENCES, 41, 141-154.  doi: 10.1007/s00376-023-3001-1
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    [10] Lei ZHU, Zhiyong MENG, Yonghui WENG, Fuqing ZHANG, 2022: Assimilation of All-sky Geostationary Satellite Infrared Radiances for Convection-Permitting Initialization and Prediction of Hurricane Joaquin (2015), ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1859-1872.  doi: 10.1007/s00376-022-2015-4
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    [12] Rong FENG, Wansuo DUAN, 2019: Indian Ocean Dipole-related Predictability Barriers Induced by Initial Errors in the Tropical Indian Ocean in a CGCM, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 658-668.  doi: 10.1007/s00376-019-8224-9
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    • Manuscript History

    Manuscript received: 10 July 2001
    Manuscript revised: 10 July 2001
    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

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    Predictions of ENSO with a Coupled Atmosphere-Ocean General Circulation Model

    • 1. LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
    • Manuscript received: 2001-07-10
    • Manuscript revised: 2001-07-10

    Abstract: Predictions of ENSO are described by using a coupled atmosphere-ocean general circulation model. The initial conditions are created by forcing the coupled system using SST anomalies in the tropical Pacific at the background of the coupled model climatology. A series of 24-month hindcasts for the period from November 1981 to December 1997 are carried out to validate the performance of the coupled system. Correlations of SST anomalies in the Nino3 region exceed 0.54 up to 15 months in advance and the rms errors are less than 0.9℃. The system is more skillful in predicting SST anomalies in the 1980s and less in the 1990s. The model skills are also seasonal-dependent, which are lower for the predictions starting from late autumn to winter and higher for those from spring to autumn in a year-time forecast length. The prediction, beginning from March, persists 8 months long with the correlation skill exceeding 0.6, which is important in predictions of summer rainfall in China. The predictions are succesful in many aspects for the 1997-2000 ENSO events.

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