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October 2025 Quick Look

Published: October 20, 2025

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 2025, the equatorial Pacific remains in an ENSO-neutral state, with sea surface temperatures in the Niño 3.4 region close to average but exhibiting a gradual cooling trend. The IRI ENSO plume forecast indicates a moderate probability (65%) of La Niña conditions developing during October–December 2025. These La Niña conditions are expected to persist through the boreal winter of 2025/2026 (53%). However, beginning in January–March, ENSO-neutral conditions are forecasted to return with probabilities of 57%. The chances of El Niño development remain very low—below 10%—through March–May 2026, but increases to 14%, 22%, and 30% during April–June, May–July, and Jun-Aug 2026 respectively.

Figures 1 ((the official CPC ENSO probability forecast) and 3 (the objective model-based IRI ENSO probability forecast) are often quite similar. However, occasionally they may differ noticeably. There can be several possible reasons for differences. One is the human forecasters, using their experience and judgment, may disagree to some degree with the models, which may have known biases. Another reason is 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, typically about a week apart, with the IRI forecast run later. Also note that the CPC forecast starts on the previous season while the IRI forecast starts on the current season.
Click on the for more information on each figure.

Historically Speaking

    El Niño and La Niña events tend to develop during the period Apr-Jun and they
  • Tend to reach their maximum strength during October - February
  • Typically persist for 9-12 months, though occasionally persisting for up to 2 years
  • Typically recur every 2 to 7 years

IRI ENSO Forecast

IRI Technical ENSO Update and Model-Based Probabilistic ENSO Forecast

Published: October 20, 2025

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 2025 season was -0.28 °C, and for September, it was -0.44 °C. The most recent weekly average (week centered on October 15, 2025) of the NINO3.4 index was -0.6 °C. While these values still remain within the ENSO-neutral range, they reflect a clear cooling trend, indicating that cold conditions are gradually developing in the equatorial Pacific Ocean. The IRI’s definition of El Niño, similar to NOAA/Climate Prediction Center’s, requires that the monthly SST anomaly in the NINO3.4 region (5°S-5°N; 170°W-120°W) exceed +0.5 °C. Similarly, for La Niña, the anomaly must be -0.5 °C or colder.

As of mid-October 2025, both atmospheric and oceanic indicators continue to show ENSO-neutral conditions. The traditional Southern Oscillation Index (SOI) for September 2025 was +0.2, while and equatorial SOI registered a value of +1.2 respectively. Low-level (850-hPa) wind anomalies were easterly across the east-central and eastern Pacific. Upper-level (200-hPa) wind anomalies were westerly over the eastern equatorial Pacific. Below-average OLR, indicating enhanced convection and increased precipitation, was observed over parts of Indonesia, while above-average OLR, associated with suppressed convection and reduced precipitation, was present over and around the Date Line. Over the past few months, below-average sea surface temperatures have gradually intensified in the east-central and eastern equatorial Pacific. These anomalies have continued to strengthen, supported by enhanced trade winds across the equatorial Pacific. If this cooling trend persists, it may signal the onset of La Niña conditions in the coming months. Meanwhile, above-average subsurface temperatures have persisted in the western equatorial Pacific, reflecting the ongoing east–west subsurface temperature gradient across the equatorial ocean. Overall, current conditions continue to reflect an ENSO-neutral state.

Expected Conditions

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

The El Niño/Southern Oscillation (ENSO) Diagnostic Discussion released on 09 October 2025 by the Climate Prediction Center (CPC)/NCEP/NWS, issued a “La Niña Advisory” forecasting the presence of La Niña conditions that are favored to persist through December 2025 – February 2026, with a transition to ENSO-neutral likely in January-March 2026 (55% chance).

The latest set of ENSO prediction models from mid-October 2025 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 plume forecast issued by the IRI in October 2025, there is a moderate probability (65%) of La Niña conditions developing during October–December 2025, while the likelihood of ENSO-neutral conditions continuing is estimated at around 35%. For the overlapping seasonal periods of November–January, and December–February, the forecast probabilities for La Niña are 62%, and 53%, respectively. In contrast, the likelihood of ENSO-neutral conditions during these periods is 38%, and 46%. Subsequently, the probability of ENSO-neutral conditions gradually becomes dominant again—rising from 57% in January–March to 69% in February–April, 78% in March–May, 74% in April–June, 63% in May–July, and 54% in Jun-Aug 2026. During the same period, the chances of La Niña are estimated to decrease from 40% to 12%. The probability of El Niño development remains below 10% through the boreal spring of 2026, but increases to 14%, 22%, and 30% during April–June, May–July, and Jun-Aug 2026 respectively. 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.

