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\begin{document}

\title{Multi-stage Ensemble-learning-based Model Fusion for Surface Ozone
Simulations: A Focus on CMIP6 Models}



\author[1]{Zhe Sun}%
\author[2]{Alexander Archibald}%
\affil[1]{Centre for Atmospheric Science}%
\affil[2]{Centre for Atmospheric Sciences, Yusuf Hamied Department of Chemistry, University of Cambridge}%


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  \date{June 13, 2024}


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\begin{abstract}
Accurately simulating global surface ozone has long been one of the
principal components of chemistry-climate modelling, but divergences in
simulation outcomes have been reported as a result of the mechanistic
complexity of tropospheric ozone budget. Settling the cross-model
discrepancies to achieve higher accuracy thus is a task of priority.
Building on the Coupled Model Intercomparison Project Phase 6 (CMIP6),
we have transplanted a conventional ensemble learning approach, and also
constructed an innovative 2-stage enhanced space-time Bayesian neural
network to fuse an ensemble of 57 simulations together with a prescribed
ozone dataset, both of which have realised outstanding performances
(R-square \textgreater{} 0.95, RMSE \textless{} 2.12 ppbV). The
conventional ensemble learning approach is computationally cheaper and
results in higher overall performance, but at the expense of oceanic
ozone being overestimated and the learning process being
uninterpretable. The Bayesian approach performs better in spatial
generalisation and enables perceivable interpretability, but requires
heavier computational burdens. Both of these multi-stage learning-based
approaches provide frameworks for improving the fidelity of
composition-climate model outputs for use in future impact studies.%
\end{abstract}%



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