A monthly summary of the status of El Niño, La Niña, and the Southern Oscillation, or ENSO, based on the NINO3.4 index (120-170W, 5S-5N)
During November through early December the observed ENSO conditions remained neutral. Most of the ENSO prediction models indicate a continuation of neutral ENSO into early 2014. During northern spring and summer a warming tendency is seen in both dynamical and statistical models.
The SST anomaly in the Nino3.4 region has been in the neutral range lately, through mid-December 2013. For November 2013 the Nino3.4 SST anomaly was 0.01 C, indicative of neutral ENSO conditions, and for September-November it was -0.13 C. The IRI’s definition of El Niño, like NOAA/Climate Prediction Center’s, requires that the SST anomaly in the NINO3.4 region (5S-5N; 170W-120W) exceed 0.5 C. Similarly, for La Niña, the anomaly must be -0.5 C or less. The climatological probabilities for La Niña, neutral, and El Niño conditions vary seasonally, and are shown in a table at the bottom of this page for each 3-month season. The most recent weekly SST anomaly in the NINO3.4 region was 0.0 C, indicating neutral ENSO-related SST conditions in the tropical Pacific; this is virtually identical to the 0.01 C level observed in November.
What is the outlook for the ENSO status going forward? The most recent official diagnosis and outlook was issued earlier this month in the NOAA/Climate Prediction Center ENSO Diagnostic Discussion, produced jointly by CPC and IRI; it called for a high likelihood of neutral ENSO conditions enduring through winter 2013-14 and into spring 2014, with probabilities of El Niño or La Niña each less than 30% until Apr-Jun 2014 when El Niño probabilities rise above that level but stay less than 50% through summer 2014. The latest set of model ENSO predictions, from mid-December, now available in the IRI/CPC ENSO prediction plume, is discussed below. Currently, Nino3.4 SST anomalies are in the ENSO-neutral range, are slightly above average in the far western and west-central part of the basin and continue to be slightly below average in the eastern quarter of the basin. Subsurface temperature anomalies across the central and eastern equatorial Pacific have been slightly above average since mid-May, and have now become more clearly and strongly positive. In the atmosphere, the basin-wide sea level pressure pattern (e.g. the SOI), and the low-level zonal winds have been approximately average across much of the basin, although enhanced trades are observed in the western part of the basin. The upper level zonal winds are also mainly near-average across the tropical Pacific. Anomalous convection (as measured by OLR) has generally been negative in the west-central tropical Pacific, and positive in the far western part of the basin and in Indonesia. Together, these features reflect ENSO-neutral conditions.
As of mid-December, 4% of the set of dynamical and statistical models models predicts weak La Niña SST conditions for the Dec-Feb 2013-14 season, 0% predicts El Niño conditions, and 96% indicates neutral ENSO. At lead times of 3 or more months into the future, statistical and dynamical models that incorporate information about the ocean’s observed subsurface thermal structure generally exhibit higher predictive skill than those that do not. For the Mar-May 2014 season, among models that do use subsurface temperature information, 76% predicts ENSO-neutral SSTs, 24% predicts El Niño conditions and none predicts La Niña conditions. For all model types, the probability for neutral ENSO conditions is above 90% for Dec-Feb 2013-14 and Jan-Mar 2014, above 80% through Apr-Jun 2014, and 67%-73% for May-Jul through Aug-Oct 2014 at the end of the forecast period. Probabilities for El Niño are below 20% through Apr-Jun 2014, and rise to approxiately 30% from May-Jul through Aug-Oct.
Note – Only models that produce a new ENSO prediction every month are included in the above statement.
Caution is advised in interpreting the distribution of model predictions as the actual probabilities. At longer leads, the skill of the models degrades, and skill uncertainty must be convolved with the uncertainties from initial conditions and differing model physics, leading to more climatological probabilities in the long-lead ENSO Outlook than might be suggested by the suite of models. Furthermore, the expected skill of one model versus another has not been established using uniform validation procedures, which may cause a difference in the true probability distribution from that taken verbatim from the raw model predictions.