It is worth noting that the Relative Oceanic Niño Index, which measures sea surface temperature anomalies in the eastern equatorial Pacific relative to the rest of the equatorial band, has exceeded the -0.5 La Niña threshold for the past several overlapping seasons (with values of -0.50 for Jun–Aug 2024, -0.63 for Jul–Sep, -0.75 for Aug–Oct, -0.81 for Sep–Nov, -0.92 for Oct–Dec, -1.07 for Nov–Jan, -1.12 for Dec–Feb, -0.89 for Jan–Mar 2025, -0.67 for Feb–Apr, -0.53 for Mar–May, and -0.49 for Apr–Jun 2025); however, it has returned to ENSO-neutral values (-0.43 for May–Jul, and -0.48 for Jun-Aug, 2025). For Jul-Sep, RONI registered a value of -0.63. The Pacific Decadal Oscillation Index in September 2025 recorded a value of -2.38, following its record-breaking value of -4.12 in July, and -3.18 in August, 2025.

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 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 65 35 0
NDJ 62 38 0
DJF 53 46 1
JFM 40 57 3
FMA 27 69 4
MAM 15 78 7
AMJ 12 74 14
MJJ 15 63 22
JJA 16 54 30

ENSO Forecast

IRI ENSO Predictions Plume

Published: October 20, 2025

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 (2025 – 2026)
Model OND NDJ DJF JFM FMA MAM AMJ MJJ JJA
Dynamical Models
AUS-ACCESS -0.37 -0.27 -0.03 0.27
BCC DIAP -0.11 -0.26 -0.20 -0.13 -0.09 -0.02 0.09 0.13 0.19
CMC CANSIP -0.65 -0.62 -0.45 -0.22 -0.03 0.14 0.30 0.44 0.52
COLA CCSM4 -0.59 -0.67 -0.77 -0.68 -0.41 -0.06 0.28 0.55 0.84
CS-IRI-MM -0.52 -0.47 -0.37 -0.19 0.06 0.31
IOCAS ICM -0.71 -0.77 -0.82 -0.70 -0.51 -0.31 -0.18 -0.08 0.01
JMA -0.68 -0.69 -0.56 -0.34 -0.13
KMA -0.69 -0.65 -0.51 -0.28 -0.02
LDEO -0.71 -0.73 -0.64 -0.49 -0.33 -0.14 0.04 0.18 0.25
MetFRANCE -0.98 -0.96 -0.81 -0.58 -0.34
NASA GMAO -1.23 -1.33 -1.09 -0.67 -0.23 0.08 0.23
NCEP CFSv2 -0.83 -0.68 -0.41 -0.12 0.14 0.39 0.58
SINTEX-F -0.31 -0.15 0.04 0.22 0.32 0.43 0.56 0.73 0.85
UKMO -0.88 -0.89 -0.68 -0.38
Average, Dynamical models -0.662 -0.653 -0.521 -0.307 -0.130 0.091 0.237 0.325 0.443
Statistical Models
BCC_RZDM -0.61 -0.67 -0.65 -0.53 -0.38 -0.27 -0.18 -0.09 -0.04
CPC CA -0.38 -0.38 -0.32 -0.20 -0.03 0.11 0.32 0.38 0.37
CPC MRKOV -1.13 -1.05 -0.92 -0.78 -0.67 -0.55 -0.43 -0.32 -0.24
CSU CLIPR -0.31 -0.25 -0.18 -0.12 -0.04 0.04 0.12 0.10 0.09
IAP-NN -0.65 -0.68 -0.66 -0.61 -0.52 -0.41 -0.27 -0.13 0.01
NTU CODA -0.57 -0.61 -0.64 -0.54 -0.47 -0.34 -0.24 -0.15 -0.18
UCLA-TCD -0.61 -0.64 -0.57 -0.45 -0.30 -0.13 0.03 0.15 0.23
UW PSL-CSLIM -0.55 -0.59 -0.58 -0.49 -0.36 -0.21 -0.09 0.00 0.03
UW PSL-LIM -0.53 -0.48 -0.41 -0.36 -0.35 -0.34 -0.31 -0.25 -0.18
XRO -0.59 -0.67 -0.70 -0.65 -0.56 -0.46 -0.37 -0.31 -0.27
Average, Statistical models -0.593 -0.602 -0.563 -0.473 -0.368 -0.256 -0.143 -0.062 -0.019
Average, All models -0.633 -0.632 -0.539 -0.376 -0.238 -0.091 0.026 0.083 0.154

