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In optical models snow is commonly treated as a disperse collection of particles. In this representation, the penetration depth of solar radiation is sensitive to the shape of the particles, in particular to the absorption enhancement parameter, B, that quantifies the lengthening of the photon path inside grains due to internal multiple reflections. Spherical grains, with theoretical B = 1.25, are often used. We propose an experimental method to determine B, and apply it to 36 snow samples and 56 snow strata. The method is based on radiative transfer modeling and combined measurements of reflectance and irradiance profiles. Such measurements are performed in the laboratory and in the field, in Antarctica and the French Alps. The retrieved values of B are in the range 0.7–2.4, with a wide peak between 1.4 and 1.8. An analysis of measurement error propagation based on a Bayesian framework shows that the uncertainty on B is ± 0.1, which is the order of magnitude of variations between different snow types. Thus, no systematic link between B and snow type can be inferred. Here we recommend using shapes with B = 1.6 to model snow optical properties, rather than spherical grains.
Using a snow/atmosphere coupled model, the evolution of the surface and near-surface snow temperature is modeled at Dome C, Antarctica, during the period 20–30 January 2010. Firstly, the detailed multilayer snow model Crocus is run in stand-alone mode, with meteorological input forcing data provided by local meteorological observations. The snow model is able to simulate the evolution of surface temperature with good accuracy. It reproduces the observed downward propagation of the diurnal heatwave into the upper 50 cm of the snowpack reasonably well. Secondly, a fully coupled 3-D snow/atmosphere simulation is performed with the AROME regional meteorological model, for which the standard single-layer snow parameterization is replaced by Crocus. In spite of a poor simulation of clouds, the surface and near-surface snow temperatures are correctly simulated, showing neither significant bias nor drifts during the simulation period. The model reproduces particularly well the average decrease of the diurnal amplitude of air temperature from the surface to the top of the 45 m instrumented tower. This study highlights the potential of snow/atmosphere coupled models over the Antarctic plateau and the need to improve cloud microphysics and data assimilation over polar regions.
Time series of observed microwave brightness temperatures at Dome C, East Antarctic plateau, were modeled over 27 months with a multilayer microwave emission model based on dense-medium radiative transfer theory. The modeled time series of brightness temperature at 18.7 and 36.5 GHz were compared with Advanced Microwave Scanning Radiometer–EOS observations. The model uses in situ high-resolution vertical profiles of temperature, snow density and grain size. The snow grain-size profile was derived from near-infrared (NIR) reflectance photography of a snow pit wall in the range 850–1100 nm. To establish the snow grain-size profile, from the NIR reflectance and the specific surface area of snow, two empirical relationships and a theoretical relationship were considered. In all cases, the modeled brightness temperatures were overestimated, and the grain-size profile had to be scaled to increase the scattering by snow grains. Using a scaling factor and a constant snow grain size below 3 m depth (i.e. below the image-derived snow pit grain-size profile), brightness temperatures were explained with a root-mean-square error close to 1 K. Most of this error is due to an overestimation of the predicted brightness temperature in summer at 36.5 GHz.
Spaceborne microwave radiometers are an attractive tool for observing Antarctic climate because their measurements are related to the snow temperature. However, the conversion from microwave emission to snow temperature is not simple and strongly depends on the emissivity through snow properties. This difficulty in predicting the snow property profile for Antarctic conditions is the main bottleneck in the retrieval of accurate climate information from microwave radiometers. We attempt to explain the vertically polarized emissivity at 19.3 and 37 GHz derived from brightness temperatures acquired by the Special Sensor Microwave/Imager (SSM/I) and physical temperature from the ERA-40 re-analysis. In Antarctica the snow emissivities at 19.3 and 37 GHz are nearly equal, although a decrease with frequency is expected. To explain this, we consider various profiles of snow grain size and density and predict their emissivity using a dense-medium radiative transfer (DMRT) model. The results show that the emissivities cannot be explained by constant profiles of grain size and density. Heterogeneous snowpacks need to be considered. We first test random variations of snow density and grain radius with depth and then monotonic and continuous variations in the snow grain radius. In both cases, we show that an overall increase of the snow grain radius with depth is required to match the observed emissivity in Antarctica. In addition, two parameters characterizing the snow grain profiles are retrieved and compared with (1) in situ measurements of grain size at various locations in East Antarctica, (2) grain size estimated using visible spaceborne radiometers and (3) a semi-empirical relationship for grain growth.
A surface energy balance model is forced by 13 years of high-quality hourly observations from the Antarctic coastal station Neumayer. The model accurately reproduces observed surface temperatures. Surface sublimation is significant in summer, when absorbed solar radiation heats the surface. Including a first order estimate of snowdrift sublimation in the calculation more than triples the total sublimation, removing 19% of the solid precipitation, indicating that snowdrift sublimation is potentially important for the mass balance of Antarctic ice shelves. Surface melt occurs at Neumayer in all summers, but all the meltwater refreezes. In two-thirds of the cases, the refreezing is quasi-instantaneous (within the model timestep of 6 min), so that no liquid water remains in the snow. For all other events, the occurrence of liquid water in the snowpack at Neumayer agrees well with satellite-based liquid water detection.
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