Collaborative Research, Type 1, L02170206: Climate Sensitivity, Stochastic Models and GCM-EaSM Optimization

合作研究，类型 1，L02170206：气候敏感性、随机模型和 GCM-EaSM 优化

• 批准号: 1049095
• 负责人: Annalisa Bracco
• 金额: $ 20.56万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-04-15 至 2015-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1049095&HistoricalAwards=false
• 关键词: Collaborative; Research; Type; L02170206; Climate

Ghil, 1049253Bracco, 1049095Wang, 1049114Zaliapin, 1049092 The project team is made up of climate dynamicists and of applied mathematicians. The investigators (a) formulate a mathematical theory of climate sensitivity and (b) devise a set of optimization algorithms for general circulation models and Earth System Models. The team brings together strengths in dynamical systems, partial differential equations, and numerical methods, with depth and broad coverage in the study of atmospheric, oceanic, and climate dynamics. The project's three main objectives are to: (i) continue developing powerful new methods for the fundamental understanding of climate sensitivity and predictability; (ii) extend earlier work of the investigators on modes of low-frequency variability associated with the El Nino-Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO), interannual as well as decadal; and (iii) combine items (i) and (ii) in analyzing the sensitivity and predictability of these modes when subjected to climate change. All three objectives are pursued across a full hierarchy of models, from conceptual "toy" models through intermediate climate models and on to Earth system Models of Intermediate Complexity. Ghil and his associates have recently worked on extending the theory of random dynamical systems and applying it to the climate system. This theory allows one to (1) investigate the effect of random perturbations ("weather") on nonlinear dynamical systems ("climate variability"); (2) evaluate the robustness and sensitivity of a random dynamical system to changes in either the system or its forcing, whether deterministic (e.g., slow, anthropogenic changes in greenhouse gas or aerosol concentrations) or stochastic (e.g., volcanic eruptions); and (3) obtain sharper results on the system's predictability by accounting for the effect of the random perturbations. Methods developed for the systematic study of parameter dependence in a streamlined global circulation model, the ICTP-AGCM, have promising parallels to results published by the PIs and co-workers on idealized models. The team obtains rigorous results on the latter kinds of models, as well as on random dynamical system bifurcations, sensitivity, and predictability, while extending the ICTP-AGCM results to models of intermediate complexity like SPEEDO, and eventually to full Earth System Models like the Community Climate System Model (CCSM). This work leads to a deeper understanding of the causes and mechanisms of climate sensitivity. It also provides efficient ways to evaluate and improve the ability of global circulation models and Earth System Models to simulate past and present climate, and to predict our environment's future evolution. It helps strengthen the basis for robust climate projections on decade-to-century time scales, and it provides a systematic way to evaluate and improve both deterministic and stochastic parameterizations in such models. The results of this work have implications for other areas in which complex deterministic dynamics interacts with external forcing, deterministic as well as random. This situation characterizes the life and socio-economic sciences, as well as climate science and the geosciences. Strong interactions across disciplinary boundaries -- among team members themselves and with colleagues in other areas -- help accelerate the transfer of new methods and results to other disciplines.

GHIL，1049253BRACCO，1049095WANG，1049114ZALIAPIN，1049092项目团队由气候动力学家和应用数学家组成。 研究人员（a）制定了气候灵敏度的数学理论，（b）为通用循环模型和地球系统模型设计了一组优化算法。 该团队在动态系统，部分微分方程和数值方法中汇集了优势，并在大气，海洋和气候动力学的研究中具有深度和广泛的覆盖范围。 该项目的三个主要目标是：（i）继续开发强大的新方法，以基本了解气候敏感性和可预测性； （ii）扩展了研究人员对与El Nino-Southern振荡（ENSO）和北大西洋振荡（NAO），年际和年婚姻和际交往相关的低频变异性模式的早期工作； （iii）将项目（i）和（ii）结合在分析这些模式的灵敏度和可预测性中，并受到气候变化的影响。 从概念性的“玩具”模型到中间气候模型到中间复杂性的地球系统模型，都在整个模型的完整层次结构中追求所有三个目标。 Ghil和他的同事最近致力于扩展随机动力学系统的理论并将其应用于气候系统。 该理论允许（1）研究随机扰动（“天气”）对非线性动力学系统（“气候变异性”）的影响； （2）评估随机动力学系统对系统或其强迫变化的鲁棒性和灵敏度，无论是确定性（例如，温室气体或气溶胶浓度的慢速，人为变化）还是随机性的（例如，火山喷发）； （3）通过考虑随机扰动的影响，从系统的可预测性中获得更清晰的结果。 在简化的全局循环模型ICTP-AGCM中，为参数依赖的系统研究而开发的方法与PIS和同事在理想化模型上发表的结果具有有希望的相似之处。 该团队在后一种模型以及随机动力学系统分叉，灵敏度和可预测性上获得了严格的结果，同时将ICTP-AGCM结果扩展到了Speedo（例如Speedo）的模型，并最终延伸到像社区气候系统模型（CCSM）之类的完整地球系统模型。 这项工作导致对气候敏感性的原因和机制有了更深入的了解。 它还提供了有效的方法来评估和提高全球循环模型和地球系统模型模拟过去和现在气候的能力，并预测环境的未来发展。 它有助于加强十年到世纪时间尺度上强大气候预测的基础，并提供了一种系统的方法来评估和改善此类模型中的确定性和随机参数。 这项工作的结果对复杂确定性动态与外部强迫，确定性和随机性相互作用的其他领域具有影响。 这种情况描述了生活和社会经济科学，气候科学和地球科学。 在团队成员本身以及其他领域的同事之间，跨学科边界之间的强烈互动有助于加速新方法和结果向其他学科的转移。