Abstract. In the present work we simulate the equatorial Atlantic variability at annual and interannual timescales using a coupled mixed layer-isopycnal ocean general circulation model (

Long-range empirical forecasts of North Atlantic anomalous conditions are issued, using sea ice concentration anomalies in the same region as predictors. Conditions in the North Atlantic are characterized by anomalies of sea surface temperature, of 850 hPa air temperature and of sea level pressure. Using the Singular Value Decomposition of the cross-covariance matrix between the sea ice field (the predictor) and each of the predictand variables, empirical models are built, and forecasts at lead times from 3 to 18 months are presented. The forecasts of the air temperature anomalies score the highest levels of the skill, while forecasts of the sea level pressure anomalies are the less sucessful ones.To investigate the sources of the forecast skill, we analyze their spatial patterns. In addition, we investigate the influence of major climatic signals on the forecast skill. In the case of the air temperature anomalies, the spatial pattern of the skill may be connected to El Niñ o Southern Oscillation (ENSO) influences. The ENSO signature is present in the predictor field, as shown in the composite analysis. The composite pattern indicates a higher (lower) sea ice concentration in the Labrador Sea and the opposite situation in the Greenland-Barents Seas during the warm (cold) phase of ENSO. The forecasts issued under the El Niñ o conditions show improved skill in the Labrador region, the Iberian Peninsula and south of Greenland for the lead times considered in this paper. For the Great Lakes region the skill increases when the predictor is under the influence of a cold phase. Some features in the spatial structure of the skill of the forecasts issued in the period of the Great Salinity Anomaly present similarities with those found for forecasts made during the cold phase of ENSO. The strength of the dependence on the Great Salinity Anomaly makes it very difficult to determine the influence of the North Atlantic Oscillation.

The interannual variability of the tropical Atlantic is characterized by warmings and coolings similar to the Pacific ones (El Nifio), and by an interhemispheric signal of decadal variability. The magnitudes of the Gulf of Guinea warmings are less and, therefore, they do not significantly influence the earth's climate, as the El NiiidSouthern Oscillation (ENSO) does. In the past, they have been studied because of their connections with the recurrent droughts in the Sahel region. Recently, a number of modelling studies have tried to establish their dependence on ENSO. The real existence of an interhemispheric decadal signal, and its predictability, is also a widely discussed topic. Forecast studies have recently appeared for both the north tropical Atlantic and the Gulf of Guinea regions, and are now operationally available from the National Oceanic and Atmospheric Administration.In the present work we try first to understand the tropical Atlantic variability in terms of forcings external to the basin. These are identified from 48 years of monthly anomalies of sea surface temperature (SST) data obtained by combining the Comprehensive Ocean and Atmosphere Dataset (COADS) and the Integrated Global Ocean Services System (IGOSS) dataset, and then using the Bayesian theory of estimation. Besides the ENSOrelated scales, our analysis retains a decadal time scale in the data variability.Next, a model is built to forecast the most important features of the equatorial Atlantic interannual variability. These features are monitored through two indices, the Gulf of Guinea and the north tropical Atlantic indices ('the predictands'). Predictor fields are identified from our preliminary analysis, as those time series significantly correlated with the predictands. These correspond to grid points in the tropical Pacific (mainly the Nifio3 region, (5OS-5' N. 150°W-900W)) and tropical Indian oceans. Forecasts were issued for 28 years, at three-monthly intervals. For the north tropical Atlantic index, we have a good forecast skill at leads greater than four months with predictors obtained from east equatorial Pacific time series. For the Gulf of Guinea index, a good forecast skill can be obtained only when we include time series of the equatorial Indian ocean, as well as the east equatorial Pacific, among the predictors. Any of the forecasts presented here show useful forecast skill that, at least, beats persistence at leads greater than four months