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IRI ENSO Forecast

2016 September Quick Look

Published: September 15, 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-September 2016 the tropical Pacific SST anomaly was close to -0.5C, the weak La Niña threshold. However, not all of the atmospheric variables support weak La Niña conditions. Although the upper level winds in the tropical Pacific are somewhat suggestive of La Niña, the lower level winds remain near average. The Southern Oscillation index and the pattern of cloudiness and rainfall in the equatorial Pacific are somewhat suggestive of weak La Niña conditions, but could also be interpreted as being in the cool-neutral range. The collection of ENSO prediction models indicates SSTs hovering at levels near borderline La Niña during fall, then weakening to cool-neutral in late fall and into winter.

During early September 2016 the tropical Pacific SST anomaly was close to -0.5C, near the weak La
Niña threshold. However, only about half of the atmospheric variables indicate borderline or weak La
Niña conditions. Although the upper level winds in the tropical Pacific are somewhat suggestive of La
Niña, the lower level winds remain near average. The Southern Oscillation index and the pattern of
cloudiness and rainfall in the equatorial Pacific are close to the La Niña threshold, but still only in the
cool-neutral range. The collection of ENSO prediction models indicates SSTs retreating from currently
near-La Niña levels toward cool-neutral from the present through fall and into winter.

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: September 8, 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:  Not Active

Synopsis: ENSO-Neutral conditions are slightly favored (between 55-60%) during the upcoming Northern Hemisphere fall and winter 2016-17.

ENSO-Neutral conditions were observed over the past month, although sea surface temperatures (SSTs) were below-average over the east-central equatorial Pacific Ocean (Fig. 1). While the Niño-3.4 and Niño-3 regions remained around -0.5°C for most of the month, Niño-4 and Niño 1+2 were -0.1°C and +0.3°C, respectively, by the end of the month (Fig. 2). Subsurface temperatures across the eastern and central Pacific remained below average (Fig. 3), and negative temperature anomalies remained weak across the western Pacific (Fig. 4). Atmospheric anomalies over the tropical Pacific Ocean largely indicated ENSO-Neutral conditions. The traditional Southern Oscillation index and the equatorial Southern Oscillation index were weakly positive during August. The lower-level winds were near average, while the upper-level winds were anomalously westerly in a small region to the east of the International Date Line. Convection was suppressed over the western and central tropical Pacific, although less suppressed compared to last month (Fig. 5). Overall, the combined ocean and atmosphere system continues to reflect ENSO-Neutral.

The multi-model averages favor borderline Neutral-La Niña conditions (3-month average Niño-3.4 index less than or equal to -0.5°C) during the Northern Hemisphere fall, continuing into winter (Fig. 6). However, the more recently updated model runs from the North American Multi-Model Ensemble (NMME) more strongly favor ENSO-Neutral (Fig. 7). The forecaster consensus prefers this outcome, which is supported by the lack of significant anomalies in several indicators over the past month (winds, convection, subsurface temperatures). Overall, ENSO-Neutral conditions are slightly favored (between 55-60%) during the upcoming Northern Hemisphere 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 13 October 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
JAS 2016 48% 52% 0%
ASO 2016 43% 57% 0%
SON 2016 41% 58% 1%
OND 2016 40% 57% 3%
NDJ 2016 39% 57% 4%
DJF 2016 36% 56% 8%
JFM 2017 30% 57% 13%
FMA 2017 26% 57% 17%
MAM 2017 22% 58% 20%

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: September 15, 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

ENSO-neutral conditions were observed in most ENSO-related variables from May through July 2016. For August 2016 the average NINO3.4 SST anomaly was -0.54 C, indicative of weak La Niña SST conditions. For Jun-Aug it was -0.38 C, in 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. However, accompanying this ocean condition, is a mixture of some indications of La Niña and some of ENSO-neutral. Lower-level zonal wind anomalies have been near-zero. Convection anomalies across the equatorial Pacific are somewhat suggestive of La Niña, as is the Southern Oscillation Index (SOI). Overall, despite the SST, the case for weak La Niña is not beyond reason, but also not very compelling so far.

