A numerical sensitivity study on the snow-darkening effect by black carbon deposition over the Arctic in spring - Z. Zhang; Z. Zhang; L. Zhou; L. Zhou; L. Zhou; M. Zhang; M. Zhang (Atmospheric Chemistry and Physics)
The rapid warming of the Arctic, driven by glacial and sea ice melt, poses significant challenges to Earth's climate, ecosystems, and economy. Recent evidence indicates that the snow-darkening effect (SDE), caused by black carbon (BC) deposition, plays a crucial role in accelerated warming. However, high-resolution simulations assessing the impacts from the properties of snowpack and land–atmosphere interactions on the changes in the surface energy balance of the Arctic caused by BC remain scarce. This study integrates the Snow, Ice, and Aerosol Radiative (SNICAR) model with a polar-optimized version of the Weather Research and Forecasting model (Polar-WRF) to evaluate the impacts of snow melting and land–atmosphere interaction processes on the SDE due to BC deposition. The simulation results indicate that BC deposition can directly affect the surface energy balance by decreasing snow albedo and its corresponding radiative forcing (RF). On average, BC deposition at 50 ng g−1 causes a daily average RF of 1.6 W m−2 in offline simulations (without surface feedbacks) and 1.4 W m−2 in online simulations (with surface feedback). The reduction in snow albedo induced by BC is strongly dependent on snow depth, with a significant linear relationship observed when snow depth is shallow. In regions with deep snowpack, such as Greenland, BC deposition leads to a 25 %–41 % greater SDE impact and a 19 %–40 % increase in snowmelt compared to in areas with shallow snow. Snowmelt and land–atmosphere interactions play significant roles in assessing changes in the surface energy balance caused by BC deposition based on a comparison of results from offline and online coupled simulations via Polar-WRF and the community Noah land surface model (LSM) with multiple parameterization options (Noah-MP) and SNICAR. Offline simulations tend to overestimate SDE impacts by more than 50 % because crucial surface feedback processes are excluded. This study underscores the importance of incorporating detailed physical processes in high-resolution models to improve our understanding of the role of the SDE in Arctic climate change.