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建模的定性改进。研究人员正在为气候建模进行直接统计方法进行跨学科计划，该计划有可能显着提高我们对不同气候过程如何在短期和长时间尺度上以及大型和大空间尺度相互作用的理解。该计划为新的气候模型奠定了基础，这些基础可能会补充甚至最终取代现有的气候模型。它有两个平行的轨道。首先，研究人员正在开发直接计算气候统计数据的方法，这些方法最终可能取代常规数值模拟，从而导致计算上更有效的气候模型。其次，他们正在开发低云的改进表示形式，并对与大规模循环的云相互作用有了改进的理解，从而导致更准确的气候模型，并减少当前大型不确定性在其中云的表示。直接计算气候统计的方法基于累积的扩展，其中快速模式被整合在一起。这将重新关注对低频模式的评估和理解，这在气候变化中占主导地位。基于云和亚网格尺度动力学的概率封闭而开发了低云的改进表示，可以系统地改善准确性。这些都集成到一般循环模型（GCM）中，该模型将云耦合到大规模流量，从而系统地研究了它们的相互作用。这项工作扩展了过去对统计数据直接计算的地球物理流量模型的直接计算，以提高复杂性和现实主义的GCM，同时提高统计方法和GCM本身的准确性。