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April 2021 Quick Look

Published: April 19, 2021

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)

In mid-April, SSTs in the east-central Pacific are roughly 0.4 degree C below average, and the evolution of most key atmospheric variables are consistent with weakening La Niña conditions. A large majority of the model forecasts predict SSTs to return to near-normal during spring, though a La Niña advisory remains in effect for now. The new official CPC/IRI outlook issued earlier this month is similar to these model forecasts, calling for a transition in Apr-May-Jun, which is likely to happen in April or May. A La Niña advisory remains in effect.

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: April 8, 2021

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

ENSO Alert System Status: La Niña Advisory

Synopsis: A transition from La Niña to ENSO-Neutral is likely in the next month or so, with an 80% chance of ENSO-neutral during May-July 2021. 

La Niña continued during March, reflected by negative sea surface temperatures (SST) anomalies, which extended across much of the equatorial Pacific Ocean (Fig. 1). SST anomalies weakened but continue to oscillate week-to-week in most of the Niño index regions, particularly in the eastern Pacific Ocean (Fig. 2). With the exception of Niño-1+2, the latest weekly Niño index values were at or near -0.5ºC.  Sub-surface ocean temperatures also weakened during the month, with the integrated anomalies averaged between the 180-100°W becoming positive during the middle of the month (Fig. 3). Currently, negative subsurface anomalies are present from the surface to approximately ~100m below the surface only in the eastern Pacific between 110°W and 80°W (Fig. 4). Low-level easterly wind anomalies are present but weak across the equatorial Pacific, and are most notable in the far western Pacific. Upper-level wind anomalies were westerly across the most of the tropical Pacific.  The suppression of tropical convection over the western and central Pacific persisted during March, although the enhancement of rainfall around the Philippines and Indonesia weakened (Fig. 5). The Southern Oscillation and Equatorial Southern Oscillation were weakly positive in March.  Overall, the coupled ocean-atmosphere system is consistent with a weakening La Niña.

Most of the models in the IRI/CPC plume predict a transition to ENSO-neutral during the Northern Hemisphere spring 2021 (Fig. 6). The forecaster consensus agrees that a transition is imminent, with a 50-50% chance of La Niña or ENSO-neutral for the March-May average, and then predicts ENSO-neutral to continue at least through the Northern Hemisphere summer.  In part, due to the uncertainty in predictions made at this time of year, the forecast for the Northern Hemisphere Fall 2021 remains lower confidence with a 40-50% chance of either La Niña or ENSO-Neutral, with a small chance for El Niño.  In summary, a transition from La Niña to ENSO-Neutral is likely in the next month or so, with ENSO-neutral favored with a ~80% chance during May-July 2021 (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). Additional perspectives and analysis are also available in an ENSO blog.  A probabilistic strength forecast is available here.

The next ENSO Diagnostics Discussion is scheduled for 13 May 2021. 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 Official Probabilistic ENSO Forecasts

Season La Niña Neutral El Niño
MAM 2021 50% 50% 0%
AMJ 2021 21% 79% 0%
MJJ 2021 15% 81% 4%
JJA 2021 20% 68% 12%
JAS 2021 30% 57% 13%
ASO 2021 37% 50% 13%
SON 2021 41% 46% 13%
OND 2021 46% 41% 13%
NDJ 2022 47% 40% 13%

IRI ENSO Forecast

IRI Technical ENSO Update

Published: April 19, 2021

Note: The SST anomalies cited below refer to the OISSTv2 SST data set, and not ERSSTv5. OISSTv2 is often used for real-time analysis and model initialization, while ERSSTv5 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 ERSSTv5, and during very strong events the two datasets may differ by as much as 0.5 C. Additionally, the ERSSTv5 may tend to be cooler than OISSTv2, because ERSSTv5 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. In February 2021, both datasets were updated using the 1991-2020 climatology period.

