The potential role of the land surface state in improving predictions of seasonal climate is investigated with a coupled land-atmosphere climate model. Climate simulations for 18-years, as well as 4-month integrations for boreal spring and summer seasons (1982-1999) have been conducted with specified observed sea surface temperature (SST). The impact on prediction skill of initial land surface state (interannually varying versus climatological soil wetness) and the effect of errors in downward surface fluxes (precipitation, longwave and shortwave radiation) over land are investigated with a number of parallel experiments. Flux errors are addressed by replacing the downward fluxes with observed values in various combinations to ascertain the separate roles of water and energy flux errors on land surface state variables, upward water and energy fluxes from the land surface, and the important climate variables of precipitation and near-surface air temperature.

Strong systematic errors are found in the model, which are only mildly alleviated by the specification of realistic initial soil wetness. The specification of the snow initial condition in spring run does improve the simulation of near surface air temperature, but does not significant helpful for precipitation results. Replacement of the downward surface fluxes has a clear positive impact on systematic errors, suggesting that the land-atmosphere feedback is helping to exacerbate climate drift. Improvement in the simulation of year-to-year variations in climate is even more evident. This suggests that the land surface can communicate climate anomalies to the atmosphere, given proper meteorological forcing.

Comparing long-term and seasonal runs, we investigate the differences in the signal-to-noise ratios between the runs, and which factors contribute to the skill of simulation results. We also analyze the sensitive regions responding to the replacement of the downward surface fluxes. The changes in skill under flux replacement suggest which of the parameterizations within the atmospheric model need critical attention and improvement, and also raise the hope that a different coupling strategy (e.g., flux adjustment or anomaly coupling) between land and atmosphere may significantly improve climate prediction.