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

用于天气预报和气候模拟的大气全球环流模型（cgm）通常将大气划分为一个网格，在这个网格上，温度和压力等量取一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传统上使用的网格间距为100公里或更大，在此范围内不可能表示单个云甚至大型云系统。产生强降水和其他重要影响的积云，在这些模式中通过对流参数化间接表示，试图捕捉它们在网格单元上的聚集行为。随着计算机能力的提高，以足够高的分辨率运行gcm已经成为可能，进入“灰色地带”，其中最大积云群的部分运动在模型网格上被明确表示，但较小的云仍然必须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更大一部分积云活动的明确解决，该参数化可以自动无缝地进行调整。在较粗的分辨率下，参数化代表了云大小和运动的整个频谱，但在较细的分辨率下，参数化有效地关闭了自己，并允许明确地模拟云。该方案将细分辨率云分辨模式（crm）和粗尺度云分辨模式（gcm）对云的处理统一起来，因此被称为“统一参数化”。统一的参数化实现在两个模型中，一个是CRM，具有有限的区域域，能够显式的云模拟。进行了分辨率为1-2km的参考模拟，并将这些模拟与使用统一参数化的逐渐粗糙分辨率的模拟进行了比较。模拟评估了统一方案模拟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接近它。这些模拟旨在测试和发展灰色区域精细尺度末端的统一参数化。另一种是GCM，即群落大气模式（CAM），该模式的实验探讨了该区域粗尺度侧的参数化行为。由于统一参数化有可能改进用于天气预报和预测气候变化可能后果的模式，因此这项工作具有更广泛的影响。积云参数化的缺陷是天气和气候模式偏差的持续来源，并降低了它们在研究和业务应用中的价值。此外，随着天气和气候模式采用更高的分辨率，工作中讨论的混合隐式-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其提供的免费模型，使用它作为该计划的测试平台，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，从而支持下一代模型开发研究人员。该项目是在美国国家科学基金会下属的大气过程多尺度模拟中心（CMMAP）的支持下开始的研究的延续。

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.