Intermittent snow, which appears and disappears multiple times over the course of a winter, provides critical feedbacks to the fields of atmospheric science (by altering surface albedo and temperature), hydrology (through melt contributions to rain-on-snow floods), soil science (through insulating the land surface), and ecology (through insulation and water supply). Despite its scientific and societal importance, intermittent snow is a modeling challenge. Focusing on intermittent snow in both the Washington Cascades and the plains of Colorado, this research project will use a mix of observations and physically-based modeling to improve understanding of how different snow processes combine to influence snowpack evolution. Controlled numerical experiments will examine (1) multiple methods to estimate fluxes at the snow-atmosphere interface, including approaches used to estimate the surface albedo, the turbulent fluxes of sensible and latent heat, and the partitioning of precipitation between rain and snow; (2) multiple methods to simulate internal processes within the snowpack, including heat conduction, penetration of shortwave radiation, vertical drainage of liquid water, and compaction of the snowpack associated with metamorphism of the snow crystals; and (3) multiple methods to simulate fluxes at the lower boundary associated with heat transfer in the soil. Model simulations, isolating one process at a time, will be compared with detailed measurements, both at point locations and distributed across the landscape. This research aims to provide a better understanding of dominant processes in the intermittent snow zone, a better understanding of major modeling uncertainties, and a path forward towards an improved, coupled atmosphere-hydro model. Because intermittent snow is almost always ripe to melt, it responds immediately to energy inputs, resulting in a change in snow water equivalent (SWE) rather than just a change in internal snowpack temperature. This readiness-to-melt makes intermittent snow an extra sensitive indicator of snow model performance. Therefore, any model improvements vetted in this area will translate into better snow modeling everywhere, including the seasonal snow zone (where snow lasts all winter).
Although intermittent snow is only present part of the winter, it has important impacts on the atmosphere, the land surface, and society. Snow increases the reflectivity of the Earth?s surface and lowers the temperature, and it also insulates the soil, protecting the ground surface from potentially damaging frost. During rain-on-snow storms, this lower-elevation snow melts and contributes to flooding (a hazard), but at other times, melting snow from this zone contributes to summer water supplies (a resource). Intermittent snow in cities and along major highways hinders transportation and city operations. The intermittent snow zone has been clearly identified as the most sensitive region to climate change, and many areas that currently have seasonal snow are predicted to shift to an intermittent snow regime. For all of these reasons, it is important to model intermittent snow correctly. This project will improve the next generation of snow models used for hydrologic and climate prediction.
