Using neural network emulations of model physics in numerical model ensembles

In this paper the use of the neural network emulation technique, developed earlier by the authors, is investigated in application to ensembles of general circulation models used for the weather prediction and climate simulation. It is shown that the neural network emulation technique allows us: (1) to introduce fast versions of model physics (or components of model physics) that can speed up calculations of any type of ensemble up to 2 -3 times; (2) to conveniently an naturally introduce perturbations in the model physics (or a component of model physics) and to develop a fast versions of perturbed model physics (or fast perturbed components of model physics), and (3) to make the computation time for the entire ensemble (in the case of short term perturbed physics ensemble introduced in this paper) comparable with the computation time that is needed for a single model run.