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October 2024 Quick Look

Published: October 18, 2024

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)

As of mid-October 2024, ENSO-neutral conditions persist in the equatorial Pacific. The IRI ENSO prediction plume forecasts show 53% chances for ENSO-neutral conditions to continue during Oct-Dec, 2024. Borderline La Niña conditions are forecasted during Nov-Jan and Dec-Feb, 2025. ENSO-neutral conditions subsequently re-emerge as the most likely category and remain so during the first and second quarter of 2025.

According to the most recent official CPC ENSO Outlook (issued on October 10, 2024), the La Nina onset is forecasted in Sep-Nov, with 60% chances and that persist till Jan-Mar, 2025. However, the objective IRI model-based ENSO outlook forecasts indicate the continuation of ENSO-neutral conditions for Oct-Dec 2024 (53% chances). Thus, there is a notable difference between the probability values in the early-month CPC Outlook and mid-month IRI ENSO forecast. The CPC ENSO Outlook predicts a clear preference for La Niña onset in Sep-Nov with continuation until Jan-Mar 2025. In contrast, the objective IRI ENSO forecasts show weak and short-lived La Niña conditions in Nov-Jan, and Dec-Feb, 2025. Both the CPC ENSO Outlook and the IRI ENSO forecast indicate a return to ENSO-neutral conditions boreal spring of 2025.

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: October 10, 2024

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 favored to emerge in September-November (60% chance) and is expected to persist through January-March 2025.

During September 2024, ENSO-neutral continued with near-average sea surface temperatures (SSTs) observed across most of the central and eastern equatorial Pacific Ocean (Fig. 1). Similar to this time last month, the latest weekly Niño indices ranged from +0.2°C (Niño-4) to -0.4°C (Niño-1+2; Fig. 2). Below-average subsurface temperatures persisted (Fig. 3) across the east-central and eastern equatorial Pacific Ocean (Fig. 4). Low-level wind anomalies were easterly over the east-central equatorial Pacific, and upper-level wind anomalies were westerly over the eastern Pacific.  Convection was near average over Indonesia and was slightly suppressed over the Date Line (Fig. 5). Collectively, the coupled ocean-atmosphere system reflected ENSO-neutral.

The IRI plume predicts a weak and a short duration La Niña, as indicated by the Niño-3.4 index values less than -0.5°C (Fig. 6). The latest North American Multi-Model Ensemble (NMME) forecasts were warmer this month, but still predict a weak La Niña.  As a result of the warmer predictions and the recent weakening of equatorial trade winds, the team still favors a weak event, but has lowered the chances of La Niña.  A weaker La Niña implies that it would be less likely to result in conventional winter impacts, though predictable signals could still influence the forecast guidance (e.g., CPC’s seasonal outlooks).  In summary, La Niña is favored to emerge in September-November (60% chance) and is expected to persist through January-March 2025  (Fig. 7).

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 14 November 2024. 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.

Season La Niña Neutral El Niño
SON 60 40 0
OND 71 29 0
NDJ 75 25 0
DJF 71 28 1
JFM 60 38 2
FMA 46 51 3
MAM 32 63 5
AMJ 20 68 12
MJJ 18 63 19

IRI ENSO Forecast

IRI Technical ENSO Update

Published: October 18, 2024

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. These two products may differ, particularly during ENSO events. The difference between the two datasets may be as much as 0.5 °C. Additionally in some years, the ERSSTv5 may tend to be cooler than OISSTv2 in the context of warming trends, 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. In February 2021, both datasets were updated to reflect the 1991-2020 climatology period.

Recent and Current Conditions

The SST anomaly in the NINO3.4 region during the Jul-Sep 2024 season was 0.0 °C, and for the month of September 2024 it was -0.15 °C. The most recent weekly average (week centered on 09 October 2024) of the NINO3.4 index was -0.50 °C. These values indicate that the current tropical Pacific state is ENSO-neutral. The IRI’s definition of El Niño, like NOAA/Climate Prediction Center’s (CPC), requires that the monthly 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 colder.

