El Niño and the Science of Climate Prediction (ENSO=El Niño-Southern Oscillation)
Predicting ENSO
It is common experience that weather forecasts become rather rapidly less reliable when projected more than a few days in advance. This loss of "skill" is the result of the atmosphere’s chaotic nature.
Forecasts of the weather rely heavily on what are called initial conditions — the array of temperatures and pressures which the forecaster’s model uses as a starting point for his or her projection. The more closely these describe what really pertains at that moment, the more accurate the forecast. Minor differences in initial conditions can significantly alter what is forecast, and the more distant the projection, the greater the disparity. Yet we can never know the actual initial state of the atmosphere precisely, because of incomplete and imperfect measurements. Thus, there are limits to how far ahead we can predict with useful skill, even if our predictive models were absolutely perfect. In the face of this reality, can we ever hope to predict climate, or future ENSO events?
Why Climate Can Be Predicted
First, climate prediction does not aim to foretell the details of day-to-day weather, one or more seasons ahead. Rather, it aims to predict some aspect of the statistics of weather, the simplest and most common being a seasonal mean: such as, precipitation in the western states will be lower than normal this winter. But even these more generalized projections would be hopeless were it not for the fact that additional (and potentially simpler) factors become more dominant in the climate system at longer time scales.
Most important among these, for ENSO, is the ocean. Theory dictates that ENSO is fundamentally a coupled process, arising from a tight linkage between atmosphere and ocean. Due to its great mass and thermal inertia, the ocean changes rather slowly, and by virtue of the strong coupling, it imparts to the atmosphere a degree of order, or determinism, that it might not otherwise possess. Interactions between the atmosphere and the land surface could potentially do the same, since the land is also slow to change.
As we noted earlier, predicting the behavior of ENSO is muddled somewhat by the presence of a seemingly chaotic element, and by the influence of shorter-term weather variations that we have called noise. Thus, we know ENSO prediction skill must always be limited. But the limits will be determined by the properties of the ocean-atmosphere coupling, and not the atmosphere alone. The distinction can change the time scales of useful predictions from days to seasons.
The Track Record: Where do we Stand?
Today, more than twenty separate forecast systems are being run routinely to predict the evolution of ENSO. How good are they? Are the forecasts more reliable at some times than others?
In order to provide reliable answers to such questions, one needs a large number of forecasts, spanning varied states of the real climate system. A continual stumbling block is the lack of extensive data to make such evaluations. Prior to the 1980s, the quantity and quality of observational data decreases substantially, seriously hindering the ability to adequately initialize forecast models. We do not know enough about the state of the ocean at the time of onset of these earlier events. In addition, many forecast models have not been run extensively for past years because of resource limitations, and especially computer resources.
What we can do is evaluate the performance of available forecasts over the limited period of the past ten to fifteen years. This gives a sense, if not a robust measure, of current capabilities. As of 1994, one such study was done. It compared two dynamical coupled models, two purely statistical models, and one hybrid model. The finding was that each type of forecast model was, at that time, giving a comparable level of skill; namely, at the level of 65 to 70 percent accuracy for 6-8 month lead times. Though a respectable score, it left room for improvement. An interesting finding was that for all forecast systems, both seasonal and year-to-year variations in skill were apparent.
The explanation for these variations in skill is still unresolved, but the prevailing view is that the actual climate system undergoes fluctuations in predictability, depending on the season and the state of ENSO. Variable predictability is not uncommon in chaotic systems of many types. In the case of ENSO prediction, it carries a warning that the reliability of forecasts is not always the same. Future work will address ways to incorporate such information into the forecast products.
ENSO and Global Greenhouse Warming
For the past two or three decades, ENSO and global warming have been the two most studied climate problems. Yet, until very recently, they have been pursued independently, by nearly independent groups of researchers. The early global warming (or "global change") studies focused primarily on the role of atmospheric processes, with little if any emphasis on ocean dynamics, while ENSO research was focused first and foremost on this very topic. But as global change research has progressed, the importance of active coupling among atmosphere, ocean, land surface, and ice have been amply demonstrated, emphasizing, among other things, the probable connections between global change and ENSO.
How might ENSO and global change be related? It has been known for some time that ENSO has a significant effect on both local and globally averaged surface temperature. El Niño years stand out in any casual inspection of the record of global mean surface temperature. This is hardly a surprise, since ENSO alters the temperature of the surface ocean over an area that covers nearly one quarter of the Earth’s surface. Indeed, the prevalence of warm ENSO events in recent years has contributed significantly to the recorded increase in the average surface temperature of the Earth. But is there a causal connection between the two?
Some studies have suggested that the characteristics of ENSO might be very much modified by other aspects of the global environment. For example, some ENSO models have indicated significant increases in ENSO variability, and in the magnitude of ENSO extremes, as the Earth warms, overall. Unfortunately, the results depend on the details of temperature changes beneath the ocean surface, which is an issue of considerable uncertainty.
It can be done, its just that most of the models currently have to factor in so many variables that it limits the forecast.
Another interesting article from
RealClimate.org
Is Climate Modelling Science?
