Argentina

El Niño–Southern Oscillation (ENSO) is the most important driver of interannual natural climate variability on a global scale. ENSO alternates between El Niño (warm phase) and La Niña (cool phase) approximately every 3–7 years, with neutral periods in between, shaping worldwide patterns of temperature, rainfall, and extreme events such as droughts, floods, and heatwaves. ​

DuringEl Niño, the eastern Pacific experiences warmer and wetter-than-usual conditions, while the western Pacific tends to become drier. La Niña reflects the opposite pattern. ENSO activity typically peaks in boreal winter (DJF), while its impact on global mean surface temperature is delayed, with the strongest signal appearing about 6–7 months later. These shifts have far-reaching consequences for agriculture, health, infrastructure, and livelihoods—particularly in vulnerable regions. Moreover, during the warm phase, weaker trade winds and reduced upwelling in the eastern Pacific limit the supply of cold, nutrient-rich waters, disrupting marine ecosystems and fisheries in the region. ​

El Niño events are commonly classified based on the location of maximum sea surface temperature (SST) anomalies in the tropical Pacific. Two primary types are recognized: Eastern Pacific (EP) El Niño and Central Pacific (CP) El Niño. EP El Niños—often referred to as "canonical" events—are characterized by pronounced SST warming in the eastern equatorial Pacific, particularly off the coasts of South America. These events are typically associated with strong atmospheric responses, including heavy rainfall and flooding in countries like Peru and Ecuador.In contrast, CP El Niños—sometimes called "El Niño Modoki"—exhibit peak SST anomalies farther west, near the dateline in the central Pacific. These events tend to produce weaker or more spatially displaced atmospheric impacts, and recent studies suggest they have become more frequent in recent decades.​

In this analysis, we present correlations between ERA5 climate data and the Oceanic Niño Index (ONI), which primarily reflects SST anomalies averaged over the central equatorial Pacific (Niño 3.4 region). This makes it particularly relevant for capturing the influence of CP El Niño events.​​

Now, El Niño or La Niña events occur on top of the long-term climate change baseline, meaning that the combined extremes can be more severe than those caused by ENSO or climate change alone. Understanding how El Niño - La Niña influences specific regions is therefore essential for preparedness and adaptation planning.​

While ENSO dominates at the global scale, other regional modes of variability can play a stronger role regionally—such as the Indian Ocean Dipole (IOD), North Atlantic
Oscillation (NAO), or the Southern Annular Mode (SAM).

 

In our analysis, we use the Oceanic Niño Index (ONI) to represent central Pacific Niño, which uses SST data in the central equatorial Pacific (5° N–5° S, 170° W–120° W, also known as 3.4 region) (see more https://www.ncei.noaa.gov/access/monitoring/enso/sst ). An El Niño or La Niña event is typically defined when the index exceeds ±0.5 °C for six months or more. Other regions and indices capture different “flavors” of ENSO, and these can be explored in further analyses.

What you can see in this figure​
In this analysis, we explore how variations in the ENSO index relate to changes in key climate variables seasonally across the globe. We plot the correlation between the climate variable and the ENSO index. ​​

The key questions are: In which direction do temperature and precipitation shift under ENSO, and how strong are those shifts compared to other regions? When is the correlation stronger throughout the year? Equally important is how temperature and precipitation interact—for example, where ENSO drives hotter and drier conditions, or hotter and wetter ones.​​

Understanding the Data: Implications and Utility ​
A positive correlation between a climate variable and the ENSO index in each season means that the variable (temperature or precipitation) tends to increase during El Niño and decrease during La Niña. A negative correlation indicates the opposite pattern. For example, in the temperature maps, red regions show warming during El Niño and cooling during La Niña, while blue regions show cooling during El Niño and warming during La Niña. These correlations are strongest near the tropical Pacific, where ENSO originates, and around Dec-Feb, but teleconnections—climatic effects at a distance—extend across the globe. The patterns are also highly seasonal. ​​

What are the key caveats and limitations to consider?​

This is one approach to visualizing and quantifying the influence of ENSO on temperature and precipitation, though other methods and visualizations exist. The current results are based on simultaneous correlations, showing how conditions in the Pacific are linked to immediate responses elsewhere. However, ENSO’s impacts are often delayed by several months, and since it typically peaks in December–February, one could develop analysis that include lagged correlations.​

Weak correlations are disregarded—for reference,  at least ±0.235 is needed for significance at the 95% level. ​

To isolate interannual variability for our analysis, the climate time series were de-trended prior to calculating correlation with a simple quadratic fit. This ensures that our correlation analysis reflects only year-to-year fluctuations, rather than being influenced by long-term linear trends.​

Because this analysis relies on ERA5 data, any biases in that dataset will also be reflected in the ENSO–climate correlations presented here.​

What you can see in this figure​​

This figure shows the time series of temperature and precipitation anomalies for the aggregated spatial unit. Red indicates El Niño months and blue indicates La Niña months. Anomalies are calculated for each month relative to the 1950–2020 climatological average.​​

Understanding the Data: Implications and Utility​​

Because of climate change, El Niño and La Niña events now occur against a shifted baseline—generally warmer, and either drier or wetter depending on the region. When these events are superimposed on long-term trends, their impacts can be amplified. For example, a heatwave or storm triggered during an ENSO phase may become more severe due to accumulated effects. This figure helps us understand the extremes that emerge from the interaction between long-term climate change and ENSO variability.​​

What are the key caveats and limitations to consider?​​

There is still limited scientific confidence in how climate change may be altering the intensity and frequency of ENSO events. What is shown here focuses only on their combined effects rather than on changes in ENSO itself.​

What you can see in this figure​​

This figure shows the distribution of monthly and seasonal anomalies for El Niño, La Niña, and neutral ENSO conditions. The distributions are approximated using Gaussian curves. Anomalies are computed relative to a long-term trend estimated with a simple quadratic fit (which in general remains flat initially and increases more rapidly in recent years for temperature).

Understanding the Data: Implications and Utility​

The degree of separation between the El Niño and La Niña distributions reflects the strength of ENSO’s influence on temperature or precipitation. In regions where ENSO strongly modulates climate, these distributions are clearly distinct, whereas in areas dominated by other sources of natural variability, they overlap more closely. The long-term climate change trend has been removed to isolate differences attributable solely to ENSO.