Embedding machine-learnt sub-grid variability improves climate model biases

TL;DR

This paper introduces a machine learning approach using a Multi-Output Gaussian Process to embed sub-grid variability into climate models, significantly reducing biases in precipitation predictions and improving climate simulation accuracy.

Contribution

It presents a novel hybrid climate modeling method that integrates ML-derived sub-grid variability into an atmospheric model, enhancing bias correction in climate simulations.

Findings

Global precipitation bias reduced by 18%

Tropical bias reduced by 22%

Improved representation of cloud-related processes

Abstract

The under-representation of cloud formation is a long-standing bias associated with climate simulations. Parameterisation schemes are required to capture cloud processes within current climate models but have known biases. We overcome these biases by embedding a Multi-Output Gaussian Process (MOGP) trained on high resolution Unified Model simulations to represent the variability of temperature and specific humidity within a climate model. A trained MOGP model is coupled in-situ with a simplified Atmospheric General Circulation Model named SPEEDY. The temperature and specific humidity profiles of SPEEDY are perturbed at fixed intervals according to the variability predicted from the MOGP. Ten-year predictions are generated for both control and ML-hybrid models. The hybrid model reduces the global precipitation bias by 18\% and over the tropics by 22\%. To further understand the drivers of these improvements, physical quantities of interest are explored, such as the distribution of lifted index values and the alteration of the Hadley cell. The control and hybrid set-ups are also run in a plus 4K sea-surface temperature experiment to explore the effects of the approach on patterns relating to cloud cover and precipitation in a warmed climate setting.