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This study describes and evaluates a new downscaling scheme that specifically addresses the need for hillslope-scale atmospheric-forcing time series for modelling the local impact of regional climate change projections on the land surface in complex terrain. The method has a global scope in that it does not rely directly on surface observations and is able to generate the full suite of model forcing variables required for hydrological and land surface modelling in hourly time steps. It achieves this by utilizing the previously published TopoSCALE scheme to generate synthetic observations of the current climate at the hillslope scale while accounting for a broad range of surface–atmosphere interactions. These synthetic observations are then used to debias (downscale) CORDEX climate variables using the quantile mapping method. A further temporal disaggregation step produces sub-daily fields. This approach has the advantages of other empirical–statistical methods, including speed of use, while it avoids the need for ground data, which are often limited. It is therefore a suitable method for a wide range of remote regions where ground data is absent, incomplete, or not of sufficient length. The approach is evaluated using a network of high-elevation stations across the Swiss Alps, and a test application in which the impacts of climate change on Alpine snow cover are modelled. [ABSTRACT FROM AUTHOR]

This study describes and evaluates a new downscaling scheme that specifically addresses the need for hillslope scale atmospheric forcing time-series for modeling the local impact of regional climate change projections on the land surface in complex terrain. The method has a global scope and is able to generate the full suite of model forcing variables required for hydrological and land surface modeling at hourly timesteps. It achieves this by utilising the previously published TopoSCALE scheme (Fiddes et al. 2014) to generate a synthetic observation of current climate at hillslope scale while accounting for a broad range of surface-atmosphere interactions. These synthetic observations are then used to debias (downscale) CORDEX climate variables using the quantile mapping method. A further temporal disaggregation step produces sub-daily fields. This approach has the advantages of other empirical-statistical methods, namely speed of use while avoiding the need for ground data, which is often limited. It is therefore a suitable method for a wide range of remote regions where ground data is absent, incomplete, or not of sufficient length. The approach is evaluated using a network of high elevation stations across the Swiss Alps and a test application of modelling climate change impacts on Alpine snow cover is given. [ABSTRACT FROM AUTHOR]