On the potential impact of sea surface salinity observations on ENSO predictions

Abstract

Multiple regression analysis is used here to construct statistical prediction models for the El Niño/Southern Oscillation (ENSO) to explore the potential impact of monitoring Pacific Ocean sea surface salinity (SSS) on prediction of equatorial Pacific sea surface temperature (SST). This study, one of the firsts focusing on the direct role of SSS in ENSO predictions, is motivated by proposed missions for remote sensing of SSS. A forward stepwise method is used to extract significant predictors of the Niño‐3 SST index from observed monthly anomalies of tropical SST, SSS, sea level, freshwater flux, and components of the wind stress. The results indicate that SSS monitoring would have small impact on the statistical nowcast (reconstruction) of ENSO but a potential role in the 6–12 month forecasts. Correlation maps show two regions of high correlation: an equatorial region (between 170°E and 160°W) and an off‐equatorial region (between 170°E and 140°W and 5°S and 20°S). Short lag correlations display the negative relationship between the warm phase of ENSO and the negative equatorial SSS anomalies related with the increase of local rainfall. Such an equatorial negative correlation coexists with an area of positive correlations off the equator. The region with positive correlations moves eastward as the lag increases, reaching the geographical limit of the SSS observations at 6 months lag. The region of negative correlation moves northward and becomes weaker as the lag increases (it is nonsignificant for 9 months lag). For lags longer than 9 months, significant positive correlations are found south of the equator (5°S–10°S). At these lags, positive salinity anomalies have the potential to modify the subsurface stratification of the western Pacific as they are subducted westward. Thus, the availability of continuous remotely sensed SSS data might add considerably to ENSO predictions at longer lead times as a result of SSS‐induced changes in the subsurface density field.