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IRI ENSO Forecast

2020 March Quick Look

Published: March 19, 2020

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

SSTs in the east-central Pacific were neutral, but above average during mid-March. Patterns in atmospheric variables are split between neutral and borderline El Niño conditions. Most model forecasts favor warm-neutral SST conditions during spring, cooling to average by early summer. The official CPC/IRI outlook is consistent with these model forecasts, calling for continuation of ENSO-neutral.

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: March 12, 2019

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 is favored for the Northern Hemisphere spring 2020 (~65% chance), continuing through summer 2020 (~55% chance).

During February 2020, above-average sea surface temperatures (SSTs) were evident across the western, central, and far eastern Pacific Ocean (Fig. 1). The latest weekly Niño-3.4 and Niño-3 indices were near-to-above average (+0.5°C and +0.1°C, respectively), with the Niño-4 and Niño-1+2 indices warmer, at +1.1°C (Fig. 2). Equatorial subsurface temperatures (averaged across 180°-100°W) remained above average during the month (Fig. 3), with positive anomalies spanning the western to the east-central equatorial Pacific, from the surface to ~150m depth (Fig. 4). Also during the month, low-level westerly wind anomalies persisted over the western tropical Pacific Ocean, while upper-level wind anomalies were mostly westerly over the eastern half of the basin.  Tropical convection remained suppressed over Indonesia and was enhanced near and just west of the Date Line (Fig. 5). While the equatorial Southern Oscillation index (SOI) was negative, the traditional SOI was near average.  Overall, the combined oceanic and atmospheric system remained consistent with ENSO-neutral.

The majority of models in the IRI/CPC plume (Fig. 6) favor ENSO-neutral (Niño-3.4 index between -0.5°C and +0.5°C) through the Northern Hemisphere fall.  Despite elevated Niño 3.4 index values in the near-term, the forecaster consensus expects the Niño-3.4 index values will decrease gradually through the spring and summer. In summary, ENSO-neutral is favored for the Northern Hemisphere spring 2020 (~65% chance), continuing through summer 2020 (~55% chance; 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 9 April 2020. 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.

CPC/IRI Early-Month Official ENSO Forecast Probabilities

Season La Niña Neutral El Niño
FMA 2020 1% 60% 39%
MAM2020 2% 67% 31%
AMJ 2020 8% 66% 26%
MJJ 2020 16% 60% 24%
JJA 2020 25% 53% 22%
JAS 2020 31% 48% 21%
ASO 2020 35% 45% 20%
SON 2020 38% 43% 19%
OND 2020 40% 41% 19%

 

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: March 19, 2020

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-March 2020, SSTs were above average, and near the threshold of weak El Niño in the NINO3.4 region. The February SST anomaly was 0.39 C, in the warm-neutral range, and for Dec-Feb it was 0.47 C, also warm-neutral but near the El Niño threshold. 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 C, at the borderline of warm-neutral and weak El Niño. SSTs are near average in the eastern equatorial Pacific, slightly or somewhat above average in the east-central Pacific and more strongly positive near the Date Line and in the west-central Pacific. Some of the key atmospheric variables such as the low-level and upper-level zonal wind anomalies, and patterns of sea level pressure and cloudiness and rainfall have been exhibiting neutral ENSO conditions, while some (low-level wind anomalies and cloudiness/anomalous convection patterns) have intermittently shown El Niño-like patterns. The sea level pressure pattern across the tropical Pacific has been near-average. Subsurface temperature anomalies from the dateline eastward in the equatorial Pacific have been somewhat above average in February and early March. Despite recent SSTs, low-level winds and anomalous convection leaning in the direction of El Niño, overall conditions continue to suggest an ENSO-neutral state.

