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

顾名思义，全球变暖是全球范围内对温室气体增加造成的能量不平衡的反应，温室气体在全球范围内扩散并混合成统一的浓度。但是，尽管全球变暖具有全球性，但它的影响即使在小区域内也会有所不同，这取决于当地地形、地面覆盖、风向和其他因素的差异。这种局部效应可以用全球气候模式（GCMs）来研究，但全球模式对小尺度效应的表征能力有限。即使GCM的结果是可靠的和物理上合理的，如果发生在太小的尺度上的物理机制不能被GCM充分代表，那么它们仍然可能具有误导性，而不是它们所捕获的更大的尺度效应。另一种策略是使用区域气候模型（RCM），它利用来自全球模型的信息模拟有限区域的天气和气候，将该区域与世界其他地区联系起来。RCM可以代表比GCM更小尺度的过程，而GCM和RCM结果之间的不匹配可以表明“中尺度惊喜”，即从大尺度考虑预期的局部气候变化被小尺度过程所取代（中尺度是一个技术术语，指的是比锋面天气系统小但比单个累积云大的空间尺度）。该项目旨在了解太平洋西北地区（PNW）中尺度物理过程在塑造气候变化中的作用，该地区的山地地形、靠近海岸以及陆上和海上风模式的交替，为研究中尺度惊喜创造了理想的自然实验室。例如，gcm表明，洪水的最大增加将发生在径流来自雨水和融雪混合的河流流域，因为变暖减少了积雪，而积雪对水流有调节作用。但在更适合代表山区降水的RCM模拟中，洪水风险的最大增加发生在低海拔、以雨为主的河流流域。这些盆地更容易受到暴雨的影响，因此它们更容易受到气候变暖时降水强度增加的影响。这里进行的工作使用了RCM模拟的集合，每个RCM模拟都使用不同GCM模拟的输出，以检查全球变暖对PNW天气和气候的影响。研究中使用的RCM是天气研究与预报（WRF）模式，GCM模拟来自耦合模式比对项目（CMIP）。使用集合可以评估结果对模型公式差异的敏感性，并确保研究重点放在最有可能具有简单物理解释的鲁棒模型行为上。研究中的一个关键问题是偏差对GCM预测未来气候变化的影响。例如，积雪的损失对当地气候和水文具有强烈的影响，因此，一个模型如果不正确地模拟一个通常没有积雪覆盖的地区的积雪，就可能高估该地区对变暖的敏感性。该项目还使用RCM集合来研究气候变化增加PNW野火风险的可能性。这项工作具有社会和科学意义，因为应对气候变化的大部分努力都发生在地方层面。主要研究人员与一些参与气候变化规划的当地组织合作，包括金县自然资源部、美国林务局和西雅图城市之光。该项目涉及华盛顿大学Bothell校区的本科生，这是一个主要的本科生机构。它还支持一名博士后，从而为该研究领域的未来科学劳动力提供支持。该奖项反映了美国国家科学基金会的法定使命，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。

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.