ENSO Conditions and Coral Bleaching

I) CPC Official Probabilistic Forecast
(based on a consensus of NOAA's CPC and Columbia University's IRI forecasters, using observational information and model output, along with human judgment):

January 8, 2026 (updated early-month)

Alert Status: La Niña Advisory
(definitions)

La Niña persists, followed by a 75% chance of a transition to ENSO-neutral during January-March 2026. ENSO-neutral is likely through at least Northern Hemisphere late spring 2026.

DIAGNOSTIC DISCUSSION:

In December 2025, La Niña was reflected in the continuation of below-average sea surface temperatures (SSTs) across the east-central and eastern equatorial Pacific Ocean. The latest weekly Niño-3.4 index value was -0.5°C, with the Niño-3 and Niño-1+2 indices remaining cooler at -0.8°C and -0.7°C, respectively. The equatorial subsurface temperature index (average from 180°-100°W) became slightly positive, reflecting the expansion of above-average temperatures from the western to east-central Pacific at depth.

Atmospheric anomalies across the tropical Pacific Ocean remained consistent with La Niña. For most of the month, easterly wind anomalies were present over the central equatorial Pacific, and upper-level westerly wind anomalies continued across the equatorial Pacific. Enhanced convection persisted over Indonesia and suppressed convection strengthened near the Date Line. The equatorial Southern Oscillation index was positive. Collectively, the coupled ocean-atmosphere system remains consistent with La Niña.

Last month, the IRI multi-model predictions indicated ENSO-neutral will emerge during January-March (JFM) 2026. In conjunction with the North American Multi-Model Ensemble, the team favors ENSO-neutral to develop during JFM 2026. Even after equatorial Pacific SSTs transition to ENSO-neutral, La Niña may still have some lingering influence through the early Northern Hemisphere spring 2026 (e.g., CPC's seasonal outlooks). For longer forecast horizons, there are growing chances of El Niño, though there remains uncertainty given the lower accuracy of model forecasts through the spring.

... Read more at the NOAA's Diagnostic Discussion, CPC ENSO Update, and CPC's Official Probabilistic ENSO Forecast sites, all updated at the same time in the early-month.

** Note: IRI's Monthly Ouick Look updates twice per month and presents the latest update between the (I) early-month CPC official Probabilistic Forecast (shown in this section) and (II) mid-month IRI Model-Based Probabilistic Forecast (shown in the following two sections).

(The update of the CPC ENSO diagnostics discussion and forecast is scheduled for the second Thursday of each month.)

II) IRI Model-Based Probabilistic Forecast
(a purely objective ENSO probability forecast that uses as input the model predictions from IRI's ENSO Predictions Plume):

January 20, 2026 (updated mid-month)

The probability of La Niña at 31% and ENSO-neutral conditions at 69% as the dominant category for Jan–Mar 2026.

As of mid-January 2026, the equatorial Pacific remains in a La Niña state. The CCSR/IRI ENSO plume forecast places the probability of La Niña at 31% for Jan–Mar 2026 and shifts the odds in favor of ENSO-neutral conditions (about 69%) for the same period. ENSO-neutral remains the dominant category through May-Jul 2026. Beyond that, El Niño probabilities become higher than ENSO-neutral, although they remain in the range of 48% to 51%, with ENSO-neutral still the second most likely outcome.

... Read more at the IRI Technical ENSO Update, IRI Model-Based Probabilistic ENSO Forecast, IRI ENSO Predictions Plume, and IRI Forecast Probability Distribution Based on the IRI ENSO Prediction Plume sites, all updated at the same time in the mid-month.

** Note: IRI's Monthly Ouick Look updates twice per month and presents the latest update between the (I) early-month CPC official Probabilistic Forecast (shown in this section) and (II) mid-month IRI Model-Based Probabilistic Forecast (shown in the following two sections).

(The update of the IRI ENSO forecast is scheduled for the 19th of each month. If the 19th falls on a weekend or holiday, it is released on the closest business day.)

III) IRI ENSO Predictions Plume
The following graph shows forecasts made by a set of dynamical and statistical models for SST in the Niño-3.4 region, for nine, overlapping, three-month periods.

January 20, 2026 (updated mid-month)

The CCSR/IRI ENSO prediction plume indicates a high likelihood of ENSO-neutral conditions during Jan–Mar 2026. The multimodel mean of statistical and dynamical models suggests that La Niña is likely to transition to ENSO-neutral in Jan–Mar 2026 (69% chance), with ENSO-neutral conditions remaining the dominant category through May–Jul 2026 (58% chance). Over this period, the probability of El Niño development gradually increases from 1% in Feb–Apr to 37% by May–Jul 2026, and El Niño becomes the leading category during the final four overlapping seasons of the forecast (Jun to Nov), although probabilities remain in the low (between 48% to 51%). However, such long-lead forecasts remain highly uncertain, as they pass through the spring predictability barrier.

... Read more here, where any specific model can be interactively examined, along with other detailed information.

(The update of the IRI ENSO forecast is scheduled for the 19th of each month. If the 19th falls on a weekend or holiday, it is released on the closest business day.)

Historically Speaking & More:

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

The spring predictability barrier: 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. This is a well-known challenge for forecaster and it can impact statistical models more than dynamical models.

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