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，因为它们运行在21世纪的模拟中，以便在气候变化的细节上形成共识。这种融合的“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，模型的融合是基于通常观察到的混沌系统在仅通过许多变量中的几个变量连接时的同步趋势。混沌的同步已经作为一种正在进行的观测的数据同化的观点被推进到一个运行的模型中，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为同步参数和状态，因此链接不同模型对中的不同变量对的连接系数可以动态地适应。在一系列日益复杂的模式层级中进行了一系列实验后，共识方案最终将被应用于融合三个全复杂性全球气候模式，每个模式的最佳预测特征将通过使用20世纪的数据在训练运行中调整连接系数来自动选择。该项目将通过探索同步理论和了解如何最好地在实践中进行同步来朝着同步气候模型的目标迈出第一步。该项目的广泛影响在于提供了一个基于自适应同步的共识形成的例子，可以应用于任何情况下，任何情况下使用可选的模型集合来表示任何类型的正在进行的物理过程。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具有更高的客观性，从而产生更大的影响。

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.