Collaborative Research: Type 1 -- LOI02170139: Direct Statistical Approaches to Large-Scale Dynamics, Low Cloud Dynamics, and their Interaction

合作研究：类型 1 -- LOI02170139：大规模动力学、低云动力学及其相互作用的直接统计方法

• 批准号: 1048701
• 负责人: John Marston
• 金额: $ 43.65万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-05-01 至 2015-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1048701&HistoricalAwards=false
• 关键词: Collaborative; Research; Type; LOI02170139; Direct

Transformative research is needed to realize qualitative improvements in the modeling of climate. The investigators are carrying out an interdisciplinary program of direct statistical approaches to climate modeling that has the potential to dramatically improve our understanding of how different climate processes interact over short and long time scales and small and large spatial scales. The program lays the foundations for a new class of climate models that may complement or even eventually replace existing climate models. It has two parallel tracks. First, the investigators are developing methods for the direct computation of climate statistics that may eventually replace conventional numerical simulations, leading to computationally more efficient climate models. Second, they are developing improved representations of low clouds and an improved understanding of cloud interactions with large-scale circulations, leading to more accurate climate models and reducing the currently large uncertainties in the representation of clouds in them. The methods for the direct computation of climate statistics are based upon cumulant expansions in which fast modes are integrated out. This refocuses attention on the evaluation and understanding of low-frequency modes, which dominate climate variability. Improved representations of low clouds are developed based on probabilistic closures of cloud and subgrid-scale dynamics, which can be systematically improved in accuracy. These are integrated into a general circulation model (GCM) that couples the clouds to the large-scale flows, allowing a systematic study of their interactions. The work extends past work on the direct computation of statistics in relatively simple models of geophysical flows to GCMs of increasing complexity and realism, while improving the accuracy of the statistical methods and of the GCMs themselves.

需要进行变革性研究，以实现气候模型的质量改进。研究人员正在开展一个直接统计气候建模的跨学科项目，该项目有可能极大地提高我们对不同气候过程在短时间和长时间尺度以及小空间和大空间尺度上如何相互作用的理解。该计划为一类新的气候模型奠定了基础，这些模型可能会补充甚至最终取代现有的气候模型。它有两条平行的轨道。首先，研究人员正在开发直接计算气候统计数据的方法，这些方法最终可能会取代传统的数值模拟，从而导致计算上更有效的气候模型。其次，他们正在改进低云的表示，并改进对云与大规模环流相互作用的理解，从而产生更准确的气候模型，并减少目前云表示中的巨大不确定性。直接计算气候统计量的方法是基于累积量展开，在累积量展开中积分出快速模式。这使人们的注意力重新集中在对低频模式的评估和理解上，低频模式主导着气候变化。改进的低云表示是基于云的概率闭包和次网格尺度的动力学，可以系统地提高精度。这些都被集成到一个将云与大尺度流动耦合起来的一般环流模式(GCM)中，从而允许系统地研究它们之间的相互作用。这项工作将过去在相对简单的地球物理流动模型中直接计算统计数据的工作扩展到日益复杂和现实的全球气候模型，同时提高统计方法和全球气候模型本身的准确性。