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 (MLPS) 是天气风暴，产生南亚大部分季风降雨，包括最强降雨事件。这个人口稠密的地区已经很容易受到极端降水的严重影响，而人们对多层保护对变暖的敏感性知之甚少。由于空间分辨率较粗，气候模型无法产生真实的 MLPS，因为模拟的热带极端降水高度依赖于模型如何通过数值参数化来描绘对流。该提案旨在更好地了解 MLPS 及其变化如何影响季风极端降水。这些发现将推进改善极端降水如何应对气温升高的区域预测的目标，并对减缓气候变化产生直接影响。拟议的活动包括指导本科生和研究生进行研究和公共宣传活动，以提高科学素养。具体来说，该研究将利用大气模拟框架系统的理想化高分辨率、允许对流配置和扩展的赤道beta 平面、固定海面温度 (SST) 以及陆地的简单表示。该分析将研究热带土地的存在和伴随的天气动态如何影响极端降水对均匀海表温度变暖的敏感性。这项工作将利用潮湿的动力学框架，并利用几种替代理论来描述极端降水和 MLPS 动力学。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。