Please refer to our licensing agreement for permission to use IRI ENSO materials. The CPC/IRI materials are not included in this licensing.
IRI ENSO Forecast
December 2021 Quick Look
Published: December 20, 2021
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)
Use the navigation menu on the right to navigate to the different forecast sections
In mid-December, Sea Surface Temperatures remain well below normal in the central-eastern equatorial Pacific. The evolution of key oceanic and atmospheric variables is consistent with weak La Niña conditions, and therefore, a La Niña Advisory remained in place for Dec 2021. A large majority of the models predict SSTs to stay below-normal during boreal winter, and then return to ENSO-neutral levels during spring. Similar to the most-recent official CPC/IRI ENSO Outlook issued on December 9, 2021, this objective model-based ENSO outlook also anticipates a continuation of the weak La Niña event with high probability during Dec-Feb, persisting until Feb-Apr, dissipating in Mar-May (36%) and return to ENSO-neutral conditions with high probabilities for rest of the forecast period.
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.
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
CPC/IRI ENSO Update
Published: December 9, 2021
El Niño/Southern Oscillation (ENSO) Diagnostic Discussion issued jointly by the Climate Prediction Center/NCEP/NWS and the International Research Institute for Climate and Society
ENSO Alert System Status: La Niña Advisory
Synopsis: La Niña is favored to continue through the Northern Hemisphere winter 2021-22 (~95% chance) and transition to ENSO-neutral during the spring 2022 (~60% chance during April-June).
In November, the continuation of La Niña was reflected in the below-average sea surface temperatures (SSTs) extending across the equatorial Pacific Ocean (Fig. 1). In the last week, all of the Niño index values were between -0.7ºC and -1.0ºC, with the coolest anomalies in the Niño-3.4 region (Fig. 2). Below-average subsurface temperatures weakened slightly compared to the previous month (Fig. 3), but a large pool of negative temperature anomalies still extended across the central and eastern Pacific, down to ~200m depth (Fig. 4). Low-level easterly and upper-level westerly wind anomalies persisted over most of the equatorial Pacific. Enhanced convection and rainfall were observed over Indonesia and convection was suppressed over the central and western equatorial Pacific (Fig. 5). The Southern Oscillation Index and Equatorial Southern Oscillation Index were more positive than the previous month. Overall, the coupled ocean-atmosphere system was consistent with La Niña.
The IRI/CPC plume average of forecasts for the Niño-3.4 SST index indicates La Niña will continue through the February-April 2022 season (Fig. 6). The forecaster consensus anticipates a transition to ENSO-neutral sometime during the Northern Hemisphere spring, with chances for La Niña declining below 50% after March-May 2022. The chance of a moderate-strength La Niña declined slightly from last month’s update, but there is still a 59% chance of the Niño-3.4 index reaching a value less than -1.0ºC for the November 2021 – January 2022 season. In summary, La Niña is favored to continue through the Northern Hemisphere winter 2021-22 (~95% chance) and transition to ENSO-neutral during the spring 2022 (~60% chance during April-June; click CPC/IRI consensus forecast for the chances in each 3-month period).
La Niña is anticipated to affect temperature and precipitation across the United States during the upcoming months (the 3-month seasonal temperature and precipitation outlooks will be updated on Thurs. Dec. 16th).
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 13 January 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 |
|---|---|---|---|
| NDJ | 100 | 0 | 0 |
| DJF | 95 | 5 | 0 |
| JFM | 81 | 19 | 0 |
| FMA | 65 | 35 | 0 |
| MAM | 50 | 49 | 1 |
| AMJ | 34 | 62 | 4 |
| MJJ | 25 | 63 | 12 |
| JJA | 22 | 58 | 20 |
| JAS | 22 | 52 | 26 |
Please refer to our licensing agreement for permission to use IRI ENSO materials. The CPC/IRI materials are not included in this licensing.
IRI ENSO Forecast
IRI Technical ENSO Update
Published: December 20, 2021
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
La Niña conditions are well established, with Sea Surface Temperatures (SST) well below average across most of the equatorial Pacific Ocean. The November SST anomaly for NINO3.4 region (5S-5N; 170W-120W) was -0.89 C, and for Sep-Nov season it was -0.80. The IRI’s definition of a weak La Niña, like NOAA/Climate Prediction Center’s, requires that the SST anomaly in the NINO3.4 region be between -0.5 C and -1.0 C. The most recent weekly SST anomaly in the NINO3.4 region for the week ending 8 December 2021 was -1.1 C. Subsurface temperatures in the eastern equatorial Pacific remain below-average, the traditional and equatorial Southern Oscillation Indices show sustained positive values, and above-normal Trade Winds are observed near and west of the Date Line. The upper-level westerly wind anomalies that would accompany a large-scale response to La Niña conditions are present, together with reduced cloudiness near the date line and increased rainfall over Indonesia, all of which are consistent with weak La Niña conditions.
