Stochastic parametrisation schemes represent sources of uncertainty in atmospheric model and several types of these schemes are in widespread use in general circulation models across a variety of temporal and spatial resolutions. We introduce a new stochastic scheme for use in global atmospheric models, which uses a machine learning model trained on high-resolution convection-permitting simulation data to estimate properties of the distribution of subgrid variability in potential temperature. This then informs the profile of  stochastic perturbations being applied to the inputs of traditional parametrisation schemes. This scheme is tested in single column model experiments over the tropical west Pacific and is shown to improve model performance in this case.