ered sequential and arises from the social and physical learning engendered by a series of forecasts (some correct and some incorrect) and the responses they provoke in the user community. This section discusses current and potential uses of ENSO forecasts and some possible new directions in making climate prediction more useful.
In theory, because the coupled atmosphere and ocean have global extent, a model that predicts SST in the tropical Pacific should also predict SST and the concomitant atmospheric response (temperature, pressure, precipitation) everywhere on the globe. As data in the ocean become more abundant, as global coupled models become better, and as computers get faster, predictions will approach the theoretical limit of predictability. At present, however, data for the world ocean outside the tropical Pacific are inadequate to initialize seasonal-to-interannual predictions. Therefore, different practical strategies are used. Either a limited region of the atmosphere-ocean system that includes the tropical Pacific is used to predict SST or a global coarse resolution atmosphere-ocean model is used to initialize only the tropical Pacific part of the ocean and therefore to predict Pacific SST only.
In order to go from tropical Pacific SST to quantities of wider usefulness, especially air temperature and precipitation, an additional step is required. Given a prediction of tropical Pacific SST, a high-resolution atmospheric model is run using climatological SST (i.e., normal SSTs for that time of year) everywhere but in the tropical Pacific, where the predicted values are used instead. Such models make predictions from tropical Pacific SST to climate in many other parts of the world, as shown in Figure 2-1. The models directly predict tropical Pacific precipitation, atmospheric temperature, and surface winds. To forecast these quantities at higher latitudes, the model must be run for a month or so to predict averages of land temperature, precipitation and winds, and it must be run many times with differing initial atmospheric and oceanic conditions, all consistent with the imperfect specification of these initial conditions.
The resulting ensemble of forecasts provides a distribution of all the conditions that could occur given these imperfectly known initial conditions. The resulting forecast can then be converted into a probability distribution. For example, Figure 2-3 shows a seasonal forecast of precipitation over North America for the unusually warm phase of ENSO that occurred during the winter of 1997-1998, in which the southern tier of states was predicted to have 60 percent chance of above average rainfall (these forecasts are available at http://iri.ucsd.edu/forecast/net_asmt). This method of presenting the forecast is valuable in that it makes explicit