Increased ENSO sea surface temperature variability under four IPCC emission scenarios
Open accessAbstract
Sea surface temperature (SST) variability of El Niño–Southern Oscillation (ENSO) underpins its global impact, and its future change is a long-standing science issue. In its sixth assessment, the IPCC reports no systematic change in ENSO SST variability under any emission scenarios considered. However, comparison between the 20th and 21st century shows a robust increase in century-long ENSO SST variability under four IPCC plausible emission scenarios.
AI snapshot
Written from the paper’s full text.
TL;DR
Using CMIP6 models, the paper finds a robust inter-model consensus that ENSO SST variability increases in the 21st century relative to the 20th century under all four IPCC SSP scenarios, contrary to the IPCC AR6 assessment of no systematic change. The increase is statistically significant in multi-model means and is linked to stronger nonlinear Bjerknes feedback and air–sea coupling.
Key findings
- Under SSP585, 38 out of 43 (88.4%) CMIP6 models show greater Niño3.4 SST variability in 2000–2099 than in 1900–1999; the multi-model ensemble mean increase is 16.1%, statistically significant by a bootstrap test, and 15.7% when inter-model differences in ENSO amplitude are removed.
- Under SSP126, 34 out of 39 (87.2%) models show an increase in Niño3.4 SST variability, with a statistically significant multi-model mean increase of 11.7%.
- Across the four SSP scenarios, inter-model consensus ranges are 78.6–88.4% for Niño3.4, 76.2–81.6% for Niño3, and 73.8–81.4% for Niño4.
- With 50-year periods, 34 out of 43 (79.1%) models under SSP585 and 27 out of 39 (69.2%) under SSP126 produce increased ENSO SST variability; the relationship between stronger nonlinear Bjerknes feedback and greater future ENSO variability is statistically significant above the 99% confidence level.
Methods
- Analyzed all available CMIP6 SST outputs under SSP126 (39 models), SSP245 (42), SSP370 (38), and SSP585 (43), each covering 1900–2099, using one experiment per model; historical forcing was applied to 2014 and scenario forcing thereafter.
- Constructed monthly climatology over 1900–1999, computed SST anomalies, quadratically detrended them over the full 200 years, and calculated standard deviations over Niño3, Niño4, and Niño3.4 regions; compared 2000–2099 versus 1900–1999 and 2050–2099 versus 1965–2014.
- Used a bootstrap method with 10,000 resamples to test whether multi-model mean increases exceed internal variability at the 95% confidence level; an alternative normalization by each model’s full 200-year standard deviation was also applied.
Limitations
- Stated: the results are based on IPCC 21st-century transient warming scenarios, whereas studies showing decreased ENSO variability after instantaneous CO2 doubling/quadrupling concern equilibrium states; ocean mesoscale eddy-induced vertical heat transport is not resolved in CMIP6, so its effect is unknown.
- Stated: the conclusion is based solely on CMIP6 models, while a study using 36 CMIP5 and 20 CMIP6 models finds a large spread with no inter-model consensus, suggesting sensitivity to model generation; the result is also opposite to an observed ENSO variability decline since 1999/2000, which might be masked by decadal variability and not yet detectable.
- Evident from the text: short 30-year sampling periods, such as those used in the AR6 assessment, are strongly influenced by decadal climate fluctuations, stochastic noise, and the butterfly effect, which can obscure greenhouse-forced changes; the paper addresses this by using 100-year and 50-year periods.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
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Cites
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Claims the paper makes
17 of 37 cited sources checked · 12 supported · 0 flagged · accuracy not yet measured
- Premisecitation§ Increased ENSO sea surface temperature variability under four IPCC emission scenarios
Studies based on CMIP5 models alone, or on a combination of CMIP5 and a smaller set of earlier available CMIP6 models, have found that models better simulating ENSO dynamics tend to generate increased ENSO SST variability.
“Studies based on CMIP5 models alone, or on a combination of CMIP5 and a smaller set of earlier available CMIP6 models 9,10 , have found that models better simulating ENSO dynamics tend to generate increased ENSO SST variability.”
Quote in the paperSubstantial claim1 of 2 sources checked - Premisecitation§ Increased ENSO sea surface temperature variability under four IPCC emission scenarios
Under greenhouse warming, an increased air-sea coupling arising from an enhanced upper equatorial ocean stratification underpins the increase in ENSO variability.
“Under greenhouse warming, an increased air-sea coupling arising from an enhanced upper equatorial ocean stratification underpins the increase in ENSO variability 10 .”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [10] Cai, W. et al. Increased variability of eastern Pacific El Niño under greenhouse warming. Nature 564, 201–206 (2018). source
The paper’s summary states that enhanced upper-equatorial-Pacific stratification under greenhouse warming strengthens wind–ocean coupling and increases eastern Pacific El Niño SST variance. The citing sentence broadens this specific finding to ENSO variability generally; the passage does not establish that all ENSO variability increases. (LLM, conf=0.85)
Experimental — accuracy not yet measured
- Premisecitation§ Abstract
The IPCC AR6 statement on ENSO SST variability is contrary to studies 5-7 based on CMIP6 climate models.
“The IPCC AR6 statement on ENSO SST variability is contrary to studies 5-7 based on climate models participating in the sixth phase of the Coupled Model Intercomparison Project 8 (CMIP6).”
Quote in the paperSubstantial claim3 of 4 sources checked- cites [5] Fredriksen, H.-B., Berner, J., Subramanian, A. C. & Capotondi, A. How does El Niño-Southern Oscillation change under global warming – a first look at CMIP6. Geophys. Res. Lett. 47, e2020GL090640 (2020). source
The Results (§3.2) and Summary and Conclusion (§4) report increased ENSO-related SST variance in most of the 11 CMIP6 models, supporting the description of this paper as a CMIP6 study of ENSO SST variability. The paper does not discuss the IPCC AR6 statement or establish that its findings are contrary to it; that comparison is the citing authors’ framing. (LLM, conf=0.84)
Experimental — accuracy not yet measured - cites [6] Yang, Y. et al. Greenhouse warming intensifies north tropical Atlantic climate variability. Sci. Adv. 7, eabg9690 (2021). source
- cites [7] Cai, W. et al. Changing El Niño–Southern Oscillation in a warming climate. Nat. Rev. Earth Environ. https://doi.org/10.1038/s43017-021-00199-z (2021). source
- cites [8] Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016). source
- Premisecitation§ Abstract
There was a lack of inter-model consensus among CMIP5 models unless model selection was applied on the basis of the realism of the simulated ENSO.
“There was a lack of inter-model consensus among available models that participated in the previous instalments of CMIP5, unless model selection was applied on the basis of the realism of the simulated ENSO 9,10 .”
