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

使用模型层次结构中的模拟对流评估对流参数化方案及其尺度感知

基本信息

批准号：
1549259
负责人：
Guang Zhang
金额：
$ 52.64万
依托单位：
University of California-San Diego Scripps Inst of Oceanography
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：
2016-04-01 至 2020-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1549259&HistoricalAwards=false
关键词：
Evaluating Convective Parameterization Schemes Their

项目摘要

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.

该项目将对控制对流发生和数量的因素产生新的见解。它将导致人们更好地了解世界主要气候模型中现有的大气对流表示的优点和缺点。随着全球气候模型网格间距减小到约 10 公里或更小，在此类模型中表示大气对流是一个巨大的挑战。所提出的利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工作是创新的，将为进一步改进全球气候模型铺平道路。对流和相关云的表现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与气候变化相关的自然灾害的发生。该项目还将通过培训博士后研究员并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。大气对流影响的表征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，尽管在改善气候模型中物理过程的处理方面付出了巨大的努力，但在模拟重要的气候系统时仍然存在重大问题。这些缺陷很大程度上与模型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以及使用层次化数值模型确定大气对流表示中的对流活动量的假设（称为闭合假设）。目标是评估最先进的全球气候模型中表示大气对流的多种不同方式，从而确定其优缺点并进一步改进。为了实现这一目标，通过能够解析对流的高分辨率数值模型对大气对流进行模拟，以评估对流的起始标准和闭合假设。统计分析方法，包括技能评分计算、超前滞后相关性和综合技术，将用于分析模型输出数据。对流模拟还将用于研究当模型的网格间距减小到约 10 公里或更小（即所谓的灰色区域尺度）时，当前全球气候模型中表示对流的算法是否仍然可以使用。目前间距约为 100 公里或更大。分析得出的在全球气候模型中表示对流的新想法和公式将使用社区大气模型 CAM5 进行测试。