About the Project
Background
Since 2020, the energy imbalance at the top of the Earth’s atmosphere has grown at a rate substantially exceeding our expectations. This energy imbalance has contributed to record global mean temperatures and a marked rise in the frequency and intensity of regional extreme events. The mechanisms driving this recent acceleration of Earth’s global warming remain poorly understood, resulting in substantial uncertainty in our predictions. Global km-scale resolution models are at the forefront of…
current weather prediction and climate modelling. They resolve key processes which are not explicitly represented in more commonly used lower-resolution climate models. There is a potential that these models help to substantially improve understanding and prediction of climate variability and change and therefore help to reduce uncertainty. Prediction ensemble sizes are small, however, and appropriate statistical approaches will need to be developed to quantify uncertainty in this context.
PhD Project
In the project we will investigate the recently-observed increase in Earth’s energy imbalance using a global km-scale model. The main goal is to explore whether high-resolution simulations can help to reduce the uncertainty in the prediction of recent climate variability and change, and related extreme events. Because the high-resolution models are expensive to run, the ensemble sizes of the simulations are relatively small. Therefore, there is a need to develop appropriate statistical techniques, such as Bayesian statistical emulators, to design ensemble experiments and quantify the uncertainty in this context. This will allow for robustly assessing whether a possible reduction in uncertainty can be achieved and detected, especially in regional extreme events which pose great risks to society.
Focussing on the period 2021-2026, we aim not only to understand recent annual mean values, but also to analyse individual months. Modelling experiments will be conducted with prescribed sea surface temperatures. The experiments will explore to what extent the observed energy imbalance and temperature evolution can be reproduced by varying key uncertain parameters in the model. A stratified sampling strategy will be developed which is guided by expert knowledge. The sampling will consider different model parameters, parameter values, years, and months. Lower-resolution experiments and the use of a Bayesian statistical or machine learning emulator will support and guide the experimental design. The global km-resolution sensitivity experiments can also be used to explore the validity of a new cloudy energy balance model which could be used to develop and statistically assess techniques to quantify uncertainty in key aspects of the high-resolution simulations.
The project has secured CASE support from the Met Office and provides the opportunity for visits to the Met Office. Dedicated climate model simulations will be performed at the Met Office supercomputing facilities.
Applicant Profile
Both students with a background in meteorology and climate science, or in statistics and mathematics are suited for this project. Some experience in data analysis tools such as the Python programming language and an interest in physical processes governing climate change are required.
Funding Notes
This project is part of the UNRISK CDT, which offers 15-18 fully-funded NERC studentships, covering full university tuition fees; a personal stipend at standard UKRI rates; £6000 individual research and training costs; £5000 (per student) of cohort-level training; and a ‘Flexible Fund’ for special projects.
International applicants will need to cover costs related to applying for a student visa and the international health surcharge (IHS)
Applications are open to UK and international applicants. The number of awards for international applicants is limited by UKRI rules.
More information is available on the UNRISK website: View Website
References
https://wmo.int/news/media-centre/earths-climate-swings-increasingly-out-of-balance
https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2024AV001636
https://pure.mpg.de/rest/items/item_3711842_3/component/file_3712334/content