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IRI ENSO Forecast

2016 July Quick Look

Published: July 21, 2016

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

During mid-July 2016 the tropical Pacific SST anomaly was slightly below zero, indicating ENSO-neutral conditions. The key atmospheric variables also indicate neutral ENSO conditions. This includes near-average upper and lower level tropical Pacific winds, as well as mainly near-normal cloudiness and rainfall patterns in the central and eastern equatorial Pacific. Most ENSO prediction models indicate neutral ENSO conditions during July, with likely development of La Niña during August or September, lasting through fall and into winter. Most likely strength is weak.

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 Dec-Feb
  • Typically persist for 9-12 months, though occasionally persisting for up to 2 years
  • Typically recur every 2 to 7 years

Figure 1 is based on a consensus of CPC and IRI forecasters, in association with the official CPC/IRI ENSO Diagnostic Discussion

Figure 3 is purely objective, based on regression, using equally weighted model predictions from the plume

IRI ENSO Forecast

CPC/IRI ENSO Update

Published: July 14, 2016

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 Watch

Synopsis: La Niña is favored to develop during August – October 2016, with about a 55-60% chance of La Niña during the fall and winter 2016-17.

ENSO-neutral conditions were observed during the past month, as indicated by near-to-below average surface temperatures (SST) across the eastern equatorial Pacific Ocean (Fig. 1). While the Niño-4 region was slightly above average, the other Niño indices were either slightly below average or near zero during June (Fig. 2). Below-average subsurface temperatures continued (Fig. 3) and extended to the surface in parts of the central and eastern equatorial Pacific (Fig. 4). Atmospheric anomalies over the tropical Pacific Ocean also indicated ENSO-neutral conditions. The traditional Southern Oscillation index was slightly positive while the equatorial Southern Oscillation index was near zero. The upper and lower-level winds were both near average across most of the tropical Pacific. Convection was slightly suppressed over portions of the western tropical Pacific and enhanced over part of Indonesia (Fig. 5). Collectively, these atmospheric and oceanic anomalies reflect ENSO-neutral conditions.

Many models favor La Niña (3-month average Niño-3.4 index less than or equal to -0.5°C) by the end of the Northern Hemisphere summer, continuing during fall and lasting into winter (Fig. 6). Statistical models predict a later onset time (i.e., mid-fall) than dynamical models, and also predict a relatively weaker event. The forecaster consensus is somewhat of a compromise between the two model types, favoring La Niña onset during the August-October season, and predicting a weak event (Niño-3.4 index between -0.5°C and -1.0°C), if an event were to form. Overall, ENSO-neutral conditions currently prevail and La Niña is favored to develop by August – October 2016, with about a 55-60% chance of La Niña during the fall and winter 2016-17 (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 11 Aug 2016. 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

CPC/IRI Early-Month Official ENSO Forecast Probabilities

Season La Niña Neutral El Niño
JJA 2016 21% 77% 2%
JAS 2016 39% 59% 2%
ASO 2016 50% 48% 2%
SON 2016 57% 41% 2%
OND 2016 59% 38% 3%
NDJ 2016 61% 36% 3%
DJF 2017 58% 38% 4%
JFM 2017 54% 41% 5%
FMA 2017 48% 46% 6%

IRI ENSO Forecast

IRI Technical ENSO Update

Published: July 21, 2016

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 usually shows stronger anomalies than ERSSTv4, and during very strong events the two datasets may differ by as much as 0.5 C.

Recent and Current Conditions

Following the end of the strong 2015-16 El Niño in May, ENSO-neutral conditions have prevailed. For June 2016 the average NINO3.4 SST anomaly was -0.12 C, indicative of neutral ENSO conditions, and for Mar-May it was 0.42 C, in the upper portion of the ENSO-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 -0.6, at a weak La Niña level, and the first week to be -0.5 or cooler for roughly two years. Accompanying this ocean condition, a generally neutral condition is observed in the atmosphere, including weak lower-level wind anomalies and weak convection anomalies across the equatorial Pacific.  The Southern Oscillation Index (SOI) and the equatorial SOI have been slightly positive, leaning mildly toward La Niña, but neutral ENSO conditions are indicated, overall.

