Background
Asian monsoons provide water resources for billions of people across South and East Asia, yet their response to human activities remains one of the largest uncertainties in regional climate science. Monsoon strength and timing strongly influence floods, droughts, air quality and hydropower generation, making reliable projections of future monsoon behaviour critical for climate adaptation and risk management.
Black carbon and other atmospheric aerosols may alter monsoon circulation by absorbing and scattering sunlight and modifying clouds, but climate models disagree on whether these processes strengthen or weaken monsoons, resulting in markedly different projections of rainfall distribution and intensity. As Asian aerosol emissions decline over coming decades, improving confidence in their influence on regional climate is essential for providing robust evidence to inform adaptation, infrastructure planning and climate resilience.
PhD project
World class regional climate models produce very different predictions of how monsoons will respond to changes in atmospheric pollution because many of the physical processes that control aerosols, clouds and rainfall cannot be represented directly. Instead, these processes are approximated using simplified descriptions, and different choices can lead to very different outcomes. As a result, it remains unclear which physical mechanisms are responsible for the contrasting behaviour seen across regional climate models, and which differences reflect genuine scientific uncertainty.
This project will investigate these questions using the UK Met Office regional climate model. The research will require a large ensemble of regional climate simulations in which key aspects of aerosol processes are varied systematically to determine how they influence monsoon behaviour. While this approach has been widely applied to global climate models, it has rarely been used for higher-resolution regional models, where it has the potential to provide much greater insight into the processes controlling monsoon systems.
The project will identify which aspects of aerosol physics have the greatest influence on monsoon behaviour, explain why different models produce contrasting responses, and investigate how observations can be used to distinguish between competing explanations. The results will improve confidence in regional climate projections and provide new methods for understanding uncertainty in regional climate models more generally.
In this research you will gain interdisciplinary training in atmospheric science, regional climate modelling, scientific computing, statistics and data analysis, with opportunities to collaborate with researchers working on climate prediction, atmospheric composition and climate-risk assessment.
Applicant Profile
Our experience shows that motivated STEM student with a genuine interest in climate modelling can rapidly develop the skills needed to excel in this field of research. Although the approach draws on advanced methods in data science and machine learning, students typically find that with guidance and curiosity they quickly acquire the necessary expertise to make important insights and make meaningful contributions to understanding how aerosols and clouds shape our future climate. If you’re eager to push the boundaries of knowledge, whilst acquiring highly transferable skills, reach out to our group members to find out more.
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://doi.org/10.5194/acp-25-7647-2025
https://doi.org/10.5194/acp-26-2293-2026
https://doi.org/10.1002/essoar.10507903.2