Abstract. Observations of spectral albedo and bidirectional reflectance in the wavelength region of X = 0.35-2.5 tzm were made together with snow pit work on a flat snowfield in eastern Hokkaido, Japan. The effects of snow impurities, density, layer structure, and grain size attained by in situ and laboratory measurements were taken into account in snow models for which spectral albedos were calculated using a multiple-scattering model for the atmosphere-snow system. Comparisons of these theoretical albedos with measured ones suggest that the snow impurities were concentrated at the snow surface by dry fallout of atmospheric aerosols. The optically equivalent snow grain size was found to be of the order of a branch width of dendrites or of a dimension of narrower portion of broken crystals. This size was smaller than both the mean grain size and the effective grain size obtained from micrographs by image processing. The observational results for the bidirectional reflection distribution function (BRDF) normalized by the radiance at the nadir showed that the anisotropic reflection was very significant in the near-infrared region, especially for X > 1.4 tzm, while the visible normalized BRDF (NBRDF) patterns were relatively flat. Comparison of this result with two kinds of theoretical NBRDFs, where one having been calculated using single-scattering parameters by Mie theory and the other using the same parameters except for Henyey-Greenstein (HG) phase function obtained from the same asymmetry factor as in the Mie theory, showed that the observed NBRDF agreed with the theoretical one using the HG phase function rather than with that using the Mie phase function, while the albedos calculated with both phase functions agreed well with each other. IntroductionSnow cover is very sensitive to a climate change and has large feedback effects on the climate system. The former is because local climate affects the phase change of ice (snow) and the latter is caused by the high albedo in the visible region.

[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.

[1] Snow pit work of several-day intervals was performed simultaneously with radiation budget observations during two winters in eastern Hokkaido, Japan. From these data we investigated the effects of elapsed time after snowfall (snow aging), air temperature, snow surface temperature, snow grain size, and snow impurities on the visible and the near infrared albedos for improving the snow albedo scheme in the land surface process from an empirical model to a physically based model. The dependence of albedos on elapsed time after snowfall could be clearly classified by dividing the snow-covered period into a dry snow season and a wet snow season rather than by snow surface temperature. The albedo reduction by snow aging statistically depends on the snow surface temperature, which is often used to predict the snow albedo in the empirical model of land surface process. However, the albedo reduction rate was very scattered for snow surface temperatures above À10°C. This is because the snow albedo reduction essentially depends on the snow grain size and the concentration of snow impurities. Using the radiative transfer model for the atmosphere-snow system, the effects of these snow physical parameters on broadband albedos are calculated and compared with the observed ones. The measured broadband albedos fell close to the range of theoretically calculated ones as functions of these snow physical parameters. In particular, the measured near infrared albedo agreed well with the theoretically calculated ones both for the dependence of snow grain size and snow impurities but not as well for the visible albedo in detail. In the near infrared region the light absorption by ice is strong, and thus the snow albedo contains the information of snow physical parameters near the surface where these parameters are measured. In contrast, the visible albedo contains the snow information in the deeper layer because the ice is relatively transparent in the visible region. This suggests the necessity of the multiple-snow-layer model for the visible region in the physically based snow albedo model.

Abstract. Observations and models agree that the Greenland Ice Sheet (GrIS) surface mass balance (SMB) has decreased since the end of the 1990s due to an increase in meltwater runoff and that this trend will accelerate in the future. However, large uncertainties remain, partly due to different approaches for modelling the GrIS SMB, which have to weigh physical complexity or low computing time, different spatial and temporal resolutions, different forcing fields, and different ice sheet topographies and extents, which collectively make an inter-comparison difficult. Our GrIS SMB model intercomparison project (GrSMBMIP) aims to refine these uncertainties by intercomparing 13 models of four types which were forced with the same ERA-Interim reanalysis forcing fields, except for two global models. We interpolate all modelled SMB fields onto a common ice sheet mask at 1 km horizontal resolution for the period 1980–2012 and score the outputs against (1) SMB estimates from a combination of gravimetric remote sensing data from GRACE and measured ice discharge; (2) ice cores, snow pits and in situ SMB observations; and (3) remotely sensed bare ice extent from MODerate-resolution Imaging Spectroradiometer (MODIS). Spatially, the largest spread among models can be found around the margins of the ice sheet, highlighting model deficiencies in an accurate representation of the GrIS ablation zone extent and processes related to surface melt and runoff. Overall, polar regional climate models (RCMs) perform the best compared to observations, in particular for simulating precipitation patterns. However, other simpler and faster models have biases of the same order as RCMs compared with observations and therefore remain useful tools for long-term simulations or coupling with ice sheet models. Finally, it is interesting to note that the ensemble mean of the 13 models produces the best estimate of the present-day SMB relative to observations, suggesting that biases are not systematic among models and that this ensemble estimate can be used as a reference for current climate when carrying out future model developments. However, a higher density of in situ SMB observations is required, especially in the south-east accumulation zone, where the model spread can reach 2 m w.e. yr−1 due to large discrepancies in modelled snowfall accumulation.

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