Magazine article Oceanus

The El Nino/Southern Oscillation Phenomenon: Seeking Its "Trigger" and Working toward Prediction

Magazine article Oceanus

The El Nino/Southern Oscillation Phenomenon: Seeking Its "Trigger" and Working toward Prediction

Article excerpt

The El Nino/Southern Oscillation (ENSO) phenomenon, an eastward shift of warm water in the tropical Pacific and associated effects on the atmosphere, is at the heart of global interannual climate variability. The just completed, decade-long Tropical Ocean/Global Atmosphere (TOGA) program was dedicated to understanding and working toward predicting ENSO by bringing together oceanographers and atmospheric scientists in a coordinated observational and numerical modeling research program. TOGA has not answered all the questions: We have not uncovered the physical mechanisms of the elusive ENSO "trigger" nor have our best coupled air/sea numerical models been as successful in predicting the rather irregular ENSO signal of the 1990s as they were in predicting the regular events of the 1980s and hindcasting the events of the late 1960s through the 1970s.

Prediction is the ultimate goal of ENSO research. It is also the ultimate test for an ENSO model and the theory underlying the model. During the last decade, a number of forecast models have shown predictive skills in both retrospective and real time forecasting, and they are now being used for routine ENSO prediction. Nevertheless, the skill of even the best available models is far from perfect, and there is still considerable room for improvement in modeling, observation, and forecasting techniques.

Factors that limit the current skill of ENSO forecasts include:

* an inherent limit to predictability because of the chaotic and random nature of the natural system,

* model flaws such as oversimplified physics,

* gaps in the observing system, and

* flaws in the way the data is used (data assimilation and initialization).

It seems likely that the inherent predictability limit for ENSO is years rather than weeks or months, though more theoretical study is needed in this area. The observing system is improving, but still far from satisfactory. Thus a challenge facing the modelers is to improve model forecasts by making the most reasonable and efficient use of available data.

In the past few years much effort has been devoted to assimilating various observational data into the initial state of forecast models. The most common approach is to improve the initial ocean conditions by assimilating observations of sea surface temperature, thermocline (region of rapid temperature decline) depth, or sea level into an ocean model prior to coupling it with an atmosphere model. One problem with this approach is that no attention is paid to the ocean-atmosphere interaction during initialization, so the coupled system may not be well balanced initially and may experience a shock when the forecast starts. A new initialization/assimilation procedure significantly improves the predictive skill of one of our most promising coupled models, which was constructed by Mark Cane and Steve Zebiak (Lamont-Doherty Earth Observatory).

In the new methodology the model is initialized in a coupled manner, using a simple data assimilation scheme in which the coupled model wind stress anomalies are "nudged" toward observations. The new procedure improves the model's predictive ability as measured by a variety of statistical scores. It also eliminates the so-called "spring prediction barrier," a marked drop of skill in forecasts that try to predict across the boreal spring, found in many previous ENSO forecast systems. The success of the new initialization procedure is attributed to its explicit consideration of ocean-atmosphere coupling, and the associated reduction of initialization shock and random noise.

As an example, the forecasts made by the improved model are compared to observations in the figure on page 39 in terms of the sea surface temperature anomaly averaged over an area in the eastern/central equatorial Pacific (5 [degrees] S to 5 [degrees] N and 90 [degrees]W to 150 [degrees] W). The model is capable of forecasting ENSO more than one year in advance. …

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