Figures 1 ((the official CPC ENSO probability forecast) and 3 (the objective model-based CCSR/IRI ENSO probability forecast) are often quite similar. However, occasionally they may differ noticeably. There can be several possible reasons for differences. One is the human forecasters, using their experience and judgment, may disagree to some degree with the models, which may have known biases. Another reason is the models are not run at the same time that the forecasters make their assessment, so that the starting ENSO conditions may be slightly different between the two times. The charts on this Quick Look page are updated at two different times of the month, typically about a week apart, with the CCSR/IRI forecast run later. Also note that the CPC forecast starts on the previous season while the CCSR/IRI forecast starts on the current season.
Click on the for more information on each figure.

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  Figure 1.

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  Figure 3.

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  Figure 2

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  Figure 4.

Historically Speaking

El Niño and La Niña events tend to develop during the period Apr-Jun and they
• Tend to reach their maximum strength during October - February
• Typically persist for 9-12 months, though occasionally persisting for up to 2 years
• Typically recur every 2 to 7 years

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  Figure 5

  Predictions of ENSO are probabilistic. The ensemble mean prediction is only a best single guess. On either side of that prediction, there is a substantial uncertainty distribution, or error tolerance. The second plot (Figure 2) shows the estimated probability distribution of the predictions, showing a set of percentiles within that distribution for each lead time. The distribution is modeled as a normal (Gaussian) distribution, so that the overall mean forecast represents the center, or 50 percentile, in the distribution. The overall mean is formed using equal weighting among all models. On either side, other percentile values are shown symmetrically, ranging from 1 to 99 and including some intermediate percentiles (5 and 95, 15 and 85, and 25 and 75). The plot enables a user to estimate the probability of the Niño3.4 SST anomaly to be greater or less than some critical value, or within some interval. If, for example, the 85 percentile falls at 1.8° C above average, the probability of the SST exceeding 1.8° C can be estimated at 15%. Probabilities for exceeding or not exceeding values not exactly on percentile line can be roughly interpolated by eye. The overall width of the probability distribution is derived from the historical skill of the hindcasts of the models, from 1982 to present, for the specific forecast start time and lead time. This method of defining the probability distribution represents one of two general approaches, the other approach being a direct counting of ensemble members within each of the percentile bands. This second approach assumes that the ensemble spreads of the models are true representations of the uncertainty. Individual model spreads have often been found to be somwehate narrower than they should be, although in multi-model ensembles this tendency has been shown to be milder or even eliminated.

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  Figure 6

  Figure 6, sometimes called a spaghetti diagram, shows synthetically generated prediction scenarios that are equally likely. Here, 100 scenarios are shown; any number can be generated for such a diagram. Each scenario is produced using a random number generator, combined with knowledge of the mean forecast and its uncertainty, as well as the amount of persistence of anomalies. The degree of persistence of anomalies is based on the correlation of prediction errors from one lead time to another. In other words, the individual lines are designed to show the correct amount of persistence as expected in nature, rather than jumping around more randomly from one lead time to the next. The uncertainty and persistence statistics are based on the set of 7 NMME (North American Multimodel Ensemble) models, as it is assumed that these statistics are approximately applicable to all of the models. Sometimes the “spaghetti density” may appear asymmetric about the mean of all the forecasts or outside of the 85 and 15 percentile lines. This is purely sampling variability, and would not occur if many thousands of such lines were plotted. But with that many lines, most of the plot would be too crowded to get a sense of the behavior of the lines near the center of the distribution. The main purpose of the diagram is to serve users who want to assess realistic individual scenarios of ENSO behavior rather than statistical summaries of the forecast like the percentiles shown in the second plot.