A Highly Efficient Machine Learning-based Ozone Parameterization for Climate Models

Atmospheric ozone is a crucial absorber of solar radiation and an important greenhouse gas. However, explicitly representing ozone in climate models is computationally expensive. A recent study introduced a simple linear machine learning-based ozone parameterization scheme (mloz) for daily ozone prediction based on temperature. Here we develop and implement the mloz in the UK Earth System Model (UKESM) for long-term idealized climate simulations. It produces stable ozone predictions over 50 years with a computational cost of less than 0.5% of the total runtime. The scheme accurately predicts ozone distribution, with climatology field errors of less than 10% in the stratosphere. It also realistically represents ozone variabilities, including seasonal and Quasi-Biennial Oscillation-related variabilities, despite a slight underestimation of amplitudes over the stratospheric polar regions. Additionally, we further demonstrated its generalizability by successfully transferring the mloz trained on UKESM to the ICOsahedral Nonhydrostatic model (ICON). Over 30 years of climate sensitivity tests indicate that it can effectively represent the response of ozone to the sudden quadrupling of CO₂, significantly outperforming the simplified linearized ozone photochemistry scheme (Linoz) in the troposphere. This implies that the mloz can be transferred to other climate models without a full chemistry module to enable an efficient explicit ozone simulation.