Quote in the paperSubstantial claim1 of 2 sources checked- cites [9] Cai, W. et al. ENSO and greenhouse warming. Nat. Clim. Change 5, 849–859 (2015). source
- cites [10] Cai, W. et al. Increased variability of eastern Pacific El Niño under greenhouse warming. Nature 564, 201–206 (2018). source
The main text says earlier assessments lacked inter-model consensus on the response of Niño3 SST variability, and the model-selection analysis says selecting models that better simulate ENSO enhanced consensus. But the same analysis reports reasonable consensus for increased E-index variance even without selection: 24 of 34 CMIP5 models (71%). The claim therefore overstates selection as necessary for consensus in general. (LLM, conf=0.88)
Experimental — accuracy not yet measured
- Premisecitation§ Increased ENSO sea surface temperature variability under four IPCC emission scenarios
An inter-model consensus of increased ENSO SST variability emerges in a subset of models that simulate a reasonably realistic nonlinear positive feedback.
“As a result, an inter-model consensus of increased ENSO SST variability emerges in a subset of models that simulate a reasonably realistic nonlinear positive feedback 10 .”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [10] Cai, W. et al. Increased variability of eastern Pacific El Niño under greenhouse warming. Nature 564, 201–206 (2018). source
- Premisecitation§ Increased ENSO sea surface temperature variability under four IPCC emission scenarios
Models generally underestimate the nonlinear Bjerknes feedback.
“Models generally underestimate this feedback 10 .”
Quote in the paperSubstantial claim1 of 1 sources checked- cites [10] Cai, W. et al. Increased variability of eastern Pacific El Niño under greenhouse warming. Nature 564, 201–206 (2018). source
- Premisecitation§ Abstract
ENSO SST variability underpins consequential ENSO-related climate variability and climate extremes within and outside the Pacific.
“Because ENSO SST variability underpins consequential ENSO-related climate variability and climate extremes within and outside the Pacific 6,11,12 , how ENSO SST variability might respond to greenhouse warming is a fundamental issue in climate change science 7,9,13 , with critical societal ramifications.”
Quote in the paperSubstantial claim2 of 3 sources checked- cites [6] Yang, Y. et al. Greenhouse warming intensifies north tropical Atlantic climate variability. Sci. Adv. 7, eabg9690 (2021). source
- cites [11] Ashok, K., Behera, S. K., Rao, S. A., Weng, H. Y. & Yamagata, T. El Niño Modoki and its possible teleconnection. J. Geophys. Res. 112, C11007 (2007). source
- cites [12] Cai, W. et al. Climate impacts of the El Niño–Southern Oscillation on South America. Nat. Rev. Earth Environ. 1, 215–231 (2020). source
- Premisecitation§ Abstract
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] .”
Quote in the paperSubstantial claim2 of 3 sources checked- cites [14] Maher, N., Matei, D., Milinski, S. & Marotzke, J. ENSO change in climate projections: forced response or internal variability? Geophys. Res. Lett. 45, 11–390 (2018). source
- cites [15] Zheng, X.-T., Hui, C. & Yeh, S. W. Response of ENSO amplitude to global warming in CESM large ensemble: uncertainty due to internal variability. Clim. Dyn. 50, 4019–4035 (2018). source
- cites [16] Cai, W. et al. Butterfly effect and a self-modulating El Niño response to global warming. Nature 585, 68–73 (2020). source
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
- Findingown data§ Abstract
In the SSP126 scenario, 34 out of 39 (87.2%) CMIP6 models generate an increase in Niño3.4 SST variability.
“In the SSP126 scenario (Fig. 1b), which represents a strong mitigation path under the Paris Agreement to limit warming to 1.5-2.0°C relative to the pre-industrial level, a total of 34 out of 39 models (87.2%) generate an increase in Niño3.4SST variability.”
Quote in the paperSubstantial claim - Findingown data§ Abstract
A similar increase of 15.7% in Niño3.4 SST variability under SSP585 is obtained when inter-model differences in representing ENSO amplitude are removed.
“A similar increase (15.7%) is obtained when inter-model differences in representing ENSO amplitude are removed (Extended Data Fig. 2).”
Quote in the paperNot a substantial claim - Findingown data§ Abstract
Under SSP585, the 21st century (2000-2099) sees greater Niño3.4 SST variability than the 20th century (1900-1999) in 38 out of 43 (88.4%) CMIP6 models.
“Under SSP585, a 'high emissions' scenario in the 21st century (2000-2099) sees greater Niño3.4 variability than in the 20th century (1900-1999) in a total of 38 out of 43 (88.4%)models (Fig. 1a).”
Quote in the paperSubstantial claim - Findingown data§ Abstract
ENSO SST variability is stronger in the 21st century than in the 20th century in four plausible emission scenarios considered in the IPCC AR6.
“However, comparison between the 20th and 21st century shows a robust increase in century-long ENSO SST variability under four IPCC plausible emission scenarios.”
- Also stated in: § Abstract · boosted
“Here we show that, contrary to the AR6 assessment, ENSO SST variability is stronger in the 21st century than in the 20th century in four plausible emission scenarios considered in the IPCC AR6.”
Quote in the paperSubstantial claim - Also stated in: § Abstract · boosted
- Findingown data§ Increased ENSO sea surface temperature variability under four IPCC emission scenarios
There is a statistically significant tendency (above 99% confidence) for models that simulate stronger nonlinear Bjerknes feedback to generate a greater future increase in ENSO variability, and vice versa.
“There is a tendency for models that simulate stronger nonlinear Bjerknes feedback to generate a greater future increase in ENSO variability, and vice versa, and this relationship is statistically significant above the 99% confidence level (Extended Data Fig. 6).”
Quote in the paperSubstantial claim - Findingown data§ Extended Data
The inter-model consensus range is 73.8-81.4%.
“The inter-model consensus range is 73.8-81.4%.”
Quote in the paperNot a substantial claim - Findingown data§ Extended Data
Under SSP585, 88.4% of CMIP6 models show an increase in ENSO SST variability from the 20th to the 21st century.
“with 88.4% and 87.2% of models respectively showing an increase in ENSO variability.”
Quote in the paperSubstantial claim - Findingown data§ Extended Data
The inter-model consensus range for increased ENSO Niño3 variability in the 21st century across the SSP585, SSP370, SSP245, and SSP126 scenarios is 76.2-81.6%.
“The inter-model consensus range is 76.2-81.6%.”
Quote in the paperSubstantial claim - Findingown data§ Increased ENSO sea surface temperature variability under four IPCC emission scenarios
There is an inter-model consensus of increased ENSO SST variability in the 21st century compared to the 20th century under all four SSP scenarios considered by IPCC AR6 using CMIP6 models.
“To conclude, in contrast to the IPCC AR6 assessment, there is in fact an inter-model consensus of increased ENSO SST variability in the 21st century from that of the 20th century under all four plausible SSP scenarios considered by the IPCC AR6 using CMIP6 models.”
