10.1 Heterogeneity of Mountain Snow: Measurement, Modeling, and Implications

Thursday, 16 July 2020: 10:35 AM
Virtual Meeting Room
Ethan Gutmann, NCAR, Boulder, CO; and T. H. Painter, L. Bearup, K. Andreadis, C. Vuyovich, G. E. Liston, D. Reynolds, and J. D. Lundquist

Handout (16.2 MB)

Extreme spatial heterogeneity of snow is everywhere in the mountains, most notably above treeline. This heterogeneity makes measurements difficult, and to date the best measurements come from airborne lidar such as the Airborne Snow Observatory (ASO), with satellite snow covered area providing a longer record, but with more limited information. In situ measurements are challenged by the heterogeneity, though there is a history of snow courses (manually measured transects) that provide extremely valuable information for ground truth. Modeling this heterogeneity presents additional challenges, because the dominant processes controlling spatial heterogeneity occur on very fine (1-10 meter) length scales. Here we evaluate state of the art snow redistribution modeling with the spatially explicit SnowModel. SnowModel provides the capability to represent the important physical processes involved, including estimating the wind field on fine spatial scales. The windfield in particular is challenging, especially over large spatial domains. We present approaches to improve this wind field, including analytical solutions, empirical solutions, and fully explicit large eddy simulations with an immersed boundary condition. We propose that a combination of these methods is likely to provide the most fruitful path forward in the near term, and that mountain meteorologists with more knowledge of the important turbulent boundary layer processes should engage the snow community to improve such models. Finally, we present a few key implications of better understanding the heterogeneity of mountain snowpack ranging from streamflow timing to the sensitivity of the snow albedo feedback and climate change.

Supplementary URL: https://youtu.be/7EYI4mqTlUM

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