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2016 August Quick Look

Published: August 18, 2016

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 mid-August 2016 the tropical Pacific SST anomaly was close to -0.5C, approaching the weak La Niña threshold. However, most key atmospheric variables continue to indicate neutral ENSO conditions. Although the upper level winds in the tropical Pacific are slightly suggestive of La Niña, the lower level winds remain near average. The Southern Oscillation index and the pattern of cloudiness and rainfall in the equatorial Pacific also indicate neutral ENSO despite a mild tilt toward La Niña. The collection of ENSO prediction models indicate SSTs most likely near the borderline of cool-neutral and weak La Niña from the present through fall and into winter.

Figures 1 and 3 (the official CPC ENSO probability forecast and the objective model-based IRI 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

ENSO Forecast

CPC ENSO Update

Published: August 11, 2016

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

ENSO Alert System Status:  La Niña Watch

Synopsis: La Niña is slightly favored to develop during August – October 2016, with about a 55-60% chance of La Niña during the fall and winter 2016-17.

ENSO-neutral conditions were observed during the past month, featuring slightly below average sea surface temperatures (SSTs) close to the equator across the eastern tropical Pacific Ocean (Fig. 1). While the weekly Niño-1+2 and Niño-4 regions were near average, the Niño-3 and Niño-3.4 indices were slightly below average (approaching -0.5oC) during July.  (Fig. 2). Although below-average subsurface temperatures continued, they weakened during the past month (Fig. 3) but remained near the surface in parts of the central and eastern equatorial Pacific (Fig. 4). traditional Southern Oscillation index and the equatorial Southern Oscillation index were near average during July, while the upper and lower-level winds also were near average across most of the tropical Pacific. Convection was suppressed over portions of the western and central tropical Pacific and enhanced over part of Indonesia (Fig. 5). Overall, the combined ocean and atmosphere system is reflective of ENSO-neutral.

Many models favor La Niña (3-month average Niño-3.4 index less than or equal to -0.5°C) by the beginning of the Northern Hemisphere fall, continuing into winter (Fig. 6). Statistical models predict a slightly later onset time (i.e., mid- to late fall) than dynamical models, and also predict a slightly weaker event. The forecaster consensus favors La Niña onset during the August-October season, and predicts a weak event (Niño-3.4 index between -0.5°C and -1.0°C) if La Niña forms. Overall, La Niña is slightly favored to develop during August – October 2016, with about a 55-60% chance of La Niña during the fall and winter 2016-17 (click 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 8 September 2016. 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
JAS 2016 41% 59% 0%
ASO 2016 51% 49% 0%
SON 2016 56% 43% 1%
OND 2016 59% 40% 1%
NDJ 2016 60% 39% 1%
DJF 2016 55% 42% 3%
JFM 2017 50% 46% 4%
FMA 2017 44% 51% 5%
MAM 2017 38% 54% 8%

IRI ENSO Forecast

IRI Technical ENSO Update

Published: August 18, 2016

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 usually shows stronger anomalies than ERSSTv4, and during very strong events the two datasets may differ by as much as 0.5 C.

Recent and Current Conditions

Since May 2016, ENSO-neutral conditions have prevailed. For July 2016 the average NINO3.4 SST anomaly was -0.49 C, indicative of neutral ENSO conditions but close to the threshold for weak La Niña conditions. For May-Jul it was -0.10 C, in the ENSO-neutral category. 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 -0.6 at a weak La Niña level. However, accompanying this ocean condition, a generally neutral condition is observed in the atmosphere, including weak lower-level wind anomalies, and convection anomalies across the equatorial Pacific that are only weakly suggestive of La Niña so far.  The Southern Oscillation Index (SOI) and the equatorial SOI have been just weakly positive, leaning toward La Niña but still in La Niña territory. Overall, neutral ENSO conditions are still indicated.

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 roughly 55-60% likelihood of La Niña during fall and winter 2016-17.  The latest set of model ENSO predictions, from mid-August, now available in the IRI/CPC ENSO prediction plume, is discussed below. Currently, the Nino3.4 SST anomalies are at the level of minimal La Niña. Subsurface temperature anomalies across the eastern equatorial Pacific continue to be somewhat below average. Although enhanced easterly trade winds have not yet been observed, the slightly below-average SSTs and the negative subsurface heat content anomaly is a good backdrop for the onset of such enhanced trades in the eastern tropical Pacific. When and if these develop, the SST is likely to fall further into to weak La Niña category by late August or September, and if this happens the La Niña condition would likely last, even if only weak, through the remainder of the year. However, the collection of the latest model predictions suggest that this scenario is only slightly more likely than not to happen.

