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穆穆（Mu Mu）
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穆穆

特聘教授/中国科学院院士/发展中国家科学院院士/

中国工业与应用数学学会会士/中国运筹学会会士/博士生导师

mumu@fudan.edu.cn

                             电话：021-31248899  传真：021-31248888




研究兴趣

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


教育背景

学士学位（1975.9 - 1978.8），应用数学，安徽大学

硕士学位（1978.9 - 1981.8），应用数学，安徽大学

博士学位（1982.3 - 1985.7），基础数学，复旦大学


研究经历

2016.3-至今,特聘教授,复旦大学

2010.12-2016.3,研究员,中国科学院海洋研究所

1993.1-2010.11,研究员,中国科学院大气物理研究所

1989.4-1992.12,副研究员,中国科学院大气物理研究所

1987.4-1989.3,博士后,中国科学院大气物理研究所

1985.8-1987.3,讲师,上海交通大学应用数学系


承担课题

2020.09.01-2023.08.31 基于风云卫星智能精准观测针对极端天气事件的长三角航空运行安全应对研究，20dz1200700，上海市2020年度“科技创新行动计划”社会发展科技攻关项目，上海市科委，主持

2018.01.01-2022.12.31 北极海-冰-气系统对欧亚大陆冬季极端天气事件可预报性的影响，41790475，国家自然科学基金重大项目，基金委，参与（张人禾）

2015.01.01-2019.12.31 黑潮及延伸体海域海气相互作用机制及其气候效应，41490644，国家自然科学基金重大项目，基金委，参与（张荣华）

2015.01.01-2020.12.31 西太平洋海洋环流动力过程，41421005，国家自然科学基金创新群体，基金委，参与（袁东亮）

2013.01.01-2017.12.31 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，41230420，国家自然科学基金重点项目，基金委，主持

2013.11.01-2017.12.31 NEC和STCC的变异对黑潮上游段及其可预报性的影响，XDA11010303，中科院先导性专项，中科院，参与（王凡）

2012.11.01-2016.08.31 EI Nino可预报性、模式及同化技术改进，国家重点基础项目研究发展计划（973计划），参与


学术兼职

现任：

国际气象学和大气科学协会（IAMAS）执委会委员（Member at Large）

IAMAS中国委员会主席

《Advances in Atmospheric Sciences》共同主编

《中国科学：地球科学》与《Science China: Earth Sciences》副主编

《气候与环境研究》副主编

曾任：

国家自然科学基金委员会地球科学部第八届专家咨询委员会委员

国际气象学和大气科学协会（IAMAS）动力气象委员会（ICDM）和行星大气及其演变委员会（ICPAE）委员

美国气象学会'每月天气评论'associate editor

英国皇家气象学会季刊（QJRMS）编委

国务院学位委员会大气科学评议组召集人


获奖情况

1994 国家杰出青年科学基金

2001 中国科学院自然科学一等奖（第一完成人）

2005 国家人事部“全国优秀博士后”称号

2006 中国科学院“宝洁优秀研究生导师”奖

2006 中国科学院研究生院“优秀教师”称号

2010 何梁何利基金科学与技术进步奖

2020-2022 爱思维尔中国高被引学者（大气科学）

2024 国家自然科学奖二等奖


发表论文（本人名称加粗，通讯作者加*号）

1. Ji, C., Mu, M., Fang, X., & Tao, L. (2023). Improving the Forecasting of El Niño Amplitude Based on an Ensemble Forecast Strategy for Westerly Wind Bursts. Journal of Climate, 36(24), 8675-8694. https://doi.org/10.1175/jcli-d-23-0233.1

2. Ke, J., Mu, M., & Fang, X. (2023). Influence of Physically Constrained Initial Perturbations on the Predictability of Mei-Yu Heavy Precipitation. Monthly Weather Review, 151(8), 2115-2138. https://doi.org/10.1175/mwr-d-22-0302.1

3. Gao, Y., Mu, M., & Dai, G. (2023). Targeted observations on Arctic sea ice concentration for improving extended-range prediction of Ural Blocking. Journal of Geophysical Research: Atmospheres, 128(22), e2023JD039630. https://doi.org/10.1029/2023JD039630 

4. Ren, Q. J., M. Mu, G. D. Sun, and Q. Wang. 2022: A new sensitivity analysis approach using conditional nonlinear optimal perturbations and its preliminary application. Adv. Atmos. Sci., https://doi.org/10.1007/s00376-022-1445-3.

5. Chen, H., Chen, S. X., & Mu, M. (2023). A statistical review on the optimal fingerprinting approach in climate change studies. Climate Dynamics. https://doi.org/10.1007/s00382-023-06975-5

6. Dai, G., Ma, X., Mu, M., Han, Z., Li, C., Jiang, Z., & Zhu, M. (2023). Optimal Arctic sea ice concentration perturbation in triggering Ural blocking formation. Atmospheric Research, 289. https://doi.org/10.1016/j.atmosres.2023.106775

7. Dai, G., Mu, M., Han, Z., Li, C., Jiang, Z., Zhu, M., & Ma, X. (2023). The Influence of Arctic Sea Ice Concentration Perturbations on Subseasonal Predictions of North Atlantic Oscillation Events. Advances in Atmospheric Sciences, 40(12), 2242-2261. https://doi.org/10.1007/s00376-023-2371-8

8. Duan, W., Yang, L., Mu, M., Wang, B., Shen, X., Meng, Z., & Ding, R. (2023). Recent Advances in China on the Predictability of Weather and Climate. Advances in Atmospheric Sciences, 40(8), 1521-1547. https://doi.org/10.1007/s00376-023-2334-0

9. Han, Z., Dai, G., Mu, M., Li, C., Li, S., Ma, X., & Zhu, M. (2023). Extent of the Impact of Arctic Atmospheric Uncertainty on Extended‐Range Forecasting of Cold Events in East Asia. Journal of Geophysical Research: Atmospheres, 128(9). https://doi.org/10.1029/2022jd037187

10. Li, C., Dai, G., Mu, M., Han, Z., Ma, X., Jiang, Z., . . . Zhu, M. (2023). Influence of Arctic Sea-ice Concentration on Extended-range Forecasting of Cold Events in East Asia. Advances in Atmospheric Sciences, 40(12), 2224-2241. https://doi.org/10.1007/s00376-023-3010-0

11. Mu, M., & Wang, L. (2023). Preface to the Special Issue on the National Report to the 28th IUGG General Assembly by CNC-IAMAS (2019–2022). Advances in Atmospheric Sciences, 40(8), 1337-1338. https://doi.org/10.1007/s00376-023-3003-z

12. Sun, G., & Mu, M. (2023). Applications of CNOP-P Method to Predictability Studies of Terrestrial Ecosystems. Atmosphere, 14(4). https://doi.org/10.3390/atmos14040617

13. Sun, G., Mu, M., Zhang, Q., Ren, Q., & You, Q. (2023). Application of the CNOP‐P Ensemble Prediction (CNOP‐PEP) Method in Evapotranspiration Forecasting Over the Tibetan Plateau to Model Parameter Uncertainties. Journal of Advances in Modeling Earth Systems, 15(3). https://doi.org/10.1029/2022ms003110

14. Sun, G., Mu, M., Zhang, Q., Ren, Q., & You, Q. (2023). Application of the Observation‐Oriented CNOP‐P Sensitivity Analysis Method in Evapotranspiration Simulation and Prediction Over the Tibetan Plateau. Water Resources Research, 59(8). https://doi.org/10.1029/2022wr033216

15. Tao, L., Mu, M., Wang, L., Fang, X., Duan, W., & Zhang, R. H. (2023). Impacts of Initial Zonal Current Errors on the Predictions of Two Types of El Niño Events. Journal of Geophysical Research: Oceans, 128(6). https://doi.org/10.1029/2023jc019833

16. Zhou L., Zhang K., Wang Q. Mu M. Optimally growing initial error for predicting the sudden shift in the Antarctic Circumpolar Current transport and its application to targeted observation. Ocean Dynamics (2022). https://doi.org/10.1007/s10236-022-01531-x

17. Mu Mu; Zhang Kun; Wang Qiang (2022): Recent progress in applications of the conditional nonlinear optimal perturbation approach to atmosphere-ocean sciences. Chinese Annals of Mathematics, Series B, 43(6), 1033-1048.

18. Zhang, H., Wang, Q., Mu, M., & Liu, X. (2022). Local energetics mechanism for the short-term shift between Kuroshio Extension bimodality. Journal of Geophysical Research: Oceans, 127, e2022JC018794. https://doi.org/10.1029/2022JC018794 

19. Ke, J., M. Mu, and X. Fang, 2022: Impact of Optimally Growing Initial Errors on the Mesoscale Predictability of Heavy Precipitation Events along the Mei-Yu Front in China. Mon. Wea. Rev., 150, 2399–2421.

20. Sun, G., Mu, M., Ren, Q., Zhang, Q., & You, Q. (2022). Determinants of physical processes and their contributions for uncertainties in simulated evapotranspiration over the Tibetan Plateau. Journal of Geophysical Research: Atmospheres, 127, e2021JD035756. https://doi.org/10.1029/2021JD035756

21. Sun, G., and M. Mu. 2022. Role of hydrological parameters in the uncertainty in modeled soil organic carbon using a coupled water-carbon cycle model. Ecological Complexity,50,100986,https://doi.org/10.1016/j.ecocom.2022.100986 

22. Li, J., Jiang, Z., Dong, Y., Zhang, L., Ying, T., Zhang, Z., and Mu Mu. (2022) The IAMAS-CNC Early Career Scientists Nobel Prize Online Interpretation Workshop. Adv. Atmos. Sci. https://doi.org/10.1007/s00376-021-1455-6

23. Ma, X., Mu, M., Dai, G., Han, Z., Li, C., & Jiang, Z. (2022). Influence of Arctic sea ice concentration on extended-range prediction of strong and long-lasting Ural blocking events in winter. Journal of Geophysical Research: Atmospheres, 127, e2021JD036282. https://doi.org/10.1029/2021JD036282 

24. Xu, Z. Z., J. Chen, M. Mu, G. K. Dai, and Y. N. MA, (2022). A nonlinear representation of model uncertainty in a convective-scale ensemble prediction system. Adv. Atmos. Sci., https://doi.org/10.1007/s00376-022-1341-x.