Expected Conditions

What is the outlook for the ENSO status going forward? The most recent official diagnosis and outlook was issued one week ago in the NOAA/Climate Prediction Center ENSO Diagnostic Discussion, produced jointly by CPC and IRI; it called for a roughly 55-60% likelihood of La Niña during fall and winter 2016-17.  The latest set of model ENSO predictions, from mid-July, now available in the IRI/CPC ENSO prediction plume, is discussed below. Currently, the Nino3.4 SST anomalies are in the ENSO-neutral category, but subsurface temperature anomalies across the eastern equatorial Pacific are below average. With the onset of enhanced easterly trade winds in the equatorial Pacific fairly likely, the SST is poised to fall to weak  La Niña levels by early fall, and if this happens the La Niña condition would likely last through the remainder of the year.

As of mid-July, 38% of the dynamical or statistical models predicts La Niña conditions for the initial Jul-Sep 2016 season, while 62% predict neutral ENSO.  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 Oct-Dec 2016 season, among models that do use subsurface temperature information, 43% predicts ENSO-neutral conditions and 57% predicts La Niña conditions. For all model types, the probabilities for La Niña are close to 50% from Aug-Oct 2016 through Nov-Jan 2016-17, about 55-60% for Dec-Feb and Jan-Mar 2017, and drop back below 50% beginning in Feb-Apr 2017. No model predicts El Niño for any of the periods forecast.

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 at 44% for Jul-Sep 2016, 50% for Aug-Oct, and rising to between about 55% and 60% from Oct-Dec 2016 through Jan-Mar 2017, and dropping below 50% beginning in the final forecast period of Mar-May 2017. Probabilities for El Niño are 8% or lower throughout the forecast period.  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 likelihood for La Niña conditions increasing to greater than 50% from Aug-Oct through early 2017, but never more than about 55-60% during fall through mid-winter 2016-17.  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 in early October by CPC and IRI, which will include some human judgement 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

IRI/CPC Model-Based Probabilistic ENSO Forecast

Published: July 21, 2016



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

Season La Niña Neutral El Niño
JAS 2016 44% 55% 1%
ASO 2016 50% 47% 3%
SON 2016 53% 42% 5%
OND 2016 55% 39% 6%
NDJ 2016 55% 37% 8%
DJF 2016 58% 36% 6%
JFM 2016 54% 41% 5%
FMA 2017 51% 46% 3%
MAM 2017 43% 55% 2%

IRI ENSO Forecast

CPC/IRI Official Probabilistic ENSO Forecast

Published: July 14, 2016



CPC/IRI Early-Month Official ENSO Forecast Probabilities

Season La Niña Neutral El Niño
JJA 2016 21% 77% 2%
JAS 2016 39% 59% 2%
ASO 2016 50% 48% 2%
SON 2016 57% 41% 2%
OND 2016 59% 38% 3%
NDJ 2016 61% 36% 3%
DJF 2017 58% 38% 4%
JFM 2017 54% 41% 5%
FMA 2017 48% 46% 6%

