Butterfly effect and a self-modulating El Niño response to global warming
Open accessAbstract
El Niño and La Niña, collectively referred to as the El Niño-Southern Oscillation (ENSO), are not only highly consequential 1-6 but also strongly nonlinear 7-14. For example, the maximum warm anomalies of El Niño, which occur in the equatorial eastern Pacific Ocean, are larger than the maximum cold anomalies of La Niña, which are centred in the equatorial central Pacific Ocean 7-9. The associated atmospheric nonlinear thermal damping cools the equatorial Pacific during El Niño but warms it during La Niña 15,16. Under greenhouse warming, climate models project an increase in the frequency of strong El Niño and La Niña events, but the change differs vastly across models 17, which is partially attributed to internal variability 18-23. Here we show that like a butterfly effect 24, an infinitesimal random perturbation to identical initial conditions induces vastly different initial ENSO variability, which systematically affects its response to greenhouse warming a century later. In experiments with higher initial variability, a greater cumulative oceanic heat loss from ENSO thermal damping reduces stratification of the upper equatorial Pacific Ocean, leading to a smaller increase in ENSO variability under subsquent greenhouse warming. This self-modulating mechanism operates in two large ensembles generated using two different models, each commencing from identical initial conditions but with a butterfly perturbation 24,25; it also operates in a large ensemble generated with another model commencing from different initial conditions 25,26 and across climate models participating in the Coupled Model Intercomparison Project 27,28. Thus, if the greenhouse-warming-induced increase in ENSO variability 29 is initially suppressed by internal variability, future ENSO variability is likely to be enhanced, and vice versa. This self-modulation linking ENSO variability across time presents a different perspective for understanding the dynamics of ENSO variability on multiple timescales in a changing climate.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Sampling ENSO SST variability over relatively short 30-year running periods is subject to a strong influence from decadal climate fluctuations such that stochastic noise and butterfly effect can obscure the impact of greenhouse forcing.
“However, sampling ENSO SST variability over relatively short 30-year running periods would be subject to a strong influence from decadal climate fluctuations such that stochastic noise and butterfly effect can obscure the impact of greenhouse forcing [14][15][16] .”
From Increased ENSO sea surface temperature variability under four IPCC emission scenarios (2022) · cites this paper as [16]
The Conclusion and implications says decadal variability, stochastic forcing and butterfly perturbations can shape ENSO variability and its response to greenhouse warming. That supports the general concern about obscuring a forced signal, but our copy does not establish it specifically for 30-year running periods; its main comparisons use 50-year periods. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., et al. (2020) · Quarterly Journal of the Royal Meteorological Society · doi:10.1002/qj.3803 · Cited by 31,882 · see citing works
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., & Taylor, K. E. (2016) · Geoscientific Model Development · doi:10.5194/gmd-9-1937-2016 · Cited by 11,981 · see citing works
Cai, W., Santoso, A., Wang, G., Yeh, S.-W., An, S.-I., Cobb, K. M., Collins, M., Guilyardi, E., Jin, F.-F., Kug, J.-S., et al. (2015) · Nature Climate Change · doi:10.1038/nclimate2743 · Cited by 891 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 4
- Frauen, C. & Dommenget, D. El Niño and La Niña amplitude asymmetry caused by atmospheric feedbacks. Geophys. Res. Lett. 37, L18801 (2010).
- Takahashi, K., Montecinos, A., Goubanova, K. & Dewitte, B. ENSO regimes: Reinterpreting the canonical and Modoki El Niño. Geophys. Res. Lett. 38, L10704 (2011).
- Guan, C., & McPhaden, M. J. Ocean processes affecting the twenty-first-century shift in ENSO SST variability. J. Climate 29(19), 6861-6879, (2016).
- Xie, S.-P. The shape of continents, air-sea interaction, and the rising branch of the Hadley circulation. The Hadley Circulation: Present, Past and Future, H. F. Diaz and R. S. Bradley, Eds., Advances in Global Change Research, Vol. 21, Kluwer Academic Publishers, 121-152 (2005).
Cited by
Sorted by citations
Increased ENSO sea surface temperature variability under four IPCC emission scenarios
Cai, W., Ng, B., Wang, G., Santoso, A., Wu, L., & Yang, K. (2022) · Nature Climate Change · doi:10.1038/s41558-022-01282-z · Cited by 263 · see citing works
Self-citationRobust decrease in El Niño/Southern Oscillation amplitude under long-term warming
Callahan, C. W., Chen, C., Rugenstein, M., Bloch-Johnson, J., Yang, S., & Moyer, E. J. (2021) · Nature Climate Change · doi:10.1038/s41558-021-01099-2 · Cited by 87 · see citing works
Enhanced North Pacific impact on El Niño/Southern Oscillation under greenhouse warming
Jia, F., Cai, W., Gan, B., Wu, L., & Di Lorenzo, E. (2021) · Nature Climate Change · doi:10.1038/s41558-021-01139-x · Cited by 78 · see citing works
Self-citation
- Authors
- Cai, W., Ng, B., Geng, T., Wu, L., Santoso, A., & McPhaden, M. J.
- Affiliations
- CSIRO Oceans and Atmosphere (AU)
- Qingdao National Laboratory for Marine Science and Technology (CN)
- NOAA Pacific Marine Environmental Laboratory (US)
- Corresponding author
- Cai, Wenju
- Wu, Lixin
- Venue
- Nature
- Published
- 2020-09-02
- Volume
- 585
- Issue
- 7823
- Pages
- 68-73
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W3082203506
- Cited by
- 111
- References
- 3
- Citations by year
- 2026: 10 · 2025: 24 · 2024: 19 · 2023: 28 · 2022: 13 · 2021: 15 · 2020: 2
- FWCI
- 5.40
- Type
- journal article
- Language
- en
- Update notices
- Open access
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- NOAA Institutional Repository (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- UNSWorks (UNSW Sydney) (submittedVersion, cc-by-nc-nd)
- Funders
- U.S. Department of Energy
- Australian Government
- Commonwealth Scientific and Industrial Research Organisation
- Chinese Academy of Sciences (XDB40000000)
- Centre for Southern Hemisphere Oceans Research
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Climate variability and models
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Geology and Paleoclimatology Research
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Meteorological Phenomena and Simulations
- Keywords
- ENSO, ENSO variability, butterfly effect, self-modulation, global warming, natural climate variability, climate model ensembles, CMIP
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review