Simulating high elevation snowpack: The impact of snow albedo and multi-layer snow treatment
Jinwon Kim, UCLA, Los Angeles, CA; and D. E. Waliser, Y. Xue, A. Hall, K. N. Liou, S. Kapnick, and F. De Sale
The impact of snow albedo and the use of a multi-layer snow model on simulating snowpack have been investigated using the WRF model. The impact of snow albedo on the cold season Sierra Nevada snowpack is examined in five cold season simulations with five different sets of snow albedo values. The control run utilizes the default snow albedo values provided with the Noah LSM in the WRF model. In the four sensitivity runs, the default snow albedo values were modified at 75, 90, 110, and 125% of the control run. The two smaller snow albedo values represent the cases in which black carbon emissions in California will continue to increase in the future. The two larger snow albedo values assume the cases in which anthropogenic emissions, hence black carbon, will be reduced by successful implementation of recent mandates by California's governor.
The WRF model runs were performed over a 12km-resolution California domain. The sensitivity of the Sierra snowpack to snow albedo tends to increase with increasing terrain elevation, especially during late winter and early spring. In higher elevation regions, the period of largest sensitivity occurs later in the season compared to lower elevations. Similarly, the timing for peak increase in snow water equivalence (SWE) due to increased snow albedo occurs later in the season as the terrain elevation increases. In response to the changes in snow albedo, snowmelt in high elevation regions varies significantly. With larger albedos, snowmelt is delayed by as much as one month. With the decrease in snow albedo, snowmelt in earlier months increase, and the timing of the increased snowmelt appears in later months in higher elevations. Thus, the most notable impact of the decrease in snow albedo is enhanced (reduced) snowmelt in earlier (later) part of the cold season, resulting in adverse impacts on warm season water resources in California. The two experiments with larger snow albedo values show that an increase in snow albedo will suppress snowmelt in the early part of the cold season and will increase in the later part of the season. This can partially alleviate the adverse impact of global warming on California water resources which will promote earlier snow depletion. The timing of peak increase also varies with elevation in a similar way as in the case of reduced snow albedo. A preliminary test shows that of a 3-layer snow model in conjunction with the SSiB LSM could improve snowpack simulation from the simulation in which the default Noah LSM is used.
Poster Session 7, Regional climate modeling
Thursday, 15 January 2009, 9:45 AM-11:00 AM, Hall 5
Previous paper Next paper
Browse or search entire meeting
AMS Home Page