Subpixel cloud contamination is one of the major issues plaguing passive satellite aerosol remote sensing. Its impact on the aerosol optical thickness (AOT) retrieval has been analyzed/evaluated by many studies. However, the question of how it influences the AOT trend remains to be answered. In this paper, four long-term advanced very high resolution radiometer (AVHRR) AOT data sets from 1981 to 2009 over global oceans for four different definitions of clear sky, respectively, are produced by applying a two-channel aerosol retrieval algorithm to the AVHRR clear-sky reflectances derived by combining NOAA Pathfinder Atmosphere's Extended AVHRR climate data record level-2b all-sky reflectances with the cloud probability parameter determined from the Bayesian probabilistic cloud detection technique. A global analysis of the effect of cloud contamination on the AVHRR AOT retrieval as well as on its long-term trend is then performed by comparing the results from the four data sets. It was found that cloud contamination imposes not only a positive bias on AOT values but also a positive bias on its long-term trend such that negative trends become less negative and positive trends become more positive. A cloud probability value of ≤1% has been identified as an optimal criterion for clear-sky definition to minimize the cloud contamination in the AVHRR aerosol retrieval while still retaining strong aerosol signals. In order for a satellite aerosol product to be useful and reliable in aerosol trend detection, the cloud contamination effect on aerosol trends needs to be studied/evaluated carefully along with the effects of calibration error, surface disturbance, and aerosol model assumptions.