近日，我系博士后陈晓丹、李宏、Safi Ullah荣获2020年上海市“超级博士后”激励计划资助。
上海市“超级博士后”计划是由市人力资源和社会保障局、市财政局联合开展的一项激励计划。旨在落实《国务院办公厅关于改革完善博士后制度的意见》，深化推进具有全球影响力的上海科技创新中心建设，为保障和提高优秀博士后研究人员待遇水平，进一步激发青年专业技术人才创新创造活力。
陈晓丹， 2020年6月获中国科学院大气物理研究所气象学博士学位，硕博连读期间在《Journal of Climate》、《Geophysical Research Letters》等国际主流刊物上发表论文6篇。2018-2019年间在美国哥伦比亚大学Lamont-Doherty地球研究所进行学术访问。2020年7月进入我系博士后流动站，与温之平教授、Aiguo Dai教授合作从事研究工作。已获得"博新计划"、博后面上基金、上海市超级博士后计划的资助。主要研究方向包括北极海冰与大气环流相互作用、气候变化背景下中纬度极端天气、中高纬度大气动力等。
李宏，2020年6月获得清华大学地球系统科学系理学博士学位，同年8月进入我系王桂华教授团队从事博士后工作。曾开发了我国首款在国际上公开发布的全球海洋Argo网格资料集，被国内外广泛应用。博士期间主要研究夏季西太副高的东西移动机理，发现西太副高的东西移动（强度变化）是大气对海表温度异常的斜压（正压）响应的结果，并指出副高的东西移动可以影响南海热带气旋生成频率的变化。目前研究兴趣为海洋中尺度涡，主持1项中国博士后科学基金项目。2019年入选卫星海洋环境动力学国家重点实验室青年访问海星学者。
Safi, He’s from Khyber Pakhtunkhwa, Pakistan. He has obtained his Ph.D. in Climate System and Climate Change from Nanjing University of Information Science and Technology (NUIST), Nanjing, China. His Ph.D. dissertation focused on “Observed and projected spatiotemporal changes in temperature and its extremes over the China-Pakistan Economic Corridor”. During his Ph.D. studies, he has published five papers in scientific citation index (SCI) journals such as Atmospheric Research, Climate Dynamics, and International Journal of Climatology. Due to his outstanding academic and research performances, he has been awarded several excellent awards during his studies at NUIST, i.e., Jiangsu Provincial Excellent International student award, NUIST Excellent International student award, and NUIST Outstanding International Graduate.
In July 2020, he joined our department as a Postdoctoral Fellow under the supervision of Professor Qinglong You. Recently, he has been selected for Shanghai Super Postdoctoral Fellowships, which is one of the prestigious fellowships in China. During his Postdoc period, he will work on “Projected changes in temperature extremes and their probability over South Asia using CMIIP6 models. Besides, he will assess population exposure to these extremes in South Asia. He hope that the findings of these studies would help to improve the prediction level of extreme events and will provide a basis for climate change mitigation and adaptation in the region.
