The decrease in mountain snowpack associated with global warming is difficult to estimate in the presence of the large year-to-year natural variability in observations of snow-water equivalent (SWE). A more robust approach for inferring the impacts of global warming is to estimate the temperature sensitivity (λ) of spring snowpack and multiply it by putative past and future temperature rises observed across the Northern Hemisphere. Estimates of λ can be obtained from (i) simple geometric considerations based on the notion that as the seasonal-mean temperature rises by the amount δT, the freezing level and the entire snowpack should rise by the increment δT/Γ, where Γ is the mean lapse rate; (ii) the regression of 1 April SWE measurements upon mean winter temperatures; (iii) a hydrological model forced by daily temperature and precipitation observations; and (iv) the use of inferred accumulated snowfall derived from daily temperature and precipitation data as a proxy for SWE. All four methods yield an estimated sensitivity of 20% of spring snowpack lost per degree Celsius temperature rise. The increase of precipitation accompanying a 1°C warming can be expected to decrease the sensitivity to 16%. Considering observations of temperature rise over the Northern Hemisphere, it is estimated that spring snow-water equivalent in the Cascades portion of the Puget Sound drainage basin should have declined by 8%–16% over the past 30 yr resulting from global warming, and it can be expected to decline by another 11%–21% by 2050. These losses would be statistically undetectable from a trend analysis of the region’s snowpack over the past 30 yr.

A hierarchical clustering algorithm using Ward's method has been applied to the 500-hPa geopotential height field in the Pacific-North American sector. In contrast to previous clustering studies that measure distance between records by using all the grid points within the domain (full-field method), the procedure outlined here, referred to as the limited-contour method, focuses on the coordinates of the 540-dam contour as the distance measure. Comparison between the regimes emerging from the two methods shows that the limited-contour method is more efficient than the full-field method with respect to grouping maps with ridges located at similar longitudes. The four regimes emerging from the limited-contour clustering analysis have been named as follows: Off-Shore Trough, Alaskan Ridge, Coastal Ridge, and Rockies Ridge. The frequencies of occurrence of the regimes have a significant relationship with the phase of the El Niño-Southern Oscillation. El Niño winters exhibit a strong preference for the Rockies Ridge pattern; La Niña winters exhibit a greater diversity of regimes. The frequencies of occurrence of extreme cold outbreaks and episodes of heavy precipitation in the Pacific Northwest show a relatively strong connection to the regime type. For other regions in the western portion of the United States, only the frequency of occurrence of cold outbreaks exhibits a significant relationship to regime type.

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Despite overall improvements in numerical weather prediction and data assimilation, large short-term forecast errors of sea level pressure and 2-m temperature still occur. This is especially true for the west coast of North America where short-term numerical weather forecasts of surface low pressure systems can have large position and central pressure errors. In this study, forecast errors of sea level pressure and temperature in the Pacific Northwest are related to the shape of the large-scale flow aloft. Applying a hierarchical limitedcontour clustering algorithm to historical 500-hPa geopotential height data produces four distinct weather regimes. The Rockies ridge regime, which exhibits a ridge near the axis of the Rocky Mountains and nearly zonal flow across the Pacific, experiences the highest magnitude and frequency of large sea level pressure errors. On the other hand, the coastal ridge regime, which exhibits a ridge aligned with the North American west coast, experiences the highest magnitude and frequency of large 2-m minimum temperature errors.