Snow topography on undeformed Arctic sea ice captured by an idealized "snow dune" model

TL;DR

This paper introduces a simple Gaussian 'snow dune' model to accurately describe snow topography on flat Arctic sea ice, improving predictions of melt pond formation and energy balance in climate models.

Contribution

The paper develops and validates a Gaussian dune-based model of snow topography that generalizes previous models and links snow distribution to melt pond geometry and heat flux calculations.

Findings

Model accurately matches LiDAR snow depth data on flat ice.

Predicts melt pond coverage evolution during early pond formation.

Provides a criterion for ice to remain pond-free during summer.

Abstract

Our ability to predict the future of Arctic sea ice is limited by ice's sensitivity to detailed surface conditions such as the distribution of snow and melt ponds. Snow on top of the ice decreases ice's thermal conductivity, increases its reflectivity (albedo), and provides a source of meltwater for melt ponds during summer that decrease the ice's albedo. In this paper, we develop a simple model of pre-melt snow topography that accurately describes snow cover of flat, undeformed Arctic sea ice on several study sites for which data was available. The model considers a surface that is a sum of randomly sized and placed "snow dunes" represented as Gaussian mounds. This model generalizes the "void model" of Popovi\'c et al. (2018) and, as such, accurately describes the statistics of melt pond geometry. We test this model against detailed LiDAR measurements of the pre-melt snow topography. We show that the model snow-depth distribution is statistically indistinguishable from the measurements on flat ice, while small disagreement exists if the ice is deformed. We then use this model to determine analytic expressions for the conductive heat flux through the ice and for melt pond coverage evolution during an early stage of pond formation. We also formulate a criterion for ice to remain pond-free throughout the summer. Results from our model could be directly included in large-scale models, thereby improving our understanding of energy balance on sea ice and allowing for more reliable predictions of Arctic sea ice in a future climate.