An alternative way to assess the probabilities of the three possible ENSO conditions is more quantitatively precise and less vulnerable to sampling errors than the categorical tallying method used above. This alternative method uses the mean of the predictions of all models on the plume, equally weighted, and constructs a standard error function centered on that mean. The standard error is Gaussian in shape, and has its width determined by an estimate of overall expected model skill for the season of the year and the lead time. Higher skill results in a relatively narrower error distribution, while low skill results in an error distribution with width approaching that of the historical observed distribution. This method shows probabilities for La Niña at 1% for Dec-Feb 2013-14, remaining at 10% or less through the end of the forecast period in Aug-Oct 2014. Model probabilities for ENSO-neutral conditions are more than 90% from Dec-Feb 2013-14 to Jan-Mar 2014, dropping steadily during northern spring 2014 to become slightly less than 50% from Jun-Aug through the end of the forecast period in Aug-Oct 2014. Probabilities for El Niño are below 10% from Dec-Feb 2013-14 to Feb-Apr 2014, thereafter steadily increasing to exceed 30% by May-Jul 2014 and to between 40% and 50% from Jun-Aug to Aug-Oct 2014 (maximizing at 45% for both Jul-Sep). It is clear that the models collectively favor neutral ENSO conditions into northern spring 2014; then by Jun-Aug El Niño probabilities become more competitive with ENSO-neutral probabilities, until they are approximately equally likely for Jul-Sep and Aug-Oct 2014. A plot of the probabilities generated from this most recent IRI/CPC ENSO prediction plume using the multi-model mean and the Gaussian standard error method summarizes the model consensus out to about 10 months into the future. The same cautions mentioned above for the distributional count of model predictions apply to this Gaussian standard error method of inferring probabilities, due to differing model biases and skills. In particular, this approach considers only the mean of the predictions, and not the total range across the models, nor the ensemble range within individual models.
The probabilities derived from the models on the IRI/CPC plume describe, on average, maintenance of neutral ENSO conditions into northern spring 2014. The possibility of El Niño development is seen starting Jun-Aug 2014, but the objective model-based probabilities for El Niño still remain below 50% for Jun-Aug through Aug-Oct 2014. A caution regarding this latest set of model-based ENSO plume predictions, is that factors such as known specific model biases and recent changes that the models may have missed will be taken into account in the next official outlook to be generated and issued in early October by CPC and IRI, which will include some human judgement in combination with the model guidance.
Using the 0.5 C thresholds, the climatological probabilities of La Nina, neutral, and El Nino conditions for each 3-month season are as follows:
| Season | La Niña | Neutral | El Niño |
|---|---|---|---|
| DJF | 37% | 28% | 35% |
| JFM | 34% | 37% | 29% |
| FMA | 30% | 48% | 22% |
| MAM | 26% | 54% | 20% |
| AMJ | 24% | 54% | 22% |
| MJJ | 25% | 51% | 24% |
| JJA | 25% | 50% | 25% |
| JAS | 27% | 46% | 27% |
| ASO | 29% | 40% | 31% |
| SON | 32% | 34% | 34% |
| OND | 34% | 31% | 35% |
| NDJ | 37% | 27% | 36% |
| Season | La Niña | Neutral | El Niño |
|---|---|---|---|
| DJF 2014 | 1% | 99% | ~0% |
| JFM 2014 | 2% | 96% | 2% |
| FMA 2014 | 4% | 88% | 8% |
| MAM 2014 | 4% | 80% | 16% |
| AMJ 2014 | 7% | 64% | 29% |
| MJJ 2014 | 9% | 53% | 38% |
| JJA 2014 | 9% | 48% | 43% |
| JAS 2014 | 9% | 46% | 45% |
| ASO 2014 | 10% | 46% | 44% |
A purely objective ENSO probability forecast, based on regression, using as input the model predictions from the plume of dynamical and statistical forecasts shown in the ENSO Predictions Plume. Each of the forecasts is weighted equally. It is updated near or just after the middle of the month, using forecasts from the plume models that are run in the first half of the month. It does not use any human interpretation or judgment. This is updated on the third Thursday of the month.
| Season | La Niña | Neutral | El Niño |
|---|---|---|---|
| DJF 2014 | 1% | 99% | ~0% |
| JFM 2014 | 2% | 96% | 2% |
| FMA 2014 | 4% | 88% | 8% |
| MAM 2014 | 4% | 80% | 16% |
| AMJ 2014 | 7% | 64% | 29% |
| MJJ 2014 | 9% | 53% | 38% |
| JJA 2014 | 9% | 48% | 43% |
| JAS 2014 | 9% | 46% | 45% |
| ASO 2014 | 10% | 46% | 44% |
The following graph and table show forecasts made by dynamical and statistical models for SST in the Nino 3.4 region for nine overlapping 3-month periods. Note that the expected skills of the models, based on historical performance, are not equal to one another. The skills also generally decrease as the lead time increases. Thirdly, forecasts made at some times of the year generally have higher skill than forecasts made at other times of the year--namely, they are better when made between June and December than when they are made between February and May. Differences among the forecasts of the models reflect both differences in model design, and actual uncertainty in the forecast of the possible future SST scenario.
