Modelling hydrologic impacts of light absorbing aerosol deposition on snow at the catchment scale

Abstract

Abstract. 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.

Assessing Satellite‐Derived Radiative Forcing From Snow Impurities Through Inverse Hydrologic Modeling

“…This scenario provides input for a catchment‐scale hydrologic model coupled to the Snow, Ice, and Aerosol Radiation (SNICAR) model (Flanner et al, , ). The model includes a dynamic snow albedo calculation considering LAISI (Figure ‐1; details can be found in Matt et al, ). Using an optimized set of parameters, we derive snow properties for the region on a daily time scale.…”

“…Matt et al () introduced a snowpack routine within a catchment‐scale hydrologic model that allows for the input of wet and dry deposition of light‐absorbing aerosols, in addition to standard meteorological forcing variables. The model uses a two‐layer energy balance‐based snow algorithm coupled to the SNICAR model (Flanner et al, , ), which accounts for the reduction in albedo due to LAISI.…”

“…Inclusion of LAISI in physically based snow albedo models improves the representation of this sensitive and dynamic variable and modifies snow metamorphism (Tuzet et al, ). Models of different scales consider the effects of LAISI, ranging from multilayer point models (Tuzet et al, ), hydrologic catchment models (Matt et al, ), to regional (Oaida et al, ) and global (Flanner et al, ) climate models.…”

“…From this it is then assumed that a good estimate of the LAISI mixing ratios in surface snow reflects the model's ability to simulate RFS. Though reasonable estimates of surface mixing ratios of LAISI may be a necessity to determine RFS, it is not a sufficiency: the model representation of surface snow (Matt et al, ), optical grain radius (OGR) of the snow (Warren & Wiscombe, ), and irradiance characteristics (Wiscombe & Warren, ) play an essential role. However, observations of all variables required to conduct a complete evaluation of model predictions are rare and often discontinuous in time and space.…”

“…However, observations of all variables required to conduct a complete evaluation of model predictions are rare and often discontinuous in time and space. On the other hand, continuous observations are required due to strong temporal fluctuations in the surface mixing ratios and subsequent RFS, driven by post depositional redistribution of LAISI in the snowpack (Matt et al, ; Qian et al, ). These dynamics, and in particular the role of LAISI transport and removal from the snowpack with percolating meltwater (Doherty et al, , ; Sterle et al, ; Xu et al, ), are still poorly understood (Lazarcik et al, ).…”