SGER: Assessment of the Severe Weather Environment Simulated by Global Climate Models

SGER：全球气候模型模拟的恶劣天气环境评估

• 批准号: 0550178
• 负责人: David Karoly
• 金额: $ 5.26万
• 依托单位: University of Oklahoma Norman Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-05-15 至 2007-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0550178&HistoricalAwards=false
• 关键词: SGER; Assessment; Severe; Weather; Environment

Severe thunderstorms and tornados are very important mesoscale weather events in the central United States because of their high frequency and intensity in this region, and the damage and loss of life that they cause every year. Recently, it has been shown that the frequency of favorable conditions for significant severe thunderstorms and tornados can be estimated for the United States and other regions using global atmospheric re-analyses with spatial resolution on the order of 200 km and temporal resolution of 6 hours. Global climate models are unable to simulate severe thunderstorms and tornados because their spatial resolution is too coarse to be able to simulate such mesoscale events. However, they should be able to simulate the environmental conditions under which such severe weather develops, including abundant lower tropospheric moisture, steep mid-tropospheric lapse rates, and strong tropospheric wind shear. High space and time resolution data from control simulations with global climate models archived at NCAR will be used to estimate the frequency of favorable conditions for severe weather, as simulated by the models. The climatological distribution of the severe weather environment in the model simulations will be compared with that from the reanalyses, including the seasonal and geographical variations and its interannual variability. The frequency distributions of the important factors determining the severe weather environment also will be assessed in the models. The research is exploratory. Only recently have climate model data sets with sufficient vertical, horizontal and time resolution become available to undertake this study. Outcomes from this research will include a detailed assessment of global climate model simulations of the environmental conditions determining severe weather. This will provide a better understanding of some of the causes of model problems with simulation of warm season intense continental convective. Broader Impacts: If the approach is successful, subsequent research will provide estimates for the first time of changes in the frequency of severe weather in the United States under future climate conditions. The method can then be applied to other climate model simulations to provide greater confidence in the estimates of future changes in the severe weather environment. In addition to the scientific outcomes, there will be important training outcomes from this project. The graduate student employed on this project will gain valuable training and experience in climate diagnostics, climate modeling, and severe weather, which will allow him/her to contribute better to future research and development in climate change and its impacts in the United States. In addition, the PIs anticipate that the datasets generated will be used in the OU National Weather Center's NSF-sponsored Research Experiences for Undergraduates as the basis for projects.

强雷暴和龙卷风是美国中部非常重要的中尺度天气事件，因为它们在该地区发生的频率和强度很高，并且每年都会造成损失和生命损失。最近，研究表明，可以利用空间分辨率为 200 公里量级、时间分辨率为 6 小时的全球大气再分析来估计美国和其他地区发生重大雷暴和龙卷风的有利条件的频率。全球气候模型无法模拟严重的雷暴和龙卷风，因为它们的空间分辨率太粗糙，无法模拟此类中尺度事件。然而，它们应该能够模拟这种恶劣天气发生的环境条件，包括丰富的对流层低层水分、陡峭的对流层中层递减率和强烈的对流层风切变。 NCAR 存档的全球气候模型控制模拟的高空间和时间分辨率数据将用于估计模型模拟的恶劣天气有利条件的频率。将模型模拟中恶劣天气环境的气候分布与重新分析的气候分布进行比较，包括季节和地理变化及其年际变化。模型还将评估决定恶劣天气环境的重要因素的频率分布。该研究是探索性的。直到最近，具有足够垂直、水平和时间分辨率的气候模型数据集才可用于开展这项研究。这项研究的成果将包括对决定恶劣天气的环境条件的全球气候模型模拟进行详细评估。这将有助于更好地理解暖季强大陆对流模拟中模型出现问题的一些原因。更广泛的影响：如果该方法成功，后续研究将首次对未来气候条件下美国恶劣天气频率的变化进行估计。然后，该方法可以应用于其他气候模型模拟，以便对恶劣天气环境的未来变化的估计提供更大的信心。除了科学成果外，该项目还将产生重要的培训成果。该项目雇用的研究生将获得气候诊断、气候建模和恶劣天气方面的宝贵培训和经验，这将使他/她能够更好地为美国未来气候变化及其影响的研究和开发做出贡献。此外，PI 预计生成的数据集将用于 OU 国家气象中心的 NSF 资助的本科生研究经验项目，作为项目的基础。