Cloud Parameterization Frameworks

云参数化框架

• 批准号: 0415184
• 负责人: David Randall
• 金额: --
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0415184&HistoricalAwards=false
• 关键词: Cloud; Parameterization; Frameworks

The PI will continue his development of parameterizations of convective cloud systems and the planetary boundary layer (PBL). His new PBL parameterization will include a mechanistic representation of the vertical transport of horizontal momentum by roll circulations. The model predicts the width and orientation of the rolls, and the perturbation wind field is diagnosed. This information is used to compute the perturbation pressure field from the anelastic pressure equation, and this in turn is used to compute the pressure correlations needed to predict the vertical velocity variance and other statistics. The parameterization will be tested using large-eddy simulation (LES) results. The PBL parameterization will be altered to use an explicit PBL depth, with about 10 or fewer layers inside. The PI's intention is to test this parameterization in the Colorado State University general circulation model and eventually in the Community Climate System Model. In order to predict the depth, he must parameterize the entrainment rate. He will develop an entrainment parameterization that is designed for use with a vertically resolved PBL and predicted statistics such as the skewness of the vertical velocity field. This fresh look at the entrainment problem, from a different perspective, may lead to an improvement in the understanding of the basic physics. Some emphasis will be placed on the role of evaporative cooling in enhancing the entrainment rate and reducing the fractional cloudiness. The top-hat probability density function (PDF) used in his earlier work will be replaced by a more realistic and flexible "spatial distribution function." Finally, the new parameterization will include a representation of precipitation processes. The parameterization of deep convection will be altered to abandon the Arakawa-Schubert approach based on an entraining plume cloud model with a spectrum of cloud types. In its place the PI will put a single convective updraft at each level, but with a resolved internal structure. This will improve the scaling of the parameterization as the vertical resolution of the model is increased. Convective downdrafts and stratiform clouds will also be included in this framework. The PI's revised parameterization of deep convection and the attendant stratiform clouds will make use of a new cloud model for parameterization. In the Arakawa-Schubert parameterization there are many cloud types, each with a crudely idealized internal structure. The PI will replace this by a single "cloud type" with a more realistic internal structure, including joint variations (at a given level) of the vertical velocity and the thermodynamic variables. Convective downdrafts will be represented through a second PDF. The PDF of the cloud model can include both convective and stratiform clouds in a unified framework. In fact, the PDF can even include such things as spatial variability of the water vapor mixing ratio in clear air. It has been suggested that humid mesoscale regions (surrounded by much drier air) can provide nurturing environments for the growth of deep cumuli, and that in the absence of such mesoscale humid regions deep convection is suppressed. By parameterizing the mesoscale variability of water vapor in clear air, the PI can explore this idea in the context of a large-scale model. His work will be guided by the results obtained with high-resolution cloud models. To complete the parameterization, his new cloud model will be combined with a prognostic closure. He will predict multiple moments of the vertical velocity as functions of height, as well as multiple moments of the thermodynamic variables. This research will represent a step towards unification of the parameterizations of the PBL and deep convection.Broader Impacts:The research will pave the way for improved weather forecasts and improved simulations of climate change. Deficiencies in cloud parameterizations are widely acknowledged to be among the most serious obstacles standing in the way of more reliable simulations of climate change. The research will also contribute to the training of graduate students and postdoctoral researchers for careers in atmospheric science.

PI将继续发展对流云系统和行星边界层(PBL)的参数化。他的新的PBL参数化将包括通过滚转环流垂直输送水平动量的机械表示。该模式预测了卷曲的宽度和方位，并诊断了扰动风场。这些信息被用来从滞弹性压力方程计算扰动压力场，而这又被用来计算预测垂直速度变化和其他统计所需的压力关联。该参数化将使用大涡模拟(LES)结果进行测试。PBL参数化将被更改为使用显式PBL深度，内部约有10层或更少的层。PI的目的是在科罗拉多州立大学的大气环流模式中测试这种参数化，并最终在社区气候系统模式中进行测试。为了预测深度，他必须将夹带率参数化。他将开发一种卷吸参数，该参数设计用于垂直分辨的PBL和预测统计数据，如垂直速度场的偏斜度。这种从不同的角度看待夹带问题的新视角，可能会导致对基本物理的理解有所改善。部分重点将放在蒸发冷却在提高夹带率和减少部分云量方面的作用。他早期工作中使用的顶帽概率密度函数(PDF)将被更现实、更灵活的“空间分布函数”所取代。最后，新的参数化将包括降水过程的表示。深对流的参数化将被改变，以放弃基于卷吸羽状云模式的Arakawa-Schubert方法。在它的位置上，PI将在每一层放置一个单一的对流上升气流，但有一个确定的内部结构。随着模型垂直分辨率的提高，这将改进参数化缩放。对流下沉气流和层状云也将包括在这个框架内。PI修改后的深对流和伴随的层状云的参数化将使用一个新的云模式进行参数化。在Arakawa-Schubert参数化中，有许多云类型，每一种都有一个粗略理想化的内部结构。PI将用一个具有更真实内部结构的单一“云型”来取代它，包括垂直速度和热力学变量的联合变化(在给定的水平)。对流下沉气流将通过第二个PDF表示。云模型的PDF可以在统一的框架中包括对流云和层状云。事实上，PDF甚至可以包括晴朗空气中水蒸气混合比的空间变异性等内容。有人认为，湿润的中尺度区域(被干燥得多的空气包围)可以为深层积云的生长提供滋养环境，如果没有这样的中尺度湿润区域，深层对流就会受到抑制。通过将晴朗空气中水蒸气的中尺度可变性参数化为参数，PI可以在大尺度模式的背景下探索这一想法。他的工作将以使用高分辨率云模型获得的结果为指导。为了完成参数化，他的新云模型将与预测闭合结合在一起。他将预测垂直速度的多个矩作为高度的函数，以及热力学变量的多个矩。这项研究将代表着朝着统一PBL和深对流的参数化迈出的一步。更广泛的影响：这项研究将为改进天气预报和改进气候变化模拟铺平道路。云参数的缺陷被广泛认为是阻碍更可靠的气候变化模拟的最严重障碍之一。这项研究还将有助于为大气科学的职业培训研究生和博士后研究人员。