C41A-0318:
Ensemble Predictions of Future Snowfall Scenarios in the Karakorum and Hindu-Kush Mountains Using Downscaled GCM Data

Thursday, 18 December 2014
Thomas M. Mosier, David F Hill and Kendra V. Sharp, Oregon State University, Corvallis, OR, United States
Abstract:
Climate change is affecting the seasonality and mass of snow, and impacting the water resources of hundreds of millions of people who depend on streamflow originating in High Asia. Global climate model (GCM) outputs are the primary forcing data used to investigate future projections of changes in snow and glacier processes; however, these processes occur at a much finer spatial scale than the resolution of current GCMs. To facilitate studying the cryosphere in High Asia, we developed a software package to downscale monthly GCM data to 30-arcseconds for any global land area. Using this downscaling package, we produce an ensemble of downscaled GCM data from 2020-2100, corresponding to representative concentration pathways (RCPs) 4.5 and 8.5. We then use these data to model changes to snowfall in the Karakorum and Hindu Kush (KHK) region, which is located in High Asia. The ensemble mean of these data predict that total annual snowfall in 2095 will decrease by 22% under RCP 4.5 and 46% under RCP 8.5, relative to 1950-2000 climatological values. For both scenarios, the changes in snowfall are dependent on elevation, with the maximum decreases in snowfall occurring at approximately 2,300 m. While total snowfall decreases, an interesting feature of snowfall change for the RCP 8.5 scenario is that the ensemble mean projection shows an increase in snowfall for elevations between 3,000- 5,000 m relative to historic values. These fine-scale spatial, temporal, and elevation-dependent patterns of changes in projected snowfall significantly affect the energy balance of the snowpack, in turn affecting timing of melt and discharge. Therefore, our work can be coupled with a glacio-hydrological model to assess effects of these snowfall patterns on other processes or compared to existing model results to assess treatment of snow processes in the existing model. Our method is designed to downscale climate data for any global land area, allowing for the production of these fine-scale climate and cryosphere forcing data for other high altitude areas as well.