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

Ghil，1049253 Braveli，1049095 Wang，1049114 Zaliapin，1049092 该项目小组由气候动力学家和应用数学家组成。 研究人员（a）制定气候敏感性的数学理论和（B）设计一套用于大气环流模型和地球系统模型的优化算法。 该团队汇集了动力系统，偏微分方程和数值方法的优势，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：（一）继续发展强有力的新方法，以便从根本上了解气候的敏感性和可预测性;（二）扩大调查人员早先就与厄尔尼诺-南方涛动和北大西洋涛动有关的年际和十年期低频变率模式开展的工作;以及（iii）将第（i）项和第（ii）项联合收割机结合起来，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一个完整的模型层次中实现的，从概念性的“玩具”模型到中等复杂性的地球系统模型。 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。 该理论允许人们（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，无论是确定性的（例如，温室气体或气溶胶浓度的缓慢的、人为的变化）或随机的（例如，火山爆发）;（3）通过考虑随机扰动的影响，获得关于系统可预报性的更清晰的结果。 为系统研究简化的全球环流模式（ICTP-AGCM）中的参数依赖性而开发的方法与PI及其同事在理想化模型上发表的结果有着相似之处。 该团队在后一种模型以及随机动力系统分叉，灵敏度和可预测性方面获得了严格的结果，同时将ICTP-AGCM结果扩展到SPEEDO等中等复杂度的模型，并最终扩展到社区气候系统模型（CCSM）等完整的地球系统模型。 这项工作有助于更深入地了解气候敏感性的原因和机制。 它还提供了有效的方法来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的能力，并预测我们的环境的未来演变。 它有助于加强对十年至世纪时间尺度的可靠气候预测的基础，并为评价和改进这类模型中的确定性和随机性参数化提供了一种系统的方法。 这项工作的结果有影响的其他领域中，复杂的确定性动力学与外部强迫，确定性以及随机相互作用。 生命科学和社会经济科学以及气候科学和地球科学都是这种情况。 跨学科界限的强有力的互动--团队成员之间以及与其他领域的同事之间的互动--有助于加速新方法和新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.