The Dynamics of Teleconnection Patterns and Their Relationship with the General Circulation

远相关型态的动力学及其与一般环流的关系低层大气的逐月和年变率表现为一组周期性型态，称为远相关。远程连接调节季节性气候：热波和寒潮；季节性干旱和周期性洪水。费尔德斯坦将研究设定这些模式结构的动力机制，并决定它们随时间的行为。将讨论五个具体问题：1。什么动态过程决定了远距连接如何随时间变化？平流层是如何影响主导着整个温带气候的高纬度模式的？远距连接模式代表不同的体制还是连续的状态？纬向平均喷流呈现向极地传播的时期。这样的周期可以预测吗？如果是这样，对预测天气有什么影响？风暴是如何导致北大西洋和北太平洋主要遥相关模式的发展的？这些问题将通过分析现有的大气数据集以及使用理想数值模式进行的大量模拟来回答。模型模拟将包括具有地形和已解析平流层的运行。这项研究的一个更广泛的影响将是为社区提供大量模型模拟的输出。一名研究生将得到培训，并为本科生荣誉学生提供研究机会。这些研究可能最终导致季节内预测的改进。

The dynamics of teleconnection patterns and their relationship with the general circulationThe month-to-month and year-to-year variability of the lower atmosphere appears in a set of recurrent patterns, denoted teleconnections. Teleconnections mediate the seasonal climate: heat and cold waves; seasonal droughts and periods of flooding. Feldstein will investigate the dynamical mechanisms that set the structures of these patterns and determine their behavior over time. Five specific questions will be addressed:1. What dynamical processes determine how teleconnections vary over time?2. How does the stratosphere influence the leading high-latitude patterns, which organize weather throughout the extratropics?3. Do teleconnection patterns represent distinct regimes or continua of states?4. The zonal-mean jet exhibits periods when it propagates poleward. Can such periods be predicted? If so, what are the implications for forecasting the weather?5. How do storms lead to the development of the leading teleconnection patterns over the North Atlantic and North Pacific Oceans?These questions will be answered by analyzing existing atmospheric data sets together with an extensive set of simulations using an idealized numerical model. The model simulations will include runs with topography and with a resolved stratosphere.A broader impact of the research will be to make available, to the community, the output of the extensive set of model simulations. A graduate student will be trained, and research opportunities will be provided for undergraduate honors students. These studies may ultimately lead to improvements in intraseasonal prediction.