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.

用于天气预报和气候模拟的大气全球环流模型 (CGM) 通常将大气划分为网格，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 传统上使用一百公里或以上的网格间距，在这种情况下不可能表示单个云甚至大型云系统。 积云会产生强烈的降水并具有其他重要影响，在这些模型中通过对流参数化间接表示，这些参数化试图捕获它们在网格单元上的聚合行为。随着计算机能力的增强，以足够高的分辨率运行 GCM 已成为可能，以进入“灰色区域”，其中最大积云系的运动的某些部分明确地表示在模型网格上，但较小的云仍必须是通过对流参数化间接表示。 这里进行的工作的目标是开发一种参数化，随着网格间距的减小和大部分积云活动的明确解决，该参数化可以自动无缝地调整。 在较粗的分辨率下，参数化代表了云的大小和运动的整个范围，但在精细的分辨率下，参数化有效地自我关闭并允许显式地模拟云。 该方案被称为“统一参数化”，因为它统一了精细分辨率云解析模型（CRM）和粗尺度 GCM 之间对云的处理。统一参数化在两种模型中实现，一种是具有有限区域域的 CRM，它是能够进行显式云模拟。 执行分辨率为 1-2km 的参考模拟，并将这些模拟与使用统一参数化的逐渐变粗的分辨率进行的模拟进行比较。 模拟评估了统一方案模仿精细分辨率模拟行为的程度，并随着分辨率的增加更忠实地近似它。 这些模拟旨在测试和开发灰色区域精细尺度端的统一参数化。 另一个是 GCM，即社区大气模型 (CAM)，并且使用该模型进行的实验探索了区域粗尺度侧的参数化行为。由于统一参数化有可能改善环境质量，因此这项工作具有更广泛的影响。用于天气预报和预测气候变化可能后果的模型。 积云参数化的缺陷是天气和气候模型偏差的持续来源，并降低了它们的研究和业务应用价值。此外，随着天气和气候模型采用更高分辨率，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。 使用 CAM（由研究界开发并为研究界免费提供的模型）作为该计划的测试平台，使社区能够获取研究结果。 此外，该项目还将支持和培训一名研究生，从而支持下一代模型开发研究人员。 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建模中心 (CMMAP) 的支持下开始的研究的延续。