Drivers of coupled model ENSO error dynamics and the spring predictability barrier

Abstract

Despite recent improvements in ENSO simulations, ENSO predictions ultimately remain limited by error growth and model inadequacies. Determining the accompanying dynamical processes that drive the growth of certain types of errors may help the community better recognize which error sources provide an intrinsic limit to predictability. This study applies a dynamical analysis to previously developed CCSM4 error ensemble experiments that have been used to model noise-driven error growth. Analysis reveals that ENSO-independent error growth is instigated via a coupled instability mechanism. Daily error fields indicate that persistent stochastic zonal wind stress perturbations $$(\tau_{x}^{\prime } )$$ near the equatorial dateline activate the coupled instability, first driving local SST and anomalous zonal current changes that then induce upwelling anomalies and a clear thermocline response. In particular, March presents a window of opportunity for stochastic $$\tau_{x}^{\prime }$$ to impose a lasting influence on the evolution of eastern Pacific SST through December, suggesting that stochastic $$\tau_{x}^{\prime }$$ is an important contributor to the spring predictability barrier. Stochastic winds occurring in other months only temporarily affect eastern Pacific SST for 2–3 months. Comparison of a control simulation with an ENSO cycle and the ENSO-independent error ensemble experiments reveals that once the instability is initiated, the subsequent error growth is modulated via an ENSO-like mechanism, namely the seasonal strength of the Bjerknes feedback. Furthermore, unlike ENSO events that exhibit growth through the fall, the growth of ENSO-independent SST errors terminates once the seasonal strength of the Bjerknes feedback weakens in fall. Results imply that the heat content supplied by the subsurface precursor preceding the onset of an ENSO event is paramount to maintaining the growth of the instability (or event) through fall.