Implementation and evaluation of the unified parameterization in NCAR Community Atmospheric Model

NCAR社区大气模型统一参数化的实现与评估

• 批准号: 1538532
• 负责人: David Randall
• 金额: $ 44.94万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1538532&HistoricalAwards=false
• 关键词: Implementation; evaluation; unified; parameterization; NCAR

Atmospheric global circulation models (CGMs) used for weather prediction and climate simulation typically divide the atmosphere into a grid on which quantities like temperature and pressure take on a single value to represent mean conditions across each grid cell. For reasons of computational expense GCMs have traditionally used grid spacings of a hundred kilometers or more, at which it is not possible to represent individual clouds or even large cloud systems. Cumulus clouds, which produce intense precipitation and have other important effects, are represented indirectly in these models by convective parameterizations, which attempt to capture their aggregate behavior over a grid cell. With increasing computer power it has become possible to run GCMs at resolutions high enough to enter the "gray zone", in which some portion of the motions of the largest cumulus ensembles is explicitly represented on the model grid, but the smaller clouds must still be represented indirectly through convective parameterization. The goal of the work performed here is to develop a parameterization which adjusts automatically and seamlessly as grid spacing decreases and a greater portion of the cumulus activity is resolved explicitly. At coarser resolutions the parameterization represents the entire spectrum of cloud sizes and motions, but at fine resolutions the parameterization effectively shuts itself off and allows the clouds to be explicitly simulated. The scheme is called the "unified parameterization" because it unifies the treatment of clouds between fine resolution cloud resolving models (CRMs) and coarse-scale GCMs.The unified parameterization is implemented in two models, one a CRM with a limited regional domain which is capable of explicit cloud simulation. Reference simulations with a resolution of 1-2km are performed, and these are compared to simulations at progressively coarser resolutions using the unified parameterization. The simulations assess the extent to which the unified scheme mimics the behavior of the fine-resolution simulation and approximates it more faithfully as resolution increases. These simulations are meant to test and develop the unified parameterization at the fine-scale end of the gray zone. The other is a GCM, the Community Atmosphere Model (CAM), and experiments with this model explore the behavior of the parameterization on the coarse-scale side of the zone.The work has broader impacts due to the potential of the unified parameterization to improve models used for weather forecasting and for anticipating the likely consequences of climate change. Deficiencies in cumulus parameterization are a persistent source of bias in weather and climate models, and diminish their value for research and operational applications. Moreover, the issue of mixed implicit-explicit cloud representation addressed in the work will become increasingly important as higher resolutions are adopted for weather and climate models. The use of CAM, a freely available model developed by and for the research community, as a testbed for the scheme enables community access to results of the research. In addition, the project will support and train a graduate student, thereby supporting the next generation of researchers in model development. The project is a continuation of research begun with the support of the Center for Multiscale Modeling of Atmospheric Processes (CMMAP), an NSF Science and Technology Center.

用于天气预测和气候模拟的大气全球环流模型（CGMs）通常将大气划分为一个网格，其中温度和压力等量采用单个值来表示每个网格单元的平均条件。 由于计算费用的原因，GCM传统上使用100公里或更大的网格间距，在这种情况下，不可能代表单个云或甚至大的云系统。 积云，产生强烈的降水，并有其他重要的影响，间接表示在这些模式的对流参数化，试图捕捉他们的聚集行为在一个网格单元。随着计算机能力的提高，已经有可能以足够高的分辨率运行GCM，以进入“灰色区域”，其中最大积云集合的运动的一部分在模式网格上显式表示，但较小的云仍然必须通过对流参数化间接表示。 在这里进行的工作的目标是开发一个参数化，自动调整和无缝网格间距的减少和更大的积云活动的一部分是明确解决。 在较粗的分辨率下，参数化表示云的大小和运动的整个频谱，但在精细的分辨率下，参数化有效地关闭了自己，并允许显式地模拟云。 该方案统一了细分辨率云分辨模式（CRM）和粗尺度大气环流模式（GCM）对云的处理，被称为“统一参数化”方案。 参考模拟的分辨率为1- 2公里，这些进行了比较，模拟逐步粗糙的分辨率使用统一的参数化。 模拟评估的程度，统一的计划模仿的行为的精细分辨率模拟和近似它更忠实地作为分辨率增加。 这些模拟是为了测试和开发灰色区域细尺度端的统一参数化。 另一个是GCM，社区大气模式（CAM），与此模式的实验探索的行为的参数化的粗尺度一侧的区域。由于统一的参数化，以改善用于天气预报和预测气候变化的可能后果的模式的潜力，这项工作具有更广泛的影响。 积云参数化中的缺陷是天气和气候模式中偏差的一个持久来源，并降低了它们在研究和业务应用中的价值。此外，随着天气和气候模型采用更高的分辨率，工作中解决的混合隐式-显式云表示问题将变得越来越重要。 CAM是一个由研究界开发并为研究界免费提供的模型，作为该计划的试验平台，使社区能够获得研究结果。 此外，该项目将支持和培训一名研究生，从而支持下一代研究人员进行模型开发。 该项目是在美国国家科学基金会科学技术中心大气过程多尺度模拟中心（CMMAP）的支持下开始的研究的延续。