Collaborative Research: EaSM 2: Stochastic Simulation and Decadal Prediction of Large-Scale Climate

合作研究：EaSM 2：大尺度气候的随机模拟和年代际预测

• 批准号: 1243158
• 负责人: Sergey Kravtsov
• 金额: $ 26.07万
• 依托单位: University of Wisconsin-Milwaukee
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-02-15 至 2017-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1243158&HistoricalAwards=false
• 关键词: Collaborative; Research; EaSM; Stochastic; Simulation

Under this project, large-scale, low-frequency modes (LFMs) will be studied in observations and simulations of state-of-the-art general circulation models (GCMs) of Earth's climate. This project will build on previous NSF- and DOE-funded work on LFMs arising from the ocean's wind-driven and overturning circulations in the Atlantic and Pacific sectors, as well as from their interactions with the atmosphere and tropical climate variability. This project will focus on: (a) improving current understanding of LFMs and interactions between them; (b) developing, revising and testing statistical methods for probabilistic decadal prediction based on these modes; and (c) using these decadal predictions in conjunction with observations and GCM simulations to gain insight into the dynamical causes of climate change and climate variability at decadal and longer time scales. This project will continue the identification of decadal modes in the Atlantic and Pacific, from both observations and the simulations from the two most recent international climate intercomparison modeling projects, using advanced, data-adaptive spectral methods. These methods will include multi-channel singular spectrum analysis (MSSA), as well as harmonic Koopman analysis (HKA), along with stochastic modeling based on empirical model reduction (EMR). The investigators will further apply novel methods for the study of synchronized chaotic oscillators developed at UCLA and UWM, to gain additional understanding of decadal-scale teleconnections within the Pacific and between the Atlantic and the Pacific, as well as their relationships with the El Nino-Southern Oscillation (ENSO). Using the LFMs studied in the first part of the project, the investigators will examine their decadal predictability and assess the skill of retrospective decadal forecasts made with our new empirical forecast models based on EMR, MSSA and HKA. These empirical forecasts will be compared against the initialized climate predictions being made as part of the IPCC's 5th Assessment Report. This comparison will help quantify potential skill due to intrinsic decadal modes and their interactions with ENSO, as well as with climate change. The combination of these activities will lead to advanced stochastic simulation tools for creating benchmark scenarios of future climate, based on a combination of observed data and the most robust and predictable elements of near-term climate change and climate variability, out to about 2050.The intellectual merit of this project is in developing versatile statistical tools and methodologies for climate prediction and the validation of dynamical climate models. The project will also advance our understanding of global coupling between prominent large-scale low-frequency modes. An expected outcome of the project is improved estimates of reliability of climate projections, by developing and testing novel metrics - associated with coupling and synchronization between multiple LFMs across the globe - for validating climate model predictions.The project's broader impacts lie in addressing a problem of utmost societal importance, i.e., understanding climate variability and change. The project's investigators are committed to making their stochastic simulations easily available to the climate community. This work will foster scientific partnerships between UCLA and UWM, and further the development of graduate curricula in climate dynamics at both institutions. The principal investigators will broadly disseminate the results by means of a dedicated web site, refereed publications, seminars and presentations at national and international meetings.

在该项目下，将在地球气候最先进的大气环流模型（GCM）的观测和模拟中研究大尺度低频模式（LFM）。该项目将建立在之前由 NSF 和 DOE 资助的关于 LFM 的工作的基础上，这些 LFM 是由大西洋和太平洋区域的海洋风驱动和翻转环流及其与大气和热带气候变化的相互作用产生的。该项目将侧重于： (a) 提高目前对 LFM 及其之间相互作用的理解； (b) 开发、修订和测试基于这些模式的十年概率预测统计方法； (c) 将这些十年预测与观测和 GCM 模拟结合起来，以深入了解十年和更长时间尺度上气候变化和气候变率的动力原因。该项目将利用先进的数据自适应光谱方法，根据最近两个国际气候比对建模项目的观测和模拟，继续识别大西洋和太平洋的年代际模式。这些方法将包括多通道奇异谱分析（MSSA）、谐波库普曼分析（HKA）以及基于经验模型简化（EMR）的随机建模。研究人员将进一步应用加州大学洛杉矶分校和威斯康星大学开发的同步混沌振荡器的新方法来研究，以进一步了解太平洋内部以及大西洋与太平洋之间的十年尺度遥相关，以及它们与厄尔尼诺-南方涛动（ENSO）的关系。使用项目第一部分研究的 LFM，研究人员将检查其十年预测能力，并评估使用我们基于 EMR、MSSA 和 HKA 的新实证预测模型进行回顾性十年预测的技巧。这些经验预测将与作为 IPCC 第五次评估报告一部分的初始气候预测进行比较。这种比较将有助于量化由于内在十年模式及其与 ENSO 以及气候变化的相互作用而产生的潜在技能。这些活动的结合将产生先进的随机模拟工具，用于创建未来气候的基准情景，基于观测数据和近期气候变化和气候变率的最可靠和可预测的要素的组合，到2050年左右。该项目的智力价值在于开发用于气候预测和动态气候模型验证的通用统计工具和方法。该项目还将增进我们对重要的大规模低频模式之间的全局耦合的理解。该项目的预期成果是通过开发和测试新指标（与全球多个 LFM 之间的耦合和同步相关）来验证气候模型预测，从而改进对气候预测可靠性的估计。该项目的更广泛影响在于解决最重要的社会问题，即了解气候变率和变化。该项目的研究人员致力于让气候界能够轻松获得他们的随机模拟。这项工作将促进加州大学洛杉矶分校和威斯康辛大学之间的科学合作伙伴关系，并进一步开发两所机构的气候动力学研究生课程。主要研究人员将通过专门网站、参考出版物、研讨会以及在国内和国际会议上的演讲等方式广泛传播研究结果。