El Niño’s Four-Month Link to Atmospheric CO₂ Growth Helps Explain Short-Term Swings
A four-month lag connects El Niño to changes in atmospheric CO₂ growth, while two unusual dips remain after accounting for that influence.
Year-to-year changes in atmospheric CO2 growth reflect more than emissions. A comparison with the NINO3.4 index points to a strong connection with El Niño, delayed by about four months. Accounting for that pattern sharpens the view of two periods when CO2 growth fell below its expected level. It also shows why a change in atmospheric growth alone cannot establish what happened to emissions.
Earlier analysis tracked the changing rate at which atmospheric CO2 has increased. That rate has accelerated over decades, but it also fluctuates over shorter periods. Two dips stood out: one in 1991–1992, associated in the original analysis with an economic slowdown after the breakup of the former Soviet Union, and another in 2025–2026. The latter was initially linked to a proposed explanation involving reduced economic activity. The new comparison asks whether El Niño could instead account for either episode.
Separating the long-term rise from shorter swings
The analysis first estimates the CO2 growth rate over time, then removes its long-term linear trend. This detrending step makes it easier to compare shorter fluctuations without the underlying multidecade increase dominating the result. The comparison uses the relative NINO3.4 index, a measure associated with El Niño conditions in the tropical Pacific.
A cross-correlation analysis finds the strongest relationship when the NINO3.4 index is shifted by four months. In other words, changes in the index tend to line up most closely with changes in detrended CO2 growth several months later. A lag of that order is consistent with the time it can take for climate patterns to affect conditions across the world.
Why a climate pattern affects atmospheric CO₂
El Niño alters precipitation patterns, which can affect vegetation growth. Plants take up CO2 as they grow and release it through processes such as decay and respiration. When rainfall and growing conditions shift across large regions, the balance of those flows can change how much CO2 accumulates in the atmosphere over a given period.
That mechanism helps explain why atmospheric growth can vary even while the longer-term rise continues. It doesn’t mean El Niño changes the underlying trend, or that the index accounts for every short-term fluctuation. It offers one measurable influence to consider when interpreting the atmospheric record.
To show the relationship, the analysis compares detrended CO2 growth with a scaled NINO3.4 index shifted by four months. The two series track one another much of the time. The author then fits a model relating detrended CO2 growth to the index and examines what remains after that relationship is accounted for.
What remains after accounting for El Niño
The residuals—the differences between the observed growth rate and the model’s estimate—make the two periods of unusually low growth more prominent, rather than explaining them away. On this analysis, El Niño is not the main cause of those dips. That result narrows one possible explanation, but it doesn’t identify the cause on its own.
Atmospheric CO2 growth and emissions are related, but they are not interchangeable measures. The atmospheric rate reflects the net effect of emissions and the amount of carbon absorbed or released by natural systems. A dip in growth can therefore prompt questions about emissions without proving that emissions fell, or showing which sources changed.
Emissions data could help test the proposed explanation for 2025, though those data are less current than atmospheric CO2 measurements. The analysis also notes that the author has less confidence in emissions data. That leaves a practical next step: compare the atmospheric pattern with emissions estimates as they become available, while checking whether other influences could explain the remaining departures.
A reader’s comment prompted the El Niño comparison. Its value is methodological as well as scientific: testing an overlooked influence improves the interpretation of the record. For now, the four-month relationship accounts for a substantial recurring pattern, while the residual dips remain questions for further evidence.
