Significant socio-economic gains may be made if uncertainties in the projection of future climate for increased GHG levels are reduced. The super-modeling strategy has the potential to significantly reduce model systematic error, long before that could occur through the slow, but essential process of model improvement. In particular, the project has demonstrated the potential of supermodeling to predict the statistics of extreme events, more accurately than any of the individual models or any ex post facto combination of model outputs. And of course, projection of extreme climate behavior is central to the socio-economic benefits of climate projection.
Outside of climate science, supermodeling will provide a generic approach for modeling in complex application domains where different expert models are available. One can envision applications to complex biological, social, economic, and environmental processes, in situations where there are a small number of competing models. Toward the promotion of supermodeling, the PI was the lead guest editor for a recent Focus Issue of Chaos (1)
The question of whether supermodeling is useful when the constituent models make similar errors has been squarely addressed, and answered in the affirmative, both empirically as in previous work, and with theoretical understanding of how the errors common to the different models might be corrected in the supermodel. The situation of common error among the world-class climate models currently in use is typical, so the result that a supermodel can surpass combinations of these models in regard to such errors is important.
The demonstrated relationship between data assimilation and learning in neural networks (possibly including biological networks) has larger implications for machine learning, since a very large assortment of methods for data assimilation have been explored, and heretofore not linked to the problem of training neural networks. A picture of brain function emerges in which semi-autonomous components assimilate data from each other, realizing a form of self-perception. Mutual benefits for cognitive science and computational science will ensue.
(1) Duane, G.S. Grabow, C., Selten, F., and Ghil, M., Introduction fo focus issue: Synchronization in large networks and continuous media - data, models, and supermodels, Chaos 27, 126601 (2017)
