James Briant(He/Him)University College London
James Briant is a PhD candidate in the Department of Statistical Science at University College London. His research interests concern Bayesian calibration for computer models, statistical emulation of computer models and experimental design. He works closely with the UK Met Office on projects such as hybrid machine learning climate simulations and upper atmosphere weather forecasts following space weather events. |
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TalksHybrid climate simulation including machine-learnt subgrid variability from kilometre-scale weather simulation22-Apr-24Underrepresentation of cloud formation is a known failing in current climate simulations. The coarse grid resolution required by the computational constraint of integrating over long time scales does not permit the inclusion of underlying cloud generating physical processes. This work employs a multi-output Gaussian Process (MOGP) trained on high resolution Unified Model (UM) simulation data to predict the variability of temperature and specific humidity profiles within the climate model. A proof-of-concept study has been carried out where a trained MOGP model is coupled in-situ with a simplified Atmospheric General Circulation Model (AGCM) named SPEEDY. The temperature and specific humidity profiles of the SPEEDY model outputs are perturbed at each timestep according to the predicted high resolution informed variability. 10-year forecasts are generated for both default SPEEDY and ML-hybrid SPEEDY models and output fields are compared ensuring hybrid model predictions remain representative of Earth's atmosphere. Some changes in the precipitation, outgoing longwave and shortwave radiation patterns are observed indicating modelling improvements in the complex region surrounding India and the Indian sea. More generally, this approach creates a computationally efficient path towards more realistic future climate change predictions. |