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2020 September Quick Look

Published: September 18, 2020

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-September, SSTs in the east-central Pacific are below average, and most of the atmospheric variables are consistent with La Niña conditions. The majority of the model forecasts exceeds the threshold of La Niña SST conditions for fall and winter and implies about an 80% chance for La Niña for fall, 70% for winter. The official CPC/IRI outlook is similar to these model forecasts, calling for a 75% chance of La Niña for fall and winter. A La Niña advisory is posted.

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: September 10, 2020

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

ENSO Alert System Status: La Niña Advisory

Synopsis: La Niña conditions are present and are likely to continue through the Northern Hemisphere winter (~75% chance). 

In August, La Niña conditions were present, with below-average sea surface temperatures (SSTs) extending across the central and eastern equatorial Pacific Ocean (Fig. 1). In the last week, all Niño indices were negative, with the Niño-3.4 index at -0.9ºC and the Niño-1+2 and Niño-3 indices cooler than -1.0ºC (Fig. 2). Equatorial subsurface temperature anomalies averaged across 180°-100°W were negative (Fig. 3), with the largest departures observed in the east-central Pacific from the surface to 200m depth (Fig. 4). Atmospheric circulation anomalies over the tropical Pacific were also generally consistent with La Niña, despite sub-seasonal variability during the month.  The low-level and upper-level winds were near average for the month as a whole, but enhanced low-level easterly winds were prominent across the equatorial Pacific Ocean during early and late August.  Tropical convection remained suppressed over the western and central Pacific, and was near average over Indonesia (Fig. 5). Both the Southern Oscillation and Equatorial Southern Oscillation indices were positive.  Overall, the coupled ocean-atmosphere system was consistent with La Niña conditions.

A majority of the models in the IRI/CPC plume predict the continuation of La Niña (Niño-3.4 index less than -0.5°C) through the Northern Hemisphere winter 2020-21 (Fig. 6). The forecaster consensus supports that view, and favors a borderline moderate event (Niño-3.4 index near -1.0ºC) during the peak November-January season.  In summary, La Niña conditions are present and are likely to continue through the Northern Hemisphere winter (~75% chance; click CPC/IRI consensus forecast for the chance of each outcome for 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). Forecasts are also updated monthly in the Forecast Forum section of CPC’s Climate Diagnostics Bulletin. Additional perspectives and analysis are also available in an ENSO blog.

The next ENSO Diagnostics Discussion is scheduled for 8 October 2020. 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.


CPC/IRI Early-Month Official ENSO Forecast Probabilities

Season La Niña Neutral El Niño
ASO 2020 73% 27% 0%
SON 2020 77% 22% 1%
OND 2020 79% 20% 1%
NDJ 2020 78% 20% 2%
DJF 2020 74% 23% 3%
JFM 2020 65% 29% 6%
FMA 2020 52% 40% 8%
MAM 2020 38% 50% 12%
AMJ 2020 29% 53% 18%

 

IRI ENSO Forecast

IRI Technical ENSO Update

Published: September 18, 2020

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. During ENSO events, OISSTv2 often shows stronger anomalies than ERSSTv5, and during very strong events the two datasets may differ by as much as 0.5 C. Additionally, the ERSSTv5 may tend to be cooler than OISSTv2, 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 and so, half of the time, is based on a slightly older period and does not account as much for the slow warming trend in the tropical Pacific SST.

Recent and Current Conditions

In mid-September 2020, SSTs were below average, and in the weak/moderate La Niña range in the NINO3.4 region. The August SST anomaly was -0.60 C, in the weak La  Niña range, and for Jun-Aug it was -0.43 C, still in the neutral category. 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 less. 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. The most recent weekly anomaly in the NINO3.4 region was -1.0 C, at the borderline between weak and moderate La Niña. Most of the key atmospheric variables, such as the low-level and upper-level zonal wind anomalies and patterns of cloudiness and rainfall, suggest La Niña conditions. The traditional and equatorial Southern Oscillation Indices have been positive, and anomalously dry conditions have been observed around the date line and in the west-central part of the basin. Subsurface temperature anomalies from the dateline eastward in the equatorial Pacific were below-average during early and mid-September. In summary, overall current conditions are suggestive of La Niña. A La Niña advisory was initially issued earlier this month.

Expected Conditions

What is the outlook for the ENSO status going forward? The most recent official diagnosis and outlook was issued approximately one week ago in the NOAA/Climate Prediction Center ENSO Diagnostic Discussion, produced jointly by CPC and IRI; it states that La Niña conditions are present and are most likely to continue through fall and winter, with a 75% chance. The latest set of model ENSO predictions from mid-September, now available in the IRI/CPC ENSO prediction plume, is next discussed: As of mid-September, 85% of the dynamical or statistical models predicts La Niña conditions for the initial Sep-Nov season, while 15% predicts neutral. Percentages of models predicting La Niña drop slowly going forward into winter: 77% for Nov-Jan, and 62% for Dec-Feb. The percentage of models predicting neutral increases to 35% for Dec-Feb, and begins exceeding 50% starting in Feb-Apr. By Jan-Mar, the percentage of models predicting La Niña drops to 54%, and for Feb-Apr and beyond neutral is the dominant probability.

