  This work introduces an approach to enhancing the computational efficiency of
3D atmospheric simulations by integrating a machine-learned surrogate model
into the OASIS global circulation model (GCM). Traditional GCMs, which are
based on repeatedly numerically integrating physical equations governing
atmospheric processes across a series of time-steps, are time-intensive,
leading to compromises in spatial and temporal resolution of simulations. This
research improves upon this limitation, enabling higher resolution simulations
within practical timeframes. Speeding up 3D simulations holds significant
implications in multiple domains. Firstly, it facilitates the integration of 3D
models into exoplanet inference pipelines, allowing for robust characterisation
of exoplanets from a previously unseen wealth of data anticipated from JWST and
post-JWST instruments. Secondly, acceleration of 3D models will enable higher
resolution atmospheric simulations of Earth and Solar System planets, enabling
more detailed insights into their atmospheric physics and chemistry. Our method
replaces the radiative transfer module in OASIS with a recurrent neural
network-based model trained on simulation inputs and outputs. Radiative
transfer is typically one of the slowest components of a GCM, thus providing
the largest scope for overall model speed-up. The surrogate model was trained
and tested on the specific test case of the Venusian atmosphere, to benchmark
the utility of this approach in the case of non-terrestrial atmospheres. This
approach yields promising results, with the surrogate-integrated GCM
demonstrating above 99.0% accuracy and 101 factor GPU speed-up of the entire
simulation compared to using the matched original GCM under Venus-like
conditions.
