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2019 February Quick Look

Published: February 19, 2019

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)

SSTs in the tropical Pacific cooled to a borderline El Niño level in January and early February, while subsurface waters continued to be warmer than average. However, some atmospheric patterns of El Niño that had been lacking, finally developed in late January and February. Collective forecasts of models show a return to weak El Niño-level SSTs into summer. The official CPC/IRI outlook, now carrying an El Niño advisory, calls for a 65% chance of El Niño prevailing during Feb-Apr, decreasing to 50% for Apr-Jun.

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: February 14, 2019

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

ENSO Alert System Status: El Niño Advisory

Synopsis: Weak El Niño conditions are present and are expected to continue through the Northern Hemisphere spring 2019 (~55% chance).

El Niño conditions formed during January 2019, based on the presence of above-average sea surface temperatures (SSTs) across most of the equatorial Pacific Ocean (Fig. 1). and corresponding changes in the overlying atmospheric circulation.  The weekly Niño indices remained above average during the month, although decreasing in the Niño-3 and Niño-3.4 regions (Fig. 2). However, the Niño-4 region remained elevated, with a value of +0.8°C in early February. Positive subsurface temperature anomalies (averaged across 180°-100°W) increased in the last couple weeks (Fig. 3), in association with a downwelling Kelvin wave that contributed to above-average temperatures in the central Pacific (Fig. 4). Compared to last month, the region of enhanced equatorial convection expanded near the Date Line, while anomalies remained weak over Indonesia (Fig. 5). Low-level wind anomalies became westerly across the western Pacific Ocean, while upper-level wind anomalies were mostly westerly over the eastern Pacific. The equatorial Southern Oscillation index was negative (-0.6 standard deviations).  Overall, these features are consistent with borderline, weak El Niño conditions.

The majority of models in the IRI/CPC plume predict a Niño 3.4 index of +0.5°C or greater through at least the Northern Hemisphere spring 2019 (Fig. 6). Given the recent downwelling Kelvin wave and the forecast of westerly wind anomalies, most forecasters expect SST anomalies in the east-central Pacific to increase slightly in the upcoming month or so.  Because forecasts through the spring tend to be more uncertain and/or less accurate, the predicted chance that El Niño will persist beyond the spring is 50% or less. In summary, weak El Niño conditions are present and are expected to continue through the Northern Hemisphere spring 2019 (~55% chance; click the CPC/IRI consensus forecast for the chance of each outcome for each 3-month period).

Due to the expected weak strength, widespread or significant global impacts are not anticipated. However, the impacts often associated with El Niño may occur in some locations during the next few months (the 3-month seasonal outlook will be updated on Thursday February 21st).

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 Forumsection 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 March 2019. 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.


CPC/IRI Early-Month Official ENSO Forecast Probabilities

Season La Niña Neutral El Niño
JFM 2019 0% 23% 77%
FMA 2019 1% 34% 65%
MAM 2019 1% 43% 56%
AMJ 2019 3% 47% 50%
MJJ 2019 4% 48% 48%
JJA 2019 8% 49% 43%
JAS 2019 10% 48% 42%
ASO 2019 15% 46% 39%
SON 2019 17% 45% 38%

IRI ENSO Forecast

IRI Technical ENSO Update

Published: February 19, 2019

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-February 2019, warm-neutral to borderline El Niño SST conditions were observed in the NINO3.4 region. The January SST anomaly was 0.52 C, near the “bottom” of the weak El Niño range, and for Nov-Jan it was 0.82 C, indicative of a weak El Niño. 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, indicating weak El Niño conditions. Between mid-January and mid-February, positive SST anomalies in the east-central Pacific weakened to just below weak El Niño thresholds, while anomalies remained more strongly positive near and just west of the dateline. On the other hand, before mid-January, many of the key atmospheric variables, such as the lower level zonal wind anomalies, sea level pressure pattern (e.g., the traditional and equatorial Southern Oscillation indices) and outgoing longwave radiation pattern (convection) did not suggest El Niño conditions, but rather a continuation of ENSO-neutral conditions. But since the last half of January some important atmospheric variables finally became El Niño-like, including westerly low-level zonal wind anomalies and above-average convection near the dateline. Thus, the coupling of the atmosphere to the oceanic conditions finally commenced, despite that the SST has cooled to below- or just borderline El Niño levels. The subsurface temperature anomalies across the eastern equatorial Pacific remain above-average. These warmed waters at depth extend to the surface, resulting in above-average temperatures, and also presaging likely continuation or possible resurgence of somewhat more strongly above-average SST in the coming one to two months. Given the recent atmospheric participation and the subsurface profile, it appears likely that the SST will return to weak El Niño levels for the Mar-May season.

