Figures 1 and 3 (the official ENSO probability forecast and the objective model-based 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.

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  Figure 1.

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  Figure 3.

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  Figure 2

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  Figure 4.

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 Alert System Status: La Niña Advisory

Synopsis: Though La Niña is favored to continue, the odds for La Niña decrease into the late Northern Hemisphere summer (58% chance in August-October 2022) before slightly increasing through the Northern Hemisphere fall and early winter 2022 (61% chance).

Below-average sea surface temperatures (SSTs) persisted during April across most of the central and eastern equatorial Pacific Ocean (Fig. 1). Over the past month, the Niño index values decreased, with the latest weekly values ranging from -1.1ºC to -1.5ºC (Fig. 2), which are quite negative for this time of year.  Subsurface temperatures anomalies (averaged between 180º-100ºW and 0-300m depth) remained negative (Fig. 3), reflecting an extensive area of below-average temperatures from the surface to ~100m depth across the central and eastern equatorial Pacific Ocean (Fig. 4). For the monthly average, low-level easterly and upper-level westerly wind anomalies dominated the equatorial Pacific.  Convection remained significantly suppressed around the Date Line and was enhanced over the Philippines (Fig. 5). Overall, the coupled ocean-atmosphere system reflected the continuation of La Niña.

The most recent IRI/CPC plume average for the Niño-3.4 SST index forecasts borderline La Niña conditions during the Northern Hemisphere summer, with increasing odds for La Niña into the fall (Fig. 6). Similar to last month, the forecaster consensus predicts Niño-3.4 index values to weaken into the summer, but remaining below the threshold of La Niña (Niño-3.4 values equal to or less than -0.5ºC).  In the near-term, westerly wind anomalies are predicted for mid-late May which supports the weakening of below-average surface and subsurface oceanic temperatures in the coming months.  However, much of the model guidance is also hinting at a re-strengthening of La Niña conditions again in the fall and upcoming winter.  In summary, though La Niña is favored to continue, the odds for La Niña decrease into the late Northern Hemisphere summer (58% chance in August-October 2022) before slightly increasing through the Northern Hemisphere fall and early winter 2022 (61% chance; click CPC/IRI consensus forecast for the chances in 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 9 June 2022. 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.

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Season	La Niña	Neutral	El Niño
AMJ	100	0	0
MJJ	87	13	0
JJA	69	31	0
JAS	59	39	2
ASO	58	38	4
SON	61	35	4
OND	61	34	5
NDJ	61	34	5
DJF	58	37	5

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List of Models Used

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Forecast SST Anomalies (deg C) in the Nino 3.4 Region

	Seasons (2022 – 2023)
Model	AMJ	MJJ	JJA	JAS	ASO	SON	OND	NDJ	DJF
Dynamical Models
NASA GMAO	-0.77	-1.01	-1.14	-1.13	-1.22	-1.35	-1.44
NCEP CFSv2	-1.08	-1.19	-1.21	-1.10	-1.01	-1.03	-1.05
JMA	-0.79	-0.69	-0.58	-0.50	-0.50
BCC_CSM11m	-0.36	0.12	0.54	0.76	0.83	0.86	0.94	1.11	1.30
SAUDI-KAU	-0.86	-0.72	-0.61	-0.60	-0.58	-0.55	-0.45	-0.25	0.05
LDEO	-0.65	-0.46	-0.25	-0.13	-0.13	-0.17	-0.18	-0.29	-0.46
AUS-ACCESS	-0.58	-0.40	-0.23	-0.17
ECMWF	-0.70	-0.54	-0.36	-0.28	-0.31
UKMO	-0.71	-0.59	-0.49	-0.47
KMA	-0.25	-0.15	-0.06	-0.11	-0.02
IOCAS ICM	-0.92	-0.96	-1.02	-1.12	-1.29	-1.54	-1.79	-2.01	-2.16
COLA CCSM4	-0.82	-0.90	-1.04	-1.17	-1.30	-1.41	-1.46	-1.34	-1.01
MetFRANCE	-1.16	-1.14	-0.87	-0.61	-0.48	-0.55	-0.66
SINTEX-F	-0.42	-0.31	-0.11	0.02	0.07	0.12	0.19	0.29	0.41
CS-IRI-MM	-0.48	-0.35	-0.23	-0.24	-0.30	-0.46
GFDL SPEAR	-0.54	-0.35	-0.21	-0.20	-0.32	-0.46	-0.52	-0.40	-0.14
CMC CANSIP	-0.80	-0.61	-0.40	-0.30	-0.35	-0.49	-0.59	-0.60	-0.50
Average, Dynamical models	-0.699	-0.603	-0.486	-0.432	-0.460	-0.586	-0.638	-0.436	-0.314
Statistical Models
NTU CODA	-0.59	-0.40	-0.18	-0.12	-0.24	-0.41	-0.23	-0.23	-0.25
BCC_RZDM	-0.95	-0.84	-0.80	-0.88	-1.08	-1.27	-1.48	-1.61	-1.57
CPC MRKOV	-0.91	-0.75	-0.65	-0.56	-0.46	-0.37	-0.24	-0.06	0.11
CPC CA	-0.56	-0.46	-0.36	-0.36	-0.33	-0.32	-0.27	-0.20	-0.02
CSU CLIPR	-0.65	-0.64	-0.62	-0.61	-0.59	-0.57	-0.55	-0.50	-0.45
IAP-NN	-0.78	-0.62	-0.46	-0.30	-0.13	0.04	0.17	0.24	0.26
UCLA-TCD	-0.59	-0.48	-0.49	-0.59	-0.75	-0.89	-0.97	-0.95	-0.83
Average, Statistical models	-0.718	-0.599	-0.509	-0.489	-0.511	-0.542	-0.510	-0.473	-0.392
Average, All models	-0.705	-0.601	-0.493	-0.449	-0.476	-0.569	-0.588	-0.453	-0.351

Discussion of Current Forecasts

Season	La Niña	Neutral	El Niño
AMJ	80	20	0
MJJ	61	39	0
JJA	49	49	2
JAS	47	48	5
ASO	49	44	7
SON	54	38	8
OND	54	36	10
NDJ	48	38	14
DJF	43	42	15

Summary of forecasts issued over last 22 months

The following plots show 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 also 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. The first plot shows forecasts for dynamical models, the second for statistical models, and the third for all models. For less difficult readability, forecasts are shown to a maximum of only the first five lead times. Below the third plot, we provide a mechanism for highlighting the forecasts of one model at a time against a background of more lightly colored lines for all other models.

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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.

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 1971-2000 period as the base period, or a period not very different from it.

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  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.

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  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.