LAUSR

Seasonal climate predictions are formulated from known, present conditions, and simulate the near-term climate for approximately a year in the future. Recent efforts in seasonal climate prediction include coupled general circulation model (CGCM) ensemble predictions, but other efforts have included atmospheric general circulation model (AGCM) ensemble predictions that are forced by time-varying sea surface temperatures (SSTs). CGCMs and AGCMs have differences in the way surface energy fluxes are simulated, which may lead to differences in skill and predictability. Concerning model biases, forecasted SSTs have errors compared to observed SSTs, which may also affect skill and predictability. This manuscript focuses on the role of the ocean in climate predictions, and includes the influences of ocean-atmosphere coupling and SST errors on skill and predictability. We perform a series of prediction experiments comparing coupled and uncoupled Community Climate System Model version 4.0 (CCSM4) predictions, and forecasted versus observed SSTs to determine which is the leading cause for differences in skill and predictability. Overall, prediction skill and predictability are only weakly influenced by ocean-atmosphere coupling, with the exception of the western Pacific, while errors in forecasted SSTs significantly impact skill and predictability. Comparatively, SST errors lead to more significant and robust differences in prediction skill and predictability versus inconsistencies in ocean-atmosphere coupling.