IAP-24-024

Understanding pathways to extreme heat using large ensembles

The impacts of human-caused climate change are already being experienced around the world, with the rapidly increasing risk of severe heatwaves posing a particular threat to human health, infrastructure and natural ecosystems. In recent years, several extreme heatwaves have seen temperatures far exceed those previously observed, including the first occurrence of 40°C in the UK during the summer of 2022.

To prepare effectively for more frequent and intense heatwaves, it is essential to first understand the dynamics of extreme temperature events in the current climate. However, observations serve as a poor guide to extremes in a rapidly changing climate. The type and frequency of heatwaves varies dramatically from year-to-year, influenced by the natural variability of the climate system on seasonal, interannual and decadal timescales. The atmospheric, oceanic and surface conditions required to generate extreme heat can be rare and may not have even occurred in the current climate. This combination limits our understanding of pathways to extreme heat, and the ability to trust and respond to model forecasts of unprecedented events weeks to months ahead of time. As the climate continues to warm, addressing these knowledge gaps will enable the necessary anticipatory action to be taken to mitigate impacts.

The goal of this project is to transform our understanding of the conditions which can generate extreme heatwaves and their representation in models used operationally for subseasonal-to-decadal prediction.

The project will build on recent studies of large-scale circulation regimes, theory and changing risk of heatwaves led by the groups of supervisors Dr Simon Lee (Lee et al. 2023), Dr Michael Byrne (Byrne 2021) and Dr Nick Dunstone (Kay et al. 2020), combining analysis of large ensembles of weather and climate models together with theory and model experiments. The project will address three key objectives:

1. Develop and apply statistical methods to extract plausible extreme heat events from very large ensembles in different regions.
2. Separate the relative roles of large-scale dynamics versus local-scale processes in the most extreme events.
3. Apply new physical understanding to near-term climate predictions for extreme temperatures.

To address the project objectives, the initial focus will be on analysing the Met Office operational decadal prediction system (DePreSys3; Dunstone et al. 2016). This is a leading dynamical prediction system which contributes real-time decadal forecasts every year to the World Meteorological Organisation’s Lead Centre for Annual-to-Decadal Climate Prediction (http://www.wmolc-adcp.org). These large ensembles will be mined to extract large-scale weather patterns linked to extreme heat, focusing on Europe and North America (Lee et al. 2023), where observed heatwave trends strongly differ. The large ensembles will enable a much better understanding of extremely rare configurations of the climate system.

A next step will be to decompose the physical processes which set apart the most extreme events, considering the relative roles of large-scale dynamics versus local-scale processes in the context of new theory (Byrne 2021; Zhang and Boos 2023). Broadening the analysis to examine other prediction systems, including daily large ensemble initialisations of the ECMWF subseasonal forecast system (https://doi.org/10.21957/fv6k37c49h) and a new generation of climate prediction systems being developed as part of the EU ASPECT project (https://www.aspect-project.eu), will help develop the extent to which models differ in their treatment of extremes, including the role of biases. For example, differences in climatological land relative humidity, as suggested by theory, could be explored and compared to the Met Office observed gridded global surface humidity dataset (HadISDH). It is anticipated that the project student will then design new model experiments involving the Met Office DePreSys prediction system or the ECMWF OpenIFS model, such as perturbing or nudging the land or sea surface, to test hypotheses.

Not only will these innovative simulations advance fundamental understanding of heatwave mechanisms and predictability, they will also develop valuable skills for the PhD student in coding, scientific modelling and high-performance computing.

Year 1

The first year will involve a literature review to allow the student to develop their understanding of heatwave dynamics and physical aspects of climate science. The student will also begin working with the Met Office DePreSys decadal climate prediction data, including a short early visit to the Met Office to gain understanding of operational seasonal-to-decadal prediction and activities of the WMO Lead Centre. The student will also attend the annual UK National Climate Dynamics workshop to broaden their understanding of current research in the area and network with other researchers.

Year 2

The second year will focus on developing statistical frameworks to extract plausible scenarios for extreme heat across the multi-model large ensembles, developing hypotheses that might be tested in bespoke modelling experiments that the student will design during an extended visit to the Met Office. The student will present their findings at a national conference, such as the Royal Meteorological Society’s Annual Weather and Climate conference.

Year 3

The third year will focus on understanding the roles of local versus large-scale processes in generating the most extreme heat events. This will involve substantial data analysis, and the student will also draft a research article on this portion of the project. The student will present the key results at an international conference, such as the European Geosciences Union annual meeting in Vienna.

Year 3.5

The final six months will focus on writing the PhD thesis and drafting a research article on the analyses previously conducted.

The student will be trained in several aspects of physical climate science including atmospheric dynamics, ensemble prediction, climate modelling and climate change. The student will also be trained in highly sought-after technical skills in computational modelling, high-performance computing, and quantitative ‘Big Data’ analysis.