Broadband Surface Albedo for Snow Derived from Satellite Observations

Le résumé fourni par la source

Abstract Spectral surface albedo depends on the surface type. Broadband surface albedo is calculated as the integral of the spectral albedo weighted by the surface downward irradiance spectrum. Consequently, unlike spectral albedo, broadband albedo depends not only on surface properties but also on atmospheric conditions, such as whether the sky is clear or cloudy. The difference between clear- and cloudy-sky broadband albedos is particularly significant for snow-covered surfaces for two main reasons. First, snow exhibits a strong contrast in spectral albedo between the visible and near-infrared (NIR) regions. Second, cloud absorption is stronger in the NIR, which reduces the surface downward irradiance relative to the visible region. When clouds are present, broadband snow albedo increases nearly by 0.1 above clear-sky albedo. In the Clouds and the Earth’s Radiant Energy System (CERES) radiative transfer model, satellite-observed broadband surface albedos are used to constrain assumptions about surface spectral albedo. However, such satellite observations are available only under clear-sky conditions. The current algorithm, which derives the initial spectral albedo for the radiative transfer model, normalizes a cloudy-sky spectral albedo to a clear-sky broadband albedo derived directly from CERES observations. We find that this normalization underestimates surface broadband albedo over snow and ice under cloudy-sky conditions. We demonstrate that, even in the presence of clouds, because the numerator in the normalization is based on a clear-sky observation, assuming an initial clear-sky spectral albedo improves the representation of surface albedo for snow and ice under clouds.

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