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November 2022 Quick Look

Published: November 18, 2022

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)

In mid-November, sea surface temperatures in the central-eastern equatorial Pacific remain below-average. Key oceanic and atmospheric variables have remained consistent with La Niña conditions, though there is a large drop in SOI value recently (+6.9 as of 16 November, 2022). A CPC La Niña Advisory still remains in place for November 2022. Several models in the plume predict SSTs to remain below-normal at the level of a La Niña until at least Jan-Mar 2023. Similar to the most-recent official CPC ENSO Outlook issued on November 10, 2022, the IRI objective model-based ENSO outlook forecasts a continuation of the La Niña event with high probability during Dec-Feb, which decreases to 56% in Jan-Mar 2023. Based on objective ENSO forecasts, La Niña is expected to transition into ENSO-neutral during Feb-Apr 2023, which remains the most likely category until Jun-Aug 2023. The likelihood of El Niño remains very low through Apr-Jun, but rises to 30% in May-Jul 2023, and becomes the dominant category at 47% in Jul-Sep 2023.

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: November 10, 2022

El Niño/Southern Oscillation (ENSO) Diagnostic Discussion issued by the Climate Prediction Center/NCEP/NWS

ENSO Alert System Status: La Niña Advisory

Synopsis: There is a 76% chance of La Niña during the Northern Hemisphere winter (December-February) 2022-23, with a transition to ENSO-neutral favored in February-April 2023 (57% chance).

Below-average sea surface temperatures (SSTs) strengthened in the east-central Pacific Ocean during the past month (Fig. 1). All of the latest weekly Niño index values were near -1.0ºC, with the exception of Niño-1+2 which was at -1.8ºC (Fig. 2). Since late July 2022, negative subsurface temperature anomalies have been quite persistent (Fig. 3), reflecting the stationary pattern of below-average temperatures across the eastern Pacific Ocean (Fig. 4). For the monthly average, low-level easterly wind anomalies and upper-level westerly wind anomalies were evident across most of the equatorial Pacific.  However, in the last week, the low-level trade winds weakened in association with sub-seasonal tropical variability.  Convection remained suppressed over the western and central tropical Pacific and enhanced over Indonesia (Fig. 5). Overall, the coupled ocean-atmosphere system continued to reflect La Niña.

The most recent IRI plume forecast of the Niño-3.4 SST index indicates La Niña will persist into the Northern Hemisphere winter 2022-23, and then transition to ENSO-neutral in February-April 2023 (Fig. 6). The forecaster consensus, which also considers the North American Multi-Model Ensemble (NMME), is in agreement with the timing of this transition.  The recent weakening of the trade winds suggest below-average SSTs may be near their minimum, though considerable uncertainty remains over how gradually the anomalies will decay.  In summary, there is a 76% chance of La Niña during the Northern Hemisphere winter (December-February) 2022-23, with a transition to ENSO-neutral favored in February-April 2023 (57% chance; 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 8 December 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.



Season La Niña Neutral El Niño
OND 100 0 0
NDJ 92 8 0
DJF 76 24 0
JFM 59 40 1
FMA 40 57 3
MAM 24 70 6
AMJ 13 72 15
MJJ 10 64 26
JJA 9 54 37

IRI ENSO Forecast

IRI Technical ENSO Update

Published: November 18, 2022

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

Recent and Current Conditions

The SST anomaly for NINO3.4 during the Aug-Oct season was -0.92 °C, and for the month of October it was -0.85 °C. The most recent weekly (09 Nov 2022) anomaly in the NINO3.4 region was -1.0 °C, indicating persistent La Niña conditions in the tropical Pacific. The IRI’s definition of El Niño, like NOAA/Climate Prediction Center’s, requires that the 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.

Many of the key atmospheric variables remain indicative of La Niña conditions, such as the traditional and equatorial Southern Oscillation Indices, which had shown a significant increase in September, now has a downward trend, but still remained positive (as of 16 November, 2022, last observed value was +6.9). The low-level easterly winds are stronger than normal across the central-eastern Pacific, while upper-level wind anomalies remain westerly across the tropical Pacific. Anomalously dry conditions have been observed over the central and western Pacific Ocean (west of the Date Line). Across the equatorial Pacific Ocean, subsurface ocean temperatures are above average in the western Pacific. However, negative subsurface temperatures are still observed near the surface and at depth in the central and eastern Pacific.

In summary, tropical Pacific atmospheric and oceanic conditions remain consistent with La Niña and a La Niña advisory is still in place.

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?

El Niño/Southern Oscillation (ENSO) Diagnostic Discussion issued on 10 November 2022 by the Climate Prediction Center/NCEP/NWS indicates a continuation of the current La Niña event.

The latest set of ENSO prediction models from mid-November is now available in the IRI ENSO prediction plume. These are used to assess the probabilities of the three possible ENSO conditions by using the average value of the NINO3.4 SST anomaly predictions from all models in the plume, equally weighted. Please note that the BCC model was not factored into these probabilities, even though it appears in the ENSO plume-of-models graphic. A standard Gaussian error is imposed over that average forecast, and its width is 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.

A large number of the models in the plume predict SSTs to remain below-normal at the level of a weak La Niña until Jan-Mar 2023. In particular, the chances of La Niña during Dec-Feb are 75%, and 56% for Jan-Mar, while those of the ENSO-neutral category are 25%, and 44% respectively. ENSO-neutral becomes the most likely category in Feb-Apr 2023 (65% chances), which remains the case until Jun-Aug 2023. The likelihood of El Niño is very low during first four seasons (less than 10%), but rises to 30% in May-Jul,41% in Jun-Aug, and 47% in Jul-Sep, 2023. 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 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 probabilities derived from the models in the IRI ENSO forecast plume indicate a preference for the continuation of the La Niña event until Dec-Feb or Jan-Mar 2023, while ENSO-neutral conditions are most likely thereafter till May-Jul 2023. The likelihood of El Niño development remains very low during boreal winter and spring, but gradually increases to greater than 47% in Jul-Sep 2023. However, there is a significant amount of uncertainty beyond boreal spring.

