CAREER: Predicting global climate change through fluctuation-dissipation: A practical computational strategy for complex multiscale dynamics

该奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。该方案描述了复杂混沌非线性多尺度动力系统对外部强迫参数变化响应的实用计算框架。它是基于PI最近成功地为波动耗散定理创建了一种数值方法，以预测非线性混沌强迫耗散动力系统对外部扰动的线性响应，并改进了技巧，基于具有混沌吸引子的系统的精确几何响应公式。虽然所开发的方法在相对简单的动力系统中表现良好，但复杂的现实气候模式计算成本更高，在多尺度上具有非线性相互作用，有时还包括随机参数化过程。在这里，PI提出了几种数值策略，使新方法适用于复杂的多尺度非线性强迫耗散，甚至可能是随机参数化的多变量和高度非高斯平衡状态的动力系统，并降低了其计算成本。所提出的算法的成功实施将有助于为全球气候变化预测创建一个新的计算框架。该提案还建议开发一套研究生水平的课程，重点放在混沌非线性动力学，大气和海洋物理以及计算天气和气候预测等基础学科上，这些是成为跨学科天气和气候研究科学家所需的关键主题。这些课程将使研究生有机会直接与PI的研究互动，并通过研究生指导直接从PI那里学习各种先进的理论方法和数值方法。线性波动耗散方法识别产生灾难性气候响应的参数扰动范围的能力，有助于确定对地球全球气候周期的潜在有害的人为干预类型。这些数据可以提供额外的信息，以帮助确定经济、政治和立法举措，以保护我们的环境，并开发更可靠的环保可再生能源系统的先进技术。此外，这种方法也适用于气候变化逆问题，即利用过去气候变化的地质证据来计算触发这些气候变化的物理强迫参数的范围，从而有助于确定这些气候变化在行星尺度上的原因。PI开发的一系列课程可能会发展成为研究生水平的跨学科教育计划，以培养具有严重数学和计算偏见的未来气候和天气研究科学家，最终可能被采纳为气候和天气研究的基本教育标准。这一计划的实施将减轻国家天气和气候研究中心和实验室的负担，这些中心和实验室目前在培养博士后水平的员工方面花费了大量的精力。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). The proposal describes a practical computational framework for the response of a complex chaotic nonlinear multiscale dynamical system to changes in external forcing parameters. It is based on the PI's recent successful efforts to create a numerical approach for the fluctuation-dissipation theorem to predict linear response of a nonlinear chaotic forced-dissipative dynamical system to an external perturbation with improved skill, based on a precise geometric response formula for systems with chaotic attractors. While the method developed is observed to perform well for relatively simple dynamical systems, complex realistic climate models are more computationally expensive, have nonlinear interactions on multiple scales, and sometimes include stochastically parameterized processes. Here the PI proposes several numerical strategies to adapt the new approach for complex multiscale nonlinear forced-dissipative, and, possibly, stochastically parameterized dynamical systems with many variables and highly non-Gaussian equilibrium state, as well as reduce its computational cost. Successful implementation of proposed algorithms should help create a novel computational framework for global climate change prediction. The proposal also suggests development of a set of graduate-level courses with strong emphasis on the basic subjects of chaotic nonlinear dynamics, atmospheric and oceanic physics, and computational weather and climate prediction, which are the key topics needed to become an interdisciplinary weather and climate research scientist. These courses will give graduate students an opportunity to interact directly with the PI's research and learn a variety of advanced theoretical approaches and numerical methods directly from the PI through graduate advising.The ability of the linear fluctuation-dissipation approach to identify the ranges of parameter perturbations which produce catastrophic climate response can be helpful in determining potentially harmful types of anthropogenic intervention into the Earth's global climate cycle. This data may provide additional information to help define economic, political and legislative initiatives to preserve our environment and to develop advanced technologies for more reliable environmentally-friendly renewable power systems. In addition, such an approach is also suitable for the inverse climate change problem, where the geological evidence of past climate changes is used to compute the range of physical forcing parameters which triggered these climate changes, which can help to determine the cause of these climate changes on the planetary scale. The set of courses under the PI's development may potentially evolve into a consistent interdisciplinary educational program on the graduate level to train future climate and weather research scientists with heavy mathematical and computational bias, which could eventually be adopted as a basic educational standard for climate and weather research. Implementation of this program will reduce the burden on national weather and climate research centers and laboratories which currently spend substantial efforts on the training of their employees at the postdoctoral level.