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IRI ENSO Forecast

2017 January Quick Look

Published: January 19, 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)

Use the navigation menu on the right to navigate to the different forecast sections

During mid-January 2016 the tropical Pacific SST anomaly was near -0.5C, the threshold for weak La Niña. Many of the atmospheric variables across the tropical Pacific also remain consistent with weak La Niña conditions, although some have become only weakly so. The upper and lower atmospheric winds have continued to be weakly suggestive of a strengthened Walker circulation, and the cloudiness and rainfall remain suggestive of La Niña conditions. The collection of ENSO prediction models indicates SSTs, now near the threshold of La Niña, is in the process of dissipating to neutral levels by February.

Figures 1 and 3 (the official ENSO probability forecast and the objective model-based ENSO probability forecast, respectively) are often quite similar. However, occasionally they may differ noticeably. There can be several reasons for differences. One possible reason is that the human forecasters, using their experience and judgment, may disagree to some degree with the models, which may have known biases. Another reason is related to the fact that the models are not run at the same time that the forecasters make their assessment, so that the starting ENSO conditions may be slightly different between the two times. The charts on this Quick Look page are updated at two different times of the month, so that between the second and the third Thursday of the month, the official forecast (Fig. 1) has just been updated, while the model-based forecasts (Figs. 3 and 4) are still from the third Thursday of the previous month. On the other hand, from the third Thursday of the month until the second Thursday of the next month, the model-based forecasts are more recently updated, while the official forecasts remain from the second Thursday of the current month.
Click on the for more information on each figure.

Historically Speaking

    El Niño and La Niña events tend to develop during the period Apr-Jun and they
  • Tend to reach their maximum strength during October - February
  • Typically persist for 9-12 months, though occasionally persisting for up to 2 years
  • Typically recur every 2 to 7 years

IRI ENSO Forecast

CPC/IRI ENSO Update

Published: January 12, 2017

El Niño/Southern Oscillation (ENSO) Diagnostic Discussion issued jointly by the Climate Prediction Center/NCEP/NWS and the International Research Institute for Climate and Society

ENSO Alert System Status: La Niña Advisory

Synopsis: A transition to ENSO-neutral is expected to occur by February 2017, with ENSO-neutral then continuing through the first half of 2017.

La Niña continued during December, with negative sea surface temperature (SST) anomalies continuing across the central and eastern equatorial Pacific (Fig. 1). The weekly Niño index values fluctuated during the last month, with the Niño-3 and Niño-3.4 regions hovering near and slightly warmer than -0.5°C (Fig. 2). The upper-ocean heat content anomaly was near zero when averaged across the eastern Pacific (Fig. 3), though near-to-below average subsurface temperatures were evident closer to the surface (Fig. 4). Atmospheric convection remained suppressed over the central tropical Pacific and enhanced over Indonesia (Fig. 5). The low-level easterly winds were slightly enhanced over the western Pacific, and upper-level westerly anomalies were observed across the eastern Pacific. Overall, the ocean and atmosphere system remained consistent with a weak La Niña.

The multi-model averages favor an imminent transition to ENSO-neutral (3-month average Niño-3.4 index between -0.5°C and 0.5°C), with ENSO-neutral lasting through August-October (ASO) 2017 (Fig. 6). Along with the model forecasts, the decay of the subsurface temperature anomalies and marginally cool conditions at and near the ocean surface portends the return of ENSO-neutral over the next month. In summary, a transition to ENSO-neutral is expected to occur by February 2017, with ENSO-neutral then continuing through the first half of 2017 (click CPC/IRI consensus forecast for the chance of each outcome for each 3-month period).

Even as the tropical Pacific Ocean returns to ENSO-neutral conditions, the atmospheric impacts from La Niña could persist during the upcoming months (the 3-month seasonal outlook will be updated on Thursday January 19th). The current seasonal outlook for JFM 2017 favors above-average temperatures and below-median precipitation across much of the southern tier of the U.S., and below-average temperatures and above-median precipitation in portions of the northern tier of the U.S.

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 9 February 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

CPC/IRI Early-Month Official ENSO Forecast Probabilities

Season La Niña Neutral El Niño
DJF 2017 43% 57% 0%
JFM 2017 28% 70% 2%
FMA 2017 19% 74% 7%
MAM 2017 14% 74% 12%
AMJ 2017 12% 67% 21%
MJJ 2017 12% 60% 28%
JJA 2017 13% 54% 33%
JAS 2017 15% 50% 35%
ASO 2017 15% 49% 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 Technical ENSO Update

Published: January 19, 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 based on a slightly older period and does not account for the slow warming trend in the tropical Pacific SST.

Recent and Current Conditions

Since August 2016, the NINO3.4 SST anomaly has been near or slightly cooler than -0.5 C, indicative of a weak La Niña SST condition. For December the SST anomaly was -0.42, and for Sep-Nov it was -0.57 C. 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 -0.3, in the ENSO-neutral level. However, accompanying this ocean condition are atmospheric variables that mainly continue to indicate borderline or weak La Niña. The lower-level trade winds have been enhanced only weakly, while the upper level has shown slightly more convincing westerly anomalies. The Southern Oscillation Index (SOI) had been positive but has averaged just weakly so since November. On the other hand, convection anomalies across the equatorial Pacific have been suggestive of La Niña. Subsurface temperature anomalies across the eastern equatorial Pacific have essentially returned to average. Overall, given the SST and the atmospheric conditions, the diagnosis of weak La Niña remains appropriate but the event is thought likely to be in the process of dissipation.

