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Please refer to our licensing agreement for permission to use IRI ENSO materials. The CPC/IRI materials are not included in this licensing.

IRI ENSO Forecast

2018 May Quick Look

Published: May 18, 2018

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)

Use the navigation menu on the right to navigate to the different forecast sections

In mid-May 2018, the east-central tropical Pacific waters reflected ENSO-neutral conditions. Most key atmospheric variables also indicated neutral conditions, although the upper level wind anomalies show remnants of La Niña. The subsurface water temperature continued to be above-average. The official CPC/IRI outlook calls for neutral conditions through the September-Novemeber season, with a nearly 50% chance of El Niño development by year’s end. The latest forecasts of statistical and dynamical models collectively favor weak El Niño development by year’s end, but forecasters hedge on this due to low confidence at this time of year.

Figures 1 and 3 (the official ENSO probability forecast and the objective model-based ENSO probability forecast, respectively) are often quite similar. However, occasionally they may differ noticeably. There can be several reasons for differences. One possible reason is that the human forecasters, using their experience and judgment, may disagree to some degree with the models, which may have known biases. Another reason is related to the fact that 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, so that between the second and the third Thursday of the month, the official forecast (Fig. 1) has just been updated, while the model-based forecasts (Figs. 3 and 4) are still from the third Thursday of the previous month. On the other hand, from the third Thursday of the month until the second Thursday of the next month, the model-based forecasts are more recently updated, while the official forecasts remain from the second Thursday of the current month.
Click on the for more information on each figure.

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

IRI ENSO Forecast

CPC/IRI ENSO Update

Published: May 10, 2018

El Niño/Southern Oscillation (ENSO) Diagnostic Discussion issued jointly by the Climate Prediction Center/NCEP/NWS and the International Research Institute for Climate and Society

ENSO Alert System Status: Final La Niña Advisory

Synopsis: ENSO-neutral is favored through September-November 2018, with the possibility of El Niño nearing 50% by Northern Hemisphere winter 2018-19.

During April 2018, the tropical Pacific returned to ENSO-neutral, as indicated by mostly near-to- below average sea surface temperatures (SSTs) along the equator (Fig. 1). The latest weekly Niño indices were near zero in all regions (between +0.2°C and -0.3°C), except for Niño-1+2, which remained negative (-0.6°C; Fig. 2). Subsurface temperature anomalies (averaged across 180°-100°W) remained positive (Fig. 3), due to the continued influence of a downwelling oceanic Kelvin wave (Fig. 4). Atmospheric indictors related to La Niña also continued to fade.  While convection remained suppressed near and east of the Date Line, rainfall near Indonesia was also below average during the month (Fig. 5). Low-level winds were near average over most of the tropical Pacific Ocean, and upper-level winds were anomalous westerly over the eastern Pacific.  Overall, the ocean and atmosphere system reflected a return to ENSO-neutral.

The majority of models in the IRI/CPC plume predict ENSO-neutral to continue at least through the Northern Hemisphere summer 2018 (Fig. 6). As the fall and winter approaches, many models indicate an increasing chance for El Niño. Therefore, the forecaster consensus hedges in the direction of El Niño as the winter approaches, but given the considerable uncertainty in ENSO forecasts made at this time of year, the probabilities for El Niño are below 50%.  In summary, ENSO-neutral is favored through September-November 2018, with the possibility of El Niño nearing 50% by Northern Hemisphere winter 2018-19 (see the CPC/IRI consensus forecast for the chance of each outcome for each 3-month period).

This discussion is a consolidated effort of the National Oceanic and Atmospheric Administration (NOAA), NOAA’s National Weather Service, and their funded institutions. Oceanic and atmospheric conditions are updated weekly on the Climate Prediction Center web site (El Niño/La Niña Current Conditions and Expert Discussions). Forecasts are also updated monthly in the Forecast Forum section of CPC’s Climate Diagnostics Bulletin. Additional perspectives and analysis are also available in an ENSO blog.

The next ENSO Diagnostics Discussion is scheduled for 14 June 2018. To receive an e-mail notification when the monthly ENSO Diagnostic Discussions are released, please send an e-mail message to: ncep.list.enso-update@noaa.gov.

