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循环模型（CGM）通常将大气分为一个网格，在该网格上，温度和压力等数量以单个值为单一值以表示每个网格电池的平均条件。 出于计算费用的原因，GCM传统上使用了一百公里或更多的网格间距，在该网格间距不可能代表单个云甚至大型云系统。 产生强烈降水并具有其他重要作用的积云云通过对流参数化在这些模型中间接表示，这些参数试图在网格细胞上捕获其骨料行为。随着计算机功率的增加，可以在高足够高的分辨率上运行GCM，以进入“灰色区域”，其中最大的集团合奏的某些运动在模型网格上明确表示，但是必须通过对流参数来间接地表示较小的云。 此处执行的工作的目的是开发一个参数化，该参数化随着网格间距的降低而自动和无缝地调整，并且明确解析了库卢斯活动的较大部分。 在粗糙分辨率下，参数化表示云大小和动作的整个光谱，但是在精细的分辨率下，参数化有效地关闭了自身，并允许明确模拟云。 该方案称为“统一的参数化”，因为它可以在良好的分辨率云解析模型（CRM）和粗尺度GCM之间统一云处理。在两个模型中实现了统一的参数化，一种具有有限的区域域的CRM，能够具有显着云模拟。 进行分辨率为1-2公里的参考模拟，并使用统一的参数化将其与逐渐擦拭的分辨率进行比较。 模拟评估统一方案模仿精细分辨率模拟的行为的程度，并随着分辨率的增加而更忠实地近似它。 这些模拟旨在在灰色区域的细尺度测试和开发统一的参数化。 另一个是GCM，社区大气模型（CAM），并且该模型的实验探索了该区域粗尺度一侧的参数化行为。由于统一参数化的潜力改善了用于天气预测的模型，并且用于预测气候变化的可能后果，这项工作具有更大的影响。 积云参数化的缺陷是天气和气候模型中偏见的持续来源，并降低了其对研究和操作应用的价值。此外，随着天气和气候模型采用更高的分辨率，工作中涉及的混合隐性云表示的问题将变得越来越重要。 CAM的使用是由研究社区开发的一种免费可用的模型，作为该计划的测试床，使社区可以访问研究结果。 此外，该项目将支持和培训研究生，从而为下一代研究人员提供模型开发的支持。 该项目是在NSF科学技术中心的多尺度建模中心（CMMAP）的支持下进行研究的延续。