AGS-PRF: Leveraging High-Resolution Modeling and Dynamical Theory to Elucidate Changes in Extreme Precipitation in a Monsoonal Regime

AGS-PRF：利用高分辨率建模和动力学理论来阐明季风气候下极端降水的变化

• 批准号: 2123372
• 负责人: Jane Smyth
• 金额: $ 19万
• 依托单位: Smyth, Jane E
• 依托单位国家: 美国
• 项目类别: Fellowship Award
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2123372&HistoricalAwards=false
• 关键词: AGS; PRF; Leveraging; Resolution; Modeling

Monsoon low pressure systems (MLPS) are synoptic storms that generate the majority of monsoon rainfall over South Asia, including the heaviest rainfall events. This densely populated region is already prone to severe impacts from extreme precipitation, and the sensitivity of MLPS to warming is poorly understood. With coarse spatial resolutions, climate models do not produce realistic MLPS, as the simulated tropical precipitation extremes are highly dependent on how models portray convection via numerical parameterizations. This proposal seeks to better understand MLPS and how their changes affect the monsoonal precipitation extremes. The findings will advance the goal of improving regional predictions of how precipitation extremes will respond to increasing temperatures, with direct implications for climate change mitigation. The proposed activities include the mentoring of undergraduate and graduate students in research and public outreach activities to promote increased science literacy.Specifically, the study will utilize an idealized high-resolution, convection-permitting configuration of the System for Atmospheric Modeling framework with an extended equatorial beta-plane, fixed sea surface temperatures (SST), and a simple representation of land. The analysis will examine how the presence of tropical land and the accompanying synoptic dynamics influence the extreme precipitation sensitivity to uniform SST warming. The work will utilize a moist dynamical framework and draw on several alternative theories to characterize precipitation extremes and MLPS dynamics.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.

季风低压系统（MLP）是天气风暴，在南亚造成大部分季风降雨，包括最重的降雨事件。这个人口稠密的区域已经很容易受到极端降水的严重影响，并且MLPS对变暖的敏感性知之甚少。通过粗糙的空间分辨率，气候模型不会产生现实的MLP，因为模拟的热带降水极端高度取决于模型如何通过数值参数化描绘对流的方式。该提案旨在更好地了解MLP以及它们的变化如何影响季风降水极端。这些发现将促进对降水极端将如何响应温度响应的区域预测的目标，这对缓解气候变化的影响直接影响。拟议的活动包括在研究和公共宣传活动中对本科和研究生的指导，以促进科学素养的提高。具体来说，该研究将利用理想化的高分辨率，对流 - 渗透配置，用于大气建模框架，并使用扩展的赤道beta beta-beta-plane-beta-plane-beta-plane-beta-beta-beta-plane，固定的海面温度（SST）和简单的土地代表。该分析将研究热带土地的存在和随附的天气动力学如何影响对均匀SST变暖的极端降水敏感性。这项工作将利用一个潮湿的动力框架，并利用几种替代理论来表征降水极端和MLPS动态。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估标准来通过评估来获得支持的。