The present study examines the correspondence between short- and long-term systematic errors in five atmospheric models by comparing the sixteen 5-day hindcast ensembles from the Transpose-AMIP II for the July-August 2009 (short-term), to the climate simulations from the CMIP5/AMIP for the June-August mean conditions of the years of 1979-2008 (long-term). Because the short-term hindcasts were conducted with identical climate models used in the CMIP5/AMIP simulations, one can diagnose over what time scale systematic errors in these climate simulations develop, thus yielding insights into their origin through a seamless modeling approach.
The analysis suggests that most systematic errors of precipitation, clouds, and radiation processes in the long-term climate runs are present by Day 5 in ensemble average 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 since the atmospheric large-scale states remain close to observations in the first two to three days. However biases associated with model physics can have impacts on the large-scale states by Day 5, such as zonal winds, 2 meter temperature and sea level pressure, and our analysis further indicates a good correspondence between short- and long-term biases for these large-scale states. Therefore, improving individual model parameterizations in the hindcast mode could lead to the improvement of most climate models in simulating their climate mean state and potentially their future projections.