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

雪的绝缘和反射特性对北冰洋海冰的生长和消融有很大影响。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。在地球系统模型中对雪的有限处理主要是基于多年冰的现场实验的数据集，没有捕捉到不断变化的雪的性质和过程。越来越普遍的更年轻、更薄的冰携带着不同的积雪，而且可能比过去多年的冰对降雪条件更加敏感。预测北极气候要求我们了解海冰上的雪及其与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它的精细空间重新分布。风驱动的雪重新分布到沙丘和漂流中，控制着热通量和融化池塘的形成，对冰的质量平衡施加了相当大的控制。该项目的主要研究人员将利用综合野外观测和模拟方法研究雪的分布、变化及其对冰块平衡的影响。该项目将以多种方式促进STEM劳动力的发展。它将为一位处于成长阶段的早期科学家提供支持。它将支持一名研究生的培训。它将吸引本科生和高中实习生参与研究工作。将通过博客和课堂讲演，与主要调查员机构附近的当地学校进行外联。该项目将启动一个旨在提高巴罗学校科学参与度的外展项目。现场项目将跟踪一个为期数月的实验过程中的降雪分布，同时建模工作将寻求重现观察到的降雪条件的演变。激光雷达技术将在漂流建立、侵蚀和迁移时跟踪雪面位置，创建精度为厘米级的三维雪面模型的时间序列。在坑道研究中观察到的雪特性将与表面位置图合成，以构建用于模型初始化和集合体雪热特性研究的三维雪地层学。这些观测将被整合到一对可分辨尺度的雪和海冰模型中，以通过改变热传导和融化池塘的形成来量化雪重新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表示，并将量化雪过程对年度冰质量平衡的重要性。将使用以前的实地观察库和对近海地点的短期访问来验证实地地点的一般性，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的较粗分辨率最好地聚合(或参数化)雪的特性和过程。结果和结果将与地球系统模型界分享，以支持改进的海上冰雪表示法的开发。

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.