QUEST: QUasi-biennial oscillation: Enhancing Stratospheric Theoretical understanding

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.