Ensemble of Neural Network Emulations for Climate Model Physics: The Impact on Climate Simulations

A new application of the NN ensemble approach is presented. It is applied to NN emulations of model physics in complex numerical climate models, and aimed at improving the accuracy of climate simulations. In particular, this approach is applied to NN emulations of the long wave radiation of the widely used National Center for Atmospheric Research Community Atmospheric Model. It is shown that practically all individual neural network emulations that we have trained in the process of development an optimal NN LWR emulation can be used within the NN ensemble approach for climate simulation. Using the NN ensemble results in a significant reduction of climate simulation errors, namely: the systematic and random errors, the magnitudes of the extreme errors or outliers and, in general, the number of large errors.