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: 1049114
• 负责人: Shouhong Wang
• 金额: $ 4.46万
• 依托单位: Indiana University
• 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 2014-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1049114&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.

1049092项目团队由气候动态学家和应用数学家组成。研究人员(A)阐述了气候敏感性的数学理论，(B)为大气环流模型和地球系统模型设计了一套优化算法。该团队汇集了动力系统、偏微分方程式和数值方法的优势，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面。该项目的三个主要目标是：(I)继续开发强有力的新方法，从根本上了解气候敏感性和可预测性；(Ii)扩大研究人员先前关于厄尔尼诺-南方涛动(ENSO)和北大西洋涛动(NAO)相关低频变化模式、年际和年代际模式的工作；(Iii)结合项目(I)和(Ii)分析这些模式在受到气候变化影响时的敏感性和可预测性。所有这三个目标都是在整个模型层次上追求的，从概念性的“玩具”模型到中间气候模型，再到中等复杂性的地球系统模型。Ghil和他的同事最近致力于扩展随机动力系统的理论，并将其应用于气候系统。这一理论使人们能够(1)调查随机扰动(“天气”)对非线性动力系统(“气候变异性”)的影响；(2)评估随机动力系统对系统或其强迫的变化的稳健性和敏感性，无论是确定性的(例如，温室气体或气溶胶浓度的缓慢人为变化)还是随机的(例如，火山喷发)；以及(3)通过考虑随机扰动的影响，获得关于系统可预测性的更准确的结果。ICTP-AGCM是为系统研究简化的全球环流模式中的参数相关性而开发的方法，其结果与PI及其同事在理想化模式上发表的结果有很好的相似之处。该团队在后一类模型以及随机动力系统的分叉、敏感性和可预测性方面获得了严格的结果，同时将ICTP-AGCM的结果扩展到中等复杂性的模型，如Speedo，并最终扩展到整个地球系统模型，如社区气候系统模型(CCSM)。这项工作有助于更深入地理解气候敏感性的原因和机制。它还提供了评估和改进全球环流模型和地球系统模型模拟过去和现在气候以及预测我们环境未来演变的能力的有效方法。它有助于加强在十年到世纪的时间尺度上进行稳健气候预测的基础，并提供了一种系统的方法来评估和改进这种模式中的确定性和随机性参数。这项工作的结果对复杂的确定性动力学与外部强迫相互作用的其他领域也有影响，这些外部强迫既是确定性的，也是随机的。这种情况是生命科学和社会经济科学以及气候科学和地球科学的特点。跨学科界限的强大互动--团队成员之间以及与其他领域的同事--有助于加速将新方法和结果转移到其他学科。