Collaborative Research: North American Warm-season Extremes in a Changing Climate: Large-scale Drivers and Local Feedbacks

合作研究：气候变化中的北美暖季极端事件：大规模驱动因素和当地反馈

• 批准号: 2203515
• 负责人: Walter Robinson
• 金额: $ 88.84万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2203515&HistoricalAwards=false
• 关键词: Collaborative; Research; North; American; Warm

Throughout much of the US climate change will be felt largely through its effects on warm season extreme events like flooding rains, heat waves, fires, and droughts. Basic thermodynamics suggests that the severity and frequency of these events should increase, for instance the hottest heat waves are likely to get hotter in a warming climate and storm intensity is likely to increase because warmer air holds more moisture. The thermodynamic arguments help but the full suite of processes that affect extreme events is extensive and involves a broad range of spatial scales, from the multi-kilometer scale of thunderstorms to the hemispheric scale of the jet streams that drive weather systems. The broad scale range complicates efforts to study extreme event change using weather and climate models, as a brute force effort to simulate all the relevant processes at all the relevant spatial scales, occurring over the decades-long progression of global warming, is not practical even on the largest computers. Climate models can simulate the full global climate system for decades and even centuries but at resolutions too coarse (perhaps 100km grid spacing) to represent the scales of intense storms. In particular they do not capture the mesoscale convective systems (MCSs) which account for much of the severe weather over the continental US. An alternative approach called pseudo-global warming (PGW) uses a high-resolution model to simulate an observed extreme event, and the simulation is repeated with modifications to the ambient conditions to represent the warmer climate. PGW simulations are quite valuable but they only allow consideration of how climate change affects the severity of specific events, thus they do not enable research on changes in the frequency of occurrence of extreme events. Also, PGW simulations are typically conducted using regional models and thus do not properly represent the effects of changes in the hemispheric-scale atmospheric circulation.This project develops a methodology for looking at extreme event change in a warming climate which addresses the multi-scale issue and enables examination of extreme event frequency and other aggregate statistics. First, a high-resolution global model, the Model for Prediction Across Scales (MPAS) is used to simulate the weather and climate of the past 30 years (1990 to 2019). With a grid spacing of 15km the model is capable of representing MCSs. Second, extreme events are identified in this "nature run" and resimulated with modifications to sea surface temperatures and other surface conditions to represent future warming. The modifications are generated using climate model simulations from the Coupled Model Intercomparison Project (CMIP). The resimulations are a form of PGW only with a global domain, so that changes in intensity can be examined accounting for the full range of spatial scales. Third, a set of 30 warm season (May to November) MPAS simulations using CMIP model output is generated to represent future climate change. The warm season simulations follow the PGW approach but the full season duration means that the simulations do not follow particular events but instead show how a typical season of extreme events changes due to warmer conditions. One issue to be addressed with these simulations is the effect of changes in the jet streams over North America on floods and heat waves, as climate models typically show a reduction in jet-level wind speed over the continental US with increases in speed to the north and south.The work is of societal as well as scientific interest given the damaging effects of extreme events and the value of better information on extreme event change to guide decision making. The project also provides support and training to five graduate students and an undergraduate research assistant. Simulations generated in the project are made available to the research community, and reduced versions of the output are hosted on a JupyterHub to provide access to researchers at the universities participating in the project through Jupyter Notebooks. Outreach is conducted through the Junior Curator program North Carolina Museum of Natural Sciences (NCMNS), a program for high school students interested in field biology and conservation. The students collect field mesaurements of local weather events and their impacts, including insect outbreaks, mold, flooding, and other after-effects of heavy rain. Activity guides are created based on these activities and disseminated through the National Association of Geoscience Teachers.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.

在整个美国的大部分地区，都会通过对温暖季节的极端事件（例如洪水，热浪，火灾和干旱和干旱）的影响而感受到它。 基本的热力学表明，这些事件的严重程度和频率应增加，例如，在温暖的气候和风暴强度下，最热的热浪可能会变得更热，因为较温暖的空气会保持更多的水分。 热力学论证有助于有助于影响极端事件的整个过程套件，并且涉及广泛的空间尺度，从雷暴的多公斤尺度到驱动天气系统的喷气流的半球标度。 广泛的规模范围复杂化了使用天气和气候模型研究极端事件变化的努力，这是一项蛮力的努力，以模拟所有相关空间尺度的所有相关过程，即使在最大的计算机上，几十年来全球变暖的发展中发生的所有相关过程也不是实际的。 气候模型可以模拟数十年甚至几个世纪以来的全球气候系统，但在决议中太粗糙（也许是100公里的网格间距），无法代表剧烈风暴的尺度。 特别是，它们没有捕获中尺度对流系统（MCS），这些系统占美国大陆的大部分恶劣天气。 一种称为伪全球变暖（PGW）的替代方法使用高分辨率模型来模拟观察到的极端事件，并重复对环境条件进行修改以代表温暖的气候。 PGW模拟非常有价值，但它们仅允许考虑气候变化如何影响特定事件的严重性，因此它们不能够研究极端事件发生频率的变化。 同样，PGW模拟通常是使用区域模型进行的，因此不能正确地表示半球大气循环中变化的影响。该项目开发了一种方法，用于在变暖气候中查看极端事件变化，以解决多规模问题，并可以检查极端事件频率和其他聚集统计。 首先，一个高分辨率的全球模型，跨量表（MPA）的预测模型用于模拟过去30年的天气和气候（1990年至2019年）。 通过15公里的网格间距，该模型能够代表MCS。 其次，在这种“自然运行”中确定了极端事件，并通过对海面温度和其他表面条件进行修改以代表未来的变暖进行了修改。 修改是使用耦合模型对比项目（CMIP）的气候模型模拟生成的。 重复拟合仅是具有全局域的PGW形式，因此可以检查强度的变化，以说明整个空间尺度的范围。 第三，使用CMIP模型输出产生了一组30个温暖季节（5月至11月）MPAS模拟，以代表未来的气候变化。 温暖的季节模拟遵循PGW方法，但是整个季节的持续时间意味着模拟不会遵循特定事件，而是显示了由于条件较高而导致的极端事件的典型季节如何变化。 这些模拟要解决的一个问题是北美喷气流变化对洪水和热浪的变化的影响，因为气候模型通常显示出北部和南部速度的速度提高到北部和南方的射流风速降低。鉴于对极端事件的极端事件以及对极端事件的极端信息的价值，北部和南方的工作既具有社会兴趣，又降低了社会利益。 该项目还为五名研究生和一名本科研究助理提供了支持和培训。 项目中生成的仿真可供研究社区提供，并且减少的产出版本托管在Jupyterhub上，以通过Jupyter Notebooks参与该项目的大学的研究人员访问。 外展活动是通过北卡罗来纳州自然科学博物馆（NCMNS）进行的，这是针对对现场生物学和保护感兴趣的高中学生的计划。 学生会收集当地天气事件及其影响的野外核心，包括昆虫爆发，霉菌，洪水和其他大雨的后效应。 活动指南是根据这些活动创建的，并通过全国地球科学教师协会进行了传播。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优点和更广泛的影响审查标准通过评估来支持的。