Plane Parallel Albedo Biases from Satellite Observations. Part I: Dependence on Resolution and Other Factors

Abstract

Due to cloud heterogeneity and the nonlinear dependence of albedo on cloud water content, the average albedo of a cloudy scene found by calculating the albedo of independent pixels within the scene tends to be different from the albedo calculated using the average cloud water in the scene. This difference, termed the plane parallel albedo bias (PPH bias), which has previously been estimated from limited case studies, is evaluated here for the first time using an extensive set of Advanced Very High Resolution Radiometer data over oceanic scenes. This dataset yields visible PPH biases that range from 0.02 to 0.30, depending in part on the size of the scene, the viewing?illumination directions, and the assumptions made retrieving cloud optical depths. The PPH biases increase when atmospheric effects are accounted for but are relatively insensitive to assumptions about cloud microphysics. Due to the limitations of a one-dimensional retrieval, they tend to increase with solar zenith angle and to be larger in the backscattering than the forward scattering direction. Placed in the context of those general circulation models that do not provide subgrid-scale information on cloud amount, these biases are even larger. PPH biases in the broadband-reflected shortwave flux from general circulation models are estimated to exceed 30 W m?2, typically requiring the introduction of a compensatory bias in the model?s treatment of cloud water content. The resolution of the satellite sensor and the averaging/sampling of the satellite substantially influences the calculated PPH bias. The authors find a significant drop in albedo bias (?0.02?0.05) when averaging/sampling original local area coverage (LAC) data to global area coverage (GAC) resolution or when Landsat data were averaged to LAC resolution. These results, along with stochastic simulations of internal LAC pixel variability indicate that the bias discrepancies among variable resolution satellite data are mostly due to the neglect of subpixel cloud fraction, which makes clouds appear thinner than they actually are.