Snow albedo models have been previously used to investigate changes of the reflectance of a snow cover due to altered microphysical parameters of the snowpack, atmospheric conditions diffusing the
incident radiation, effects of the SZA on light propagation in the snow and the presence of LAI in the snowpack. A model permitted the study of single parameters within a controlled environment.
The incentive for this model study was to set up and test a sophisticated radiative transfer model, based on the DISORT method, in which the propagation of solar radiation in atmosphere and snowpack is coupled, thus allowing multiple reflections. The model setup developed here follows the principle of the setup formulated in the study by Gardner and Sharp [2010] and is specialised for the conditions in the Arctic and snow on top of sea ice. Thismodel, called SoSIM, was used here for studies with a set of parameters that is expected to represent Arctic conditions based on literature research.
SoSIM is not only able to simulate the albedo of a snowpack, but also features of the atmosphere can be investigated. This is needed in order to study the effects of atmospheric backscatter on airborne surface albedo measurements as a function of altitude, as mentioned below in more detail. This type of model offers further possibilities for studies involving snow on the ground.
For example, evaluating the impact of Arctic Haze on the energy balance of the boundary layer could be possible. Thus, there are further applications for SoSIM in connection with the diverse measurements conducted during airborne campaigns like PAMARCMiP. It would be possible to include measured vertical profiles of atmospheric state (e.g. temperature, pressure and humidity), aerosols, such as BC, and trace gases, such as ozone, into the model. However, even though this data was (partly) available for this thesis, the given time did not allow to prepare the data in a way that it could have been included into the model atmosphere properly. Nevertheless, there is a tool and data for further studies.
Comparison of model results
Model results of the spectral albedo were compared with a set of models based on different approaches, that are described in Section 2.5. The comparison was done with regard especially to the effects of BC added to the snow crystals. An agreement of the albedo reduction due to BC better than ±1.1% was found in the VIS. However, the model results reveal a slightly different sensitivity of the UV and VIS albedo to equal concentrations of BC in the snow. SoSIM showed very good agreement to other models in the wavelength region near 1300 nm, where the albedo is very sensitive to the microphysical properties of the snow and to the SZA. At higher wavelengths, the relative differences between model results are generally greater, especially near the absorption band of ice and water. Comparisons of the broadband albedo with values calculated with another model and a physically based parametrisation revealed an agreement better than about 1%. An attempt to re-simulate field measurements of the spectral albedo of a real snowpack with complex vertical structure demonstrated the complexity of snow reflectance. It could be achieved to generally match the measurements by tuning the model parameters. However, using spectral measurements as a sophisticated validation of SoSIM would need accurately known snow parameters and a less complex snow stratigraphy.
Albedo changes by single parameters
For typical conditions in the Arctic sea-ice area, it was found that the size of spherical snow grains and the angle of the sun above the horizon ultimately determine both spectral and broadband
for Arctic conditions already high, BC concentration of 40 ng/g added to pure snow only lowers the spectral albedo by less than 2% around 500 nm and the broadband albedo is lowered by less than 1%. The effect depends on snow grain size and the MAC of BC, the property that describes how efficient absorption by a certain mass concentration of BC is. Nevertheless, the decreasing effect of BC or other light absorbing substances on snow albedo can be significant if a high mass concentration is present in the snow. It is known that the spectral signature of BC is very similar to that of a thinning snow cover on top of a darker surface [Warren, 2013, Wiscombe and Warren, 1980]. With the model results obtained here it can be shown that the two effects closely resemble each other for parameters that are likely to occur on Arctic sea ice (Figure 3.9).
Implications for modelling and measurements
The characteristic behaviour of the spectral albedo for changes of certain snow parameters could be used to formulate retrieval algorithms. A problem with all possible retrievals of snow properties from spectral albedo measurements is that too many factors influence the spectral albedo. Table 3.5 gives a priority of the parameters based on model calculations showing which albedo changes can be expected during measurements on Arctic sea ice. Parameters with high influence on the albedo have to be accurately known to infer others with smaller influence. A similar argumentation holds if parameters have to be assumed for modelling purposes. The following point can be seen as guidelines for measurements or modelling:
• The thickness of an optically thin snowpack has to be accurately known, however the albedo does not change significantly for a snow depth above 20 cm. A fixed value can be assumed for the snow density since changes influence the albedo byπ1%for most snowpacks.
• The SZA must be known with an accuracy of at least±1%because of its strong influence on NIR albedo. This effect vanishes if no direct radiation reaches the snow surface, i. e. for an overcast sky.
• Changes of the snow grain size have a strong impact on the spectral albedo. If assumptions are made, the possible large uncertainties have to be accounted for.
When modelling the hemispheric reflectance, or albedo, the exact angular distribution of the reflected radiation is not of particular interest, therefore a description of all snow grains as equally sized spherical particles is feasible [Grenfell and Warren, 1999]. The approximation is favoured by the fact that angular details in the phase function of complex snow grains are smeared by multiple scattering [Warren, 1982]. However, a high SZA or very thin snow reduce the number of scattering events within the snowpack, thus a model utilising the assumption of spherical grains should be limited. SoSIM was not used to model snow below a thickness of 1 cm and an SZA above 80¶.