Interannual variability of mountain snowpack has important consequences for ecological and socioeconomic systems, yet changes in variability have not been widely examined under future climates. Physically based snowpack simulations for historical (1970–1999) and high‐emission scenario (RCP 8.5) mid‐21st century (2050–2079) periods were used to assess changes in the variability of annual maximum snow water equivalent (SWEmax) and SWEmaxtiming across the western United States. Models show robust declines in the interannual variability of SWEmaxin regions where precipitation is projected to increasingly fall as rain. The average frequency of consecutive snow drought years (SWEmax< historical 25th percentile) is projected to increase from 6.6% to 42.2% of years. Models also project increases in the variability of SWEmaxtiming, suggesting reduced reliability of when SWEmaxoccurs. Differences in physiography and regional climate create distinct spatial patterns of change in snowpack variability that will require adaptive strategies for environmental resource management.
The decline in snowpack across the western United States is one of the most pressing threats posed by climate change to regional economies and livelihoods. Earth system models are important tools for exploring past and future snowpack variability, yet their coarse spatial resolutions distort local topography and bias spatial patterns of accumulation and ablation. Here, we explore pattern-based statistical downscaling for spatially-continuous interannual snowpack estimates. We find that a few leading patterns capture the majority of snowpack variability across the western US in observations, reanalyses, and free-running simulations. Pattern-based downscaling methods yield accurate, high resolution maps that correct mean and variance biases in domain-wide simulated snowpack. Methods that use large-scale patterns as both predictors and predictands perform better than those that do not and all are superior to an interpolation-based “delta change” approach. These findings suggest that pattern-based methods are appropriate for downscaling interannual snowpack variability and that using physically meaningful large-scale patterns is more important than the details of any particular downscaling method.
more » « less- Award ID(s):
- 1803995
- NSF-PAR ID:
- 10367579
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Climate Dynamics
- Volume:
- 58
- Issue:
- 11-12
- ISSN:
- 0930-7575
- Page Range / eLocation ID:
- p. 3225-3241
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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