In summary, the most recent observations of key oceanic and atmospheric variables indicate well established, though weak La Niña conditions. A La Niña advisory from CPC remained in effect.
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? The most recent official diagnosis and outlook was issued on 09 December 2021 in the NOAA/Climate Prediction Center ENSO Diagnostic Discussion, produced jointly by CPC and IRI. It states that La Niña conditions continued during the month of November, and highly favored during the Northern Hemisphere winter, gradually decreasing and transitioning to ENSO-Neutral in spring of 2022.
The latest set of model ENSO predictions from mid-December is now available in the the IRI/CPC 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. Currently, however, the NASA-GMAO model is not factored into the probabilistic update, even though it appears on 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.
Using this method, La Niña is highly probable (91%) during Dec-Feb, while chances for ENSO-neutral is just 9%. Going forward, probabilities for La Niña decrease to 80% for Jan-Mar, 59% for Feb-Apr, 36% for Mar-May, and less than the La Niña climatological threshold probabilities for the rest of the forecast period. While highly probable until boreal spring, the plume diagram indicates a gradual further weaking of the current La Niña event. Chances of the ENSO-neutral state rise above 50% beginning in Mar-May, reaching above 71% for Apr-Jun, and decreasing afterwards; thus, the ENSO-neutral state becomes the most likely outcome from Mar-May 2022 onwards. El Niño probabilities start at 1% in Mar-May and reach up to 29% at the end of the forecast period (Aug-Oct). A plot of the probabilities summarizes the forecast evolution. The climatological probabilities for La Niña, 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/CPC plume indicate a high preference for a weak La Niña relative to neutral conditions during boreal winter and possibly extending into the early spring of 2022, after which ENSO-neutral conditions becomes the most likely outcome through the remaining forecast periods. The likelihood for El Niño development remains very low during winter and spring time; however, it increases up to 29% at the end of the forecast period.
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 IRI, and which will include some human judgment in combination with the model guidance.
| Season | La Niña | Neutral | El Niño |
|---|---|---|---|
| DJF | 91 | 9 | 0 |
| JFM | 80 | 20 | 0 |
| FMA | 59 | 41 | 0 |
| MAM | 36 | 63 | 1 |
| AMJ | 25 | 71 | 4 |
| MJJ | 22 | 66 | 12 |
| JJA | 22 | 57 | 21 |
| JAS | 22 | 52 | 26 |
| ASO | 25 | 46 | 29 |
Please refer to our licensing agreement for permission to use IRI ENSO materials. The CPC/IRI materials are not included in this licensing.
IRI ENSO Forecast
IRI/CPC Model-Based Probabilistic ENSO Forecast
Published: December 20, 2021
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 |
|---|---|---|---|
| DJF | 91 | 9 | 0 |
| JFM | 80 | 20 | 0 |
| FMA | 59 | 41 | 0 |
| MAM | 36 | 63 | 1 |
| AMJ | 25 | 71 | 4 |
| MJJ | 22 | 66 | 12 |
| JJA | 22 | 57 | 21 |
| JAS | 22 | 52 | 26 |
| ASO | 25 | 46 | 29 |
IRI ENSO Forecast
CPC/IRI Official Probabilistic ENSO Forecast
Published: December 9, 2021
The official CPC/IRI 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/IRI 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 |
|---|---|---|---|
| NDJ | 100 | 0 | 0 |
| DJF | 95 | 5 | 0 |
| JFM | 81 | 19 | 0 |
| FMA | 65 | 35 | 0 |
| MAM | 50 | 49 | 1 |
| AMJ | 34 | 62 | 4 |
| MJJ | 25 | 63 | 12 |
| JJA | 22 | 58 | 20 |
| JAS | 22 | 52 | 26 |
Please refer to our licensing agreement for permission to use IRI ENSO materials. The CPC/IRI materials are not included in this licensing.
IRI ENSO Forecast
IRI/CPC ENSO Predictions Plume
Published: December 20, 2021
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.
Because of occasional data corrections and late model runs following the time of ENSO product issuance, the data shown in the ENSO forecast table and the ENSO plume graph may not always match. The best source of the ENSO forecast data is http://iri.columbia.edu/~forecast/ensofcst/Data/ensofcst_ALLtoMMYY where MM is the month number and YY is the year.