25. Xu, Z., Chen, J., Mu, M. et al. A stochastic and nonlinear representation of model uncertainty in a convective-scale ensemble prediction system. Quart. J. Royal Meteor. Soc.(2022). https://doi.org/10.1002/qj.4322

26. Mu, M., Luo, D., & Zheng, F. (2022). Preface to the Special Issue on Extreme Cold Events from East Asia to North America in Winter 2020/21. Advances in Atmospheric Sciences, 39(4), 543-545. https://doi.org/10.1007/s00376-021-1004-3

27. Liu, X., Wang, Q., & Mu, M. (2022). Identifying the sensitive areas in targeted observation for predicting the Kuroshio large meander path in a regional ocean model. Acta Oceanologica Sinica, 41(2), 3-14. https://doi.org/10.1007/s13131-021-1838-7

28. Ren, Q., Mu, M., Sun, G., & Wang, Q. (2022). A New Sensitivity Analysis Approach Using Conditional Nonlinear Optimal Perturbations and Its Preliminary Application. Advances in Atmospheric Sciences, 40(2), 285-304. https://doi.org/10.1007/s00376-022-1445-3

29. Dai, G., Mu, M., Li, C., Han, Z., & Wang, L. (2021). Evaluation of the Forecast Performance for Extreme Cold Events in East Asia with Subseasonal-to-Seasonal Datasets from ECMWF. Journal of Geophysical Research: Atmospheres, 126(1), e2020JD033860.

30. Dai, G., Mu, M, & Wang, L. (2021). The Influence of Sudden Arctic Sea-Ice Thinning on North Atlantic Oscillation Events. Atmosphere-Ocean, 59(1), 39-52.

31. Sun, G., and M. Mu. 2021. Impacts of two types of errors on the predictability of terrestrial carbon cycle. Ecosphere, 12(1): e03315.

32. Sun, G., and M. Mu. 2021. Uncertainty range of projected soil carbon responses to climate warming in China. Meteorological Applications,28(3),e1993.https://doi.org/10.1002/met.1993

33. Zhou, L., Wang, Q., Mu, M., & Zhang, K. (2021). Optimal precursors triggering sudden shifts in the Antarctic circumpolar current transport through Drake Passage. Journal of Geophysical Research: Oceans, 126, e2021JC017899.

34. 张坤, 穆穆& 王强. 数值模式在海洋观测设计中的重要作用:回顾与展望. Sci. Sin. Terrae 51, 653–665 (2021)

35. Zhang, Q., Ng, CP., Dai, K.Jun Xu, Jian Tang, Juanzhen Sun and Mu Mu: Lessons Learned from the Tragedy during the 100 km Ultramarathon Race in Baiyin, Gansu Province on 22 May 2021. Adv. Atmos. Sci.38,1803–1810 (2021). https://doi.org/10.1007/s00376-021-1246-0 

36. 张人禾,刘哲,穆穆,等.气候系统和气候变化研究获2021年诺贝尔物理学奖的启示[J].中国科学基金, 2021, 35(6):4.

37. Dai, G., & Mu, M. (2020). Arctic Influence on the Eastern Asian Cold Surge Forecast: A Case Study of January 2016. Journal of Geophysical Research: Atmospheres, 125(23), e2020JD033298.

38. Dai G-K, and Mu M 2020. Influence of the Arctic on the Predictability of Eurasian Winter Extreme Weather Events. Advances in Atmospheric Sciences, 37: 307-317.

39. Wei Y-T, Ren H-L, Mu M, and Fu J-X. 2020. Nonlinear optimal moisture perturbations as excitation of primary MJO events in a hybrid coupled climate model. Climate Dynamics, 54: 675-699.

40. Xie R-H, Mu M, and Fang X-H. 2020. New Indices for Better Understanding ENSO by Incorporating Convection Sensitivity to Sea Surface Temperature. Journal of Climate, 33: 7045-7061.

41. Yang Z-Y, Fang X-H, and Mu M. 2020. The Optimal Precursor of El Niño in the GFDL CM2p1 Model. Journal of Geophysical Research: Oceans, 125: e2019JC015797.

42. Wang Q, Mu M, and Pierini S. 2020: The fastest growing initial error in prediction of the Kuroshio Extension state transition processes and its growth. Climate Dynamics, 2020, 54(3): 1953-1971.

43. Geng Y, Wang Q, Mu M, and Zhang K. 2020: Predictability and error growth dynamics of the Kuroshio Extension state transition process in an eddy-resolving regional ocean model. Ocean Modelling, 2020, 153: 101659.

44. Sun, G. D., F. Peng, and M. Mu, 2020, Application of targeted observation in model physical parameters for simulation and forecast of heat flux with a land surface model. Meteorological Applications, 27(1): e1883

45. Fei Peng, Mu Mu and Guodong Sun, 2020, Evaluations of Uncertainty and Sensitivity in Soil Moisture Modeling on the Tibetan Plateau, Tellus A: Dynamic Meteorology and Oceanography, 72:1, 1-16

46. Zhang K, Mu M, Wang Q. 2020: Increasingly important role of numerical modeling in oceanic observation design strategy: A review. Science China Earth Sciences, 63

47. Jin, Z., You, Q., Mu, M., Sun, G., and Pepin, N. (2020). Fingerprints of anthropogenic influences on vegetation change over the Tibetan Plateau from an ecohydrological diagnosis. Geophysical Research Letters, 47, e2020GL087842.

48. Gao, Y., Mu, M. and Zhang, K. 2020. Impacts of parameter uncertainties on deep chlorophyll maximum simulation revealed by the CNOP-P approach. J.Ocean. Limnol. 38, 1382–1393.

49. Wang Q, Mu M, and Sun G-D. 2020. A useful approach to sensitivity and predictability studies in geophysical fluid dynamics: conditional non-linear optimal perturbation. National Science Review: 1-10

50. Sun, G., Mu, M., & You, Q. (2020). Identification of Key Physical Processes and Improvements for Simulating and Predicting Net Primary Production Over the Tibetan Plateau. Journal of Geophysical Research: Atmospheres, 125(23). https://doi.org/10.1029/2020jd033128

51. Sun G,Mu M .Hydrodynamics-driven uncertainty study in simulating terrestrial carbon cycle with a coupled water-carbon cycle model[J]. 2020.DOI:10.21203/rs.3.rs-108448/v1

52. Dai, G., Mu, M., Li, C., Han, Z., & Wang, L. (2021). Evaluation of the Forecast Performance for Extreme Cold Events in East Asia With Subseasonal‐to‐Seasonal Data Sets From ECMWF. Journal of Geophysical Research: Atmospheres, 126(1). https://doi.org/10.1029/2020jd033860

53. Wei Y-T, Mu M, Ren H-L, and Fu J-X. 2019. Conditional nonlinear optimal perturbations of moisture triggering primary MJO initiation. Geophysical Research Letters, 46: 3492-3501.

54. Dai G-K, Mu M, and Jiang Z-N. 2019. Evaluation of the forecast performance for North Atlantic Oscillation onset. Advances in Atmospheric Sciences, 36: 753–765.

55. Dai G-K, Mu M, and Jiang Z-N. 2019. Targeted observations for improving predictions of the NAO event onset. Journal of Meteorological Research, 33: 1-16

56. Zhang K, Mu M, Wang Q, Yin B, and Liu S. 2019. CNOP-based adaptive observation network designed for improving upstream Kuroshio transport prediction. Journal of Geophysical Research: Oceans, 124: 4350-4364

57. Du J, Berner J, Buizza R, Charron M, Houtetamer P, Hou D, Jankov I, Mu M, Wang X, Wei M, and Yuan H. 2019. Ensemble Methods for Meteorological Predictions. Handbook of Hydrometeorological Ensemble Forecasting,. Qea Duan, Ed., Springer,1-52

58. Zhou Q, Mu M, and Duan W-S. 2019. The Initial Condition Errors Occurring in the Indian Ocean Temperature That Cause “Spring Predictability Barrier” for El Niño in the Pacific Ocean. Journal of Geophysical Research: Oceans, 124: 1244-1261

59. Liang P, Mu M, Wang Q, and Yang L. 2019. Optimal Precursors Triggering the Kuroshio Intrusion Into the South China Sea Obtained by the Conditional Nonlinear Optimal Perturbation Approach. Journal of Geophysical Research: Oceans, 124: 3941-3962

60. Huang K, Ren H-L, Liu X, Ren P, Wei Y-T, and Mu M. 2019. Parameter Modulation of Madden-Julian Oscillation Behaviors in BCC_CSM1.2: The Key Role of Moisture-Shallow Convection Feedback Atmosphere, 10: 1-26

61. Wei Y-T, Ren H-L, Mu M, and Fu J-X. 2019. Nonlinear optimal moisture perturbations as excitation of primary MJO events in a hybrid coupled climate model. Climate Dynamics54:675-699

62. Duan, W.-S., and M. Mu (2018), Predictability of El Niño-Southern Oscillation events, OxfordResearch Encyclopedia of Climate Science, 1-41, doi:10.1093/acrefore/9780190228620.013.80

63. Fang, X.-H., and M. Mu(2018), Both air-sea components are crucial for El Niño forecast from boreal spring,Scientific Reports, 8(10501), 1-8,doi:10.1038/s41598-018-28964-z

64. Fang, X.-H., and M. Mu(2018), A three-region conceptual model for central Pacific El Niño including zonal advective feedback,Journal of Climate, 31(13),4965-4979, doi:10.1175/JCLI-D-17-0633.1.

65. Liu, X., M. Mu, and Q. Wang (2018), The Nonlinear Optimal Triggering Perturbation of the Kuroshio Large Meander and Its Evolution in a Regional Ocean. Journal of Physical Oceanography, 48(8), 1771-1786, doi:10.1175/JPO-D-17-0246.1.

