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Volume 28 Issue 6

Nov.  2011

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Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2011 >  28(6): 1279-1290

Effect of Stochastic MJO Forcing on ENSO Predictability

  • 1.

    The State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, Department of Military Oceanography, Dalian Naval Academy, Dalian 116018, Institute of Meteorology, PLA University of Science and Technology, Nanjing 211101,The State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,Institute of Meteorology, PLA University of Science and Technology, Nanjing 211101


doi: 10.1007/s00376-011-0126-4

  • Within the frame of the Zebiak-Cane model, the impact of the uncertainties of the Madden--Julian Oscillation (MJO) on ENSO predictability was studied using a parameterized stochastic representation of intraseasonal forcing. The results show that the uncertainties of MJO have little effect on the maximum prediction error for ENSO events caused by conditional nonlinear optimal perturbation (CNOP); compared to CNOP-type initial error, the model error caused by the uncertainties of MJO led to a smaller prediction uncertainty of ENSO, and its influence over the ENSO predictability was not significant. This result suggests that the initial error might be the main error source that produces uncertainty in ENSO prediction, which could provide a theoretical foundation for the data assimilation of the ENSO forecast.
  • [1] Ben TIAN, Wansuo DUAN, 2016: Comparison of Constant and Time-variant Optimal Forcing Approaches in El Niño Simulations by Using the Zebiak-Cane Model, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 685-694.  doi: 10.1007/s00376-015-5174-8
    [2] XUE Hai-Le, SHEN Xue-Shun, CHOU Ji-Fan, 2013: A Forecast Error Correction Method in Numerical Weather Prediction by Using Recent Multiple-time Evolution Data, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1249-1259.  doi: 10.1007/s00376-013-2274-1
    [3] XUE Haile, SHEN Xueshun, CHOU Jifan, 2015: An Online Model Correction Method Based on an Inverse Problem: Part I——Model Error Estimation by Iteration, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1329-1340.  doi: 10.1007/s00376-015-4261-1
    [4] XUE Haile, SHEN Xueshun, CHOU Jifan, 2015: An Online Model Correction Method Based on an Inverse Problem: Part II——Systematic Model Error Correction, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1493-1503.  doi: 10.1007/s00376-015-4262-0
    [5] LING Jian, LI Chongyin, ZHOU Wen, JIA Xiaolong, Chidong ZHANG, 2013: Effect of Boundary Layer Latent Heating on MJO Simulations, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 101-115.  doi: 10.1007/s00376-012-2031-x
    [6] ZHENG Qin, DAI Yi, ZHANG Lu, SHA Jianxin, LU Xiaoqing, 2012: On the Application of a Genetic Algorithm to the Predictability Problems Involving ``On--Off'' Switches, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 422-434.  doi: 10.1007/s00376-011-1054-z
    [7] JIANG Zhina, WANG Xin, WANG Donghai, 2015: Exploring the Phase-Strength Asymmetry of the North Atlantic Oscillation Using Conditional Nonlinear Optimal Perturbation, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 671-679.  doi: 10.1007/s00376-014-4094-3
    [8] SUN Guodong, MU Mu, ZHANG Yale, 2010: Algorithm Studies on How to Obtain a Conditional Nonlinear Optimal Perturbation (CNOP), ADVANCES IN ATMOSPHERIC SCIENCES, 27, 1311-1321.  doi: 10.1007/s00376-010-9088-1
    [9] WANG Bo, and HUO Zhenhua, 2013: Extended application of the conditional nonlinear optimal parameter perturbation method in the Common Land Model, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 1213-1223.  doi: 10.1007/s00376-012-2025-8
    [10] ZHANG Yuli, LIU Yi, LIU Chuanxi, V. F. SOFIEVA, 2015: Satellite Measurements of the Madden-Julian Oscillation in Wintertime Stratospheric Ozone over the Tibetan Plateau and East Asia, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1481-1492.  doi: 10.1007/s00376-015-5005-y
    [11] Li Wei, Yu Rucong, Zhang Xuehong, 2001: Impacts of Sea Surface Temperature in the Tropical Pacific on Interannual Variability of Madden-Julian Oscillation in Precipitation, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 429-444.  doi: 10.1007/BF02919322
