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One of the most visible and widely felt impacts of climate warming is the change (mostly loss) of low-elevation snow cover in the midlatitudes. Snow cover that accumulates at temperatures close to the ice-water phase transition is at greater risk to climate warming than cold climate snowpacks because it affects both precipitation phase and ablation rates. This study maps areas in the Pacific Northwest region of the United States that are potentially at risk of converting from a snow-dominated to a rain-dominated winter precipitation regime, under a climate-warming scenario. A data-driven, climatological approach of snow cover classification is used to reveal these “at risk” snow zones and also to examine the relative frequency of warm winters for the region. For a rain versus snow temperature threshold of 0°C the at-risk snow class covers an area of about 9200 km2 in the Pacific Northwest region and represents approximately 6.5 km3 of water. Many areas of the Pacific Northwest would see an increase in the number of warm winters, but the impacts would likely be concentrated in the Cascade and Olympic Ranges. A number of lower-elevation ski areas could experience negative impacts because of the shift from winter snows to winter rains. The results of this study point to the potential for using existing datasets to better understand the potential impacts of climate warming.

Remote sensing offers local, regional and global observations of seasonal snow, providing key information on snowpack processes. This brief review highlights advancements in instrumentation and analysis techniques that have been developed over the past decade. Areas of advancement include improved algorithms for mapping snow-cover extent, snow albedo, snow grain size, snow water equivalent, melt detection and snow depth, as well as new uses of instruments such as multiangular spectroradiometers, scatterometry and lidar. Limitations and synergies of the instruments and techniques are discussed, and remaining challenges such as multisensor mapping, scaling issues, vegetation correction and data assimilation are identified.