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% |
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% |
