672 Arctic Sea Ice: Snow Depth Distributions Over Variable Ice Types

Wednesday, 13 January 2016
Hall D/E ( New Orleans Ernest N. Morial Convention Center)
Marissa Dattler, University of Maryland, College Park, MD; and S. L. Farrell and T. Newman

The entire sea ice system affects, and is affected by the changing climate. One way the sea ice impacts the climate system is through its high albedo. As the sea ice melts, lower albedo ocean surface is exposed, resulting in greater solar absorption. In addition to albedo, melting sea ice causes an influx of fresh water to the ocean, influencing ocean circulation patterns. The diminishing sea ice pack could potentially be influencing Northern Hemisphere winter weather.

The snow on the surface of the sea ice is an important parameter of the Arctic sea ice system which impacts several important measurements. Depending on its thickness and density, snow acts to restrict the heat flux between the ocean and atmosphere. Snow has a higher albedo than ice or ocean. The amount of snow on the surface of the ice changes the mass balance calculation of sea ice thickness, and influences the location and extent of summer melt ponds.

Due to the harsh conditions of the Arctic, the data available to estimate snow thickness on the surface of the ice is limited. Here, snow depth data from previous field campaigns are used to quantify the characteristic form of the snow depth distribution with respect to ice topography, also known as ice class. The ice classes include level ice, slightly rougher ice/hummocky multiyear ice, and deformed ice.

In this study, each snow depth distribution, separated by ice class, is modeled by a fit. Across all field study sites, snow on level ice was lognormally distributed, whereas the snow cover on rougher, hummocky ice tends to be better fit by a Weibull distribution. Deformed ice remains difficult to characterize, as snow over pressure ridges has higher spatial heterogeneity than snow than on level ice. Our analysis brings together Arctic snow depth observations from several in-situ field campaigns conducted over the last fifteen years. Therefore, this project provides vital information for more accurate characterization of snow state in numerical sea ice models.

- Indicates paper has been withdrawn from meeting
- Indicates an Award Winner