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的水平动量垂直传输的机械表示。该模型可以预测滚动的宽度和方向，并诊断出扰动风场。该信息用于从无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量所需的压力相关性。参数化将使用大涡模拟（LES）结果测试。 PBL参数化将被更改以使用显式的PBL深度，内部大约有10层或更少的层。 PI的目的是在科罗拉多州立大学一般循环模型中测试此参数化，并最终在社区气候系统模型中测试。为了预测深度，他必须参数化夹带率。他将开发一个夹带参数化，该参数化旨在与垂直分辨的PBL和预测的统计数据一起使用，例如垂直速度场的偏度。从不同的角度来看，这种对夹带问题的新鲜感可能会改善对基本物理学的理解。将重点放在蒸发冷却在提高夹带率和减少分数浑浊方面的作用上。他早期工作中使用的顶级概率密度函数（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更改，以放弃基于具有各种云类型的插入的羽流云模型的Arakawa-Schubert方法。 PI代替了每个级别的对流上升气流，但内部结构已解决。随着模型的垂直分辨率的增加，这将改善参数化的缩放。对流下降和层状云也将包括在此框架中。 PI修订了深对流的参数化和随之而来的层状云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，每种云类型都有理想化的内部结构。 PI将用更现实的内部结构替代单个“云类型”，包括垂直速度的关节变化（在给定水平上）和热力学变量。对流下降将通过第二个PDF表示。云模型的PDF可以在统一框架中包括对流和层状云。实际上，PDF甚至可以包括透明空气中水蒸气混合比的空间变异性。有人提出，潮湿的中尺度地区（被更干燥的空气包围）可以为深度库利的生长提供养育环境，并且在没有这样的中尺度的潮湿区域的情况下，深对流被抑制。通过参数化透明空气中水蒸气的中尺度变异性，PI可以在大型模型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后的封闭结合在一起。他将预测垂直速度的多个矩作为高度的功能以及热力学变量的多个矩。这项研究将代表统一PBL参数和深度对流的参数化的一步。Broader的影响：研究将为改善天气预测和改善气候变化的模拟铺平道路。云参数化的缺陷被广泛认为是最严重的障碍之一，阻碍了更可靠的气候变化模拟。这项研究还将有助于培训大气科学职业的研究生和博士后研究人员。