Accurate and Fast Neural Network Emulations of Model Radiation for the NCEP Coupled Climate Forecast System: Climate Simulations and Seasonal Predictions*

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...

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