CMG COLLABORATIVE RESEARCH: Novel Mathematical Strategies for Superparameterization in Atmospheric and Oceanic Flows

CMG 合作研究：大气和海洋流超参数化的新数学策略

• 批准号: 1025468
• 负责人: Andrew Majda
• 金额: $ 97万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1025468&HistoricalAwards=false
• 关键词: CMG; COLLABORATIVE; RESEARCH; Novel; Mathematical

Improving our understanding of, and ability to model atmospheric and oceanic circulation impacts human society in a very direct way. Atmospheric and oceanic flows, however, encompass a wide range of scales, from the planetary scale, of order 10000km, to the microscale, on the order of a few millimeters. This raises a particular challenge for global climate simulations, which cannot directly resolve scales below a few kilometers at best, even with the most powerful computers. The proposed project will develop novel methods to represent the effects of unresolved scales on directly simulated scales in atmospheric and oceanic models through ʻsuperparameterizationʼ (SP), defined broadly as a general set of techniques by which small-scale dynamics can be represented mathematically and numerically as an embedded sub-grid model. Superparameterization is a relatively new approach that has had great success in the parameterization of clouds, but has yet to be applied to many other relevant problems in climate models. The present project will apply novel SPtechniques to accurately incorporate the effects of a wide variety of important unresolved processes in atmospheric and oceanic models.Our research will focus on the application of SP strategies to highly relevant and difficult geophysical problems, including moist convection in the atmosphere, mesoscale and submesoscale eddies in the oceans, and moist geostrophic turbulence associated with the midlatitude weather system, which combines elements of both moist convection and geostrophic turbulence. We approach these diverse problems with a systematic, three step SP process: (1) the development of multi-scale equations for the physical problem;(2) the use of a periodic approximation for the fluctuations; and (3) exploitation of intermittency to replace fluctuating dynamics with reduced models that may be solved by either inexpensive numerical methods or by analytic solutions. Our goals are to develop and test this SP approach in a set of idealized models, and ultimately to provide new parameterization algorithms that may be incorporated in operational global climatemodels.

提高我们对大气和海洋环流的理解和建模能力，以非常直接的方式影响人类社会。然而，大气和海洋的流动包含了很大的尺度，从10000公里量级的行星尺度到几毫米量级的微尺度。这对全球气候模拟提出了一个特殊的挑战，即使使用最强大的计算机，也无法直接解决几公里以下的尺度。拟议的项目将开发新的方法，通过超级参数化（SP）来表示大气和海洋模型中直接模拟尺度上未解决尺度的影响，超级参数化（SP）被广泛定义为一套通用技术，通过这些技术，小尺度动态可以在数学上和数值上表示为嵌入式子网格模型。超级参数化是一种相对较新的方法，在云的参数化方面取得了巨大成功，但尚未应用于气候模式中的许多其他相关问题。本项目将应用新的SP技术，在大气和海洋模式中精确地纳入各种重要的未解决过程的影响，我们的研究将集中在SP策略在高度相关和困难的地球物理问题中的应用，包括大气中的湿对流，海洋中的中尺度和亚中尺度涡旋，以及与中纬度天气系统相关的湿地转湍流，它结合了湿对流和地转湍流的元素。我们接近这些不同的问题与一个系统的，三个步骤SP过程：（1）多尺度方程的物理问题的发展;（2）使用周期近似的波动;（3）开发的不稳定性，以取代波动动力学与减少模型，可以通过廉价的数值方法或解析解来解决。我们的目标是在一组理想化的模式中开发和测试这种SP方法，并最终提供新的参数化算法，可用于业务全球气候模型。