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: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=680402
• 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.

气候模式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式的平台。它们最大的弱点与它们无法解决的涉及云的过程有关。大多数气候模式将大气分成水平方向约100公里、垂直方向约1公里的方框。这些方框太大，无法捕捉大多数云中的运动。在夏季，热带和中纬度地区的大部分降雨是由雷暴等对流云产生的。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，这些云的水平和垂直运动不能被气候模式解决。相反，气候模型使用了所谓的参数化。参数化是必要的，因为如果没有参数化，气候模式就会失效或变得非物理化。然而，参数化表示小尺度过程（如云）的方法是不完整的。自50年前第一个气候模型出现以来，如何将对流云纳入气候模型的问题一直存在。虽然这个问题永远无法完全解决，但随着计算机模型的改进和对对流云的观测越来越多，越来越好的方法也在不断发展。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。这包括采用气候模式中已分解气流的某些方面，例如对流层下层的上升运动，并使用该变量来计算在一个柱中产生多少对流降雨。它是利用已知的观测到的大尺度运动和对流降雨之间的关系的捷径。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、热带地区干旱和季风的季节性模式、与ENSO相关的气候变化以及马登朱利安涛动（MJO）。MJO是太平洋上与赤道平行向东移动的一大片降雨增强区域。预报MJO能力的提高，将大大改善中纬度地区的天气预报。