报告题目：A minimal model for stratosphere-troposphere coupling on sub-seasonal timescales（次季节尺度上平流层-对流层耦合的一个极简模型）
报告专家：Andrew Charlton-Perez教授
报告时间：2024年03月07日（周四）15:00
报告地点：气象楼423会议室（同步腾讯会议ID：702384299）
主持人：周波涛教授
专家简介：
Andrew Charlton-Perez is Professor of Meteorology and Head of School of Mathematical, Physical and Computational Sciences at the University of Reading. He has worked on the dynamics of the stratosphere throughout his career publishing more than 80 peer reviewed publications on this topic. He has led a number of international efforts to improve understanding of the stratospheric role in predictability including as a member of the SPARC DynVar committee and as the founding co-chair of the SPARC SNAP initiative. He recently co-chaired the SPARC General Assembly the first large climate meeting to be held in a multi-hub format with the aim of reducing the carbon impact of the meeting. He also has significant interests in improving climate education for all. He was the founding co-chair of the WCRP Academy lighthouse activity and advises the UK government on climate education and green jobs. He leads the Climate Ambassadors project, a new initiative to find ways to harness climate and sustainability expertise for the good of education settings for all young people which is now part of the core delivery mechanisms for the UK Sustainability and Climate Change in Education strategy.
报告摘要：
Over the past twenty years a large weight of evidence has been built that demonstrates the importance of polar stratospheric variability for sub-seasonal predictability on weekly to monthly timescales. However, despite this evidence, and a number of multi-system comparisons our understanding of how sub-seasonal prediction systems capture this important source of predictability is still in its early stages. One tool needed in our toolbox is a simple statistical model that can be used for thought experiments and for interrogating comprehensive modelling systems. In this talk I will outline one suggested model for this purpose that we have developed at Reading. The model helps us to understand, for example, how and why the tropospheric correlation skill following SSW events is elevated while the root-mean square error is unchanged. Having explained the model in detail I will show one application of the model to a series of sub-seasonal hindcasts, revealing important differences in behaviour between the systems and a link between the signal-to-noise ratio in the stratosphere and troposphere.
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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
气候与应用前沿研究院
大气科学学院
2024.03.05