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IRI ENSO Forecast

2016 January Quick Look

Published: January 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-January 2015 the tropical Pacific SST was at a strong El Niño level, having peaked in November and December. All atmospheric variables strongly support the El Niño pattern, including weakened trade winds and excess rainfall in the east-central tropical Pacific. The consensus of ENSO prediction models indicate continuation of strong El Niño conditions during the January-March 2016 season in progress. The beginning of a gradual weakening of the SST anomaly is underway, with the event dissipating to neutral conditions by late spring or early summer 2016.

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: January 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: El Niño Advisory

Synopsis: A strong El Niño is expected to gradually weaken through spring 2016, and to transition to ENSO-neutral during late spring or early summer.

A strong El Niño continued during December, with well above-average sea surface temperatures (SSTs) across the central and eastern equatorial Pacific Ocean (Fig. 1) All weekly Niño indices decreased slightly from the previous month (Fig. 2). The subsurface temperatures in the central and eastern Pacific, while still well above average, weakened  (Figs. 3) due to an upwelling equatorial oceanic Kelvin wave (Fig. 4). Significant low-level westerly wind anomalies and upper-level easterly wind anomalies continued over much of the tropical Pacific. During the last week, another westerly wind burst occurred in the east-central Pacific. The traditional and equatorial Southern Oscillation Index (SOI) values remained strongly negative. Also, convection remained strong over the central and east-central tropical Pacific, and suppressed over Indonesia (Fig. 5). Collectively, these atmospheric and oceanic anomalies reflect the continuation of a strong El Niño episode.

Most models indicate that a strong El Niño will weaken with a transition to ENSO-neutral during the late spring or early summer (Fig. 6). The forecasters are in agreement with the model consensus, though the exact timing of the transition is difficult to predict. A strong El Niño is expected to gradually weaken through spring 2016, and to transition to ENSO-neutral during late spring or early summer (click CPC/IRI consensus forecast for the chance of each outcome for each 3-month period).

El Niño has already produced significant global impacts. and is expected to affect temperature and precipitation patterns across the United States during the upcoming months (the 3-month seasonal outlook will be updated on Thursday January 21st). The seasonal outlooks for January – March indicate an increased likelihood of above-median precipitation across the southern tier of the United States, and below-median precipitation over the northern tier of the United States. Above-average temperatures are favored in the West and northern half of the country with below-average temperatures favored in the southern Plains and along the Gulf Coast.

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 February 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 Consensus ENSO Forecast Probabilities

Season La Niña Neutral El Niño
DJF 2016 ~0% ~0% 100%
JFM 2016 ~0% 1% 99%
FMA 2016 ~0% 4% 96%
MAM 2016 1% 12% 87%
AMJ 2016 4% 34% 62%
MJJ 2016 13% 48% 39%
JJA 2016 22% 52% 26%
JAS 2016 33% 49% 18%
ASO 2016 40% 46% 14%

IRI ENSO Forecast

IRI Technical ENSO Update

Published: January 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. Therefore, the anomalies cited below for this strong 2015-16 event are likely larger than those that will later be cited officially, particularly in comparisons with other strong El Niño events like 1997-98 and 1982-83.

Recent and Current Conditions

The SST anomaly in the NINO3.4 region has been at a strong El Niño level since around mid-July 2015. For December the average NINO3.4 SST anomaly was 2.82 C, indicative of strong El Niño conditions, and for Oct-Dec it was 2.74 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 SST anomaly in the Nino3.4 region was 2.6 C, in the category of strong El Niño. Accompanying this SST has been a clear and strong El Niño atmospheric pattern, including westerly low-level wind anomalies and positive anomalies of convection near and east of the dateline. The Southern Oscillation Index (SOI) and the equatorial SOI have also been quite negative, indicative of El Niño conditions.