You can highlight a specific model by hovering over it either on the chart or the legend. Selecting An item on the legend will toggle the visibility of the model on the page. You can also select DYN MODELS or STAT MODELS to toggle them all at once. Clicking on the "burger" menu above the legend will give you options to download the image or expand to full screen. If you have any feedback on this new feature, please let us know at webmaster@iri.columbia.edu.
| Seasons (2013-2014) | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Model | DJF | JFM | FMA | MAM | AMJ | MJJ | JJA | JAS | ASO |
| Dynamical models | |||||||||
| NCEP CFS version 2 | -0.2 | -0.2 | 0 | 0.2 | 0.4 | 0.6 | 0.8 | 0.9 | |
| NASA GMAO model | 0.1 | -0.1 | -0.1 | -0.1 | 0.1 | 0.4 | 0.6 | ||
| Japan Met. Agency model | 0.1 | 0.1 | 0.1 | 0.2 | 0.3 | ||||
| Scripps Inst. HCM | 0.3 | 0.4 | 0.5 | 0.6 | 0.7 | 0.7 | 0.7 | 0.8 | 0.9 |
| Lamont-Doherty model | -0.3 | -0.3 | -0.3 | -0.2 | -0.1 | 0.1 | 0.2 | 0.2 | 0.4 |
| POAMA (Austr) model | -0.2 | -0.2 | -0.2 | -0.2 | -0.1 | 0 | 0 | ||
| ECMWF model | 0.1 | 0 | 0.1 | 0.2 | 0.4 | ||||
| UKMO model | -0.2 | -0.4 | -0.4 | ||||||
| KMA (Korea) SNU model | 0.4 | 0.5 | 0.6 | 0.6 | 0.6 | 0.5 | 0.5 | 0.5 | 0.5 |
| ESSIC Intermed. Coupled model | 0.1 | 0.1 | 0.2 | 0.3 | 0.4 | 0.4 | 0.4 | 0.4 | 0.4 |
| COLA CCSM3 model | -0.1 | -0.1 | -0.1 | -0.2 | -0.2 | -0.3 | -0.3 | -0.4 | -0.4 |
| MÉTÉO FRANCE model | 0 | 0 | 0.1 | 0.2 | 0.3 | ||||
| CSIR-IRI 3-model MME | -0.2 | -0.3 | -0.2 | -0.1 | 0.1 | ||||
| GFDL CM2.1 Coupled Climate model | 0.1 | 0.3 | 0.6 | 0.7 | 1 | 1.2 | 1.4 | 1.4 | 1.1 |
| Canadian Coupled Fcst Sys | 0.2 | 0.3 | 0.5 | 0.6 | 0.7 | 0.7 | 0.8 | 0.7 | 0.6 |
| Average, dynamical models | 0 | 0 | 0.1 | 0.2 | 0.3 | 0.4 | 0.5 | 0.6 | |
| Statistical models | |||||||||
| NCEP/CPC Markov model | -0.1 | 0 | 0.1 | 0.1 | 0.2 | 0.2 | 0.2 | 0.3 | 0.3 |
| NOAA/CDC Linear Inverse | -0.2 | -0.2 | -0.2 | -0.3 | -0.2 | -0.2 | -0.1 | -0.1 | 0 |
| NCEP/CPC Constructed Analog | -0.2 | -0.2 | -0.2 | 0 | 0.1 | 0.3 | 0.3 | 0.3 | 0.2 |
| NCEP/CPC Can Cor Anal | -0.6 | -0.5 | -0.4 | -0.2 | 0 | 0.2 | 0.3 | 0.4 | 0.4 |
| Landsea/Knaff CLIPER | 0 | 0 | 0 | 0.1 | 0.1 | 0.1 | 0.2 | 0.3 | 0.4 |
| Univ. BC Neural Network | 0 | 0 | 0.1 | 0.2 | 0.2 | 0.2 | 0.3 | 0.3 | 0.3 |
| FSU Regression | 0.1 | 0.1 | 0.1 | 0.2 | 0.4 | 0.5 | 0.5 | 0.5 | 0.6 |
| TDC – UCLA | 0.3 | 0.2 | 0.2 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 |
| Average, statistical models | -0.1 | -0.1 | 0 | 0 | 0.1 | 0.2 | 0.2 | 0.3 | 0.3 |
| Average, all models | 0 | 0 | 0 | 0.1 | 0.2 | 0.3 | 0.4 | 0.4 | 0.4 |
Most of the set of dynamical and statistical model predictions issued during late November and early December 2013 predict neutral ENSO conditions into early 2014, with a warming tendency during northern spring and summer 2014. Development of weak El Nino conditions appears possible by the middle of 2014. In the most recent week, the SST anomaly in the Nino3.4 region was 0.0C. Based on the multi-model mean predictions, and the expected skill of the models by start time and lead time, the probabilities (X100) for La Nina, neutral and El Nino conditions (using -0.5C and 0.5C thresholds) over the coming 9 seasons are:
| Season | La Niña | Neutral | El Niño |
|---|---|---|---|
| DJF 2014 | 1% | 99% | ~0% |
| JFM 2014 | 2% | 96% | 2% |
| FMA 2014 | 4% | 88% | 8% |
| MAM 2014 | 4% | 80% | 16% |
| AMJ 2014 | 7% | 64% | 29% |
| MJJ 2014 | 9% | 53% | 38% |
| JJA 2014 | 9% | 48% | 43% |
| JAS 2014 | 9% | 46% | 45% |
| ASO 2014 | 10% | 46% | 44% |
The following interactive plot shows the model forecasts issued not only from the current month (as in the plot above),
but also from the 21 months previous to this month. The observations are shown up to the most recently completed
3-month period. The plots allow comparison of plumes from the previous start times, or examination of the forecast
behavior of a given model over time.
