Exploring uncertainty due to clouds in modelled future climate change systematically

Background

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 models, which typically have “structurally” different parametrisations written in terms of different functions. Progress has been made through “”perturbed physics ensembles””, which perturb uncertain parameters within one model structure. However, differences between model structures, which dominate uncertainty, remain undefined.
Recent work now also uses high-resolution models that resolve more cloud-relevant atmospheric processes physically. However, high resolution models do not produce better simulations of all aspects of clouds, meaning that model comparisons with each other and observations remain key.

PhD Opportunity

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. Clouds are the most uncertain process affecting the interaction between radiation and climate, playing a key role in the degree to which the planet will warm in future. This project will start by writing down cloud parametrisations and high-resolution model realisations of cloud using CSP.
The research can then take a number of directions:

1) A Gaussian process emulator or neural network can be applied in conjunction with CSP to provide a more complete description of a parametrised process while remaining human-readable. Our preliminary work indicates that this is helpful for cloud, but more work is needed to constrain low cloud, which is notoriously difficult to understand.

2) Emulation can fill in the CSP parameter space, giving us a prediction of key cloud responses between the parametrisations that we have. History matching will be used to find regions of parameter space not inconsistent with observations, and highlight new areas of parameter space that model parametrisations should explore, to allow climate models to better explore future climate impacts.

3) The CSP emulator can be placed directly within the Met Office Unified Model climate model to explore the potential impacts of
uncertainty in cloud parametrisation on future surface climate.

The project is linked to the UK Met Office with collaborator Mark Webb providing expertise in clouds and climate modelling. Co-supervisor
James Salter is a statistician who will provide expertise in emulation and history matching. The project will interact with the Model Uncertainty Model Intercomparison Project (MUMIP).

Applicant Profile

This project will suit students with a strong background in mathematics or statistics or another numerate degree such as Physics
or Meteorology, and an interest in Earth’s atmosphere, climate and climate change. Experience in coding in python or another
high-level language would be an advantage, but is not essential.

Other information

Supervisors involved:
Hugo Lambert: https://fhl202.github.io/
James Salter: https://experts.exeter.ac.uk/26439-james-salter
Mark Webb: https://www.metoffice.gov.uk/research/people/mark-webb

MUMIP consortium: https://mumip.web.ox.ac.uk/home

Reading:

Continuous Structural Parametrisation:
https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020MS002085

History matching:
https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020MS002217

Uncertainty in climate change due to errors in modelling of
cloud:
https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2021JD035198