66. Liu, X., Q. Wang, andM. 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(11), 1362-1371, doi:10.1007/s00376-018-8003-z.

67. Sun, G.-D., and M. Mu (2018), Assessing the characteristics of net primary production due to future climate change and CO2 under RCP4.5 in China, Ecological Complexity, 34, 58-68, doi:10.1016/j.ecocom.2018.04.001 

68. Tang, Y.-M., R.-H. Zhang, T. Liu, W.-S. Duan, D.-J. Yang, F. Zheng, H.-L. Ren, T. Lian, C. Gao, D.-K. Chen and M. Mu (2018), Progress in ENSO prediction and predictability study, National Science Review, nwy105, doi:10.1093/nsr/nwy105 

69. Wei, Y.-T., F. Liu,M. Mu, and H.-L. Ren (2018), Planetary scale selection of the Madden-Julian Oscillation in an air-sea coupled dynamic moisture model, Climate Dynamics, 50, 3441-3456, doi:10.1007/s00382-017-3816-5.

70. 张星,穆穆,王强,张坤(2018),条件非线性最优扰动方法在黑潮目标观测研究中的应用,海洋气象学报, 38(1), 1-9, doi:10.19513/j.cnki.issn2096-3599.2018.01.001.

71. 穆穆，段晚锁，唐佑民 (2017), 大气-海洋运动的可预报性:思考与展望, 中国科学：地球科学, doi: 10.1360/N072016-00420.

72. Mu, M., W.-S. Duan, and Y.-m. Tang (2017), The predictability of atmospheric and oceanic motions: Retrospect and prospects, Science China: Earth Sciences,60:2001-2012

73. Feng, R., W.-S. Duan, and M. Mu (2017), Estimating observing locations for advancing beyond the winter predictability barrier of Indian Ocean dipole event predictions, Climate Dynamics, 48(3-4), 1173-1185, doi:10.1007/s00382-016-3134-3.

74. Li, C., Y. Hu, F. Zhang, J. Chen, Z. Ma, X. Ye, X. Yang, L. Wang, X. Tang, R. Zhang, M. Mu, G. Wang, H. Kan, X. Wang, and A. Mellouki (2017), Multi-pollutant emissions from the burning of major agricultural residues in China and the related health-economic effects. Atmospheric Chemistry and Physics, 17(8), 4957-4988, doi:10.5194/acp-17-4957-2017.

75. Mu, M., R. Feng, and W.-S. Duan (2017), Relationship between optimal precursors for Indian Ocean Dipole events and optimally growing initial errors in its prediction, Journal of Geophysical Research: Oceans, 122(2), 1141-1153, doi:10.1002/2016JC012527.

76. 穆穆, 王强 (2017), 非线性最优化方法在大气-海洋科学研究中的若干应用, 中国科学：数学, 47(10), 1-16, doi:10.1360/N012016-00200.

77. Mu, M., and H.-L. Ren (2017), Enlightenments from researches and predictions of 2014-2016 super El Niño event, Science China: Earth Sciences, 60(9), 1569-1571, doi:10.1007/s11430-017-9094-5.

78. Peng, F., M. Mu, and G.-D. Sun (2017), Responses of soil moisture to climate change based on projections by the end of the 21st century under the high emission scenario in the 'Huang-Huai-Hai Plain' region of China, Journal of Hydro-environment Research, 14, 105-118, doi:10.1016/j.jher.2016.10.003.

79. Sun, G.-D., and M. Mu (2017), 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., 128(3-4), 587-601, doi:10.1007/s00704-015-1690-9.

80. Sun, G.-D., and M. Mu (2017b), Responses of terrestrial ecosystem to climate change: results from approach of conditional nonlinear optimal perturbation of parameters, in Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications, edited by S. K. Park and L. Xu, pp. 527-547, Springer International Publishing, Switzerland, doi:10.1007/978-3-319-43415-5_24.

81. Sun, G.-D., F. Peng, and M. Mu (2017), Variations in soil moisture over the 'Huang-Huai-Hai Plain' in China due to temperature change using the CNOP-P method and outputs from CMIP5, Science China: Earth Sciences, doi:10.1007/s11430-016-9061-3, Accepted.

82. Wang, Q., and M. Mu (2017), Application of conditional nonlinear optimal perturbation to target observations for high-impact ocean-atmospheric environmental events, in Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications, edited by S. K. Park and L. Xu, pp. 513-526, Springer International Publishing, Switzerland, doi:10.1007/978-3-319-43415-5_23.

83. Yu, H.-Z., H.-L. Wang, Z.-Y. Meng, M. Mu, X.-Y. Huang, and X. Zhang (2017), A WRF-based tool for forecast sensitivity to the initial perturbation: the conditional nonlinear optimal perturbations versus the first singular vector method and comparison to MM5, Journal of Atmospheric and Oceanic technology, 34(1), 187-206, doi:10.1175/JTECH-D-15-0183.1.

84. Zhang, K., M. Mu, and Q. Wang (2017), Identifying the sensitive area in adaptive observation for predicting the upstream Kuroshio transport variation in a 3-D ocean model, Science China: Earth Sciences, 60(5), 866-875, doi:10.1007/s11430-016-9020-8.

85. Zhang, X., M. Mu, Q. Wang, and S. Pierini (2017), Optimal precursors triggering the Kuroshio extension state transition obtained by the conditional nonlinear optimal perturbation approach, Advances in Atmospheric Sciences, 34(6), 685-699, doi:10.1007/s00376-017-6263-7.

86. Zhang, X., Q. Wang, and M. Mu (2017), The impact of global warming on Kuroshio Extension and its southern recirculation using CMIP5 experiments with a high-resolution climate model MIROC4h, Theor. Appl. Climatol., 127(3-4), 815-827, doi: 10.1007/s00704-015-1672-y.

87. Zhang, K., Q. Wang, M. Mu and P. Liang, 2016. Effects of optimal initial errors on predicting the seasonal reduction of the upstream Kuroshio transport. Deep-Sea Research I, 116: 220-235

88. Zu, Z., M. Mu, 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. J. Phys. Oceanogr., 46(7):2029-2047.

89. Zou, G., Q. Wang and M. Mu, 2016. Identifying sensitive areas of adaptive observations for prediction of the Kuroshio large meander using a shallow-water model. Chin. J. Ocean. Limnol., 34: 1122-1133

90. Dai, G., M. Mu and Z. Jiang, 2016. Relationships between Optimal Precursors Triggering NAO Onset and Optimally Growing Initial Errors during NAO Prediction. J. Atmos. Sci., 73(1): 293-317

91. Ding, Y., Mu, M., Zhang, J., Jiang, T., Zhang, T., Wang, C., ... & Zhang, S.(2016). Impacts of climate change on the environment, economy, and society of China. In Climate and Environmental Change in China: 1951–2012 (pp. 69-92). Springer, Berlin, Heidelberg

92. Zhou, Q., W. S. Duan, M. Mu, and R. Feng, 2015: Influence of positive and negative Indian Ocean dipoles on ENSO via the Indonesian Throughflow: Results from sensitivity experiments. Adv. Atmos. Sci., 32(6), 783–793

93. Wang Qiang, and Mu Mu, 2015: A new application of conditional nonlinear optimal perturbation approach to boundary condition uncertainty. Journal of Geophysical Research: Oceans. doi: 10.1002/2015JC011095

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

95. 穆穆，周菲凡，2015：基于CNOP方法的台风目标观测研究进展，气象科技进展，5, 6-17.

96. 罗德海, & 穆穆. (2015). 混沌理论之父——1983 年 Crafoord 奖获得者洛伦茨教授成就解读. 中国科学: 地球科学, (001), 1-7.

97. Guodong Sun , Mu Mu (2014): The analyses of the net primary production due to regional and seasonal temperature differences in eastern China using the LPJ model. Ecological Modelling，289,66-76.

98. Wang, Q., and M. Mu (2014): Responses of the ocean planktonic ecosystem to finite-amplitude perturbations,J. Geophys. Res. Oceans, 119, doi:10.1002/ 2014JC010339.

99. Feng, R., W. Duan , and M. Mu (2014): The ‘‘winter predictability barrier’’ for IOD events and its error growth dynamics: Results from a fully coupled GCM,J. Geophys. Res. Oceans, 119, doi:10.1002/2014JC010473

100. Mu Mu , Yanshan Yu , Hui Xu and Tingting Gong (2014) : Similarities between optimal precursors for ENSO events and optimally growing initial errors in El Niño predictions. Theor．Appl．Climatol．,115:461–469,doi: 10.1007/s00704-013-0909-x.

101. 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. Theor Appl. Climatol., 118(3),523-534.

102. Qin, X. H., and M. Mu. (2014): Can adaptive observations improve tropical cyclone intensity forecasts? Adv. Atmos.Sci., 31(2), 252–262, doi: 10.1007/s00376-013-3008-0.

103. Yu, L., M. Mu, and Y. S. Yu(2014): Role of parameter errors in the spring predictability barrier for ENSO events in the Zebiak–Cane model. Adv. Atmos. Sci., 31(3), 647–656, doi: 10.1007/s00376-013-3058-3.

104. Stefano Pierini, Henk A. Dijkstra and 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.

105. Mu Mu , Wang Qiang , Duan Wansuo , Jiang Zhina (2014): Application of Conditional Nonlinear Optimal Perturbation to Targeted Observation Studies of the Atmosphere and Ocean. Journal of Meteorological Research,28(5),923-933.

106. Mu, M., & Zhang, R. (2014). Addressing the issue of fog and haze: A promising perspective from meteorological science and technology. Science China. Earth Sciences, 57(1), 1.

107. 穆穆, & 张人禾. (2014). 应对雾霾天气: 气象科学与技术大有可为. 中国科学: 地球科学, 44(1), 1-2.

108. 穆穆, 王强, 段晚锁, & 姜智娜. (2014). 条件非线性最优扰动法在大气与海洋目标观测研究中的应用.气象学报, 72(5), 1001-1011.

109. Sun Guodong , Mu Mu.(2013):Using the Lind-Potsdam-Jena model to understand the different responses of three woody plants to land use in China. Advances in Atmospheric Sciences ,30(2), 515-524.

