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Volume 16 Issue 3

Jul.  1999

Article Contents
Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 1999 >  16(3): 405-418

A Hybrid Coupled Ocean-Atmosphere Model and ENSO Prediction Study

  • 1.

    Department of Atmospheric Sciences, Nanjing University, Nanjing 210093,Department of Atmospheric Sciences, Nanjing University, Nanjing 210093


doi: 10.1007/s00376-999-0019-y

  • A hybrid coupled ocean－tmosphere model is designed, which consists of a global AGCM and a simple anomaly ocean model in the tropical Pacific。 Retroactive experimental predictions in-itiated in each season from 1979 to 1994 are performed. Analyses indicate that: (1) The overall predictive capability of this model for SSTA over the central-eastern tropical Pacific can reach one year, and the error is not larger than 0.8?C. (2) The prediction skill depends greatly on the season when forecasts start. However, the phenomenon of SPB (spring prediction barrier) is not found in the model. (3) The ensemble forecast method can effectively improve prediction results. A new initialization scheme is discussed.
  • [1] 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
    [2] 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
    [3] 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
    [4] LIN Zhaohui, WANG Huijun, ZHOU Guangqing, CHEN Hong, LANG Xianmei, ZHAO Yan, ZENG Qingcun, 2004: Recent Advances in Dynamical Extra-Seasonal to Annual Climate Prediction at IAP/CAS, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 456-466.  doi: 10.1007/BF02915572
    [5] 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
    [6] Zhou Guangqing, Zeng Qingcun, 2001: Predictions of ENSO with a Coupled Atmosphere-Ocean General Circulation Model, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 587-603.  doi: 10.1007/s00376-001-0047-8
    [7] Lili LEI, Yangjinxi GE, Zhe-Min TAN, Yi ZHANG, Kekuan CHU, Xin QIU, Qifeng QIAN, 2022: Evaluation of a Regional Ensemble Data Assimilation System for Typhoon Prediction, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1816-1832.  doi: 10.1007/s00376-022-1444-4
    [8] ZHU Jiang, LIN Caiyan, WANG Zifa, 2009: Dust Storm Ensemble Forecast Experiments in East Asia, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 1053-1070.  doi: 10.1007/s00376-009-8218-0
    [9] T. N. Krishnamurti, Mukul Tewari, Ed Bensman, Wei Han, Zhan Zhang, William K. M. Lau, 1999: An Ensemble Forecast of the South China Sea Monsoon, ADVANCES IN ATMOSPHERIC SCIENCES, 16, 159-182.  doi: 10.1007/BF02973080
    [10] 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
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    [12] Yuejian ZHU, 2005: Ensemble Forecast: A New Approach to Uncertainty and Predictability, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 781-788.  doi: 10.1007/BF02918678
    [13] Saleh AMINYAVARI, Bahram SAGHAFIAN, Majid DELAVAR, 2018: Evaluation of TIGGE Ensemble Forecasts of Precipitation in Distinct Climate Regions in Iran, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 457-468.  doi: 10.1007/s00376-017-7082-6
    [14] Zhenhua HUO, Wansuo DUAN, Feifan ZHOU, 2019: Ensemble Forecasts of Tropical Cyclone Track with Orthogonal Conditional Nonlinear Optimal Perturbations, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 231-247.  doi: 10.1007/s00376-018-8001-1
    [15] Jihang LI, Zhiyan ZHANG, Lu LIU, Xubin ZHANG, Jingxuan QU, Qilin WAN, 2021: The Simulation of Five Tropical Cyclones by Sample Optimization of Ensemble Forecasting Based on the Observed Track and Intensity, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1763-1777.  doi: 10.1007/s00376-021-0353-2
    [16] Jiangshan ZHU, Fanyou KONG, Xiao-Ming HU, Yan GUO, Lingkun RAN, Hengchi LEI, 2018: Impact of Soil Moisture Uncertainty on Summertime Short-range Ensemble Forecasts, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 839-852.  doi: 10.1007/s00376-017-7107-1
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    [18] Junya HU, Rong-Hua ZHANG, Chuan GAO, 2019: A Hybrid Coupled Ocean-Atmosphere Model and Its Simulation of ENSO and Atmospheric Responses, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 643-657.  doi: 10.1007/s00376-019-8197-8
    [19] LIU Zhengyu, WU Shu, ZHANG Shaoqing, LIU Yun, RONG Xinyao, , 2013: Ensemble Data Assimilation in a Simple Coupled Climate Model: The Role of Ocean-Atmosphere Interaction, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1235-1248.  doi: 10.1007/s00376-013-2268-z
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    • Manuscript History

    Manuscript received: 10 July 1999
    Manuscript revised: 10 July 1999
    通讯作者: 陈斌, bchen63@163.com
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      沈阳化工大学材料科学与工程学院 沈阳 110142

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    A Hybrid Coupled Ocean-Atmosphere Model and ENSO Prediction Study

    • 1. Department of Atmospheric Sciences, Nanjing University, Nanjing 210093,Department of Atmospheric Sciences, Nanjing University, Nanjing 210093
    • Manuscript received: 1999-07-10
    • Manuscript revised: 1999-07-10

    Abstract: A hybrid coupled ocean－tmosphere model is designed, which consists of a global AGCM and a simple anomaly ocean model in the tropical Pacific。 Retroactive experimental predictions in-itiated in each season from 1979 to 1994 are performed. Analyses indicate that: (1) The overall predictive capability of this model for SSTA over the central-eastern tropical Pacific can reach one year, and the error is not larger than 0.8?C. (2) The prediction skill depends greatly on the season when forecasts start. However, the phenomenon of SPB (spring prediction barrier) is not found in the model. (3) The ensemble forecast method can effectively improve prediction results. A new initialization scheme is discussed.

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