58 Snow surface temperature update process in Noah land surface model: a modified approach

Tuesday, 25 January 2011
Washington State Convention Center
Rebeka Sultana, University of California, Irvine, CA; and K. L. Hsu, J. Li, and S. Sorooshian

Noah, the community land surface model of NCEP-NCAR, is widely used to describe the land surface processes either in stand-alone (e.g. NLDAS, GLDAS) or in coupled land-atmospheric model systems (e.g. WRF). Although the model has been under development since 1984, it is recognized that snow water equivalent (SWE) can be underestimated. During the snow build-up season, snow grain size and density rapidly changes after the fresh snowfall, and therefore snow albedo changes as snow ages which is not effectively simulated in the Noah LSM. Another critical mechanism which is missing in Noah LSM is the liquid water holding capacity of the warm snow. This holding capacity allows storage of the melted water in the snowpack and thus inclusion of this phase delays snow melt. Although our experiments show that, improving above elements in Noah can reduce bias of SWE estimation, in many cases SWE is still underestimated.

We have further considered snow surface skin temperature to be different than average snowpack temperature in the energy and water balance calculation of Noah LSM. This was done by adaptation of the Utah Energy Balance (UEB) model (Tarboton and Luce, 1996). Like Noah LSM, the UEB model represents snow in a single snow layer and applies energy balance to compute average snowpack temperature at every time step. However, unlike Noah, within a single snow layer, UEB acknowledges snow surface skin temperature to be different than average snowpack temperature using an equilibrium gradient approach which is based on surface energy balance. Experiments with the modified approach were conducted in several Sierra Nevada SNOw TELemetry (SNOTEL) sites. Four water years – 2002 to 2005 with NLDAS forcing data were used for model inputs. The results show better simulation of snow accumulation and less bias when compared to that of the SNOTEL observation.

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