QUEST: QUasi-biennial oscillation: Enhancing Stratospheric Theoretical understanding

任务：准两年一次的振荡：增强平流层理论理解

• 批准号: NE/W00819X/1
• 负责人: Alison Donna Ming
• 金额: $ 77.56万
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FW00819X%2F1
• 关键词: QUEST; QUasi; biennial; oscillation; Enhancing

Understanding the sources of variability in the atmosphere is vital for weather and climate prediction. The Quasi-Biennial Oscillation (QBO) is a regular pattern of wind, between 15 and 40 km high in the tropical stratosphere, that reverses direction every 14 months. It is arguably the largest source of stratospheric year-to-year variability. Despite being located in the tropics, the influence of the QBO extends globally. Its effects include changing monsoon precipitation, affecting winter flooding in Europe and modifying North Atlantic hurricane frequency. However, our best models struggle to simulate a realistic QBO. If we cannot reproduce an accurate QBO in our models, we will not be able to confidently predict its impacts on the surface both now and under future climate change. On the other hand, a good representation of the QBO will lead to substantial improvements in seasonal predictability and a better understanding of how a key part of the stratospheric circulation responds to increasing carbon dioxide levels.The current state-of-the-art models that do produce a QBO are often high-resolution models with many complex processes and are thus computationally expensive to run. We also do not know if the QBO in these models is being simulated for the right reasons. This is because the interactions between the different processes in the stratosphere are not well understood. If these models are not including the correct interactions, the risk is that predictions of the QBO under climate change, and their subsequent surface impact, will be wrong. Indeed, recent studies have found wildly different behaviours of the QBO under climate change with some models predicting that the period of the oscillation will become longer and others predicting it becoming shorter.A significant challenge is to understand how the different processes such as chemistry, transport, heating and the circulation in the stratosphere interact with each other within the QBO. Studying the QBO in a state-of-the-art model is difficult because of the computational cost and the lack of control over the many complex processes. For this reason, we need to develop new and better tools to study the QBO. During this fellowship, I will construct a fast model that has all the key processes so that long simulations can be performed quickly and where each individual interaction can be turned on and off. This will allow me to understand, in detail, how each process changes QBO behaviour and to construct mathematical frameworks of the mechanisms. This new understanding will, in turn, allow me to explain the failings of state-of-the-art models and devise strategies to improve them.My new mathematical and numerical frameworks will pave the way for me to address a wide range of climate problems, starting with the impact of the QBO on the surface, and moving towards improving seasonal predictability.

了解大气变化的来源对于天气和气候预测至关重要。准两年振荡（Quasi-Biennial Oscillation，QBO）是一种有规律的风模式，在热带平流层15到40公里之间，每14个月改变一次方向。它可以说是平流层年与年之间变化的最大来源。尽管位于热带地区，QBO的影响力扩展到全球。其影响包括改变季风降水，影响欧洲冬季洪水，改变北大西洋飓风频率。然而，我们最好的模型很难模拟真实的QBO。如果我们不能在我们的模型中重现准确的QBO，我们将无法自信地预测它对现在和未来气候变化的影响。另一方面，QBO的良好表现将导致季节性可预测性的实质性改善，并更好地了解平流层环流的关键部分如何对增加的二氧化碳水平作出反应。目前，产生QBO的最先进的模式通常是具有许多复杂过程的高分辨率模式，因此运行计算成本很高。我们也不知道这些模型中的QBO是否是出于正确的原因进行模拟的。这是因为平流层中不同过程之间的相互作用还没有得到很好的理解。如果这些模型没有包括正确的相互作用，那么气候变化下QBO的预测及其随后的地表影响将是错误的。事实上，最近的研究发现QBO在气候变化下的行为有很大的不同，一些模型预测振荡的周期会变长，而另一些模型预测振荡的周期会变短。一个重要的挑战是了解QBO中不同的过程，如化学，传输，加热和平流层环流是如何相互作用的。由于计算成本和对许多复杂过程缺乏控制，在最先进的模型中研究QBO是困难的。因此，我们需要开发新的更好的工具来研究QBO。在此期间，我将构建一个快速模型，其中包含所有关键过程，以便可以快速执行长时间模拟，并且可以打开和关闭每个单独的交互。这将使我能够详细了解每个过程如何改变QBO行为，并构建机制的数学框架。这种新的理解反过来将使我能够解释最先进的模型的失败之处，并设计改进它们的策略。我新的数学和数值框架将为我解决广泛的气候问题铺平道路，从QBO对地表的影响开始，并朝着改善季节可预测性的方向发展。