- Also stated in: § Extended Data · plain
“An inter-model consensus on increased ENSO SSt variability.”
Quote in the paperSubstantial claim- Rests on an assumption: A long analysis period reduces the effect of internal variability, allowing a better detection of the greenhouse-forced effect.
- Also stated in: § Extended Data · plain
- Findingown data§ Extended Data
The difference in the multi-model mean ENSO SST variability between the 20th and 21st centuries is statistically significant above the 95% confidence level.
“The difference in the multi-model mean is statistically significant above the 95% confidence level.”
- Also stated in: § Extended Data · plain
“The difference in the multi-model mean is statistically significant above the 95% confidence level.”
Quote in the paperSubstantial claim - Also stated in: § Extended Data · plain
- Findingown data§ Extended Data
Under the SSP585 scenario, 88.4% of 43 available CMIP6 models show an increase in ENSO Niño3.4 SST variability from the 20th century (1900-1999) to the 21st century (2000-2099).
“from a 43 available CMIP6 models under the SSP585 scenario, and b 39 CMIP6 models under the SSP126 scenario, with an 88.4% and 87.2% of models showing an increase in ENSO variability.”
Quote in the paperSubstantial claim - Findingown data§ Bootstrap test.
The statistical significance of the increased multi-model mean ENSO SST variability holds when each model's ENSO SST index is normalized by its full 200-year standard deviation.
“In this approach, the statistical significance holds (Extended Data Fig. 2).”
Quote in the paperNot a substantial claim - Findingown data§ Extended Data
There is inter-model consensus on increased ENSO Niño4 variability in the 21st century.
“Inter-model consensus on increased ENSO Niño4 variability in 21 st century.”
Quote in the paperSubstantial claim - Findingown data§ Extended Data
There is inter-model consensus on increased ENSO Niño3 variability in the 21st century.
“Inter-model consensus on increased ENSO Niño3 variability in 21 st century.”
Quote in the paperSubstantial claim - Findingown data§ Extended Data
Under the SSP126 scenario, 87.2% of 39 available CMIP6 models show an increase in ENSO Niño3.4 SST variability from the 20th century (1900-1999) to the 21st century (2000-2099).
“from a 43 available CMIP6 models under the SSP585 scenario, and b 39 CMIP6 models under the SSP126 scenario, with an 88.4% and 87.2% of models showing an increase in ENSO variability.”
Quote in the paperSubstantial claim - Findingown data§ Extended Data
There is an inter-model consensus on increased ENSO SST variability.
“An inter-model consensus on increased ENSO SSt variability.”
Quote in the paperSubstantial claim - Findingown data§ Extended Data
Using 50-year periods, 34 out of 43 (79.1%) CMIP6 models under SSP585 produce increased ENSO SST variability.
“Using the period length of 50 years sees a total of 34 out of 43 (79.1%)models under SSP585, and 27 out of 39 (69.2%) models under SSP126 produce increased ENSO SST variability.”
Quote in the paperSubstantial claim - Findingown data§ Extended Data
Under SSP126, 87.2% of CMIP6 models show an increase in ENSO SST variability from the 20th to the 21st century.
“with 88.4% and 87.2% of models respectively showing an increase in ENSO variability.”
Quote in the paperSubstantial claim - Findingown data§ Bootstrap test.
A larger number of CMIP6 (79%) than CMIP5 (55%) models simulate greater than one third of the observed nonlinearity parameter.
“In terms of this nonlinearity parameter, a larger number of CMIP6 (79%) than CMIP5 (55%) models simulate greater than one third of the observed parameter.”
Quote in the paperNot a substantial claim - Findingown data§ Extended Data
There is inter-model consensus on increased ENSO Niño3.4 variability in the 21st century.
“Inter-model consensus on increased ENSO Niño3.4 variability in 21 st century.”
Quote in the paperSubstantial claim - Findingown data§ Extended Data
The inter-model consensus range for increased ENSO Niño3.4 variability in the 21st century across the SSP585, SSP370, SSP245, and SSP126 scenarios is 78.6-88.4%.
“The inter-model consensus range is 78.6-88.4%.”
Quote in the paperSubstantial claim - Findingown data§ Extended Data
ENSO Niño4 sea surface temperature variability increases in the 21st century.
“increased ENSO Niño4 variability in 21 st century.”
Quote in the paperSubstantial claim - Findingown data§ Extended Data
Using 50-year periods, 27 out of 39 (69.2%) CMIP6 models under SSP126 produce increased ENSO SST variability.
“Using the period length of 50 years sees a total of 34 out of 43 (79.1%)models under SSP585, and 27 out of 39 (69.2%) models under SSP126 produce increased ENSO SST variability.”
Quote in the paperSubstantial claim - Premisecitation§ Abstract
CMIP6 models exhibit a notably higher inter-model consensus than CMIP5 models under a high emission scenario.
“However, CMIP6 models exhibit a notably higher inter-model consensus than CMIP5 models under a high emission scenario 7 .”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [7] Cai, W. et al. Changing El Niño–Southern Oscillation in a warming climate. Nat. Rev. Earth Environ. https://doi.org/10.1038/s43017-021-00199-z (2021). source
- Interpretationreasoning§ Increased ENSO sea surface temperature variability under four IPCC emission scenarios
The discrepancy between the paper's result and the recent study suggests a sensitivity of the paper's result to model generations.
“In addition, our conclusion is based solely on CMIP6 models, but a recent study based on 36 CMIP5 models and 20 CMIP6 models 23 finds a large spread in the projected ENSO change with no inter-model consensus; the discrepancy suggests a sensitivity of our result to model generations.”
Quote in the paperNot a substantial claim - Study design and conductnone given§ Increased ENSO sea surface temperature variability under four IPCC emission scenarios
Ocean mesoscale eddy-induced vertical heat transport is not resolved in CMIP6 models.
“Further, ocean mesoscale eddy-induced vertical heat transport is part of ENSO dynamics 22 , but is not resolved in CMIP6 models; as such, whether or how incorporation of ocean mesoscale eddies alters our conclusion is not known.”
Quote in the paperNot a substantial claim - Interpretationown data§ Increased ENSO sea surface temperature variability under four IPCC emission scenarios
The increased inter-model consensus in CMIP6 is facilitated by improvements in the representation of ENSO processes, for example, nonlinear positive feedbacks.
“The increased inter-model consensus in CMIP6 is facilitated by improvements in the representation of ENSO processes, for example, nonlinear positive feedbacks, thus underscoring the importance of sustained model improvement in a multi-model approach toward reliable future projections.”
Quote in the paperSubstantial claim - Interpretationcitation§ Increased ENSO sea surface temperature variability under four IPCC emission scenarios
The paper's result is opposite to an observed decline in ENSO variability since 1999/2000.
