Including debris cover effects in a distributed model of glacier ablation

Distributed glacier melt models generally assume that the glacier surface consists of bare exposed ice and snow. In reality, many glaciers are wholly or partially covered in layers of debris that tend to suppress ablation rates. In this paper, an existing physically based point model for the ablation of debris-covered ice is incorporated in a distributed melt model and applied to Haut Glacier d’Arolla, Switzerland, which has three large patches of debris cover on its surface. The model is based on a 10 m resolution digital elevation model (DEM) of the area; each glacier pixel in the DEM is defined as either bare or debris-covered ice, and may be covered in snow that must be melted off before ice ablation is assumed to occur. Each debris-covered pixel is assigned a debris thickness value using probability distributions based on over 1000 manual thickness measurements. Locally observed meteorological data are used to run energy balance calculations in every pixel, using an approach suitable for snow, bare ice or debris-covered ice as appropriate. The use of the debris model significantly reduces the total ablation in the debris-covered areas, however the precise reduction is sensitive to the temperature extrapolation used in the model distribution because air near the debris surface tends to be slightly warmer than over bare ice. Overall results suggest that the debris patches, which cover 10% of the glacierized area, reduce total runoff from the glacierized part of the basin by up to 7%

A Distributed Energy-balance Melt Model of an Alpine Debris-covered Glacier

Distributed energy-balance melt models have rarely been applied to glaciers with extensive supraglacial debris cover. This paper describes the development of a distributed melt model and its application to the debris-covered Miage glacier, western Italian Alps, over two summer seasons. Sub-debris melt rates are calculated using an existing debris energy-balance model (DEB-Model), and melt rates for clean ice, snow and partially debris-covered ice are calculated using standard energy-balance equations. Simulated sub-debris melt rates compare well to ablation stake observations. Melt rates are highest, and most sensitive to air temperature, on areas of dirty, crevassed ice on the middle glacier. Here melt rates are highly spatially variable because the debris thickness and surface type varies markedly. Melt rates are lowest, and least sensitive to air temperature, beneath the thickest debris on the lower glacier. Debris delays and attenuates the melt signal compared to clean ice, with peak melt occurring later in the day with increasing debris thickness. The continuously debris-covered zone consistently provides ∼30% of total melt throughout the ablation season, with the proportion increasing during cold weather. Sensitivity experiments show that an increase in debris thickness of 0.035 m would offset 1°C of atmospheric warming

Sea-ice production over the Laptev Sea shelf inferred from historical summer-to-winter hydrographic observations of 1960s-1990s

The winter net sea-ice production (NSIP) over the Laptev Sea shelf is inferred from continuous summer-to-winter historical salinity records of 1960s–1990s. While the NSIP strongly depends on the assumed salinity of newly formed ice, the NSIP quasi-decadal variability can be linked to the wind-driven circulation anomalies in the Laptev Sea region. The increased wind-driven advection of ice away from the Laptev Sea coast when the Arctic Oscillation (AO) is positive implies enhanced coastal polynya sea-ice production and brine release in the shelf water. When the AO is negative, the NSIP and seasonal salinity amplitude tends to weaken. These results are in reasonable agreement with sea-ice observations and modeling

Growth Rate and Salinity Profile of First-Year Sea Ice in the High Arctic

AbstractThis paper describes the growth of sea ice and the salinity profiles observed in Eclipse Sound near Pond Inlet, Baffin Island, Canada, during the winter of 1977–78. A numerical method of calculation has been developed to incorporate the variations in snow conditions and physical properties of ice and snow during the growth season. It is shown that the growth rate can be predicted reasonably well. It is also shown that the vertical salinity profile in the ice towards the end of the season, provides a record of previous climatological conditions. A dependence has been shown between the predicted growth rate and the measured salinity.</jats:p