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Relationships between the Limit of Predictability and Initial Error in the Uncoupled and Coupled Lorenz Models


doi: 10.1007/s00376-012-1207-8

  • In this study, the relationship between the limit of predictability and initial error was investigated using two simple chaotic systems: the Lorenz model, which possesses a single characteristic time scale, and the coupled Lorenz model, which possesses two different characteristic time scales. The limit of predictability is defined here as the time at which the error reaches 95% of its saturation level; nonlinear behaviors of the error growth are therefore involved in the definition of the limit of predictability. Our results show that the logarithmic function performs well in describing the relationship between the limit of predictability and initial error in both models, although the coefficients in the logarithmic function were not constant across the examined range of initial errors. Compared with the Lorenz model, in the coupled Lorenz model---in which the slow dynamics and the fast dynamics interact with each other---there is a more complex relationship between the limit of predictability and initial error. The limit of predictability of the Lorenz model is unbounded as the initial error becomes infinitesimally small; therefore, the limit of predictability of the Lorenz model may be extended by reducing the amplitude of the initial error. In contrast, if there exists a fixed initial error in the fast dynamics of the coupled Lorenz model, the slow dynamics has an intrinsic finite limit of predictability that cannot be extended by reducing the amplitude of the initial error in the slow dynamics, and vice versa. The findings reported here reveal the possible existence of an intrinsic finite limit of predictability in a coupled system that possesses many scales of time or motion.
  • [1] Qian ZHOU, Wansuo DUAN, Xu WANG, Xiang LI, Ziqing ZU, 2021: The Initial Errors in the Tropical Indian Ocean that Can Induce a Significant “Spring Predictability Barrier” for La Niña Events and Their Implication for Targeted Observations, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1566-1579.  doi: 10.1007/s00376-021-0427-1
    [2] Ming ZHANG, Ruiqiang Ding, Quanjia Zhong, Jianping Li, Deyu Lu, 2024: Application of Conditional Nonlinear Local Lyapunov Exponent to the Second Kind Predictability, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-024-3297-5
    [3] Yong LI, Siming LI, Yao SHENG, Luheng WANG, 2018: Data Assimilation Method Based on the Constraints of Confidence Region, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 334-345.  doi: 10.1007/s00376-017-7045-y
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    [6] Qian ZOU, Quanjia ZHONG, Jiangyu MAO, Ruiqiang DING, Deyu LU, Jianping LI, Xuan LI, 2023: Impact of Perturbation Schemes on the Ensemble Prediction in a Coupled Lorenz Model, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 501-513.  doi: 10.1007/s00376-022-1376-z
    [7] Ruiqiang DING, Baojia LIU, Bin GU, Jianping LI, Xuan LI, 2019: Predictability of Ensemble Forecasting Estimated Using the Kullback-Leibler Divergence in the Lorenz Model, ADVANCES IN ATMOSPHERIC SCIENCES, , 837-846.  doi: 10.1007/s00376-019-9034-9
    [8] 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
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    [12] Rong FENG, Wansuo DUAN, 2018: Investigating the Initial Errors that Cause Predictability Barriers for Indian Ocean Dipole Events Using CMIP5 Model Outputs, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 1305-1320.  doi: 10.1007/s00376-018-7214-7
    [13] Xuan LI, Ruiqiang DING, Jianping LI, 2019: Determination of the Backward Predictability Limit and Its Relationship with the Forward Predictability Limit, ADVANCES IN ATMOSPHERIC SCIENCES, 36, 669-677.  doi: 10.1007/s00376-019-8205-z
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Manuscript History

Manuscript received: 10 September 2012
Manuscript revised: 10 September 2012
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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Relationships between the Limit of Predictability and Initial Error in the Uncoupled and Coupled Lorenz Models

  • 1. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029

Abstract: In this study, the relationship between the limit of predictability and initial error was investigated using two simple chaotic systems: the Lorenz model, which possesses a single characteristic time scale, and the coupled Lorenz model, which possesses two different characteristic time scales. The limit of predictability is defined here as the time at which the error reaches 95% of its saturation level; nonlinear behaviors of the error growth are therefore involved in the definition of the limit of predictability. Our results show that the logarithmic function performs well in describing the relationship between the limit of predictability and initial error in both models, although the coefficients in the logarithmic function were not constant across the examined range of initial errors. Compared with the Lorenz model, in the coupled Lorenz model---in which the slow dynamics and the fast dynamics interact with each other---there is a more complex relationship between the limit of predictability and initial error. The limit of predictability of the Lorenz model is unbounded as the initial error becomes infinitesimally small; therefore, the limit of predictability of the Lorenz model may be extended by reducing the amplitude of the initial error. In contrast, if there exists a fixed initial error in the fast dynamics of the coupled Lorenz model, the slow dynamics has an intrinsic finite limit of predictability that cannot be extended by reducing the amplitude of the initial error in the slow dynamics, and vice versa. The findings reported here reveal the possible existence of an intrinsic finite limit of predictability in a coupled system that possesses many scales of time or motion.

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