Micrometeorological processes driving snow ablation in an Alpine catchment

Abstract

Abstract. Mountain snow covers typically become patchy over the course of a melting season. The snow pattern during melt is mainly governed by the end of winter snow depth distribution and the local energy balance. The objective of this study is to investigate micro-meteorological processes driving snow ablation in an Alpine catchment. For this purpose we combine a meteorological boundary-layer model (Advanced Regional Prediction System) with a fully distributed energy balance model (Alpine3D). Turbulent fluxes above melting snow are further investigated by using data from eddy-correlation systems. We compare modeled snow ablation to measured ablation rates as obtained from a series of Terrestrial Laser Scanning campaigns covering a complete ablation season. The measured ablation rates indicate that the advection of sensible heat causes locally increased ablation rates at the upwind edges of the snow patches. The effect, however, appears to be active over rather short distances of about 4-6 m. Measurements suggest that mean wind velocities of about 5 m s −1 are required for advective heat transport to increase snow ablation over a long fetch distance of about 20 m. Neglecting this effect, the model is able to capture the mean ablation rates for early ablation periods but strongly overestimates snow ablation once the fraction of snow coverage is below a critical value of approximately 0.6. While radiation dominates snow ablation early in the season, the turbulent flux contribution becomes important late in the season. Simulation results indicate that the air temperatures appear to overestimate the local air temperature above snow patches once the snow coverage is low. Measured turbulent fluxes support these findings by suggesting a stable internal boundary layer close to the snow surface causing a strong decrease of the sensible heat flux towards the snow cover.Correspondence to: R. Mott (mott@slf.ch) Thus, the existence of a stable internal boundary layer above a patchy snow cover exerts a dominant control on the timing and magnitude of snow ablation for patchy snow covers.

Statistical modelling of the snow depth distribution in open alpine terrain

“…1a) is a small alpine catchment that is located in the eastern part of the Swiss Alps. The site has been an intensive investigation area for snow studies in recent years Grünewald and Lehning, 2011;Mott et al, , 2011Mott et al, , 2013Wirz et al, 2011;Egli et al, 2012). Most of the area is above the local tree line and no tall vegetation is present.…”

“…McKay and Gray, 1981;Pomeroy and Gray, 1995;Essery et al, 1999;Trujillo et al, 2007;Lehning et al, 2008). Moreover, snow can be relocated by avalanches and sloughing (Blöschl and Kirnbauer, 1992;Gruber, 2007;Bernhardt and Schulz, 2010), and the local radiation and energy balance influence the spatially varying ablation processes (Cline et al, 1998;Pomeroy et al, 1998Pomeroy et al, , 2004Pohl et al, 2006;Mott et al, 2011).…”

Snow Temperature Changes within a Seasonal Snowpack and Their Relationship to Turbulent Fluxes of Sensible and Latent Heat

“…12a). Dewpoint temperature is notoriously difficult to measure (e.g., Bohren and Albrecht 1998, 181-271) and is even more complicated in the subcanopy, where turbulent mixing is weak and large humidity gradients can exist between the lowest measurement-level and the air just above the snow surface (Mott et al 2011). If air with T d near the snow surface temperature passes over the snowpack, condensation on the snowpack surface can occur, which would release latent heat energy, warm the snowpack, and initiate snowmelt.…”

“…In an effort to better understand the meteorological conditions that affect snowmelt, energy budgets of snowpacks have been studied for nearly 80 yr. Many studies use slow-response meteorological instruments with empirical bulk aerodynamic formulas to estimate the turbulent energy fluxes (e.g., Niederdorfer 1933;Sverdrup 1936;Takahashi et al 1956;Schlatter 1972;Male and Granger 1981;Marks and Dozier 1992;Marsh and Pomeroy 1996;Cline 1997;Hood et al 1999;Hawkins and Walton 2007), whereas more recent studies use fast-response instrumentation with the eddy covariance technique to calculate turbulent energy exchange (Hicks and Martin 1972;McKay and Thurtell 1978;Yen 1995;Mahrt and Vickers 2005;Hayashi et al 2005;Molotch et al 2007;Marks et al 2008;Reba et al 2009;Mott et al 2011). Marks et al (2008) showed that, for a snowpack under a pine canopy, the mean differences calculated over several weeks between the eddy covariance and bulkmethod fluxes were within 1-4 W m 22 (with better agreement during the day than at night); hourly differences, however, were significantly larger than the longterm mean difference.…”

“…This does not imply that the partitioning of surface energy (or sublimation/ablation rates) at Soddie and NWT are the same. Across such a complex landscape, it is well known that the surfaceatmosphere energy exchanges will vary depending on slope aspect, wind exposure, vegetation density, snow cover, and a host of other factors (e.g., Gustafson et al 2010;Mott et al 2011). To better understand the differences in energy partitioning at Soddie and NWT, similar instrumentation and a site-specific comparison would be necessary.…”