IRI – International Research Institute for Climate and Society (2022)

July 2022 Quick Look

Published: July 19, 2022

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)

In mid-July, sea surface temperatures in the central-eastern equatorial Pacific remain below-average. Key oceanic and atmospheric variables have remained consistent with La Niña conditions, although weakened. A La Niña Advisory still remains in place for July 2022. A large majority of the models in the plume predict SSTs to remain below-normal at the level of a weak La Niña until at least Sep-Nov 2022. Similar to the most-recent official CPC/IRI ENSO Outlook issued on July 14, 2022, the objective model-based ENSO outlook forecasts a continuation of the La Niña event with moderate probability (68% chance) during Aug-Oct 2022, continuing into boreal fall and early winter with 63-70% likelihood, with ENSO-neutral becoming the most likely category in Jan-Mar 2023 onward.

  • Figure 1.
  • Figure 3.
  • Figure 2
  • Figure 4.

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

CPC/IRI ENSO Update

Published: July 14, 2022

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:La Niña is favored to continue through 2022 with the odds for La Niña decreasing into the Northern Hemisphere late summer (60% chance in July-September 2022) before increasing through the Northern Hemisphere fall and early winter 2022 (62-66% chance).

During June, below-average sea surface temperatures (SSTs) weakened across most of the central and eastern equatorial Pacific Ocean with SSTs returning to near-average in the east-central Pacific (Fig. 1), as reflected by the Niño indices, which ranged from -0.4ºC to -1.2ºC during the past week (Fig. 2). Subsurface temperatures anomalies averaged between 180°-100°W and 0-300m depth were weakly positive in June (Fig. 3). Below-average subsurface temperatures persisted near the surface to ~75m depth in the eastern equatorial Pacific Ocean, with above-average temperatures at depth (~100 to 200m) in the western and central Pacific Ocean (Fig. 4). Low-level easterly wind anomalies prevailed in the western and central equatorial Pacific, while upper-level westerly wind anomalies continued over most of the equatorial Pacific. Convection remained suppressed over the western and central Pacific and enhanced over Indonesia (Fig. 5). Overall, the coupled ocean-atmosphere system was consistent with La Niña conditions.

The most recent IRI/CPC plume average for the Niño-3.4 SST index now forecasts La Niña to persist into the Northern Hemisphere winter 2022-23 (Fig. 6). The forecaster consensus also predicts La Niña to persist during the remainder of 2022, with odds for La Niña remaining at 60% or greater through early winter. Lowest odds occur during the next few months with a 60% chance of La Niña and a 39% chance of ENSO-neutral during July-September 2022. Subsequently, chances of La Niña increase slightly during the fall and early winter. In summary, La Niña is favored to continue through 2022 with the odds for La Niña decreasing into the Northern Hemisphere late summer (60% chance in July-September 2022) before increasing through the Northern Hemisphere fall and early winter 2022 (62-66% chance; click CPC/IRI consensus forecastfor the chances in each 3-month period).

(Video) Columbia University's International Research Institute for Climate and Society

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). Additional perspectives and analysis are also available in an ENSO blog. A probabilistic strength forecast is available here. The next ENSO Diagnostics Discussion is scheduled for 11 August 2022. 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.

IRI – International Research Institute for Climate and Society (5)

SeasonLa NiñaNeutralEl Niño
JJA74260
JAS60391
ASO62362
SON63352
OND66322
NDJ65323
DJF56395
JFM45487
FMA34588

IRI – International Research Institute for Climate and Society (6)

IRI – International Research Institute for Climate and Society (7)

IRI – International Research Institute for Climate and Society (8)

IRI – International Research Institute for Climate and Society (9)

IRI – International Research Institute for Climate and Society (10)

IRI – International Research Institute for Climate and Society (11)

IRI Technical ENSO Update

Published: July 19, 2022

Note: The SST anomalies cited below refer to the OISSTv2 SST data set, and not ERSSTv5. OISSTv2 is often used for real-time analysis and model initialization, while ERSSTv5 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. These two products may differ, particularly during ENSO events. The difference between the two datasets may be as much as 0.5 °C. Additionally in some years, the ERSSTv5 may tend to be cooler than OISSTv2 in the context of warming trends, because ERSSTv5 is expressed relative to a base period that is updated every 5 years, while the base period of OISSTv2 is updated every 10 years. In February 2021, both datasets were updated to reflect the 1991-2020 climatology period.

Recent and Current Conditions

The SST anomaly for NINO3.4 during the Apr-Jun season was -0.89 °C, and for the month of June it was -0.68 °C. The most recent weekly (13 Jul 2022) anomaly in the NINO3.4 region was -0.6 °C, indicating borderline La Niña conditions. 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 colder.

Many of the key atmospheric variables remain indicative of La Niña conditions, such as the traditional and equatorial Southern Oscillation Indices, which decreased in June 2022, but remained positive. The low-level easterly winds are near normal in the eastern Pacific, and stronger than normal in the central-western Pacific, while upper-level wind anomalies remain westerly across the tropical Pacific. Anomalously dry conditions have been observed over the central and western Pacific Ocean. Across the equatorial Pacific Ocean, subsurface temperatures are above average in the western (100 to 200 meters depth) and eastern Pacific between 130W to 100W and at the depth of 50 to 100 meters. Negative subsurface temperatures are evident near the surface and at depth (100 to 150 meters) in the central and far eastern Pacific near 100W-80W.