“We also note that our result is opposite to an observed decline in ENSO variability since 1999/2000 (ref.24 ); however, any greenhouse warming-induced ENSO variability change in the two decades since 1999/2000 might be masked by decadal variability and not yet detectable.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [24] Hu, Z.-Z. et al. The interdecadal shift of ENSO properties in 1999/2000: a review. J. Climate 33, 4441–4462 (2020). source
- Premisecitation§ Increased ENSO sea surface temperature variability under four IPCC emission scenarios
The impact of North Pacific SST variability on ENSO is enhanced under warmer background SSTs.
“Finally, the impact of North Pacific SST variability on ENSO is more realistically simulated in CMIP6 than in CMIP5, and the impact is enhanced under warmer background SSTs, contributing to enhanced future ENSO SST variability 25 .”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [25] Jia, F. et al. Enhanced North Pacific impact on El Niño/Southern Oscillation under greenhouse warming. Nat. Clim. Change 11, 840–847 (2021). source
- Premisecitation§ Increased ENSO sea surface temperature variability under four IPCC emission scenarios
The enhanced impact of North Pacific SST variability on ENSO under warmer background SSTs contributes to enhanced future ENSO SST variability.
“Finally, the impact of North Pacific SST variability on ENSO is more realistically simulated in CMIP6 than in CMIP5, and the impact is enhanced under warmer background SSTs, contributing to enhanced future ENSO SST variability 25 .”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [25] Jia, F. et al. Enhanced North Pacific impact on El Niño/Southern Oscillation under greenhouse warming. Nat. Clim. Change 11, 840–847 (2021). source
- Study design and conductown data§ Increased ENSO sea surface temperature variability under four IPCC emission scenarios
The paper's conclusion is based solely on CMIP6 models.
“In addition, our conclusion is based solely on CMIP6 models, but a recent study based on 36 CMIP5 models and 20 CMIP6 models 23 finds a large spread in the projected ENSO change with no inter-model consensus; the discrepancy suggests a sensitivity of our result to model generations.”
Quote in the paperSubstantial claim - Premisecitation§ Increased ENSO sea surface temperature variability under four IPCC emission scenarios
The impact of North Pacific SST variability on ENSO is more realistically simulated in CMIP6 than in CMIP5.
“Finally, the impact of North Pacific SST variability on ENSO is more realistically simulated in CMIP6 than in CMIP5, and the impact is enhanced under warmer background SSTs, contributing to enhanced future ENSO SST variability 25 .”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [25] Jia, F. et al. Enhanced North Pacific impact on El Niño/Southern Oscillation under greenhouse warming. Nat. Clim. Change 11, 840–847 (2021). source
- Study design and conductown data§ Increased ENSO sea surface temperature variability under four IPCC emission scenarios
The paper's result of increased ENSO SST variability is based on the IPCC 21st century 'transient' warming scenarios.
“thus, it is worth highlighting that our result of increased ENSO SST variability is based on the IPCC 21st century 'transient' warming scenarios.”
Quote in the paperSubstantial claim
- Classified as probably not a substantial claim. 8
- May combine two claims. 8
Probably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation§ Increased ENSO sea surface temperature variability under four IPCC emission scenariosMore CMIP6 models simulate realistic ENSO nonlinear feedbacks.
“Besides some improvements in the representation of the tropical Pacific climate 20 , more CMIP6 models simulate realistic ENSO nonlinear feedbacks 7 .”
Quote in the paperNot a substantial claim1 of 2 sources checked- cites [20] Planton, Y. et al. Evaluating climate models with the CLIVAR 2020 ENSO metrics package. Bull. Am. Meteorol. Soc. 102, E193–E217 (2021). source
The Summary reports improved ENSO patterns and other metrics in CMIP6, but it does not establish that more models simulate realistic nonlinear feedbacks. The Evaluation of ENSO processes says none of its five feedback-process metrics improved significantly from CMIP5 to CMIP6, and one degraded; it also notes that numerous nonlinear processes were not included in that short metric list. (LLM, conf=0.83)
Experimental — accuracy not yet measured - cites [7] Cai, W. et al. Changing El Niño–Southern Oscillation in a warming climate. Nat. Rev. Earth Environ. https://doi.org/10.1038/s43017-021-00199-z (2021). source
May combine two claims.
Premisecitation§ Increased ENSO sea surface temperature variability under four IPCC emission scenariosNonlinear Bjerknes feedback is particularly important: once warm SST anomalies establish deep atmospheric convection in the equatorial eastern Pacific, the response of equatorial zonal wind anomalies increases nonlinearly to further sea surface warming, amplifying El Niño growth.
“Nonlinear Bjerknes feedback is particularly important, in which once warm SST anomalies establish deep atmospheric convection in the equatorial eastern Pacific, the response of equatorial zonal wind anomalies increases nonlinearly to further sea surface warming 19 , amplifying El Niño growth 10 .”
Quote in the paperSubstantial claim1 of 2 sources checked- cites [19] Dommenget, D., Bayr, T. & Frauen, C. Analysis of the non-linearity in the pattern and time evolution of El Niño–Southern Oscillation. Clim. Dyn. 40, 2825–2847 (2013). source
- cites [10] Cai, W. et al. Increased variability of eastern Pacific El Niño under greenhouse warming. Nature 564, 201–206 (2018). source
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Premisecitation§ Increased ENSO sea surface temperature variability under four IPCC emission scenariosA recent study based on 36 CMIP5 models and 20 CMIP6 models finds a large spread in the projected ENSO change with no inter-model consensus.
“In addition, our conclusion is based solely on CMIP6 models, but a recent study based on 36 CMIP5 models and 20 CMIP6 models 23 finds a large spread in the projected ENSO change with no inter-model consensus; the discrepancy suggests a sensitivity of our result to model generations.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites [23] Beobide-Arsuaga, G. et al. Uncertainty of ENSO-amplitude projections in CMIP5 and CMIP6 models. Clim. Dyn. 56, 3875–3888 (2021). source
Classified as probably not a substantial claim.
Premisecitation§ Bootstrap test.SST skewness in the eastern equatorial Pacific is an integral component of a parameter that also encapsulates skewness in the central Pacific to depict ENSO nonlinearity as a whole.
“SST skewness in the eastern equatorial Pacific is an integral component of a parameter that also encapsulates skewness in the central Pacific to depict ENSO nonlinearity as a whole 7,10 .”
Quote in the paperSubstantial claim1 of 2 sources checked- cites [7] Cai, W. et al. Changing El Niño–Southern Oscillation in a warming climate. Nat. Rev. Earth Environ. https://doi.org/10.1038/s43017-021-00199-z (2021). source
- cites [10] Cai, W. et al. Increased variability of eastern Pacific El Niño under greenhouse warming. Nature 564, 201–206 (2018). source
Probably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation§ AbstractPrevious studies found a stronger rainfall response to ENSO SST variability even if ENSO SST variability itself does not change.
