The Art and Science of Reduced-Complexity Modeling in the Environmental Sciences

环境科学中降低复杂性建模的艺术与科学

• 批准号: 1263851
• 负责人: Laurel Larsen
• 金额: $ 3.9万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-12-15 至 2014-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1263851&HistoricalAwards=false
• 关键词: Art; Science; Reduced; Complexity; Modeling

Numerical models are a critical tool in many disciplines for generating and testing hypotheses, examining sensitivity to perturbation, hindcasting or filling in data gaps, and making predictions about future behavior. Numerical experiments offer advantages over field or laboratory experiments in that they provide complete control over critical variables and can expand the spatial and temporal scales over which experiments are run. Although improvements in computing technology have allowed for highly detailed simulations, does the ability to see everything (i.e., the perfect experiment) improve our overall understanding of phenomena? An alternative to highly detailed simulations, reduced complexity models (RCMs) have persisted and are even becoming more pervasive in the environmental sciences. RCMs offer advantages in their ability to couple physical, biological, and chemical dynamics and provide highly intuitive yet quantitative understanding of system behavior and sensitivities. However, these advantages depend on the rigor of the processes simulated and the assumptions involved in model simplification. A diverse array of assumptions and strategies used in formulating RCMs is present in ecology, hydrology, climate science, and other environmental science fields, highlighting a need for the community to come together for synthesis. In this workshop, participants from diverse fields will come together to discuss common strategies for reducing complexity in models, probe assumptions, and evaluate issues of scale and parameterization of RCMs. Strategies for testing the rigor of RCM processes and assumptions using direct numerical simulations, physical models, and/or databases will also be developed.RCMs are common elements of larger climate change simulations and are also used to predict how large ecosystems such as deltas, wetlands, and desert landscapes will likely change as a result of restoration, urbanization, and/or changes in climate. The workshop will result in tools for the scientific community that will make the use of RCMs more efficient, reliable, and effective for use in management of thee complex environmental systems. The tools include online resources (code, model test cases, data, descriptions of common steps in formulating RCMs) that will help streamline and validate RCM development. Two synthesis papers, targeted for general publications of geophysical and ecological societies will also result. Because of their simplicity and their ability to produce intuitive understanding of how complex environmental systems function, RCMs, or ?toy models,? are an ideal teaching tool. Part of the workshop will focus on developing online teaching resources using RCMs. An additional workshop session focused on graduate student research will reinforce an emphasis on training a diverse assemblage of early-career scientists.

数值模型是许多学科中的重要工具，用于生成和测试假设、检查对扰动的敏感性、事后预测或填补数据空白以及对未来行为进行预测。数值实验比现场或实验室实验具有优势，因为它们可以完全控制关键变量，并且可以扩展实验运行的空间和时间尺度。尽管计算技术的进步已经允许进行高度详细的模拟，但是看到一切的能力（即完美的实验）是否可以提高我们对现象的整体理解？作为高度详细模拟的替代方案，降低复杂度模型 (RCM) 一直存在，甚至在环境科学中变得越来越普遍。 RCM 的优势在于能够耦合物理、生物和化学动力学，并提供对系统行为和敏感性高度直观且定量的理解。然而，这些优势取决于模拟过程的严格性以及模型简化中涉及的假设。生态学、水文学、气候科学和其他环境科学领域存在用于制定 RCM 的各种假设和策略，这凸显了社区团结起来进行综合的需要。在本次研讨会上，来自不同领域的参与者将齐聚一堂，讨论降低模型复杂性的共同策略、探讨假设并评估 RCM 的规模和参数化问题。还将制定使用直接数值模拟、物理模型和/或数据库来测试 RCM 过程和假设的严谨性的策略。RCM 是大型气候变化模拟的常见要素，也用于预测三角洲、湿地等大型生态系统的变化程度。 ，沙漠景观可能会因恢复、城市化和/或气候变化而发生变化。该研讨会将为科学界提供工具，使 RCM 的使用更加高效、可靠和有效，以管理复杂的环境系统。这些工具包括在线资源（代码、模型测试用例、数据、制定 RCM 的常见步骤的描述），这将有助于简化和验证 RCM 开发。还将产生两篇综合论文，供地球物理学会和生态学会的一般出版物使用。由于 RCM 或“玩具模型”的简单性以及能够直观地理解复杂环境系统如何运作的能力，是理想的教学工具。研讨会的一部分将重点讨论使用 RCM 开发在线教学资源。一个以研究生研究为重点的额外研讨会将加强对培养多样化的早期职业科学家的重视。