导师信息
博士生导师
硕士生导师
 
 
 
地址:青岛市市南区南海路7号
 
电话:0532-82898650

传真:0532-82898654

邮政编码:266071 

电子邮件:yjsb@ms.qdio.ac.cn
  

穆照片穆穆,研究员,博导。1978年毕业于安徽大学数学系;1985年获得复旦大学理学博士学位;1987-1988年在中国科学院大气物理研究所做博士后研究工作,博士后出站后留该所工作。曾在多伦多大学、剑桥大学牛顿研究所、香港科技大学、法国动力气象实验室与夏威夷大学太平洋研究中心进行过合作研究。1993年被批准为博士生导师。2007年当选为中国科学院院士。2008年当选为发展中国家科学院(TWAS,原第三世界科学院)院士。  

 

 

l  研究领域

天气与气候可预报性 , 大气海洋动力学 , 气候预测与资料同化 , 集合预报 , 目标观测地球流体动力学                                                                                                                                                          

l  招生专业及方向

物理海洋学专业:海洋环流与气候环境变化方向

海洋气象专业:海洋气象方向                                                       

l  联系方式                                                                                                                                      

中国科学院海洋研究所 青岛市南海路7 5#411

Email:mumu@qdio.ac.cn;电话:0532-82897988

l  承担的主要科研项目

主要主持的项目:

1、国家自然科学基金重点基金:可预报性研究中最优前期征兆与增长最快初始误差的相似性及其在目标观测中的应用,(2013-2017);

2、中国科学院战略性先导专项子课题:NECSTCC的变异对黑潮上游段及其可预报性的影响,(2013-2017);

3、公益性(气象)行业专项:印度洋通过印尼贯穿流对ENSO及其可预报性影响的研究,(2013-2015);

4、国家自然科学基金重点项目:条件非线性最优扰动方法在台风目标观测中的应用研究,(2009-2012);

5、中国科学院知识创新工程重要方向项目:条件非线性最优扰动方法在黑潮路径变异可预报性研究中的应用,(2011-2013.

                                                                                                                                                        

l  研究成果及奖励                                                                             

天气、气候的可预报性与非线性()稳定性研究,是当前国际大气-海洋科学前沿热点领域。穆穆研究员主要从事了该领域天气、气候的可预报性,资料同化,集合预报与适应性观测方面的研究。他提出了条件非线性最优扰动CNOP方法,用该方法研究了厄尔尼诺春季预报障碍、海洋热盐环流对淡水通量扰动的敏感性、热带气旋的目标观测等问题,在国际学术界享有很高的知名度。   

穆穆研究员在国内外权威杂志上发表论文百余篇。多次在国际重要学术会议上做特邀报告。是首届国家杰出青年科学基金获得者, 2001年获中国科学院自然科学一等奖(第一完成人)。2005年获国家人事部"全国优秀博士后"称号, 2006年获中国科学院"宝洁优秀研究生导师"奖与中国科学院研究生院"优秀教师"称号。2010年荣获何梁何利基金科学与技术进步奖。指导的博士研究生曾获中科院优秀博士论文与全国百篇优秀博士论文奖。                                                                              

l  代表性论文及著作

1.        Zu Ziqing, Mu Mu, and H. A. Dijkstra, 2016: Optimal Initial Excitations of Decadal Modification of the Atlantic Meridional Overturning Circulation under the Prescribed Heat and Freshwater Flux Boundary Conditions, Journal of Physical Oceanography, 46, 2029-2047

2.        Sun Guodong and Mu Mu, 2016: A new approach to identify the sensitivity and importance of physical parameters combination within numerical models using the Lund–Potsdam–Jena (LPJ) model as an example, Theor Appl Climatol, DOI 10.1007/s00704-015-1690-9

3.        Dai Guokun,  Mu Mu, and Jiang Zhina, 2016: Relationships between Optimal Precursors Triggering NAO Onset and Optimally Growing Initial Errors during NAO Prediction, J. Atmos. Sci., 73, 293-317

4.        Wang Qiang, and Mu Mu, 2015: A new application of conditional nonlinear optimal perturbation approach to boundary condition uncertainty, J. Geophys. Res., 120, 7979-7996

5.        Mu Mu, Wansuo Duan, Dake Chen, Weidong Yu, 2015: Target observations for improving initialization of high-impact ocean-atmospheric environmental events forecasting, National Science Review, 2, 226–236

