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

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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.