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

该项目将对控制对流的开始和数量的因素产生新的见解。它将使人们更好地了解世界上主要气候模式中现有的大气对流表示的优缺点。当全球气候模式网格间距减小到~10 km或更小时，在这种模式中表示大气对流是一个很大的挑战。本文提出的利用大气对流的云分辨模式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工作具有创新性，并将为进一步改进全球气候模式铺平道路。对流和相关云的表现强烈地影响当前和未来气候中极端事件的统计。它也是预测气候变化不确定性的一个主要来源。因此，所提出的研究将有助于在不同时间尺度上模拟和预测与气候变率相关的自然灾害的发生。该项目还将通过培养一名博士后研究员和为本科生暑期实习生提供学习机会，为培养下一代气候科学家做出贡献。大气对流效应的表征是气候模式中最具挑战性的科学问题之一。在过去的几十年里，尽管人们在改善气候模式对物理过程的处理方面做出了巨大的努力，但在模拟重要气候系统方面仍然存在重大问题。这些缺陷在很大程度上与模式中缺乏对大气对流的准确表示有关。该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），在使用数值模式层次的大气对流表示中。目标是评估在最先进的全球气候模式中表示大气对流的许多不同方法，从而确定其优缺点并进一步改进它们。为了实现这一目标，利用能够解析对流的精细分辨率数值模式对大气对流进行模拟，以评估对流的开始准则和闭合假设。统计分析方法，包括技能得分计算，领先-滞后相关和复合技术，将用于分析模型输出数据。对流的模拟还将用于研究当前全球气候模式中表示对流的算法，当模式的网格间距从目前的~100 km或更大的间距减小到~10 km或更小时，是否仍然可以使用。通过分析得出的在全球气候模式中表示对流的新想法和公式将使用群落大气模式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.