The impact of cloud masking on the climatology of sea surface temperature gradients

Abstract

ABSTRACT Cloud masking is a critical step in the estimation of Sea Surface Temperature (SST) from satellite observations. It can affect the validation statistics of SST on synoptic scales but also on long-term climatologies. One of the main challenges in cloud masking is the discrimination between clouds and ocean sharp fronts as both of these are associated with high spatial variability. In this study, we investigate the impact of cloud masking on the climatology of SST gradient magnitudes. Using night-time Level 2 SST derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua for almost the entire mission, i.e. 2003 to 2018 over the Southwestern Atlantic Ocean (SAO), we show that misclassification of sharp ocean thermal fronts as clouds leads to (1) significant underestimation of thermal frontal activity in monthly and longer-term climatologies and (2) different spatial distributions and temporal variability of SST gradient magnitudes.