Representing moisture fluxes and phase changes in glacier debris cover using a reservoir approach

Due to the complexity of treating moisture in supraglacial debris, surface energy balance models to date have neglected moisture infiltration and phase changes in the debris layer. The latent heat flux (QL) is also often excluded due to the uncertainty in determining the surface vapour pressure. To quantify the importance of moisture on the surface energy and climatic mass balance (CMB) of debris-covered glaciers, we developed a simple reservoir parameterization for the debris ice and water content, as well as an estimation of the latent heat flux. The parameterization was incorporated into a CMB model adapted for debris-covered glaciers. We present the results of two point simulations, using both our new “moist” and the conventional “dry” approaches, on the Miage Glacier, Italy, during summer 2008 and fall 2011. The former year coincides with available in situ glaciological and meteorological measurements, including the first eddy-covariance measurements of the turbulent fluxes over supraglacial debris, while the latter contains two refreeze events that permit evaluation of the influence of phase changes. The simulations demonstrate a clear influence of moisture on the glacier energy and mass-balance dynamics. When water and ice are considered, heat transmission to the underlying glacier ice is lower, as the effective thermal diffusivity of the saturated debris layers is reduced by increases in both the density and the specific heat capacity of the layers. In combination with surface heat extraction by QL, subdebris ice melt is reduced by 3.1% in 2008 and by 7.0% in 2011 when moisture effects are included. However, the influence of the parameterization on the total accumulated mass balance varies seasonally. In summer 2008, mass loss due to surface vapour fluxes more than compensates for the reduction in ice melt, such that the total ablation increases by 4.0 %. Conversely, in fall 2011, the modulation of basal debris temperature by debris ice results in a decrease in total ablation of 2.1 %. Although the parameterization is a simplified representation of the moist physics of glacier debris, it is a novel attempt at including moisture in a numerical model of debris-covered glaciers and one that opens up additional avenues for future research

A Factorial Snowpack Model (FSM 1.0)

A model for the coupled mass and energy balances of snow on the ground requires representations of absorption of solar radiation by snow, heat conduction in snow, compaction of snow, transfer of heat to snow from the air and retention and refreezing of meltwater in snow. Many such models exist, but it has proven hard to relate their relative performances to the complexity of their process representations. This paper describes the systematic development of an open-source snowpack model with two levels of representation for each of the five processes mentioned above, allowing factorial experimental designs with 32 different model configurations. The model is demonstrated using driving and evaluation data recorded over one winter at an alpine site

Aerodynamic and Radiative Controls on the Snow Surface Temperature

Abstract The snow surface temperature (SST) is essential for estimating longwave radiation fluxes from snow. SST can be diagnosed using finescale multilayer snow physics models that track changes in snow properties and internal energy; however, these models are heavily parameterized, have high predictive uncertainty, and require continuous simulation to estimate prognostic state variables. Here, a relatively simple model to estimate SST that is not reliant on prognostic state variables is proposed. The model assumes that the snow surface is poorly connected thermally to the underlying snowpack and largely transparent for most of the shortwave radiation spectrum, such that a snow surface energy balance among only sensible heat, latent heat, longwave radiation, and near-infrared radiation is possible and is called the radiative psychrometric model (RPM). The RPM SST is sensitive to air temperature, humidity, ventilation, and longwave irradiance and is secondarily affected by absorption of near-infrared radiation at the snow surface that was higher where atmospheric deposition of particulates was more likely to be higher. The model was implemented with neutral stability, an implicit windless exchange coefficient, and constant shortwave absorption factors and aerodynamic roughness lengths. It was evaluated against radiative SST measurements from the Canadian Prairies and Rocky Mountains, French Alps, and Bolivian Andes. With optimized and global shortwave absorption and aerodynamic roughness length parameters, the model is shown to accurately predict SST under a wide range of conditions, providing superior predictions when compared to air temperature, dewpoint, or ice bulb calculation approaches.</jats:p