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Numerical simulations of land surface processes are important in order toperform landscape-scale assessments of earth systems. This task isproblematic in complex terrain due to (i) high-resolution grids required tocapture strong lateral variability, and (ii) lack of meteorological forcing datawhere they are required. In this study we test a topography and climateprocessor, which is designed for use with large-area land surface simulation,in complex and remote terrain. The scheme is driven entirely by globallyavailable data sets. We simulate air temperature, ground surface temperatureand snow depth and test the model with a large network of measurements in theSwiss Alps. We obtain root-mean-squared error (RMSE) values of 0.64 °C for air temperature,0.67–1.34 °C for non-bedrock ground surface temperature, and44.5 mm for snow depth, which is likely affected by poor input precipitationfield. Due to this we trial a simple winter precipitation correction methodbased on melt dates of the snowpack. We present a test application of thescheme in the context of simulating mountain permafrost. The scheme producesa permafrost estimate of 2000 km<sup>2</sup>, which compares well to publishedestimates. We suggest that this scheme represents a useful step inapplication of numerical models over large areas in heterogeneous terrain

Large-area land surface simulations in heterogeneous terrain driven by global data sets: application to mountain permafrost