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Nicholas Siler
,
Gerard Roe
, and
Dale Durran

Abstract

Washington State’s Cascade Mountains exhibit a strong orographic rain shadow, with much wetter western slopes than eastern slopes due to prevailing westerly flow during the winter storm season. There is significant interannual variability in the magnitude of this rain-shadow effect, however, which has important consequences for water resource management, especially where water is a critically limited resource east of the crest. Here the influence of the large-scale circulation on the Cascade rain shadow is investigated using observations from the Snowfall Telemetry (SNOTEL) monitoring network, supplemented by stream gauge measurements. Two orthogonal indices are introduced as a basis set for representing variability in wintertime Cascade precipitation. First, the total precipitation index is a measure of regionwide precipitation and explains the majority of the variance in wintertime precipitation everywhere. Second, the rain-shadow index is a measure of the east–west precipitation gradient. It explains up to 31% of the variance west and east of the crest. A significant correlation is found between the winter-mean rain shadow and ENSO, with weak (strong) rain shadows associated with El Niño (La Niña). The analysis is supported by streamflow data from eastern and western watersheds. A preliminary review of individual storms suggests that the strongest rain shadows are associated with warm-sector events, while the weakest rain shadows occur during warm-frontal passages. This is consistent with known changes in storm tracks associated with ENSO, and a variety of mechanisms likely contribute.

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Alison M. Anders
,
Gerard H. Roe
,
Dale R. Durran
, and
Justin R. Minder

Abstract

Persistent, 10-km-scale gradients in climatological precipitation tied to topography are documented with a finescale rain and snow gauge network in the Matheny Ridge area of the Olympic Mountains of Washington State. Precipitation totals are 50% higher on top of an ∼800-m-high ridge relative to valleys on either side, 10 km distant. Operational fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) runs on a 4-km grid produce similar precipitation patterns with enhanced precipitation over high topography for 6 water years.

The performance of the MM5 is compared to the gauge data for 3 wet seasons and for 10 large precipitation events. The cumulative MM5 precipitation forecasts for all seasons and for the sum of all 10 large events compare well with the precipitation measured by the gauges, although some of the individual events are significantly over- or underforecast. This suggests that the MM5 is reproducing the precipitation climatology in the vicinity of the gauges, but that errors for individual events may arise due to inaccurate specification of the incident flow.

A computationally simple model of orographic precipitation is shown to reproduce the major features of the event precipitation pattern on the windward side of the range. This simple model can be coupled to landscape evolution models to examine the impact of long-term spatial variability in precipitation on the evolution of topography over thousands to millions of years.

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