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 the official outlooks, which are generated and issued early in the month by CPC, and which will include some human judgment in combination with the model guidance.

Season La Niña Neutral El Niño
NDJ 91 9 0
DJF 75 25 0
JFM 56 44 0
FMA 33 65 2
MAM 17 78 5
AMJ 9 77 14
MJJ 9 61 30
JJA 10 49 41
JAS 10 43 47

ENSO Forecast

IRI Model-Based Probabilistic ENSO Forecast

Published: November 18, 2022

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.



Season La Niña Neutral El Niño
NDJ 91 9 0
DJF 75 25 0
JFM 56 44 0
FMA 33 65 2
MAM 17 78 5
AMJ 9 77 14
MJJ 9 61 30
JJA 10 49 41
JAS 10 43 47

ENSO Forecast

CPC Official Probabilistic ENSO Forecast

Published: November 10, 2022

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.



Season La Niña Neutral El Niño
OND 100 0 0
NDJ 92 8 0
DJF 76 24 0
JFM 59 40 1
FMA 40 57 3
MAM 24 70 6
AMJ 13 72 15
MJJ 10 64 26
JJA 9 54 37

ENSO Forecast

IRI ENSO Predictions Plume

Published: November 18, 2022

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 (2022 – 2023)
Model NDJ DJF JFM FMA MAM AMJ MJJ JJA JAS
Dynamical Models
AUS-ACCESS -0.73 -0.47 -0.17 0.10
BCC_CSM11m -0.25 0.41 0.93 1.22 1.42 1.67 1.98 2.27 2.43
CMC CANSIP -0.85 -0.69 -0.42 -0.12 0.14 0.34 0.58 0.84 1.01
COLA CCSM4 -1.06 -0.94 -0.80 -0.54 -0.30 -0.14 -0.02 0.10 0.18
CS-IRI-MM -1.08 -0.88 -0.59 -0.27 0.05 0.35
ECMWF
GFDL SPEAR -0.54 -0.33 -0.09 0.12 0.34 0.54 0.75 0.88 0.89
IOCAS ICM -1.08 -1.13 -1.16 -1.07 -0.92 -0.80 -0.70 -0.59 -0.52
JMA -0.94 -0.70 -0.51 -0.30 -0.16
KMA -0.90 -0.72 -0.49 -0.24 0.01
LDEO -0.57 -0.37 -0.20 -0.11 -0.10 -0.09 -0.01 0.07 0.08
MetFRANCE -0.96 -0.74 -0.49 -0.26
NASA GMAO -1.20 -0.87 -0.39 -0.07 0.11 0.28 0.51
NCEP CFSv2 -1.01 -0.76 -0.49 -0.23 -0.03 0.14 0.31
SAUDI-KAU -0.80 -0.47 -0.25 -0.08 0.06 0.13 0.12 0.12 0.15
SINTEX-F -0.63 -0.49 -0.24 0.01 0.23 0.48 0.74 1.00 1.19
UKMO -1.24 -1.11 -0.77 -0.46
Average, Dynamical models -0.864 -0.642 -0.384 -0.144 0.066 0.265 0.426 0.587 0.677
Statistical Models
BCC_RZDM -0.92 -0.87 -0.71 -0.39 -0.09 0.31 0.62 0.86 0.86
CPC CA -1.36 -1.16 -0.91 -0.54 -0.25 0.09 0.34 0.57 0.67
CPC MRKOV -1.23 -0.96 -0.70 -0.49 -0.26 -0.05 0.14 0.29 0.47
CSU CLIPR -0.88 -0.75 -0.63 -0.50 -0.39 -0.27 -0.16 0.17 0.49
IAP-NN -1.10 -1.03 -0.87 -0.66 -0.43 -0.22 -0.04 0.11 0.23
NTU CODA -1.15 -1.21 -1.03 -0.85 -0.75 -0.55 -0.31 -0.14 -0.27
UCLA-TCD -0.72 -0.58 -0.38 -0.15 0.09 0.31 0.48 0.61 0.71
Average, Statistical models -1.052 -0.938 -0.747 -0.511 -0.296 -0.055 0.154 0.352 0.452
Average, All models -0.921 -0.732 -0.494 -0.256 -0.061 0.140 0.314 0.477 0.572

Discussion of Current Forecasts

For two seasons (Dec-Feb, and Jan-Mar 2023), all statistical and several dynamical models indicate a continuation of the current La Niña event. Overall, there is a high chance for La Niña to persist during boreal winter (75% chance in Dec-Feb 2023), and to transition to ENSO-neutral in Feb-Apr (65% chance) and remain dominant during boreal spring time (78% in Mar-May, and 77% in Apr-Jun). The probabilities for El Niño conditions remain very low till boreal spring (less than 10%), but increasing to 47% in Jul-Sep 2023. Based on the multi-model mean prediction, and the expected skill of the models by start time and lead time, the probabilities (X100) 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
NDJ 91 9 0
DJF 75 25 0
JFM 56 44 0
FMA 33 65 2
MAM 17 78 5
AMJ 9 77 14
MJJ 9 61 30
JJA 10 49 41
JAS 10 43 47

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.


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.

ENSO Forecast

Forecast Probability Distribution Based on the IRI ENSO Prediction Plume

Published: November 18, 2022


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.


  • 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.

  • 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.

The CPC ENSO forecast is released at 9am (Eastern Time) on the second Thursday of each month.

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.