“consistent with findings by previous studies of a stronger rainfall response to ENSO SST variability even if ENSO SST variability itself does not change 2-4 .”
Quote in the paperNot a substantial claim1 of 3 sources checked- cites [2] Power, S. B., Delage, F., Chung, C., Kociuba, G. & Keay, K. Robust twenty-first century projections of El Niño and related precipitation variability. Nature 502, 541–545 (2013). source
- cites [3] Cai, W. et al. Increasing frequency of extreme El Niño events due to greenhouse warming. Nat. Clim. Change 4, 111–116 (2014). source
- cites [4] Wang, G. et al. Continued increase of extreme El Niño frequency long after 1.5 °C warming stabilization. Nat. Clim. Change 7, 568–572 (2017). source
May combine two claims.
Findingown data§ AbstractThe multi-model ensemble mean increase in Niño3.4 SST variability under SSP585 is 16.1% and statistically significant according to a Bootstrap method.
“The multi-model ensemble mean increase (16.1%) is statistically significant according to a Bootstrap method 18 (see 'Bootstrap test' in Methods and Extended Data Fig. 1).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [18] Austin, P. C. & Tu, J. V. Bootstrap methods for developing predictive models. Am. Stat. 58, 131–137 (2004). source
May combine two claims.
Findingown data§ AbstractThe multi-model ensemble mean increase in Niño3.4 SST variability under SSP126 is 11.7% and statistically significant.
“The multi-model ensemble mean increase (11.7%) is statistically significant.”
Quote in the paperSubstantial claimMay combine two claims.
Findingown data§ Increased ENSO sea surface temperature variability under four IPCC emission scenariosUsing 50-year periods (current climate 1965-2015 and future climate 2050-2099), an inter-model consensus emerges: 34 out of 43 (79.1%) models under SSP585 and 27 out of 39 (69.2%) models under SSP126 generate an increase in Niño3.4 SST variability.
“Using statistics over another longer period, for example, 50 years of the current climate of 1965-2015 and the future climate of 2050-2099, an inter-model consensus emerges, with a total of 34 out of 43 (79.1%)models under SSP585, and 27 out of 39 (69.2%) models under SSP126, generating an increase in Niño3.4SST variability (Fig. 2a,b).”
Quote in the paperSubstantial claimMay combine two claims.
Findingreasoning§ Increased ENSO sea surface temperature variability under four IPCC emission scenariosThe skewness in three model outliers that project a decrease in ENSO variability in Figs. 1 and 2 is either small (UKESM1-0-LL) or negative (BCC-CSM2-MR, CESM2).
“As such, the skewness in three model outliers that project a decrease in ENSO variability in Figs. 1 and 2 is either small (UKESM1-0-LL) or negative (BCC-CSM2-MR, CESM2).”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown data§ AbstractInter-model consensus for increased ENSO SST variability is seen in all four SSP emission scenarios, ranging between 78.6-88.4% for Niño3.4, 76.2-81.6% for Niño3, and 73.8-81.4% for Niño4 across emission scenarios.
“Inter-model consensus is seen in all four SSP emission scenarios (Extended Data Figs.3, 4 and 5), ranging between 78.6-88.4% for Niño3.4,76.2-81.6% for Niño3, and 73.8-81.4% for Niño4 across emission scenarios.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation§ AbstractEven on centennial timescales, ENSO variability is influenced by internal climate fluctuations.
“Even on centennial timescales, ENSO variability is influenced by internal climate fluctuations 17 .”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [17] Li, J. et al. Interdecadal modulation of El Niño amplitude during the past millennium. Nat. Clim. Change 1, 114–118 (2011). source
May combine two claims.
Interpretationown data§ Increased ENSO sea surface temperature variability under four IPCC emission scenariosAs the period for calculating ENSO statistics lengthens, the influence of internal variability tapers off, whereas the effect of greenhouse warming is better diagnosed.
“Thus, as the period for calculating ENSO statistics lengthens, the influence of internal variability tapers off, whereas the effect of greenhouse warming is better diagnosed.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Interpretationreasoning§ Increased ENSO sea surface temperature variability under four IPCC emission scenariosAny greenhouse warming-induced ENSO variability change in the two decades since 1999/2000 might be masked by decadal variability and not yet detectable.
“We also note that our result is opposite to an observed decline in ENSO variability since 1999/2000 (ref.24 ); however, any greenhouse warming-induced ENSO variability change in the two decades since 1999/2000 might be masked by decadal variability and not yet detectable.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation§ Increased ENSO sea surface temperature variability under four IPCC emission scenariosOcean mesoscale eddy-induced vertical heat transport is part of ENSO dynamics.
“Further, ocean mesoscale eddy-induced vertical heat transport is part of ENSO dynamics 22 , but is not resolved in CMIP6 models; as such, whether or how incorporation of ocean mesoscale eddies alters our conclusion is not known.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [22] Wengel, C. et al. Future high-resolution El Niño/Southern Oscillation dynamics. Nat. Clim. Change 11, 758–765 (2021). source
Probably not a substantial claim: may not pass S3 (load-bearing).
Premisecitation§ Increased ENSO sea surface temperature variability under four IPCC emission scenariosFindings of decreased ENSO variability in equilibrium state after an instantaneous doubling/quadrupling of CO2 seemingly contradict the paper's conclusion.
“we note findings of decreased ENSO variability 21,22 in equilibrium state after an instantaneous doubling/quadrupling of CO 2 , which seemingly contradict our conclusion”
Quote in the paperNot a substantial claim0 of 2 sources checked
The paper relies on this without support.
A long analysis period reduces the effect of internal variability, allowing a better detection of the greenhouse-forced effect.
“A long analysis period reduces the effect of internal variability, allowing a better detection of the greenhouse-forced effect.”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Current-climate comparison period has two end dates
MinorThe text gives the current-climate period for the 50-year comparison as 1965-2015, while Methods gives it as 1965-2014. Only the latter spans 50 years when both endpoints are included.
“50 years of the current climate of 1965-2015 and the future climate of 2050-2099”
“between the 50-year future climate of 2050-2099 and current climate of 1965-2014 (Fig. 2).”
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“For example, the majority of latest Coupled Model Intercomparison Project (CMIP6) models indicate that ENSO amplitude will likely increase even under strict mitigation targets, 188 while some large ensemble simulations suggest nonlinear and time-dependent changes.”
Self-citationProjections of faster onset and slower decay of El Niño in the 21st century
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“It is worth noting that “strong gradient La Niñas” have been identified in observations (Johnson, 2013), and are expected by climate change model projections later in the 21st century (Cai et al., 2022; Cai et al., 2015a; Cai et al., 2015b).”
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“%) models generate an increased ENSO amplitude with a multimodel mean increase of ~11.4%, consistent with a recent finding 25 .”