110. Sun Guodong, Mu Mu. (2013):Understanding variations and seasonal characteristics of net primary production under two types of climate change scenarios in China using the LPJ model. Climate Change,120,755-769.

111. Chunzai Wang, Chunxiang Li, Mu Mu, Wansuo Duan(2013):Seasonal modulations of different impacts of two types of ENSO events on tropical cyclone activity in the western North Pacific. Climate Dynamics,40,2877-2902.

112. Zhina Jiang, Mu Mu, Dehai Luo(2013):A study of the North Atlantic Oscillation using conditional nonlinear optimal perturbation. J. Atmos. Sci.,70,855-875.

113.Qiang Wang , Mu Mu, Henk A. Dijkstra(2013):The similarity between optimal prescursor and optimally growing initial error in prediction of Kuroshio large meander and its application to targeted observation. J. Geo. Res.: Ocean,118,869-884.

114. Boyu Chen, Mu Mu, Qin Xiaohao(2013):The impact of assimilating dropwindsonde data deployer at different sites on typhoon track forecasts. Mon. Wea. Rev.,141,2669-2682.

115. Xiaohao Qin , Wansuo Duan , Mu Mu(2013):Conditions under which CNOP sensitivity is valid for tropical cyclone adaptive observations. Q. J. R. Meteorol. Soc., 139:1544–1554. doi:10.1002/qj.2109.

116. Mu Mu, Yanshan Yu , Hui Xu, Tingting Gong(2013):Similarities between optimal prescursors for ENSO events and optimally growing initial errors in El Nino predictions. Theor. Appl. Climatol,115:461-469.

117. Zu Ziqing , Mu Mu, Henk A. Dijkstra(2013):Three-dimensional structure of optimal nonlinear excitation for decadal variability of the thermohaline circulation. Atmospheric and Oceanic Science Letters,6(6):410-416.

118. Mu Mu, 2013:Methods,current status ,and prospect of targeted observation. Science China :Earth Science,56(12):1997-2005.

119. Zu Ziqing , Mu Mu, Henk A. Dijkstra(2013):Optimal nonlinear excitation of decadal variability of the North Atlantic thermohaline circulation. Chinese Journal of Oceanology and Limnology ,31(6):1356-1362.

120. Xie Dongdong, Sun Guodong, Shao Aimei, Mu Mu. 2013:A study of simulation uncertainties caused by parameter uncertainties in a grassland ecosystem model. Climatic and Environmental Research (in Chinese), 18 (3): 375–386.

121. Mu Mu, Duan Wansuo.(2013): Applications of conditional nonlinear optimal perturbation to the studies of predictability problems. Chinese Journal of Atmospheric Sciences (in Chinese 条件非线性最优扰动在可预报性问题研究中的应用), 37 (2): 281–296.

122. 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., doi:10.1029/2011JC007758,

123. Yu, Y., Mu M.,and W. Duan, 2012: Does Model Parameter Error Cause a Significant ‘‘Spring Predictability Barrier’’ for El Nin˜o Events in the Zebiak–Cane Model?,J.Climate,25,1263-1277.

124. Feifan Zhou, Mu Mu. (2012) The impact of horizontal resolution on the CNOP and on its identified sensitive areas for tropical cyclone predictions. Advances in Atmospheric Sciences 29:1, 36-46.

125. Wang, Q., Mu Mu, and H. A. Dijkstra, 2012: Application of the conditional nonlinear optimal perturbation method to the predictability study of the Kuroshio large meander. Adv. Atmos. Sci., 29(1),118-134.

126. Boyu Chen, Mu Mu. (2012) The roles of spatial locations and patterns of initial errors in the uncertainties of tropical cyclone forecasts. Advances in Atmospheric Sciences 29:1, 63-78.

127. Qin, Xiaohao, Mu Mu, 2012: Influence of conditional nonlinear optimal perturbations sensitivity on typhoon track forecasts. Quarterly Journal of Royal Meteorological Society, 138,662,185-197.

128. Zhou, F., & Mu, M. (2012). The time and regime dependencies of sensitive areas for tropical cyclone prediction using the CNOP method. Advances in Atmospheric Sciences, 29(4), 705-716. https://doi.org/10.1007/s00376-012-1174-0

129. 穆穆,秦晓昊,周菲凡,等.加强目标观测,服务防灾减灾[J].成都信息工程学院学报, 2012, 27(1):7.DOI:10.3969/j.issn.1671-1742.2012.01.002.

130. 王凡,胡敦欣,穆穆,et al.热带太平洋海洋环流与暖池的结构特征、变异机理和气候效应[J].地球科学进展, 2012, 27(6):8.DOI:CNKI:SUN:DXJZ.0.2012-06-000.

131. 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.

132. Qin, Xiaohao, Mu Mu, A Study on the Reduction of Forecast Error Variance by Three Adaptive Observation Approaches for Tropical Cyclone Prediction. Mon. Wea. Rev., 2011，139：2218–2232.

133. HONGLI WANG, Mu Mu, XIANG-YU HUANG, 2011, Application of conditional non-linear optimal perturbations to tropical cyclone adaptive observation using the Weather Research Forecasting (WRF) model. Tellus, 63(5), 939-957.

134. Sun, G. D., and M. Mu, 2011: Response of a grassland ecosystem to climate change in a theoretical model. Adv. Atmos. Sci., 28(6), 1266-1278. SCI A5

135. Mu Mu，Boyu Chen, Feifan Zhou and Yu, Y., 2011: Methods and Uncertainties of Meteorological Forecasts. Meteorological Monthly, 37(1): 1-13.(in Chinese)

136. 穆穆,陈博宇,周菲凡,等.气象预报的方法与不确定性[J].气象, 2011, 37(1):13.DOI:10.7519/j.issn.1000-0526.2011.1.001.

137. Zhou, F., & Mu, M. (2011). The impact of verification area design on tropical cyclone targeted observations based on the CNOP method. Advances in Atmospheric Sciences, 28(5), 997-1010. https://doi.org/10.1007/s00376-011-0120-x

138. Sun, Guodong, Mu Mu, Y. Zhang, 2010: Algorithm studies on how to obtain a conditional nonlinear optimal perturbation (CNOP). Adv. Atmos. Sci., 27(6), 1311-1321.

139. 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.

140. Jiang Zhina and Mu Mu, A comparison study of the methods of conditional nonlinear optimal perturbations and singular vectors in ensemble prediction. Adv. Atmos. Sci., 2009, 26, 465-470.

141. Sun, Guodong and Mu Mu, Nonlinear feature of the abrupt transitions between multiple equilibria states of an ecosystem model. Adv. Atmos. Sci., 2009, 26, 293–304.

142. Mu Mu，Zhou Feifan, Wang Hongli， A method to identify the sensitive areas in targeting for tropical cyclone prediction: conditional nonlinear optimal perturbation. Mon. Wea. Rev., 2009, 137，1623-1639.

143. Wu Xiaogang, Mu Mu, Impact of Wind-driven Ocean Gyres on the Nonlinear Stability of Thermohaline Circulation in a Modified Box Model, J. Phys. Oceanogr., 2009，39，798-805.

144. Wansuo Duan, Xinchao Liu, Keyun Zhu, and Mu Mu, Exploring the initial error that causes a significant spring predictability barrier for El Nino events, J. Geophysical Research. 2009，114, C04022, doi:10.1029/2008JC004925.

145. Wansuo Duan，Mu Mu, Conditional nonlinear optimal perturbation: applications to stability, sensitivity, and predictability, Science in China(D), 2009, 52，884-906.

146. Yu Yanshan, Wansuo Duan, Hui Xu, Mu Mu, Dynamics of nonlinear error growth and season-dependent predictability of El Nino events in the Zebiak-Cane model, Quarterly Journal of Royal Meteorological Society, 2009, DOI: 10.1002/qj.526.

147. Zhina Jiang, Mu Mu，Donghai Wang, Experiments of ensemble forecast by conditional nonlinear optimal perturbation, Science in China（D），2009: 52（4），511-518.

148. Duan, W., Xue, F., & Mu, M. (2009). Investigating a nonlinear characteristic of El Niño events by conditional nonlinear optimal perturbation. Atmospheric Research, 94(1), 10-18. https://doi.org/10.1016/j.atmosres.2008.09.003

149. Mu Mu, Zhina Jiang, A Method to Determine Perturbations That Trigger Blocking Onset: Conditional Nonlinear Optimal Perturbations. J. Atmos. Sci., 2008, 65, 3935-3946.

150. Zhina Jiang, Mu Mu, Donghai Wang ,Conditional nonlinear optimal perturbation of a T21L3 quasi-geostrophic model, The Quarterly Journal of the Royal Meteorological Society, 2008，134（633）：1027

1038.

151. Wansuo Duan, Hui Xu, Mu Mu, Decisive role of nonlinear temperature advection in El Nino and La Nina amplitude asymmetry, J. Geophysical Research, 2008, VOL. 113, C01014, doi:10.1029/2006JC003974.

152. Wansuo Duan, Xue Feng, and Mu Mu, Investigating a nonlinear characteristic of ENSO events by conditional nonlinear optimal perturbation, Atmosphere Research, 2008, doi:10.1016/j.atmosres.2008.09.003.

153. Mu Mu, Jiang Zhina, A new approach to the generation of initial perturbations for ensemble prediction: Conditional nonlinear optimal perturbation, Chinese Science Bulletin, 2008，53（13）: 2062-2068.

154. Xiaogang Wu, Mu Mu, Impact of wind-driven ocean gyres on the decadal variability of THC----Analysis by box-models, Advance in Marine Science, 2008, 26（4），411-417 (in Chinese).

155. 李崇银,穆穆,周广庆,et al.ENSO机理及其预测研究[J].大气科学, 2008, 32(4):21.DOI:CNKI:SUN:DQXK.0.2008-04-005.

156. 吴晓刚,穆穆.风生涡旋对热盐环流年代际变率的影响——基于盒子模型的分析[J].海洋科学进展, 2008, 26(4):7.DOI:10.3969/j.issn.1671-6647.2008.04.001.

