| description abstract | The Tropical Atmosphere Ocean (TAO) array of moored buoys in the tropical Pacific Ocean is a major source of data for understanding and predicting the El Niño?Southern Oscillation (ENSO). Despite the importance of the TAO array, limited work has been performed to date on the number and locations of observations required to predict ENSO effectively. To address this issue, this study performs a series of observing system simulation experiments (OSSEs) with a linearized intermediate coupled ENSO model, stochastically forced. ENSO forecasts are simulated for a number of observing network configurations, and forecast skill averaged over 1000 years of simulated ENSO events is compared. The experiments demonstrate that an OSSE framework can be used with a linear, stochastically forced ENSO model to provide useful information about requirements for ENSO prediction. To the extent that the simplified model dynamics represent ENSO dynamics accurately, the experiments also suggest which types of observations in which regions are most important for ENSO prediction. The results indicate that, using this model and experimental setup, subsurface ocean observations are relatively unimportant for ENSO prediction when good information about sea surface temperature (SST) is available; adding subsurface observations primarily improves forecasts initialized in late summer. For short lead-time (1?2 month) forecasts, observations within approximately 3° of the equator are most important for skillful forecasts, while for longer lead-time forecasts, forecast skill is increased by including information at higher latitudes. For forecasts longer than a few months, the most important region for observations is the eastern equatorial Pacific, south of the equator; a secondary region of importance is the western equatorial Pacific. These regions correspond to those where the leading singular vector for the ENSO model has a large amplitude. In a continuation of this study, these results will be used to develop efficient observing networks for forecasting ENSO in this system. | |
