Tracking Seasonal Prediction Models

A machine learning algorithm for combining predictions is applied to seasonal predictions of the NINO3.4 index from six coupled atmosphere-ocean models. The algorithm adaptively tracks a dynamic sequence of “best experts” and produces a probability that a particular expert is best. Averaging based on this probability effectively yields a multi-model prediction. The algorithm gives seasonal predictions that are more skillful than any individual model and better than the multi-model mean.