Future Snow Changes over the Columbia Mountains, Canada, using a Distributed Snow Model

M. Mortezapour (), B. Menounos, P. L. Jackson and A. R. Erler
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M. Mortezapour: University of Northern British Columbia
B. Menounos: University of Northern British Columbia
P. L. Jackson: University of Northern British Columbia
A. R. Erler: Aquanty

Climatic Change, 2022, vol. 172, issue 1, No 6, 24 pages

Abstract: Abstract In western North America, many communities rely on runoff from mountain snowpacks. Projections of how future climate change will affect the seasonal snowpack are thus of interest to water managers, communities and policy makers. We investigate projected changes in seasonal snow cover for the twenty-first century for the Canadian portion of the Columbia River Basin using a physically based snow distribution model (SnowModel) at 500 m horizontal resolution. Forcing data for the reference (1979–1994) and future (2045–2059, 2085–2099) periods originate from a 4-member initial condition ensemble of global Community Earth System Model (CESM1) simulations based on the Representative Concentration Pathway (RCP) 8.5 scenario. The ensemble was dynamically downscaled (DD) to 10 km resolution using the Weather Research and Forecasting model (WRF). We also evaluate the performance of SnowModel using publicly available, statistically downscaled (SD) temperature and precipitation. We project a 38%/28% and 30%/15% decrease in WRF/SD-simulated snow depth and SWE, respectively, by the end of this century relative to the reference period over the entire domain. Our results indicate that the projected loss of snowpack depends largely on elevation and season. Snow depth and snow water equivalent (SWE) are most affected for elevations below 2000 m asl, with a reduction of more than 60%. While both simulations show SWE losses in most areas by the end of the century, a stronger projected thinning of the snowpack occurs for the DD-forced simulations compared to the SD-forced simulations.

Keywords: Climate projections; Snow distribution; Physically based snow model; Columbia Basin; Dynamically downscaled (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10584-022-03360-9

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