IRI ENSO Forecast
IRI Technical ENSO Update
Published: October 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 updated every 10 years and so, half of the time, is based on a slightly older period and does not account as much for the slow warming trend in the tropical Pacific SST.
Recent and Current Conditions
In mid-October 2017, the NINO3.4 SST anomaly was near the borderline of the weak La Niña category. For September the SST anomaly was -0.47 C, in the upper portion of the ENSO-neutral range, and for July-September it was -0.07 C, in the ENSO-neutral range. The IRI’s definition of El Niño, like NOAA/Climate Prediction Center’s, requires that the SST anomaly in the Nino3.4 region (5S-5N; 170W-120W) exceed 0.5 C. Similarly, for La Niña, the anomaly must be -0.5 C or less. The climatological probabilities for La Niña, neutral, and El Niño conditions vary seasonally, and are shown in a table at the bottom of this page for each 3-month season. The most recent weekly anomaly in the Nino3.4 region was -0.5, at the borderline of ENSO-neutral and weak La Niña. The pertinent atmospheric variables, including the upper and lower level zonal wind anomalies, have been showing patterns suggestive of near-La Niña, and he Southern Oscillation Index (SOI) has also been somewhat above average. Subsurface temperature anomalies across the eastern equatorial Pacific are somewhat below average. Despite recent SST anomalies and some clear signs of La Niña patterns in some key atmospheric variables, the combination of the SST and the atmospheric conditions continues to warrant an official diagnosis of ENSO-neutral for the recent 1-month period.
Expected Conditions
What is the outlook for the ENSO status going forward? The most recent official diagnosis and outlook was issued approximately one week ago in the NOAA/Climate Prediction Center ENSO Diagnostic Discussion, produced jointly by CPC and IRI; it stated that La Niña is favored for fall and into winter, with slightly lower chances for ENSO-neutral. A La Niña watch was issued with that Discussion, for the second consecutive month. The latest set of model ENSO predictions, from mid-October, now available in the IRI/CPC ENSO prediction plume, is discussed below. Those predictions suggest that the SST has the greatest chance for being in the weak La Niña range for October-December through January-March 2017, with a slightly lower but significant probability for ENSO-neutral during that period.
As of mid-October, about 65 to 70% of the dynamical or statistical models predicts La Niña conditions from the initial Oct-Dec 2017 season through to the Jan-Mar 2018 season. During this period, about 30 to 35% of models predict neutral conditions, while no models 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 Jan-Mar 2018 season, among models that do use subsurface temperature information, 33% of models predicts neutral conditions and 67% predicts La Niña conditions. For all models, at longer lead times reaching through the first half of 2018, predictions for ENSO-neutral conditions dominate, with probabilities from 70% to higher levels, except for the final season of Jun-Aug 2018 when the probability for El Niño rises to 35% and for La Niña decreases to near zero.
Note – Only models that produce a new ENSO prediction every month are included in the above statement.
Caution is advised in interpreting the distribution of model predictions as the actual probabilities. At longer leads, the skill of the models degrades, and skill uncertainty must be convolved with the uncertainties from initial conditions and differing model physics, leading to more climatological probabilities in the long-lead ENSO Outlook than might be suggested by the suite of models. Furthermore, the expected skill of one model versus another has not been established using uniform validation procedures, which may cause a difference in the true probability distribution from that taken verbatim from the raw model predictions.
An alternative way to assess the probabilities of the three possible ENSO conditions is more quantitatively precise and less vulnerable to sampling errors than the categorical tallying method used above. This alternative method uses the mean of the predictions of all models on the plume, equally weighted, and constructs a standard error function centered on that mean. The standard error is Gaussian in shape, and has its width determined by an estimate of overall expected model skill for the season of the year and the lead time. Higher skill results in a relatively narrower error distribution, while low skill results in an error distribution with width approaching that of the historical observed distribution. This method shows probabilities for La Niña between 65 and 70% from Oct-Dec to Dec-Feb, with 30-35% probabilities for neutral conditions during these seasons and near-zero probabilities for El Niño. Probabilities for ENSO-neutral rise to approximately 75% during Mar-May and Apr-Jun when the likely La Niña conditions are expected to have returned to neutral. For the longer lead forecasts for Apr-Jun to Jun-Aug 2018, chances for El Niño rise to 32% by the final season as chances for neutral remain greater than 50% and for La Niña decrease to 15-20%. A plot of the probabilities generated from this most recent IRI/CPC ENSO prediction plume using the multi-model mean and the Gaussian standard error method summarizes the model consensus out to about 10 months into the future. The same cautions mentioned above for the distributional count of model predictions apply to this Gaussian standard error method of inferring probabilities, due to differing model biases and skills. In particular, this approach considers only the mean of the predictions, and not the total range across the models, nor the ensemble range within individual models.
In summary, the probabilities derived from the models on the IRI/CPC plume describe, on average, a preference for weak La Niña conditions from Oct-Dec 2017 to Jan-Mar 2018, with neutral regaining highest probability status from Feb-Apr through the end of the forecast period in summer 2018. Chances for El Niño are very small through Mar-May 2018, rising to near-climatological probabilities for May-Jul and slightly higher for Jun-Aug 2018. A caution regarding this latest set of model-based ENSO plume predictions, is that factors such as known specific model biases and recent changes that the models may have missed will be taken into account in the next official outlook to be generated and issued in early June 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/CPC Mid-Month Model-Based ENSO Forecast Probabilities
Season |
La Niña |
Neutral |
El Niño |
OND 2017 |
67% |
33% |
0% |
NDJ 2017 |
70% |
30% |
0% |
DJF 2018 |
67% |
33% |
0% |
JFM 2018 |
57% |
42% |
1% |
FMA 2018 |
41% |
58% |
1% |
MAM 2018 |
22% |
75% |
3% |
AMJ 2018 |
16% |
73% |
11% |
MJJ 2018 |
15% |
61% |
24% |
JJA 2018 |
16% |
52% |
32% |