Understanding the Mesoscale Response to Climate Change Using a Regional Climate Model Ensemble

使用区域气候模型集合了解对气候变化的中尺度响应

基本信息

批准号：
2040626
负责人：
Eric Salathe
金额：
$ 67.2万
依托单位：
University of Washington
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-06-01 至 2025-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2040626&HistoricalAwards=false
关键词：
Understanding Mesoscale Response Climate Change

项目摘要

As the name suggests, global warming is a planet-wide response to the energy imbalance caused by increases in greenhouse gases, which spread out and mix to a uniform concentration around the globe. But despite its global nature the effects of global warming can vary even within small regions, depending on local contrasts in topography, ground cover, wind direction, and other factors. Such local effects can be be studied with global climate models (GCMs), but global models have limited ability to represent small-scale effects. Even when GCM results are robust and physically reasonable they can still be misleading if physical mechanisms occurring on scales too small to be adequately represented by GCMs turn out to be more influential than the larger-scale effects that they do capture. An alternative strategy is to use a regional climate model (RCM), which simulates weather and climate over a limited area using information from the global model to connect that region to the rest of the world. The RCM can represent processes on smaller scales than the GCM, and a mismatch between GCM and RCM results can indicate a "mesoscale surprise", in which a local climate change expected from large-scale considerations is upstaged by smaller-scale processes (mesoscale is a technical term referring to spatial scales which are smaller than frontal weather systems but larger than individual cumulus clouds).This project seeks to understand the role of mesoscale physical processes in shaping climate change in the Pacific Northwest (PNW), where the combination of mountainous terrain, proximity to the coast, and alternation of onshore and offshore wind patterns creates an ideal natural laboratory for studying mesoscale surprises. As one example, GCMs suggest that the greatest increase in flooding will occur in river basins in which runoff comes from a mix of rain and snow-melt, since warming reduces snowpack and snowpack has a moderating effect on streamflow. But in RCM simulations, which are better suited to representing precipitation in mountainous regions, the greatest increase in flood risk happens in low-elevation, rain-dominated river basins. These basins are more exposed to heavy rain, thus they are more susceptible to the increase in precipitation intensity that occurs as climate warms.Work performed here uses an ensemble of RCM simulations, each one performed using output from a different GCM simulation, to examine the effects of global warming on the weather and climate of the PNW. The RCM used in the study is the Weather Research and Forecasting (WRF) model, and GCM simulations are taken from the Coupled Model Intercomparison Project (CMIP). The use of an ensemble allows an assessment of the sensitivity of results to differences in model formulation, and ensures that the research focuses on the robust model behaviors which are most likely to have simple physical explanations. A key concern in the research is the effect of biases on GCM projections for future climate change. For instance the loss of snowpack has strong implications for local climate and hydrology, thus a model which incorrectly simulates snowpack in a region which is not typically snow covered will likely overestimate sensitivity to warming in that region. The project also uses the RCM ensemble to look at the likelihood that climate change will increase the risk of wildfires in the PNW.The work is of societal as well as scientific interest as much of the effort in responding to climate change occurs at the local level. The Principal Investigators are engaged with a number of local organizations involved in planning for climate change, including the King County Department of Natural Resources, the US Forest Service, and Seattle City Light. The project involves undergraduate students through the Bothell campus of the University of Washington, which is a primarily undergraduate institution. It also supports a postdoctoral associate, thereby providing for the future scientific workforce in this research area.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.

顾名思义，全球变暖是对温室气体增加引起的能量不平衡的范围反应，温室气体的增加并散布并混合到全球周围均匀的浓度。但是，尽管它具有全球性质，但全球变暖的影响即使在小区域内也有所不同，具体取决于地形，地面覆盖，风向和其他因素的局部对比度。可以通过全球气候模型（GCM）研究这种局部效应，但是全球模型代表小规模效应的能力有限。即使GCM结果稳健且身体合理，如果发生在太小的尺度上而无法充分用GCM代表的尺度上，它们仍然可能会误导它们，这比它们捕获的大规模效应更具影响力。另一种策略是使用区域气候模型（RCM），该模型使用来自全球模型的信息模拟有限区域的天气和气候，以将该地区与世界其他地区联系起来。 The RCM can represent processes on smaller scales than the GCM, and a mismatch between GCM and RCM results can indicate a "mesoscale surprise", in which a local climate change expected from large-scale considerations is upstaged by smaller-scale processes (mesoscale is a technical term referring to spatial scales which are smaller than frontal weather systems but larger than individual cumulus clouds).This project seeks to understand the role of在太平洋西北（PNW）塑造气候变化的中尺度物理过程中，山区地形，靠近海岸以及陆上和近海风模式的交替的结合创造了研究中尺度惊喜的理想自然实验室。一个例子，GCMS表明，河流中洪水的最大增加将来自雨水和雪融合的混合，因为变暖减少了积雪和积雪对流量的调节作用。但是在RCM模拟中，更适合代表山区的降水量，洪水风险的最大增加发生在低海拔，以雨水为主的河流盆地中。这些盆地更容易受到大雨的暴露，因此它们更容易受到随着气候温暖的降水强度的增加而感染的。此处执行的工作使用RCM模拟集合，每一个模拟都使用来自不同GCM模拟的输出进行，以检查全球变暖对PNW的天气和气候的影响。该研究中使用的RCM是天气研究和预测模型（WRF）模型，GCM模拟取自耦合模型对比项目（CMIP）。合奏的使用允许评估结果对模型公式的差异的敏感性，并确保研究的重点是最有可能具有简单物理解释的稳健模型行为。研究的关键问题是偏见对GCM预测对未来气候变化的影响。例如，积雪的损失对局部气候和水文学具有很大的影响，因此，该模型错误地模拟了一个区域中的积雪，而Snowpack通常不会被覆盖，这可能会高估该地区对温暖的敏感性。该项目还使用RCM合奏来研究气候变化会增加PNW中野火风险的可能性。这项工作是社会和科学兴趣，以及在应对气候变化的努力上发生在地方一级。首席调查人员与许多参与计划气候变化的地方组织，包括金县自然资源部，美国森林服务局和西雅图城市灯。该项目涉及通过华盛顿大学的Bothell校园的本科生，该大学主要是一家本科机构。它还支持博士后合伙人，从而为该研究领域提供了未来的科学劳动力。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评估的评估来支持的。