Note  – Only models that produce a new ENSO prediction every month are included in the above statement.

Caution is advised in interpreting the distribution of model predictions as the actual probabilities. At longer leads, the skill of the models degrades, and skill uncertainty must be convolved with the uncertainties from initial conditions and differing model physics, leading 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 from that taken verbatim from the raw model predictions.

An alternative way to assess the probabilities of the three possible ENSO conditions is more quantitatively precise and less vulnerable to sampling errors than the categorical tallying method used above. This alternative method uses the mean of the predictions of all models on the plume, equally weighted, and constructs a standard error function centered on that mean. The standard error is Gaussian in shape, and has 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. Using this method, chances for La Niña are 79% for the Sep-Nov season, while chances for ENSO-neutral are 21%. Going forward, probabilities for La Niña slowly decrease to 75% for Nov-Jan, 68% for Dec-Feb, 51% for Jan-Mar and below 20% for Mar-May through May-Jul. Chances for neutral rise above 30% beginning in Dec-Feb, to 63% by Feb-Apr and remain dominant through the final season of May-Jul.  El Niño probabilities are less than 10% through Mar-May, then jump to 28% by May-Jul. A plot of the probabilities generated from this most recent IRI/CPC ENSO prediction plume using the multi-model mean and the Gaussian standard error method summarizes the model consensus out to about 10 months into the future.

The same cautions mentioned above for the distributional count of model predictions apply to this Gaussian standard error method of inferring probabilities, due to differing model biases and skills. 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.

In summary, the probabilities derived from the models on the IRI/CPC plume describe, on average, a slim chance for El Niño conditions throughout most of the forecast period, and a preference for La Niña conditions relative to neutral conditions from the initial season of Sep-Nov extending through Jan-Mar, after which time the chances for La Niña fall rapidly as neutral becomes more likely outcome through the remainder of the forecast periods.

A caution regarding this latest set of model-based ENSO plume predictions, is that factors such as known specific model biases and recent changes that the models may have missed will be taken into account in the next official outlook to be generated and issued early next month by CPC and IRI, which will include some human judgment in combination with the model guidance.


IRI ENSO Forecast Histogram Image

IRI/CPC Mid-Month Model-Based ENSO Forecast Probabilities

Season La Niña Neutral El Niño
SON 2020 79% 21% 0%
OND 2020 77% 23% 0%
NDJ 2020 75% 24% 1%
DJF 2021 68% 31% 1%
JFM 2021 51% 46% 3%
FMA 2021 32% 63% 5%
MAM 2021 18% 75% 7%
AMJ 2021 13% 71% 16%
MJJ 2021 13% 59% 28%

 

ENSO Forecast

IRI Model-Based Probabilistic ENSO Forecast

Published: September 18, 2020

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.


IRI ENSO Forecast Histogram Image


IRI/CPC Mid-Month Model-Based ENSO Forecast Probabilities

Season La Niña Neutral El Niño
SON 2020 79% 21% 0%
OND 2020 77% 23% 0%
NDJ 2020 75% 24% 1%
DJF 2021 68% 31% 1%
JFM 2021 51% 46% 3%
FMA 2021 32% 63% 5%
MAM 2021 18% 75% 7%
AMJ 2021 13% 71% 16%
MJJ 2021 13% 59% 28%

 

ENSO Forecast

CPC Official Probabilistic ENSO Forecast

Published: September 10, 2020

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.


NOAA?CPC ENSO Forecast Image
NOAA/CPC ENSO Forecast Graphic, courtesy of NOAA/CPC

CPC/IRI Early-Month Official ENSO Forecast Probabilities

Season La Niña Neutral El Niño
ASO 2020 73% 27% 0%
SON 2020 77% 22% 1%
OND 2020 79% 20% 1%
NDJ 2020 78% 20% 2%
DJF 2020 74% 23% 3%
JFM 2020 65% 29% 6%
FMA 2020 52% 40% 8%
MAM 2020 38% 50% 12%
AMJ 2020 29% 53% 18%

 