As of mid-August, 52% of the dynamical or statistical models predicts La Niña conditions for the initial Aug-Oct 2016 season, while 48% predict 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 Nov-Jan 2016-17 season, among models that do use subsurface temperature information, 35% predicts ENSO-neutral conditions and 65% predicts La Niña conditions. For all model types, the probabilities for La Niña are between 50% and 60% from Aug-Oct 2016 through Nov-Jan 2016-17, near 45% for Dec-Feb and Jan-Mar 2017, and drop back below 40% beginning in Feb-Apr 2017. No model predicts El Niño for any of the periods forecast.

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 53% for Aug-Oct 2016, rising slightly to a maximum of 58% for Nov-Jan 2016-17, and remaining above 50% through Jan-Mar. Probabilities for El Niño are 5% or lower throughout the forecast period ending in Apr-Jun 2017. Probabilities for ENSO-neutral exceed 50% beginning in Feb-Apr 2017. 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 likelihood for La Niña conditions of greater than 50% from Aug-Oct through early 2017, but never more than about 55-60% during fall through mid-winter 2016-17.  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.

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%

 

IRI ENSO Forecast Histogram Image

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

Season La Niña Neutral El Niño
ASO 2016 53% 47% 0%
SON 2016 54% 43% 3%
OND 2016 56% 39% 5%
NDJ 2016 58% 37% 5%
DJF 2017 56% 39% 5%
JFM 2017 53% 43% 4%
FMA 2017 44% 53% 3%
MAM 2017 37% 60% 3%
AMJ 2017 35% 61% 4%

ENSO Forecast

IRI Model-Based Probabilistic ENSO Forecast

Published: August 18, 2016

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 ENSO Forecast Histogram Image

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

Season La Niña Neutral El Niño
ASO 2016 53% 47% 0%
SON 2016 54% 43% 3%
OND 2016 56% 39% 5%
NDJ 2016 58% 37% 5%
DJF 2017 56% 39% 5%
JFM 2017 53% 43% 4%
FMA 2017 44% 53% 3%
MAM 2017 37% 60% 3%
AMJ 2017 35% 61% 4%

ENSO Forecast

CPC Official Probabilistic ENSO Forecast

Published: August 11, 2016

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.

NOAA?CPC ENSO Forecast Image
NOAA/CPC ENSO Forecast Graphic, courtesy of NOAA/CPC

CPC/IRI Early-Month Official ENSO Forecast Probabilities

Season La Niña Neutral El Niño
JAS 2016 41% 59% 0%
ASO 2016 51% 49% 0%
SON 2016 56% 43% 1%
OND 2016 59% 40% 1%
NDJ 2016 60% 39% 1%
DJF 2016 55% 42% 3%
JFM 2017 50% 46% 4%
FMA 2017 44% 51% 5%
MAM 2017 38% 54% 8%

ENSO Forecast

IRI ENSO Predictions Plume

Published: August 18, 2016

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.

