Improving the Prediction of Tropical Precipitation using a new Convective Parameterization

使用新的对流参数化改进热带降水的预测

• 批准号: 155649-2012
• 负责人: Folkins, Ian
• 金额: $ 1.97万
• 依托单位: Dalhousie University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=569483
• 关键词: Improving; Prediction; Tropical; Precipitation; using

Climate models are the main tools used to predict how the earth's atmosphere will respond to the current increases in carbon dioxide. Climate models are widely used in seasonal weather forecasting, such as in the prediction of droughts and cold winters. Although climate models have been very successful, they do have important weaknesses. These weaknesses originate from difficulties in trying to represent processes that are turbulent or involve clouds. Convective clouds are especially difficult to represent in climate models. They are the main cloud type in the tropics and are very common in mid-latitudes during spring and summer. They range in size from small fair weather cumulus to thunderstorms, and are often organized into larger patterns such as squall lines and hurricanes. With current computing power, it is impossible to represent the vertical circulations of individual convective clouds within a climate model. Instead, climate models resort to approximate methods called parameterizations. To make a successful convective rainfall forecast, a convective parameterization must accurately simulate the factors which determine the average growth rate of the convective clouds inside a model grid box. However, the growth rates of convective clouds are extremely sensitive to a large number factors in their environment. Convective clouds modify the background atmosphere in ways which suppress and enhance the growth rates of neighboring clouds. Because of these difficulties, climate and weather forecast models perform most poorly in regions where convective clouds are most frequent. The purpose of this research is to improve the representation of convective clouds in climate and weather forecast models. If successful, the research can be expected to improve the forecasting of convective precipitation, better predict the impacts of extreme weather events, generate more credible climate projections, improve the forecasting of seasonal precipitation, and by improving the way climate models couple to land surface models, improve the prediction of quantities important to agriculture such as soil moisture.

气候模型是用于预测地球大气将如何应对当前二氧化碳增加的主要工具。气候模型广泛应用于季节性天气预报，例如预测干旱和寒冷的冬季。尽管气候模型非常成功，但它们确实存在重大缺陷。这些弱点源于试图表示湍流或涉及云的过程的困难。对流云在气候模型中尤其难以表示。它们是热带地区的主要云类型，在春季和夏季的中纬度地区非常常见。它们的大小范围从小型晴天积云到雷暴，并且通常组织成更大的模式，例如飑线和飓风。以目前的计算能力，不可能在气候模型中表示单个对流云的垂直环流。相反，气候模型采用称为参数化的近似方法。为了成功进行对流降雨预报，对流参数化必须准确模拟决定模型网格盒内对流云平均增长率的因素。然而，对流云的增长率对其环境中的许多因素极其敏感。对流云以抑制和增强邻近云的增长率的方式改变背景大气。由于这些困难，气候和天气预报模型在对流云最频繁的地区表现最差。这项研究的目的是改进气候和天气预报模型中对流云的表示。如果成功，该研究有望改进对流降水的预报，更好地预测极端天气事件的影响，生成更可信的气候预测，改进季节性降水的预报，并改进气候模型与地表模型的耦合方式，改进对农业重要量（例如土壤湿度）的预测。