Evaluating Convective Parameterization Schemes and Their Scale-awareness Using Simulated Convection in a Hierarchy of Models

该项目将产生新的洞察力的对流的发生和数量的控制因素。它将导致更好地了解世界上主要气候模式中现有大气对流表示的优点和缺点。随着全球气候模式网格间距减少到10公里或更小，在这种模式中代表大气对流是一个巨大的挑战。使用大气对流的云解析模型模拟来评估广泛使用的对流表示算法的准确性和尺度意识的拟议工作是创新的，并将为进一步改进全球气候模型铺平道路。对流和相关云的代表性强烈影响当前和未来气候中极端事件的统计。这也是气候变化预测不确定性的一个主要来源。因此，拟议的研究将有助于模拟和预测不同时间尺度上与气候变异有关的自然灾害的发生。该项目还将通过培训一名博士后研究员和为本科生暑期实习生提供学习机会，为教育下一代气候科学家做出贡献。大气对流影响的表示是气候建模中最具挑战性的科学问题之一。在过去的几十年里，尽管在改善气候模式中物理过程的处理方面做出了巨大的努力，但在模拟重要的气候系统方面仍然存在重大问题。这些不足之处在很大程度上与模型中缺乏对大气对流的准确描述有关。本项目建议系统地研究用于确定对流开始的标准和确定对流活动量的假设（称为闭合假设），使用数值模型的层次结构来表示大气对流。其目标是评估在科学水平的全球气候模型中表示大气对流的许多不同方法，从而确定其优点和缺点并进一步改进它们。为了实现这一目标，通过可以解决对流的高分辨率数值模式来模拟大气对流，以评估对流的开始标准和闭合假设。统计分析方法，包括技能得分计算，领先滞后相关性和复合技术，将用于分析模型输出数据。对流模拟还将用于调查，当模型的网格间距从目前的约100公里或更大的间距减少到约10公里或更小，即所谓的灰色区域尺度时，目前全球气候模型中表示对流的算法是否仍然可以使用。从分析中得出的在全球气候模式中代表对流的新想法和公式将使用社区大气模式CAM5进行测试。

This project will yield new insight into factors governing the onset and amount of convection. It will lead to a much better understanding of the strengths and weaknesses of existing representations of atmospheric convection in major climate models in the world. As the global climate model grid spacing decreases to ~10 km or less, representing atmospheric convection in such models is a great challenge. The proposed work to use cloud-resolving model simulation of atmospheric convection to evaluate the accuracy and scale-awareness of widely used algorithms for representing convection is innovative, and will pave the way for further improving global climate models. The representation of convection and associated clouds strongly affects the statistics of extreme events in both current and future climates. It is also a major source of uncertainty in climate change projection. Thus, the proposed research will contribute to the simulation and prediction of the occurrence of natural disasters associated with climate variability at different timescales. The project will also contribute to educating future generation climate scientists through training of a postdoctoral researcher and providing learning opportunities for undergraduate summer interns.The representation of the effects of atmospheric convection is one of the most challenging scientific issues in climate modeling. Over the last few decades, despite tremendous efforts going into improving the treatment of physical processes in climate models, major problems still exist in simulating important climate systems. These deficiencies are largely associated with the lack of accurate representation of atmospheric convection in the models. This project proposes to systematically investigate the criteria used for determining the onset of convection and assumptions that determine the amount of convective activity (known as closure assumptions) in representations of atmospheric convection using a hierarchy of numerical models. The goal is to evaluate the many different ways of representing atmospheric convection in state-of-the-science global climate models, thereby identifying their strengths and weakness and further improving them. To achieve this goal, simulations of atmospheric convection by fine-resolution numerical models that can resolve convection are used to evaluate the onset criteria and closure assumptions for convection. Statistical analysis methods, including skill score calculation, lead-lag correlation, and composite techniques, will be used to analyze the model output data. The simulations of convection will also be used to investigate whether the algorithms of representing convection in current global climate models can still be used when the grid spacing of the models decreases to ~10 km or less, the so-called grey zone scales, from the current spacing of ~100 km or larger. New ideas and formulations for representing convection in global climate models resulting from the analysis will be tested using the Community Atmosphere Model, CAM5.