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Oxyfuel Combustion Makes Carbon Capture More Efficient
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“Although this increased ENSO activity can be caused by the background global warming ( 15 , 35 ), the correlation of ENSO activity with the externally forced global mean Tas is not overly strong (with a correlation of 0.33, P < 0.01; fig.”
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“In addition, anthropogenic climate change has been shown to increase the SST variability, resulting in more frequent El Niño and La Niña events (Cai et al., 2015, 2021, 2022), and the corresponding hydroclimate extremes through ocean-atmosphere coupling.”
Collapsed upwelling projected to weaken ENSO under sustained warming beyond the twenty-first century
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Exploring climate stabilisation at different global warming levels in ACCESS-ESM-1.5
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Exogenous moisture deficit fuels drought risks across China
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Self-citationOcean stratification in a warming climate
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“These interactions affect ENSO-related SST variability 211 , driving increased frequency of extreme El Niño 212 , extreme La Niña events 213 and consecutive ENSO events 214–216 in the future.”
Tailored Forecasts Can Predict Extreme Climate Informing Proactive Interventions in East Africa
Funk, C. C., Harrison, L., Segele, Z., Rosenstock, T. S., Steward, P., Anderson, C. L., de Perez, E. C., Maxwell, D., Endris, H. S., Koch, E., et al. (2023) · Earth s Future · doi:10.1029/2023ef003524 · Cited by 32 · see citing works
Li, G., Yu, Z., Li, Y., Yang, C., Gu, H., Zhang, J., & Huang, Y. (2024) · Global and Planetary Change · doi:10.1016/j.gloplacha.2024.104438 · Cited by 27 · see citing works
Dhage, L., & Widlansky, M. J. (2022) · Earth s Future · doi:10.1029/2021ef002524 · Cited by 26 · see citing works
Future Drying Subtropical East Asia in Winter: Mechanism and Observational Constraint
He, C. (2023) · Journal of Climate · doi:10.1175/jcli-d-22-0347.1 · Cited by 24 · see citing works
Climate change, migration, and health: perspectives from Latin America and the Caribbean
Batista, C., Knipper, M., Sedas, A. C., Farante, S. V., Wainstock, D. C., Borjas-Cavero, D. B., Rojas, K., España, J. C. A., & Yglesias-González, M. (2024) · The Lancet Regional Health - Americas · doi:10.1016/j.lana.2024.100926 · Cited by 23 · see citing works
Rising dengue risk with increasing El Niño–Southern Oscillation amplitude and teleconnections
Tian, Y., Xu, Y., Liang, Y., Zhou, Z., Susong, K. M., Chen, Y., Joshi, K., Campbell, A. M., Lim, A., Lin, Q., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-63655-0 · Cited by 24 · see citing works
“Here we intended to state the increasing ENSO variability under future emission scenarios based on the previous research (Cai et al., 2022) to emphasize the need to investigate the future effects of ENSO on dengue risk.”
InfluentialAerobiology over the Southern Ocean – Implications for bacterial colonization of Antarctica
Malard, L. A., Ávila-Jiménez, M. L., Schmale, J., et al. (2022) · Environment International · doi:10.1016/j.envint.2022.107492 · Cited by 22 · see citing works
The Impacts of a Weakened Atlantic Meridional Overturning Circulation on ENSO in a Warmer Climate
Liu, W., Pinto, D. D. C., Fedorov, A. V., & Zhu, J. (2023) · Geophysical Research Letters · doi:10.1029/2023gl103025 · Cited by 23 · see citing works
Enhanced North Pacific Victoria mode in a warming climate
Ji, K., Yu, J., Li, J., et al. (2024) · npj Climate and Atmospheric Science · doi:10.1038/s41612-024-00599-0 · Cited by 21 · see citing works
“Consequently, if CP SST variability remains robustly on the rise under greenhouse warming 37,42,43 , it is con-ceivablethatVMvariabilitywillbecomemorepronounced.”
Johnson, N. C., Wittenberg, A. T., Rosati, A. J., et al. (2022) · Frontiers in Climate · doi:10.3389/fclim.2022.941055 · Cited by 20 · see citing works
ENSO skewness hysteresis and associated changes in strong El Niño under a CO2 removal scenario
Liu, C., An, S., Jin, F., Stuecker, M. F., Zhang, W., Kug, J., Yuan, X., Shin, J., Xue, A., Geng, X., et al. (2023) · npj Climate and Atmospheric Science · doi:10.1038/s41612-023-00448-6 · Cited by 20 · see citing works
Ekolu, J., Dieppois, B., Tramblay, Y., Villarini, G., Slater, L. J., Mahé, G., Paturel, J., Eden, J., Moulds, S., Sidibé, M., et al. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2024.131679 · Cited by 20 · see citing works
Historical and Future Asymmetry of ENSO Teleconnections with Extremes
Lieber, R., Brown, J. R., King, A. D., & Freund, M. B. (2024) · Journal of Climate · doi:10.1175/jcli-d-23-0619.1 · Cited by 20 · see citing works
Significant West Antarctic Cooling in the Past Two Decades Driven by Tropical Pacific Forcing
Zhang, X., Wang, Y., Hou, S., et al. (2023) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-22-0153.1 · Cited by 19 · see citing works
Feng, T., Zhu, Z., Cao, S., Zhao, W., Li, M., & Wu, J. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.167664 · Cited by 21 · see citing works
Future climate change will increase risk to mangrove health in Northern Australia
Chung, C. T. Y., Hope, P. K., Hutley, L. B., et al. (2023) · Communications Earth & Environment · doi:10.1038/s43247-023-00852-z · Cited by 18 · see citing works
Balancing Livestock Environmental Footprints with Forestry-Based Solutions: A Review
Solomon, T., Gupta, V., & Ncho, C. M. (2023) · Ecologies · doi:10.3390/ecologies4040047 · Cited by 18 · see citing works
Petrova, D., Tarín‐Carrasco, P., Sekulić, A., Luković, J., Reniu, M. G., Rodó, X., & Cvijanović, I. (2024) · International Journal of Climatology · doi:10.1002/joc.8449 · Cited by 18 · see citing works
“We also found an increase in ENSO events in CMIP6 (Figure 9), similar to other studies (Cai et al., 2014; Cai et al., 2022; Maloney et al., 2014; Seager et al., 2012; Wang et al., 2019), associated with more precipitation in California (Figure 11; Cheng et al., 2021).”
