Pattern-based downscaling of snowpack variability in the western United States View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2021-12-27

AUTHORS

Nicolas Gauthier, Kevin J. Anchukaitis, Bethany Coulthard

ABSTRACT

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... »

PAGES

3225-3241

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00382-021-06094-z

DOI

http://dx.doi.org/10.1007/s00382-021-06094-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1144208046


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