| Seasons (2021 – 2022) | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| Model | DJF | JFM | FMA | MAM | AMJ | MJJ | JJA | JAS | ASO |
| Dynamical Models | |||||||||
| NASA GMAO | -2.90 | -3.19 | -2.80 | -2.32 | -1.99 | -1.71 | -1.40 | ||
| NCEP CFSv2 | -1.16 | -1.27 | -1.21 | -1.03 | -0.88 | -0.70 | -0.46 | ||
| JMA | -0.92 | -0.74 | -0.49 | -0.25 | -0.02 | ||||
| BCC_CSM11m | |||||||||
| SAUDI-KAU | -0.91 | -0.65 | -0.32 | -0.10 | 0.04 | 0.08 | 0.11 | 0.16 | 0.26 |
| LDEO | -0.28 | -0.02 | 0.20 | 0.34 | 0.41 | 0.48 | 0.57 | 0.56 | 0.50 |
| AUS-ACCESS | -0.90 | -0.70 | -0.43 | -0.20 | |||||
| ECMWF | -0.94 | -0.75 | -0.49 | -0.26 | -0.03 | ||||
| UKMO | -1.10 | -0.98 | -0.73 | -0.52 | |||||
| KMA | -1.32 | -1.22 | -1.00 | -0.79 | -0.67 | ||||
| IOCAS ICM | -0.90 | -0.84 | -0.72 | -0.63 | -0.58 | -0.52 | -0.46 | -0.42 | -0.47 |
| COLA CCSM4 | -1.12 | -1.05 | -0.81 | -0.49 | -0.23 | -0.08 | -0.03 | -0.04 | -0.07 |
| MetFRANCE | -1.16 | -1.11 | -0.81 | -0.37 | -0.36 | -0.29 | 0.10 | ||
| SINTEX-F | -0.69 | -0.59 | -0.41 | -0.25 | -0.03 | 0.24 | 0.47 | 0.58 | 0.58 |
| CS-IRI-MM | -0.77 | -0.56 | -0.29 | -0.06 | 0.14 | 0.29 | |||
| GFDL SPEAR | -0.47 | -0.27 | -0.08 | 0.09 | 0.23 | 0.35 | 0.42 | 0.37 | 0.25 |
| CMC CANSIP | -1.11 | -1.01 | -0.78 | -0.56 | -0.33 | -0.10 | 0.12 | 0.27 | 0.34 |
| Average, Dynamical models | -1.042 | -0.935 | -0.698 | -0.463 | -0.307 | -0.178 | -0.057 | 0.211 | 0.200 |
| Statistical Models | |||||||||
| NTU CODA | -0.98 | -0.84 | -0.66 | -0.50 | -0.26 | -0.20 | -0.22 | -0.27 | -0.28 |
| BCC_RZDM | |||||||||
| CPC MRKOV | -0.99 | -0.80 | -0.64 | -0.47 | -0.30 | -0.16 | -0.04 | 0.09 | 0.26 |
| CPC CA | -0.94 | -0.81 | -0.54 | -0.38 | -0.27 | -0.20 | -0.11 | -0.01 | 0.11 |
| CSU CLIPR | -0.76 | -0.61 | -0.46 | -0.31 | -0.27 | -0.23 | -0.19 | -0.11 | -0.03 |
| IAP-NN | -1.06 | -1.05 | -0.95 | -0.80 | -0.66 | -0.52 | -0.41 | -0.33 | -0.28 |
| UCLA-TCD | -0.87 | -0.77 | -0.61 | -0.44 | -0.31 | -0.23 | -0.23 | -0.31 | -0.46 |
| Average, Statistical models | -0.933 | -0.813 | -0.643 | -0.484 | -0.345 | -0.256 | -0.201 | -0.157 | -0.113 |
| Average, All models | -1.012 | -0.902 | -0.683 | -0.469 | -0.318 | -0.206 | -0.111 | 0.041 | 0.056 |
Discussion of Current Forecasts
A slim majority of the models in the set of dynamical and statistical model predictions issued during mid-December 2021 show moderate La Niña SST anomalies, while various other models suggest a weak La Niña conditions in next two seasons (Dec-Feb, and Jan-Mar), which gradually decreases during spring of 2022. The ENSO-neutral state is likely to be increasing steadily during this time, and dominant in Mar-May and during rest of the forecast period, while the chances of El Niño conditions are very low throughout the forecast period.
Dynamical and statistical models show consistent and high probabilities of La Niña conditions during boreal winter time, and then decreasing in early spring of 2022. ENSO-neutral conditions are again more likely category than La Niña during rest of forecast period. 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, 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 |
|---|---|---|---|
| DJF | 91 | 9 | 0 |
| JFM | 80 | 20 | 0 |
| FMA | 59 | 41 | 0 |
| MAM | 36 | 63 | 1 |
| AMJ | 25 | 71 | 4 |
| MJJ | 22 | 66 | 12 |
| JJA | 22 | 57 | 21 |
| JAS | 22 | 52 | 26 |
| ASO | 25 | 46 | 29 |
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.
Please refer to our licensing agreement for permission to use IRI ENSO materials. The CPC/IRI materials are not included in this licensing.
IRI ENSO Forecast
Forecast Probability Distribution Based on the IRI/CPC ENSO Prediction Plume
Published: December 20, 2021
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.
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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.