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

项目团队由气候动力学家和应用数学家组成。研究人员(a)制定了气候敏感性的数学理论，(b)为大气环流模式和地球系统模式设计了一套优化算法。该团队汇集了动力系统，偏微分方程和数值方法方面的优势，在大气，海洋和气候动力学研究方面具有深度和广泛的覆盖范围。该项目的三个主要目标是：(i)继续发展强大的新方法，以从根本上了解气候敏感性和可预测性；（ii）扩展研究人员关于厄尔尼诺-南方涛动（ENSO）和北大西洋涛动（NAO）相关的低频变率模式的早期工作，年际和年代际；（iii）结合(i)和（ii）项分析这些模态在气候变化影响下的敏感性和可预测性。所有这三个目标都是通过一个完整的模型层次来实现的，从概念上的“玩具”模型到中等气候模型，再到中等复杂程度的地球系统模型。吉尔和他的同事最近致力于扩展随机动力系统理论，并将其应用于气候系统。这一理论允许人们(1)研究随机扰动（“天气”）对非线性动力系统（“气候变率”）的影响；(2)评估随机动力系统对系统或其强迫变化的鲁棒性和敏感性，无论这些变化是确定性的（如温室气体或气溶胶浓度缓慢的人为变化）还是随机性的（如火山喷发）；(3)通过考虑随机扰动的影响，对系统的可预测性得到更清晰的结果。为系统研究流线型全球环流模式（ICTP-AGCM）的参数依赖性而开发的方法，与pi及其同事在理想化模式上发表的结果有希望相似。该团队在后一种模型以及随机动力系统分岔、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程度的模型，如SPEEDO，最终扩展到完整的地球系统模型，如社区气候系统模型（CCSM）。这项工作使人们对气候敏感性的原因和机制有了更深入的了解。它还为评估和提高全球环流模式和地球系统模式模拟过去和现在气候以及预测我们环境未来演变的能力提供了有效的方法。它有助于加强十年到世纪时间尺度上可靠的气候预估的基础，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参数化。这项工作的结果对其他领域的影响，其中复杂的确定性动力学与外部强迫，确定性和随机相互作用。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。跨学科边界的强大互动——团队成员之间以及与其他领域的同事之间——有助于加速新方法和成果向其他学科的转移。

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.