ENSO Forecast

IRI ENSO Predictions Plume

Published: September 18, 2020

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 (2020 – 2021)
Model SON OND NDJ DJF JFM FMA MAM AMJ MJJ
Dynamical Models
NASA GMAO -1.63 -1.92 -1.92 -1.62 -1.17 -0.71 -0.34
NCEP CFSv2 -1.13 -1.51 -1.7 -1.61 -1.25 -0.8 -0.44 -0.16
JMA -1.05 -1.12 -1.1 -0.9 -0.59
BCC_CSM11m -0.9 -0.67 -0.4 -0.16 0.12 0.4 0.68 0.96 1.22
SAUDI-KAU -0.66 -0.49 -0.38 -0.25 -0.11 0.01 0.08 0.13 0.19
LDEO 0.05 0.16 0.3 0.46 0.58 0.59 0.57 0.51 0.48
AUS/ACCESS -0.98 -1.27 -1.37 -1.23
ECMWF -0.81 -0.91 -0.98 -0.95 -0.79
UKMO -1.15 -1.41 -1.47 -1.29
KMA SNU -0.25 -0.07 0.11 0.25 0.34 0.38 0.4 0.42 0.47
IOCAS ICM -1.16 -1.37 -1.52 -1.55 -1.41 -1.16 -0.96 -0.79 -0.65
COLA CCSM4 -0.98 -1.25 -1.56 -1.64 -1.47 -1.15 -0.82 -0.57 -0.43
MetFRANCE -1.04 -1.04 -0.96 -0.8 -0.58
SINTEX-F -0.79 -0.74 -0.61 -0.42 -0.21 -0.04 0.05 0.12 0.2
CS-IRI-MM -0.48 -0.53 -0.51 -0.41 -0.16 0.09
GFDL CM2.1 -0.44 -0.31 -0.18 0.07 0.38 0.67 0.86 1.03 1.18
CMC CANSIP -1.05 -1.26 -1.37 -1.3 -1.07 -0.77 -0.54 -0.35 -0.16
GFDL FLOR -0.64 -0.57 -0.46 -0.25 0.02 0.28 0.51 0.71 0.94
Average, Dynamical Models -0.84 -0.90 -0.89 -0.76 -0.46 -0.17 0.00 0.18 0.34
Statistical Models
NTU CODA -0.76 -0.9 -1.1 -1.07 -0.99 -0.8 -0.69 -0.61 -0.56
BCC_RZDM -0.83 -0.93 -1.01 -1 -0.88 -0.63 -0.4 -0.15 0.06
CPC MRKOV -0.79 -0.73 -0.6 -0.44 -0.31 -0.23 -0.13 -0.02 0.07
CPC CA -0.52 -0.53 -0.6 -0.6 -0.49 -0.23 -0.05 0.14 0.22
CSU CLIPR -0.63 -0.69 -0.74 -0.8 -0.59 -0.38 -0.17 -0.12 -0.06
IAP-NN -0.71 -0.77 -0.8 -0.76 -0.67 -0.52 -0.37 -0.22 -0.11
FSU REGR -0.6 -0.61 -0.61 -0.55 -0.48 -0.37 -0.27 -0.16 -0.06
UCLA-TCD -0.93 -1.01 -0.99 -0.87 -0.68 -0.48 -0.3 -0.16 -0.08
Average, Statistical Models -0.72 -0.77 -0.81 -0.76 -0.64 -0.46 -0.30 -0.16 -0.07

Discussion of Current Forecasts

Many of the models in the set of dynamical and statistical model predictions issued during mid-September 2020 show weak or moderate La Niña SST conditions for the  fall, becoming more likely weak La Niña conditions by winter.  In the most recent week, the SST anomaly in the NINO3.4 region was -1.0 C, at the threshold of moderate La Niña, and -0.60 C for the month of August, in the weak La Niña category. During mid-September the subsurface water temperatures were moderately below-average, while the SST had cooled further than the levels observed through most of August. Approximately 85% of the dynamical and statistical models predict weak La Niña conditions for the Sep-Nov season, decreasing to 54% by Jan-Mar and below 40% thereafter. Objective model-based La Niña probabilities are near 80% for Sep-Nov, slowly dropping to 75% for Nov-Jan, 51% by Jan-Mar and much lower thereafter. 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 -0.5C and 0.5C thresholds) over the coming 9 seasons are:

IRI/CPC Mid-Month Model-Based ENSO Forecast Probabilities

Season La Niña Neutral El Niño
SON 2020 79% 21% 0%
OND 2020 77% 23% 0%
NDJ 2020 75% 24% 1%
DJF 2021 68% 31% 1%
JFM 2021 51% 46% 3%
FMA 2021 32% 63% 5%
MAM 2021 18% 75% 7%
AMJ 2021 13% 71% 16%
MJJ 2021 13% 59% 28%

 

Summary of forecasts issued over last 22 months

The following interactive plot shows 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 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.
Hovering over any single model will highlight that particular model in the chart.
Clicking a particular model will hide/show that model in the chart.
At the bottom of the plot, you can select which models to show in the chart: all the models, the dynamical models only, or the statistical models only.


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.

Once an IRI ENSO probability forecast has been published, the results stand even if a model reports an error and changes their data. When this happens we will update the plume with the model's correct values even though our forecast hasn't changed. What this means is that our forecast is always the same, but the underlying data may be different from what we based our forecast on.

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 1991-2020 period as the base period, or a period not very different from it.

Historical SST Anomalies Image

ENSO Forecast

Forecast Probability Distribution Based on the IRI ENSO Prediction Plume

Published: September 18, 2020


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.


  • Model Based Prediction Percentiles Image

    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.

  • Model Based Prediction Distribution Image

    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.

All data from this website is covered under the Creative Commons Attribution 4.0 License. When citing IRI ENSO images or data, please use "Images [or Data] provided by The International Research Institute for Climate and Society, Columbia University Climate School", with a link to https://iri.columbia.edu/ENSO.