Advancing understanding of interannual variability and extreme events in the thermal structure of large lakes under historical and future climate scenarios

增进对历史和未来气候情景下大型湖泊热结构的年际变化和极端事件的了解

• 批准号: 2319044
• 负责人: Ayumi Fujisaki-Manome
• 金额: $ 51.95万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2026-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2319044&HistoricalAwards=false
• 关键词: Advancing; understanding; interannual; variability; extreme

The overarching goal is to advance understanding of the North American Great Lakes thermal variability and occurrence of extreme events in both the historical period and under future climate scenarios. The project will fill critical knowledge gaps in how Great Lakes thermal structure responds to climate change by examining changes in lake surface temperatures and ice cover as well as how temporal variabilities have changed in the past and how they will continue to change under future climate scenarios. Advanced understanding of the role of teleconnection patterns in driving Great Lakes climate variability will help distinguish between changes related to anthropogenic global warming and those related to natural climate variability, an important distinction in the discussion of climate change mitigation. Hydrodynamic-ice simulation results will be generated for the historical period and under future climate scenarios for the Great Lakes. Improved understanding of historical and future occurrence of extreme conditions in Great Lakes’ thermal conditions will benefit decision making in shipping, fishing, recreational use of the lakes, as well as coastal management and climate adaptation efforts.The project will investigate interannual variabilities of ice coverage, lake heat content, stratification strength and duration, as well as timing and trends of lake surface temperatures, occurrence of extreme conditions, and how these variabilities are related to the large-scale atmospheric circulations. Numerical experiments with a three-dimensional hydrodynamic-ice model will be conducted with atmospheric reanalysis and the projected future surface meteorology out to the end-century from select climate model outputs. The historical simulations will be verified by comparing with the publicly available observational datasets. Changes in thermal conditions and their variabilities under historical and projected climate conditions will be evaluated using timeseries analysis, changes in probability density functions and the Schmidt stability index, and extreme value analysis. The research will address how extreme events and climate variability in the Great Lakes change over time and the role of teleconnection patterns and low-frequency oscillations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

总体目标是增进对北美五大湖热变率以及历史时期和未来气候情景下极端事件发生情况的了解。该项目将填补五大湖热结构如何应对气候变化的关键知识空白，方法是研究湖面温度和冰盖的变化，以及过去时间变异性如何变化，以及在未来气候情景下它们将如何继续变化。进一步了解遥相关模式在驱动五大湖气候变异性方面的作用，将有助于区分与人为全球变暖有关的变化和与自然气候变异性有关的变化，这是减缓气候变化讨论中的一个重要区别。将生成五大湖历史时期和未来气候情景下的水动力冰模拟结果。提高对五大湖历史和未来热条件极端情况的认识将有利于航运、渔业、湖泊娱乐利用以及海岸管理和气候适应工作的决策。该项目将调查冰层覆盖、湖泊热含量、分层强度和持续时间的年际变化，以及湖泊表面温度的时间和趋势，极端条件的发生，以及这些变化如何与大尺度大气环流有关。将利用三维水动力冰模式进行数值试验，并根据选定的气候模式输出结果进行大气再分析和预测到本世纪末的未来地面气象。将通过与公开的观测数据集进行比较来验证历史模拟。将使用时间序列分析、概率密度函数和施密特稳定指数的变化以及极值分析来评价热条件的变化及其在历史和预测气候条件下的可变性。该研究将解决如何极端事件和气候变化的五大湖随着时间的推移和遥相关模式和低频振荡的作用。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。