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[1] Simulations of snow-covered area (SCA) over Northern Hemisphere lands by a suite of general circulation models (GCMs) are evaluated. Results from GCM experiments submitted by an international array of research groups participating in the second phase of the Atmospheric Model Intercomparison Project (AMIP-2) are compared to a data set derived primarily from visible band satellite imagery provided by the United States National Oceanic and Atmospheric Administration. At continental to hemispheric scales we find improvements over AMIP-1 models, including the elimination of temporal and spatial biases in simulations of the seasonal cycle of SCA, as well as improved simulations of the magnitude of interannual variability. At regional spatial scales, while no consistent model biases are identified over North America, regions over Eurasia are identified where models consistently either underestimate or overestimate SCA at the southern boundary of the seasonal snowpack. The region of greatest model bias is eastern Asia. While SCA biases are associated with temperature and precipitation biases, over only one region do we find a relationship between the magnitudes of SCA biases and the magnitudes of temperature and/or precipitation biases.

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Eighteen global atmospheric general circulation models (AGCMs) participating in the second phase of the Atmospheric Model Intercomparison Project (AMIP-2) are evaluated for their ability to simulate the observed spatial and temporal variability in snow mass, or water equivalent (SWE), over North America during the AMIP-2 period (1979–95). The evaluation is based on a new gridded SWE dataset developed from objective analysis of daily snow depth observations from Canada and the United States with snow density estimated from a simple snowpack model. Most AMIP-2 models simulate the seasonal timing and the relative spatial patterns of continental-scale SWE fairly well. However, there is a tendency to overestimate the rate of ablation during spring, and significant between-model variability is found in every aspect of the simulations, and at every spatial scale analyzed. For example, on the continental scale, the peak monthly SWE integrated over the North American continent in AMIP-2 models varies between ±50% of the observed value of ∼1500 km3. The volume of water in the snowpack, and the magnitudes of model errors, are significant in comparison to major fluxes in the continental water balance. It also appears that the median result from the suite of models tends to do a better job of estimating climatological mean features than any individual model. Year-to-year variations in large-scale SWE are only weakly correlated to observed variations, indicating that sea surface temperatures (specified from observations as boundary conditions) do not drive interannual variations of SWE in these models. These results have implications for simulations of the large-scale hydrologic cycle and for climate change impact assessments.