Collaborative Research: Understanding Climate Processes with Causal Discovery and Graphs of Information Flow in the Coupled Atmosphere-Land-Ocean System

合作研究：通过大气-陆地-海洋耦合系统中的因果发现和信息流图来了解气候过程

• 批准号: 1445956
• 负责人: Yi Deng
• 金额: $ 32.25万
• 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: 2015
• 资助国家: 美国
• 起止时间: 2015-01-15 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1445956&HistoricalAwards=false
• 关键词: Collaborative; Research; Understanding; Climate; Processes

The goal of this project is to develop graphical models of causal discovery and apply them to topics the field of climate dynamics. In these models lagged correlations are used to identify pathways of causal influence among components of a coupled system, and the model is constructed by representing each component as one node of a directed graph. Edges between node are assigned values representing the extent to which one component is predictable given prior knowledge of another. A key issue in causal discovery is accounting for indirect effects, in which component A influences component B which in turn influences component C, as opposed to the case in which component A directly influences component C. In graphical terms the distinction is whether or not an edge exists between A and C, or whether A's connection to C is only through B. This evaluation is done through a statistical calculation of conditional probabilities. Previous work by the PIs applied causal discovery to identify relationships among four well-known teleconnection patterns, the Western Pacific and Eastern Pacific Oscillations (WPO and EPO), the North Atlantic Oscillation (NAO), and the Pacific/North America (PNA) pattern. In this case the method shows that the EPO can affect the NAO 18 days later, while the NAO can influence the PNA pattern three to six days later. Although the application to atmospheric science is novel, the method has been used successfully in other disciplines including economics, systems engineering, and bioinformatics.Work under the project will further develop the modeling technique, with a goal of increasing computational speed by a factor of 100 through improvements in parallelization and localization of memory access. Visualization tools will also be developed to display results. In addition to these development efforts, the PIs will apply the technique to address three topics in climate dynamics: the onset dynamics of the Asian summer monsoon, the subseasonal variation of the Northern Hemisphere annual mode (NAM), and changes in the characteristics of information flow in global warming simulations. Work on monsoon onset will focus on three questions: What are the critical processes responsible for the establishment of the cross-equatorial flow? How important is the mechanical and thermal forcing associated with the Tibetan Plateau for the onset compared to local air-sea interaction in the Bay of Bengal and South China Sea? Is the onset a direct manifestation of atmospheric instability? For subseasonal NAM variability, the research goal is to investigate the relative importance of tropospheric dynamics, stratosphere-troposphere coupling, and troposphere-surface coupling in determining the phase transition of the NAM. The work on global warming is based on a preliminary analysis of one simulation in which information flows in the Northern midlatitudes become weaker as the climate warms, suggesting a reduction in atmospheric predictability in the tropics and mid-latitudes.The work has broader impacts through the introduction of a methodology developed in one scientific discipline (economics) into another (climate dynamics). The PIs will promote the adoption of the modeling technique by making their algorithms and software available to the community of climate dynamics researchers through a web portal. They will also offer tutorials at scientific conferences including the annual meetings of the American Meteorological Society and the American Geophysical Union. Tutorial materials will also be made available online and through published papers. In addition, the project will support and train a graduate students and a postdoctoral fellow, thereby providing for the future workforce in this research area.

该项目的目标是开发因果发现的图形模型，并将其应用于气候动力学领域的主题。在这些模型中，滞后相关性用于识别耦合系统组件之间的因果影响途径，并且通过将每个组件表示为有向图的一个节点来构建模型。节点之间的边被赋值，表示给定另一个组件的先验知识，一个组件可预测的程度。因果发现中的一个关键问题是考虑间接影响，其中组件A影响组件B，组件B反过来影响组件C，而不是组件A直接影响组件C的情况。在图形术语中，区别在于A和C之间是否存在边缘，或者A与C的联系是否仅通过B。这种评估是通过条件概率的统计计算完成的。pi先前的工作应用因果发现来确定四种众所周知的遥相关模式之间的关系，即西太平洋和东太平洋涛动（WPO和EPO）、北大西洋涛动（NAO）和太平洋/北美（PNA）模式。在这种情况下，该方法显示EPO可以在18天后影响NAO，而NAO可以在3至6天后影响PNA模式。虽然应用于大气科学是新颖的，但该方法已成功地应用于其他学科，包括经济学，系统工程和生物信息学。该项目的工作将进一步发展建模技术，目标是通过改进并行化和内存访问的本地化，将计算速度提高100倍。还将开发可视化工具来显示结果。除了这些开发工作之外，pi还将应用该技术来解决气候动力学中的三个主题：亚洲夏季风的开始动力学，北半球年模态（NAM）的亚季节变化，以及全球变暖模拟中信息流特征的变化。季风开始的研究将集中在三个问题上：跨赤道气流形成的关键过程是什么？这是大气不稳定的直接表现吗？对于亚季节的不结盟运动变率，研究目标是探讨对流层动力学、平流层-对流层耦合和对流层-地面耦合在确定不结盟运动相变中的相对重要性。关于全球变暖的工作是基于对一个模拟的初步分析，在这个模拟中，随着气候变暖，北部中纬度地区的信息流变弱，这表明热带和中纬度地区的大气可预测性降低。通过将一门学科（经济学）开发的方法引入另一门学科（气候动力学），这项工作产生了更广泛的影响。pi将通过一个门户网站向气候动力学研究人员社区提供他们的算法和软件，从而促进建模技术的采用。他们还将在科学会议上提供指导，包括美国气象学会和美国地球物理联合会的年度会议。教程材料也将在网上和通过发表的论文提供。此外，该项目将支持和培养一名研究生和一名博士后，从而为该研究领域的未来劳动力提供支持。