About the model summary
Warm and cool conditions
The most common definitions of El Niño and La Niña refer to changes in sea surface temperatures (SSTs) over the tropical Pacific Ocean. Warm conditions (El Niño) mean that SSTs are significantly warmer than their climatological average values for a given time of year, while cool conditions (La Niña) mean that SSTs are colder than normal. Generally, warm conditions correspond to negative values of the SOI and in eastern Australia usually to dry conditions. Cool conditions tend to correspond to positive values of the SOI and in eastern Australia usually to wet conditions.
NINO3.4 and other indices
The National Climate Centre (NCC) uses the "NINO3.4 index" to classify ENSO conditions (see "Note:" below). The NINO3.4 index is defined as the average of SST anomalies over the region 5°N – 5°S and 170° – 120°W. NCC classifies the NINO3.4 temperature anomaly as "warm" if it exceeds 0.8°C, which is about one standard deviation above average.Similarly, anomaly predictions below –0.8°C are tabled as "cool", with those in between classed "neutral". There are also other "NINO" indices that refer to SST anomalies over different areas of the Pacific Ocean. The regions covered by the NINO indices are shown in this map of the tropical Pacific Ocean.
The main variable that is considered from the coupled climate models' ENSO forecasts is the NINO3.4 index. All models that NCC surveys run a suite of forecasts known as an ensemble. Both the ensemble mean (the average of these forecasts) and the individual forecasts are useful: the spread amongst individual forecasts indicates how sensitive the forecasts are to the exact initial conditions from which they are run. Note that the temperature conditions cannot always be determined precisely from published materials and then a subjective assessment is made from available material. This material is linked to the institutions listed below. The information given here is intended only as a quick summary of forecasts. For more detail visitors are advised to visit the linked websites.
The fraction or proportion of models categorized as warm, neutral or cold, should not be taken as an official Bureau or National Climate Centre view as to the likelihood of these various outcomes.
Nearly all the forecast systems whose results are included in the ENSO forecast have been documented in the reputable peer-reviewed international scientific literature and have a respectable level of skill in predicting ENSO. They are all coupled General Circulation Models, so they simulate both atmospheric and ocean processes and are dynamic rather than statistical models. Most have been run routinely for a number of years. All models use an ensemble method, where several model forecasts are run at the same time with minor tweaks to the initial conditions and the model parameters. Each line in a plume graph represents a model run (or ensemble member) with the number of ensemble members included varying from model to model.
Some of the models are regarded as experimental, and most are subject to regular revision and update with the aim of continuous improvement. However, they represent the state-of-the-art as far as dynamical ENSO prediction systems are concerned and offer useful guidance up to about nine months.
The system details are given in the table below. Follow the group links to the Home Page of each of the models. Most of these models provide verification statistics for the WMO Long Range Forecast Verification System and are Global Producing Centres for Long Range Forecasts (WMO).
|POAMA||Bureau of Meteorology (CAWCR)||30 ensemble members|
|System4||ECMWF (EU)||51 ensemble members|
|GloSea5||UK Met Office||42 ensemble members|
|CSFv2||NCEP (US)||40 ensemble members|
|GEOS5||NASA Goddard GMAO (US)||20 ensemble members|
|ARPEGE||MeteoFrance||51 ensemble members|
|JMA.MRI-CGCM||Japan Met Agency||51 ensemble members|
Note: Until recently NCC used NINO3 index to monitor ENSO conditions. However, NINO3.4 is now used since this has been shown to be more closely related to Australian climate than NINO3. See, for example, Wang, G. and Hendon, H. H., 2007: Sensitivity of Australian rainfall to inter-El Niño variations. Journal of Climate, 20, 4211-4226.