Doctoral Dissertation Research: Visualizing Mechanistic Controls of North American Climate Variability Through Cartographic Animation

极端气候变化，如干旱和洪水，对农业和水资源产生广泛的社会和经济后果。然而，气候研究人员面临的一个主要挑战是有效地制定和呈现气候变化的描述。本博士论文研究项目的目标是在多个时空尺度上建立一个北美气候控制的概念模型，并使用动画制图方法说明该模型。具体来说，该项目旨在提供基于过程的大气环流日变化和逐月变化的一般描述，这些变化会导致地面气候异常，如干旱。这项研究还将描述厄尔尼诺等现象对北美气候年际变化的影响。本项目数据由美国国家环境预测中心（NCEP）和美国国家大气研究中心（NCAR） 50年再分析项目提供。这些数据源包含许多不能直接观测到的气象场，可用于分析气候系统的时空变异。每个变量的长期平均值提供了通过季节周期的大尺度气候变化的信息。复合异常型显示了大尺度大气环流型与地表气候响应之间的关系。使用这些数据的制图动画将时间进程纳入气候变化的分析中，从而提供了将干旱和洪水等异常事件的形成和消亡可视化的机会。该项目的结果将使人们更好地了解北美气候变率所涉及的过程，以及这些过程如何随时间和空间变化。这项研究将展示控制现代气候记录中干旱等极端事件的地表和大气气候过程，以帮助理解是什么驱动了过去的气候变化。该项目还将为预测干旱对未来气候的影响提供工具。本项目采用的基于过程的方法将有助于理解在多个空间尺度（如区域到大陆）和时间尺度（如日到年）上引起气候变率的机制和反馈。此外，本研究将透过动画在数据探索和结果交流方面的应用，加强制图领域。这项研究制作的动画将通过互联网提供给研究人员、教育工作者和公众，作为教学和资源工具。这些产品在实验室研究和课堂学习之间建立了联系。例如，在K-12教育中，教师将能够使用为该项目创建的基于网络的材料来帮助他们开展以学生为中心的学习活动。作为博士论文研究改进奖，该奖项还将提供支持，使有前途的学生建立一个强大的独立研究生涯。

Extreme climate variations, such as droughts and floods, have broad social and economic consequences for agriculture and water resources. A major challenge to climate researchers, however, are to effectively formulate and present depictions of climate change. The objectives of this doctoral dissertation research project are to develop a conceptual model of the controls of North American climate on multiple spatial and temporal scales and to illustrate this model using animated cartographic methods. Specifically, this project is designed to provide a general process-based description of daily and month-to-month variations of atmospheric circulation that contribute to surface climate anomalies such as drought. This study will also describe the influence that such phenomena like El Nino have on year-to-year variations of North American climate. Data for this project are provided through the National Center for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) 50-year Reanalysis Project. These data sources, which contain meteorological fields, many not directly observable, can be used for analyzing the spatial and temporal variability of the climate system. The long-term averages of each variable provide information about the broad-scale climate variations through the seasonal cycle. Composite-anomaly patterns show relationships between large-scale atmospheric circulation patterns and surface climate responses. Cartographic animations using these data incorporate time progression into analyses of climate variability, thus providing an opportunity to visualize the build-up and demise of anomalous events such as droughts and floods.Results from this project will provide a greater understanding of the processes involved in climate variability in North America and how those processes vary through time and space. This research will show the surface and atmospheric climate processes that control extreme events like drought in the modern climate record in order to help understand what has driven past climate change. The project also will provide tools for predicting the impact of drought in future climates. The process-based approach taken in this project will contribute to understanding of the mechanisms and feedbacks that cause climate variability on multiple spatial scales (e.g., regional to continental) and temporal scales (e.g., daily to yearly). In addition, this research will enhance the field of cartography through the application of animations for both data exploration and communication of results. Animations created from this research will be made available via the internet to researchers, educators and the general public for use as teaching and resource tools. Such products provide a link between research in the lab and learning in the classroom. In K-12 education, for example, teachers will be able to use the web-based materials created for this project to help develop their own student-centered learning activities. As a Doctoral Dissertation Research Improvement award, this award also will provide support to enable a promising student to establish a strong independent research career.