Climate Prediction Center
National Centers for Environmental Prediction
NOAA/National Weather Service
College Park, MD 20740

CPC/IRI Early-Month Official ENSO Forecast Probabilities

Season La Niña Neutral El Niño
AMJ 2018 17% 82% 1%
MJJ 2018 10% 80% 10%
JJA 2018 9% 68% 23%
JAS 2018 10% 58% 32%
ASO 2018 10% 52% 38%
SON 2018 10% 48% 42%
OND 2018 11% 44% 45%
NDJ 2018 11% 40% 49%
DJF 2019 11% 40% 49%

Please refer to our licensing agreement for permission to use IRI ENSO materials. The CPC/IRI materials are not included in this licensing.

IRI ENSO Forecast

IRI Technical ENSO Update

Published: May 18, 2018

Note: The SST anomalies cited below refer to the OISSTv2 SST data set, and not ERSSTv4. OISSTv2 is often used for real-time analysis and model initialization, while ERSSTv4 is used for retrospective official ENSO diagnosis because it is more homogeneous over time, allowing for more accurate comparisons among ENSO events that are years apart. During ENSO events, OISSTv2 often shows stronger anomalies than ERSSTv4, and during very strong events the two datasets may differ by as much as 0.5 C. Additionally, the ERSSTv4 may tend to be cooler than OISSTv2, because ERSSTv4 is expressed relative to a base period that is updated every 5 years, while the base period of OISSTv2 is updated every 10 years and so, half of the time, is based on a slightly older period and does not account as much for the slow warming trend in the tropical Pacific SST.

Recent and Current Conditions

In mid-May 2018, the NINO3.4 SST anomaly showed neutral ENSO conditions. For April the SST anomaly was -0.41 C, indicating cool-neutral conditions, and for February-April it was -0.68 C, indicating weak La Niña. 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 anomaly in the Nino3.4 region was -0.1, showing neutral conditions. Additionally, most of the key atmospheric variables, including the lower level zonal wind anomalies, the Southern Oscillation Index and the anomalies of outgoing longwave radiation (convection) suggest neutral conditions. The upper level wind anomalies still show weak remnants of the now ended La Niña of 2017-18.  The subsurface temperature anomalies across the eastern equatorial Pacific have warmed to moderately above-average, suggesting the possibility of a warming of the SST in the coming months. Given the current and recent SST anomalies, the subsurface profile and the conditions of most key atmospheric variables, we will likely remain in ENSO-neutral conditions for at least a few months, with a chance for a warming leading to El Niño development later in the year.

Expected Conditions

What is the outlook for the ENSO status going forward? The most recent official diagnosis and outlook was issued approximately one week ago in the NOAA/Climate Prediction Center ENSO Diagnostic Discussion, produced jointly by CPC and IRI; it stated that the La Niña is no longer with us, and that ENSO-neutral is expected during summer and into autumn. The latest set of model ENSO predictions, from mid-May, now available in the IRI/CPC ENSO prediction plume, is discussed below. Those predictions also suggest that the SST is likely to remain in the ENSO-neutral range into autumn, and that El Niño development is more than 50% likely from late autumn into winter.

As of mid-May, about 95% of the dynamical or statistical models predict neutral conditions for the initial May-Jul 2018 season, with about 5% showing a continuation of La Niña conditions. Over the course of the rest of 2018, probabilities for neutral remain greater than 50% through Jul-Sep, after which probabilities for El Niño rise to over 50% for Sep-Nov and to about 80% for Dec-Feb 2018-19 and Jan-Mar 2019. 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 Aug-Oct 2018 season, among models that do use subsurface temperature information, 50% of models predicts neutral conditions and 45% predict El Niño conditions. For all models, predictions for La Niña probabilities are about 5% or below for the entire time period of May-Jul through Jan-Mar 2019. Probabilities for neutral are greater than 50% from May-Jul to Jul-Sep, and probabilities for El Niño begin low but exceed 50% by Sep-Nov and peak near 80% for Dec-Feb and Jan-Mar 2018-19.

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 approximately 10% or lower for the full range of seasons from May-Jul 2018 through to Jan-Mar 2019. Probabilities for neutral conditions begin at about 90% for May-Jul, fall below 50% beginning in Aug-Oct, and decrease further to near 30% for the last four seasons of Oct-Dec to Jan-Mar. Meanwhile, the probabilities for El Niño, which begin at 5% for May-Jul, rise to 50% for Sep-Nov, and to approximately 60-65% for Nov-Jan to Jan-Mar.  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.

