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2017 November Quick Look

Published: November 20, 2017

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 2017, the tropical Pacific reflected weak La Niña conditions, with SSTs in the east-central tropical Pacific past the threshold of La Niña and most atmosphere variables showing patterns suggestive of weak La Niña conditions. The collection of latest ENSO prediction models indicates weak La Niña as the most likely scenario for the remainder of Northern Hemisphere fall and for the winter. The official CPC/IRI outlook favors continuation of La Niña through winter, and carries a La Niña advisory.

Figures 1 ((the official CPC ENSO probability forecast) and 3 (the objective model-based IRI ENSO probability forecast) are often quite similar. However, occasionally they may differ noticeably. There can be several possible reasons for differences. One is the human forecasters, using their experience and judgment, may disagree to some degree with the models, which may have known biases. Another reason is 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, typically about a week apart, with the IRI forecast run later. Also note that the CPC forecast starts on the previous season while the IRI forecast starts on the current season.
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 09, 2017

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 predicted to continue (~65-75% chance) at least through the Northern Hemisphere winter 2017-18.

During October, weak La Niña conditions emerged as reflected by below-average sea surface temperatures (SSTs) across most of the central and eastern equatorial Pacific Ocean (Fig. 1). The weekly Niño indices were variable during the month, with values near -0.5° C during the past week in the Niño-3.4 and Niño-3 regions (Fig. 2). Sub-surface temperatures remained below average during October (Fig. 3), reflecting the anomalously shallow depth of the thermocline across the central and eastern Pacific (Fig. 4). Also, convection was suppressed near the International Date Line and slightly enhanced over parts of the Maritime Continent and the Philippines (Fig. 5). Over the equatorial Pacific Ocean, low-level trade winds were mainly near average, but the upper-level winds were strongly anomalously westerly and the Southern Oscillation Index was positive.  Overall, the ocean and atmosphere system reflects the onset of La Niña conditions.

For the remainder of the Northern Hemisphere fall and winter 2017-18, a weak La Niña is favored in the model averages of the IRI/CPC plume (Fig. 6). and also in the North American Multi-Model Ensemble (NMME) (Fig. 7). The consensus of forecasters is for the event to continue through approximately February-April 2018.  In summary, La Niña conditions are predicted to continue (~65-75% chance) at least through the Northern Hemisphere winter (click CPC/IRI consensus forecast for the chance of each outcome for each 3-month period).

La Niña is likely 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 Thursday November 16th). The outlooks generally favor above-average temperatures and below-median precipitation across the southern tier of the United States, and below-average temperatures and above-median precipitation across the northern tier of the United States.

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 14 November 2017. 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.

Climate Prediction Center
National Centers for Environmental Prediction
NOAA/National Weather Service
College Park, MD 20740

NOAA?CPC ENSO Forecast Image

CPC/IRI Early-Month Official ENSO Forecast Probabilities

Season La Niña Neutral El Niño
OND 2017 72% 28% 0%
NDJ 2017 76% 24% 0%
DJF 2017 73% 26% 1%
JFM 2017 64% 34% 2%
FMA 2018 51% 45% 4%
MAM 2018 35% 56% 9%
AMJ 2018 24% 61% 15%
MJJ 2018 18% 60% 22%
JJA 2018 18% 57% 25%

IRI ENSO Forecast

IRI Technical ENSO Update and Model-Based Probabilistic ENSO Forecast

Published: November 20, 2017

Note: The SST anomalies cited below refer to the OISSTv2 SST data set, and not ERSSTv4. OISSTv2 is often used for real-time analysis and model initialization, while ERSSTv4 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 ERSSTv4, and during very strong events the two datasets may differ by as much as 0.5 C. Additionally, the ERSSTv4 may tend to be cooler than OISSTv2, because ERSSTv4 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-November 2017, the NINO3.4 SST anomaly was in the weak La Niña category, and during the most recent weak was near the borderline of moderate La Niña. For October the SST anomaly was -0.46 C, near the borderline of ENSO-neutral and weak La Niña, and for August-October it was -0.35 C, in the cool ENSO-neutral range. 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 -1.1, qualifying for moderate La Niña for just that week. The pertinent atmospheric variables, including the upper and lower level zonal wind anomalies, have been showing patterns suggestive of La Niña, and he Southern Oscillation Index (SOI) has also been above average. Subsurface temperature anomalies across the eastern equatorial Pacific are somewhat below average. Given the current and recent SST anomalies, the subsurface profile and the La Niña patterns in most key atmospheric variables, an official diagnosis of La Niña is warranted and a La Niña Advisory has been issued.

