Impact of in-consistency between the climate model and its initial conditions on climate prediction

We investigated the influence of dynamical in-consistency of initial conditions on the predictive skill of decadal climate predictions. The investigation builds on the fully coupled global model “Coupled GCM for Earth Simulator” (CFES). In two separate experiments, the ocean component of the coupled model is full-field initialized with two different initial fields from either the same coupled model CFES or the GECCO2 Ocean Synthesis while the atmosphere is initialized from CFES in both cases. Differences between both experiments show that higher SST forecast skill is obtained when initializing with coupled data assimilation initial conditions (CIH) instead of those from GECCO2 (GIH), with the most significant difference in skill obtained over the tropical Pacific at lead year one. High predictive skill of SST over the tropical Pacific seen in CIH reflects the good reproduction of El Niño events at lead year one. In contrast, GIH produces additional erroneous El Niño events. The tropical Pacific skill differences between both runs can be rationalized in terms of the zonal momentum balance between the wind stress and pressure gradient force, which characterizes the upper equatorial Pacific. In GIH, the differences between the oceanic and atmospheric state at initial time leads to imbalance between the zonal wind stress and pressure gradient force over the equatorial Pacific, which leads to the additional pseudo El Niño events and explains reduced predictive skill. The balance can be reestablished if anomaly initialization strategy is applied with GECCO2 initial conditions and improved predictive skill in the tropical Pacific is observed at lead year one. However, initializing the coupled model with self-consistent initial conditions leads to the highest skill of climate prediction in the tropical Pacific by preserving the momentum balance between zonal wind stress and pressure gradient force along the equatorial Pacific.