Impact of Annual Cycle on ENSO Variability and Predictability

Abstract:

Low-order Linear Inverse Models (LIMs) have been shown to be competitive with comprehensive coupled atmosphere-ocean models at reproducing many aspects of tropical oceanic variability and predictability. This paper presents an extended cyclo-stationary Linear Inverse Model (CS-LIM) that includes the annual cycles of the background state and stochastic forcing of tropical sea surface temperature (SST) and sea surface height (SSH) anomalies. Compared to a traditional stationary LIM that ignores such annual cycles, the CS-LIM is better at representing the seasonal modulation of ENSO-related SST anomalies and their phase locking to the annual cycle. Its deterministic as well as probabilistic hindcast skills, assessed in terms of a Relative Operating Characteristic (ROC), are comparable to the skills of the North American Multi-Model Ensemble (NMME) of comprehensive global coupled models.

The explicit inclusion of annual-cycle effects in the CS-LIM improves the forecast skill of both SST and SSH anomalies through SST-SSH coupling. The impact on the SSH skill is particularly marked at longer forecast lead times over the western Pacific and in the vicinity of the Pacific North Equatorial Countercurrent (NECC), which transports warm waters from the western Pacific warm pool to the eastern Pacific and influences El Niño development there. The higher CS-LIM skill thus results from improving the representation of both ENSO phase-locking and Pacific NECC variations. These improvements result not only from explicitly accounting for the annual cycle of the background state, but also that of the stochastic forcing.