In 2015, the Olympic Mountains contain 255 glaciers and perennial snowfields totaling 25.34 ± 0.27 km2, half of the area in 1900, and about 0.75 ± 0.19 km3 of ice. Since 1980, glaciers shrank at a rate of -0.59 km2 yr-1 during which 35 glaciers and 16 perennial snowfields disappeared. Area changes of Blue Glacier, the largest glacier in the study region, was a good proxy for glacier change of the entire region. A simple mass balance model of the glacier, based on monthly air temperature and precipitation, correlates with glacier area change. The mass balance is highly sensitive to changes in air temperature rather than precipitation, typical of maritime glaciers. In addition to increasing summer melt, warmer winter temperatures changed the phase of precipitation from snow to rain, reducing snow accumulation. Changes in glacier mass balance are highly correlated with the Pacific North American index, a proxy for atmospheric circulation patterns and controls air temperatures along the Pacific Coast of North America. Regime shifts of sea surface temperatures in the North Pacific, reflected in the Pacific Decadal Oscillation (PDO), trigger shifts in the trend of glacier mass balance. Negative (‘cool’) phases of the PDO are associated with glacier stability or slight mass gain whereas positive (‘warm’) phases are associated with mass loss and glacier retreat. Over the past century the overall retreat is due to warming air temperatures, almost +1oC in winter and +0.3oC in summer. The glaciers in the Olympic Mountains are expected to largely disappear by 2070.

The Snow Ensemble Uncertainty Project (SEUP) is an effort to establish a baseline characterization of snow water equivalent (SWE) uncertainty across North America with the goal of informing global snow observational needs. An ensemble-based modeling approach, encompassing a suite of current operational models, is used to assess the uncertainty in SWE and total snow storage (SWS) estimation during the 2009-2017 period. The highest modeled SWE uncertainty is observed in mountainous regions, likely due to the relatively deep snow, forcing uncertainties, and variability between the different models in resolving the snow processes over complex terrain. This highlights a need for high-resolution observations in mountains to capture the high spatial SWE variability. The greatest SWS is found in Tundra regions where, even though the spatiotemporal variability in modeled SWE is low, there is considerable uncertainty in the SWS estimates due to the large areal extent over which those estimates are spread. This highlights the need for high accuracy in snow estimations across the Tundra. In mid-latitude boreal forests, large uncertainties in both SWE and SWS indicate that vegetation-snow impacts are a critical area where focused improvements to modeled snow estimation efforts need to be made. Finally, the SEUP results indicate that SWE uncertainty is driving runoff uncertainty and measurements may be beneficial in reducing uncertainty in SWE and runoff, during the melt season at high latitudes (e.g., Tundra and Taiga regions) and in the Western mountain regions, whereas observations at (or near) peak SWE accumulation are more helpful over the mid-latitudes.