With the advancement of modern parallel computer architectures, General Circulation Models (GCMs) are becoming capable of running operationally at higher horizontal resolutions than ever before. However, using GCMs for tropical cyclone studies remains difficult due to the relatively small size of the storms, the intense convection and a host of large-scale small-scale interactions. These are mostly unresolved at high GCM resolutions of about 30-60 km, and still challenged at the highest resolutions between 12-30 km. Nevertheless, high-resolution GCMs are becoming a tool of choice to evaluate tropical cyclones in current and future climate conditions. This raises questions concerning the fidelity of GCMs for tropical cyclone assessments. In particular, the physical and dynamical components of GCMs need to be carefully evaluated to assess their reliability for tropical cyclone studies.

An idealized tropical cyclone test case for high-resolution GCMs is implemented in aqua-planet mode with constant sea surface temperatures. The initial conditions are based on an initial vortex seed that is in gradient-wind and hydrostatic balance and intensifies over a 10-day period. In particular, we investigate the role of the choice of the dynamical and physical components within NCAR's hydrostatic Community Atmosphere Model CAM 5.1. We utilize two different dynamical cores available in CAM, including the default Finite-Volume (FV) and the next generation Spectral Element (SE) dynamics packages. Special attention is given to the SE dynamical core and its interaction with the GCM physical parameterization suite. The impact of small variations in the initial conditions on the evolution of the tropical cyclone is also assessed. Therefore, the investigation also sheds light on the role of uncertainty and structural differences within GCMs in tropical cyclone simulations. We note that our model setup is more idealized than that used for full climate or realistic tropical cyclone assessments. However, this represents a deliberate approach to more clearly isolate the causes and effects of the GCM modeling choices and their uncertainties as they relate to extreme storms.