A Global land snow scheme (GLASS) v1.0 for the GFDL Earth System Model: Formulation and evaluation at instrumented sites

Abstract. Snowpack modulates water storage over extended land regions, and at the same time plays a central role in the surface albedo feedback, impacting the climate system energy balance. Despite the complexity of snow processes and their importance for both land hydrology and global climate, several state-of-the-art land surface models and Earth System Models still employ relatively simple descriptions of snowpack dynamics. In this study we present a newly-developed snow scheme tailored to the Geophysical Fluid Dynamics Laboratory (GFDL) Land Model version 4.1. This new snowpack model, named GLASS ("Global LAnd-Snow Scheme"), includes a refined and dynamical vertical layering snow structure which allows us to track in each snow layer the temporal evolution of snow grain properties, while at the same time limiting the model computational expense, as necessary for a model suited to global-scale climate simulations. In GLASS, the evolution of snow grain size and shape is explicitly resolved, with implications for predicted bulk snow properties, as they directly impact snow depth, snow thermal conductivity and optical properties. Here we describe the physical processes in GLASS and their implementation, as well as the interactions with other surface processes and the land-atmosphere coupling in the GFDL Earth System Model. The performance of GLASS is tested over 10 experimental sites, where in-situ observations allow for a comprehensive model evaluation. We find that, when compared to previous version of GFDL snow model, GLASS improves predictions of seasonal snow water equivalent and soil temperature under the snowpack.