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Application of the Conditional Nonlinear Optimal Perturbation Method to the Predictability Study of the Kuroshio Large Meander


doi: 10.1007/s00376-011-0199-0

  • A reduced-gravity barotropic shallow-water model was used to simulate the Kuroshio path variations. The results show that the model was able to capture the essential features of these path variations. We used one simulation of the model as the reference state and investigated the effects of errors in model parameters on the prediction of the transition to the Kuroshio large meander (KLM) state using the conditional nonlinear optimal parameter perturbation (CNOP-P) method. Because of their relatively large uncertainties, three model parameters were considered: the interfacial friction coefficient, the wind-stress amplitude, and the lateral friction coefficient. We determined the CNOP-Ps optimized for each of these three parameters independently, and we optimized all three parameters simultaneously using the Spectral Projected Gradient 2 (SPG2) algorithm. Similarly, the impacts caused by errors in initial conditions were examined using the conditional nonlinear optimal initial perturbation (CNOP-I) method. Both the CNOP-I and CNOP-Ps can result in significant prediction errors of the KLM over a lead time of 240 days. But the prediction error caused by CNOP-I is greater than that caused by CNOP-P. The results of this study indicate not only that initial condition errors have greater effects on the prediction of the KLM than errors in model parameters but also that the latter cannot be ignored. Hence, to enhance the forecast skill of the KLM in this model, the initial conditions should first be improved, the model parameters should use the best possible estimates.
  • [1] Xia LIU, Qiang WANG, Mu MU, 2018: Optimal Initial Error Growth in the Prediction of the Kuroshio Large Meander Based on a High-resolution Regional Ocean Model, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 1362-1371.  doi: 10.1007/s00376-018-8003-z
    [2] DUAN Wansuo, ZHANG Rui, 2010: Is Model Parameter Error Related to a Significant Spring Predictability Barrier for El Nino events? Results from a Theoretical Model, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 1003-1013.  doi: 10.1007/s00376-009-9166-4
    [3] ZHENG Qin*, SHA Jianxin, SHU Hang, and LU Xiaoqing, 2014: A Variant Constrained Genetic Algorithm for Solving Conditional Nonlinear Optimal Perturbations, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 219-229.  doi: 10.1007/s00376-013-2253-6
    [4] MU Mu, DUAN Wansuo, XU Hui, WANG Bo, 2006: Applications of Conditional Nonlinear Optimal Perturbation in Predictability Study and Sensitivity Analysis of Weather and Climate, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 992-1002.  doi: 10.1007/s00376-006-0992-3
    [5] Se-Hwan YANG, LI Chaofan, and LU Riyu, 2014: Predictability of Winter Rainfall in South China as Demonstrated by the Coupled Models of ENSEMBLES, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 779-786.  doi: 10.1007/s00376-013-3172-2
    [6] Bin MU, Juhui REN, Shijin YUAN, Rong-Hua ZHANG, Lei CHEN, Chuan GAO, 2019: The Optimal Precursors for ENSO Events Depicted Using the Gradient-definition-based Method in an Intermediate Coupled Model, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-019-9040-y
    [7] BEI Naifang, Fuqing ZHANG, 2014: Mesoscale Predictability of Moist Baroclinic Waves: Variable and Scale-dependent Error Growth, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 995-1008.  doi: 10.1007/s00376-014-3191-7
    [8] 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
    [9] ZHU Benlu, LIN Wantao, ZHANG Yun, 2010: Analysis Study on Perturbation Energy and Predictability of Heavy Precipitation in South China, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 382-392.  doi: 10.1007/s00376-009-8164-x
    [10] 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
    [11] ZHOU Feifan, DING Ruiqiang, FENG Guolin, FU Zuntao, DUAN Wansuo, 2012: Progress in the Study of Nonlinear Atmospheric Dynamics and Predictability of Weather and Climate in China (2007--2011), ADVANCES IN ATMOSPHERIC SCIENCES, 29, 1048-1062.  doi: 10.1007/s00376-012-1204-y
    [12] LANG Xianmei, WANG Huijun, 2005: Seasonal Differences of Model Predictability and the Impact of SST in the Pacific, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 103-113.  doi: 10.1007/BF02930873
    [13] WangHuijun, Xue Feng, Bi Xunqiang, 1997: The Interannual Variability and Predictability in a Global Climate Model, ADVANCES IN ATMOSPHERIC SCIENCES, 14, 554-562.  doi: 10.1007/s00376-997-0073-2
    [14] QIN Xiaohao, MU Mu, 2014: Can Adaptive Observations Improve Tropical Cyclone Intensity Forecasts?, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 252-262.  doi: 10.1007/s00376-013-3008-0
    [15] DUAN Wansuo, LUO Haiying, 2010: A New Strategy for Solving a Class of Constrained Nonlinear Optimization Problems Related to Weather and Climate Predictability, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 741-749.  doi: 10.1007/s00376-009-9141-0
    [16] 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
    [17] 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
    [18] SUN Guodong, MU Mu, 2011: Response of a Grassland Ecosystem to Climate Change in a Theoretical Model, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 1266-1278.  doi: 10.1007/s00376-011-0169-6
    [19] SUN Guodong, MU Mu, 2013: Using the Lund-Potsdam-Jena Model to Understand the Different Responses of Three Woody Plants to Land Use in China, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 515-524.  doi: 10.1007/s00376-012-2011-1
    [20] WANG Huijun, FAN Ke, SUN Jianqi, LI Shuanglin, LIN Zhaohui, ZHOU Guangqing, CHEN Lijuan, LANG Xianmei, LI Fang, ZHU Yali, CHEN Hong, ZHENG Fei, 2015: A Review of Seasonal Climate Prediction Research in China, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 149-168.  doi: 10.1007/s00376-014-0016-7

