This study examines the correspondence between short- and long-term systematic errors in five atmospheric models. This is accomplished by comparing short hindcasts from the Transpose-AMIP II archive to AMIP simulations for the same models. The focus is on June-August, though results are expected to hold for other seasons.  The comparison allows one to diagnose the time scales over which systematic errors develop, and yields insights into their origin through a seamless modeling approach.  The results show that systematic errors in precipitation, clouds, and radiation processes in the climate simulations manifest in only a few days in the hindcasts in all models. Errors typically saturate after few days of hindcasts with amplitudes comparable to the climate errors, and the impacts of initial conditions on the simulated ensemble mean errors are relatively small. This robust bias correspondence suggests that these systematic errors across different models likely are initiated by model parameterizations because the atmospheric large-scale state remains close to observations in the first few days. The biases associated with parameterized physics can feed back on the large-scale state on synoptic time scales, and impact fields such as winds, surface temperature, and sea level pressure.  This analysis further indicates a good correspondence between short- and long-term biases for these large-scale state variables. These results suggest that changes to a model to improve hindcasts will likely also improve long-term climate errors.