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Identifying Coherence Across End-of-Century Montane Snow Projections in the Western United States
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  • Justin Pflug,
  • Sujay Kumar,
  • Ben Livneh,
  • Ethan D. Gutmann,
  • Sudershan Gangrade,
  • Shih-Chieh Kao
Justin Pflug
University of Maryland

Corresponding Author:[email protected]

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Sujay Kumar
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Ben Livneh
CIRES, University of Colorado
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Ethan D. Gutmann
National Center for Atmospheric Research (UCAR)
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Sudershan Gangrade
Oak Ridge National Laboratory (DOE)
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Shih-Chieh Kao
Oak Ridge National Laboratory
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Montane snowpack is a vital source of water supply in the Western United States. However, the future of snow in these regions in a changing climate is uncertain. Here, we use a large-ensemble approach to evaluate the consistency across 124 statistically downscaled snow water equivilent (SWE) projections between end-of-century (2076 – 2095) and early 21st century (2106 – 2035) periods. Comparisons were performed on dates corresponding with the end of winter (15 April) and spring snowmelt (15 May) in five western US montane domains. By benchmarking SWE climate change signals using the disparity between snow projections, we identified relationships between SWE projections that were repeatable across each domain, but shifted in elevation. In low to mid-elevations, 15 April average projected decreases to SWE of 48% or larger were greater than the disparity between models. Despite this, a significant portion of 15 April SWE volume (39 – 93%) existed in higher elevation regions where the disparities between snow projections exceeded the projected changes to SWE. Results also found that 15 April and 15 May projections were strongly correlated (r 0.82), suggesting that improvements to the spread and certainty of 15 April SWE projections would translate to improvements in later dates. The results of this study show that large-ensemble approaches can be used to measure coherence between snow projections and identify both 1) the highest-confidence changes to future snow water resources, and 2) the locations and periods where and when improvements to snow projections would most benefit future snow projections.
13 Aug 2023Submitted to ESS Open Archive
14 Aug 2023Published in ESS Open Archive