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

职业：通过波动耗散预测全球气候变化：复杂多尺度动力学的实用计算策略

• 批准号: 0845760
• 负责人: Rafail Abramov
• 金额: $ 47.3万
• 依托单位: University of Illinois at Chicago
• 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: 2009-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0845760&HistoricalAwards=false
• 关键词: CAREER; Predicting; global; climate; change

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.

该奖项是根据2009年《美国复苏和再投资法案》(公法111-5)提供资金的。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的响应的实用计算框架。它基于PI最近的成功工作，为涨落耗散定理建立了一种数值方法，以改进的技巧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，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型的计算成本更高，在多个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，以适应新方法的复杂的多尺度非线性强迫耗散，并可能随机参数化的多变量和高度非高斯平衡态的动力系统，同时降低其计算成本。所提出的算法的成功实施应该有助于为全球气候变化预测创建一个新的计算框架。该提案还建议发展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，这些都是成为跨学科天气和气候研究科学家所需的关键课题。这些课程将使研究生有机会直接与PI的研究互动，并通过研究生建议直接从PI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的范围的能力，有助于确定对地球全球气候循环的潜在有害人为干预类型。这些数据可能提供更多信息，以帮助确定经济、政治和立法倡议，以保护我们的环境，并为更可靠、更环保的可再生能源系统开发先进技术。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，这可以帮助确定这些气候变化在行星尺度上的原因。PI开发的这套课程可能会演变为研究生水平上一致的跨学科教育计划，以培养未来具有严重数学和计算偏见的气候和天气研究科学家，这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博士后培训方面投入了大量精力。