For two decades, global measurements of sea surface temperature (SST) have been produced by the National Oceanic and Atmospheric Administration (NOAA) using infrared data obtained from the Advanced Very High Resolution Radiometer (AVHRR) on board NOAA polar orbiting satellites. The conventional retrieval algorithms are derived from regression analyses of AVHRR window channel brightness temperatures against in-situ buoy measurements under non-cloudy conditions which provide a correction for infrared attenuation due to molecular water vapor absorption. However for atmospheric conditions with anomalously high aerosol content (e.g., arising from dust, biomass burning and volcanic eruptions), such algorithms lead to significant negative biases in SST due to unaccounted attenuation arising from aerosol absorption and scattering. This research presents the derivation and implementation of a first-phase aerosol-robust daytime correction algorithm for AVHRR SST. To accomplish this, a long-term (1990-1998), global AVHRR-buoy matchup database was created by merging the Pathfinder Atmospheres (PATMOS) and Oceans (PFMDB) data sets. The merged data set is unique in that it includes daytime estimates of aerosol optical depth (AOD) derived from AVHRR channel 1 (0.63 micron) under conditions of significant aerosol loading. Histograms of retrieved AOD reveal monomodal, lognormal distributions for both tropospheric and stratospheric aerosol modes. It is then empirically shown that the SST bias caused under each aerosol mode can be expressed as a linear function of observed AVHRR channel 1 slant-path AOD. Based on these relationships, aerosol correction equations are derived for the daytime nonlinear SST (NLSST) algorithm. Separate sets of coefficients are utilized for the two aerosol modes. The elimination of cold biases in the AVHRR SST, as demonstrated in this work, will greatly improve its utility for the general user community.