Oceanic and atmospheric conditions across the tropical Pacific are indicative of ENSO-neutral conditions. Both the traditional and equatorial Southern Oscillation Indices are in the ENSO-neutral range. During September 2024, the trade winds were significantly weaker across the eastern equatorial Pacific, hindering the development of La Niña in the tropical Pacific. Consequently, model forecasts initialized during September show a much less pronounced forecasted evolution towards La Niña compared to the forecasts made in August 2024. The convection over the central Pacific is near normal, while above normal cloudiness is observed over Indonesia. The colder waters have persisted (though weakened slightly) in the central-eastern equatorial Pacific Ocean. The warming in the western Pacific has been sustaining and extended slightly to the east of Dateline. Together, these observed conditions in the coupled ocean-atmosphere system indicate ongoing ENSO-neutral conditions in the equatorial Pacific.

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 El Niño/Southern Oscillation (ENSO) Diagnostic Discussion released on October 10, 2024, indicated that despite warmer forecasts, there is a 60% probability of a weak La Niña, expected to persist through January-March 2025

The latest set of ENSO prediction models from mid-October 2024 is now available in the IRI ENSO prediction plume. These are used to assess the probabilities of the three ENSO categories by using the average value of the NINO3.4 SST anomaly predictions from all models in the plume, equally weighted. A standard Gaussian error is imposed over that averaged forecast, with 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.

According to the ENSO forecast issued by the IRI in October 2024, a continuation of ENSO-neutral conditions is favored at 53% during Oct-Dec, while chances for La Niña are 47%. The chances of La Niña conditions increase to 53% during Nov-Jan, and Dec-Feb, 2025, while ENSO-neutral is estimated at 46% for both seasons. Subsequently, ENSO-neutral returns as the most-likely category in Jan-Mar, 2025 (55%), remaining for the rest of the forecast period with probabilities of 65% in Feb-Apr, 72% in Mar-May, 71% in Apr-Jun, 63% in May-Jul, and 55% in Jun-Aug, 2025. During Jan-Aug 2025, the estimated La Niña probability is within the range of 27% to 32%. The probability of El Niño remains very low throughout the forecast period [less than 10% except May-Jul (14%), and Jun-Aug (18%), 2025]. A plot of the probabilities summarizes the forecast evolution. The climatological probabilities for La Niña, ENSO-neutral, and El Niño conditions vary seasonally, and are shown by the lines on the plot, and are given in a table at the bottom of this page for each 3-month season.

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 October, 2024 issued forecast indicates moderate chances for continued ENSO-neutral conditions in Oct-Dec, 2024. The forecast then favors weak La Niña conditions that remain during Nov-Jan, and Dec-Feb, 2025, while the continuation of ENSO-neutral state is the second dominant category. In Jan-Mar 2025, ENSO-neutral becomes again the most likely category and remains so for the remainder of the forecast period.

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 the models may have missed, are not considered. This approach is purely objective. Those issues are taken into account in CPC’s official outlooks, which are issued early in the month, and which will include some human judgment in combination with the model guidance.

IRI ENSO Forecast Histogram Image
Season La Niña Neutral El Niño
OND 47 53 0
NDJ 53 46 1
DJF 53 46 1
JFM 43 55 2
FMA 32 65 3
MAM 24 72 4
AMJ 22 71 7
MJJ 23 63 14
JJA 27 55 18

ENSO Forecast

IRI Model-Based Probabilistic ENSO Forecast

Published: October 18, 2024

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

Season La Niña Neutral El Niño
OND 47 53 0
NDJ 53 46 1
DJF 53 46 1
JFM 43 55 2
FMA 32 65 3
MAM 24 72 4
AMJ 22 71 7
MJJ 23 63 14
JJA 27 55 18

ENSO Forecast

CPC Official Probabilistic ENSO Forecast

Published: October 10, 2024

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
Season La Niña Neutral El Niño
SON 60 40 0
OND 71 29 0
NDJ 75 25 0
DJF 71 28 1
JFM 60 38 2
FMA 46 51 3
MAM 32 63 5
AMJ 20 68 12
MJJ 18 63 19

