PhD Studentship: Exploring uncertainty due to clouds in modelled future climate change systematically. UNRISK NERC Centre for Doctoral Training PhD studentship 2025/26 Entry.

Understanding Uncertainty to Reduce Climate Risks (UNRISK) is a Centre for Doctoral Training – Recruiting now!

UNRISK is a Centre for Doctoral Training with fully funded PhD research opportunities at the University of Leeds, University College London, and the University of Exeter collaborating with over 40 external partners. UNRISK will train students with the multidisciplinary knowledge and skills across climate science, data science and decision science to tackle the pressing challenge of reducing the risks associated with rapid climate change. UNRISK will fund 40 PhD students in cohorts of 12-15 per year over three years, providing them with a stipend, university fees and residential training for 3 years and 9 months. Find out more at https://unrisk-cdt.ac.uk/ and browse the projects at https://unrisk-cdt.ac.uk/projects/.

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Changes in clouds are one of the biggest uncertainties affecting predictions of future climate change. Because their size is typically much smaller than the gridlength of numerical climate models, clouds must be “parametrised”, meaning that they are represented approximately using information from larger-scale conditions.

It is difficult to quantify the difference between climate models, which typically have different model parametrisations written in terms of different functions. Some progress has been made through “perturbed physics ensembles”, which take one model structure and perturb uncertain model parameters through their ranges of possible values. However, climate models are expensive to run, meaning that only a few parameter combinations can be tried. Features of model behaviour for unexplored parameter combinations must instead be estimated via statistical emulation. Parameter values that are unrealistic given available data are then identified via “history matching”.

We have developed Continuous Structural Parametrisation (CSP), which is a way of approximating structurally different model parametrisations as functions of the same variables, effectively writing them as members of a perturbed physics ensemble. CSP also allows us to represent observations or high-resolution process models within the same structure, allowing us to benchmark our parametrisations.

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