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

数值模型是许多学科的重要工具，用于生成和测试假设，检查对扰动的敏感性，后推或填补数据空白，以及对未来行为进行预测。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。虽然计算技术的改进已经允许高度详细的模拟，但看到一切的能力（即，完美的实验）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，简化复杂性模型（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、生物和化学动力学的能力方面具有优势，并提供了对系统行为和灵敏度的高度直观但定量的理解。然而，这些优势取决于模拟过程的严格性和模型简化中所涉及的假设。在生态学、水文学、气候科学和其他环境科学领域，制定区域协调机制时使用的各种假设和策略，突出了社区走到一起进行综合的必要性。在这次研讨会上，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过程和假设的严格性的策略。RCM是大型气候变化模拟的常见要素，也用于预测三角洲、湿地和沙漠景观等大型生态系统由于恢复、城市化和/或气候变化而可能发生的变化。研讨会将为科学界提供工具，使区域协调机制的使用更有效、可靠和有效，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、制定区域协调机制的共同步骤说明），将有助于简化和验证区域协调机制的制定。还将产生两篇针对地球物理和生态学会一般出版物的综合论文。由于其简单性和他们的能力，以产生直观的理解如何复杂的环境系统的功能，区域协调机制，或？玩具模型？是理想的教学工具。研讨会的一部分将侧重于使用区域协调机制开发在线教学资源。一个额外的研讨会会议侧重于研究生的研究将加强对培训的早期职业科学家的多样化组合的重视。

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.