BackgroundResultCentral-Pacific El Niño-Southern Oscillation less predictable under greenhouse warming
Chen, H., Jin, Y., Liu, Z., Sun, D., Chen, X., McPhaden, M. J., Capotondi, A. M., & Lin, X. (2024) · Nature Communications · doi:10.1038/s41467-024-48804-1 · Cited by 21 · see citing works
Mohsin, M., Xuhua, H., Sarfraz, M., et al. (2023) · Sustainable Development · doi:10.1002/sd.2734 · Cited by 17 · see citing works
Chung, C. T. Y., Boschat, G., Taschetto, A. S., et al. (2023) · Journal of Southern Hemisphere Earth System Science · doi:10.1071/es23002 · Cited by 17 · see citing works
Self-citationSolaimani, K., Ahmadi, S. B., & Shokrian, F. (2024) · Ecological Indicators · doi:10.1016/j.ecolind.2024.111629 · Cited by 16 · see citing works
Strengthened ENSO Amplitude Contributed to Regime Shift in the Hadley Circulation
Feng, J., Wang, S., & Li, J. (2024) · Geophysical Research Letters · doi:10.1029/2023gl106006 · Cited by 16 · see citing works
“On the other hand, increased variability in the ENSO is projected under different greenhouse warming scenarios (Cai et al., 2022; Fredriksen et al., 2020; McGregor et al., 2022), and exploration of associated changes in HC variability would be worthwhile.”
BackgroundBayr, T., Lübbecke, J. F., Vialard, J., & Latif, M. (2024) · Journal of Climate · doi:10.1175/jcli-d-24-0071.1 · Cited by 18 · see citing works
Zhang, H., Ye, X., Zhu, P., et al. (2022) · Remote Sensing · doi:10.3390/rs14071596 · Cited by 14 · see citing works
Liu, J., Yu, J., Lin, C., et al. (2024) · Earth system science data · doi:10.5194/essd-16-4949-2024 · Cited by 13 · see citing works
“Land and sea surface temperatures (LST and SST) are essential variables frequently utilized in the climate research community (Cai et al., 2022; Hong et al., 2022). this study, we incorporated a classical land surface temperature algorithm (Ulivieri and Cannizzaro, 1985) into the NANO_SCS sys-tem,…”
MethodInfluentialLe, T. H. (2023) · Scientific Reports · doi:10.1038/s41598-023-41590-8 · Cited by 16 · see citing works
Abd‐Elaty, I., Kuriqi, A., Ramadan, E. M., et al. (2023) · Journal of Hydrology Regional Studies · doi:10.1016/j.ejrh.2023.101600 · Cited by 13 · see citing works
“Bear (2012) proposed optimizing model for SWI based on the abstraction rates.”
Dynamics of Peatland Fires in South Sumatra in 2019: Role of Groundwater Levels
Irfan, M., Koriyanti, E., Saleh, K., et al. (2024) · Land · doi:10.3390/land13030373 · Cited by 13 · see citing works
Huang, Z., Li, X., Mao, F., Huang, L., Zhao, Y., Song, M., Yu, J., & Du, H. (2025) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2025.110462 · Cited by 14 · see citing works
Tipping points in ocean and atmosphere circulations
Loriani, S., Aksenov, Y., McKay, D. I. A., Bala, G., Born, A., Chiessi, C. M., Dijkstra, H. A., Donges, J. F., Drijfhout, S., England, M. H., et al. (2025) · Earth System Dynamics · doi:10.5194/esd-16-1611-2025 · Cited by 13 · see citing works
Projected changes in the interannual variability of surface air temperature using CMIP6 simulations
Shi, J., Tian, Z., Lang, X., & Jiang, D. (2023) · Climate Dynamics · doi:10.1007/s00382-023-06923-3 · Cited by 13 · see citing works
“…affected by the large-scale climate modes generated by SST variabilities, such as El Niño or La Niña in the eastern to central equatorial Pacific (Diaz et al. 2001; Yang and DelSole 2012); these modes were projected to strengthen under future scenarios in a recent study (Cai et al. 2022).”
Climate extremes and socioeconomic impact of El Niño and La Niña events
Nizamani, M. M., Hughes, A. C., Wang, Y., Zhang, H.-L., & Lai, Z. (2025) · Environmental Development · doi:10.1016/j.envdev.2025.101276 · Cited by 12 · see citing works
Mangroves as nature-based mitigation for ENSO-driven compound flood risks in a large river delta
Pelckmans, I., Belliard, J., Gourgue, O., Domínguez‐Granda, L., & Temmerman, S. (2024) · Hydrology and earth system sciences · doi:10.5194/hess-28-1463-2024 · Cited by 12 · see citing works
Global climate-driven sea surface temperature and chlorophyll dynamics.
Venegas, R., Rivas, D., & Treml, E. (2024) · Marine Environmental Research · doi:10.1016/j.marenvres.2024.106856 · Cited by 12 · see citing works
Thermodynamic processes prolong triple La Niña events in a hybrid coupled ocean-atmosphere model
Dong, X. L., Zhang, R., Hu, J., Gao, C., & Chen, M. (2025) · Climate Dynamics · doi:10.1007/s00382-024-07564-w · Cited by 12 · see citing works
“The El Niño-Southern Oscillation (ENSO) is the strongest interannual variability signal in the tropical Pacific, which considerably impacts global weather, climate, and socio-economics (Cai et al. 2022; Chen and Li 2023; Geng et al. 2023; Huang et al. 2022; Zhang et al. 2022).”
A Regime View of ENSO Flavors Through Clustering in CMIP6 Models
Ayar, P. V., Battisti, D. S., Li, C., King, M. P., Vrac, M., & Tjiputra, J. F. (2023) · Earth s Future · doi:10.1029/2022ef003460 · Cited by 11 · see citing works
“Using traditional Niño.x indices, Cai et al. (2022) and Maher et al. (2023) find enhanced ENSO variability over the course of the 21st Century compared to the end of the 20th Century (although the increase in variance is much smaller than the bias in the variance in the typical CMIP6 model).”
BackgroundSpatial and seasonal variation in thermal sensitivity within North American bird species
Cohen, J. M., Fink, D., & Zuckerberg, B. (2023) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2023.1398 · Cited by 11 · see citing works
“Given that climatic variability is increasing with climate change [19 – 21], these findings highlight the importance of considering population-level variation in thermal breadth [8].”
BackgroundQuantifying causal teleconnections to drought and fire risks in Indonesian Borneo
Lam, T., Catto, J. L., Barciela, R. M., Harper, A., Challenor, P., & Arribas, A. (2024) · International Journal of Climatology · doi:10.1002/joc.8442 · Cited by 11 · see citing works
“…et al., 2021; Inter-governmental Panel on Climate Change [IPCC], 2021), there is an emerging consensus that the occurrence of strong El Niño and positive IOD events may increase in the future (Cai et al., 2022; Li et al., 2021; McKenna et al., 2020; Power et al., 2013; Stevenson, 2012).”
