Pattern-based downscaling of snowpack variability in the western United States
AffiliationSchool of Geography, Development and Environment, University of Arizona
Laboratory of Tree-Ring Research, University of Arizona
KeywordsCanonical correlation analysis
Empirical orthogonal functions
Snow water equivalent
MetadataShow full item record
PublisherSpringer Science and Business Media LLC
CitationGauthier, N., Anchukaitis, K. J., & Coulthard, B. (2021). Pattern-based downscaling of snowpack variability in the western United States. Climate Dynamics.
Rights© The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractThe 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.
NoteOpen access article
VersionFinal published version
Sponsorsnational science foundation
Except where otherwise noted, this item's license is described as © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License.