Improved Understanding of the Response of Mean and Extreme Precipitation to Climate Change

更好地了解平均降水量和极端降水量对气候变化的响应

基本信息

批准号：
1552195
负责人：
Paul O'Gorman
金额：
$ 42.02万
依托单位：
Massachusetts Institute of Technology
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-07-01 至 2020-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1552195&HistoricalAwards=false
关键词：
Improved Understanding Response Mean Extreme

项目摘要

The goal of this work is to understand the basic mechanisms which determine how increases in global temperature affect precipitation, including both changes in the spatial distribution of precipitation and changes in the intensity of the most extreme precipitation events. Model simulations of the response of the climate system to greenhouse gas increases show substantial changes in precipitation as a consequence of global warming, but models disagree on the details of these changes and the mechanisms for them are not well understood. One mechanism commonly invoked to explain the changes is predicated on the fact that the moisture content of air typically increases with warming, so moisture convergence and subsequent precipitation increase in regions where moisture is already converging and causing precipitation in the present-day climate (drying is likewise expected in regions which are already dry). But previous work by the PI shows that this "wet get wetter" argument does not adequately account for precipitation changes over land, where they have the greatest societal impacts. Work pursued here seeks to better understand the precipitation response over land by considering the roles played by spatial differences in land surface warming and relative humidity change, through experiments with a simplified atmospheric general circulation model and analysis of simulations from the Climate Model Intercomparison Project version 5 (CMIP5) . Another argument holds that precipitation will increase over the tropical oceans where local sea surface temperature (SST) exceeds the overall warming of tropical SSTs, as the warmer SSTs cause the overlying atmosphere to be less stable than in neighboring regions. But this "warmer gets wetter" argument neglects potential contributions from near-surface wind convergence, the radiative effects of water vapor and clouds, and changes in dry static stability. These effects will be examined together using a diagnostic model in which precipitation is related to a shallow vertical mode which responds to low-level convergence, and a second mode which captures the dependence of deep convection on relative SST change.Research on changes in the intensity of extreme precipitation events uses a cloud-system resolving model (CRM, specifically the System for Atmospheric Modeling) in idealized configurations to make up for the limitations of climate models in representing extreme precipitation. Some simulations are performed using hypohydrostatic scaling, in which the vertical momentum equation is artificially modified to reduce the scale gap between the small scales on which convective precipitation occurs and the much larger scales of typical of the weather systems and high and low pressure centers found on weather maps. This approach enables experiments incorporating both scales which would otherwise be too computationally expensive. A further topic to be addressed is the effect of warming on extreme snowfall events. The PI's previous work posits an optimal temperature for snowfall extremes which occurs because precipitation extremes increase with temperature whereas the fraction of precipitation that falls as snow decreases sharply in a range near the freezing point. Work conducted here uses observed snowfall data and model outputs to test this theory and explore its implications for a warming climate.The work has broader impacts due to the potential impacts of changes in mean precipitation and the severity of extreme precipitation events. Mean precipitation is important for agriculture and for water resources and their management, while extreme precipitation is often disruptive to society, and extreme snowfall events are associated with a number of costs in urban environments. The project also supports and train a graduate student, thus contributing to workforce development in this research area. The project also provides summer support for an undergraduate student.

这项工作的目的是了解确定全球温度升高如何影响降水的基本机制，包括降水的空间分布的变化以及最极端降水事件强度的变化。气候系统对温室气体的响应的模型模拟会增加由于全球变暖而导致的降水变化，但是在这些变化的细节上，模型不同意这些变化的细节和它们的机制。一种通常被引用来解释这些变化的机制是基于以下事实：空气的水分含量通常会随着变暖而增加，因此水分收敛性和随后在当今气候下水分已经收敛并引起降水的区域的降水增加（预计在已经干燥的区域中，干燥也是如此）。但是，PI的先前工作表明，这种“湿润的”论点并不能充分解释土地上的降水变化，而它们具有最大的社会影响。这里从事此处的工作，试图通过考虑通过简化的大气通用循环模型和对气候模型项目对比的模拟项目5（CMIP5）的模拟分析的实验，以考虑到土地表面变暖和相对湿度变化的空间差异所起的作用，以更好地了解土地上的降水反应。另一个论点认为，由于局部海面温度（SST）超过热带SST的整体变暖，降水量将在热带海洋上增加，因为温暖的SST导致上覆的大气比邻近地区不那么稳定。但是，这种“温暖变得更湿”的论点忽略了近地表风收敛的潜在贡献，水蒸气和云的辐射效应以及干静态稳定性的变化。将使用诊断模型一起检查这些效果，在该模型中，降水与浅层垂直模式有关，该模式对低级别的融合响应，并捕获了深层对流对相对SST变化的依赖性的第二种模式。研究极端降水事件的强度的变化。极端降水事件的强度的变化使用云系统解决模型（特定于crm appertairs for Apperipation cormiviate for Airdleseric模型），在理想的模型中，在理想的模型中，在理想的模型中，在理想的模型中，在理想化的模型中进行了限制，以实现理想的模型，从而在理想的模型中进行限制，以实现大气的模型，从而实现了限制模型，以实现限制模型。降水。使用低液静态缩放进行一些模拟，其中对垂直动量方程进行了人工修改，以减少发生对流降水的小尺度之间的尺度差距，并在天气图上发现的天气系统和高压中心的典型典型典型尺度。这种方法使实验结合了两个量表，否则在计算上太昂贵了。要解决的另一个主题是对极端降雪事件的变暖的影响。 PI的先前工作对降雪极端的温度提出了最佳的温度，因为降水极端会随温度而增加，而随着降雪急剧下降的降水，降水量的比例急剧下降。此处进行的工作使用观察到的降雪数据和模型输出来测试该理论并探索其对温暖气候的影响。由于平均降水量变化的潜在影响以及极端降水事件的严重性，这项工作具有更大的影响。平均降水对于农业和水资源及其管理很重要，而极端降水通常会对社会造成破坏，而极端的降雪事件与城市环境中的许多成本有关。该项目还支持并培训研究生，从而为该研究领域的劳动力发展做出了贡献。该项目还为本科生提供了夏季的支持。