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Progress in the Study of Nonlinear Atmospheric Dynamics and Predictability of Weather and Climate in China (2007--2011)


doi: 10.1007/s00376-012-1204-y

  • Recent progress in the study of nonlinear atmospheric dynamics and related predictability of weather and climate in China (2007--2011) are briefly introduced in this article. Major achievements in the study of nonlinear atmospheric dynamics have been classified into two types: (1) progress based on the analysis of solutions of simplified control equations, such as the dynamics of NAO, the optimal precursors for blocking onset, and the behavior of nonlinear waves, and (2) progress based on data analyses, such as the nonlinear analyses of fluctuations and recording-breaking temperature events, the long-range correlation of extreme events, and new methods of detecting abrupt dynamical change. Major achievements in the study of predictability include the following: (1) the application of nonlinear local Lyapunov exponents (NLLE) to weather and climate predictability; (2) the application of condition nonlinear optimal perturbation (CNOP) to the studies of El Ni\~{n}o-Southern Oscillation (ENSO) predictions, ensemble forecasting, targeted observation, and sensitivity analysis of the ecosystem; and (3) new strategies proposed for predictability studies. The results of these studies have provided greater understanding of the dynamics and nonlinear mechanisms of atmospheric motion, and they represent new ideas for developing numerical models and improving the forecast skill of weather and climate events.
  • [1] 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
    [2] Mu Mu, Duan Wansuo, Wang Jiacheng, 2002: The Predictability Problems in Numerical Weather and Climate Prediction, ADVANCES IN ATMOSPHERIC SCIENCES, 19, 191-204.  doi: 10.1007/s00376-002-0016-x
    [3] DUAN Wansuo, JIANG Zhina, XU Hui, 2007: Progress in Predictability Studies in China (2003--2006), ADVANCES IN ATMOSPHERIC SCIENCES, 24, 1086-1098.  doi: 10.1007/s00376-007-1086-6
    [4] DUAN Anmin, WU Guoxiong, LIU Yimin, MA Yaoming, ZHAO Ping, 2012: Weather and Climate Effects of the Tibetan Plateau, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 978-992.  doi: 10.1007/s00376-012-1220-y
    [5] 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
    [6] Zhiyong MENG, Eugene E. CLOTHIAUX, 2022: Contributions of Fuqing ZHANG to Predictability, Data Assimilation, and Dynamics of High Impact Weather: A Tribute, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 676-683.  doi: 10.1007/s00376-021-1362-x
    [7] 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
    [8] 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
    [9] S. PANCHEV, T. SPASSOVA, 2005: Simple General Atmospheric Circulation and Climate Models with Memory, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 765-769.  doi: 10.1007/BF02918720
    [10] 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
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    [12] DING Ruiqiang, FENG Guolin, LIU Shida, LIU Shikuo, HUANG Sixun, FU Zuntao, 2007: Nonlinear Atmospheric and Climate Dynamics in China (2003--2006): A Review, ADVANCES IN ATMOSPHERIC SCIENCES, 24, 1077-1085.  doi: 10.1007/s00376-007-1077-7
    [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] 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
    [15] Xiaoran ZHUANG, Jinzhong MIN, Liu ZHANG, Shizhang WANG, Naigeng WU, Haonan ZHU, 2020: Insights into Convective-scale Predictability in East China: Error Growth Dynamics and Associated Impact on Precipitation of Warm-Season Convective Events, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 893-911.  doi: 10.1007/s00376-020-9269-5
    [16] Ning ZENG, 2003: Glacial-Interglacial Atmospheric CO2 Change--The Glacial Burial Hypothesis, ADVANCES IN ATMOSPHERIC SCIENCES, 20, 677-693.  doi: 10.1007/BF02915395
    [17] Yunyun LIU, Zeng-Zhen HU, Renguang WU, Xing YUAN, 2022: Causes and Predictability of the 2021 Spring Southwestern China Severe Drought, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1766-1776.  doi: 10.1007/s00376-022-1428-4
    [18] DONG Wenjie, CHOU Jieming, FENG Guolin, 2007: A New Economic Assessment Index for the Impact of Climate Change on Grain Yield, ADVANCES IN ATMOSPHERIC SCIENCES, 24, 336-342.  doi: 10.1007/s00376-007-0336-y
    [19] FAN Lei, Zhengyu LIU, LIU Qinyu, 2011: Robust GEFA Assessment of Climate Feedback to SST EOF Modes, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 907-912.  doi: 10.1007/s00376-010-0081-5
    [20] Wang Zhiren, Wu Dexing, Chen Dake, Wu Huiding, Song Xuejia, Zhang Zhanhai, 2002: Critical Time Span and Nonlinear Action Structure of Climatic Atmosphere and Ocean, ADVANCES IN ATMOSPHERIC SCIENCES, 19, 741-756.  doi: 10.1007/s00376-002-0013-0

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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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Progress in the Study of Nonlinear Atmospheric Dynamics and Predictability of Weather and Climate in China (2007--2011)

  • 1. Laboratory of Cloud-Precipitation Physics and Severe Storms, 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 Sciences, Chinese Academy of Sciences, Beijing 100029;Laboratory for Climate Studies of China Meteorological Administration, National Climate Center, Beijing 100081;School of Physics, Peking University, Beijing 100871;State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Sciences, Chinese Academy of Sciences, Beijing 100029

Abstract: Recent progress in the study of nonlinear atmospheric dynamics and related predictability of weather and climate in China (2007--2011) are briefly introduced in this article. Major achievements in the study of nonlinear atmospheric dynamics have been classified into two types: (1) progress based on the analysis of solutions of simplified control equations, such as the dynamics of NAO, the optimal precursors for blocking onset, and the behavior of nonlinear waves, and (2) progress based on data analyses, such as the nonlinear analyses of fluctuations and recording-breaking temperature events, the long-range correlation of extreme events, and new methods of detecting abrupt dynamical change. Major achievements in the study of predictability include the following: (1) the application of nonlinear local Lyapunov exponents (NLLE) to weather and climate predictability; (2) the application of condition nonlinear optimal perturbation (CNOP) to the studies of El Ni\~{n}o-Southern Oscillation (ENSO) predictions, ensemble forecasting, targeted observation, and sensitivity analysis of the ecosystem; and (3) new strategies proposed for predictability studies. The results of these studies have provided greater understanding of the dynamics and nonlinear mechanisms of atmospheric motion, and they represent new ideas for developing numerical models and improving the forecast skill of weather and climate events.

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