Can understanding large-scale dynamical drivers of the regional climate reduce uncertainty in European projections? (4965)

Project Background

Climate projections for Europe exhibit considerable uncertainty (Palmer et al., 2023). These uncertainties arise from different representations of dynamical and thermodynamical processes in different climate models. This can result in very different projections of meteorological hazards, by shifting regions of extreme precipitation and varying the intensity of impacts such as heatwaves and windstorms.

Understanding why different models have different projections, and which ones represent circulation patterns and features such as storm tracks, jet position and atmospheric blocking the most accurately is of great importance for improving our understanding of how European climate may change in the future. This project will analyse the latest ensemble of global and regional climate models over Europe to understand the variability in climate model performance and ultimately reduce uncertainty in the projections of meteorological hazards for Europe.

Project Aims and Methods

The aim of this project is to quantify the ability of climate models to represent large-scale features that are relevant to European climate. These drivers include Arctic Amplification, the structure of the jet stream, sea surface temperatures in the North Atlantic, and Arctic Sea Ice, to name a few. The project will utilise the latest generation of global climate models (CMIP6), and also high-resolution regional models (UKCP18 and EURO-CORDEX) to investigate these features.

Using a storylines approach (Harvey et al., 2023; Zappa and Shepherd 2017) the student will quantify plausible changes in atmospheric circulation and uncertainty in meteorological hazards such as heatwaves and extreme precipitation. Through constraining models and downweighting unphysical models, uncertainty in projections can be reduced.

The candidate, with the guidance of supervisors at the University of Exeter and the Met Office, will be able to modify the research focus (e.g. circulation features examined) to reflect their interests as the project evolves. Project partners

This project is supported by a CASE partnership with the Met Office with Dr Tamzin Palmer as the main Met Office supervisor. Links with the Met Office will ensure access to data and expertise from their seamless modelling capability across future climate scenarios. Visits to the Met Office will allow the candidate to learn their computer systems, undertake training on key software tools such as Python, and communicate results to the key stakeholders such as model developers and operational forecasters.