Revisiting ENSO Coupled Instability Theory and SST Error Growth in a Fully Coupled Model

Abstract:

In an effort to untangle certain mechanisms contributing to the initiation of ENSO events, a coupled model framework is presented to isolate coupled instability induced SST error (or anomaly) growth in the ENSO region. The modeling framework using CCSM4 allows for seasonal ensembles of initialized simulations that are utilized to quantify the spatial and temporal behavior of coupled instabilities and the associated implications for ENSO predictability. The experimental design allows for unstable growth of initial perturbations that are not prescribed and several cases exhibit sufficiently rapid growth to produce ENSO events that do not require a previous ENSO event, large-scale wind trigger, or subsurface heat content precursor. Without these precursors, however, ENSO amplitude is reduced, suggesting that a combination of processes is essential to achieving peak amplitude in CCSM4. The results imply that even without classical precursors, including western Pacific “preconditioning,” ENSO events can be excited via coupled instabilities in fully coupled models. By removing the subsurface heat content precursor, however, essentially a lower bounds for ENSO predictability in CCSM4 is established, although seasonal ensembles initialized later in the calendar year retain some predictability.

The initial error growth exhibits strong seasonality with fastest growth during spring and summer and also dependence on the initialization month with fastest growth occurring in the July ensemble. The error growth displays a well-defined seasonal limit with ensembles initialized in the winter or spring exhibiting a clear seasonal halt in error growth around September, consistent with increased background stability typical during fall. Overall, dynamically driven error growth in CCSM4 is deemed best characterized by strong seasonality, dependence on the initialization month, and nonlinearity. The results pose real implications for predictability because the final error structure is ENSO-like and occurs without a subsurface precursor, which studies have shown to be essential to ENSO predictability.