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

这个项目将对控制对流的开始和数量的因素产生新的见解。这将使我们更好地理解世界上主要气候模式中现有的大气对流表示的优缺点。随着全球气候模式的网格间距减小到~10公里或更小，在这种模式中描述大气对流是一个巨大的挑战。利用大气对流的云分辨模式模拟来评估广泛使用的表示对流的算法的精度和尺度意识的工作具有创新性，将为进一步改进全球气候模式铺平道路。对流和相关云层的表现对当前和未来气候中极端事件的统计有很大影响。这也是气候变化预测的一个主要不确定性来源。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性有关的自然灾害的发生。该项目还将通过培训博士后研究员和为本科生暑期实习生提供学习机会，为培养未来一代气候科学家做出贡献。大气对流效应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，尽管在改进气候模型中对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。这些缺陷在很大程度上与模型中缺乏大气对流的准确表示有关。本项目拟利用一系列数值模式，系统地调查用于确定对流开始的标准和确定大气对流表示中对流活动量的假设(称为闭合假设)。其目标是评估在最先进的全球气候模型中表示大气对流的许多不同方法，从而确定它们的优缺点并进一步改进它们。为了实现这一目标，利用能够分解对流的高分辨率数值模式来模拟大气对流，以评估对流的起始判据和闭合假设。将使用统计分析方法，包括技能分数计算、领先-滞后相关性和复合技术来分析模型输出数据。对流模拟还将被用来研究当前全球气候模式中表示对流的算法在模式的网格间距从目前的~100公里或更大减小到~10公里或更小时是否仍然适用，即所谓的灰色区域尺度。将使用共同体大气模式CAM5测试由分析产生的用于在全球气候模式中表示对流的新想法和公式。

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.