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 carries a La Niña advisory but called for the weak La Niña to return to neutral by February. The latest set of model ENSO predictions, from mid-January, now available in the IRI/CPC ENSO prediction plume, is discussed below. Those predictions suggest that the SST is most likely to be in the ENSO-neutral range from January-March season forward through most of 2017, but with increased uncertainty from around May onward.

As of mid-January, 12% of the dynamical or statistical models predicts La Niña conditions for the initial Jan-Mar 2017 season, while 88% 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 Apr-Jun 2017 season, among models that do use subsurface temperature information, no model predicts La Niña conditions, 90% predicts ENSO-neutral conditions, and 10% predicts El Niño conditions. For all model types, the probabilities for La Niña are below 10% for from Feb-Apr through Sep-Nov 2017. The probability for neutral conditions is near or above 90% from Jan-Mar through Apr-Jun 2017, dropping to between 60 and 65% from Jun-Aug through Sep-Nov. Probabilities for El Niño are near zero initially, rise to 25% by May-Jul 2017, and to near 35% from Jun-Aug to Sep-Nov.

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 13% for Jan-Mar 2017, decreasing to near 5% from Feb-Apr to Apr-Jun, and slowly rising again to near 20% by Aug-Oct and Sep-Nov. Probabilities for ENSO-neutral are near 85% for Jan-Mar 2017, rising to near 90% for Feb-Apr and Mar-May, then falling to 50-55% for Jun-Aug and down to about 40% by Sep-Nov. Probabilities for El Niño are less than 10% for Jan-Mar through Mar-May, and slowly rise to about 25% by May-Jul and to about 35-40% for Jun-Aug through Sep-Nov 2017.  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 high likelihood for neutral ENSO conditions for Jan-Mar season despite weak La Niña conditions still present at the very beginning of January. ENSO-neutral is predicted to remain the most likely of the three possibilities throughout most of 2017, although the probability of El Niño rises to 35-40% toward the end of 2017. 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: January 19, 2017

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/CPC Mid-Month Model-Based ENSO Forecast Probabilities

Season La Niña Neutral El Niño
JFM 2017 13% 86% 1%
FMA 2017 7% 90% 3%
MAM 2017 4% 89% 7%
AMJ 2017 5% 80% 15%
MJJ 2017 11% 63% 26%
JJA 2017 13% 53% 34%
JAS 2017 14% 48% 38%
ASO 2017 19% 44% 37%
SON 2017 22% 39% 39%

IRI ENSO Forecast

CPC/IRI Official Probabilistic ENSO Forecast

Published: January 12, 2017

The official CPC/IRI ENSO probability forecast, based on a consensus of CPC and IRI forecasters. It is updated during the first half of the month, in association with the official CPC/IRI ENSO Diagnostic Discussion. It is based on observational and predictive information from early in the month and from the previous month. It uses human judgment in addition to model output, while the forecast shown in the Model-Based Probabilistic ENSO Forecast relies solely on model output. This is updated on the second Thursday of every month.



CPC/IRI Early-Month Official ENSO Forecast Probabilities

Season La Niña Neutral El Niño
DJF 2017 43% 57% 0%
JFM 2017 28% 70% 2%
FMA 2017 19% 74% 7%
MAM 2017 14% 74% 12%
AMJ 2017 12% 67% 21%
MJJ 2017 12% 60% 28%
JJA 2017 13% 54% 33%
JAS 2017 15% 50% 35%
ASO 2017 15% 49% 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 ENSO Predictions Plume

Published: January 19, 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.


Because of occasional data corrections and late model runs following the time of ENSO product issuance, the data shown in the ENSO forecast table and the ENSO plume graph may not always match. The best source of the ENSO forecast data is http://iri.columbia.edu/~forecast/ensofcst/Data/ensofcst_ALLtoMMYY where MM is the month number and YY is the year.


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

Discussion of Current Forecasts

Most of the models in the set of dynamical and statistical model predictions issued during late December 2016 and early January 2017 predicts neutral ENSO conditions during the January-March period.  Neutral ENSO is predicted with high probability through spring 2017, and with moderate probability later in the year when there is more uncertainty.  In the most recent week, the SST anomaly in the Nino3.4 region was -0.3 C, at a cool-neutral level, and -0.42 C for the month of December, close to the threshold of a weak La Niña SST condition. The atmospheric variables continue to reflect mainly borderline or weak La Niña. The pattern of convection across the tropical Pacific makes a stronger case for weak La Niña. But the forecasts indicate this episode is in the process of dissipating. 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
JFM 2017 13% 86% 1%
FMA 2017 7% 90% 3%
MAM 2017 4% 89% 7%
AMJ 2017 5% 80% 15%
MJJ 2017 11% 63% 26%
JJA 2017 13% 53% 34%
JAS 2017 14% 48% 38%
ASO 2017 19% 44% 37%
SON 2017 22% 39% 39%

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.