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和行星边界层的参数化。他新的边界层参数化将包括一个机械表示的水平动量的垂直运输滚动环流。该模式预报了卷的宽度和方向，并诊断了扰动风场。该信息用于从滞弹性压力方程计算扰动压力场，这又用于计算预测垂直速度方差和其他统计所需的压力相关性。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个或更少的层。PI的目的是在科罗拉多州立大学的大气环流模型中测试这种参数化，并最终在社区气候系统模型中进行测试。为了预测深度，他必须以卷吸率为参数。他将开发一个夹带参数化，是专为使用垂直解决PBL和预测的统计数据，如垂直速度场的偏度。从不同的角度对夹带问题的这种新的看法可能会导致对基础物理学的理解的提高。重点将放在蒸发冷却在提高卷吸率和减少云量方面的作用上。在他早期的工作中使用的大礼帽概率密度函数（PDF）将被一个更现实和灵活的“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，放弃Arakawa-Schubert方法的基础上夹带羽流云模式与云类型的频谱。在它的位置上，PI将在每一层放置一个单独的对流上升气流，但具有一个解析的内部结构。随着模式垂直分辨率的增加，这将改善参数化的比例。对流下沉气流和层状云也将包括在这个框架中。PI修正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将用具有更真实内部结构的单一“云类型”来取代它，包括垂直速度和热力学变量的联合变化（在给定水平上）。对流下降气流将通过第二个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甚至可以包括诸如晴空中水汽混合比的空间变异性之类的东西。有人认为，潮湿的中尺度区域（周围有更干燥的空气）可以为深积云的生长提供滋养环境，而在没有这种中尺度潮湿区域的情况下，深对流会受到抑制。通过参数化晴空中水汽的中尺度变率，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相结合。他将预测垂直速度的多个时刻作为高度的函数，以及热力学变量的多个时刻。这项研究将代表着边界层和深对流参数化统一的一步。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。云参数化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培养研究生和博士后研究人员从事大气科学工作。