资源环境科技发展态势分析平台

Satellite quantification of aerosol effects on clouds relies on aerosol optical depth (AOD) as a proxy for aerosol concentration or cloud condensation nuclei (CCN). However, the lack of error characterization of satellite-based results hampers their use for the evaluation and improvement of global climate models. We show that the use of AOD for assessing aerosol-cloud interactions (ACIs) is inadequate over vast oceanic areas in the subtropics. Instead, we postulate that a more physical approach that consists of matching vertically resolved aerosol data from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite at the cloud-layer height with Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua cloud retrievals reduces uncertainties in satellite-based ACI estimates. Combined aerosol extinction coefficients (sigma) below cloud top (sigma(BC)) from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and cloud droplet number concentrations (N-d) from MODIS Aqua yield high correlations across a broad range of sigma(BC )values, with sigma(BC )quartile correlations >= 0.78. In contrast, CALIOP-based AOD yields correlations with MODIS N-d of 0.54-0.62 for the two lower AOD quartiles. Moreover, sigma(BC) explains 41 % of the spatial variance in MODIS N-d, whereas AOD only explains 17 %, primarily caused by the lack of spatial covariability in the eastern Pacific. Compared with sigma(BC), near-surface sigma weakly correlates in space with MODIS N-d, accounting for a 16 % variance. It is concluded that the linear regression calculated from ln(N-d)-ln(sigma(BC)) (the standard method for quantifying ACIs) is more physically meaningful than that derived from the N-d-AOD pair.