157. 王铁,穆穆.REM模式伴随系统的建立及其四维变分资料同化初步试验[J].气象学报, 2008, 66(6):13.DOI:10.3321/j.issn:0577-6619.2008.06.010.

158. 姜智娜,穆穆,王东海.基于条件非线性最优扰动方法的集合预报试验[J].中国科学:D辑, 2008, 038(011):1444-1451.DOI:CNKI:SUN:JDXK.0.2008-11-013.

159. Mu Mu , Xu Hui, Duan Wansuo, A kind of initial errors related to “spring predictability barrier“ for El Nino event in Zebiak-Cane model. Geophysical Research Letters, 2007, Vol. 34, L03709, doi:10.1029/2006GL027412.

160. Mu Mu, Duan Wansuo,Wang Bin, Season-dependent dynamics of nonlinear optimal error growth and ENSO predictability in a theoretical model. Journal of Geophysical Research, 2007,Vol. 112,D10113,doi:10.1029/2005JD006981.

161. Mu Mu , B.Wang, Nonlinear instability and sensitivity of a theoretical grassland ecosystem to finite-amplitude perturbations. Nonlinear Processes in Geophysics, Vol. 14, 409-423, 2007,

162. Mu Mu , Jiang Zhina, A new approach to the generation of initial perturbations for ensemble prediction: conditional nonlinear optimal perturbation, Chin. Sci. Bull., 2007,1457-1462.

163. Wang Tie, Mu Mu, The application of the adjoint modeling system and nonlinear optimization method in the study of predictability of the REM with observational data, Chinese J. Atmos. Sci., 2007,Vol 31, 987-998. (in Chinese)

164. Mu Mu, Wang Hongli, Zhou Feifan, A preliminary application of conditional nonlinear optimal perturbation to adaptive observation, Chinese J. Atmos. Sci., 2007, Vol. 31, 1102-1112. (in Chinese)

165. Mu Mu, Zhang Zhiyue, Conditional nonlinear optimal perturbations of a two-dimensional quasigeostrophic model, Journal of the Atmospheric Sciences, Vol.63, 2006, 1587-1604.

166. Duan Wansuo, Mu Mu , Investigating decadal variability of El Nino Southern Oscillation events by conditional nonlinear optimal perturbation, Journal of Geophysical Research, 2006, Vol.111, C07015, doi:10.1029/2005JC003458.

167. Duan Wansuo, Mu Mu, Advance and prospect of the studies of El Nino predictability by nonlinear optimization method, Chinese J. Atmos. Sci., Vol.30, 2006, 759-766. (in Chinese)

168. 王铁,穆穆.伴随系统及非线性优化方法在REM模式可预报性研究中的实际个例应用[J].大气科学, 2007, 31(5):12.DOI:10.3878/j.issn.1006-9895.2007.05.21.

169. 穆穆,姜智娜.集合预报初始扰动产生的一个新方法: 条件非线性最优扰动[J].科学通报, 2007, 52(12):6.DOI:10.3321/j.issn:0023-074X.2007.12.016.

170. Mu Mu , Duan Wansuo, Xu Hui, Wang Bo, Applications of conditional nonlinear optimal perturbation in weather and climate predictability and sensitivity, Advance in Atmospheric Sciences, Vol.23, 2006, 992-1002.

171. Zheng Qin and Mu Mu , The effects of the model errors generated by discretization of “on-off” processes on VDA, Nonlinear Process in Geophysics, Vol.13, 2006, 309–320.

172. 段晚锁,穆穆.用非线性最优化方法研究El Niño可预报性的进展与前瞻[J].大气科学, 2006, 30(5):8.DOI:10.3878/j.issn.1006-9895.2006.05.05.

173. Duan, W., & Mu, M. (2006). Investigating decadal variability of El Nino–Southern Oscillation asymmetry by conditional nonlinear optimal perturbation. Journal of Geophysical Research: Oceans, 111(C7). https://doi.org/10.1029/2005jc003458

174. Mu, M., and Z. Zhang, 2006: Conditional Nonlinear Optimal Perturbations of a Two-Dimensional Quasigeostrophic Model. J. Atmos. Sci.,63, 1587–1604,https://doi.org/10.1175/JAS3703.1.

175. Jiafeng Wang, Mu Mu and Qin Zheng, Initial condition and parameter estimation in physical “On-Off” processes by variational data assimilation, Tellus, Vol.57A, 2005, 736-741

176. Mu Mu and Qin Zheng, Zigzag oscillations in variational data assimilation with physics “On-off” processes, Mon. Wea. Rev., Vol.133, 2005, 2711-2720.

177. Duan Wansuo, Mu Mu , Applications of nonlinear optimization method to numerical studies of atmospheric and oceanic sciences, Applied Mathematics and Mechanics, Vol.26, 2005, 636-646.

178. Sun Liang, Mu Mu , Dejun Sun, and Xieyuan Yin, Passive mechanism of decadal variation of thermohaline circulation, Journal of Geophysical Research-Ocean, 2005, Vol. 110, C07025, doi:10.1029/2005JC002897.

179. Duan Wansuo, Mu Mu , Applications of nonlinear optimization methods to quantifying the predictability of a numerical model for El Nino-Southern Oscillation, Progress in Natural Science, 15, 2005, 915-921.

180. Duan Wansuo, Conditional nonlinear optimal perturbation and its applications in weather and climate predictability, Chinese Sci. Bull., Vol.50, 2005, 2401-2407

181. Wansuo, Mu Mu and Bin Wang, Conditional nonlinear optimal perturbations as the optimal precursors for El Nino-Southern Oscillation events, J. Geophysical Research, D23105, 2004,1029-1041.

182. Wansuo Duan and Jifan Chou, Recent advances in predictability studies in China (1999-2002), Adv. Atmos. Sci., Vol.21, 2004, 437-443.

183. Ping, Zheng Qin, Ru Rucong and Mu Mu, Application of the optimization arithmetic to aseertain the entrainment velocity in the top of the well-mixed layer, Chinese Journal of Atmospheric Sciences, Vol.28, 2004, 112-123.

184. 段晚锁,穆穆.非线性优化方法在大气和海洋科学数值研究中的若干应用[J].应用数学和力学, 2005, 26(5):10.DOI:10.3321/j.issn:1000-0887.2005.05.012.

185. Xu Hui, Mu Mu and Luo Dehai, Application of nonlinear optimization method to sensitivity analysis of numerical model, Progress in Natural Science, Vol.14, 2004, 546-549.

186. Mu Mu , Liang Sun and H. A. Dijkstra, Sensitivity and stability of thermolhaline circulation of ocean to finite amplitude perturbations, J. Physical Oceanography, Vol.34, 2004, 2305-2315.

187. Le Dimet, F. X., Shutyaev, V. P., Wang, J. and Mu, M., The problem of data assimilation for soil water movement, ESAIM: Control, Optimization and Calculus of Variations (COCV), Vol.10, 2004, 331-345.

188. Mu, M., Wansuo, D. & Jifan, C. Recent advances in predictability studies in China (1999–2002).Adv. Atmos. Sci.21, 437–443 (2004). https://doi.org/10.1007/BF02915570

189. Ping L , Qin Z , Rucong Y ,et al.Application of the Optimization Arithmetic to Ascertain the Entrainment Velocity in the Top of the Well-Mixed Layer[J].Chinese Journal of Atmospheric Sciences, 2004.DOI:10.1117/12.528072.

190. Duan, W. , Mu, M. , & Wang, B. . (2004). Conditional nonlinear optimal perturbations as the optimal precursors for el nino–southern oscillation events. Journal of Geophysical Research: Atmospheres, 109.

191. Mu Mu and Wang Jiafeng, A method to adjoint variational data assimilation with physical "on-off" processes, J. Atmos. Sci., Vol.60, 2003, 2010-2018.

192. Mu Mu and Duan Wansuo, A new approach to study ENSO predictability: Conditional nonlinear optimal perturbation, Chinese Science Bulletin, Vol.48, 2003, 1045-1047.

193. Mu Mu , Wansuo Duan and Bin Wang, Conditional nonlinear optimal perturbation and its applications, Nonlinear Processes in Geophysics, Vol.10, 2003, 493-501.

194. Liu Yongming, Mu Mu and Qiu Lincun, Nonlinear stability of zonally symmetric continuously stratified quasigeostrophic flows, Progress in Natural Science, Vol.13, 2003, 378-382 (in Chinese).

195. Sun Liang and Mu Mu, Advances in the research of thermohaline circulation and its decadal variability, Acta Oceanologica Sinica, Vol.25, 2003, 111-118 (in Chinese).

196. Mu Mu , Ji Zhongzhen, Wang Bin and Li Yang, Achievments in geophysical fluid dynamics, Chinese Journal of Atmospheric Sciences, Vol.27, 2003, 689-711 (in Chinese).

197. 孙亮,穆穆.温盐环流稳定性以及年代际变率的研究进展[J].海洋学报, 2003, 25(4):8.DOI:10.3321/j.issn:0253-4193.2003.04.014.

198. 刘永明,穆穆,邱令存.纬向对称的连续层结准地转流的非线性稳定性[J].自然科学进展, 2003(4):378-382.DOI:10.3321/j.issn:1002-008X.2003.04.008.

199. 徐辉,穆穆,罗德海.非线性优化方法在数值模式敏感性分析中的应用[J].自然科学进展, 2003, 13(11):4.DOI:10.1007/BF02873153.

200. 穆穆,段晚锁.ENSO可预报性研究的一个新方法:条件非线性最优扰动[J].科学通报, 2003, 48(7):3.DOI:CNKI:SUN:KXTB.0.2003-07-023.

201. Wang Jiafeng, Mu Mu and Zheng Qin, Adjoint approach to VAD of "on-off" processes based on nonlinear perturbation equation, Progress in Natural Science, Vol.12, 2002, 869-873. (SCI) A8

202. Mu Mu , Wansuo Duan and Jiafeng Wang, Nonlinear optimization problems in atmospheric and oceanic sciences, East-west Journal of Mathematics, Thailand, Special Volume, 2002, 155-164.

