At a dozen or so institutes around the world, comprehensive climate models are being developed and improved. Each model provides reasonable simulations of the observed climate, each with its own strengths and weaknesses. In the current multi-model ensemble approach model simulations are combined a posteriori. Recently, it has been proposed to dynamically combine the models and so construct one supermodel. The supermodel parameters are learned from historical observations. Supermodeling has been successfully developed and tested on small chaotic dynamical systems, like the Lorenz 63 system. In this chapter we review and discuss several supermodeling dynamics and learning mechanisms. Methods are illustrated by applications to low dimensional chaotic systems: the three dimensional Lorenz 63 and Lorenz 84 models, as well as a 30 dimensional two-layer atmospheric model.
W Wiegerinck, M Mirchev, W Burgers, FM Selten. Supermodeling Dynamics and Learning Mechanisms.
published, Consensus and Synchronization in Complex Networks, 2014, Springer, yes