    [12] Lifeng LI, Xin LI, Xiong CHEN, Chongyin LI, Jianqi ZHANG, Yulong SHAN, 2020: Modulation of Madden-Julian Oscillation Activity by the Tropical Pacific-Indian Ocean Associated Mode, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 1375-1388.  doi: 10.1007/s00376-020-0002-1
    [13] Ping LIANG, Zeng-Zhen HU, Yihui DING, Qiwen QIAN, 2021: The Extreme Mei-yu Season in 2020: Role of the Madden-Julian Oscillation and the Cooperative Influence of the Pacific and Indian Oceans, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 2040-2054.  doi: 10.1007/s00376-021-1078-y
    [14] Bin Wang, Yihui Ding, 1992: An Overview of the Madden-Julian Oscillation and Its Relation to Monsoon and Mid-Latitude Circulation, ADVANCES IN ATMOSPHERIC SCIENCES, 9, 93-111.  doi: 10.1007/BF02656934
    [15] Ling-Jiang TAO, Rong-Hua ZHANG, Chuan GAO, 2017: Initial Error-induced Optimal Perturbations in ENSO Predictions, as Derived from an Intermediate Coupled Model, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 791-803.  doi: 10.1007/s00376-017-6266-4
    [16] Xiaomeng SONG, Renhe ZHANG, Xinyao RONG, 2019: Influence of Intraseasonal Oscillation on the Asymmetric Decays of El Niño and La Niña, ADVANCES IN ATMOSPHERIC SCIENCES, , 779-792.  doi: 10.1007/s00376-019-9029-6
    [17] JIANG Zhina, 2006: Applications of Conditional Nonlinear Optimal Perturbation to the Study of the Stability and Sensitivity of the Jovian Atmosphere, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 775-783.  doi: 10.1007/s00376-006-0775-x
    [18] Yueyue LI, Li DAN, Jing PENG, Junbang WANG, Fuqiang YANG, Dongdong GAO, Xiujing YANG, Qiang YU, 2021: Response of Growing Season Gross Primary Production to El Niño in Different Phases of the Pacific Decadal Oscillation over Eastern China Based on Bayesian Model Averaging, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1580-1595.  doi: 10.1007/s00376-021-0265-1
    [19] Xing ZHANG, Mu MU, Qiang WANG, Stefano PIERINI, 2017: Optimal Precursors Triggering the Kuroshio Extension State Transition Obtained by the Conditional Nonlinear Optimal Perturbation Approach, ADVANCES IN ATMOSPHERIC SCIENCES, 34, 685-699.  doi: 10.1007/s00376-017-6263-7
    [20] WANG Qiang, MU Mu, Henk A. DIJKSTRA, 2012: Application of the Conditional Nonlinear Optimal Perturbation Method to the Predictability Study of the Kuroshio Large Meander, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 118-134.  doi: 10.1007/s00376-011-0199-0
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    • Manuscript History

    Manuscript received: 10 November 2011
    Manuscript revised: 10 November 2011
    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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    Effect of Stochastic MJO Forcing on ENSO Predictability

    • 1. The State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, Department of Military Oceanography, Dalian Naval Academy, Dalian 116018, Institute of Meteorology, PLA University of Science and Technology, Nanjing 211101,The State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,Institute of Meteorology, PLA University of Science and Technology, Nanjing 211101
    • Manuscript received: 2011-11-10
    • Manuscript revised: 2011-11-10

    Abstract: Within the frame of the Zebiak-Cane model, the impact of the uncertainties of the Madden--Julian Oscillation (MJO) on ENSO predictability was studied using a parameterized stochastic representation of intraseasonal forcing. The results show that the uncertainties of MJO have little effect on the maximum prediction error for ENSO events caused by conditional nonlinear optimal perturbation (CNOP); compared to CNOP-type initial error, the model error caused by the uncertainties of MJO led to a smaller prediction uncertainty of ENSO, and its influence over the ENSO predictability was not significant. This result suggests that the initial error might be the main error source that produces uncertainty in ENSO prediction, which could provide a theoretical foundation for the data assimilation of the ENSO forecast.

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