The seasonal cycle and interannual variability of two estimates of soil (or mineral) dust aerosols are compared: advanced very high resolution radiometer (AVHRR) aerosol optical thickness (AOT) and Total Ozone Mapping Spectrometer (TOMS) aerosol index (AI). Both data sets, comprising more than a decade of global, daily images, are commonly used to evaluate aerosol transport models. The present comparison is based on monthly averages, constructed from daily images of each data set for the period between 1984 and 1990, a period that excludes contamination from volcanic eruptions. The comparison focuses on the Northern Hemisphere subtropical Atlantic Ocean, where soil dust aerosols make the largest contribution to the aerosol load, and are assumed to dominate the variability of each data set. While each retrieval is sensitive to a different aerosol radiative property (absorption for the TOMS AI versus reflectance for the AVHRR AOT), the seasonal cycles of dust loading implied by each retrieval are consistent, if seasonal variations in the height of the aerosol layer are taken into account when interpreting the TOMS AI. On interannual timescales, the correlation is low at most locations. It is suggested that the poor interannual correlation is at least partly a consequence of data availability. When the monthly averages are constructed using only days common to both data sets, the correlation is substantially increased: this consistency suggests that both TOMS and AVHRR accurately measure the aerosol load in any given scene. However, the two retrievals have only a few days in common per month, so these restricted monthly averages have a large uncertainty. Calculations suggest that at least 7 to 10 daily images are needed to estimate reliably the average dust load during any particular month, a threshold that is rarely satisfied by the AVHRR AOT due to the presence of clouds in the domain. By rebinning each data set onto a coarser grid, the availability of the AVHRR AOT is increased during any particular month, along with its interannual correlation with the TOMS AI. The latter easily exceeds the sampling threshold due to its greater ability to infer the aerosol load in the presence of clouds. Whether the TOMS AI should be regarded as a more reliable indicator of interannual variability depends on the extent of contamination by subpixel clouds. ¿ 2001 American Geophysical Union