In much of the boreal forests, snow covers the ground for half of the year. Since these boreal forests comprise approximately 15% of the land normally covered by snow during the winter and upwards of 40% of the land surface normally snow-covered during the spring and autumn, reliable measures of snow cover and snow mass are required for improved energy-balance and water-balance estimates. In this study, results from snow-depth climatological data (SDC), passive microwave satellite data, and output from general circulation models (GCMs) have been intercompared for the boreal forests of both North America and Eurasia. In Eurasia, during the winter months, snowmass estimates from these data sets correspond rather well; however, in North America, the passive microwave estimates are smaller than the estimates from the climatological data and the modeled data. The underestimation results primarily from the effects of vegetation on the microwave signal. The reason why the underestimation is a bigger problem in North America than in Eurasia is likely due to the use of global microwave algorithms that have not accounted for regional differences in the size of snow grains. The GCMs generally produce too much snow in the spring season. This is a result of the models having moisture amounts that are greater and temperatures that are slightly lower than observed, in the late winter and early spring periods. The models compare more favorably with the SDC in the Eurasian boreal forest than in the forests of North America during the winter season. However, in the spring, the model results for the North America boreal forest are in better agreement with the SDC than are the forests of Eurasia.