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 将继续开发对流云系统和行星边界层 (PBL) 的参数化。他的新 PBL 参数化将包括通过滚动循环垂直传输水平动量的机械表示。该模型预测卷的宽度和方向，并诊断扰动风场。该信息用于根据滞弹性压力方程计算扰动压力场，而这又用于计算预测垂直速度方差和其他统计数据所需的压力相关性。将使用大涡模拟（LES）结果来测试参数化。 PBL 参数化将更改为使用显式 PBL 深度，内部层数约为 10 或更少。 PI 的目的是在科罗拉多州立大学大气环流模型中以及最终在社区气候系统模型中测试此参数化。为了预测深度，他必须参数化夹带率。他将开发一种夹带参数化，该参数化设计用于垂直解析 PBL 和预测统计数据，例如垂直速度场的偏度。从不同的角度重新审视夹带问题可能会提高对基础物理学的理解。重点将放在蒸发冷却在提高夹带率和减少混浊度方面的作用。他早期工作中使用的高顶帽概率密度函数（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，放弃基于具有一系列云类型的夹带羽流云模型的荒川-舒伯特方法。 PI 将在每个级别放置一个对流上升气流，但具有已解决的内部结构。随着模型垂直分辨率的增加，这将改善参数化的缩放比例。对流下沉气流和层状云也将包含在该框架中。 PI 修订后的深对流和随之而来的层状云参数化将利用新的云模型进行参数化。在 Arakawa-Schubert 参数化中，有许多云类型，每种云类型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）。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含对流云和层状云。事实上，PDF 甚至可以包括诸如晴空气中水蒸气混合比的空间变化之类的内容。有人认为，潮湿的中尺度区域（被更干燥的空气包围）可以为深层积云的生长提供培育环境，并且在没有这种中尺度潮湿区域的情况下，深层对流会受到抑制。通过参数化晴空气中水蒸气的中尺度变化，PI 可以在大型模型的背景下探索这个想法。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后闭包相结合。他将预测作为高度函数的垂直速度的多个矩，以及热力学变量的多个矩。这项研究将代表 PBL 和深对流参数化的统一迈出了一步。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。人们普遍认为，云参数化的缺陷是更可靠地模拟气候变化的最严重障碍之一。该研究还将有助于培养大气科学领域的研究生和博士后研究人员。