Using internally consistent albedo, aerosol, cloud, and surface data from the Multiangle Imaging Spectroradiometer (MISR) instrument onboard the Terra satellite, top-of-atmosphere (TOA) spectral albedo change (dα) in the presence of aerosols over land is estimated and its dependence on aerosol and surface properties is analyzed. Linear regressions between spectral TOA albedo and aerosol optical depth (AOD) for different surface types are examined to derive the aerosol-free TOA albedo. MISR surface BiHemispherical Reflectance (BHR) values are used to differentiate surface types. We find relatively high correlations between spectral TOA albedo and AOD for BHR-stratified data in 2° × 2° grid cells. The global mean values of cloud-free dα over land for June–September 2007 are estimated to be 0.018 ± 0.003 (blue), 0.010 ± 0.003 (green), 0.007 ± 0.003 (red), and 0.008 ± 0.006 (near-infrared). Individual regions show large variations from these values. Global patterns of dα are determined mainly by AOD and aerosol radiative efficiency. Large positive values of dα are observed over regions with high aerosol loading and large single-scattering albedo, where the aerosol scattering effect is dominant. The presence of light-absorbing aerosols reduces aerosol radiative efficiency and dα. Surface reflectance influences both aerosol scattering and absorbing effects. Generally, the aerosol radiative efficiency decreases with increasing BHR. We also examined dα-AOD correlations over different vegetation types. We find the smallest dα values are over needleleaf forests and shrublands, whereas the largest values are over cropland and barren regions. The aerosol radiative efficiencies are lowest over needleleaf forests and barren regions and highest over grasslands and croplands.

The effect of an increase in atmospheric aerosol concentrations on the distribution and radiative properties of Earth's clouds is the most uncertain component of the overall global radiative forcing from preindustrial time. General circulation models (GCMs) are the tool for predicting future climate, but the treatment of aerosols, clouds, and aerosol-cloud radiative effects carries large uncertainties that directly affect GCM predictions, such as climate sensitivity. Predictions are hampered by the large range of scales of interaction between various components that need to be captured. Observation systems (remote sensing, in situ) are increasingly being used to constrain predictions, but significant challenges exist, to some extent because of the large range of scales and the fact that the various measuring systems tend to address different scales. Fine-scale models represent clouds, aerosols, and aerosol-cloud interactions with high fidelity but do not include interactions with the larger scale and are therefore limited from a climatic point of view. We suggest strategies for improving estimates of aerosol-cloud relationships in climate models, for new remote sensing and in situ measurements, and for quantifying and reducing model uncertainty.