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 this strong El Niño to weaken during spring and return to neutral by late spring or early summer 2016. The latest set of model ENSO predictions, from mid-Jan, now available in the IRI/CPC ENSO prediction plume, is discussed below. Currently, besides the most recent Nino3.4 SST anomalies being in the strong El Niño category, subsurface temperature anomalies across the eastern equatorial Pacific have been at well above average levels, although they have generally weakened somewhat over the last two months. The strong positive heat content anomaly has promoted far above-average SST over the last 6 or more months. So far during December and January a slight weakening of the SST anomalies has been observed.  In the atmosphere, the basin-wide sea level pressure anomaly pattern (e.g. the SOI) has been very clearly at El Niño levels.  Anomalous convection (as measured by OLR) has been above average both near and somewhat east of the dateline.  Together, the oceanic and atmospheric features reflect strong El Niño conditions for late December through mid-January.

As of mid-January, none of the dynamical or statistical models models predicts La Niña or neutral SST conditions for the initial Jan-Mar 2016 season; 100% predicts El Niño 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 Apr-Jun 2016 season, among models that do use subsurface temperature information, 79% predicts El Niño SST conditions, while 21% predicts ENSO-neutral conditions and none predicts La Niña conditions. For all model types, the probabilities for El Niño are near 100% (i.e., higher than 99.5%) for Jan-Feb through Mar-May 2016, dropping toward to 71% for Apr-Jun and to below 30% by May-Jul and later. No model predicts La Niña conditions for any forecast period through Apr-Jun 2016, but the chances rise to near 25% by Jun-Aug and slightly over 50% for Aug-Oct and Sep-Nov. Chances for neutral ENSO conditions are near 0% through Mar-May 2016, then rising to near 65% for May-Jul and Jun-Aug, and falling back slightly for later seasons.

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 near-zero from Jan-Mar through Apr-Jun 2016, rising to between 50% and 60% for Aug-Oct and Sep-Nov.  Model probabilities for neutral ENSO conditions are less than 5% through Mar-May 2016, close to 30% for Apr-Jun and approximately 50% to 65% for May-Jul and Jun-Aug, dropping back to less than 40% by Aug-Oct. Probabilities for El Niño are near 100% for Jan-Mar and Feb-Apr 2016, in the upper 90s for Mar-May, near 70% for Apr-Jun, and below 30% beginning in May-Jul, dropping to near 10% beginning in Jun-Aug.  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.

The probabilities derived from the models on the IRI/CPC plume describe, on average, extremely high certainty for El Niño conditions for the Jan-Mar through Mar-May 2016 seasons. In terms of magnitude, the models suggest that we have already hit the peak SST anomaly values, around roughly 2.75C for the NDJ season, but still predict between 1.8 and 2.4 for Jan-Mar. Model forecast spread still exists, implying possibilities of being outside of that interval on either side. 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 Plume-Based Probabilistic ENSO Forecast

Published: January 21, 2016



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

Season La Niña Neutral El Niño
JFM 2016 ~0% ~0% 100%
FMA 2016 ~0% ~0% 100%
MAM 2016 ~0% 1% 99%
AMJ 2016 ~0% 32% 68%
MJJ 2016 10% 62% 28%
JJA 2016 32% 54% 14%
JAS 2016 47% 44% 9%
ASO 2016 53% 37% 10%
SON 2016 58% 32% 10%

IRI ENSO Forecast

CPC/IRI Consensus Probabilistic ENSO Forecast

Published: January 14, 2016



CPC/IRI Early-Month Consensus ENSO Forecast Probabilities

Season La Niña Neutral El Niño
DJF 2016 ~0% ~0% 100%
JFM 2016 ~0% 1% 99%
FMA 2016 ~0% 4% 96%
MAM 2016 1% 12% 87%
AMJ 2016 4% 34% 62%
MJJ 2016 13% 48% 39%
JJA 2016 22% 52% 26%
JAS 2016 33% 49% 18%
ASO 2016 40% 46% 14%

IRI ENSO Forecast

IRI/CPC ENSO Predictions Plume

Published: January 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.