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 stated that La Niña is favored for late fall and winter, with decidedly lower chances for ENSO-neutral. A La Niña Advisory was issued with that Discussion, following two months with a La Niña watch. The latest set of model ENSO predictions, from mid-November, now available in the IRI/CPC ENSO prediction plume, is discussed below. Those predictions suggest that the SST has the greatest chance for staying in the weak La Niña range for November-January through January-March 2017, with a lower but non-negligible probability for ENSO-neutral during that period.

As of mid-November, about 75% of the dynamical or statistical models predicts La Niña conditions for the initial Nov-Jan 2017-18 season, dropping a bit to 68% for the Jan-Mar 2018 season. During this period, no model predicts El Niño conditions, so that the remaining probability is only for neutral conditions. At lead times of 3 or more months into the future, statistical and dynamical models that incorporate information about the ocean’s observed subsurface thermal structure generally exhibit higher predictive skill than those that do not. For the Feb-Apr 2018 season, among models that do use subsurface temperature information, 38% of models predicts neutral conditions and 62% predicts La Niña conditions. For all models, at longer lead times beginning with Mar-May 2018, predictions for ENSO-neutral conditions dominate, with probabilities of 85% or more for Mar-May to May-Jul. At the end of the forecast range, Jun-Aug and Jul-Sep, the probability for El Niño rises to nearly 30% and La Niña probabilities decrease to near zero, leaving just over 70% for neutral.

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. This method shows probabilities for La Niña between 70 and 75% for Nov-Jan and Dec-Feb, decreasing thereafter to 45% for Feb-Apr and to 15-20% from Apr-Jun through Jul-Sep. Probabilities for neutral conditions begin at 25% for Nov-Jan, first exceed 50% in Feb-Apr, and peak around 75% in Apr-June, after which it drops to less than 50% for Jul-Sep as El Niño probabilities rise to 38% after having been less than 10% through Apr-Jun.  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 preference for weak La Niña conditions from Nov-Jan 2017-18 to Jan-Mar 2018, with neutral regaining highest probability status from Feb-Apr through the end of the forecast period in late summer 2018. Chances for El Niño are very small through Apr-Jun 2018, rising to near-climatological probabilities for May-Jul and slightly higher for Jun-Aug and Jul-Sep 2018. 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.

Climatological Probabilities

Season La Niña Neutral El Niño
DJF 36% 30% 34%
JFM 34% 38% 28%
FMA 28% 49% 23%
MAM 23% 56% 21%
AMJ 21% 58% 21%
MJJ 21% 56% 23%
JJA 23% 54% 23%
JAS 25% 51% 24%
ASO 26% 47% 27%
SON 29% 39% 32%
OND 32% 33% 35%
NDJ 35% 29% 36%

 

IRI ENSO Forecast Histogram Image

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

Season La Niña Neutral El Niño
NDJ 2017 75% 25% 0%
DJF 2018 71% 29% 0%
JFM 2018 60% 40% 0%
FMA 2018 45% 54% 1%
MAM 2018 26% 72% 2%
AMJ 2018 16% 76% 8%
MJJ 2018 17% 61% 22%
JJA 2018 17% 51% 32%
JJA 2018 16% 46% 38%

ENSO Forecast

IRI ENSO Predictions Plume

Published: November 20, 2017

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.

Notice about the NASA-GMAO model ENSO forecasts

GMAO staff discovered a mistake in the calculation of ensemble mean fields that resulted in an under-representation of ensemble spread and an over-representation of error in the ensemble mean. The mistake impacts forecasts from Feb 2017 through July 2019, and has been corrected as of August 2019. All forecasts hence will have the correct fields. We have not corrected any previous forecast output sent to IRI. If you need the retroactive corrected fields, please contact GMAO at: anna.borovikov@nasa.gov, kazumi.nakada@nasa.gov

