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

合作研究：通过模型自适应同步进行气候预测共识

• 批准号: 0838235
• 负责人: Benjamin Kirtman
• 金额: $ 8万
• 依托单位: University of Miami
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-01-01 至 2009-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0838235&HistoricalAwards=false
• 关键词: Collaborative; Research; Consensus; Climate; Predication

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.

该项目的最终目标是融合21世纪模拟中运行的气候模型，从而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。模型的融合基于当混沌系统仅通过许多变量中的少数几个连接时，通常观察到混沌系统会同步的趋势。混沌同步已被提出作为一种数据同化的观点，用于将持续观察转化为运行模型，从而有效地将模型与现实同步。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和状态，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。经过对日益复杂的模型层次结构进行一系列实验后，最终将采用共识方案来融合三个完全复杂的全球气候模型，并通过在使用 20 阶训练运行中调整连接系数来自动选择每个模型的最佳预测特征。该项目将通过探索同步理论并加深对如何在实践中最好地进行同步的理解，向同步气候模型的目标迈出第一步。该项目的更广泛影响在于提供了一个例子基于自适应同步的共识形成，可以适用于使用替代模型集合来表示任何类型的正在进行的物理过程的任何情况。特别是，任何新的气候模型都可以通过将其纳入培训计划来添加到共识中，从而使气候变化预测具有更高的客观性，产生更大的影响。