Convective Rainfall Variability in Climate Models

气候模型中的对流降雨变化

• 批准号: RGPIN-2018-04048
• 负责人: Folkins, Ian
• 金额: $ 1.82万
• 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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=650898
• 关键词: Convective; Rainfall; Variability; Climate; Models

Climate models are used to predict changes to increases in greenhouse gases, for seasonal weather forecasting, understanding past climate change, and as a platform for improving weather forecast models. Their greatest weaknesses are associated with processes involving clouds that they cannot resolve. Most climate models divide the atmosphere into boxes of roughly 100 km in the horizontal and 1 km in the vertical. These boxes are too large to capture motions within most clouds. Convective clouds such as thunderstorms produce most of the rain in the tropics and in mid-latitudes during summer. Though large in the upper troposphere, they are fed by updrafts whose diameters are less than a few kilometers. As a result, the horizontal and vertical motions of these clouds are not resolved by climate models. Instead, climate models have used what are called parameterizations. Parameterizations are necessary because a climate model would fail or become unphysical in their absence. However, the way that parameterizations represent small scale processes such as clouds is incomplete. The problem of how to include convective clouds in climate models has continued since the first climate models fifty years ago. Although it will never be fully solved, progressively better approaches are evolving as computer models improve and more observations of convective clouds become available. The focus of my research program is on the use of convective organization variables in convective parameterizations. This involves taking some aspect of the resolved flow of the climate model, such as the upward motion in the lower troposphere, and using this variable to calculate how much convective rain is produced in a column. It is a short cut which exploits known observed relationships between larger scale motion and convective rainfall. This approach helps simulate how organized rainfall patterns propagate. This research has implications for our ability to address some of the most important challenges in climate modelling. These includes changes in the formation and intensification of hurricanes, the seasonal patterns of droughts and monsoons in the tropics, climate shifts associated with ENSO, and the Madden Julian Oscillation (MJO). The MJO is a large region of enhanced rainfall that moves eastward parallel to the equator in the Pacific Ocean. Improvements in our ability to forecast the MJO would result in significant improvements in midlatitude weather prediction.

气候模型用于预测温室气体增加的变化，季节性天气预报，了解过去的气候变化以及改善天气预测模型的平台。他们最大的弱点与涉及无法解决的云的过程有关。大多数气候模型将大气分成大约100公里的水平盒，垂直方向为1 km。这些盒子太大，无法在大多数云中捕获动作。雷暴之类的对流云在夏季的大部分降雨和热带地区的大部分降雨。尽管在对流层上方大，但它们的直径小于几公里的上升气流。结果，这些云的水平和垂直运动无法通过气候模型来解决。相反，气候模型使用了所谓的参数化。参数化是必要的，因为气候模型在不存在的情况下会失败或变得非物理。但是，参数化代表小规模过程（例如云）的方式不完整。自五十年前的最初的气候模型以来，如何将对流云加入对流云中的问题一直持续。尽管它永远不会得到充分的解决，但是随着计算机模型的改善，对流云的观察结果逐渐发展。我的研究计划的重点是在对流参数化中使用对流组织变量。这涉及采用气候模型的解决流的某些方面，例如下部对流层中的向上运动，并使用此变量来计算柱中产生多少对流雨。这是一个捷径，可利用已知的观察到的大规模运动与对流降雨之间的关系。这种方法有助于模拟有组织的降雨模式的传播方式。这项研究对我们解决气候建模中一些最重要的挑战的能力具有影响。这些包括变化的飓风形成和加强，热带地区的干旱和季风的季节性模式，与ENSO相关的气候变化以及Madden Julian振荡（MJO）。 MJO是一个大量增强的降雨区域，在太平洋中平行于赤道的向东移动。提高我们预测MJO的能力将导致中纬度天气预测的重大改善。