Understanding Heterogeneity to Improve Snow-albedo Feedbacks in a Simplified Regional Climate Model

Abstract:

Many local changes in future mean air temperature depend on the feedbacks and changes that will occur at the land surface. One of the most well known of these is the snow albedo feedback, in which warmer air causes snow to melt, which in turn changes the albedo and warms the air further. This is a dominant process in many model representations of warming at high latitudes and in mountains. Here we show that mischaracterization of the heterogeneity of snow likely overestimates the strength of this signal and underestimates the temporal duration of the signal. We show observation of snowpack in the mountains, prairies, and tundra illustrating the real spatial heterogeneity. We then incorporate a representation of that heterogeneity into a land surface model and run the Intermediate Complexity Atmospheric Research model (ICAR) with and without this representation to illustrate the impact heterogeneity has on land atmosphere interactions. In some seasons this minor change to the representation of snowpack heterogeneity can reduce the projected warming by half. Of note, ICAR itself runs fast enough that it could be embedded within a much coarser resolution Earth System Model to efficiently resolve and downscale larger heterogeneities that are below the resolution of the ESM. Finally, we discuss other potential implications of snow heterogeneity, including mixing in the Planetary Boundary Layer, and changes in the hydrologic response to warming.