203. Mu Mu , Jianping Li, Jifan Chou, Wansuo Duan and Jiacheng Wang, Theoretical research on the predictability of climate system, Climate and Environmental Research, Vol.7, 2002, 227-235 (in Chinese).

204. Mu Mu , Wansuo Duan and Jiacheng Wang, The Predictability problems in numerical weather and climate prediction, Adv. Atmos. Sci., Vol.19, 2002, 191-204.

205. Mu Mu and Yonghui Wu, Armold Nonlinear stability theorems and their application to the atmosphere and oceans, Surveys in Geophysics, Vol.22, 2002, 383-426.

206. Mu Mu , Guo Huan , Wang Jiafeng and Li Yong, Relationship between the magnitude of singular value and nonlinear stability，Progress in Natural Science, Vol.11, 2001, 476-480.

207. Mu Mu and Wang Jiacheng, Nonlinear fastest growing perturbation and the first kind of predictability, Science in China (D), Vol.44, 2001, 1128-1139.

208. Liu Yongming and Mu Mu, Nonlinear stability of generalized Eady’s Model, J. Atmos. Sci. Vol.58, 2001, 821-827.

209. Mu Mu and Guo Huan, Effect of four-dimensional variational data assimilation in case of nonlinear instability, Progress in Natural Science, Vol.11, 2001, 825-832.

210. Wu Yonghui, Mu Mu , Zeng Qingcun and Li Yang, Weak solutions to a model of climate dynamics, Nonlinear Analysis: Real World Applications, Vol.2, 2001, 507-521.

211. Mu Mu , Guo Huan, Wang Jiafeng and Li Yong, The impact of nonlinear stability and instability on the validity of the tangent linear model, Adv. Atmos. Sci., Vol.17, 2000, 375-390.

212. Mu Mu , Nonlinear singular vectors and nonlinear singular values, Science in China (D), Vol.43, 2000, 375-385.

213. Wang Bizheng, Zeng Qingcun and Mu Mu, The four-dimensional problem of vapour by means of the adjoint equation, Part I: Theory, Climate and Environment Research, Vol.5, 2000, 273-278 (in Chinese).

214. Li Yang, Mu Mu and Wu Yonghui, A study on the nonlinear stability of fronts in the Ocean on a sloping continental shelf, Adv. Atmos. Sci., Vol.17, 2000, 275-284 .

215. Mu Mu, Wu Yonghui, Tang Mozhi and Liu Haiyan, Nonlinear stability analysis of the zonal flows at middle and high latitudes, Adv. Atmos. Sci., Vol.16, 1999, 569-580.

216. V. A. Vladimirov, Mu Mu , Yonghui Wu and K. I. Ilin, On nonlinear stability of baroclinic fronts, Geophys. Astrophys. Fluid Dynamics, Vol.91, 1999, 65-84.

217. Wu Y. H. and Mu Mu, Maximal energy isolated vortices in a uniform shear flow, Nonlinear Analysis, Vol.38, 1999, 23-135.

218. Wu Y. H. and Mu Mu , Nonlinear instability of dipole vortices and the atmospheric blocking, Progress in Natural Science, Vol.9, 1999, 234-237.

219. Mu Mu and Wu Yonghui, Symmetric stability problems in the atmospheric dynamics, Nonlinear Evolution Equations and Their Applications, World Scientific, Singapore, 1999, 163-176.

220. Mu Mu, V. Vladimirov and Wu Y. H., Energy-Casimir and energy-Lagrange methods in the study of nonlinear symmetric stability problems, J. Atmos. Sci., Vol.56, 1999, 400-411.

221. Li Y , Mu M , Moon S E ,et al.Baroclinic Instability in the Three-Layer Generalized Phillips' Model :Part Ⅱ: Nonlinear Stability Theory[J]. Korean Journal of the Atmospheric Sciences, 1999, 2.

222. Mu Mu, A criterion of symmetric stability of planetary atmospheres, East Asian Monsoon and Torrential Rain in China, Meteorological Press, 1999, 476-482 (in Chinese).

223. Mu Mu and Xiang Jie, On the evolution of finite-amplitude disturbances to the barotropic and baroclinic quasigeostrophic flows, Adv. Atmos. Sci., Vol.5, 1998, 113-123.

224. Mu Mu, Energy-Casimir method in the study of nonlinear stability of the atmospheric motions, Advances in Mechanics, Vol.28, 1998, 235-249 (in Chinese).

225. Mu Mu, Optimality of a nonlinear stability criterion of two-layer Phillips model, Chinese Science Bulletin, Vol.43, 1998, 656-659.

226. Xiang Jie and Mu Mu, The lower bound on the evolution of disturbances to the nonlinear unstable basic flow in the Phillips model, Proceedings of the Third International Conferences on Nonlinear Mechanics, Shanghai University Press, 1998, 548-553.

227. Li Y., Mu Mu, S. E. Moon and K. T. Sohn, On the linear and nonlinear stability of generalized Eady model, Part I: Linear instability theorem. Kor. J. Atmos.Sci., 1998, Vol.1, 113-118.

228. Li Y., Mu Mu, S. E. Moon and K. T. Sohn, On the linear and nonlinear stability of generalized Eady model, Part II: Nonlinear stability theorem. Kor. J. Atmos. Sci., 1998, Vol.1, 119-125.

229. Yonghui W , Mu M .A remark on the nonlinearly symmetric stability criteria[J].科学通报：英文版, 1998, 43(12):4.DOI:10.1007/BF02884648.

230. Wu Y. H. and Mu Mu, A remark on the nonlinear symmetric stability criteria, Chinese Science Bulletin, Vol.43, 1998, 1050-1052.

231. Xiang Jie and Mu Mu, Saturation of nonlinear instability to parallel flow, Progress in Natural Science, Vol.7, 1997, 239-243.

232. 项杰,穆穆.平行切变流的非线性不稳定的饱和问题[J].自然科学进展：国家重点实验室通讯, 1997, 7(4):5.DOI:10.1007/s00376-997-0061-6.

233. Li Yang and Mu Mu, Baroclinic instability in the generalized Phillips model. Part I: two-layer model, Adv. Atmos. Sci. Vol.13, 1996, 33-42.

234. Li Y. and Mu Mu, On the nonlinear stability of Three-dimensional quasigeostrophic motions in spherical geometry, Adv. Atmos. Sci., Vol.13, 1996, 203-216.

235. Mu Mu , T. G. Shepherd and K. Swanson, On nonlinear symmetric stability and the nonlinear saturation of symmetric instability, J. Atmos. Sci., Vol.53, 1996, 2918-2923.

236. Liu Y. M. and Mu Mu and T. G. Shepherd, Nonlinear stability of continuously stratified quasigeostrophic flow, J. Fluid Mech., Vol.325, 1996, 419-439.

237. Liu, Y. M. and Mu Mu, Nonlinear stability theorem for Eady's model of quasigeostropic baroclinic flow, J. Atmos. Sci., Vol.53, 1996, 1459-1463.

238. Mu Mu, Some advances in the study of nonlinear instability of the atmospheric motions, Chinese Journal of Atmospheric Sciences, Vol.19, 1995, 318-334.

239. Mu Mu, On the nonlinear symmetric stability in the atmosphere, Proceedings of Sixth Asian Congress of Fluid Mechanics, Editors: Y. T. Chew and C. P. Tso, Singapore, Vol.1, 1995, 232-235.

240. Mu Mu , Zeng Q. C., Shepherd, T. G. and Liu Y. M., Nonlinear stability of multilayer quasi-geostrophic flow, J. Fluid Mech., Vol.264, 1994, 165-184.

241. Liu Y. M. and Mu Mu, Arnol'd's second nonlinear stability theorem for general multilayer quasi-geostrophic model, Adv. Atmos. Sci., Vol.11, 1994, 36-42.

242. Mu Mu and Shepherd T. G., On Arnold's second nonlinear stability theorem for two-dimensional quasigeostrophic flow, Geophys. Astrophys. Fluid Dynamics, Vol.75, 1994, 21-37.

243. Mu Mu and Shepherd T. G., Nonlinear stability of Eady's model, J. Atmos. Sci., Vol. 51, 1994, 3427-3436.

244. Mu Mu and T. G. Shepherd, Nonlinear stability criteria for quasigeostrophic motion, Climate, Environment and Geophysical Fluid Dynamics, Editors: Ye Duzheng, Zeng Qingcun, Wu guoxiong and Zhang Zuojun, China Meteorological Press, 1993, 463-474.

2445 Mu Mu and Simon J., A remark on nonlinear stability of three-dimensional quasigeostrophic motions, Chinese Science Bulletin, Vol.38, 1993, 1978-1984.

246. Liu Y. M. and Mu Mu, A problem related to nonlinear stability criteria for multi-layer quasigeostrophic flow, Adv. Atmos. Sci., Vol.9, 1992, 337-345.

247. Mu Mu, Nonlinear stability of two-dimensional quasigeostrphic motions, Geophys. and Astrophys. Fluid Dynamics, Vol.65, 1992, 57-76.

248. Mu Mu and Wang Xiyong, Nonlinear stability criteria for motions of three-dimensional quasi-geostrophic flow on a beta-plane, Nonlinearity, Vol.5, 1992, 353-371.

249. Mu Mu and Zeng Qingcun, New development on existence and uniqueness of solutions to some models in atmospheric dynamics, Adv. Atmos. Sci., Vol.8, 1991, 383-398.

250. Mu Mu, Nonlinear stability criteria for motions of multilayer quasi-geostrophic flow, Science in China, Ser. B, Vol.34, 1991, 1516-1528.

251. Mu Mu and Zeng Qingcun, Criteria for the nonlinear stability of three-dimensional quasigeostrophic motions, Adv. Atmos. Sci., Vol.8, 1991, 1-10.

