mloz: A Highly Efficient Machine Learning-Based Ozone Parameterization for CMIP Simulations

Atmospheric ozone is a crucial absorber of solar radiation and an important greenhouse gas. However, most climate models participating in the Coupled Model Intercomparison Project (CMIP) still lack an interactive representation of ozone due to the high computational costs of atmospheric chemistry schemes. Here, we introduce a machine learning parameterization (mloz) to interactively model daily ozone variability and trends across the troposphere and stratosphere in common CMIP simulations, including pre-industrial, abrupt-4xCO₂(Ma et al. 2025), historical and future Shared Socioeconomic Pathway (SSP) scenarios simulations. We demonstrate its high fidelity on decadal timescales and its flexible use online across two different climate models -- the UK Earth System Model (UKESM) and the German ICOsahedral Nonhydrostatic (ICON) model. With meteorological variables and forcing data as inputs, mloz produces stable ozone predictions around 31 times faster than the chemistry scheme in UKESM, contributing less than 4% of the respective total climate model runtimes. In particular, we also demonstrate its transferability to different climate models without chemistry schemes by transferring the parameterization from UKESM to ICON in standard climate sensitivity simulations. This highlights mloz’s potential for widespread adoption in CMIP-level climate models that lack interactive chemistry for future climate change assessments, where ozone trends and variability will significantly modulate atmospheric feedback processes.

Reference:
Ma Y, Abraham N L, Versick S, et al. mloz: A Highly Efficient Machine Learning-Based Ozone Parameterization for Climate Sensitivity Simulations[J]. arXiv preprint arXiv:2509.20422, 2025.