ENSO Forecast

2015 September Quick Look

Published: September 17, 2015

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 late August through early September 2015 the tropical Pacific SST was at a strong El Niño level. All atmospheric variables support the El Niño pattern, including weakened trade winds and excess rainfall in the east-central tropical Pacific. The consensus of ENSO prediction models indicate continuation of strong El Niño conditions during the September-November 2015 season in progress. Some further strengthening into later fall is possible, with the event lasting well into spring 2016.

ENSO Forecast

CPC ENSO Update

Published: September 10, 2015

El Niño/Southern Oscillation (ENSO) Diagnostic Discussion issued by the Climate Prediction Center/NCEP/NWS

IRI ENSO Forecast

IRI Technical ENSO Update

Published: September 17, 2015

Recent and Current Conditions

The SST anomaly in the NINO3.4 region attained a weak El Niño level beginning late February 2015, strengthened to moderate strength around mid-May, and strengthened further to a strong level beginning around mid-July. For August the average NINO3.4 SST anomaly was 2.06 C, indicative of strong El Niño conditions, and for June-August it was 1.66 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 2.3 C, in the category of strong El Niño. Accompanying this SST has been an El Niño atmospheric pattern, including westerly low-level wind anomalies and positive anomalies of convection near and east of the dateline. The Southern Oscillation Index (SOI) and the equatorial SOI have also been quite negative, indicative of El Niño conditions.

Expected Conditions

What is the outlook for the ENSO status going forward? The most recent official diagnosis and outlook was issued one week ago in the NOAA/Climate Prediction Center ENSO Diagnostic Discussion, produced jointly by CPC and IRI; it called for a greater than 95% likelihood for El Niño conditions continuing through winter 2015-16, diminishing slowly during spring 2016. The latest set of model ENSO predictions, from mid-Sep, now available in the IRI/CPC ENSO prediction plume, is discussed below. Currently, besides weekly Nino3.4 SST anomalies being in the stromg El Niño cagtegory, subsurface temperature anomalies across the eastern equatorial Pacific have been well above average levels. The strong positive heat content anomaly has promoted steady increases in SST over the last 4 months, and is likely to lead to further smaller SST increases in the coming months, depending on the strength and nature of the atmospheric-oceanic feedbacks. In the atmosphere, the basin-wide sea level pressure anomaly pattern (e.g. the SOI) has been clearly at El Niño levels.  Anomalous convection (as measured by OLR) has been above average both near and to the east of the dateline.  Together, the oceanic and atmospheric features reflect strong El Niño conditions for late August through mid-September.

As of mid-September, none of the dynamical or statistical models models predicts La Niña or neutral SST conditions for the initial Sep-Nov 2015 season; 100% predicts El Niño conditions. 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 Dec-Feb 2015-16 season, among models that do use subsurface temperature information, 100% predicts El Niño SST conditions, while none predicts ENSO-neutral conditions and none predicts La Niña conditions. For all model types, the probabilities for El Niño are near 100% (i.e., higher than 99.5%) for Sep-Nov 2015 through Feb-Apr 2016, dropping to 90% for Mar-May and down to 50% by May-Jul. No model predicts La Niña conditions for any forecast period during 2015 and through the first half of 2016. Chances for neutral ENSO conditions are near 0% through Feb-Apr 2016, 10% for Mar-May and 50% for May-Jul 2016.

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 near-zero from Sep-Nov 2015 through Apr-Jun 2016, and 5% for May-Jul 2016.  Model probabilities for neutral ENSO conditions are less than 5% through Mar-May 2016, 22% for Apr-Jun and 46% for May-Jul 2016. Probabilities for El Niño are near 100% from Sep-Nov 2015 to Jan-Mar 2016, 99% for Feb-Apr, 97% for Mar-May, and down to 78% and 49% for Apr-Jun and May-Jul 2016, respectively.  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, extremely high certainty for El Niño conditions for the Sep-nov 2015 through Feb-Apr 2016 seasons, and still very high certainty through Mar-May 2016. In terms of magnitude, the models suggest strengthening El Niño conditions through northern autumn season, nearly certainly peaking at the level of strong El Niño. Model forecast spread still exists, and peak event strength could range from about 1.7 to 2.6 C with a slight possibility of being outside of that interval on either side. 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.

IRI/CPC Mid-Month Plume-Based ENSO Forecast Probabilities

ENSO Forecast

IRI Model-Based Probabilistic ENSO Forecast

Published: September 17, 2015

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.

IRI/CPC Mid-Month Plume-Based ENSO Forecast Probabilities

ENSO Forecast

CPC Official Probabilistic ENSO Forecast

Published: September 10, 2015

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 the Model-Based Probabilistic ENSO Forecast relies solely on model output. This is updated on the second Thursday of every month.

CPC/IRI Early-Month Consensus ENSO Forecast Probabilities

ENSO Forecast

IRI ENSO Predictions Plume

Published: September 17, 2015

Note on interpreting model forecasts

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.

Interactive Chart

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.