Modes of Climate Variability
Modes of climate variability describe recurring patterns of natural variability in the climate system. They influence seasonal and year-to-year variations in temperature and precipitation across different regions. These patterns can influence the occurrence and distribution of hazards such as heavy rainfall, floods, droughts and heatwaves. Their effects vary depending on the strength and timing of individual events and other concurrent meteorological factors.
A strong El Niño developed rapidly in 2026 and is nearly certain to persist into early 2027
Explanation
This graph shows observed departures of sea-surface temperature from the long-term monthly average in the Niño 3.4 region of the tropical Pacific Ocean. Forecasts from individual forecasting centres are also shown (light blue). These anomalies help track the development and evolution of El Niño and La Niña events. Sustained warmer-than-average conditions indicate El Niño conditions, while sustained cooler-than-average conditions occur during La Niña.
Methodology
Observations and forecasts are expressed as anomalies relative to the 1993-2009 average. For observations this is done using the observed series. For the forecasts, the 1993-2009 average is calculated from hindcasts.
The observed series is based on the NOAA OISST daily sea-surface temperature dataset averaged over the Niño 3.4 region.
Forecasts from individual models are shown as thin lines and the thick line is the multi-model mean.
A strong El Niño developed rapidly in 2026 and is nearly certain to persist into early 2027
Explanation
The Niño 3.4 index is the average sea surface temperature anomaly in the central Pacific from 120°W to 170°W and from 5°S to 5°N. Peaks in the Niño 3.4 index typically correspond to El Niño events and cool periods correspond to La Niña events. The version shown here is based on data from ships, buoys and satellites.
Methodology
To produce the plot, the following processing steps were performed:
- Rebaselined to 1991-2020 for each month separately by calculating the arithmetic mean of the data over the baseline period and subtracting the mean from all data values. This is done for each month separately (Januarys, Februarys etc).
- Selected years within the range 1950 to 2025.
A strong El Niño developed rapidly in 2026 and is nearly certain to persist into early 2027
Explanation
The southern oscillation index is a measure of the pressure difference between Tahiti (in the central Pacific) and Darwin (in northern Australia). It reflects the atmospheric changes associated with El Niño and La Niña. During El Niño, the SOI is negative with below normal pressure at Tahiti and above normal pressure at Darwin. During La Niña, the index is positive.
Methodology
The southern oscillation index is a measure of the pressure difference between Tahiti (in the central Pacific) and Darwin (in northern Australia). Data are standardized anomalies relative to the 1950-2000 baseline.
The PDO has been in a predominantly negative phase since 2018
Explanation
The Pacific Decadal Oscillation is a recurring pattern of sea surface temperature anomalies in the north Pacific which has important variations that last for years or decades. The PDO affects marine ecosystems and weather patterns in the northern hemisphere. The data here have been smoothed to emphasize the long-term variations.
Methodology
The Pacific Decadal Oscillation index is calculated based on the leading Empirical Orthogonal Function (EOF: a pattern of variability) of sea-surface temperature variability in the North Pacific. The data have been smoothed to remove variations on a monthly to annual time scale.
To produce the plot, the following processing steps were performed:
- Calculated lowess smoothed series with 0.03 of data used for each fit
- Selected years within the range 1850 to 2025.
The Indian Ocean Dipole affects weather around the Indian Ocean
Explanation
The Indian Ocean dipole is a recurring pattern of sea surface temperature anomalies in the Indian Ocean. In the positive phase, the western Indian Ocean is warmer than average and the eastern Indian Ocean off Indonesia, is cooler than average. In the negative phase, the pattern is reversed. The two phases have impacts around the ocean basin.
Methodology
Note that differences between datasets reflect uncertainty in the index. In particular, the variance of the series is affected by the spatial and temporal resolution of the datasets used to calculate the index. Higher resolution datasets which include satellite data are better able to better resolve coastal features such as upwelling along the western edge of Indonesia. While the magnitude of the events depends on these factors, there is good agreement between datasets on the timing and direction of changes in the index.
To produce the plot, the following processing steps were performed:
- Calculated monthly average from values using arithmetic mean of all dates that fall within each month
- Selected years within the range 1980 to 2025.
The North Atlantic Oscillation affects winter weather in Eurasia
Explanation
The North Atlantic Oscillation (NAO) index is the difference in pressure between the Azores and Iceland, representing fluctuations in the pressure gradient between the "Azores high" and the "Icelandic low". In its positive phase, the pressure gradient is stronger, bringing mild and wet weather to northern Europe. In the negative phase, the gradient is weaker and northern Europe often experiences colder, drier conditions. For southern Europe, the pattern is reversed.
Methodology
The North Atlantic Oscillation index is calculated based on the first Empirical Orthogonal Function (EOF: patter of variability) of daily pressure variations in the North Atlantic during the winter months. The monthly average is presented here,
The Arctic Oscillation affects winter weather across the northern hemisphere
Explanation
The Arctic Oscillation (AO) index tracks changes in the pressure gradient between the north pole and the mid-latitudes of the northern hemisphere. In its positive phase, the gradient is stronger, driving stronger mid-latitude winds and confining cold air to the polar regions. In its negative phase, the gradient and the winds are weaker.
Methodology
According to the CPC webpage the AO index is obtained by projecting the AO loading pattern to the daily anomaly 1000 millibar height field over 20°N-90°N latitude. The AO loading pattern has been chosen as the first mode of EOF analysis using monthly mean 1000 millibar height anomaly data from 1979 to 2000 over 20°N-90°N
The Antarctic Oscillation affects winter weather across the southern hemisphere
Explanation
The Antarctic oscillation (AAO) index tracks changes in the pressure gradient between the south pole and the mid-latitudes of the southern hemisphere. In its positive phase, westerly winds intensify and move towards Antarctica. In the negative phase, they weaken and move further north. The Antarctic Oscillation is also known as the Southern Annular Mode.
Methodology
According to the NOAA CPC webpage - The daily AAO index is constructed by projecting the daily (00Z) 700mb height anomalies poleward of 20°S onto the loading pattern of the AAO. Please note that year-round monthly mean anomaly data have been used to obtain the loading pattern of the AAO. Since the AAO has the largest variability during the cold season, the loading pattern primarily captures characteristics of the cold season AAO pattern.