Hovering over any single model will highlight that particular model in the chart.
Clicking a particular model will hide/show that model in the chart.
At the bottom of the plot, you can select which models to show in the chart:
all the models, the dynamical models only, or the statistical models only.
Only models producing forecasts on a monthly basis are included. This means that some models whose forecasts appear in the Experimental Long-Lead Forecast Bulletin (produced by COLA) do not appear in the table.
Once an IRI ENSO probability forecast has been published, the results stand even if a model reports an error and changes their data. When this happens we will update the plume with the model's correct values even though our forecast hasn't changed. What this means is that our forecast is always the same, but the underlying data may be different from what we based our forecast on.
The SST anomaly forecasts are for the 3-month periods shown, and are for the Nino 3.4 region (120-170W, 5N-5S). Often, the anomalies are provided directly in a graph or a table by the respective forecasting centers for the Nino 3.4 region. In some cases, however, they are given for 1-month periods, for 3-month periods that skip some of the periods in the above table, and/or only for a region (or regions) other than Nino 3.4. In these cases, the following means are used to obtain the needed anomalies for the table:
The anomalies shown are those with respect to the base period used to define the normals, which vary among the groups producing model forecasts. They have not been adjusted to anomalies with respect to a common base period. Discrepancies among the climatological SST resulting from differing base periods may be as high as a quarter of a degree C in the worst cases. Forecasters are encouraged to use the standard 1991-2020 period as the base period, or a period not very different from it.
The IRI ENSO forecast is released on the 19th of each month. If the 19th falls on a weekend or holiday, it is released on the closest business day.
Forecast and model data used in our probabilistic forecast can be accessed by submitting a Request to Access IRI ENSO Data.
All data from this website is covered under the Creative Commons Attribution 4.0 License. When citing IRI ENSO images or data, please use "Images [or Data] provided by The International Research Institute for Climate and Society, Columbia University Climate School", with a link to https://iri.columbia.edu/ENSO.
The official CPC ENSO probability forecast, based on a consensus of CPC and IRI forecasters. It is updated during the first half of the month, in association with the official CPC ENSO Diagnostic Discussion. It is based on observational and predictive information from early in the month and from the previous month. It uses human judgment in addition to model output, while the forecast shown in Fig. 3 relies solely on model output. Figure 1 is updated on this page on the second Thursday of every month.
NOAA/CPC ENSO Forecast Graphic, courtesy of NOAA/CPC
×A purely objective ENSO probability forecast, based on regression, using as input the model predictions from the plume of dynamical and statistical forecasts shown in Fig. 4. Each of the forecasts is weighted equally. It is updated near or just after the middle of the month, using forecasts from the plume models that are run in the first half of the month. It does not use any human interpretation or judgment. Figure 3 is updated on the third Thursday of every month.
×Plume of forecasts of the Nino3.4 SST anomaly from dynamical and statistical models that are run during the first half of the month. A probability forecast is generated using all the models in Fig. 4, and is shown in Fig. 3. The average of the forecasts of the dynamical models is shown by the thick yellow line, and of the statistical models by the thick green line. The average of the four models run at the NOAA Climate Prediction center (CPC) is shown by the thick pink line. This figure is updated on the third Thursday of every month. Because forecasts from some models of Fig. 4 are not yet available when the official CPC ENSO probability forecast (Fig. 1) is made, the official forecast uses the previous month's Fig. 4 as one of its inputs, which is shown in the CPC ENSO Diagnostic Discussion.
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