Development of a new parameterization of tropical convection

热带对流新参数化的发展

• 批准号: 155649-2007
• 负责人: Folkins, Ian
• 金额: $ 1.54万
• 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: 2010
• 资助国家: 加拿大
• 起止时间: 2010-01-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=452103
• 关键词: Development; new; parameterization; tropical; convection

A convective cloud is one which grows vertically. Examples include fair weather cumulus clouds, and cumulonimbus clouds which grow into thunderstorms. These clouds generate almost all of the rainfall in the tropics, and most of the summer rainfall in mid-latitudes. The small size of these clouds makes them difficult to represent in global weather forecast models. These models divide the atmosphere into grid boxes with a typical horizontal length of 100 km. Not surprisingly, the rainfall generated by convective clouds is also very difficult to forecast. In fact, current weather forecast models are unable to forecast tropical rainfall more accurately than very primitive statistical methods. Much of the rainfall in the tropics occurs in association with the movement of large coherent groups of thunderstorms, of which hurricanes are one example. One of these coherent groups, known as the MJO, has been shown to have a significant influence on mid-latitude 1 - 2 week forecasts. This research is designed to improve the way global models currently represent the effects of convective clouds. These improvements should lead to better rainfall forecasts. Convective clouds also have a strong influence on the concentration of water vapor in the atmosphere. They inject moist, saturated air into the upper troposphere, and generate rainfall which evaporates and moistens the atmosphere as it falls. Water vapor is also the most important greenhouse gas in the atmosphere. Its concentration is likely to change in response to current and future increases in carbon dioxide. Uncertainty over the magnitude of this change is perhaps the dominant source of uncertainty in the projections of global climate models. Improvements in the way models represent convective clouds should therefore also improve the climate predictions of these models.

对流云是垂直生长的云。例子包括晴朗天气的积雨云，以及发展成雷暴的积雨云。这些云产生了热带地区几乎所有的降雨，以及中纬度地区夏季的大部分降雨。这些云的小尺寸使它们难以在全球天气预报模型中表示。这些模型将大气层划分为网格框，典型的水平长度为100公里。毫不奇怪，对流云产生的降雨也很难预测。事实上，目前的天气预报模型无法比非常原始的统计方法更准确地预测热带降雨。热带地区的大部分降雨都与大规模连贯的雷暴群的运动有关，飓风就是其中的一个例子。其中一个连贯的群体，被称为MJO，已被证明对中纬度1 - 2周的预测有显着的影响。这项研究旨在改善目前全球模型表示对流云影响的方式。这些改进应能带来更好的降雨预报。对流云对大气中的水蒸气浓度也有很大的影响。它们将潮湿、饱和的空气注入对流层上层，并产生降雨，降雨在福尔斯降落时蒸发并润湿大气。水蒸气也是大气中最重要的温室气体。它的浓度可能会随着当前和未来二氧化碳的增加而变化。这种变化幅度的不确定性也许是全球气候模型预测不确定性的主要来源。因此，改进模式表示对流云的方式也应该改进这些模式的气候预测。