This work sits on the periphery: there is almost nothing nearby, with the nearest neighbor at a distance of 0.563. Around it lie studies on El Niño predictability, solar cycles, and the revival of old weather records, but no one has yet drawn a direct link between El Niño and power system failures. Meanwhile, from 2000 to 2021, about 83% of major U.S. outages were weather-related, and the average annual number of such events rose by 78% in 2011–2021 compared to the previous decade. Understanding how a global climate anomaly drives local storms and heat is essential for planning a resilient energy system.
The authors assembled four datasets: U.S. power system failure reports (Form DOE-417), five El Niño indices (MEI, Niño3, Niño4, Niño3.4, SOI), daily temperature and precipitation observations at 0.5° resolution, and projections from 18 CMIP6 models. All series were detrended, converted to anomalies relative to the 2000–2023 average, and smoothed with a three-month moving window. They then computed cross-correlations with time lags from 0 to 9 months between La Niña intensity, the frequency of extreme weather events, and the number of outages in ten U.S. climate regions. To test the significance of differences between El Niño, La Niña, and neutral phases, they used ANOVA and Tukey's test.
During La Niña, the number of outages is significantly higher than during neutral periods, with the effect strongest in spring after the peak of the event. In six of ten regions, the correlation between La Niña intensity and outage counts reaches -0.68 for the entire U.S., and in some regions it goes as high as -0.82. La Niña always precedes outages, not the other way around, confirming a causal link. The mechanism works through three channels: in the western U.S., La Niña intensifies cold snaps and extreme precipitation; in the southeast, heat waves; and in the mid-eastern states, extreme precipitation. CMIP6 projections show that under the SSP 5-8.5 scenario, by 2081–2100, spring heat-related outages in the eastern U.S. will increase by a factor of 25.9 compared to 2000–2023, and under the moderate SSP 2-4.5 scenario, by a factor of 13.2.
What sets this work apart from its neighbors is that it is the first to connect a global climate index to local power system failures through specific weather intermediaries. Neighboring studies either predict El Niño itself or examine solar cycles and space weather, but they do not carry the chain through to blackouts. Here, it is shown that knowing the El Niño phase months in advance can serve as an early warning for energy planners: if a strong La Niña is forecast, one should prepare for higher failure rates in specific regions.
The topic could develop toward accounting for other climate modes, such as the Pacific Decadal Oscillation or the North Atlantic Oscillation, which also affect wind and temperature. It might be combined with El Niño forecast models to create operational warning systems. Extending the analysis to other countries would require collecting comparable outage data, which is currently limited by the availability of information from electric grid companies.
The results will affect power system planning, infrastructure risk insurance, and the development of climate services for utilities. Meteorologists could provide energy planners not only with weather forecasts but also with probabilistic outage risk assessments for the coming season.
The next step will be to test the link between El Niño and outages in other countries and regions with different climatic conditions. It is also necessary to study the combined influence of several climate modes, for example, La Niña and the North Atlantic Oscillation, on failure frequency.
For the energy sector, the unresolved problem remains quantifying the contribution of different climatic factors to equipment failures. This work shows that global modes can explain a significant portion of the interannual variability in outage rates, but separating the contributions of individual weather events within a single event remains difficult.
This section is addressed to the author of the paper and their colleagues. Neighbouring works were found by meaning-based search across our archive, not by word overlap. We do not review: we show the field around the work.
What the work does
The authors compared 24 years of U.S. power outage data with El Niño indices and found a significant link: during La Niña, outages are more frequent, especially in spring, driven by intensified extreme precipitation, cold snaps, and heat waves. CMIP6 projections show a sharp rise in heat-related outages by the end of the century under SSP 2-4.5 and SSP 5-8.5 scenarios.
Where its strength is
For the first time, a chain has been established from a global climate index to local power system failures through specific weather mediators, providing a basis for seasonal forecasting of outage risk.
Where it could go next
Worth trying
Why this may matter
This link gives power systems an early warning tool: knowing the El Niño phase, they can prepare in advance for higher outage rates in specific regions, which is especially important as extreme weather increases.
Nearby works in our archive
The archive is sparse around this work: the closest review is only 0.59, against a usual 0.64. It looks like few have drilled nearby.
In spring 2018, a storm in California brought heavy snowfall and cold, leaving 10,898 customers without power and causing a loss of 38 MW of demand. And in March 2023, a series of tornadoes with extreme precipitation blacked out about one million customers across more than ten states.