[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.

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An unprecedentedly large ensemble of climate simulations with a 60-km atmospheric general circulation model and dynamical downscaling with a 20-km regional climate model has been performed to obtain probabilistic future projections of low-frequency local-scale events. The climate of the latter half of the twentieth century, the climate 4 K warmer than the preindustrial climate, and the climate of the latter half of the twentieth century without historical trends associated with the anthropogenic effect are each simulated for more than 5,000 years. From large ensemble simulations, probabilistic future changes in extreme events are available directly without using any statistical models. The atmospheric models are highly skillful in representing localized extreme events, such as heavy precipitation and tropical cyclones. Moreover, mean climate changes in the models are consistent with those in phase 5 of the Coupled Model Intercomparison Project (CMIP5) ensembles. Therefore, the results enable the assessment of probabilistic change in localized severe events that have large uncertainty from internal variability. The simulation outputs are open to the public as a database called “Database for Policy Decision Making for Future Climate Change” (d4PDF), which is intended to be utilized for impact assessment studies and adaptation planning for global warming.

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The Rhône-Aggregation (Rhône-AGG) Land Surface Scheme (LSS) intercomparison project is an initiative within the Global Energy and Water Cycle Experiment (GEWEX)/Global Land-Atmosphere System Study (GLASS) panel of the World Climate Research Programme (WCRP). It is a intermediate step leading up to the next phase of the Global Soil Wetness Project (GSWP) (Phase 2), for which there will be a broader investigation of the aggregation between global scales (GSWP-1) and the river scale. This project makes use of the Rhône modeling system, which was developed in recent years by the French research community in order to study the continental water cycle on a regional scale. The main goals of this study are to investigate how 15 LSSs simulate the water balance for several annual cycles compared to data from a dense observation network consisting of daily discharge from over 145 gauges and daily snow depth from 24 sites, and to examine the impact of changing the spatial scale on the simulations. The overall evapotranspiration, runoff, and monthly change in water storage are similarly simulated by the LSSs, however, the differing partitioning among the fluxes results in very different river discharges and soil moisture equilibrium states. Subgrid runoff is especially important for discharge at the daily timescale and for smallerscale basins. Also, models using an explicit treatment of the snowpack compared better with the observations than simpler composite schemes. Results from a series of scaling experiments are examined for which the spatial resolution of the computational grid is decreased to be consistent with large-scale atmospheric models. The impact of upscaling on the domainaveraged hydrological components is similar among most LSSs, with increased evaporation of water intercepted by the canopy and a decrease in surface runoff representing the most consistent inter-LSS responses. A significant finding is that the snow water equivalent is greatly reduced by upscaling in all LSSs but one that explicitly accounts for subgrid-scale orography effects on the atmospheric forcing.