The ultimate goal of this project is to fuse climate models, as they run in a 21st century simulation, so as to form a consensus on the details of climate change. The proposed fused "multi-model" should be more reliable than any of the separate models or any average of their outputs, since it would use the best predictive features of each model at each point in time.
The fusion of models is based on the commonly observed tendency of chaotic systems to synchronize when they are connected through only a few of many variables. Synchronization of chaos has been advanced as a view of data assimilation for ongoing observations into a running model, effectively synchronizing the model with reality. Here, the models will assimilate information from each other. Since synchronization schemes can generally be extended to synchronize parameters as well as states, the connection coefficients linking different pairs of variables in different pairs of models can be dynamically adapted. After a series of experiments in a hierarchy of increasingly complex models, the consensus scheme would ultimately be applied to fuse three full-complexity global climate models, with the best predictive features of each selected automatically by adapting the connection coefficients in a training run using 20th century data.
This project will take the first steps towards this goal of synchronized climate models, by exploring the theory of synchronization and developing an understanding of how synchronization may best be carried out in practice.
Broader impacts of this project are in providing an example of adaptive synchronization-based consensus formation that can be applied in any situation where a collection of alternative models is used to represent an ongoing physical process of any sort. In particular, any new climate model could be added to the consensus by including it in the training scheme, thus giving climate change predictions a higher level of objectivity, with greater resulting impact.