In summary, the probabilities derived from the models on the IRI/CPC plume describe, on average, a strong preference for ENSO-neutral from May-Jul to Jun-Aug 2018, approximately equal probabilities for neutral or El Niño conditions for Aug-Oct, followed by a period from Nov-Jan through Jan-Mar 2019 when El Niño conditions are approximately 60-65% likely. Probabilities for La Niña are roughly 5-10% throughout the entire forecast period. 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 early next month by CPC and IRI, which will include some human judgment in combination with the model guidance.

Climatological Probabilities

Season La Niña Neutral El Niño
DJF 36% 30% 34%
JFM 34% 38% 28%
FMA 28% 49% 23%
MAM 23% 56% 21%
AMJ 21% 58% 21%
MJJ 21% 56% 23%
JJA 23% 54% 23%
JAS 25% 51% 24%
ASO 26% 47% 27%
SON 29% 39% 32%
OND 32% 33% 35%
NDJ 35% 29% 36%

 

Please refer to our licensing agreement for permission to use IRI ENSO materials. The CPC/IRI materials are not included in this licensing.

IRI ENSO Forecast

IRI/CPC Model-Based Probabilistic ENSO Forecast

Published: May 18, 2018

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 Model-Based ENSO Forecast Probabilities

Season La Niña Neutral El Niño
MJJ 2018 4% 91% 5%
JJA 2018 7% 67% 26%
JAS 2018 9% 54% 37%
ASO 2018 11% 44% 45%
SON 2018 12% 38% 50%
OND 2018 12% 33% 55%
NDJ 2018 10% 32% 58%
DJF 2018 6% 29% 65%
JFM 2019 4% 29% 67%

IRI ENSO Forecast

CPC/IRI Official Probabilistic ENSO Forecast

Published: May 10, 2018

The official CPC/IRI 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/IRI 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 Official ENSO Forecast Probabilities

Season La Niña Neutral El Niño
AMJ 2018 17% 82% 1%
MJJ 2018 10% 80% 10%
JJA 2018 9% 68% 23%
JAS 2018 10% 58% 32%
ASO 2018 10% 52% 38%
SON 2018 10% 48% 42%
OND 2018 11% 44% 45%
NDJ 2018 11% 40% 49%
DJF 2019 11% 40% 49%

Please refer to our licensing agreement for permission to use IRI ENSO materials. The CPC/IRI materials are not included in this licensing.

IRI ENSO Forecast

IRI/CPC ENSO Predictions Plume

Published: May 18, 2018

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.


Because of occasional data corrections and late model runs following the time of ENSO product issuance, the data shown in the ENSO forecast table and the ENSO plume graph may not always match. The best source of the ENSO forecast data is http://iri.columbia.edu/~forecast/ensofcst/Data/ensofcst_ALLtoMMYY where MM is the month number and YY is the year.


Seasons (2018-2019)
Model MJJ JJA JAS ASO SON OND NDJ DJF JFM
Dynamical models
NASA GMAO model -0.6 -0.6 -0.6 -0.5 -0.4 -0.3 -0.3
NCEP CFS version 2 -0.1 0 0.1 0.1 0.2 0.2 0.2 0.2
Japan Met. Agency model 0 0.1 0.2 0.3 0.4
Beijing Climate Center BCC-CSM1.1M 0 0.3 0.4 0.6 0.7 0.9 1.1 1.3 1.4
 King Abdulaziz University (Saudi Arabia) 0.3 0.5 0.6 0.7 0.7 0.9 1 1.1 1.1
Lamont-Doherty model 0 0.1 0.1 0.1 0.2 0.2 0 -0.2 -0.3
POAMA (Austr) model -0.4 -0.4 -0.4 -0.2 -0.1 0 0.1
ECMWF model 0.1 0.3 0.5 0.6 0.8
UKMO model 0.1 0.3 0.5 0.7
KMA (Korea) SNU model -0.2 -0.1 0.2 0.4 0.6 0.8 0.8 0.9 0.9
IOCAS (China) Intermed. Coupled model 0.2 0.4 0.4 0.4 0.5 0.7 0.8 0.7 0.5
COLA CCSM4 model 0 0.1 0.1 0.2 0.2 0.3 0.4 0.6 0.7
MÉTÉO FRANCE model -0.4 -0.2 -0.1 -0.1 0
Japan Frontier Coupled model 0.3 0.6 0.7 0.8 0.8 0.9 1 1.1 1
CSIR-IRI 3-model MME 0.1 0.3 0.4 0.6 0.7 0.7
GFDL CM2.1 Coupled Climate model 0.3 0.6 0.7 0.7 0.8 0.8 0.9 1 1.1
Canadian Coupled Fcst Sys 0.1 0.3 0.4 0.5 0.5 0.6 0.7 0.8 0.8
GFDL CM2.5 FLOR Coupled Climate model 0.2 0.5 0.7 0.8 0.8 0.9 1.1 1.1 1.2
Average, dynamical models 0 0.2 0.3 0.4 0.4 0.5 0.6 0.8 0.8
Statistical models
PSD-CIRES LIM 0.4 0.6 0.8 0.9 1 1.1 1.1 1.2 1.2
National Taiwan Univ CODA -0.3 -0.3 -0.3 -0.1 0 0.3 0.4
NCEP/CPC Markov model 0 0.1 0.2 0.4 0.6 0.8 1 1.1 1.1
NCEP/CPC Constructed Analog 0.2 0.2 0.3 0.6 0.9 1.1 1.2 1.1 1
Landsea/Knaff CLIPER 0 0.1 0.2 0.3 0.3 0.3 0.3 0.3 0.2
Univ. BC Neural Network 0.1 0.3 0.5 0.6 0.8 0.9 0.9 1 1
FSU Regression 0.3 0.6 0.7 0.8 1 1.2 1.2 1.2 1
TCD – UCLA 0.2 0.4 0.5 0.5 0.6 0.6 0.5 0.5 0.4
Average, statistical models 0.1 0.2 0.4 0.5 0.6 0.8 0.8 0.9 0.8
Average, all models 0 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.8