Discussion of Current Forecasts

The IRI ENSO prediction plume indicates a moderate likelihood of La Niña conditions during October–December 2025, with a 65% chance. The multimodel mean of statistical and dynamical models suggests La Niña conditions are likely to persist through December–February (53%). Thereafter, beginning in January–March 2026, ENSO-neutral conditions are expected to become dominant once again. These forecasts indicate a transition from the current ENSO-neutral state to La Niña conditions during October–December, which are forecasted to persist briefly through the boreal winter at relatively low probabilities, followed by a return to ENSO-neutral conditions that are expected to remain through the end of the forecast period. During this time, the chances of El Niño development remain minimal. Based on the multi-model mean (Dynamical and Statistical models) 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 65 35 0
NDJ 62 38 0
DJF 53 46 1
JFM 40 57 3
FMA 27 69 4
MAM 15 78 7
AMJ 12 74 14
MJJ 15 63 22
JJA 16 54 30

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.

ENSO Forecast

Forecast Probability Distribution Based on the IRI ENSO Prediction Plume

Published: October 20, 2025

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.

IOD Forecast

Published: October 20, 2025

Note: The Dipole Mode Index is calculated based on the ERSSTv5 data. To account for evolving background conditions and long-term warming, SST anomalies were calculated relative to a sliding monthly climatology. For each month in the time series, the climatology was computed as the mean SST for that calendar month over the prior 30 years. Then the Dipole Mode Index (DMI) is defined as the difference in sea surface temperature anomalies between the western equatorial Indian Ocean (50°E–70°E, 10°S–10°N) and the southeastern equatorial Indian Ocean (90°E–110°E, 10°S–0°), and it is used to quantify the strength and phase of the Indian Ocean Dipole (IOD).

Current Conditions

The Dipole Mode Index for the month of September was -0.71 °C, indicative of the presence of negative IOD conditions in the Indian Ocean. According to the CCSR@NASA-GISS/IRI’s criteria for defining positive and negative Indian Ocean Dipole (IOD) events, a positive IOD is classified when the DMI exceeds +0.4 °C, and a negative IOD when it falls below −0.4 °C. The IOD is considered inactive (neutral) when the DMI lies between −0.4 °C and +0.4 °C.

Model-Based IOD Outlook: Deterministic Forecasts from the NMME

Forecasts from the latest set of operational models in the North American Multi-Model Ensemble (NMME) project are used to construct deterministic IOD forecasts from each individual model using its ensemble mean Dipole Mode Index (DMI) to form an IOD forecast plume. The IOD plume plot illustrates the latest set of predictions from early October (CESM1, CFSv2, CanESM5, GEM-NEMO, NASA), along with their equally weighted multi-model mean (MME). The observed DMI (shown in black) indicates a strong downward trend from May to September 2025, entering negative IOD-neutral conditions by late August. The models all predict a continuation of the negative IOD phase during October and November 2025. Subsequently, the individual models begin to diverge, and the MME forecast indicates a transition to IOD-neutral conditions in December. The MME forecast subsequently remains mostly in the IOD-neutral range, although individual models display substantial spread in their predictions.

Probabilistic IOD Forecasts from the NMME

Based on October 2025 initialization data, the model-based probabilistic forecast of the Indian Ocean Dipole (IOD) was generated by CCSR@NASA-GISS/IRI to assess potential phase developments. Probabilities are computed using a counting method, where each ensemble member from the contributing models is evaluated individually to determine the likelihood of a negative, neutral, or positive IOD phase for the upcoming months. Climatological probabilities for each IOD phase, based on historical data, are shown as dotted lines for reference.

The forecast indicates strong model consensus on the persistence of a negative IOD event through the boreal fall and early winter. For October, the probability of a negative IOD is near or at 100%. This high confidence continues into November, with probabilities remaining above 85%, while neutral and positive phases are comparatively unlikely. By December, the likelihood of a negative IOD decreases to below 40%, with a corresponding increase in the probability of neutral conditions. From December 2025 through April 2026, the forecast shifts toward higher neutral probabilities, suggesting a gradual weakening of the negative IOD signal and a return to climatologically typical conditions. Positive IOD probabilities remain very low throughout the forecast period—around 20% during spring 2026.

In summary, the forecast suggests a highly likely continuation of the negative IOD event through October and November 2025, followed by a transition toward neutral IOD conditions thereafter.

October 20, 2025 IOD Model Based Forecast
Historical SST Anomalies Image

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.