6.        Stefano Pierini, Henk A. Dijkstra, Mu Mu, 2014: Intrinsic low-frequency variability and predictability of the Kuroshio Current and of its extension. Advances in Oceanography and Limnology, DOI: 10.1080/19475721.2014.962091

7.        Rong Feng, Mu Mu, Wansuo Duan, 2014: Study on the “winter persistence barrier” of Indian Ocean dipole events using observation data and CMIP5 model outputs. Theoretical and Applied Climatology, DOI: 10.1007/s00704-013-1083-x

8.        Mu Mu, Yanshan Yu, Hui Xu, Tingting Gong, 2014: Similarities between optimal precursors for ENSO events and optimally growing initial errors in El Niño predictions. Theoretical and Applied Climatology, 115, 461-469.

9.        Mu Mu, 2013: Methods, current status, and prospect of targeted observation. Science China (Earth Sciences), 56, 1997-2005.

10.    Wang, Q., M. Mu, and H. A. Dijkstra, 2013: Effects of nonlinear physical processes on optimal error growth in predictability experiments of the Kuroshio Large Meander. J. Geophys. Res., 118, 6425-6436.

11.    Wang, Q., M. Mu, and H. A. Dijkstra, 2013: The similarity between optimal precursor and optimally growing initial error in prediction of Kuroshio large meander and its application to targeted observation. J. Geophys. Res., 118(2), 869-884.

12.    Yu, Y., M. Mu, W. Duan, and T. Gong, 2012: Contribution of the location and spatial pattern of initial error to uncertainties in El Niño predictions. J. Geophys. Res., 117, doi:10.1029/2011JC007758

13.    Yu, Y., Mu M.,and W. Duan,2012: Does Model Parameter Error Cause a Significant “Spring Predictability Barrier” for El Niño Events in the Zebiak–Cane Model? J.Climate,25,1263-1277.

14.    Mu, Mu, Zhina Jiang, 2011: Similarities between Optimal Precursors that Trigger the Onset of Blocking Events and Optimally Growing Initial Errors in Onset Prediction. J. Atmos. Sci., 68, 2860-2877.

15.    Mu, M., W. Duan, Q. Wang, and R. Zhang, 2010: An extension of conditional nonlinear optimal perturbation approach and its applications. Nonlin. Processes Geophys., 17, 211-220.

16.    Mu, M., F. F. Zhou, and H. L. Wang, 2009: A Method for Identifying the Sensitive Areas in Targeted Observations for Tropical Cyclone Prediction: Conditional Nonlinear Optimal Perturbation. Mon. Wea. Rev., 137, 1623-1639.

17.    Mu, M., and Z. N. Jiang, 2008: A method to find perturbations that trigger blocking onset: Conditional nonlinear optimal perturbations. J. Atmos. Sci., 65, 3935-3946.

18.    Mu, M., and Z. N. Jiang, 2008: A new approach to the generation of initial perturbations for ensemble prediction: Conditional nonlinear optimal perturbation. Chinese Science Bulletin, 53, 2062-2068.

19.    Mu, M., H. Xu, and W. S. Duan2007: A kind of initial errors related to "spring predictability barrier" for El Nino events in Zebiak-Cane modelGeophysics Research Letters34 L03709, doi:10.1029/2006GL027412.

20.    Mu, M. and B. Wang, 2007: Nonlinear instability and sensitivity of a theoretical grassland ecosystem to finite-amplitude perturbations, Nonlin. Processes Geophys., 14, 409-423.

21.    Mu, M., and Z. Y. Zhang, 2006: Conditional nonlinear optimal perturbations of a two-dimensional quasigeostrophic modelJ. Atmos. Sci.63, 1587-1604.

22.    Mu, M. and Q. Zheng, 2005: Zigzag Oscillations in Variational Data Assimilation with Physical "On-Off" Processes, Month Weather Review, 133, 2711-2720.

23.    Mu, M., L. Sun and H. A. Dijkstra2004: The sensitivity and stability of thermohaline circulation of ocean to finite amplitude perturbations. Journal of Physical Oceanography342305-2315.

24.    Mu, M., and J. F. Wang2003: A method to adjoint variational data assimilation with physical "on-off" processesJ. Atmos. Sci.602010-2018.

25.    Mu, M., W. S. Duan, and B. Wang, 2003: Conditional nonlinear optimal perturbation and its applicationsNonlinear Processes in Geophysics, 10 493-501.