Abstract Snow in the Great Salt Lake Basin is a vital resource for regional agriculture, municipal water use, and the Great Salt Lake. Accumulation of light absorbing particles (LAPs) on mountain snowpacks results in lower albedos and earlier melt compared to clean snow. Though snow darkening is linked to dust events and varies spatially, snowmelt impacts have primarily been studied at the point scale. To address this gap, a spatially distributed process‐based snow model (iSnobal) was used to simulate the snowpack under different albedos: a “baseline” estimate uninfluenced by episodic LAP variability, and an “observed” scenario where MODIS snow albedo retrievals informed darkened snow conditions. Shifts in snow disappearance date (SDD) between scenarios were used to quantify the cumulative impact of snow darkening on melt over contrasting water years (WYs). The SDD shifts were greater in WY 2022, with snow disappearing 23–29 days earlier, attributed to sunny weather and darker snow. In WY 2023, SDD shifts were moderate with melt advancing 11–16 days, despite similar melt season albedos to WY 2022. Frequent storms in WY 2023 delayed darkening until later in the season, when melt progressed suddenly due to rapid albedo declines and weak longwave losses. In both years, SDD shifts were pronounced at subalpine elevations (∼2,300–2,900 m), potentially related to snow albedo declines coinciding with high solar irradiance and snowfall patterns. These findings suggest that melt sensitivity to snow darkening shows consistent spatial patterns, but the magnitude of snowmelt impacts is controlled by seasonal variability in meteorology.
Lang et al. (Sun,) studied this question.