List of Models Used

Forecast SST Anomalies (deg C) in the Nino 3.4 Region

Seasons (2016-2017)
Model ASO SON OND NDJ DJF JFM FMA MAM AMJ
Dynamical models
NASA GMAO model -0.6 -0.5 -0.4 -0.4 -0.4 -0.2 0
NCEP CFS version 2 -0.5 -0.4 -0.4 -0.6 -0.6 -0.5 -0.2 0
Japan Met. Agency model -0.9 -0.8 -0.8 -1 -1.3
Scripps Inst. HCM -1.1 -1.4 -1.5 -1.6 -1.5 -1.4 -1.3 -1.1 -0.9
Lamont-Doherty model 0 0.1 -0.1 -0.1 -0.1 0 0.1 0.1 0
POAMA (Austr) model -0.8 -0.7 -0.6 -0.6 -0.6 -0.5 -0.4
ECMWF model -0.3 -0.2 -0.1 -0.1 -0.1
UKMO model -0.7 -0.8 -0.9 -1
KMA (Korea) SNU model -0.8 -0.9 -0.9 -0.9 -0.9 -0.9 -1 -1 -1
IOCAS (China) Intermed. Coupled model -0.6 -0.9 -1.2 -1.4 -1.4 -1.3 -1.1 -1.1 -1.1
COLA CCSM4 model -0.5 -0.4 -0.5 -0.6 -0.6 -0.6 -0.4 -0.2 -0.1
MÉTÉO FRANCE model -0.2 -0.1 -0.1 0 0
Japan Frontier Coupled model -0.2 -0.2 -0.2 -0.2 -0.3 -0.3 -0.2 -0.2 -0.2
CSIR-IRI 3-model MME -0.1 -0.2 -0.3 -0.4 -0.5 -0.5
GFDL CM2.1 Coupled Climate model -0.7 -0.7 -0.6 -0.5 -0.4 -0.3 -0.3 -0.2 -0.3
Canadian Coupled Fcst Sys -0.6 -0.6 -0.6 -0.5 -0.4 -0.3 -0.2 0 0.3
GFDL CM2.5 FLOR Coupled Climate model -0.8 -0.8 -0.7 -0.5 -0.4 -0.3 -0.2 -0.1 0.1
Average, dynamical models -0.5 -0.6 -0.6 -0.6 -0.6 -0.5 -0.4 -0.4 -0.3
Statistical models
NCEP/CPC Markov model -0.4 -0.5 -0.5 -0.5 -0.4 -0.4 -0.4 -0.4 -0.3
NOAA/CDC Linear Inverse -0.3 -0.3 -0.3 -0.4 -0.4 -0.3 -0.3 -0.3 -0.3
NCEP/CPC Constructed Analog -0.2 -0.2 -0.2 -0.2 -0.1 -0.1 0 0 0
NCEP/CPC Can Cor Anal -0.9 -1.1 -1.3 -1.3 -1.3 -1.1 -0.7 -0.4 -0.3
Landsea/Knaff CLIPER -0.6 -0.8 -0.9 -1.1 -0.9 -0.8 -0.6 -0.4 -0.2
Univ. BC Neural Network -0.4 -0.4 -0.3 -0.1 0.1 0.1 0.2 0.2 0.2
FSU Regression -0.7 -0.8 -1 -1 -1 -0.8 -0.7 -0.5 -0.4
TCD – UCLA -0.5 -0.6 -0.8 -0.9 -0.9 -0.9 -0.9 -0.9 -0.8
Average, statistical models -0.5 -0.6 -0.6 -0.7 -0.6 -0.5 -0.4 -0.3 -0.3
Average, all models -0.5 -0.6 -0.6 -0.6 -0.6 -0.5 -0.4 -0.4 -0.3

Discussion of Current Forecasts

All of the set of dynamical and statistical model predictions issued during late July and early August 2016 predicts either cool-neutral or La Niña conditions during the August-October period. Most of the models suggest only very slight further cooling to either cool-neutral or mainly weak La Niña conditions by northern autumn.  In the most recent week, the SST anomaly in the Nino3.4 region was -0.6 C, reflecting a weak La Niña level, and -0.49 C for the month of May, indicating a neutral ENSO condition but very close to the threshold for minimal La Niña. However, the atmospheric variables currently reflect mainly neutral ENSO. While the upper level winds show some enhanced westerly anomalies, at the lower level the wind anomalies are close to average. The traditional and equatorial Southern Oscillation Indices lean slightly in the La Niña direction, but are not far from average. The pattern of convection across the tropical Pacific is just slightly in the direction of La Niña, but is considered only cool-neutral. 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 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
ASO 2016 53% 47% 0%
SON 2016 54% 43% 3%
OND 2016 56% 39% 5%
NDJ 2016 58% 37% 5%
DJF 2017 56% 39% 5%
JFM 2017 53% 43% 4%
FMA 2017 44% 53% 3%
MAM 2017 37% 60% 3%
AMJ 2017 35% 61% 4%

Summary of forecasts issued over last 22 months

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.

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.

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:

  • 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 1991-2020 period as the base period, or a period not very different from it.

Historical SST Anomalies Image

ENSO Forecast

Forecast Probability Distribution Based on the IRI ENSO Prediction Plume

Published: August 18, 2016

The 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.

  • Model Based Prediction Percentiles Image

    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.

  • Model Based Prediction Distribution Image

    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.

New Article

Real-time ENSO forecast skill evaluated over the last two decades, with focus on the onset of ENSO events. Ehsan, M.A., L’Heureux, M.L., Tippett, M.K., Robertson, A.W, Turmelle, J.P., npj Clim Atmos Sci, 2024.

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.