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

在许多学科中，数值模型是生成和测试假设、检查对扰动的敏感性、后推或填补数据空白以及预测未来行为的关键工具。数值实验提供了优于现场或实验室实验的优势，因为它们提供了对关键变量的完全控制，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非常详细的模拟，但看到一切（即完美的实验）的能力是否提高了我们对现象的整体理解？作为高度详细模拟的替代方案，降低复杂性模型（RCMs）一直存在，甚至在环境科学中变得越来越普遍。rcm在耦合物理、生物和化学动力学方面提供了优势，并提供了对系统行为和灵敏度的高度直观的定量理解。然而，这些优点取决于模拟过程的严谨性和模型简化中涉及的假设。在生态学、水文学、气候科学和其他环境科学领域，在制定rcm时使用了各种各样的假设和策略，这突出了社区聚集在一起进行综合的必要性。在本次研讨会中，来自不同领域的参与者将聚集在一起讨论降低模型复杂性的共同策略，探索假设，并评估rcm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/或数据库来测试RCM过程和假设的严密性的策略。rcm是大型气候变化模拟的常见元素，也用于预测三角洲、湿地和沙漠景观等大型生态系统将如何因恢复、城市化和/或气候变化而可能发生变化。讲习班将为科学界提供工具，使区域管理管理更有效、可靠和有效地用于管理三个复杂的环境系统。这些工具包括在线资源（代码、模型测试用例、数据、在制定RCM中常见步骤的描述），它们将有助于简化和验证RCM开发。还将产生两篇综合论文，目标是用于地球物理和生态学会的一般出版物。因为它们的简单性和能够产生对复杂环境系统功能的直观理解，rcm，或者？玩具模型,?是理想的教学工具。研讨会的部分内容将侧重于使用rcm开发在线教学资源。另外一个以研究生研究为重点的研讨会将加强对培养一批不同的早期职业科学家的重视。

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.