A central focus of NASA's Global Modeling and Assimilation Office (GMAO) atmospheric general circulation modeling effort is the development of an atmospheric model suitable for data assimilation, weather forecasting, and climate simulation. Ongoing developments are focused on highly parallel processing, global simulations of increasing resolutions, and increased coupling of the earth system's process models. While model computation scales very well with number of available processors, a major constraining factor on the efficiency of these simulations is the input processing of Terabyte-size source data files used by the gridded component models. Our objective in this research is to increase the efficient use of CPU time associated with these simulations by paralleling I/O processing operations using an 'I/O Staging Server' which captures and makes available the required source data asynchronously with the simulation run. More efficient I/O for reading model restarts and boundary conditions, and writing model output and checkpoint files will free up processing resources that are currently idling during I/O. As a result, we will realize a significant increase both the number of models that can be involved in the simulation and the achievable resolutions of the grid components. It is estimated that currently up to 25% of a forecast run is consumed by I/O, a factor we think can be reduced by at least 50% or more through the use of State-of-the-art I/O processing approaches and supporting software infrastructure.