[1] Thirty-three snowpack models of varying complexity and purpose were evaluated across a wide range of hydrometeorological and forest canopy conditions at five Northern Hemisphere locations, for up to two winter snow seasons. Modeled estimates of snow water equivalent (SWE) or depth were compared to observations at forest and open sites at each location. Precipitation phase and duration of above-freezing air temperatures are shown to be major influences on divergence and convergence of modeled estimates of the subcanopy snowpack. When models are considered collectively at all locations, comparisons with observations show that it is harder to model SWE at forested sites than open sites. There is no universal ''best'' model for all sites or locations, but comparison of the consistency of individual model performances relative to one another at different sites (and vice versa). Calibration of models at forest sites provides lower errors than uncalibrated models at three out of four locations. However, benefits of calibration do not translate to subsequent years, and benefits gained by models calibrated for forest snow processes are not translated to open conditions.

A five-member ensemble of regional climate model (RCM) simulations for Europe, with a high resolution nest over Germany, is analysed in a two-part paper: Part I (the current paper) presents the performance of the models for the control period, and Part II presents results for near future climate changes. Two different RCMs, CLM and WRF, were used to dynamically downscale simulations with the ECHAM5 and CCCma3 global climate models (GCMs), as well as the ERA40-reanalysis for validation purposes. Three realisations of ECHAM5 and one with CCCma3 were downscaled with CLM, and additionally one realisation of ECHAM5 with WRF. An approach of double nesting was used, first to an approximately 50 km resolution for entire Europe and then to a domain of approximately 7 km covering Germany and its near surroundings. Comparisons of the fine nest simulations are made to earlier high resolution simulations for the region with the RCM REMO for two ECHAM5 realisations. Biases from the GCMs are generally carried over to the RCMs, which can then reduce or worsen the biases. The bias of the coarse nest is carried over to the fine nest but does not change in amplitude, i.e. the fine nest does not add additional mean bias to the simulations. The spatial pattern of the wet bias over central Europe is similar for all CLM simulations, and leads to a stronger bias in the fine nest simulations compared to that of WRF and REMO. The wet bias in the CLM model is found to be due to a too frequent drizzle, but for higher intensities the distributions are well simulated with both CLM and WRF at the 50 and 7 km resolutions. Also the spatial distributions are close to high resolution gridded observations. The REMO model has low biases in the domain averages over Germany and no drizzle problem, but has a shift in the mean precipitation patterns and a strong overestimation of higher intensities. The GCMs perform well in simulating the intensity distribution of precipitation at their own resolution, but the RCMs add value to the distributions when compared to observations at the fine nest resolution.

Capsule The latest snow model intercomparison identified the same modelling issues as previous iterations over 23 years. Lack of new insights are attributed partly to human errors and intercomparison projects design.

Precipitation data from long-term high-resolution simulations with two regional climate models (CLM and REMO) are evaluated using a climatology based on observations for south-western Germany. Both models are driven by a present day climate forcing scenario from the global climate model ECHAM5. The climatological evaluation shows a strong seasonal dependence of the model deficiencies. In spring and summer there are relatively small differences between simulation results and observations. But during winter both the regional models and ECHAM5 strongly overestimate the precipitation. The frequency distributions of the model results agree well with observed data. An overestimation of the precipitation at the upwind sides of mountainous areas occurs in the regional simulations. We found that the coupling of the regional models to the driving model is stronger in winter than in summer. Therefore, in winter the large scale model have a larger impact on the performance of the regional simulations. During summer the benefit of regional climate simulations is higher. ZusammenfassungDie vorliegende Arbeit beschreibt die Ergebnisse der Evaluierung des Niederschlags von zwei hoch aufgelösten Simulationen mit den regionalen Klimamodellen CLM und REMO für die Region Südwest-Deutschland. Dazu wurde eine auf Beobachtungen basierende Klimatologie benutzt. Die Randwerte beider regionalen Modelle stammen von dem globalen Klimamodell ECHAM5, welches Antriebsdaten für das zwanzigste Jahrhundert verwendet. Die klimatologische Evaluierung zeigt eine starke jahreszeitliche Abhängigkeit derÜbereinstimmung. Beide regionalen Modelle zeigen geringe Abweichungen zu den Beobachtungen im Frühjahr und Sommer. Im Winter allerdingsüberschätzen sowohl CLM und REMO als auch ECHAM5 die Niederschläge deutlich. Die Häufigkeitsverteilung des Niederschlags stimmt bei den Modellergebnissen gut mit den Beobachtungenüberein. In den regionalen Simulationen tritt einë Uberschätzung der Niederschläge an den windwärts gelegenen Seiten von gebirgigen Regionen auf. Die vorliegende Studie zeigt, dass die Kopplung zwischen regionalem und globalem Modell im Winter stärker ist als im Sommer. Daher hat das Verhalten des globalen Modells im Winter einen größeren Einfluss. Im Sommer ist der Gewinn durch die regionalen Klimasimulationen höher.

The projected climate change signals of a five-member high resolution ensemble, based on two global climate models (GCMs: ECHAM5 and CCCma3) and two regional climate models (RCMs: CLM and WRF) are analysed in this paper (Part II of a two part paper). In Part I the performance of the models for the control period are presented. The RCMs use a two nest procedure over Europe and Germany with a final spatial resolution of 7 km to downscale the GCM simulations for the present and future A1B scenario (2021-2050) time periods. The ensemble was extended by earlier simulations with the RCM REMO (driven by ECHAM5, two realisations) at a slightly coarser resolution. The climate change signals are evaluated and tested for significance for mean values and the seasonal cycles of temperature and precipitation, as well as for the intensity distribution of precipitation and the numbers of dry days and dry periods. All GCMs project a significant warming over Europe on seasonal and annual scales and the projected warming of the GCMs is retained in both nests of the RCMs, however, with added small variations. The mean warming over Germany of all ensemble members for the fine nest is in the range of 0.8 and 1.3 K with an average of 1.1 K. For mean annual precipitation the climate change signal varies in the range of -2 to 9 % over Germany within the ensemble. Changes in the number of wet days are projected in the range of ±4 % on the annual scale for the future time period. For the probability distribution of precipitation intensity, a decrease of lower intensities and an increase of moderate and higher intensities is projected by most ensemble members. For the mean values, the results indicate that the projected temperature change signal is caused mainly by the GCM and its initial condition (realisation), with little impact from the RCM. For precipitation, in addition, the RCM affects the climate change signal significantly.