Seasons (2016-2016)
Model JFM FMA MAM AMJ MJJ JJA JAS ASO SON
Dynamical models
NASA GMAO model 2.5 2.2 1.6 0.8 0.2 -0.4 -0.6
NCEP CFS version 2 2.3 1.9 1.5 1.2 0.8 0.4 0.2 0.1
Japan Met. Agency model 1.9 1.5 1.1 0.6 0
Scripps Inst. HCM 2.2 1.8 1.2 0.6 0 -0.6 -1.2 -1.6 -2.1
Lamont-Doherty model 2.3 1.9 1.4 1 0.6 0.2 0 0.3 0.8
POAMA (Austr) model 2.1 1.6 1.1 0.7 0.3 -0.2 -0.5
ECMWF model 2.2 1.6 1 0.4 0
UKMO model 2.4 1.9 1.3 0.6
KMA (Korea) SNU model 1.7 1.5 1.3 1.1 0.8 0.6 0.3 0 -0.2
IOCAS (China) Intermed. Coupled model 2 1.5 1 0.6 0.2 -0.1 -0.4 -0.7 -1
COLA CCSM3 model 2.5 2 1.2 0.1 -0.9 -1.7 -2 -2.1 -2.2
MÉTÉO FRANCE model 2 1.6 1.2 0.8 0.3
Japan Frontier Coupled model 1.9 1.4 1 0.6 0.2 -0.2 -0.4 -0.6 -0.7
GFDL CM2.1 Coupled Climate model 2.4 2 1.6 0.9 0.2 -0.4 -0.7 -0.8 -0.7
Canadian Coupled Fcst Sys 2.2 1.7 1.2 0.4 -0.4 -1 -1.2 -1.3 -1.3
GFDL CM2.5 FLOR Coupled Climate model 2.3 1.8 1.3 0.4 -0.5 -1.3 -1.6 -1.6 -1.5
Average, dynamical models 2.2 1.7 1.2 0.7 0.1 -0.4 -0.7 -0.8 -1
Statistical models
NCEP/CPC Markov model 2.1 1.7 1.4 1.2 0.9 0.7 0.6 0.4 0.3
NOAA/CDC Linear Inverse 1.3 0.9 0.5 0.1 -0.1 -0.4 -0.6 -0.7 -0.7
NCEP/CPC Constructed Analog 2 1.4 0.9 0.4 0.1 -0.3 -0.5 -0.6 -0.7
NCEP/CPC Can Cor Anal 1.8 1.4 1 0.7 0.3 0 -0.2 -0.4 -0.5
Landsea/Knaff CLIPER 1.9 1.3 0.8 0.3 0 -0.3 -0.5 -0.7 -0.9
Univ. BC Neural Network 1.9 1.5 1.1 0.7 0.3 0.1 -0.1 -0.3 -0.4
TCD – UCLA 1.8 1.4 1 0.8 0.5 0.3 0.1 -0.2 -0.4
UNB/CWC Nonlinear PCA 2.4 2.1 1.7 1.2 0.8 0.6 0.5 0.4 0.3
Average, statistical models 1.9 1.5 1.1 0.7 0.4 0.1 -0.1 -0.2 -0.4
Average, all models 2.1 1.6 1.2 0.7 0.2 -0.2 -0.4 -0.6 -0.7

Discussion of Current Forecasts

Most of the set of dynamical and statistical model predictions issued during late December 2015 and early January 2016 predict the beginning of weakening El Niño SST conditions into late winter 2015-16, with but El Niño still remaining strong well into spring 2016. Continuation of El Niño conditions appears at least 99% likely from the current Jan-Mar 2016 season through to the Mar-May 2016 season. El Niño probabilities remain over 50% through Apr-Jun 2016, and fall rapidly to below 30% by May-July and remain low thereafter. The average of all models predicts SST anomaly around roughly 2.0C for the JFM season in progress, dropping rapidly during spring. In the most recent week, the SST anomaly in the Nino3.4 region was 2.6 C, reflecting strong El Niño conditions in this weekly time scale, and 2.82 C for the month of December, also at a strong level.  All of the atmospheric variables also reflect El Niño, including lower and upper level wind anomalies, the Southern Oscillation Index and the pattern of anomalous convection. 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 Plume-Based ENSO Forecast Probabilities

Season La Niña Neutral El Niño
JFM 2016 ~0% ~0% 100%
FMA 2016 ~0% ~0% 100%
MAM 2016 ~0% 1% 99%
AMJ 2016 ~0% 32% 68%
MJJ 2016 10% 62% 28%
JJA 2016 32% 54% 14%
JAS 2016 47% 44% 9%
ASO 2016 53% 37% 10%
SON 2016 58% 32% 10%

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.