Global climate models (GCM) have been used for nearly two decades now as a tool to investigate and analyze past, present, and future weather and climate. Even though the first several generations of climate models were very simple, today's models are very sophisticated. They use complex parameterization schemes to approximate many nonlinear physical fields. In these models, the resolution and time steps can be set to be as small or as large as desired. In either case, the model generates over 100 atmospheric variables and 20 land surface variables that can be reported daily or monthly. The Community Atmospheric Model Version Two global climate model spends over sixty percent of the time computing shortwave and longwave parameterization schemes. Our goal is to replace its shortwave scheme with empirical methods and show that accuracy of the tropospheric variables is not compromised when using these empirical methods. We found that an autoregressive moving average (ARMA) model can be used to simulate the solar radiation at the top of the model atmosphere. However, the calculated insolation value is only valid for one particular grid point. To simulate the radiation over the entire globe, many ARMA models need to be determined. We also found that large 4-10-10-1 neural networks can be used to simulate the solar radiation to within 2 W m-2. However, much smaller and manageable neural networks can be used to simulate the complete solar insolation term if the neural network only simulates the residual after the annual and diurnal cycles and removed from the field (referred to as the - method). By using the neural network in the - method and by setting the eccentricity term to a constant, we were able to cut the models processing of the solar insolation by at least a factor of four.