Discussion of Current Forecasts

Most of the models in the set of dynamical and statistical model predictions issued during mid-May 2018 indicate neutral ENSO conditions lasting from through summer and likely into fall of 2018. In the most recent week, the SST anomaly in the Nino3.4 region was -0.1 C, in the neutral range, and -0.41 C for the month of March, at a cool-neutral level. Most of the key atmospheric variables now reflect neutral conditions, unlike a month ago when they continued to show some lingering La Niña patterns. The subsurface sea temperature anomalies continue to be moderately positive. Well over half of the dynamical and statistical models predict a tendency for warming to El Niño levels toward the end of the year, but this outlook must be tempered somewhat due to low forecast confidence at this time of year. Based on the multi-model mean prediction, and the expected skill of the models by start time and lead time, the probabilities (X100) for La Niña, neutral and El Niño conditions (using -0.5C and 0.5C thresholds) over the coming 9 seasons are:

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

Season La Niña Neutral El Niño
MJJ 2018 4% 91% 5%
JJA 2018 7% 67% 26%
JAS 2018 9% 54% 37%
ASO 2018 11% 44% 45%
SON 2018 12% 38% 50%
OND 2018 12% 33% 55%
NDJ 2018 10% 32% 58%
DJF 2018 6% 29% 65%
JFM 2019 4% 29% 67%

Summary of forecasts issued over last 22 months

The following plots show 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 also 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. The first plot shows forecasts for dynamical models, the second for statistical models, and the third for all models. For less difficult readability, forecasts are shown to a maximum of only the first five lead times. Below the third plot, we provide a mechanism for highlighting the forecasts of one model at a time against a background of more lightly colored lines for all other models.


Notes on the data 

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.

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:

  • Temporal averaging
  • Linear temporal interpolation
  • Visual averaging of values on a contoured map

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 1971-2000 period as the base period, or a period not very different from it.

Please refer to our licensing agreement for permission to use IRI ENSO materials. The CPC/IRI materials are not included in this licensing.

IRI ENSO Forecast

Forecast Probability Distribution Based on the IRI/CPC ENSO Prediction Plume

Published: May 18, 2018


The three plots on this page show predictions of seasonal (3-month average) sea surface temperature (SST) anomaly in the Niño3.4 region in the east-central tropical Pacific (5°N-5°S, 120°-170°W), covering the nine overlapping seasons beginning with the current month. The predictions are based on the large (20+) set of dynamical and statistical models in the plume of model ENSO predictions.


  • Figure 1

    Figure 1 shows the ensemble mean predictions of each dynamical model, along with the statistical predictions. This plot provides some idea of the disagreement among the individual models, as well as the difference between the mean of the forecasts of dynamical versus statistical models.

  • Figure 2

    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.

  • Figure 3

    Figure 3, 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.