The Atlantic ocean spans a wide range of latitudes and exhibit numerous important climate phenomena across multiple time scales.Regarding sea surface temperature anomalies (SSTA) in the Atlantic,most previous studies have focused on the North Atlantic and tropical Atlantic,with relatively less attention given to the South Atlantic.Research has demonstrated inverse-phase sea surface temperature patterns in the eastern and southwestern parts of the Indian Ocean,a phenomenon defined as the Southern Indian Ocean Dipole (SIOD).Subsequently,studies analyzing sea surface temperature variability in the South Atlantic using empirical orthogonal function (EOF) and singular value decomposition (SVD) identified dipole-like spatial structures similar to those observed in the South Indian Ocean and South Pacific Ocean.The El Niño-Southern Oscillation (ENSO),the most prominent mode of interannual climate variability,is closely related to the sea surface temperature variations across the entire South Atlantic.Exploring the South Atlantic Subtropical Dipole (SASD) and its specific correlation with ENSO can enhance our understanding of climate variability and improve the prediction of both ENSO and SASD events.Using monthly sea surface temperature data from the Hadley Center,as well as monthly sea surface wind data,sea level pressure data and latent heat flux data from the NCEP/NCAR reanalysis for the period 1960 to 2022.This study examine the characteristics of subtropical sea surface temperature variability in the South Atlantic and its relationship with ENSO.The results show that:1) During the Northern Hemisphere winter,EOF analysis of the subtropical SSTA in the South Atlantic Ocean reveals a northeast-southwest dipole like pattern,explaining 27.82％ of the total variance.This phenomenon,termed the SASD,primarily arises due to the variability in sea surface wind associated with fluctuations in the South Atlantic subtropical high.These wind anomalies induce a northeast-southwest dipole pattern in latent heat flux anomalies,which subsequently drives sea surface temperature (SST) variability.2) Composite analysis of SST anomalies indicates that SASD exhibits strong seasonal phase-locking.It typically develops from September to November (SON),and reaches its peak during December to February (DJF),and weakens from March to May (MAM).3) Wavelet analysis shows that SASD variability is dominated by periodic fluctuations on a 4—6 years timescale over the period 1960 to 2022.4) SASD is significantly correlation with ENSO,with a correlation coefficient of 0.55.Positive (negative) SASD events are generally correspond to El Niño (La Niña) events.Furthermore,the co-occurrence of positive SASD and El Niño enhances the intensity and spatial extent of El Niño events.Additionally,when positive SASD and El Niño occurs simultaneously,they tend to trigger La Niña events the following year.Conversely,La Niña also can enhance the intensity of the negative SASD the subsequent year.This study analyses a strong correlation between ENSO and SASD,however,further investigation is needed to determine whether ENSO have an impact on the evolution of SASD and whether different ENSO types (i.e.,East-Pacific ENSO vs.Central-Pacific ENSO) exert distinct effects on SASD development.