Light-absorbing impurities in snow of the Indian Western Himalayas: impact on snow albedo, radiative forcing, and enhanced melting.

Seasonal snow cover in the Himalayas acts as source of fresh water for several Asian rivers such as Indus, Ganges, Brahmaputra, and Yangtze. Early loss of seasonal snow exposes the ice layer of the glaciers directly to sunlight, consequently leading to their ablation and alterations in discharge of glacier-fed rivers. Therefore, any alteration in the melting rate of the Himalayan snow pack can significantly affect the ecological balance in the region. Besides global warming, enhanced melting of snow, caused by light-absorbing impurities (LAIs) such as dust and elemental carbon (EC), has also been recognized as prominent cause of enhanced melting of snow in the Himalayas of China and Nepal. However, in light of vast area of the Himalayas and persistent emissions from India, studies, emphasizing the potential of LAIs to substantially affect the snow radiation budget of snow cover in IWHs, are still scanty. Therefore, in this study, field campaigns were made on three glaciers, i.e., Hamta, Beas Kund, and Deo Tibba, in IWHs to collect snow samples for estimation of LAIs. Snow of the studied glaciers was observed to be contaminated with 13.02 to 74.57 ng/g of EC and 32.14 to 216.54 μg/g of dust. Albedo simulations done using SNow and ICe Aerosol Radiation (SNICAR) model indicated that besides the changes caused by increased grain size, EC and dust, cumulatively induced 0.60 to 32.65% reduction in albedo of snow. Further assessment, constrained by measurements, illustrated that radiative forcing (RF), of 1.8 to 80 W/m2, was instigated due to enhanced thermal absorption of snow. Ten hours of daily mean RFs in this range could correspond to 3 to 9.65 mm/d of snow melt and contribute significantly in reducing the seasonal snow cover in IWHs. Considering the consequences of LAIs-induced snow melt and lack of in situ observations in the IWHs, the outcomes of this study could assist researchers and policy makers in developing efficient climate models and framing mitigation measures, respectively.