Prediction of Tropical Atlantic Sea Surface Temperatures Using Linear Inverse Modeling

Abstract

The predictability of tropical Atlantic sea surface temperature on seasonal to interannual timescales by linear inverse modeling is quantified. The authors find that predictability of Caribbean Sea and north tropical Atlantic sea surface temperature anomalies (SSTAs) is enhanced when one uses global tropical SSTAs as predictors compared with using only tropical Atlantic predictors. This predictability advantage does not carry over into the equatorial and south tropical Atlantic; indeed, persistence is a competitive predictor in those regions. To help resolve the issue of whether or not the dipole structure found by applying empirical orthogonal function analysis to tropical Atlantic SSTs is an artifact of the technique or a physically real structure, the authors combine empirically derived normal modes and their adjoints to form ?influence functions,? maps highlighting the geographical areas to which the north tropical Atlantic and the south tropical Atlantic SSTs are most sensitive at specified lead times. When the analysis is confined to the Atlantic basin, the 6-month influence functions in the north and south tropical Atlantic tend to be of the opposite sign and evolve into clear dipoles within 6 months. When the analysis is performed on global tropical SSTs, the 6-month influence functions are connected to the El Niño phenomenon in the Pacific, with the strongest signal in the north tropical Atlantic. That is, while the south tropical Atlantic region is weakly sensitive to the optimal initial structure for growth of El Niño, SST anomaly in the Niño3 region is a strong 6-month predictor of SST anomaly in the north tropical Atlantic. The results suggest that the tropical Atlantic dipole is a real phenomenon rather than an artifact of EOF analysis but that the influence of the Indo?Pacific often disrupts the northern branch so that the dipole does not dominate tropical Atlantic dynamics on seasonal timescales.