Perspectives on snow in the Third Pole and the Alps

The Hindu-Kush Karakoram Himalaya mountains and the Tibetan plateau are the world's largest snow and ice reservoir outside the polar regions and they are often referred to as the "Third Pole". These mountains feed the most important Asian river systems, and changes in snow dynamics in this area could severely impact on water availability for downstream populations, agriculture and energy production, on ecosystems and biodiversity. Despite their importance, the snowpack characteristics in the HKKH region are still poorly known, owing to the limited availability of surface observations in this remote and high elevation area. Global Climate Models still have too coarse spatial resolution to reproduce the small scale variability of precipitation and snowpack in orographically complex areas. Nevertheless, they may be effective in providing, even at a regional scale, a smooth but coherent picture of the large scale temporal and spatial patterns of snow cover and depth. The quantification of the uncertainties in GCM simulations is essential to define their skill in reproducing climate variability and to critically analyze future climate change projections. We investigate how the spatial and temporal variability of the snowpack in the HKKH region is represented in state-of-the-art GCMs participating in the CMIP5 effort, by analyzing the historical and future behavior of their simulated snow depth and snow water equivalent. We compare the model outputs in the historical period with the main, currently available datasets, including surface- and satellite-based observations and reanalysis data. We extended this work also to the Alps, making a comparison between the GCMs representation of the snowpack in the two mountain ranges. For the Alpine area, we are performing a snow modelling experiment studying how the temporal and spatial resolution of the forcing a new approach which aims at reproducing the temporal variability of the snow features at local scale using land-surface models forced by coarse-resolution meteorological variables as reanalyses.