Impact of snow darkening via dust, black carbon, and organic carbon on boreal spring climate in the Earth system

Abstract

Dust, black carbon (BC), and organic carbon (OC) aerosols, when deposited onto snow, are known to reduce the albedo of the snow (i.e., snow darkening effect (SDE)). Here using the NASA Goddard Earth Observing System Model, Version 5 (GEOS-5) with aerosol tracers and a state-of-the-art snow darkening module (GOddard SnoW Impurity Module: GOSWIM) for the land surface, we examine the role of SDE on climate in the boreal spring snowmelt season. SDE is found to produce significant surface warming (over 15 W m À2) over broad areas in midlatitudes, with dust being the most important contributor to the warming in central Asia and the western Himalayas and with BC having larger impact in the Europe, eastern Himalayas, East Asia, and North America. The contribution of OC to the warming is generally low but still significant mainly over southeastern Siberia, northeastern East Asia, and western Canada (~19% of the total solar visible absorption by these snow impurities). The simulations suggest that SDE strengthens the boreal spring water cycle in East Asia through water recycling and moisture advection from the ocean and contributes to the maintenance of dry conditions in parts of a region spanning Europe to central Asia, partially through feedback on the model's background climatology. Overall, our study suggests that the existence of SDE in the Earth system associated with dust, BC, and OC contributes significantly to enhanced surface warming over continents in northern hemisphere midlatitudes during boreal spring, raising the surface skin temperature by approximately 3-6 K near the snowline.

Impacts of absorbing aerosol deposition on snowpack and hydrologic cycle in the Rocky Mountain region based on variable-resolution CESM (VR-CESM) simulations

“…Another important factor is the light-absorbing aerosols (LAAs, e.g., black carbon (BC), organic carbon (OC), and dust) in snow (e.g., Flanner et al, 2007; et Qian et al, 2015;Yasunari et al, 2015). Previous studies have shown that LAAs in snow can significantly reduce the surface albedo (often known as the snow darkening effect; SDE), modify the surface energy budget and snowmelt, and lead to the modification of hydrologic cycles (e.g., Warren and Wiscombe, 1980;Hansen and Nazarenko, 2004;Flanner et al, 2007Flanner et al, , 2009Painter et al, 2007Painter et al, , 2010Qian et al, 2009Qian et al, , 2011Yasunari et al, 2015). Moreover, the LAA-induced snow albedo reduction may initiate positive feedback processes, which can amplify the reduction of snowpack (e.g., Flanner et al, 2009;Qian et al, 2009).…”

“…In past decades modeling studies have been undertaken to quantify the impacts of SDE by LAAs (e.g., Flanner et al, 2007;Qian et al, 2009;Oaida et al, 2015;Yasunari et al, 2015). Generally the models developed have the ability to simulate the temporal evolution of snow albedo under the influence of LAAs in snow.…”

“…Generally the models developed have the ability to simulate the temporal evolution of snow albedo under the influence of LAAs in snow. These studies have enhanced our understanding of the spatial and temporal variations in climate forcings and responses due to LAAs in snow from regional scales (e.g., Qian et al, 2009;Oaida et al, 2015) to global scales (e.g., Flanner et al, 2007;Yasunari et al, 2015).…”

“…Both snowfall and snow accumulation depend on temperature and precipitation, and thus the distribution of snowpack depends strongly on topographic variability. Current GCMs with a typical horizontal resolution of 1 to 2 • cannot resolve the snowpack over regions with complex terrains (e.g., Rocky Mountains) due to the coarse resolution Wu et al, 2017), which impedes the reliable quantification of SDE by LAAs in mountainous regions (e.g., Flanner et al, 2007;Yasunari et al, 2015). RCMs can simulate the snowpack more accurately than coarse-resolution GCMs, but they are not able to simulate the global transport of aerosols to the focused region except when aerosol transport along the boundary is prescribed (e.g., Qian et al, 2009).…”

“…RCMs can simulate the snowpack more accurately than coarse-resolution GCMs, but they are not able to simulate the global transport of aerosols to the focused region except when aerosol transport along the boundary is prescribed (e.g., Qian et al, 2009). Moreover, LAAs in snow may also influence the climate beyond the focused region (e.g., Yasunari et al, 2015), which cannot be accounted for in RCMs. Variable-resolution GCMs (VR-GCMs) can overcome these weaknesses of either coarse-resolution GCMs or RCMs and serve as a better tool to quantify the impacts of LAAs in snow.…”