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 gave a 55% chance for El Niño continuing through spring. Due to the recent onset of ocean-atmosphere coupling, an El Niño advisory was initiated. The latest set of model ENSO predictions, from mid-February, now available in the IRI/CPC ENSO prediction plume, is discussed below. As of mid-February, 77% of the dynamical or statistical models predict El Niño conditions for the Feb-Apr through Apr-Jun seasons. After Apr-Jun, the percentage of models forecasting El Niño gradually decreases, dropping to 60% for Jun-Aug and to 47% for Oct-Dec. No model predicts La Niña for any season.

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 0% from Feb-Apr through May-Jul, rising only to 6% by Jul-Sep and to 16% by Sep-Nov, 19% by Oct-Dec. Probabilities for neutral conditions begin near 25% for Feb-Apr through Apr-Jun, and rise slowly to near 35-40% for Jun-Aug through Sep-Nov. Probabilities for El Niño are near 75% for Feb-Apr through Apr-Jun, drop through the 60-69% range for May-Jul and Jun-Aug, the 50-59% range for Jul-Sep and Aug-Oct, and slightly below 50% for Sep-Nov and Oct-Dec. The failure to drop below 50% until autumn suggests a possibility for a two-year El Niño event, but at this time, with the northern spring predictability barrier in front of us, this idea is mainly speculative. 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 substantial tilt of the odds toward El Niño conditions from Feb-Apr through Apr-Jun 2019, becoming weaker but still at least 50% through the Aug-Oct season. Probabilities for La Niña are close to zero through May-Jul. 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.


IRI ENSO Forecast Histogram Image

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

Season La Niña Neutral El Niño
FMA 2019 0% 26% 74%
MAM 2019 0% 24% 76%
AMJ 2019 0% 25% 75%
MJJ 2019 1% 32% 67%
JJA 2019 3% 36% 61%
JAS 2019 6% 39% 55%
ASO 2019 11% 38% 51%
SON 2019 16% 36% 48%
OND 2019 19% 33% 48%

 

ENSO Forecast

IRI Model-Based Probabilistic ENSO Forecast

Published: February 19, 2019

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
FMA 2019 0% 26% 74%
MAM 2019 0% 24% 76%
AMJ 2019 0% 25% 75%
MJJ 2019 1% 32% 67%
JJA 2019 3% 36% 61%
JAS 2019 6% 39% 55%
ASO 2019 11% 38% 51%
SON 2019 16% 36% 48%
OND 2019 19% 33% 48%

 

ENSO Forecast

CPC Official Probabilistic ENSO Forecast

Published: February 14, 2019

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
JFM 2019 0% 23% 77%
FMA 2019 1% 34% 65%
MAM 2019 1% 43% 56%
AMJ 2019 3% 47% 50%
MJJ 2019 4% 48% 48%
JJA 2019 8% 49% 43%
JAS 2019 10% 48% 42%
ASO 2019 15% 46% 39%
SON 2019 17% 45% 38%

ENSO Forecast

IRI ENSO Predictions Plume

Published: February 19, 2019

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.


Notice about the NASA-GMAO model ENSO forecasts

GMAO staff discovered a mistake in the calculation of ensemble mean fields that resulted in an under-representation of ensemble spread and an over-representation of error in the ensemble mean. The mistake impacts forecasts from Feb 2017 through July 2019, and has been corrected as of August 2019. All forecasts hence will have the correct fields. We have not corrected any previous forecast output sent to IRI. If you need the retroactive corrected fields, please contact GMAO at: anna.borovikov@nasa.gov, kazumi.nakada@nasa.gov


