Speaker: Dan Giles, UCL
Title: Embedding sub-grid variability into hybrid climate simulations to improve convective modelling
Abstract:
Atmospheric General Circulation Models (AGCMs) play a vital role in our understanding of climate dynamics and how the climate is changing. However, carrying out climate projections using the latest AGCMs is a computationally expensive task due to long integration timescales and the need to explore the impact of different forcing pathways. As a result, climate simulations are typically carried out using spatial resolutions on the order of 100km. This coarse spatial resolution leads to biases associated with cloud formation, convection, precipitation and interactions between the water cycle and the large-scale dynamics.
This work aims to tackle these biases associated with convective scale processes by embedding a multi-output Gaussian Process (MOGP), trained to predict high resolution variability of temperature and specific humidity fields, within the AGCM. A proof-of-concept study will be presented where a trained MOGP model is coupled in-situ with a simplified AGCM. The temperature and specific humidity profiles of the AGCM model outputs are perturbed at fixed time intervals according to the predicted high resolution informed variability. Modelling improvements in the precipitation, outgoing longwave and shortwave radiation patterns are observed in a 10-year simulation run and the physical justifications for these changes will be explored. This work showcases a promising approach towards improving the overall representation of sub-grid cell processes in coarse resolution atmospheric simulations.