ENSO Forecast

IRI ENSO Predictions Plume

Published: October 18, 2024

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 (2024 – 2025)
Model OND NDJ DJF JFM FMA MAM AMJ MJJ JJA
Dynamical Models
AUS-ACCESS -0.23 -0.23 -0.13 0.13
BCC_CSM11m -0.22 -0.03 0.15 0.38 0.61 0.81 1.01 1.20 1.41
CMC CANSIP -0.50 -0.63 -0.68 -0.58 -0.47 -0.39 -0.31 -0.21 -0.12
COLA CCSM4 -0.66 -0.86 -1.09 -1.08 -0.78 -0.40 -0.09 0.07 0.14
CS-IRI-MM -0.28 -0.38 -0.39 -0.28 -0.07 0.08
DWD -0.42 -0.38 -0.35 -0.28
ECMWF -0.23 -0.29 -0.31 -0.21 -0.02
GFDL SPEAR -0.27 -0.34 -0.34 -0.26 -0.10 0.00 0.08 0.08 0.08
IOCAS ICM -0.38 -0.45 -0.56 -0.57 -0.50 -0.40 -0.34 -0.36 -0.41
JMA -0.52 -0.60 -0.59 -0.52 -0.37
KMA -0.65 -0.70 -0.67 -0.48 -0.14
LDEO -0.43 -0.44 -0.37 -0.24 -0.12 -0.05 -0.01 0.01 -0.06
MetFRANCE -0.66 -0.70 -0.56 -0.34 -0.09
NASA GMAO -1.31 -1.65 -1.71 -1.46 -1.12 -0.84 -0.63
NCEP CFSv2 -1.08 -1.08 -0.90 -0.52 -0.13 0.17 0.32
SINTEX-F -0.54 -0.58 -0.51 -0.40 -0.31 -0.23 -0.14 0.00 0.14
UKMO -0.63 -0.80 -0.81 -0.63
Average, Dynamical models -0.530 -0.597 -0.579 -0.431 -0.258 -0.125 -0.013 0.113 0.169
Statistical Models
BCC_RZDM -0.39 -0.52 -0.53 -0.42 -0.26 -0.23 -0.25 -0.28 -0.32
CPC CA -0.17 -0.30 -0.45 -0.49 -0.36 -0.24 -0.14 -0.18 -0.32
CPC MRKOV -0.44 -0.37 -0.28 -0.21 -0.19 -0.16 -0.12 -0.09 -0.07
IAP-NN -0.27 -0.25 -0.20 -0.13 -0.02 0.09 0.19 0.25 0.26
NTU CODA -0.29 -0.31 -0.33 -0.34 -0.30 -0.26 -0.40 -0.52 -0.74
TONGJI-ML -0.56 -0.56 -0.48 -0.38 -0.36 -0.46 -0.49
UCLA-TCD -0.33 -0.44 -0.52 -0.52 -0.44 -0.31 -0.18 -0.10 -0.12
UW PSL-CSLIM -0.36 -0.51 -0.64 -0.67 -0.64 -0.56 -0.47 -0.40 -0.35
UW PSL-LIM -0.45 -0.49 -0.50 -0.51 -0.54 -0.54 -0.51 -0.44 -0.36
XRO -0.44 -0.50 -0.49 -0.44 -0.40 -0.41 -0.46 -0.54 -0.61
Average, Statistical models -0.369 -0.425 -0.442 -0.411 -0.351 -0.308 -0.283 -0.256 -0.292
Average, All models -0.470 -0.533 -0.528 -0.424 -0.297 -0.216 -0.155 -0.095 -0.091

Discussion of Current Forecasts

The IRI-Plume indicates the continuation of the ENSO-neutral conditions during Oct-Dec 2024. The multimodel mean of statistical models show ENSO-neutral conditions during the entire forecasts period; however, the multimodel mean of dynamical models show borderline La Niña conditions until the boreal winter, with a return to ENSO-neutral conditions during boreal spring of 2025. Based on the multi-model mean prediction, and the expected skill of the models by start time and lead time, the probabilities (in %) for La Niña, ENSO-neutral and El Niño conditions (using -0.5 °C and 0.5 °C thresholds) over the coming 9 seasons are:

Season La Niña Neutral El Niño
OND 47 53 0
NDJ 53 46 1
DJF 53 46 1
JFM 43 55 2
FMA 32 65 3
MAM 24 72 4
AMJ 22 71 7
MJJ 23 63 14
JJA 27 55 18

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: October 18, 2024

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.