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关键工具，用于生成和测试假设，检查对扰动，后播或填补数据差距的敏感性以及对未来行为的预测。数值实验提供了比现场或实验室实验的优点，因为它们可以完全控制关键变量，并可以扩大运行实验的空间和时间尺度。尽管计算技术方面的改进允许进行高度详细的模拟，但是看到所有事物（即完美实验）的能力是否可以提高我们对现象的整体理解？替代高度详细的模拟，降低的复杂性模型（RCM）已经持续存在，甚至在环境科学中变得越来越普遍。 RCM具有搭配物理，生物学和化学动力学的能力，并对系统行为和敏感性提供了高度直观而定量的理解。但是，这些优点取决于模拟过程的严格性以及模型简化所涉及的假设。生态学，水文学，气候科学和其他环境科学领域中存在各种用于制定RCMS的假设和策略，这突出了社区需要综合综合。在这个研讨会中，来自不同领域的参与者将聚集在一起，讨论在模型，探测假设中降低复杂性的共同策略，并评估RCMS的规模和参数化问题。还将开发使用直接数值模拟，物理模型和/或数据库的RCM过程和假设严格的策略。RCMS是较大气候变化模拟的共同元素，还用于预测大型生态系统（例如三角洲，湿地和沙漠景观）等大型生态系统可能会导致恢复，预及和/或变化，或变化，或者变化，或者更改为/或变化。研讨会将为科学界提供工具，这将使RCMS的使用效率更高，可靠且有效，可用于管理复杂的环境系统。这些工具包括在线资源（代码，模型测试案例，数据，制定RCMS中常见步骤的描述），这些步骤将有助于简化和验证RCM开发。还将导致两份用于地球物理和生态社会的一般出版物的综合论文。由于它们的简单性和对复杂环境系统的功能，RCM或“玩具模型的功能”的能力？是理想的教学工具。研讨会的一部分将专注于使用RCMS开发在线教学资源。额外的针对研究生研究的研讨会将加强强调培训早期科学家的多样化组合。