Effect of forest canopy structure on wintertime Land Surface Albedo: Comparing CLM5 simulations to in-situ observations

Seasonal snow massively impacts the surface energy budget through its high reflectivity and is therefore an important component of land-atmosphere models. It affects climate through Snow Albedo Feedback (SAF), a positive feedback mechanism between a reduced snow cover extent due to climate warming and the corresponding increase of shortwave absorption, which provokes a further reduction in snow cover extent. SAF has been shown to be the largest climate feedback over the extratropical Northern Hemisphere (NH) during the snow melt period. Yet, large biases in SAF projections are linked to snow-vegetation interactions.

This study aims at investigating uncertainties associated with the representation of wintertime Land Surface Albedo (LSA) of forested environments in global climate models, which is an essential aspect when studying SAF. UAV-based observations of LSA were used to assess corresponding LSA simulations in CLM5, the land component of the NCAR Community Earth System Model. Our measurements capture a wide range of forest structure and species found in seasonally snow covered environments, spanning from Swiss sub-alpine to Finnish boreal forests, and show a strong dependency of LSA on solar angle and canopy density. CLM5 simulations failed to capture a realistic range in LSA and shortcomings were identified particularly with regards to simulations at sparsely forested sites. In these environments, Leaf Area Index as the main descriptor of canopy structure was unable to explain observed LSA differences in space and time. This study emphasizes the need to improve the representation of canopy structure in land surface models with critical implications for simulations of Snow Albedo Feedback strength over the NH extratropics.