Abstract
The Hindu Kush, Karakoram, and Himalaya (HKKH) mountain ranges feed the most important Asian river systems, providing water to about 1.5 billion people. As a consequence, changes in snow dynamics in this area could severely impact water availability for downstream populations. Despite their importance, the amount, spatial distribution, and seasonality of snow in the HKKH region are still poorly known, owing to the limited availability of surface observations in this remote and high-elevation area. This work considers global climate models (GCM) participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5) and analyzes how they represent current and future snowpack in the HKKH region in terms of snow depth and snow water equivalent. It is found that models with high spatial resolution (up to 1.25°) simulate a spatial pattern of the winter snowpack in greater agreement with each other, with observations, with reanalysis datasets, and with the orographic features of the region, compared to most lower-resolution models. The seasonal cycle of snow depth displays a unimodal regime, with a maximum in February–March and almost complete melting in summer. The models generally indicate thicker [in Hindu Kush–Karakoram (HKK)] or comparable (in the Himalayas) snow depth and higher snow water equivalent compared to the reanalyses for the control period 1980–2005. Future projections, evaluated in terms of the ensemble mean of GCM simulations, indicate a significant reduction in the spatial average of snow depth over the HKK and an even stronger decrease in the Himalayas, where a reduction between 25% and 50% is expected by the end of the twenty-first century.
Model improvement and future projection of permafrost processes in a global land surface model