IRI ENSO Forecast

IRI/CPC ENSO Predictions Plume

Published: July 21, 2016

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 produce 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 (2016-2017)
Model JAS ASO SON OND NDJ DJF JFM FMA MAM
Dynamical models
NASA GMAO model -0.4 -0.2 -0.1 -0.1 0 0.1 0.2
NCEP CFS version 2 -0.3 -0.6 -0.7 -0.8 -0.9 -0.8 -0.6 -0.3
Japan Met. Agency model -0.8 -0.7 -0.6 -0.6 -0.8
Scripps Inst. HCM -1.1 -1.4 -1.7 -1.9 -1.9 -1.9 -1.7 -1.5 -1.2
Lamont-Doherty model 0.2 0.2 0.2 0 -0.2 -0.2 -0.2 -0.1 -0.1
POAMA (Austr) model -0.9 -0.9 -0.8 -0.7 -0.7 -0.7 -0.6
ECMWF model -0.4 -0.3 -0.2 -0.2 -0.2
UKMO model -0.7 -0.8 -0.9 -1
KMA (Korea) SNU model -0.7 -0.8 -0.9 -1 -1.1 -1.1 -1.2 -1.2 -1.3
IOCAS (China) Intermed. Coupled model -0.2 -0.3 -0.6 -0.8 -0.9 -0.9 -0.9 -0.8 -0.8
COLA CCSM4 model -0.4 -0.5 -0.7 -1 -1.2 -1.2 -1 -0.7 -0.4
MÉTÉO FRANCE model -0.4 -0.4 -0.4 -0.3 -0.3
Japan Frontier Coupled model 0 0.1 0 -0.1 -0.2 -0.2 -0.2 -0.1 -0.1
CSIR-IRI 3-model MME -0.1 -0.2 -0.3 -0.3 -0.5 -0.6
GFDL CM2.1 Coupled Climate model -1.1 -1 -0.9 -0.7 -0.6 -0.6 -0.5 -0.5 -0.4
Canadian Coupled Fcst Sys -0.6 -0.7 -0.7 -0.6 -0.6 -0.5 -0.4 -0.2 0.1
GFDL CM2.5 FLOR Coupled Climate model -0.9 -0.7 -0.5 -0.5 -0.5 -0.5 -0.6 -0.6 -0.4
Average, dynamical models -0.5 -0.6 -0.6 -0.6 -0.6 -0.7 -0.6 -0.6 -0.5
Statistical models
NCEP/CPC Markov model -0.2 -0.3 -0.4 -0.4 -0.4 -0.4 -0.4 -0.4 -0.4
NOAA/CDC Linear Inverse 0 -0.1 -0.2 -0.3 -0.4 -0.4 -0.4 -0.4 -0.4
NCEP/CPC Constructed Analog -0.2 -0.2 -0.2 -0.2 -0.2 -0.2 -0.1 -0.1 -0.1
NCEP/CPC Can Cor Anal -0.6 -0.9 -1.2 -1.3 -1.4 -1.5 -1.3 -0.9 -0.6
Landsea/Knaff CLIPER -0.7 -0.7 -0.8 -0.8 -0.8 -0.7 -0.7 -0.5 -0.3
Univ. BC Neural Network -0.2 -0.3 -0.3 -0.2 -0.2 -0.1 -0.1 -0.1 -0.2
FSU Regression -0.4 -0.5 -0.6 -0.7 -0.8 -0.8 -0.7 -0.6 -0.5
TCD – UCLA -0.1 -0.2 -0.3 -0.3 -0.4 -0.5 -0.5 -0.6 -0.7
UNB/CWC Nonlinear PCA -0.2 -0.3 -0.3 -0.4 -0.4 -0.4 -0.3 -0.2 -0.1
Average, statistical models -0.3 -0.4 -0.5 -0.5 -0.5 -0.5 -0.5 -0.4 -0.3
Average, all models -0.4 -0.5 -0.5 -0.6 -0.6 -0.6 -0.6 -0.5 -0.4

Discussion of Current Forecasts

Nearly all of the set of dynamical and statistical model predictions issued during late June and early July 2016 predicts either cool-neutral or La Niña conditions during the July-September period, cooling further to either cool-neutral or mainly weak La Niña conditions by northern autumn.  In the most recent week, the SST anomaly in the Nino3.4 region was -0.6 C, reflecting a weak La Niña level for the first time for the weekly average in roughly two years, and -0.12 C for the month of May, indicating a neutral ENSO condition. Most of the atmospheric variables currently reflect neutral ENSO. This includes upper and lower level wind anomalies and the pattern of anomalous convection. The traditional and equatorial Southern Oscillation Indices, however, lean slightly in the La Niña direction. Based on the multi-model mean predictions, 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
JAS 2016 44% 55% 1%
ASO 2016 50% 47% 3%
SON 2016 53% 42% 5%
OND 2016 55% 39% 6%
NDJ 2016 55% 37% 8%
DJF 2016 58% 36% 6%
JFM 2016 54% 41% 5%
FMA 2017 51% 46% 3%
MAM 2017 43% 55% 2%

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.

IRI ENSO Forecast

IRI/CPC Experimental ENSO Prediction Plumes Based on the North American Multi-model Ensemble (NMME)

Published: July 21, 2016


The three 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 most of the models making up the set belonging to the NMME.

See below for detailed descriptions of the plots.


The first plot (Figure 1) shows the ensemble mean predictions of each of the individual models, and also the average of the individual model predictions (the NMME). Here, the NMME average is not weighted by the number of ensemble members in the individual models. This plot is intended to provide some idea of the disagreement among the individual models. Corrections for systematic biases are not done.

Predictions of ENSO are probabilistic. The ensemble mean prediction it 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 NMME forecast represents the center, or 50 percentile, in the distribution. 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 NMME 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. One reason the second approach is not used here is that the ensemble spreads may have biases in representing the real world uncertainty. Individual model spreads have often been found to be somewhat narrower than they should be, although in multi-model ensembles this tendency has been shown to be milder or even eliminated. Another reason the ensemble member counting approach is not used here is that there are not enough ensemble members in the NMME to produce a smooth probability distribution, particularly for the relatively detailed percentile bands presented here.

The third plot (Figure 3), 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 NMME 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. Sometimes the “spaghetti density” may appear asymmetric about the NMME forecast 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.