In summary, tropical Pacific atmospheric and oceanic conditions remain consistent with La Niña and a La Niña advisory is still in place.

Expected Conditions

Note – Only models that produce a new ENSO prediction every month are considered in this statement.

(Video) Mark Cane on the origins of the International Research Institute for Climate and Society

What is the outlook for the ENSO status going forward? El Niño/Southern Oscillation (ENSO) Diagnostic Discussion issued jointly on 14 July 2022 by the Climate Prediction Center/NCEP/NWS and the International Research Institute for Climate and Society indicates a continuation of the current La Niña event.

The latest set of model ENSO predictions from mid-July is now available in the IRI/CPC ENSO prediction plume. These are used to assess the probabilities of the three possible ENSO conditions by using the average value of the NINO3.4 SST anomaly predictions from all models in the plume, equally weighted. A standard Gaussian error is imposed over that average forecast, and its width is 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.

A large majority of the models in the plume predict SSTs to remain below-normal at the level of a weak La Niña until at least Sep-Nov 2022. Particularly, the chances of La Niña are 68% for the Aug-Oct season, while those of the ENSO-neutral category are 31%. The probability of La Niña for the subsequent seasons (Sep-Nov, Oct-Dec, Nov-Jan) is forecasted to be between 63-70%, decreasing to 55% in Dec-Feb 2023. ENSO-neutral becomes the most likely category during Jan-Mar (50%), Feb-Apr (64%), and Mar-May (72%), 2023. El Niño likelihoods remain very low throughout the forecast period. A plot of the probabilities summarizes the forecast evolution. The climatological probabilities for La Niña, ENSO-neutral, and El Niño conditions vary seasonally, and are shown in a table at the bottom of this page for each 3-month season.

Caution is advised in interpreting the forecast distribution from the Gaussian standard error as the actual probabilities, due to differing biases and performance of the different models. 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. At longer leads, the skill of the models degrades, and uncertainty in skill must be convolved with the uncertainties from initial conditions and differing model physics, which leads 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.

In summary, the probabilities derived from the models in the IRI/CPC plume indicate a preference for the continuation of the La Niña until Dec-Feb 2023 with moderate chances, with neutral conditions most likely thereafter. This would be the third “triple-dip La Niña” since 1950 if this event continues into the boreal winter. The likelihood of El Niño development remains very low.

A caution regarding the model-based ENSO plume predictions released mid-month, is that factors such as known specific model biases and recent changes in the tropical Pacific that the models may have missed, are not considered. This approach is purely objective. Those issues are taken into account in the official outlooks, which are generated and issued early in the month by CPC and IRI, and which will include some human judgment in combination with the model guidance.

SeasonLa NiñaNeutralEl Niño
JAS66340
ASO68311
SON70291
OND69292
NDJ63334
DJF55396
JFM43507
FMA266410
MAM137215

IRI/CPC Model-Based Probabilistic ENSO Forecast

Published: July 19, 2022

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 – International Research Institute for Climate and Society (12)

SeasonLa NiñaNeutralEl Niño
JAS66340
ASO68311
SON70291
OND69292
NDJ63334
DJF55396
JFM43507
FMA266410
MAM137215

CPC/IRI Official Probabilistic ENSO Forecast

Published: July 14, 2022

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.

IRI – International Research Institute for Climate and Society (13)

SeasonLa NiñaNeutralEl Niño
JJA74260
JAS60391
ASO62362
SON63352
OND66322
NDJ65323
DJF56395
JFM45487
FMA34588

IRI/CPC ENSO Predictions Plume

Published: July 19, 2022

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 regionfor nine overlapping 3-month periods. Note that the expected skills of the models, based on historical performance, arenot equal to one another. The skills also generally decrease as the lead time increases. Thirdly, forecasts made at sometimes of the year generally have higher skill than forecasts made at other times of the year--namely, they are better whenmade between June and December than when they are made between February and May. Differences among the forecasts of themodels reflect both differences in model design, and actual uncertainty in the forecast of the possible future SST scenario.

(Video) IRI Data Library

Interactive Chart

You can highlight a specific model by hovering over it either on the chart or the legend. Selecting An item on the legend will toggle the visibility of the model on the page. You can also select DYN MODELS or STAT MODELS to toggle them all at once. Clicking on the "burger" menu above the legend will give you options to download the image or expand to full screen. If you have any feedback on this new feature, please let us know at webmaster@iri.columbia.edu.

