CAREER: Quantifying the Effects of Liquid Water Content on the Spectral Albedo of Snow

雪的高反射特性意味着它在气候系统中起着至关重要的作用；雪反射太阳能，调节全球气温。在北极系统中，降雪过程尤为重要，因为那里的气温上升速度超过全球平均水平，部分原因是降雪融化时发生的反馈过程。随着气温的升高，雪开始融化，这降低了雪的反射率，增加了它吸收的阳光量。被吸收的光导致温度进一步升高，这一变暖过程可能对我们的气候产生深远的影响。虽然雪是自然界最具反射率的材料之一，但确切的反射率变化很大。有几个因素使雪变黑，比如较大的雪颗粒和雪中的杂质，如灰尘、煤烟和藻类。一个不太清楚的因素是雪中的液态水含量是如何降低反射率的。这在确定积雪变化将如何影响气候系统方面，特别是在北极，带来了很大的不确定性，因为由于雨雪事件更加频繁以及冰盖和冰川表面融化的范围和持续时间更长，湿雪变得更加普遍。该项目将通过在明尼苏达州和科罗拉多州的实地测量、实验室实验和建模，增强我们对湿雪反射率的理解。我们的研究结果将探讨基本的物理关系，因此，广泛适用于寒冷地区。作为这项工作的一部分，本科生将参与研究项目。他们将在工程热力学课程中收集和分析数据，并为这项工作设计和构建仪器，并在新的工程研究员计划中支持其他教师项目。研究人员还将在网上和为少数民族服务机构的教师举办的研讨会上分享有关雪反射率和气候的教材。雪的自然高反照率（或反射率）对地球表面温度提供了强有力的控制。由于这一关键作用，准确地再现积雪反照率对有效的气候模型至关重要。即使在北极，已经普遍存在的湿雪期也在增加，因为雨雪事件更加频繁，冰川表面融化的范围和持续时间也在增加；然而，几乎所有现有的雪反照率模型都采用为干雪设计的反照率方案。这些模式在融雪过程中发挥关键作用，因为雪反照率反馈过程的放大效应，其中融雪导致较低的反照率、较高的温度和进一步的融雪。因此，明确纳入液态水含量对积雪反照率的影响是提高模式精度的关键下一步。提出的工作旨在量化液态水含量对雪反照率的影响，结合几种方法。1)研究者将在明尼苏达州和科罗拉多州的Niwot Ridge进行反照率、液态水含量、颗粒大小和雪杂质的实地测量，以确定单个物理性质对整体雪反照率的影响。2)通过在工程热力学课程中实施的一项新的基于课程的本科生研究体验（CURE），学生将在实验室测量具有控制颗粒大小和液态水含量的人造雪的反射率。3)研究者将利用两种不同的建模方法来计算湿雪反照率，以研究一系列雪况，并告知基于物理的雪反照率模型的潜在变化。在不同的景观、实验室和模拟中研究这些现象将使我们能够更广泛地将我们对湿雪反照率的理解外推到寒冷地区，特别是在快速变化的北极地区。该项目将通过提供参与研究和建立科学身份的机会，通过多种途径支持学生的发展。研究者将开发一个工程研究员项目，在这个项目中，学生除了参加专业发展研讨会课程外，还将与教师一起在一个设计项目上工作一年。该研究员还将与冰钻计划教育团队合作，作为冰学院研讨会的访问科学家，并创建一个关于雪反照率的在线虚拟野外实验室。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。

The highly reflective nature of snow means that it plays a critical role in the climate system; snow reflects solar energy and regulates global temperatures. Snow processes are especially relevant in the Arctic system, where temperatures are rising more rapidly than the global average, partially because of feedback processes that take place as snow melts. As air temperatures increase, snow begins to melt, which lowers the snow’s reflectivity and increases the amount of sunlight it absorbs. The absorbed light leads to further temperature increase, and this warming process can have far-reaching implications for our climate. Although snow is one of nature’s most reflective materials, the exact reflectivity can be quite variable. Several factors darken snow, such as larger snow grain sizes and impurities in the snow like dust, soot and algae. One factor that is not well understood is how the liquid water content in snow reduces reflectivity. This presents significant uncertainty in determining how changing snowpacks will impact the climate system, particularly in the Arctic, as wet snow becomes more prevalent due to more frequent rain on snow events and larger extent and duration of surface melt on ice sheets and glaciers. This project will enhance our understanding of wet snow reflectivity through field measurements in Minnesota and Colorado, lab experiments, and modeling. Our results will probe fundamental physical relationships and therefore, broadly apply to cold regions. As part of this work, undergraduate students will be engaged in the research projects. They will collect and analyze data in an Engineering Thermodynamics class and will design and build instrumentation for this work and to support other faculty projects in a new Engineering Fellows Program. The investigator will also share teaching materials about snow reflectivity and climate online and at a workshop for faculty at minority serving institutions.The naturally high albedo (or reflectivity) of snow provides a strong control on earth’s surface temperatures. Because of this critical role, accurately reproducing snow albedo is essential for effective climate modeling. Even in the Arctic, the already prevalent periods of wet snow are increasing because of more frequent rain on snow events and increased extent and duration of glacial surface melt; however, nearly all existing snow albedo models employ albedo schemes designed for dry snow. These models play a key role during snow melt because of the amplifying effects of the snow albedo feedback process, where melting snow leads to lower albedo, higher temperatures, and further snow melt. Therefore, explicitly incorporating the effects of liquid water content on snow albedo is a critical next step in improving model accuracy. The proposed work aims to quantify the effect of liquid water content on snow albedo, combining several approaches. 1) The investigator will conduct field-based measurements of albedo, liquid water content, grain size and snow impurities in Minnesota and at Niwot Ridge in Colorado to determine the effects of individual physical properties on the overall snow albedo. Wet snow conditions at these locations represent those that are increasingly common in the Arctic. 2) Through a new course-based undergraduate research experience (CURE) implemented in the Engineering Thermodynamics class, students will conduct laboratory measurements of the reflectance of artificial snow with controlled grain size and liquid water content. 3) The investigator will complement this work with two different modeling approaches to calculate wet snow albedo to investigate an array of snow conditions and inform potential changes to physically based snow albedo models. Studying these phenomena in different landscapes, in the laboratory, and in simulations will allow us to extrapolate our understanding of wet snow albedo to cold regions more broadly, particularly in the rapidly changing Arctic. This project will support the development of students through multiple avenues by providing opportunities to engage in research and build their scientific identities. The investigator will develop an Engineering Fellows Program in which students work with faculty over the course of the year on a design project, in addition to enrolling in a professional development seminar course. The investigator will also partner with the Ice Drilling Program Education team to serve as a visiting scientist in the School of Ice workshop and create an online Virtual Field Lab on snow albedo.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.