Snow Albedo Feedbacks Enhance Snow Impurity‐Induced Radiative Forcing in the Sierra Nevada

Abstract

This study employs a fully coupled meteorology‐chemistry‐snow model to investigate the impacts of light‐absorbing particles (LAPs) on snow darkening in the Sierra Nevada. After comprehensive evaluation with spatially and temporally complete satellite retrievals, the model shows that LAPs in snow reduce snow albedo by 0.013 (0–0.045) in the Sierra Nevada during the ablation season (April‐July), producing a midday mean radiative forcing of 4.5 W m−2 which increases to 15–22 W m−2 in July. LAPs in snow accelerate snow aging processes and reduce snow cover fraction, which doubles the albedo change and radiative forcing caused by LAPs. The impurity‐induced snow darkening effects decrease snow water equivalent and snow depth by 20 and 70 mm in June in the Sierra Nevada bighorn sheep habitat. The earlier snowmelt reduces root‐zone soil water content by 20%, deteriorating the forage productivity and playing a negative role in the survival of bighorn sheep.

Midwinter Dry Spells Amplify Post‐Fire Snowpack Decline

“…However, these studies focused on the ablation season rather than the accumulation season. Dust deposition could further accelerate wildfire‐induced midwinter melt (Huang et al., 2022). Similar to dust‐on‐snow (Skiles & Painter, 2019), in post‐fire environments, radiative forcing‐induced positive feedbacks likely occur between grain size growth, albedo decline from melt‐driven LAP accumulation, and larger‐scale albedo decline as the land surface becomes snow‐free (Huang et al., 2022; Koshkin, Hatchett, & Nolin, 2022; Sterle et al., 2013; Warren, 1982).…”

“…Dust deposition could further accelerate wildfire‐induced midwinter melt (Huang et al., 2022). Similar to dust‐on‐snow (Skiles & Painter, 2019), in post‐fire environments, radiative forcing‐induced positive feedbacks likely occur between grain size growth, albedo decline from melt‐driven LAP accumulation, and larger‐scale albedo decline as the land surface becomes snow‐free (Huang et al., 2022; Koshkin, Hatchett, & Nolin, 2022; Sterle et al., 2013; Warren, 1982). Despite some of the lowest midwinter albedos on record, further investigation of why our remotely sensed WY2022 albedo values did not decline to values as low as measured in‐situ are warranted.…”

“…Snow cover errors from MODSCAG are half the size of standard MODIS products (Rittger et al, 2013) and albedo estimates from STC-MODSCAG/MODDRFS show 5% RMSE with no bias (Bair et al, 2019). STC-MODSCAG/MODDRFS data have been previously used for SWE reconstruction (Bair et al, 2016; Rittger et al, 2016), real-time estimates of SWE (Bair et al, 2018), estimating trends in snow cover at regional scales (Ackroyd et al, 2021), understanding snow darkening related to light-absorbing particles (LAP), (Huang et al, 2022;Sarangi et al, 2019Sarangi et al, , 2020 and improving snow albedo modeling (Hao et al, 2022).…”

Midwinter dry spells amplify post-fire snowpack decline

“…However, these studies focused on the ablation season rather than the accumulation season. Dust deposition could further accelerate wildfire-induced midwinter melt (Huang et al, 2022). Similar to dust-on-snow (Skiles & Painter, 2019), in post-fire environments, radiative forcing-induced positive feedbacks likely occur between grain size growth, albedo decline from melt-driven LAP accumulation, and larger-scale albedo decline as the land surface becomes snow-free (Huang et al, 2022;Sterle et al, 2013;Warren, 1982).…”

“…Snow cover errors from MODSCAG are half the size of standard MODIS products (Rittger et al, 2013) and albedo estimates from STC-MODSCAG/MODDRFS show 5% RMSE with no bias (Bair et al, 2019). STC-MODSCAG/MODDRFS data have been previously used for SWE reconstruction (Bair et al, 2016; 10.1029/2022GL101235 5 of 13 Rittger et al, 2016), real-time estimates of SWE (Bair et al, 2018), estimating trends in snow cover at regional scales (Ackroyd et al, 2021), understanding snow darkening related to light-absorbing particles (LAP), (Huang et al, 2022;Sarangi et al, 2019Sarangi et al, , 2020 and improving snow albedo modeling (Hao et al, 2022).…”