报告题目：The Role of the Stratosphere in Winter Cold Events over North America
报告专家：陈刚 教授
报告时间：2022年5月19日（周四） 上午9:30
会议形式：线上——腾讯会议（会议ID: 931-273-417）
主 持 人：虞越越 副教授
专家简介：
陈刚，教授，博导，现工作于加利福尼亚大学洛杉矶分校(UCLA)大气海洋科学学院，长期从事大气动力学研究。主要研究方向包括：1）气候变化背景下的全球大气环流与区域极端天气 2）大气水汽和化学物质的动力学传输机制。 通过理论模型和数值模拟开发了旨在描绘全球大气环流的定量分析工具，探究从热带哈德莱环流到温带风暴系统、从对流层极端天气到平流层极涡崩溃、从水汽输送到平流层-对流层-臭氧交换等相关重要物理过程。厘清这些过程对极端天气（如冬季暴风雪、极端低温或热浪）、极端水文（如干旱或洪水）或极端空气污染等高影响天气气候事件的成因及其预测具有重要意义。已在国际刊物上发表高水平学术论文近百篇。
报告摘要：
While it is expected that severe cold spells would become less frequent under global warming, there were some extreme cold events over Northern Hemisphere midlatitudes in recent decades. Some studies suggested that the stretching of the stratospheric polar vortex, characterized by planetary wave reflection, is linked to cold extremes over North America. Here I will present some recent results on the surface influences of winter stratosphere at the intraseasonal time scale using observations and numerical models. First, I will compare the surface signatures associated with the zonally symmetric and asymmetric modes of stratospheric variability, which describe the strengths of stratospheric polar vortex and planetary wave-1, respectively. The North American cooling after strong stratospheric wave events appears to be more pronounced than the cooling following weak vortex events. A nudging technique reveals that stratospheric variability can largely explain the North American cooling after strong stratospheric wave events, but not the temperature signals prior to or near the wave events. Next, I will show that the sea ice loss over the Barents-Kara Sea (BKS) region could affect the predictability of North American cold events. In the presence of the BKS sea ice loss, a weak stratospheric polar vortex is followed by the increased probability of cold spells over North America, which may provide a source of intraseasonal predictability for cold events. These findings provide a more nuanced explanation for the role of winter stratosphere in surface cold extremes and their predictability.
本次学术报告采用腾讯会议视频方式，欢迎广大师生踊跃参加！
气象灾害教育部重点实验室
气象灾害预报预警与评估协同创新中心
大气科学学院
2022.5.12