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Keywords:

  • snow;
  • land surface model;
  • validation

[1] An evaluation is undertaken of the accuracy with which the Joint UK Land Environment Simulator (JULES) can simulate snow cover and depth when driven using data from the Hadley Centre regional climate model. The JULES model provides the facility to diagnose the thermal and hydrological state of the land surface and soil given time-varying inputs of air temperature, wind speed, humidity, shortwave and longwave radiation, and precipitation. The observed data set used in this study consists of daily snow depths measurements at 601 climate stations with more than 15 years of observations in the period from January 1976 to December 2000. In this study, the JULES model was driven using two data sets at 25 km horizontal resolution: one produced using the U. K. Met Office Hadley Centre regional climate model HadRM3-P (RCM), the other in which regional climate model precipitation and air temperature data were replaced with observed values (RCM+PT). The results indicate good agreement between the land surface model simulations and observations of snow cover at climate stations. The median snow cover accuracy indices for all 601 stations were 89% and 91% for the RCM and the combined RCM+PT driving data sets, respectively, with only a small interannual variation. In contrast, the differences between modeled and measured snow depth were much larger. The median values of mean snow depth bias were similar, −0.4 cm for RCM and −1.2 cm for RCM+PT; however, the RCM simulation was found to overestimate the observed snow depth at more than 25% of climate stations. The extent to which the results from RCM-driven simulations match observed data is strongly related to the accuracy of the RCM precipitation. The large overestimation has significant impact on the snow mass simulation and the assessment of extreme values in the mountains. We note that even if snow cover can be simulated with a high degree of accuracy, this should not imply a similarly high degree of accuracy in the simulation of snow depth. Model performance was poorest in regions of significant topographic heterogeneity and our findings suggest that the most promising additional model developments should be directed toward computationally efficient representations of subgrid topography.