基本情况

宋振亚，物理海洋学博士，研究员。基金委重点项目主持人、创新团队核心成员。一直从事地球系统模式研发与应用、高性能计算、海气相互作用、机器学习等研究，发展了两代地球系统模式FIO-ESM，建立了FIO短期气候预测系统。发表学术文章60余篇，引用1100余次。

 

学习经历

2007.7-2011.6 中国海洋大学海洋与大气学院，物理海洋专业，博士学位

2003.9-2006.6 中国海洋大学海洋与大气学院，流体力学专业，硕士学位

1999.9-2003.7 中国海洋大学数学科学学院，数学与应用数学专业，学士学位

 

工作经历

2018.1-至今 自然资源部第一海洋研究所，研究员

2014.1-2017.12 自然资源部第一海洋研究所，副研究员

2013.1-2013.12 美国迈阿密大学/大西洋气象海洋实验室（AOML/NOAA），访问学者

2008.11-2013.12 自然资源部第一海洋研究所，助研

2006.07-2008.11 自然资源部第一海洋研究所，研习

 

学术兼职

北太平洋海洋科学组织（PICES, North Pacific Marine Science Organization）中国委员会物理海洋与气候专家委员会，副主席

青岛海洋科学与技术试点国家实验室区域海洋动力学与数值模拟功能实验室，“海洋与气候数值模拟体系”学术方向带头人

清华大学地球系统数值模拟教育部重点实验室/国家超级计算无锡中心，特聘研究员

 

研究方向

海洋与气候数值模拟与预测、海洋动力学与物理过程参数化、海气相互作用与气候变化、高性能计算与机器学习

 

论文发表

  1. 宋振亚，鲍颖，乔方利，FIO-ESM v2.0模式及其参与CMIP6的方案，2019，37（2），161-170. 气候变化研究进展，doi:10.12006/j.issn.1673-1719.2019.033
  2. 宋振亚，刘卫国，刘鑫，苏天赟，刘海行，尹训强，海量数据驱动下的高分辨率海洋数值模式发展与展望，海洋科学进展，2019，37（2），161-170. doi:10.3969/j.issn.1671-6647.2019.02.001
  3. Chen, X., H. Liao, X. Lei, Y. Bao, and Z. Song* (2019), Analysis of ENSO simulation biases in FIO-ESM version 1.0, Clim. Dynam., 53(11), 6933-6946. doi: 10.1007/s00382-019-04969-w
  4. Ding, N., W. Xue, Z. Song*, H. Fu, S. Xu, and W. Zheng (2019), An Automatic Performance Model-based Scheduling Tool for Coupled Climate System Models, J. Parallel. Distr. Com., 132, 204-216. doi: 10.1016/j.jpdc.2018.01.002
  5. Ding, N., S. Xu, Z. Song*, B. Zhang, J. Li, and Z. Zheng (2019), Using Hardware Counter-based Performance Model to Diagnose Scaling Issues of HPC Applications, Neural Comput. Appl., 31(5), 1563-1575, doi: 10.1007/s00521-018-3496-z
  6. Yang, X., Z. Song*, Y.-H. Tseng, F. Qiao, and Q. Shu (2017), Evaluation of three temperature profiles of a sublayer scheme to simulate SST diurnal cycle in a global ocean general circulation model, J. Adv. Model. Earth Syst., 9, 1994-2006, doi:10.1002/2017MS000927
  7. Song, Z., S.-K. Lee, C. Wang, B. Kirtman, and F. Qiao (2015), Contributions of the atmosphere–land and ocean–sea ice model components to the tropical Atlantic SST bias in CESM1, Ocean Modelling, 96(2), 280-290, doi: 10.1016/j.ocemod.2015.09.008
  8. Song, Z., Q. Shu, Y. Bao, X. Yin, and F. Qiao (2015), The prediction on the 2015/16 El Niño event from the perspective of FIO-ESM, Acta Oceanol. Sin., 34(12), 67-71, doi:10.1007/s13131-015-0787-4
  9. Song, Z., H. Liu, C. Wang, L. Zhang, and F. Qiao (2014), Evaluation of the eastern equatorial Pacific SST seasonal cycle in CMIP5 models, Ocean Sci., 10, 837-843, doi:10.5194/os-10-837-2014
  10. Song, Z., F. Qiao, X. Lei, and C. Wang (2012), Influence of Parallel Computational Uncertainty on Simulations of the Coupled General Climate Model, Geosci. Model Dev., 5, 313-319, doi:10.5194/gmd-5-313-2012
  11. Song, Z., F. Qiao, and Y. Song (2012), Response of the equatorial basin-wide SST to wave mixing in a climate model: An amendment to tropical bias, J. Geophys. Res. Oceans, 117, C00J26, doi:10.1029/2012JC007931
  12. Song, Z., F. Qiao, and C. Wang (2011), The correctness to spuriously simulated sea surface temperature semi-annual cycle in the equatorial eastern Pacific, Sci. China Earth Sci., 54(3), 438-444, doi:10.1007/s11430-011-4176-3
  13. Shu, Q., Z. Song, and F. Qiao (2015), Assessment of Sea Ice Simulations in the CMIP5 Models, The Cryosphere, 9, 399-409, doi:10.5194/tc-9-399-2015
  14. Qiao, F., Z. Song, Y. Bao, Q. Shu, C. Huang, and W. Zhao (2013), Development and evaluation of an Earth System Model with surface gravity waves, J. Geophys. Res. Oceans, 118, 4514-4524, doi: 10.1002/jgrc.20327
  15. Qiao, F., Y. Yuan, J. Deng, D. Dai, and Z. Song (2016), Wave-turbulence interaction-induced vertical mixing and its effects in ocean and climate models, Phil. Trans. R. Soc. A, 374, 20150201, doi:10.1098/rsta.2015.0201
  16. Fu, H., J. Liao, J. Yang, L. Wang, Z. Song, X. Huang, C. Yang, W. Xue, F. Liu, F. Qiao, W. Zhao, X. Yin, C. Hou, C. Zhang, W. Ge, J. Zhang, Y. Wang, C. Zhou, and G. Yang, The Sunway TaihuLight supercomputer: system and applications, Sci. China Inf. Sci., 2016, 59(7), 072001, doi: 10.1007/s11432-016-5588-7