Research explained

Making the climate model's smallest scales learnable.

Global climate models cannot resolve every cloud and turbulent motion. My research explores how super-parameterisation can provide that missing detail, and how a machine learning model might eventually reproduce it at a fraction of the cost.

The modelling stack 01 / 04
A visual explanation of the research process The diagram changes as the reader scrolls through four stages, showing a global climate model, cloud resolving models, and a machine learning emulator.

A GCM provides the large-scale state; an embedded CRM supplies detail that the grid cannot resolve.

01

Start with the scale gap

A global climate model divides the atmosphere into grid cells. The cell is useful for planetary patterns, but it is too coarse to explicitly represent many clouds and turbulent motions.

02

Super-parameterisation adds a teacher

In super-parameterisation, a small cloud-resolving model (CRM) is embedded inside each GCM grid column. It resolves more of the physics and returns the subgrid tendencies to the larger model, but running all of those CRMs is computationally demanding.

03

Learn the CRM's behaviour

The CRM becomes a high-fidelity teacher. A machine learning emulator learns the relationship between the GCM state and the CRM's tendencies, with the goal of replacing the costly inner loop while retaining the useful small-scale effects.

04

Ask whether it transfers

A model that works in one climate model is not automatically useful elsewhere. The final test is transferability: can the learned emulator work with other GCMs, grids, resolutions, and climates without losing physical credibility?

The question

Can we make high-resolution climate information both affordable and portable?

This is not only a question of prediction accuracy. It is also about stability, physical behaviour, and whether one learned representation can travel between the climate models that scientists actually use.

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