Collaborative Research: Snow, Wind, and Time: Understanding Snow Redistribution and its Effects on Sea Ice Mass Balance

雪的隔热和反射特性对北极海冰的生长和衰减有很大影响。科学界的压倒性共识是，雪和海冰相互作用的细节必须更好地纳入地球系统模型，但关于雪过程的基本信息仍然缺乏量化。地球系统模型中对雪的有限处理主要基于多年冰实地实验的数据集，无法捕捉不断变化的雪的性质和过程。越来越普遍的更年轻，更薄的冰携带不同的积雪，可能比过去的多年冰对雪况更敏感。预测北极气候需要我们了解海冰上的雪及其在气候系统其他组成部分之间的相互作用和反馈。海冰上的雪的一个特别重要的方面是其精细尺度的空间再分布。风驱动的雪重新分配到沙丘和漂移控制热通量和融化池的形成，施加相当大的控制冰质量平衡。该项目的主要研究人员将使用综合实地观测和建模方法研究雪的分布、变化及其对冰质量平衡的影响。该项目将以多种方式促进STEM劳动力的发展。 它将在早期职业科学家的形成期提供支持。 它将支助一名研究生的培训。 它将吸引本科生和高中实习生参与研究工作。 将通过博客和课堂演示，与主要研究者所在机构附近的当地学校进行外联。 该项目将启动一项旨在提高巴罗学校科学参与度的推广计划。实地项目将在为期数月的实验过程中跟踪雪的分布，而建模工作将试图重现所观察到的雪况演变。激光雷达技术将跟踪雪表面的位置，因为漂移建立，侵蚀和迁移，创建具有厘米级精度的三维雪表面模型的时间序列。在坑研究中观察到的雪特性将与表面位置图合成，以构建三维雪地层学，用于模型初始化和总雪热特性研究。这些观测结果将被整合到一对分解尺度的雪和海冰模型中，以通过改变热传导和融化池的形成来量化雪再分布对海冰质量平衡的影响。模型试验和开发将允许调查模型中雪再分布的代表性，并将量化雪过程对年度冰质量平衡的重要性。将使用先前的现场观察和对近海站点的短期访问的库来验证现场站点的一般性并评估雪分布的可变性。该模型还将用于研究如何以地球系统模型中较粗的分辨率最好地聚合（或参数化）雪的特性和过程。调查结果和成果将与地球系统建模社区分享，以支持改进海冰上的雪表示的开发。

The insulating and reflective properties of snow substantially influence Arctic sea ice growth and decay. The overwhelming consensus within the scientific community is that the details of snow and sea ice interactions must be better incorporated in Earth System models, yet basic information on snow processes remains poorly quantified. The limited treatment of snow in Earth System models is largely based on datasets from field experiments on multi-year ice and does not capture changing snow properties and processes. Increasingly pervasive younger, thinner ice carries a different snowpack and is likely much more sensitive to snow conditions than the multi-year ice of the past. Predicting Arctic climate requires that we understand snow on sea ice and its interactions and feedbacks among the rest of the climate system components. A particularly important aspect of snow on sea ice is its fine-scale spatial redistribution. Wind-driven snow redistribution into dunes and drifts controls thermal fluxes and melt pond formation, exerting considerable control over ice mass balance. The principal investigators of this project will study snow distribution, its variability, and its effects on ice mass balance using an integrated field observation and modeling approach.This project will contribute to STEM workforce development in multiple fashions. It will provide support for an early-career scientist during his formative years. It will support the training of a graduate student. It will entrain undergraduate students and high school interns into the research effort. Outreach to local schools near the institutions of the principal investigators will be enabled through blogs and classroom presentations. The project will enable an outreach program targeted at improving science engagement at the Barrow schools.Field programs will track snow distributions over the course of a multi-month experiment, while modeling efforts will seek to reproduce the observed evolution of snow conditions. Lidar technology will track snow surface position as drifts build, erode, and migrate, creating time series of three-dimensional snow surface models with cm-scale accuracy. Snow properties observed in pit studies will be synthesized with surface position maps to construct a three-dimensional snow stratigraphy for model initialization and the study of aggregate snow thermal properties. The observations will be integrated into a pair of resolved-scale snow and sea ice models to quantify impacts of snow redistribution on sea ice mass balance through alteration of thermal conduction and melt pond formation. Model trials and development will permit investigation of the representations of snow redistribution in the models and will quantify the importance of snow processes on the annual ice mass balance. A library of prior field observations and short visits to offshore sites will be used to validate the generality of the field sites and assess the variability of snow distributions. The model will also be used to investigate how to best aggregate (or parameterize) snow properties and processes at coarser resolutions found in Earth System models. Findings and results will be shared with the Earth System modeling community to support development of improved snow-on-sea-ice representations.