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 continued neutral ENSO conditions during fall and winter 2016-17, and a La Niña watch that had been posted in August was discontinued.  The latest set of model ENSO predictions, from mid-September, now available in the IRI/CPC ENSO prediction plume, is discussed below. Currently, the Nino3.4 SST anomalies are at the level of minimal La Niña. Subsurface temperature anomalies across the eastern equatorial Pacific continue to be somewhat below average, and have slightly decreased in recent weeks. Although enhanced easterly trade winds have not yet been observed, the slightly below-average SSTs and the negative subsurface heat content anomaly may support the onset of such enhanced trades in the eastern tropical Pacific. When and if these develop, the SST could fall remain in the weak La Niña category during late September and October, and if this happens the La Niña condition might last, even if only weak, through the fall. However, the collection of the latest model predictions suggest that this scenario is about as likely to happen as not to happen, with probabilities just over 50%.

As of mid-September, 62% of the dynamical or statistical models predicts La Niña conditions for the initial Sep-Nov 2016 season, while 38% 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 Dec-Feb 2016-17 season, among models that do use subsurface temperature information, 47% predicts ENSO-neutral conditions and 53% predicts La Niña conditions. For all model types, the probabilities for La Niña are between 50% and 70% from Sep-Nov 2016 through Dec-Feb 2016-17, then drop into the 40s during later winter 2016-17 and below 25% from Mar-May 2017 onward. The probability for neutral conditions rises to 50% for Nov-Jan and Dec-Feb, and still higher thereafter. No model predicts El Niño until Apr-Jun, when 6% (one model) does.

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 55% for Sep-Nov 2016, decreasing slowly to 52% by Dec-Feb 2016-17, then droping to 46% for Jan-Mar 2017 and much lower thereafter. Probabilities for El Niño are below 5% through Mar-May 2017, and rise to 12% by the final forecast period of May-Jul 2017. Probabilities for ENSO-neutral are below 50% through Dec-Feb 2016-17, 50% for Jan-Mar 2017, and rise thereafter. 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 50-55% for Sep-Nov 20167 through Dec-Feb 2016-17, and drop below 50% by early 2017 and beyond.  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%

 

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: September 15, 2016



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

Season La Niña Neutral El Niño
SON 2016 55% 45% 0%
OND 2016 54% 45% 1%
NDJ 2016 54% 44% 2%
DJF 2017 52% 44% 4%
JFM 2017 46% 50% 4%
FMA 2017 35% 61% 4%
MAM 2017 24% 72% 4%
AMJ 2017 24% 68% 8%
MJJ 2017 29% 59% 12%

IRI ENSO Forecast

CPC/IRI Official Probabilistic ENSO Forecast

Published: September 8, 2016



CPC/IRI Early-Month Official ENSO Forecast Probabilities

Season La Niña Neutral El Niño
JAS 2016 48% 52% 0%
ASO 2016 43% 57% 0%
SON 2016 41% 58% 1%
OND 2016 40% 57% 3%
NDJ 2016 39% 57% 4%
DJF 2016 36% 56% 8%
JFM 2017 30% 57% 13%
FMA 2017 26% 57% 17%
MAM 2017 22% 58% 20%