221. Mu Mu, Global smooth solutions of two-dimensional Euler equations, Chinese Sciences Bulletin, Vol.35, 1990, 1895-1900

253. Mu Mu and Zeng Qingcun, Stability of quasigeostrophic motions in atmosphere, Proceedings of Fourth Asian Congress of Fluid Mech., Editors: N. W. M. Ko and S. C. Kot, Vol.1.E16-E18, 1989, 21-25, Hong Kong.

254. Mu Mu, Global classical solutions to initial-boundary value problems for the potential vorticity equation, Journal of computational and applied mathematics, Vol.28, 1989, 327-338.

255. Mu Mu, On the boundary value problem for a degenerate elliptic equation, Comm. Appl. Math. and Comput., Vol.3, 1989, 26-30 (in Chinese).

256. Mu Mu, A class of the boundary value problem for elliptic-parabolic composite type equations, Chin. Ann. of Math., Vol.10A, 1989, 351-358 (in Chinese)

257. Mu Mu, Classical solution to 3-dimensional balanced model in numerical weather prediction, Kexue Tongbao, Vol.33, 1988, 1628-1631.

258. Mu Mu, Necessary and sufficient conditions for existence of global classical solutions of two-dimensional Euler equations in time-dependent domain, Kexue Tongbao, Vol.33, 1988, 1295-1299.

259. Mu Mu, Results about propagations of singularities for certain composite type operators, Journal of Fudan Univ. (Natural Sciences), Vol.27, 1988, 127-130 (in Chinese).

260. Mu Mu and Zeng Qingcun, On wellposedness of an initial boundary value problem for a three-dimensional balanced model, Chinese Journal of Atmospheric Sciences, Vol.12, 1988, 189-199.

261. Mu Mu, Global classical solutions of initial boundary value problems for generalized vorticity equations, Scientia Sinica (Series A), Vol.30, 1987, 359-371.

262. Mu Mu, Global classical solutions of the Cauchy problems for nonlinear vorticity equations and its application, Chin. Ann. of Math., Vol.8B(2), 1987, 199-207.

263. Mu Mu, Existence and uniqueness of global strong solutions of two models in atmospheric dynamics, Applied Math. and Mech., Vol.7, 1986, 965-970.

264. Mu Mu, Existence and uniqueness of classical solution to an initial boundary value problem in baroclinic quasigeostrophic-quasinondivergent model, Scientia Atmospherica Sinica, Vol.10, 1986, 113-120 (in Chinese).

265. Mu Mu, Global classical solutions of initial-boundary value problems for nonlinear vorticity equation and its applications, Acta Mathematica Scientia, Vol.6, 1986, 201-218 (in Chinese).

266. Mu Mu, The identity of weak and strong solutions to first order linear partial differential equations with abstract homogeneous boundary conditions, J. of Fudan Univ. (Natural Sciences), Vol.25, 1986, 25-33 (in Chinese).

267. Mu Mu, The identity of weak and strong solutions of linear partial differential equation of higher order with abstract homogeneous boundary conditions. Acta Scientiarum Naturalium Universitatis Anhuiensis, 1984, 9-16 (in Chinese).


邀请报告

[1]Mu Mu, How to explore the extreme impact of climate change on terrestrial ecosystem? WMO/IOC/ICSU Joint Scientific Committee for the World Climate Research Programme (WCRP), Nanjing University of Information Science and Technology, April 16-20, 2018, Nanjing, China.

[2]Mu Mu, Conditional Nonlinear Optimal Perturbation and its Applications to the Studies of the Atmosphere and Oceans, Understanding Nature from Computation, December 16, 2018, Shanghai, China.

[3]Mu Mu, Similarities Between optimal Precursors and Optimally Growing Initial Errors and targeted Observations in Weather and Climate Predictions, IAPSO-IAMAS-IAGA JOINT ASSEMBLY 2017, August 27-September 1, 2017, Cape Town, South Africa.

[4]Mu Mu, A nonlinear optimization approach to uncertainties of simulation and prediction of terrestrial ecosystem under global changes, 5thChina-Thailand Joint Conference on Climate Change, November 27-29, 2017,Chiang Mai, Thailand.

[5]Mu Mu, Some Progresses in the studies of Target observations, 7thInternational Conference on Atmosphere, Ocean and Climate Change, Chinese-American Oceanic and Atmospheric Association (COAA), July 27-30, 2016, Beijing, China.

[6]Mu Mu, Winter Predictability Barrier of Indian Ocean Dipole Event Predictions: The Role of Initial Errors and Targeted Observations, Asia Oceania Geosciences Society (AOGS), August 1-5, 2016, Beijing, China.

[7]Mu Mu, Applications of Nonlinear Optimization Approaches to ENSO Predictability Studies, Asia Oceania Geosciences Society (AOGS), August 1-5, 2016, Beijing, China.

[8]Mu Mu, Targeted observation studies of oceans, the Institute of Oceanology of the Chinese Academy of Sciences (IOCAS) and Research Center for Oceanography, Indonesian Institute of Sciences (RCO-LIPI), December 5-6, 2016, Bali, Indonesia.

[9]Mu Mu, Applications of Climate System Models, Target Observations for High-impact Ocean-Atmospheric Environmental Events, Second International Symposium on Climate and Earth System Modeling, Earth System Modeling Center, Nanjing University of Information Science and Technology, October 15-16, 2015, Nanjing, China.

[10]Mu Mu, Optimal precursor and optimally growing initial error in the predictability studies of Kuroshio large meander and their nonlinear evolution mechanism, EGU General Assembly 2014, April 27-May 2, 2014, Vienna, Austria.

[11]Mu Mu, Application of conditional nonlinear optimal perturbation to the predictability studies of Kuroshio large meander, AOGS 11th Annual Meeting, Jul.28-Aug.1, 2014, Sapporo, Japan.

[12]Mu Mu, Similarities between precursors for El Niño events and initial errors in the predictions and implications in targeted observation, Davos Atmosphere and Cryosphere Assembly, IAMAS, IACS and IUGG, August 8-12, 2013, Davos, Switzerland.

[13]Mu Mu, Dynamics of nonlinear optimal error growth in the studies of predictability, the RIMS International Conference on Theoretical Aspects of Variability and Predictability in Weather and Climate Systems, Kyoto University, October 22-25, 2013, Kyoto, Japan.

[14]Mu Mu, Similarities between optimal precursors and optimally growing initial errors in onset prediction—ENSO, Blocking and Kuroshio Current, EGU 2012 General Assembly, EGU, April 22-27, 2012, Vienna, Austria.

[15]Mu Mu, Applications of Conditional nonlinear optimal perturbations to the studies of ENSO and THC, NTU International Science Conference on Climate Change: Multidecadal and Beyond, Taiwan University, September 17-21, 2012, Taiwan University, Taipei.

[16]Mu Mu, A Similarity problem between signals and noises in thepredictability studies of ENSO、blocking and kuroshio current, 7thInternational symposium on atmospheric physics, climate and environment, Institute of atmospheric physics, Russian academy of sciences, July 19-21, 2012, Moscow, Russia.

[17]Mu Mu, Similarities between precursors of weather and climate events and optimally growing initial errors in their onset predictions, IUGG 2011, June 28-July 7, 2011, Melbourne, Australia.

[18]Mu Mu, Duan Wansuo, and Yu Yanshan, The error growth dynamics and spring predictability barrier of El Nino prediction, International Symposium on Boundary Current Dynamics: Its Connection with Open-ocean and Coastal Processes and Responses to Global Climate Change, Ocean University of China, May 31-June 2, 2010, Qingdao, China.

[19]Mu Mu, and Qin Xiaohao,A comparison study on adaptive observation approaches, Singular vector, conditional nonlinear optimal perturbation and ensemble transform Kalman filter, The Third International Workshop on Prevention and Mitigation of Meteorological Disasters in Southeast Asia, Kyoto University、University Consortium Oita and Japan Meteorological Agency March 1-4, 2010,Ritsumeikan Asia Pacific University, Beppu, Japan.

[20]Mu Mu, Conditional nonlinear optimal perturbation and its applications to predictability, stability and sensitivity studies, EGU General Assembly 2010, 2-7 May, 2010,Vienna, Austria.

[21]Mu Mu, Duan Wansuo, and Yu Yanshan, Dynamics of Nonlinear Error Growth and “Spring Predictability Barrier”for El Nino Predictions, 9th CTWF International Workshop Climate and Environmental Change: Challenges for Developing Countries, CAS-TWAS-WMO, November 17-19, 2010, Beijing, China.

[22]Mu Mu, Approaches to Adaptive Observation for Improving High Impact Weather Prediction: CNOP and SV, The Second International Workshop on Prevention and Mitigation of Meteorological Disasters in Southeast Asia, March 2-5, 2009, Bandung, Indonesia.

[23]Mu Mu, Progresses in the study of“spring predictability barrier”for El Nino events, 2009 LASG International Summer Symposium, August 19-21, 2009, Yinchuan, China.

[24]Mu Mu, Zhou Feifan, and Wang Hongli, An Investigation on the Applicability of Conditional Nonlinear Optimal Perturbations to Targeting Observations, Asia Oceanic Geosciences Society (AOGS), June 16-20, 2008, Busan, Korea.

[25]Mu Mu, Zhou Feifan, Wang Hongli, and Wu Xiaogang, Some new progresses in the applications of conditional nonlinear optimal perturbations, JSPS 5th university allied workshop on climate and environment studies for global sustainability, June 30-July 4, 2008, Tokyo, Japan.

[26]Mu Mu,Applications of Conditional nonlinear optimal perturbations to adaptive observations, EGU General Assembly 2008, April 13-18, 2008,Vienna, Austria.

[27]Mu Mu, Duan Wansuo, and Xu Hui, Study of“spring predictability barrier”for ENSO in Zebiak-Cane model, IUGG2007, July 2-13, 2007, Perugia, Italy.

[28]Mu Mu, Conditional nonlinear optimal perturbation and its applications, IUGG2007, July 2-13, 2007, Perugia, Italy.

[29]Mu Mu, and Duan Wansuo, Conditional nonlinear optimal perturbation, a new approach to the stability and sensitivity studies in geophysical fluid dynamics, 16th Australasian Fluid Mechanics Conference,University of Queensland, December 2-7, 2007, Brisbane, Australia.

