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Forecasting Problems to Overcome At the present time, routine predictions of ENSO are being made with a variety of statistical and dynamical models. Each forecast system has its unique problems and limitations, but all are limited by observational data in one or more ways. For some fields, such as SST and surface winds, gridded data are available for a few decades, but the quality of the data deteriorates rapidly prior to 1970. For other fields, such as thermocline depth or ocean currents, the situation is worse yet. The lack of comprehensive and reliable data for the climate system over a period of several decades is a major limitation for all forecast systems. Not only does it limit the number of retrospective forecasts with which the systems can be developed and validated, but it also limits the ability to initialize accurately the forecasts that can be attempted. Another problem concerns the injection of multiple sources of data into models – the so-called assimilation problem. The basic problem is that a typical collection of data is incompatible with any forecast model, due to data errors and model shortcomings. To date no assimilation scheme specific to the coupled atmosphere-ocean system has been utilized in the ENSO predictions. Given the coupled nature of ENSO, such a scheme would be the optimal one, without it, predictions are unnecessarily limited. For all the forecast models that have been analyzed, forecast skill is found to vary in time. One of the most striking variations concerns the seasonal cycle. It is found that predicting through the period March-May of each year is considerably more difficult than other seasons. For some models, the so-called "spring barrier" is severe enough to completely invalidate many forecasts; for other, it simply limits some forecasts relative to others. In addition, forecast skill typically varies from year to year. One possibility is that the predictability of the real climate system is variable (in which case no prediction system can overcome it). Another is that systematic errors or omissions in the models, or the lack of adequate data are limiting the models’ potential skill. It is likely that both contribute to some degree.
Satellites like these produce data for both dynamical and
statiscal models. Most believe that the best prospects for climate prediction lie ultimately with the most complete dynamical models – coupled with general circulation models. An inherent problem with the development of complex models is precisely their complexity. In practice, it is very difficult to "tune" a complex model, even if one is aware of systematic errors. An added problem is the computational expense. Complex models require an enormous amount of computing resources, even with present-day technology. The inability to produce large ensembles of predictions over an acceptable period of time seriously slows progress. Typically, analyses are done on marginal or totally inadequate samples, and the conclusions are in danger of error. The net result is that real progress is very slow indeed. A final and very important problem involves the extension from ENSO proper to global climate. One methodology is to invoke statistical relationships between ENSO manifestations (such as east Pacific SST) and climate variables in other regions of the world. Such analyses have been done , and are steadily being refined. They suggest important connections between ENSO and temperature and precipitation extremes in many regions. The statistical approach is, of course, limited by the observational data base, as well as the inability to produce new patterns – ones not appearing in the data base from which the statistical analyses is based. Clearly, nature is not similarly constrained. The other approach is to attempt to predict the regional climate signals directly with a global atmospheric model. The problems here consist of those discussed above, as well as the following: even given perfect knowledge of ENSO, the ability to predict the seasonal climate in many parts of the world (particularly outside the tropics) is extremely limited. The chaotic nature of the atmosphere in the extra-tropics can result in large seasonal anomalies even the absence of ENSO. From the climate prediction point to view, this internal "noise" adds considerable uncertainty to any prediction, and requires large ensembles of forecasts to achieve any statistical significance.
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