Physically based snow albedo model for calculating broadband albedos and the solar heating profile in snowpack for general circulation models

Abstract

[1] A physically based snow albedo model (PBSAM), which can be used in a general circulation model, is developed. PBSAM calculates broadband albedos and the solar heating profile in snowpack as functions of snow grain size and concentrations of snow impurities, black carbon and mineral dust, in snow with any layer structure and under any solar illumination condition. The model calculates the visible and near-infrared (NIR) albedos by dividing each broadband spectrum into several spectral subbands to simulate the change in spectral distribution of solar radiation in the broadband spectra at the snow surface and in the snowpack. PBSAM uses (1) the look-up table method for calculations of albedo and transmittance in spectral subbands for a homogeneous snow layer, (2) an "adding" method for calculating the effect of an inhomogeneous snow structure on albedo and transmittance, and (3) spectral weighting of radiative parameters to obtain the broadband values from the subbands. We confirmed that PBSAM can calculate the broadband albedos of single-and two-layer snow models with good accuracy by comparing them with those calculated by a spectrally detailed radiative transfer model (RTM). In addition, we used radiation budget measurements and snow pit data obtained during the two winters from 2007 to 2009 at Sapporo, Hokkaido, Japan, for simulation of the broadband albedos by PBSAM and compared the results with the in situ measurements. A five-layer snow model with one visible subband and three NIR subbands were necessary for accurate simulation. Comparison of solar heating profiles calculated by PBSAM with those calculated by the spectrally detailed RTM showed that PBSAM calculated accurate solar heating profiles when at least three subbands were used in both the visible and NIR bands.Citation: Aoki, T., K. Kuchiki, M. Niwano, Y. Kodama, M. Hosaka, and T. Tanaka (2011), Physically based snow albedo model for calculating broadband albedos and the solar heating profile in snowpack for general circulation models, J. Geophys.

Thermal diffusivity of seasonal snow determined from temperature profiles

“…On average the internal domain size was 25 cm and the changes in snow density over this depth range were small. The top boundary was chosen such that it is at least 10 cm below the snow surface to avoid regions where solar radiation may introduce non-linear effects to the heat transfer [5,6].…”

“…Furthermore, thermal diffusivity can be useful in improved prediction of snowmelt timing and streamflow discharge because typical snowmelt models are tuned to air temperature, and therefore, lack a full physical basis [4]. Heat transfer in snow occurs by conduction through the ice matrix, by conduction through air-filled pore spaces, by latent heat exchanges of water vapor (condensation and sublimation) [3] and by radiative heating [5,3,6]. In addition, convection [7][8][9] and wind pumping [10,11] can also play a role.…”

Advances in understanding and parameterization of small-scale physical processes in the marine Arctic climate system: a review

“…In particular, Hansen et al (2005) suggested that the effect of BC on snow albedo contributes substantially to rapid warming and sea ice loss in the Arctic, although recent measurements (Forsström et al, 2009(Forsström et al, , 2013Doherty et al, 2010) have shown substantially lower levels of BC than was observed in the 1980s (Clarke and Noon, 1985). In view of these findings, parameterizations of BC and soot concentration in snow have been recently developed (Flanner and Zender, 2006;Yasunari et al, 2011;Aoki et al, 2011). Evaluations of these parameterizations have revealed their capability to better reproduce the observed snow albedo and snow depth (Yasunari et al, 2011;Hadley and Kirchstetter, 2012).…”

A New Global Climate Model of the Meteorological Research Institute: MRI-CGCM3 —Model Description and Basic Performance—

“…The predicted variables for snow are temperature, SWE, density, grain size, and the aerosol deposition contents of each layer. Water phase change (snow melting) occurs when the temperature of each layer exceeds À1 C. The bottom snow melts when the temperature of the uppermost underlying soil layer is above 1 C. Snow properties, including grain size, are predicted due to snow metamorphism (Niwano et al in preparation), and the snow albedo is diagnosed from the aerosol mixing ratio, the snow properties, and the temperature (Aoki et al 2011).…”