[30]Mu Mu, and Wang Bo, The transition between grassland and desert in a theoretical grassland ecosystem, International Geographical Union (IGU), July 3-7, 2006, Brisbane, Australia.

[31]Mu Mu, and Sun Liang, Passive mechanism of decadal variation of thermohaline circulation, EGU General Assembly, April 24-29, 2005, Vienna, Austria.

[32]Mu Mu, and Zheng Qin, Removing zigzag oscillations in VDA with physical“on-off”processes, The fourth WMO International symposium on assimilation of observations in meteorology and oceanography, April 18-22, 2005, Prague, Czech.

[33]Mu Mu, Duan Wansuo, and Wang Bin, A possible mechanism of “spring  predictability barrier” for El Nino-Southern Oscillation events, EGU General  Assembly, April 24-29, 2005, Vienna, Austria.

[34]Mu Mu, and Duan Wansuo, A possible mechanism of“spring predictability barrier”for ENSO events, 1stAlexander Von Humboldt International Conference on The El Niño Phenomenon and its global impact, Centro International para la Investigación del Fenómeno de El Niño (CIIFEN) and EGU, May 16-20, 2005, Guayaquil, Ecuador.

[35]Mu Mu, Sun Liang, and D. A. Henk, Applications of conditional nonlinear optimal perturbation to the study of ocean’s thermohaline circulation, 1stEGU General Assembly, April 25-30, 2004, Nice, France.

[36]Mu Mu, Zheng Qin and Wang Jiafeng, Approaches to adjoint variational data assimilation with physical“on-off”processes, 1st  EGU General Assembly, April 25-30, 2004, Nice, France.

[37]Mu Mu, and Duan Wansuo, Applications of conditional nonlinear optimal perturbation to the study of climte predictability, The Fourth METRI-IAP Joint Research Workshop, October 9-11, 2004, Jeju, Korea.

[38]Mu Mu,and Duan Wansuo, A possible mechanism of the spring predictability barrier on ENSO events, International symposium on tropical weather and climate, LASG, November 7-11, 2004, Guangzhou, China.

[39]Mu Mu, Duan Wansuo, and Sun Liang, Applications of conditional nonlinear optimal perturbations to predictability of ENSO and sensitivity analysis of ocean’s THC, CAS-TWAS-WMO Forum, International Symposium on extreme weather and climate events, their dynamics and predictions, CAS-TWAS-WMO, October 12-16, 2004, Beijing, China.

[40]Mu Mu, Zheng Qin, and Wang Jiafeng, A method for adjoint variational data assimulation, 1stannual meeting of AOGS, July 5-9, 2004, Sigapore.

[41]Mu Mu, and Wang Jiafeng, A new adjoint method for variational data assimilation with physical“on-off”processes, European Geophysical Society 27thGeneral Assembly, EGS, April 21-26, 2002, Nice, France.

[42]Mu Mu, Duan Wansuo, and Wang Jiacheng, Predictability problems in numerical weather and climate prediction, European Geophysical Society 27thGeneral Assembly, EGS, April 21-26, 2002, Nice, France.

[43]Mu Mu,and Wang Jiachen, The first kind of predictability and nonlinear fastest growing perturbation, European Geophysical Society 26thGeneral Assembly, EGS,March 25-30, 2001, Nice, France.

[44]Mu Mu,and Wang Jiacheng, Nonlinear optimal perturbations, predictability and sensitivity analysis, International Conference on Climate and Environment Variability and Predictability (CEVP), August 7-11, 2000, Shanghai, China.

[45]Mu Mu, Arnol’d’s stability theorems, the Eliassen-Palm flux theorem, and applications to atmosphere dynamics, EGS XXV General Assembly, April 25-292000, Nice, France.

[46]Mu Mu, Nonlinear stability and instabililty of zonal wind in the atmosphere, 23th General assembly of European Geophysics  Society, April 20-24, 1998, Nice, France.

[47]Mu Mu, Numerical investigation of the nonlinear stability and instability of quasigeostrophic motions, 23th General assembly of European Geophysics Society, April 20-24, 1998, Nice, France.

[48]Mu Mu, Nonlinear symmetric instability and its saturations in the atmosphere, The tenth conference on atmospheric and oceanic waves and stability, June 5-9, 1995, Big Sky, Montana, USA.

[49]Mu Mu, Some new results on nonlinear instability in geophysical fluid dynamics, International conference on nonlinear evolution equations and infinite-dimensional dynamical system, 1994, Beijing, China.

[50]Mu Mu, Nonlinear stability criteria for the motion of quasigeostrophic flow, 19th General assembly of European Geophysics  Society, April 25-29, 1994, Grenoble, France

[51]Mu Mu, Nonlinear Arnold’s second stability criteria of atmospheric motions, International Symposium on Methods and Applications of Analysis, 1994, Hong  Kong.

[52]Mu Mu, Nonlinear stability problem of modified quasigeostrophic flow, The ninth conference on atmospheric and oceanic waves  and stability, 1993, San Antonio, Texas, USA.

[53]Mu Mu, Nonlinear stability criteria relevant  to Arnold’s second theorem  in geophysical fluid dynamics, Thirteen Annual conference of Canadian applied  methematics society, Wave phenomena, Modern Theory and applications, 1992,  Edmonton, Alberta, Canada.

[54]Mu Mu, and T. G. Shepherd, Nonlinear stability criteria for  quasigeostrophic motion, Climate, Environment and geophysical fluid dynamics,  1992, Beijing, China.

[55]Mu Mu, Well posedness and stability of  initial-boundary value problems in atmospheric dynamics, School on  qualitative aspects and applications of nonlinear evolution equations,  International center for theoretical Physics, 1990, Trieste, Italy.

[56]Mu Mu, Arnold’s second nonlinear stability criteria of  atmospheric motions, The eleventh congress on ordinary and partial  differential equations, 1990, University of Dundee, UK.

[57]Mu Mu, Existence and uniqueness of  initial-boundary value problems for quasigeostrophic equation, Theory and  numerical methods for initial-boundary value problems, 1989, Oberwolfach,  Germany

[58]Mu Mu, and Zeng Qingcun, Stability of  quasigeostrophic motions in atmosphere, Fourth Asian Congress of Fluid Mech.,  1989Hong Kong.

[59]Mu Mu, Global classical solutions of  initial-boundary value problems for potential vorticity equation, The third  International congress on computational and applied mathematics, 1988, University  of Leuven, Belgium.

[60]Mu Mu, On the boundary value problem for a  degenerate elliptic equations, The seventh international symposium on  differential geometry and differential equations, Nankai Institute of  Mathematics, 1986, Tianjing, China.

[61]Mu Mu, Global classical  solutions of nonlinear generalized vorticity equations and its application,  International workshop on applied differential equations, Tsinghua  University, 1985, Beijing, China.


会议组织

[1]Mu Mu, 2018, EGU General Assembly, Inverse problem, data assimilation,and predictability studies in geophysics. (Co-convener), Vienna, Austria.

[2]Mu Mu, 2017, EGU General Assembly, Initial error dynamics and model error physics in predictability studies of weather and climate. (Co-convener), Vienna, Austria.

[3]Mu Mu, 2016, EGU General Assembly, Inverse problem of data assimilation, Initial error and model error. (Co-convener), Vienna, Austria.

[4]Mu Mu, 2015, EGU General Assembly, Inverse Problems, Data Assimilation, Coupled Initialization, and Impact of initial and model Errors and Predictability. (Co-convener), Vienna, Austria.

[5]Mu Mu, 2015, EGU General Assembly, Initial error dynamics and model error physics in weather and climate predictability studies. (Co-convener), Vienna, Austria.

[6]Mu Mu, 2014, AOGS 11th Annual Meeting, Western Boundary Currents, Transport, Path Variability, Eddies and Continental Shelf Processes. (Co-convener), Sapporo, Japan.

[7]Mu Mu, 2014, EGU General Assembly, Initial Error dynamics and model error physics in predictability studies of weather and climate. (Co-convener), Vienna, Austria.

[8]Mu Mu, 2013, EGU General Assembly, Error growth dynamics and related predictability problems.(Co-Convener), Vienna, Austria.

[9]Mu Mu, 2012, EGU General Assembly, Nonlinear optimal modes and their applications in predictability, sensitivity and stability studies. (Co-convener), Vienna, Austria.

[10]Mu Mu, 2011, IUGG, Data assimilation and ensemble forecasting for weather and climate. (Co-convener), Melbourne, Australia.

[11]Mu Mu, 2011, EGU General Assembly, Nonlinear instabilities and predictability. (Co-convener), Vienna, Austria.

[12]Mu Mu, 2010, EGU General Assembly, Nonlinear optimal modes and their applications in predictability, sensitivity and stability studies. (Convener), Vienna, Austria.

[13]Mu Mu, 2009, Advances in Data Assimilation for Earth System Science. (Co-convener), IMMAS, Montréal, Canada.

[14]Mu Mu, 2008, AOGS2008, Predictability of weather and climate: theory and methodology, (Convener), Busan, Korea.

[15]Mu Mu, 2007, IUGG XXIV General Assembly, Data Assimilation for the Atmosphere, Ocean and Land Surface. (Co-convener), Perugia, Italy.

[16]Mu Mu, 2006, AGU Western Pacific Geophysics Meeting, Uncertainty in Numerical Weather Prediction and its Application I, (Convener), Beijing, China.

[17]Mu Mu, 2006, EGU General Assembly,Uncertainty, Random Dynamical Systems and Stochastic Modeling in Geophysics, (Co-convener), Vienna, Austria

[18]Mu Mu, 2005，IAMAS，Aeronomy of Planetary Atmospheres: Comparative Planetology, (Co-Convener),Beijing, China.

[19]Mu Mu, 2005, IAMAS, Advances in Data Assimilation, (Convener), Beijing China

[20]Mu Mu, 2005, AOGS, Joint NL4/OA10 Session - AOGS 2004. (Co-Organizer), Singapore.



#以上信息由本人提供，更新时间：2024/07/23



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