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.

总体目标是提高对北美大湖区热变异性的了解，并在历史时期和未来气候场景下发生极端事件的发生。该项目将通过检查湖面温度和冰覆盖的变化以及过去的临时变异性如何变化以及在未来的气候场景下如何继续变化，从而填补了巨大的湖泊热结构对气候变化的反应方式的关键知识差距。对远程连接模式在推动大湖气候变异性中的作用的深入了解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，这是讨论缓解气候变化的重要障碍。在历史时期和未来的气候场景下，将为大湖区生成流体动力冰模拟结果。 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循环。具有三维流体动力冰模型的数值实验将通过大气再分析进行，预计将来的表面气象从精选的气候模型输出中到达了本世纪末。历史模拟将通过与公开可用的观察数据集进行比较来验证。在历史和预测的气候条件下，热条件及其变化的变化将使用时间表分析，概率密度函数的变化和Schmidt稳定性指数以及极值分析来评估。这项研究将探讨大湖区的极端事件和气候变化如何随着时间的流逝而变化以及远程连接模式和低频振荡的作用。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和更广泛的影响审查标准来通过评估来评估的。