List of Models Used

Forecast SST Anomalies (deg C) in the Nino 3.4 Region

Seasons (2022 – 2023)
ModelJASASOSONONDNDJDJFJFMFMAMAM
Dynamical Models
NASA GMAO-1.50-1.90-1.96-1.83-1.59-1.16-0.69
NCEP CFSv2-0.76-0.81-0.87-0.94-0.92-0.74-0.44
JMA-0.64-0.64-0.70-0.71-0.64
BCC_CSM11m-0.26-0.110.070.330.701.061.321.461.56
SAUDI-KAU-0.34-0.35-0.29-0.140.080.280.430.480.46
LDEO-0.170.010.030.060.030.02-0.02-0.07-0.10
AUS-ACCESS-0.47-0.53-0.57-0.60
ECMWF-0.48-0.49-0.45-0.43-0.40
UKMO-1.13-1.37-1.51-1.52
KMA-0.53-0.86-1.05-1.10-0.76
IOCAS ICM-0.62-0.80-1.14-1.41-1.62-1.78-1.82-1.72-1.54
COLA CCSM4-0.52-0.73-0.97-1.10-1.07-0.83-0.45-0.030.24
MetFRANCE-0.51-0.55-0.65-0.80-0.89-0.83-0.52
SINTEX-F-0.63-0.63-0.61-0.48-0.29-0.090.070.180.31
CS-IRI-MM-0.29-0.28-0.34-0.36-0.31-0.17
GFDL SPEAR-0.22-0.19-0.25-0.26-0.170.020.240.450.64
CMC CANSIP-0.67-0.87-1.09-1.21-1.22-1.07-0.80-0.49-0.21
Average, Dynamical models-0.573-0.654-0.725-0.735-0.606-0.441-0.2440.0320.170
Statistical Models
NTU CODA-0.40-0.43-0.58-0.71-0.83-0.87-0.78-0.70-0.53
BCC_RZDM-0.64-0.75-0.84-0.93-1.03-1.06-0.97-0.70-0.40
CPC MRKOV-0.81-0.69-0.56-0.39-0.180.040.200.290.38
CPC CA-1.25-1.36-1.54-1.61-1.54-1.20-0.76-0.290.12
CSU CLIPR-1.14-1.07-1.00-0.93-0.78-0.63-0.48-0.220.05
IAP-NN-0.99-1.09-1.11-1.07-0.98-0.84-0.66-0.46-0.27
UCLA-TCD-0.99-1.20-1.42-1.57-1.56-1.38-1.08-0.71-0.35
Average, Statistical models-0.889-0.942-1.007-1.030-0.986-0.849-0.647-0.399-0.143
Average, All models-0.665-0.738-0.808-0.821-0.727-0.591-0.401-0.1690.024

Discussion of Current Forecasts

For Aug-Oct 2022, all statistical and most dynamical models indicate a continuation of the current La Niña event. Overall, there is a 6370% chance for a La Niña to persist during boreal fall and early winter before decreasing to 55% in Dec-Feb, and returning to ENSO-neutral thereafter. The probabilities for El Niño conditions remain below 10% during the forecast period, except for Mar-May, 2023 (15%). 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, ENSO-neutral and El Niño conditions (using -0.5 °C and 0.5 °C thresholds) over the coming 9 seasons are:

SeasonLa NiñaNeutralEl Niño
JAS66340
ASO68311
SON70291
OND69292
NDJ63334
DJF55396
JFM43507
FMA266410
MAM137215

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 completed3-month period. The plots allow comparison of plumes from the previous start times, or examination of the forecastbehavior of a given model over time. The first plot shows forecasts for dynamical models, the second for statisticalmodels, and the third for all models. For less difficult readability, forecasts are shown to a maximum of only the firstfive lead times. Below the third plot, we provide a mechanism for highlighting the forecasts of one model at a time againsta background of more lightly colored lines for all other models.

IRI – International Research Institute for Climate and Society (14)

IRI – International Research Institute for Climate and Society (15)

IRI – International Research Institute for Climate and Society (16)

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.

IRI – International Research Institute for Climate and Society (17)

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

Published: July 19, 2022

The 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.

  • IRI – International Research Institute for Climate and Society (18)

    Figure 5

    (Video) Climate & Health in Africa, narrated by Judy Omumbo, IRI

    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.

  • IRI – International Research Institute for Climate and Society (19)

    Figure 6

    Figure 6, 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.

The CPC ENSO forecast is released at 9am (Eastern Time) on the second Thursday of each month.

The IRI ENSO forecast is released on the 19th of each month. If the 19th falls on a weekend or holiday, it is released on the closest business day.

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 Fig. 3 relies solely on model output. Figure 1 is updated on this page on the second Thursday of every month.

×

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 Fig. 4. 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. Figure 3 is updated on the third Thursday of every month.

×

Plume of forecasts of the Nino3.4 SST anomaly from dynamical and statistical models that are run during the first half of the month. A probability forecast is generated using all of the models in Fig. 4, and is shown in Fig. 3. The average of the forecasts of the dynamical models is shown by the thick yellow line, and of the statistical models by the thick green line. The average of the four models run at the NOAA Climate Prediction center (CPC) is shown by the thick pink line. This figure is updated on the third Thursday of every month. Because forecasts from some of the models shown in Fig. 4 are not yet available when the official CPC/IRI ENSO probability forecast (Fig. 1) is made, the official forecast uses as one of its inputs the Fig. 4 for the previous mid-month, which is shown in the CPC/IRI ENSO Diagnostic Discussion.

×

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