Eighteen years of global climate model ensemble simulations have been performed with observed SST to assess the ability of dynamical models to predict seasonal-interannual climate variations during boreal spring and summer. In addition, test suites have been designed to assess the role in climate predictability of land-surface initial conditions, and systematic errors of precipitation and radiation fluxes at the land surface. Downward shortwave radiation has large positive errors over nearly all land areas. Longwave radiation errors are generally negative and confined to arid and semi-arid regions. Precipitation errors are generally positive, but have a great degree of spatial variability.

High skill is found in the simulation of interannual variations in surface temperature over much of the globe. Much of this skill appears to be attributable to the influence of SST anomalies. EOF analysis of near-surface climate variables shows robust patterns that persist throughout the season. However, correction of the errors in precipitation and downward longwave fluxes over land improve the skill further. Corrections to shortwave radiation do not have much benefit. Correction of these errors, which drive long-term climate drift in the land surface state variables, also allows the land surface to be more responsive to anomalous forcing, improving skill over that found in multi-decade simulations using the same prescribed SST. However, significant errors remain, suggesting that the parameterizations of atmospheric physics (convection, PBL, radiation and clouds) remain the weak link in the simulation of the coupled land-atmosphere climate system. Overall, there is a strong indication that with the proper treatment of the land surface, climate anomalies beyond the boreal cold season may be as predictable as the winter anomalies associated with El Nino.