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 states that neutral conditions are most likely to continue through the spring and summer. The latest set of model ENSO predictions from mid-March, now available in the IRI/CPC ENSO prediction plume, is next discussed: As of mid-March, 15% of the dynamical or statistical models predicts El Niño conditions for the Mar-May season, while 85% predicts ENSO-neutral. Going forward, the percentage of models predicting neutral is over 80% for Mar-May through May-Jul, gradually dropping  to near 60% by the final season of Nov-Jan 2020-21. Percentages of models predicting El Niño are below 10% from Apr-Jun through Jul-Sep and then rise to about 25% by Nov-Jan. No model predicts La Niña for Mar-May, but roughly 10-20% predicts La Niña from Jun-Aug through Nov-Jan.

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. Using this method, chances for El Niño are 20-25% for the Mar-May season, and chances for ENSO-neutral are 75-80%. Going forward, probabilities for neutral are higher than those for El Niño or La Niña for all seasons through the final season of Nov-Jan 2020-21. Probabilities for neutral are about 80% for Apr-May, drop to 50-55% for Jul-Sep and to 35-40% for Oct-Dec and Nov-Jan. El Niño probabilities drop below 20% from Apr-Jun through Aug-Oct, and then back up to just over 30% by Nov-Jan. Chances for La Niña are near-zero through Apr-Jun, rise to about 20% by JunAug and to 30-35% from Aug-Oct through Nov-Jan. 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 neutral ENSO conditions relative to El Niño or La Niña for all forecast seasons. El Niño is more likely than La Niña through the May-Jul forecast period, while La Niña is more likely than El Niño from Jun-Aug through Nov-Jan. 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 early next month by CPC and IRI, which will include some human judgment in combination with the model guidance.

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: March 19, 2020

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
MAM 2020 0% 79% 21%
AMJ 2020 1% 81% 18%
MJJ 2020 10% 73% 17%
JJA 2020 22% 61% 17%
JAS 2020 30% 53% 17%
ASO 2020 34% 47% 19%
SON 2020 36% 42% 22%
OND 2020 35% 38% 27%
NDJ 2020 31% 37% 32%

 

IRI ENSO Forecast

CPC/IRI Official Probabilistic ENSO Forecast

Published: March 12, 2019

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
FMA 2020 1% 60% 39%
MAM2020 2% 67% 31%
AMJ 2020 8% 66% 26%
MJJ 2020 16% 60% 24%
JJA 2020 25% 53% 22%
JAS 2020 31% 48% 21%
ASO 2020 35% 45% 20%
SON 2020 38% 43% 19%
OND 2020 40% 41% 19%

 

