The Physical Basis for Predicting Atlantic Sector Seasonal-to-Interannual Climate Variability*

Publisher Website
Google Scholar
Abstract
This paper reviews the observational and theoretical basis for the prediction of seasonal-to-interannual (S/I) climate variability in the Atlantic sector. The emphasis is on the large-scale picture rather than on regional details. The paper is divided into two main parts: a discussion of the predictability of the North Atlantic Oscillation (NAO)—the dominant pattern of variability in the North Atlantic—and a review of the tropical Atlantic prediction problem. The remote effects of El Niño are also mentioned as an important factor in Atlantic climate variability. Only a brief discussion is provided on the subject of South Atlantic climate predictability. Because of its chaotic dynamical nature, the NAO and its related rainfall and temperature variability, while highly significant over Europe and North America, are largely unpredictable. This also affects the predictive skill over the tropical Atlantic, because the NAO interferes with the remote influence of El Niño. That said, there appears to be an insufficiently understood, marginal signal in the NAO behavior that may be predictable and thus useful to certain end users. It is manifested in the deviation of the NAO temporal behavior from first-order autoregressive behavior. Tropical Atlantic climate variability centers on the sensitivity of the marine ITCZ to remote forcing from the equatorial Pacific and interactions with underlying sea surface temperature (SST) variability. Both mechanisms are potentially predictable—that is, given the underlying SSTs and the strength of El Niño, one could determine with a high degree of skill the anomalies in ITCZ position and intensity. However, local SSTs are easily affected by largely unpredictable North and South Atlantic phenomena, such as the NAO. In addition, the local ocean–atmosphere coupling in the Atlantic acts on relatively short time scales. Thus, in reality the level of skill indicated by forced model simulations are difficult to achieve. The use of coupled models may improve the prospects of tropical Atlantic prediction.