Predicting climate data using climate attractors derived from a global climate model

Abstract

Embodiments generally relate to methods of accurately predicting seasonal fluctuations in precipitation or other approximate functionals of a climate state space, such as the number of heating or cooling degree days in a season, maximum river flow rates, water table levels and the like. In one embodiment, a method for predicting climate comprises: deriving a climate attractor from a global climate model, wherein a tuning parameter for the climate attractor comprises a value of total energy for moving air and water on the earth's surface; estimating a predictive function for each of a plurality of computational cells within the global climate model; and predicting an approximate climate functional of interest for a given specific location utilizing a combination of the predictive functions from each of the plurality of computational cells geographically proximate the location, where at all stages, predictive functions are selected in part by comparison to historical data.