March 2026 ENSO Discussion
March 2026 ENSO Discussion
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 observed SST anomaly in the NINO3.4 region during the Dec–Feb 2026 season was -0.45 °C, and for February 2026, it was -0.20 °C. The most recent weekly average (week centered on March 11, 2026) of the NINO3.4 index was -0.0 °C. These values indicate that Pacific sea-surface temperature anomalies have weakened to ENSO-neutral levels. The CCSR/IRI's definition of El Niño, 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.
By mid-March 2026, the already weak La Niña had continued to decline in oceanic aspects (e.g., equatorial SSTs approaching neutral levels), while atmospheric features persisted in showing La Niña characteristics. In February 2026, the Southern Oscillation Index (SOI) was +11.0, while the equatorial SOI was +1.2, indicating a strengthening of the pressure gradient between the two regions. The most recent (week centered on March 16, 2026) low-level (850-hPa) winds were near average across the eastern equatorial Pacific, with slightly stronger than normal easterlies in the central-eastern Pacific region. Enhanced convection over Indonesia (below-average OLR) and suppressed near the Date Line (above-average OLR) persisted in a La Niña pattern, albeit weak/muted, reflecting the shift to ENSO-neutral. However, above-average subsurface temperatures have amplified and expanded across the Pacific Ocean, extending to the far eastern Pacific. Warmer subsurface waters now dominate much of the basin, and due to this subsurface warming and its eastward progression, the Niño 1+2 index has gradually increased over the past four weeks to +1.5 (week centered on 11 March 2026), with the Niño 3 index at +0.4. Taken together, these conditions signal the fading of La Niña conditions in the central-eastern equatorial Pacific toward ENSO-neutral, with increased chance of El Niño development.
It is worth noting that the Relative Oceanic Niño Index (RONI), which measures sea surface temperature anomalies in the eastern equatorial Pacific relative to the rest of the equatorial band, has consistently exceeded the −0.5 La Niña threshold for the past several overlapping seasons. For Jul–Sep, Aug–Oct, Sep–Nov, Oct-Dec 2025, Nov-Jan 2026, and Dec-Feb 2026 the RONI was -0.63, -0.77, -0.87, -0.93, -0.97, -0.90 °C respectively.
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 12 March 2026 by the Climate Prediction Center (CPC)/NCEP/NWS, issued both “La Niña Advisory”, and “El Niño Watch” with a transition from La Niña to ENSO-neutral is expected in the next month, with ENSO-neutral favored through May-July 2026 (55% chance). In June-August 2026, El Niño is likely to emerge (62% chance) and persist through at least the end of 2026.
The latest set of ENSO prediction models from mid-March 2026 is now available in the CCSR/ 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 March 2026 CCSR/IRI ENSO plume forecast, ENSO-neutral conditions are favored for March–May (MAM) 2026 with a 91% probability, while El Niño has only a slim 9% chance during this period. ENSO-neutral conditions are favored to remain dominant through April–June 2026 (around 53% probability), with El Niño chances increasing to about 47%. Going further forward, El Niño is expected to emerge and persist through the end of the forecast period (Nov-Jan 2026/27), with probabilities up to 72–80%. ENSO-neutral probabilities drop into the 20s, while La Niña redevelopment chances stay low (under 5%).
Caution is advised when interpreting these longer-lead ENSO forecasts, as they extend through the boreal spring predictability barrier; accordingly, long-range outlooks should be viewed with appropriate uncertainty, even if they suggest a possible emerging outcome. A plot of the probabilities summarizes theforecast 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 also 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.