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: September 15, 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 SON OND NDJ DJF JFM FMA MAM AMJ MJJ
Dynamical models
NASA GMAO model -0.4 -0.2 -0.2 -0.1 0.1 0.2 0.4
NCEP CFS version 2 -0.6 -0.6 -0.7 -0.6 -0.3 0 0.2 0.4
Japan Met. Agency model -0.7 -0.7 -0.8 -1 -1
Scripps Inst. HCM -1.2 -1.3 -1.3 -1.3 -1.3 -1.1 -1 -0.8 -0.6
Lamont-Doherty model -0.1 -0.2 -0.2 -0.1 0.1 0.2 0.1 0 -0.2
POAMA (Austr) model -0.6 -0.6 -0.5 -0.5 -0.5 -0.3 -0.2
ECMWF model -0.2 -0.1 -0.1 0 0
UKMO model -0.5 -0.5 -0.5 -0.5
KMA (Korea) SNU model -0.8 -0.8 -0.7 -0.7 -0.7 -0.8 -0.9 -0.9 -0.9
IOCAS (China) Intermed. Coupled model -0.9 -1.1 -1.2 -1.2 -1.1 -1 -0.9 -0.9 -1
COLA CCSM4 model -0.2 -0.2 -0.3 -0.4 -0.3 -0.2 0 0.1 0.1
MÉTÉO FRANCE model -0.6 -0.6 -0.6 -0.7 -0.6
CSIR-IRI 3-model MME -0.1 -0.3 -0.4 -0.6 -0.6 -0.5
GFDL CM2.1 Coupled Climate model -0.7 -0.6 -0.4 -0.3 -0.2 -0.2 -0.1 -0.1 0
Canadian Coupled Fcst Sys -0.4 -0.3 -0.2 -0.1 -0.1 0.1 0.2 0.4 0.7
GFDL CM2.5 FLOR Coupled Climate model -0.7 -0.6 -0.5 -0.4 -0.4 -0.3 -0.2 0 0.3
Average, dynamical models -0.5 -0.5 -0.5 -0.5 -0.5 -0.3 -0.2 -0.2 -0.2
Statistical models
NCEP/CPC Markov model -0.5 -0.5 -0.5 -0.4 -0.4 -0.4 -0.3 -0.3 -0.2
NOAA/CDC Linear Inverse -0.4 -0.4 -0.4 -0.3 -0.3 -0.2 -0.2 -0.2 -0.1
NCEP/CPC Constructed Analog -0.4 -0.5 -0.4 -0.3 -0.2 -0.1 -0.1 -0.1 -0.2
NCEP/CPC Can Cor Anal -0.7 -0.8 -0.8 -0.9 -0.8 -0.5 -0.3 -0.2 -0.2
Landsea/Knaff CLIPER -0.8 -0.8 -0.8 -0.8 -0.7 -0.5 -0.4 -0.3 -0.1
Univ. BC Neural Network -0.4 -0.3 -0.1 0.1 0.3 0.4 0.4 0.5 0.4
FSU Regression -0.8 -1 -1 -1 -0.8 -0.6 -0.4 -0.2 -0.1
TCD – UCLA -0.5 -0.6 -0.6 -0.6 -0.6 -0.6 -0.6 -0.5 -0.5
Average, statistical models -0.6 -0.6 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 -0.1
Average, all models -0.6 -0.5 -0.6 -0.5 -0.4 -0.3 -0.2 -0.2 -0.2

Discussion of Current Forecasts

The set of dynamical and statistical model predictions issued during late August and early September 2016 predicts either cool-neutral or La Niña conditions during the September-November period. Most of the models suggest only slight changes to the ENSO state for the second and even third season, after which weakening to cool-neutral ENSO conditions is suggested toward winter. In the most recent week, the SST anomaly in the Nino3.4 region was -0.6 C, at a weak La Niña level, and -0.54 C for the month of August, indicating a minimally weak La Niña SST condition. However, the atmospheric variables currently reflect a mixture of neutral and weak La Niña, so that the diagnosis of the curent ENSO state is unclear and could just as easily be called cool-neutral as weak La Niña.  The lower level the winds do not show enhanced trades, a major hallmark of La Niña.  The Southern Oscillation index and the pattern of convection across the tropical Pacific is slightly in the direction of La Niña. 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
SON 2016 55% 45% 0%
OND 2016 54% 45% 1%
NDJ 2016 54% 44% 2%
DJF 2017 52% 44% 4%
JFM 2017 46% 50% 4%
FMA 2017 35% 61% 4%
MAM 2017 24% 72% 4%
AMJ 2017 24% 68% 8%
MJJ 2017 29% 59% 12%

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

IRI/CPC ENSO Prediction Plumes Based on the North American Multi-model Ensemble (NMME) + Other Comprehensive Dynamical Models

Published:


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, as well as several other comprehensive coupled dynamical models.

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.

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 may not be 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.