A study of the predictability of sea surface temperature over the tropics

Abstract

In this study, reasons for spatial variability in prediction skill of seasonal mean sea surface temperature (SST) are investigated. The analysis explores the connection between the spatial variation of SST prediction skill in tropical latitudes and local simultaneous correlation between observed SST and precipitation (SST-P). The results show that high (low) SST prediction skills and slow (fast) decay in skill with lead time are generally collocated with large positive (weakly positive or negative) SST-P correlations. The reasons for spatial variation in SST-P correlation can be explained by whether the primary forcing is from the ocean to the atmosphere or vice versa. Over regions where the ocean is generally known to force the atmosphere, it is found that SST-P correlation has large positive values. Over regions where the atmosphere forces ocean, SST-P correlation is weak due to intrinsically unpredictable nature of atmospheric variability. The physical explanation for spatial variation in SST-P correlation, and apparent link between the spatial variations in SST-P correlation and spatial variations in skill of SST predictions also establishes a physical basis for the latter. As a corollary, low SST prediction skill in coupled forecast models over certain geographical regions may be due to the inherent limits on predictability in addition to the contribution from model biases or initialization errors.