Collaborative Research: Combining Self-organized Maps and Idealized Storm-scale Simulations to Investigate the Effect of Future Climate Change on Severe Convective Storms

严重的雷暴和龙卷风每年在世界范围内造成重大破坏和生命损失。目前尚不清楚未来的气候变化是否会使这种极端天气变得更糟。该项目采用了一种新的方法来研究在未来更温暖的气候中，雷暴和龙卷风可能会发生怎样的变化。这个话题很难，因为气候变化是一个全球性的现象，但雷暴是小的-通常不到几英里的直径。不同类型的计算机模型可用于了解未来的气候变化（气候模型）和模拟雷暴（风暴模型）。然而，由于计算机能力的限制，一个模型不能同时做到这两点。为了克服这一障碍，该项目首先使用基于数据的方法来研究通常产生严重雷暴的环境如何在气候模型模拟中发生变化。这些变化对严重的雷暴和龙卷风的影响，然后在模拟使用风暴模型进行测试。这两个步骤的结果可以结合在一起，以研究未来严重的雷暴可能如何变化，这将有助于社会更好地规划和适应。最近的研究表明，未来的气候变化可能会增加强对流风暴（SCS）的频率和/或严重程度，包括强雷暴和龙卷风。然而，迄今为止的研究主要集中在有利于南海活动的大尺度环境，而忽略了风暴尺度动力学的潜在变化。该项目集成了气候模型，机器学习和理想化的风暴尺度集合模拟，以了解SCS活动在未来更温暖的气候中可能如何变化。这是第一次努力研究未来的变化，在南海活动的同时占大尺度环境和风暴尺度动力学的变化。该项目有三个目标：1。使用自组织地图量化未来气候变暖时南海探测的结构和变率变化; 2.使用理想化的风暴尺度模拟，以研究这些变化在南海探测将如何改变概率的南海活动和灾害; 3。结合联合收割机的结果，以调查在未来气候变暖的情况下，SCS活动和灾害如何在地理和季节上发生变化，并了解潜在的环境与风暴规模驱动因素。这种新颖的方法大大降低了这个问题在气候状态的大小尺度上的高度复杂性。该项目的成果将与以前解决这一问题的方法进行比较，以更全面地了解SCS活动在未来变暖的气候中可能如何变化。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Severe thunderstorms and tornadoes cause significant damage and loss of life each year worldwide. It remains unclear whether future climate change will make this type of extreme weather worse. This project applies a new methodology to examine how severe thunderstorms and tornadoes may change in a future, warmer climate. This topic is difficult because climate change is a global phenomenon, yet thunderstorms are small – typically less than a few miles in diameter. Different types of computer models can be used to understand future climate change (climate models) and to simulate thunderstorms (storm models). However, one model cannot do both at the same time because of limits on computer power. To overcome this barrier, this project first uses data-based methods to examine how the environments that typically generate severe thunderstorms may change in climate model simulations. The effect of these changes on severe thunderstorms and tornadoes is then tested in simulations using a storm model. The results of both steps can be combined together to examine how severe thunderstorms may change in the future, which will help society better plan and adapt. Recent research suggests that future climate change may enhance the frequency and/or severity of severe convective storms (SCS), including severe thunderstorms and tornadoes. However, studies to date have focused principally on the large-scale environments favorable for SCS activity while neglecting potential changes in storm-scale dynamics. This project integrates climate models, machine learning, and idealized storm-scale ensemble simulations to understand how SCS activity may change in a future warmer climate. This is the first effort to examine future changes in SCS activity by simultaneously accounting for both changes in large-scale environments and storm-scale dynamics. The project has three objectives: 1. Use self-organized maps to quantify changes in structure and variability in SCS soundings in a future warmer climate; 2. Use idealized storm-scale simulations to examine how these changes in SCS soundings will alter probabilities of SCS activity and hazards; 3. Combine results to investigate how SCS activity and hazards may shift geographically and seasonally in a future warmer climate, and understand the underlying environmental vs. storm-scale drivers. This novel approach greatly reduces the high complexity of this problem at both large and small scales across climate states. The outcomes of the project will be compared to prior approaches to this problem to yield a more comprehensive understanding of how SCS activity may change in a future warmer climate.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.