Fusion of a Statistical Emulator and Climate Model to Embed High Resolution Variability into a Coarse Resolution Climate Simulation

Abstract

<p>High resolution global climate simulations are computationally intractable due to the integration over long time scales. Therefore coarse spatial resolution models are used. One of the known failings of current climate simulations is the underrepresentation of cloud formation, which is largely due to the disparity in the spatial scale of the model and the underlying cloud generating physical processes. In this work emphasis has been placed on fusing an emulator with a climate model so as to embed high resolution variability into a coarse resolution climate model - with the aim of improving the representation of convective cloud formation.</p> <p>A multi-output Gaussian Process (MOGP) is trained on high resolution Unified Model (UM) runs to predict the variability on the temperature and specific humidity fields. A proof of concept study has been carried out where the trained MOGP model is then coupled in-situ with a simplified Atmospheric General Circulation Model (AGCM) named SPEEDY. The mean profiles of the SPEEDY model are perturbed at each timestep according to the predicted high resolution informed variability. The climate statistics from the fused model are then compared to the pure model run. Improvements in the precipitation, outgoing longwave and shortwave radiation patterns are observed.</p>