List of Models Used

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

Seasons (2017-2018)
Model NDJ DJF JFM FMA MAM AMJ MJJ JJA JAS
Dynamical models
NASA GMAO model -1.2 -1.1 -0.9 -0.7 -0.5 -0.3 0
NCEP CFS version 2 -1 -1.2 -1.2 -0.9 -0.6 -0.4 -0.2 -0.1
Japan Met. Agency model -0.9 -1 -0.9 -0.7 -0.6
Beijing Climate Center BCC-CSM1.1M -0.7 -0.5 -0.2 0 0.2 0.3 0.5 0.6 0.6
 King Abdulaziz University (Saudi Arabia) -0.4 -0.2 0 0.2 0.3 0.4 0.4 0.5 0.6
Lamont-Doherty model -0.6 -0.4 -0.1 0.2 0.4 0.6 0.8 0.8 0.8
POAMA (Austr) model -0.8 -0.8 -0.8 -0.6 -0.5 -0.4 -0.3
ECMWF model -0.8 -0.9 -0.8 -0.7 -0.5
UKMO model -1.1 -1.2 -1 -0.9
KMA (Korea) SNU model -0.5 -0.4 -0.4 -0.3 -0.3 -0.2 -0.2 -0.2 -0.1
IOCAS (China) Intermed. Coupled model -0.9 -0.7 -0.6 -0.4 -0.2 0 0.1 0.3 0.3
COLA CCSM4 model -0.8 -0.9 -1 -0.7 -0.4 -0.1 -0.1 -0.1 -0.1
MÉTÉO FRANCE model -0.9 -0.9 -0.8 -0.6 -0.5
Japan Frontier Coupled model -0.7 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.2 -0.1
CSIR-IRI 3-model MME -0.5 -0.7 -0.6 -0.4 -0.3 -0.3
GFDL CM2.1 Coupled Climate model -1.2 -1.1 -0.8 -0.5 -0.2 0.1 0.4 0.8 0.9
Canadian Coupled Fcst Sys -0.9 -0.9 -0.9 -0.6 -0.4 -0.2 0 0.3 0.5
GFDL CM2.5 FLOR Coupled Climate model -1 -1 -0.9 -0.6 -0.3 0 0.3 0.7 1
Average, dynamical models -0.8 -0.8 -0.7 -0.5 -0.3 -0.1 0.1 0.3 0.4
Statistical models
 PSD-CIRES LIM -0.4 -0.4 -0.4 -0.4 -0.3 -0.3 -0.2 -0.2 -0.1
NCEP/CPC Markov model -0.8 -0.7 -0.6 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1
NCEP/CPC Constructed Analog -0.2 -0.2 -0.3 -0.3 -0.1 0 0 -0.1 -0.1
Landsea/Knaff CLIPER -0.5 -0.4 -0.3 -0.3 -0.3 -0.2 -0.2 -0.1 0
Univ. BC Neural Network -0.6 -0.7 -0.5 -0.4 -0.2 0 0.2 0.4 0.4
FSU Regression -0.6 -0.6 -0.5 -0.3 -0.3 -0.2 -0.1 0 0
TCD – UCLA -0.3 -0.2 -0.2 -0.1 0 0 0.1 0.1 0.1
Average, statistical models -0.5 -0.5 -0.4 -0.3 -0.2 -0.1 -0.1 0 0
Average, all models -0.7 -0.7 -0.6 -0.4 -0.3 -0.1 0 0.2 0.3

Discussion of Current Forecasts

Most of the models in the set of dynamical and statistical model predictions issued during mid-November 2017 predicts  weak La Niña conditions during the period of November-January 2017-18 to at least January-March 2018.  In the most recent week, the SST anomaly in the Nino3.4 region was -1.1 C, in the lower portion of the moderate La Niña range, and -0.46 C for the month of October, near the borderline of neutral and weak La Niña.  The most recent week may partly represent a temporary strengthening due to intraseasonal variability. The key atmospheric variables currently suggest weak La Niña patterns.  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
NDJ 2017 75% 25% 0%
DJF 2018 71% 29% 0%
JFM 2018 60% 40% 0%
FMA 2018 45% 54% 1%
MAM 2018 26% 72% 2%
AMJ 2018 16% 76% 8%
MJJ 2018 17% 61% 22%
JJA 2018 17% 51% 32%
JJA 2018 16% 46% 38%

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.

ENSO Forecast

Forecast Probability Distribution Based on the IRI ENSO Prediction Plume

Published: November 20, 2017

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.

Historical SST Anomalies Image

References

Real-time ENSO forecast skill evaluated over the last two decades, with focus on the onset of ENSO events. Ehsan, M.A., L’Heureux, M.L., Tippett, M.K., Robertson, A.W, Turmelle, J.P., npj Clim Atmos Sci, 2024.

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.

Forecast and model data used in our probabilistic forecast can be accessed by submitting a Request to Access IRI ENSO Data.

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.