Aerodynamic and Radiative Controls on the Snow Surface Temperature

The snow surface temperature (SST) is essential for estimating longwave radiation fluxes from snow. SST can be diagnosed using fine-scale 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 amongst only sensible heat, latent heat, longwave radiation and near-infrared radiation is possible and is called the Radiative Psychrometric Model (RPM). The RPM modelled 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 which 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 it is shown to accurately predict SST under a wide range of conditions, providing superior predictions when compared to air temperature, dew point or ice bulb calculation approaches.