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

雪的高反射性意味着它在气候系统中发挥着关键作用；雪反射太阳能并调节全球气温。降雪过程在北极系统中尤其相关，那里的气温上升速度比全球平均水平更快，部分原因是积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，增加了它对阳光的吸收。吸收的光会导致气温进一步升高，而这种变暖过程可能会对我们的气候产生深远的影响。虽然雪是自然界中反射性最强的材料之一，但确切的反射率可能会有很大的变化。有几个因素会使雪变暗，例如较大的雪粒尺寸和雪中的杂质，如灰尘、煤烟和藻类。一个还不清楚的因素是，雪中的液态水含量是如何降低反射率的。这给确定变化的积雪将如何影响气候系统带来重大不确定性，特别是在北极，因为由于降雪事件更频繁，以及冰盖和冰川表面融化的范围和持续时间更大，湿雪变得更加普遍。这个项目将通过在明尼苏达州和科罗拉多州的实地测量、实验室实验和建模来加深我们对湿雪反射率的理解。我们的结果将探索基本的物理关系，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，并将为这项工作设计和建造仪器，并在新的工程研究员计划中支持其他教员项目。研究人员还将在网上和少数族裔服务机构的教师研讨会上分享关于雪的反射率和气候的教学材料。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，准确地再现雪的反照率对于有效的气候模拟是至关重要的。即使在北极，由于雪上降雨更加频繁，冰川表面融化的程度和持续时间增加，本已普遍存在的潮湿雪期也在增加；然而，几乎所有现有的雪地反照率模型都采用了为干雪设计的反照率方案。这些模型在积雪融化过程中发挥了关键作用，因为积雪反照率反馈过程的放大效应，融化的积雪导致较低的反照率、较高的温度和进一步的积雪融化。因此，明确考虑液态水含量对雪反照率的影响是提高模型精度的关键下一步。这项拟议的工作旨在结合几种方法，量化液态水含量对雪反照率的影响。1)调查员将在明尼苏达州和科罗拉多州的尼沃特岭对反照率、液态水含量、颗粒大小和雪杂质进行实地测量，以确定个别物理性质对整体雪反照率的影响。这些地区的湿雪条件代表了北极地区日益常见的情况。2)通过在工程热力学课程中实施的以课程为基础的本科生研究体验(CURE)，学生将在实验室测量粒度和液态水含量受控的人造雪的反射率。3)调查员将用两种不同的建模方法来补充这项工作，以计算湿雪反照率，以调查一系列雪条件，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，将使我们能够将我们对湿雪反照率的理解外推到更广泛的寒冷地区，特别是在快速变化的北极地区。该项目将通过提供从事研究和建立科学认同感的机会，通过多种途径支持学生的发展。研究人员将开发一项工程研究员计划，在该计划中，学生除了参加专业发展研讨会课程外，还将在一年的课程中与教师合作进行设计项目。该研究人员还将与冰钻计划教育团队合作，担任冰雪学院研讨会的客座科学家，并创建一个关于雪的在线虚拟野外实验室。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.