“Flowering walnuts in the wood” and other bases for seasonal climate forecasting

Abstract

Although it is impossible to forecast the weather more than a few days in advance, the science of seasonal climate forecasting is premised upon an ability to predict the general weather conditions over a prolonged period of time, without trying to predict the precise weather at any specific time during that period. The forecasting is possible only because sometimes, and primarily within tropical latitudes, the atmosphere is sensitive to unusual conditions at the earth’s surface, and especially at the sea surface. El Niño, and its counterpart La Niña, are the primary examples of such forcing conditions: during El Niño events, much of the equatorial Pacific Ocean is unusually hot (cold during La Niña), and the consequent changes to the heat and moisture supplied to the atmosphere can disrupt weather conditions in many parts of the globe. However, all seasonal climate forecasts involve a great deal of uncertainty, and a key aspect of forecasting at such time scales is to estimate the uncertainty in the prediction reliably. There are two sources of uncertainty in seasonal climate forecasting: the atmosphere is nowhere completely forced by conditions at the surface, but is free to vary according to its own internal dynamics; and the models used to predict the climate system are imperfect. These two sources of uncertainty are addressed by producing a set of model predictions: different initial weather conditions are used to represent the uncertainty from the internal dynamics, and different models to account for the uncertainties arising from imperfect model physics.