A new stochastic physics scheme incorporating machine-learnt subgrid variability

Stochastic parameterisations have seen widespread use in atmospheric models. These schemes represent uncertainty by adding terms that include a random noise component directly to the equations that describe the time evolution of the model. Stochastic parameterisation development thus involves the following questions: what are the sources of uncertainty, how do we represent them, and how precisely should we formulate stochastic terms to quantify them? Common methods to quantify the uncertainty inherent in parameterisation include applying multiplicative perturbations to physics tendencies (such that the larger the tendency due to subgrid processes, the more uncertainty we should have in the tendency) or applying perturbations to physical parameters (our physics schemes often rely on parameters whose values we do not know precisely, and have complicated nonlinear responses to perturbing this set of parameters, so perturbing each one within its own specified range during the model run allows this uncertainty to feed back into the model state).

In this work we present a different approach to stochastic parameterisation. We perturb the thermodynamic profiles that constitute the inputs to parameterisation schemes. The perturbations are scaled by the degree of subgrid inhomogeneity. A representation of the subgrid inhomogeneity is estimated by a machine learning model which has been trained on coarse-grained high resolution (dx=~1.5km) model output from the Met Office Unified Model. The scheme is implemented in LFRic, the UK Met Office’s next generation modelling system, and we present results of experiments ran in single column model mode.