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: March 19, 2020

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 (2020 – 2020)
Model MAM AMJ MJJ JJA JAS ASO SON OND NDJ
Dynamical Models
NASA GMAO -0.14 -0.58 -1 -1.26 -1.4 -1.53 -1.65
NCEP CFSv2 0.21 -0.05 -0.41 -0.76 -1.05 -1.19 -1.31 -1.39
JMA 0.47 0.44 0.32 0.17 0.03
BCC_CSM11m 0.25 0.04 -0.19 -0.33 -0.29 -0.18 -0.06 0.05 0.12
SAUDI-KAU 0.62 0.56 0.47 0.3 0.12 -0.06 -0.23 -0.29 -0.2
LDEO 0.23 0.02 -0.16 -0.33 -0.44 -0.39 -0.2 0.06 0.35
AUS/ACCESS 0.57 0.37 0.1 -0.1
ECMWF 0.35 0.32 0.25 0.18 0.09
UKMO 0.45 0.33 0.12 -0.06
KMA SNU 0.22 0.18 0.17 0.26 0.4 0.58 0.66 0.67 0.62
IOCAS ICM 0.69 0.71 0.69 0.65 0.59 0.53 0.52 0.57 0.6
COLA CCSM4 0.52 0.28 -0.2 -0.71 -1.09 -1.32 -1.48 -1.64 -1.7
MetFRANCE 0.36 0.34 0.26 0.14 -0.01
SINTEX-F 0.23 0.21 0.16 0.1 0.07 0.04 0.05 0.03 0.04
CS-IRI-MM 0.36 0.26 0.09 -0.06 -0.18 -0.28
GFDL CM2.1 0.32 -0.03 -0.4 -0.55 -0.47 -0.32 -0.17 -0.03 0.14
CMC CANSIP 0.08 0.03 -0.04 -0.17 -0.31 -0.41 -0.46 -0.5 -0.53
GFDL FLOR 0.49 0.34 0.11 -0.13 -0.29 -0.38 -0.43 -0.47 -0.43
Average, Dynamical Models 0.35 0.21 0.02 -0.15 -0.26 -0.38 -0.40 -0.27 -0.10
Statistical Models
NTU CODA 0.36 0.33 0.33 0.36 0.31 0.28 0.28
BCC_RZDM 0.31 0.19 0.01 -0.19 -0.32 -0.38 -0.39 -0.43 -0.53
CPC MRKOV 0.35 0.33 0.31 0.3 0.33 0.39 0.47 0.56 0.65
CPC CA 0.24 0.28 0.25 0.19 0.01 -0.08 -0.08 0 0.01
CSU CLIPR 0.39 0.45 0.52 0.58 0.64 0.7 0.76 0.79 0.83
IAP-NN 0.06 -0.04 -0.09 -0.12 -0.14 -0.12 -0.06 0.01 0.05
FSU REGR 0.3 0.29 0.26 0.21 0.17 0.17 0.19 0.23 0.27
UCLA-TCD 0.08 0.02 -0.01 -0.04 -0.08 -0.12 -0.16 -0.17 -0.16
Average, Statistical Models 0.26 0.23 0.20 0.16 0.12 0.10 0.13 0.14 0.16

Discussion of Current Forecasts

Many of the models in the set of dynamical and statistical model predictions issued during mid-March 2019 show average to warm-neutral conditions for the spring, cooling to neutral by early summer, and continuing neutral or possibly slightly cool-neutral from late summer into fall. In the most recent week, the SST anomaly in the NINO3.4 region was 0.5 C, at the borderline of warm-neutral and weak El Niño, and 0.39 C for the month of February, in the warm-neutral range. During the first half of March, the subsurface sea temperature anomalies continued to be somewhat above average. However, only 15% of the dynamical and statistical models predict weak El Niño conditions for the Mar-May season, with objective model-based probabilities at 21%. This El Niño probability decreases to below 20% from late spring through late summer, rising slightly for later seasons. Throughout the forecast seasons, the probability for neutral conditions is higher than that of El Niño or La Niña. Based on the multi-model mean prediction, 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
MAM 2020 0% 79% 21%
AMJ 2020 1% 81% 18%
MJJ 2020 10% 73% 17%
JJA 2020 22% 61% 17%
JAS 2020 30% 53% 17%
ASO 2020 34% 47% 19%
SON 2020 36% 42% 22%
OND 2020 35% 38% 27%
NDJ 2020 31% 37% 32%

 

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

Forecast Probability Distribution Based on the IRI/CPC ENSO Prediction Plume

Published: Mar 19, 2020


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 the large (20+) set of dynamical and statistical models in the plume of model ENSO predictions.


  • <

    Figure 1

    Figure 1 shows the ensemble mean predictions of each dynamical model, along with the statistical predictions. This plot provides some idea of the disagreement among the individual models, as well as the difference between the mean of the forecasts of dynamical versus statistical models.

  • Figure 2

    Predictions of ENSO are probabilistic. The ensemble mean prediction 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 overall mean forecast represents the center, or 50 percentile, in the distribution. The overall mean is formed using equal weighting among all models. 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 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. Individual model spreads have often been found to be somwehate narrower than they should be, although in multi-model ensembles this tendency has been shown to be milder or even eliminated.

  • Figure 3

    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 mean 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. The uncertainty and persistence statistics are based on the set of 7 NMME (North American Multimodel Ensemble) models, as it is assumed that these statistics are approximately applicable to all of the models. Sometimes the “spaghetti density” may appear asymmetric about the mean of all the forecasts 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.