CMG Research: Emerging Mathematical Strategies for Stochastic Modeling and Predictability to Climate Variability

气候系统涉及大量相互作用的空间和时间尺度。开发工具以进一步了解这一非常复杂的系统及其可预测性是该项目的主要重点。研究人员已经在简单的动力系统中演示了理论上严格的方法，允许对系统进行随机建模。我们将这些扩展到气候下更复杂但实用的系统。此外，研究人员利用信息论得出了一个关于动态可预测性的全新观点。这将应用于大气和气候预测问题，也将使我们能够为一般混沌系统的动力学预测制定严格的可靠性措施。这些研究使我们能够更清楚地了解地球气候背后的复杂系统。这样的理解将对解释对未来气候变化具有重要社会意义的预测的结果大有裨益。此外，我们预计工具的发展将使我们能够可靠地决定何时特定的预报(气候或天气)可能有用，何时它可能没有任何用处。这种能力对于处理根本不确定的系统的政策制定者具有重要的实际意义。

The climate system involves a huge range of interacting spatial andtemporal scales. Development of tools to further understanding of thisvery complex system and its predictability is the major focus of thisproject. Theoretically rigorous methods allowing for the stochasticmodeling of the system have been demonstrated by the investigators insimple dynamical systems. We extend these to the more complex butpractical systems underlying climate. Additionally an entirely newperspective on dynamical predictability has been derived by theinvestigators using information theory. This will be applied to theproblems of atmospheric and climate prediction and will also enable thedevelopment of rigorous reliability measures for dynamical predictionsin general chaotic systems.These investigations enable us to gain a much clearer understandingof the complex system underlying the Earth's climate. Such anunderstanding will be of great benefit in interpreting results fromsocietally important projections of future climate change. Further weanticipate the development of tools which will enable us to reliablydecide when a particular prediction (climate or weather) is likely to beuseful and when it is not likely to have any use. This ability has important practical implications for policy makers dealing with fundamentally uncertain systems.