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极海冰的生长和衰减有很大的影响。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很少量化。地球系统模式对雪的有限处理主要基于多年冰的现场实验数据集，没有捕捉到雪的变化特性和过程。越来越普遍的更年轻、更薄的冰携带着不同的积雪，可能比过去多年的冰对雪况更敏感。预测北极气候要求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。风驱动的雪重新分布到沙丘和漂移控制了热通量和融化池的形成，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积雪分布、变率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。它将为一名早期职业科学家的成长阶段提供支持。它将支持研究生的培训。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向主要研究人员所在机构附近的当地学校进行宣传。该项目将使一项旨在提高巴罗学校科学参与度的推广计划成为可能。实地项目将在一个为期数月的实验过程中跟踪雪的分布，而建模工作将寻求再现观测到的雪条件演变。激光雷达技术将跟踪积雪表面的位置，随着积雪的形成、侵蚀和迁移，创建具有厘米尺度精度的三维积雪表面时间序列模型。将坑研究中观测到的雪性质与地表位置图综合起来，构建三维雪地层，用于模型初始化和集雪热性质研究。这些观测结果将被整合到一对分辨率尺度的雪和海冰模型中，以量化雪再分布通过改变热传导和融化池形成对海冰质量平衡的影响。模型试验和开发将允许对模型中雪再分布的表征进行调查，并将量化雪过程对年冰质量平衡的重要性。以前的实地观测和对近海地点的短期访问将用于验证实地地点的一般性和评估雪分布的变异性。该模型还将用于研究如何在地球系统模型中发现的较粗分辨率下最好地聚合（或参数化）雪的特性和过程。研究结果和结果将与地球系统建模社区共享，以支持改进海冰表征的发展。

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.