Light absorbing impurities in snow and ice (LAISI) originating from atmospheric deposition enhance snowmelt by increasing the absorption of shortwave radiation. The consequences are a shortening of the snow duration due to increased snowmelt and, at the catchment scale, a temporal shift in the discharge generation during the spring melt season.
In this study, we present a newly developed snow algorithm for application in hydrological models that allows for an additional class of input variable: the deposition mass flux of various species of light absorbing aerosols. To show the sensitivity of different model parameters, we first use the model as a 1-D point model forced with representative synthetic data and investigate the impact of parameters and variables specific to the algorithm determining the effect of LAISI. We then demonstrate the significance of the radiative forcing by simulating the effect of black carbon (BC) deposited on snow of a remote southern Norwegian catchment over a 6-year period, from September 2006 to August 2012. Our simulations suggest a significant impact of BC in snow on the hydrological cycle. Results show an average increase in discharge of 2.5, 9.9, and 21.4 %, depending on the applied model scenario, over a 2-month period during the spring melt season compared to simulations where radiative forcing from LAISI is not considered. The increase in discharge is followed by a decrease in discharge due to a faster decrease in the catchment's snow-covered fraction and a trend towards earlier melt in the scenarios where radiative forcing from LAISI is applied. Using a reasonable estimate of critical model parameters, the model simulates realistic BC mixing ratios in surface snow with a strong annual cycle, showing increasing surface BC mixing ratios during spring melt as a consequence of melt amplification. However, we further identify large uncertainties in the representation of the surface BC mixing ratio during snowmelt and the subsequent consequences for the snowpack evolution.