Abstract
Understanding future precipitation changes is critical for water supply and flood risk applications in the western United States. The North American COordinated Regional Downscaling EXperiment (NA-CORDEX) matrix of global and regional climate models at multiple resolutions (~ 50-km and 25-km grid spacings) is used to evaluate mean monthly precipitation, extreme daily precipitation, and snow water equivalent (SWE) over the western United States, with a sub-regional focus on California. Results indicate significant model spread in mean monthly precipitation in several key water-sensitive areas in both historical and future projections, but suggest model agreement on increasing daily extreme precipitation magnitudes, decreasing seasonal snowpack, and a shortening of the wet season in California in particular. While the beginning and end of the California cool season are projected to dry according to most models, the core of the cool season (December, January, February) shows an overall wetter projected change pattern. Daily cool-season precipitation extremes generally increase for most models, particularly in California in the mid-winter months. Finally, a marked projected decrease in future seasonal SWE is found across all models, accompanied by earlier dates of maximum seasonal SWE, and thus a shortening of the period of snow cover as well. Results are discussed in the context of how the diverse model membership and variable resolutions offered by the NA-CORDEX ensemble can be best leveraged by stakeholders faced with future water planning challenges.
Highlights
1.1 MotivationWater management in the western United States remains a complex challenge for society as scientists and stakeholders alike face an increasingly delicate balancing act between “feast or famine” precipitation regimes
Despite the challenges in snowfall and snow water equivalent (SWE) assessment due to limited observations, a relatively small model ensemble, and significant model spread in the historical simulations, the consistency in the sign and magnitude of projected changes in snowfall in this region still suggest a degree of projection confidence by way of model agreement that may provide value to stakeholders and decision-makers
We evaluate the representation of mean monthly and seasonal precipitation, extreme daily precipitation, and NA-CORDEX models generally reproduce the historical observed large-scale orographic precipitation enhancement features across the western U.S, they tend to overestimate mean seasonal precipitation relative to the observations used here
Summary
The NA-CORDEX experiment aims to add value to the existing body of climate model projections by using multiple resolutions and a matrix of global and regional climate models to facilitate regional climate model intercomparison studies and serve the impact and adaptation communities (Giorgi et al 2009; na-cordex.org). The present study aims to complement these analyses and contribute to the larger body of work seeking to understand what can be learned—and what can be most effectively used by water management decision-makers—from this relatively new collection of RCM projections Model datasets such as NA-CORDEX offer appeal to stakeholders because they can, in theory, provide an array of possible future climate states, derived from physically-consistent, spatially and temporally continuous gridded model output that can be used for secondary/application models. These data, by virtue of being produced by dynamical prognostic models as opposed to those based on statistical modeling using historical conditions, provide physical process insight into how and why specific climate change impacts evolve in particular model projections. We ask: what does the NA-CORDEX model dataset reveal about western U.S precipitation projections with respect to means, extremes, precipitation type, and its regional and seasonal distribution? How does the NA-CORDEX project offer unique advantages to stakeholders and end users? Does increased resolution add or change confidence based on consistency with existing climate projections? Does the NACORDEX dataset advance physical process-based insight with which to better understand the causes of projected changes?
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