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，用于季节性天气预报，了解过去的气候变化，并作为改进天气预报模型的平台。他们最大的弱点与涉及他们无法解决的云的流程相关。大多数气候模型将大气分成水平方向约100公里、垂直方向约1公里的盒子。这些盒子太大了，无法捕捉到大多数云中的运动。夏季热带和中纬度地区的大部分降雨都是由雷雨等对流云造成的。虽然它们在对流层上层很大，但它们是由直径不到几公里的上升气流提供的。因此，气候模型无法解决这些云的水平和垂直运动。取而代之的是，气候模型使用了所谓的参数化。参数化是必要的，因为气候模型在没有它们的情况下会失效或变得不实际。然而，参数化表示小规模过程(如云)的方式是不完整的。自五十年前第一个气候模型问世以来，如何在气候模型中包含对流云的问题就一直存在。尽管它永远不会被完全解决，但随着计算机模型的改进和对对流云的更多观测的出现，正在演变出越来越好的方法。我的研究项目的重点是对流组织变量在对流参数化中的使用。这涉及到气候模式分解流动的某些方面，例如对流层下部的向上运动，并使用这个变量来计算在一列中产生多少对流降雨。这是一条捷径，它利用了已知的大尺度运动和对流降雨之间的观测关系。这种方法有助于模拟有组织的降雨模式如何传播。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有影响。这些变化包括飓风的形成和加强、热带干旱和季风的季节模式、与ENSO有关的气候变化以及马登-朱利安涛动(MJO)。MJO是一大片降雨增强的区域，在太平洋中平行于赤道向东移动。我们预报MJO的能力的提高将导致中纬度天气预报的显著改善。