Collaborative Research: Consensus on Climate Predication by Adaptive Synchronization of Models

该项目的最终目标是融合气候模型，因为它们在21世纪世纪模拟中运行，以便就气候变化的细节形成共识。所提出的融合“多模型”应该比任何单独的模型或其输出的任何平均值更可靠，因为它将使用每个模型在每个时间点的最佳预测特征。混沌的同步已经被提出作为一种将正在进行的观测数据同化到运行模型中的观点，有效地使模型与现实同步。在这里，模型将相互吸收信息。由于同步方案通常可以扩展到同步参数以及状态，因此可以动态地调整链接不同模型对中的不同变量对的连接系数。在一系列日益复杂的模型层次中进行了一系列实验之后，共识方案最终将被应用于融合三个全复杂的全球气候模型，通过使用20世纪世纪数据在训练运行中调整连接系数，自动选择每个模型的最佳预测特征。通过探索同步的理论和发展如何在实践中最好地进行同步的理解。这个项目的更广泛的影响是提供了一个自适应同步的例子-基于共识的形成，可以应用于任何情况下，其中一组替代模型用于表示正在进行的物理过程任何形式的。特别是，任何新的气候模型都可以通过将其纳入培训计划而加入共识，从而使气候变化预测更加客观，产生更大的影响。

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.