Recent and Current Conditions

In mid-April 2021, SSTs are just slightly below average in the NINO3.4 region, although the tropical Pacific has exhibited La Niña conditions since August 2020. The SST anomaly for NINO3.4 during the Jan-Mar season was -0.81 C, and for the month of March it was -0.51 C, suggesting continued weakening of the La Niña conditions that peaked in late October/early November 2020. 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 -0.4 C, which is below the borderline of La Niña designation; however, there exists discernible week-to-week variability in the strength of the SST anomalies. Many of the key atmospheric variables, indicative of La Niña conditions, also continue to weaken. The traditional and equatorial Southern Oscillation Indices have dropped considerably but remain positive. The low-level easterly wind anomalies are weakened further in March and are visible now only in west of about 150W. Similarly, the westerly upper level wind anomalies that accompany La Niña events are observed over a more localized region, between about 150E and 150W. Anomalously dry conditions that were observed over the west-central part of the basin have also become more localized near the dateline. Anomalous wet conditions continue over the Maritime Continent. For the ocean, subsurface temperature anomalies in the central and eastern equatorial Pacific are quite reduced in mid-March. The cool sub-surface anomalies that had supported La Niña persistence are now removed; they have been replaced by the warm sub-surface temperature anomalies (deepened thermocline anomalies) that had been building in the western Pacific. The warm subsurface temperature anomalies have now made their way across the equator to South America. In summary, current monthly conditions still indicate a borderline moderate La Niña, but they also indicate that it is weakening. A La Niña advisory remains in effect.

Expected Conditions

Note – Only models that produce a new ENSO prediction every month are considered in this statement.

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 states that a transition from La Niña to ENSO-Neutral is likely during April, with an 80% chance of ENSO-neutral during May-July 2021.

The latest set of model ENSO predictions from mid-March is now available in the IRI/CPC ENSO prediction plume. These are used to assess the probabilities of the three possible ENSO conditions by using the average value of the NINO3.4 SST anomaly predictions from all models on the plume, equally weighted. A standard Gaussian error is imposed over that average forecast, and its width is 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. Using this method, chances for La Niña are 21% for the Apr-Jun season, while chances for ENSO-neutral are 79%. Later in the year, the collection of models suggests that the cool side of ENSO-neutral is most likely. However, it is typically difficult to accurately foresee ENSO conditions for the latter part of the year through the boreal spring prediction barrier. El Niño probabilities are less than 10% until Jun-Aug, then rise to around 20% during boreal fall. 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.

Caution is advised in interpreting the forecast distribution from the Gaussian standard error as the actual probabilities, due to differing biases and performance of the different models. 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. At longer leads, the skill of the models degrades, and uncertainty in skill must be convolved with the uncertainties from initial conditions and differing model physics, which leads 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.

In summary, the probabilities derived from the models on the IRI/CPC plume describe a very slim chance for El Niño conditions throughout most of the forecast period, and a preference for La Niña conditions relative to neutral conditions during the current season, Apr-May. By then, neutral becomes the more likely outcome through the remaining forecast periods, through the forecasts favor the cool side of the ENSO-neutral category. This scenario has been stable over the last couple months of forecasts.

A caution regarding the model-based ENSO plume predictions released mid-month, is that factors such as known specific model biases and recent changes in the tropical Pacific that the models may have missed, are not considered. This approach is purely objective. Those issues are taken into account in the official outlooks, which are generated and issued early in the month by CPC and IRI, and 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
AMJ 2021 21% 79% 0%
MJJ 2021 19% 78% 3%
JJA 2021 20% 68% 12%
JAS 2021 23% 60% 17%
ASO 2021 29% 53% 18%
SON 2021 35% 46% 19%
OND 2021 37% 43% 20%
NDJ 2022 31% 43% 26%
DJF 2022 29% 46% 25%

ENSO Forecast

IRI Model-Based Probabilistic ENSO Forecast

Published: April 19, 2021

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
AMJ 2021 21% 79% 0%
MJJ 2021 19% 78% 3%
JJA 2021 20% 68% 12%
JAS 2021 23% 60% 17%
ASO 2021 29% 53% 18%
SON 2021 35% 46% 19%
OND 2021 37% 43% 20%
NDJ 2022 31% 43% 26%
DJF 2022 29% 46% 25%

ENSO Forecast

CPC Official Probabilistic ENSO Forecast

Published: April 8, 2021

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 Official Probabilistic ENSO Forecasts

Season La Niña Neutral El Niño
MAM 2021 50% 50% 0%
AMJ 2021 21% 79% 0%
MJJ 2021 15% 81% 4%
JJA 2021 20% 68% 12%
JAS 2021 30% 57% 13%
ASO 2021 37% 50% 13%
SON 2021 41% 46% 13%
OND 2021 46% 41% 13%
NDJ 2022 47% 40% 13%

