One open question in El Niño––Southern Oscillation (ENSO) simulation and predictability is the role of random forcing by atmospheric variability with short correlation times, on coupled variability with interannual timescales. The discussion of this question requires a quantitative assessment of the stochastic component of the wind stress forcing. Self-consistent estimates of this noise (the stochastic forcing) can be made quite naturally in an empirical atmospheric model that uses a statistical estimate of the relationship between sea surface temperature (SST) and wind stress anomaly patterns as the deterministic feedback between the ocean and the atmosphere. The authors use such an empirical model as the atmospheric component of a hybrid coupled model, coupled to the GFDL ocean general circulation model. The authors define as residual the fraction of the Florida State University wind stress not explained by the empirical atmosphere run from observed SST, and a noise product is constructed by random picks among monthly maps of this residual. The impact of included or excluded noise is assessed with several ensembles of simulations. The model is run in coupled regimes where, in the absence of noise, it is perfectly periodic: in the presence of prescribed seasonal variability, the model is strongly frequency locked on a 2-yr period; in annual average conditions it has a somewhat longer inherent ENSO period (30 months). Addition of noise brings an irregular behavior that is considerably richer in spatial patterns as well as in temporal structures. The broadening of the model ENSO spectral peak is roughly comparable to observed. The tendency to frequency lock to subharmonic resonances of the seasonal cycle tends to increase the broadening and to emphasize lower frequencies. An inclination to phase lock to preferred seasons persists even in the presence of noise-induced irregularity. Natural uncoupled atmospheric variability is thus a strong candidate for explaining the observed aperiodicity in ENSO time series. Model––model hindcast experiments also suggest the importance of atmospheric noise in setting limits to ENSO predictability.
Journal of Climate – American Meteorological Society
Published: Feb 20, 1996