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Manuscript History

Manuscript received: 10 January 2012
Manuscript revised: 10 January 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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Application of the Conditional Nonlinear Optimal Perturbation Method to the Predictability Study of the Kuroshio Large Meander

  • 1. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atomspheric Physics, Chinese Academy of Sciences, Beijing 100029, Graduate University of the Chinese Academy of Sciences, Beijing 100049, Key Laboratory of Ocean Circulation and Wave, Institute of Oceanology, Chinese Academy of Sciences, Qingdao 266071,Key Laboratory of Ocean Circulation and Wave, Institute of Oceanology,Chinese Academy of Sciences, Qingdao 266071, State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atomspheric Physics, Chinese Academy of Sciences, Beijing 100029,Institute for Marine and Atmospheric Research Utrecht, Department of Physics and Astronomy, Utrecht University, 3584 CC Utrecht, the Netherlands

Abstract: A reduced-gravity barotropic shallow-water model was used to simulate the Kuroshio path variations. The results show that the model was able to capture the essential features of these path variations. We used one simulation of the model as the reference state and investigated the effects of errors in model parameters on the prediction of the transition to the Kuroshio large meander (KLM) state using the conditional nonlinear optimal parameter perturbation (CNOP-P) method. Because of their relatively large uncertainties, three model parameters were considered: the interfacial friction coefficient, the wind-stress amplitude, and the lateral friction coefficient. We determined the CNOP-Ps optimized for each of these three parameters independently, and we optimized all three parameters simultaneously using the Spectral Projected Gradient 2 (SPG2) algorithm. Similarly, the impacts caused by errors in initial conditions were examined using the conditional nonlinear optimal initial perturbation (CNOP-I) method. Both the CNOP-I and CNOP-Ps can result in significant prediction errors of the KLM over a lead time of 240 days. But the prediction error caused by CNOP-I is greater than that caused by CNOP-P. The results of this study indicate not only that initial condition errors have greater effects on the prediction of the KLM than errors in model parameters but also that the latter cannot be ignored. Hence, to enhance the forecast skill of the KLM in this model, the initial conditions should first be improved, the model parameters should use the best possible estimates.

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