List of Models Used


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

Seasons (2019 – 2019)
Model FMA MAM AMJ MJJ JJA JAS ASO SON OND
Dynamical Models
NASA GMAO 0.22 0.03 -0.22 -0.41 -0.47 -0.45 -0.45
NCEP CFSv2 0.69 0.89 1.01 1.02 1.03 0.98 0.79 0.62
JMA 0.65 0.69 0.77 0.79 0.79
BCC_CSM11m 0.58 0.59 0.56 0.48 0.41 0.39 0.4 0.41 0.41
SAUDI-KAU 0.86 1.02 1.12 1.16 1.2 1.25 1.29 1.33 1.37
LDEO 0.74 0.7 0.63 0.57 0.47 0.33 0.21 0.08 -0.04
AUS/POAMA 0.74 0.82 0.9 1.01 1.14 1.25 1.32
ECMWF 0.8 0.87 0.88 0.82 0.75
UKMO 1.03 1.1 1.09 1.05
KMA SNU 0.76 0.78 0.78 0.84 0.95 1.08 1.12 1.1 1
IOCAS ICM 0.33 0.22 0.13 0.04 -0.02 -0.08 -0.12 -0.13 -0.12
COLA CCSM4 0.96 0.96 0.92 0.87 0.77 0.65 0.56 0.51 0.51
MetFRANCE 0.85 0.93 1.02 1.12 1.16
SINTEX-F 0.84 0.86 0.76 0.61 0.44 0.25 0.14 0.11 0.09
CS-IRI-MM 0.57 0.6 0.7 0.73 0.69 0.54
GFDL CM2.1 0.97 0.99 1.03 1.01 0.85 0.58 0.28 0.11 0.07
CMC CANSIP 0.56 0.51 0.47 0.38 0.24 0.07 -0.08 -0.16 -0.2
GFDL FLOR 0.77 0.84 0.93 1 1 0.9 0.73 0.58 0.49
Average, Dynamical Models 0.72 0.74 0.75 0.73 0.67 0.55 0.48 0.41 0.36
Statistical Models
PSD-CU LIM 0.86 0.78 0.69 0.59 0.48 0.37 0.25 0.14 0.05
NTU CODA 0.79 1.05 1.39 1.72 1.59 1.33 0.95
BCC_RZDM 0.42 0.46 0.6 0.7 0.78 0.77 0.84 0.93 1.08
CPC MRKOV 0.44 0.41 0.42 0.43 0.45 0.52 0.64 0.78 0.91
CPC CA 0.63 0.59 0.58 0.48 0.44 0.42 0.39 0.42 0.53
CSU CLIPR 0.13 0.22 0.32 0.41 0.35 0.3 0.24 0.26 0.29
UBC NNET 0.81 0.96 0.92 0.88 0.84 0.77 0.71 0.64 0.53
FSU REGR 0.42 0.43 0.48 0.52 0.54 0.52 0.55 0.6 0.71
UCLA-TCD 0.45 0.36 0.27 0.21 0.18 0.15 0.13 0.1 0.08
Average, Statistical Models 0.55 0.58 0.63 0.66 0.63 0.57 0.52 0.48 0.52

Discussion of Current Forecasts

Most of the models in the set of dynamical and statistical model predictions issued during mid-February 2019 indicate weak El Niño conditions for the Feb-Apr season, continuing into summer 2019 at weaker strength, dissipating to neutral by autumn. In the most recent week, the SST anomaly in the Nino3.4 region was 0.6 C, in weak El Niño range, and 0.52 C for the month of January, also indicative of weak El Niño. Beginning in late January, some key atmospheric variables began to reflect El Niño-like conditions. The subsurface sea temperature anomalies continue to be positive. About 75-80% of the dynamical and statistical models predict El Niño conditions for the Feb-Apr season, and objective model-based probabilities are 70-80% through Apr-Jun, and about 55% for Jul-Sep. Forecasters hedge slightly on this outlook, and give a 65% probability for Feb-Apr and 55% for Mar-May. 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
FMA 2019 0% 26% 74%
MAM 2019 0% 24% 76%
AMJ 2019 0% 25% 75%
MJJ 2019 1% 32% 67%
JJA 2019 3% 36% 61%
JAS 2019 6% 39% 55%
ASO 2019 11% 38% 51%
SON 2019 16% 36% 48%
OND 2019 19% 33% 48%

 

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: February 19, 2019


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.

The CPC ENSO forecast is released at 9am (Eastern Time) on the second Thursday of each month.

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.

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.