ENSO Forecast

IRI ENSO Predictions Plume

Published: April 19, 2021

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 (2021 – 2022)
Model AMJ MJJ JJA JAS ASO SON OND NDJ DJF
Dynamical Models
NASA GMAO -1.22 -1.48 -1.53 -1.49 -1.66 -1.86 -2.00
NCEP CFSv2 -0.29 -0.15 -0.08 -0.16 -0.35 -0.47 -0.48
JMA -0.18 -0.02 0.05 0.06 0.07
BCC_CSM11m 0.04 0.35 0.60 0.72 0.77 0.81 0.90 1.04 1.17
SAUDI-KAU -0.41 -0.15 0.05 0.14 0.18 0.21 0.25 0.33 0.46
LDEO 0.02 0.25 0.45 0.54 0.53 0.54 0.59 0.48 0.17
AUS-ACCESS -0.15 0.03 0.13 0.10
ECMWF -0.23 -0.04 0.04 0.03 -0.00
UKMO -0.11 0.10 0.23 0.24
KMA SNU -0.27 -0.04 0.22 0.47 0.67 0.81 0.87 0.87 0.85
IOCAS ICM -0.30 -0.19 0.00 0.15 0.19 0.16 0.14 0.14 0.12
COLA CCSM4 -0.14 -0.17 -0.34 -0.56 -0.79 -0.98 -1.14 -1.22 -1.12
MetFRANCE -0.84 -0.60 -0.38 -0.36 -0.42 -0.50 -0.63
SINTEX-F -0.45 -0.47 -0.43 -0.45 -0.46 -0.49 -0.49 -0.44 -0.36
CS-IRI-MM -0.13 0.06 0.13 0.08 -0.02 -0.16
GFDL SPEAR -0.08 0.14 0.21 0.14 -0.02 -0.21 -0.37 -0.41 -0.29
CMC CANSIP -0.41 -0.23 -0.10 -0.06 -0.13 -0.24 -0.28 -0.29 -0.21
Average, Dynamical models -0.30 -0.15 -0.04 -0.02 -0.10 -0.18 -0.22 0.05 0.09
Statistical Models
NTU CODA -0.31 -0.32 -0.31 -0.39 -0.52 -0.68 -0.94 -1.05 -1.06
BCC_RZDM -0.14 0.01 0.11 0.13 0.16 0.19 0.22 0.17 0.10
CPC MRKOV -0.63 -0.55 -0.50 -0.47 -0.43 -0.39 -0.32 -0.18 -0.04
CPC CA -0.22 -0.05 0.04 0.01 0.01 0.05 0.15 0.18 0.23
CSU CLIPR -0.29 -0.15 -0.00 0.14 0.09 0.05 0.00 -0.01 -0.01
IAP-NN -0.52 -0.41 -0.33 -0.27 -0.22 -0.16 -0.13 -0.10 -0.08
FSU REGR -0.28 -0.20 -0.17 -0.16 -0.16 -0.18 -0.22 -0.23 -0.15
UCLA-TCD -0.22 -0.18 -0.19 -0.25 -0.34 -0.42 -0.48 -0.49 -0.45
Average, Statistical models -0.33 -0.23 -0.17 -0.16 -0.18 -0.19 -0.21 -0.21 -0.18
Average, All models -0.31 -0.18 -0.08 -0.07 -0.12 -0.19 -0.22 -0.07 -0.04

Discussion of Current Forecasts

Most of the models in the set of dynamical and statistical model predictions issued during mid-January 2021 show La Niña SST conditions likely to persist until the Mar-May season, with most transitioning to neutral during boreal spring.  In the most recent week, the SST anomaly in the NINO3.4 region was -1.2 C, indicative of moderate La Niña strength, and -1.05 C for the month of January. As of mid-February the subsurface water temperatures in the eastern equatorial Pacific remain below-average, having been reinforced by the action of the easterly wind anomalies, as warm anomalies shift into the far western equatorial Pacific.

A majority of the dynamical and statistical models predict at least weak La Niña conditions for the Feb-Apr season, decreasing to about 55% by Mar-May and below 50% thereafter. Objective model-based La Niña probabilities are 79% for Feb-Apr, dropping to about 40% by Apr-Jun. Thereafter, neutral conditions become the most likely at 62% confidence in Apr-Jun, and then decaying towards climatological odds, according to this suite of models. 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
AMJ 2021 21% 79% 0%
MJJ 2021 19% 78% 3%
JJA 2021 20% 68% 12%
JAS 2021 23% 60% 17%
ASO 2021 29% 53% 18%
SON 2021 35% 46% 19%
OND 2021 37% 43% 20%
NDJ 2022 31% 43% 26%
DJF 2022 29% 46% 25%

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: April 19, 2021


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.