| description abstract | The approach to accurate and fast-calculating model physics using neural network emulations was previously developed by the authors for both longwave and shortwave radiation parameterizations or the full model radiation, which is the most time-consuming component of model physics. It was successfully tested for a moderate-resolution uncoupled NCAR Community Atmospheric Model (CAM) that is driven by climatological SST for a decadal climate simulation mode. In this study, the approach has been further developed and implemented into the NCEP coupled Climate Forecast System (CFS) with significantly higher resolution and time-dependent CO2. The higher complexity of NCEP CFS required further adjustments to the neural network emulation methodology. Validation of the approach for the NCEP CFS has been performed through a decadal climate simulation and seasonal predictions. The developed highly-accurate neural network emulations of longwave and shortwave radiation parameterizations are, on average, 16 and 60 times faster than the original/control longwave and shortwave radiation parameterizations, respectively. The authors present a detailed comparison of parallel decadal climate simulations and seasonal predictions performed with the original NCEP model radiation parameterizations and with their neural network emulations. The differences between the parallel runs are overall within or less than the observation errors and uncertainties of reanalysis. Moreover, the differences (both in terms of bias and RMSE) are of a similar magnitude as the model?s internal variability. These results justify the practical use of efficient neural network emulations of full model radiation for climate simulations and seasonal predictions. | |
