Thursday, April 13, 2017, 12:30PM - 1:30PM
Snow pits, dubious meteorological data, and long-term snowpack simulations: Or, how snow gets cold and why it matters
Observational data are essential for advancing our understanding of physical processes in natural environments; however, the effectiveness of such data can be limited by reduced temporal resolution. Physics-based models therefore offer a promising alternative for testing hypotheses, but producing reliable output requires accurate forcing and validation data. This project leverages a 20-year snow pit record from the Niwot Ridge LTER to quantify the meteorological and energy balance controls on snowpack cold content (i.e., energy deficit) development and snowmelt generation. Snow pit observations in the alpine and subalpine were used to validate and improve snowpack simulations, which were forced with a quality controlled, serially complete meteorological dataset. Observations and simulations showed cold content increases primarily through new snowfall rather than through a negative surface energy balance. In the subalpine, fluxes exert a larger influence on cold content as a greater relative portion of the snowpack interacts with surface energy exchange. The timing of peak cold content and spring precipitation reliably predict snowmelt onset, explaining 68% of the variance in melt timing. Additionally, snow melts more rapidly in the alpine where the turbulent fluxes play a larger role in snowmelt generation. At both sites, drought years with protracted melt seasons exhibit the lowest snowmelt rates and greatest proportion of the snowpack lost to sublimation.