BackgroundIntensification of ENSO frequency drives forest disturbance in the andes during the holocene
Hagemans, K., Urrego, D. H., Gosling, W. D., Rodbell, D. T., Wagner‐Cremer, F., & Donders, T. H. (2022) · Quaternary Science Reviews · doi:10.1016/j.quascirev.2022.107762 · Cited by 11 · see citing works
A bibliographic review on anthropogenic climate change and drought
Yohannes, T., Yu, J., Kazora, J., Oo, K. T., Muleta, D., Adem, K., & Geremew, T. (2024) · Environmental Science & Policy · doi:10.1016/j.envsci.2024.103830 · Cited by 13 · see citing works
Global warming increases the risk of crop yield failures driven by climate oscillations
Li, L., Wang, B., Feng, P., Lü, C., Jägermeyr, J., Asseng, S., Luo, J., Harrison, M. T., He, Q., Liu, K., et al. (2025) · One Earth · doi:10.1016/j.oneear.2025.101318 · Cited by 12 · see citing works
Projection of ENSO using observation-informed deep learning
Zhu, Y., Zhang, R., Wang, F., Cai, W., Li, D., Guan, S., & Li, Y. (2025) · Nature Communications · doi:10.1038/s41467-025-63157-z · Cited by 10 · see citing works
Self-citationSartori, D., Scatena, G., Vrinceanu, C., & Gaion, A. (2023) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2023.115274 · Cited by 9 · see citing works
Anthropogenic climate change drives non-stationary phytoplankton internal variability
Elsworth, G. W., Lovenduski, N. S., Krumhardt, K. M., et al. (2023) · Biogeosciences · doi:10.5194/bg-20-4477-2023 · Cited by 10 · see citing works
Mondal, S., Ray, A., Osuka, K. E., Sihombing, R. I., Lee, M., & Chen, Y. (2023) · Scientific Reports · doi:10.1038/s41598-023-49984-4 · Cited by 10 · see citing works
Huang, P., Chen, Y., Li, J., & Yan, H. (2024) · npj Climate and Atmospheric Science · doi:10.1038/s41612-024-00695-1 · Cited by 11 · see citing works
“While recent climate modelstend to project anincrease in ENSO amplitude under global warming with much improved intermodel consistency 6,10 , the comprehension regarding the in fl uence of the tropical Paci fi c background state on ENSO amplitude remains constrained 11 – 13 .”
Persistently active El Niño–Southern Oscillation since the Mesozoic
Li, X., Hu, S., Hu, Y., Cai, W., Jin, Y., Lu, Z., Guo, J., Lan, J., Lin, Q., Yuan, S., et al. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2404758121 · Cited by 11 · see citing works
“More recently, it has been shown that there is a robust increase in ENSO sea surface temperature (SST) variability under all four plausible emission scenarios considered by the Intergovernmental Panel on Climate Change Sixth Assessment Report (IPCC AR6) using CMIP6 models ( 21 ).”
BackgroundSelf-citationChung, C., Power, S., Boschat, G., Gillett, Z. E., & Narsey, S. (2024) · Earth's Future · doi:10.1029/2024ef005166 · Cited by 9 · see citing works
Bonesteve, A., Lluch-Cota, S. E., Sicard, M. T., et al. (2024) · Cell Stress and Chaperones · doi:10.1016/j.cstres.2024.12.002 · Cited by 9 · see citing works
ENSO-like evolution of the tropical Pacific climate mean state and its potential causes since 300ka
Zhang, Y., Xu, J., Li, G., Lu, Z., Jiang, Z., Zhang, W., & Liu, Y. (2023) · Quaternary Science Reviews · doi:10.1016/j.quascirev.2023.108241 · Cited by 9 · see citing works
Projected changes in heat wave characteristics over India
Neethu, C. S., & Ramesh, K. V. (2023) · Climatic Change · doi:10.1007/s10584-023-03618-w · Cited by 9 · see citing works
“StudiesshowthatENSOSSTvariabilitywillbeenhancedunderthe plausibleemissionscenarioofCoupledModelIntercomparisonProject-6(CMIP6)emission scenarios(Caietal.2022).”
Estimating Uncertainty in Simulated ENSO Statistics
Planton, Y. Y., Lee, J., Wittenberg, A. T., et al. (2024) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2023ms004147 · Cited by 8 · see citing works
Pennington, L. K., Sheth, S. N., Franks, S. J., et al. (2026) · American Journal of Botany · doi:10.1002/ajb2.70214 · Cited by 8 · see citing works
The Impact of Eastern Pacific Warming on Future North Atlantic Tropical Cyclogenesis
Karnauskas, K. B., Heede, U. K., & Zhang, L. (2023) · Geophysical Research Letters · doi:10.1029/2023gl105551 · Cited by 8 · see citing works
“While the majority of Coupled Model Intercomparison Project phase 6 (CMIP6) models project a significant change in ENSO with anthropogenic forcing (Cai et al., 2022), the response varies widely depending on type of experiment, timeframe, and model (Heede & Fedorov, 2023b; Maher et al.”
BackgroundAru, H., Chen, W., Chen, S., Garfinkel, C. I., Ma, T., Dong, Z., & Hu, P. (2023) · Journal of Geophysical Research Atmospheres · doi:10.1029/2022jd037905 · Cited by 8 · see citing works
“Given recent studies (G. Beobide-Arsuage et al., 2021; Cai et al., 2018, 2022; Callahan et al., 2021) suggesting variations in ENSO amplitude due to global warming, it is imperative to meticulously assess the correlation between the present ENSO-WP relation and future ENSO amplitude projections.”
BackgroundInterannual ENSO diversity, transitions, and projected changes in observations and climate models
Freund, M. B., Brown, J. R., Marshall, A. G., Tozer, C., Henley, B. J., Risbey, J., Ramesh, N., Lieber, R., & Sharmila, S. (2024) · Environmental Research Letters · doi:10.1088/1748-9326/ad78db · Cited by 12 · see citing works
“A future increase of ENSO variability [46] could increase the amplitude of El Ni ˜ no events and consequently increase the likelihood of multi-year La Ni ˜ na events [47].”
Widening of Wind Stress Anomalies Amplifies ENSO in a Warming Climate
Stuivenvolt‐Allen, J., Fedorov, A. V., Fu, M., & Heede, U. K. (2024) · Journal of Climate · doi:10.1175/jcli-d-24-0126.1 · Cited by 8 · see citing works
- Authors
- Cai, W., Ng, B., Wang, G., Santoso, A., Wu, L., & Yang, K.
- Venue
- Nature Climate Change
- Published
- 2022-01-31
- Volume
- 12
- Issue
- 3
- Pages
- 228-231
- ISSN
- 1758-678X (ISSN-L)
- 1758-6798
- OpenAlex
- W4210819470
- Cited by
- 263
- References
- 24
- Citations by year
- 2026: 40 · 2025: 61 · 2024: 77 · 2023: 60 · 2022: 25
- FWCI
- 21.85
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Climate Change (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Australian Government
- Commonwealth Scientific and Industrial Research Organisation
- Chinese Academy of Sciences (XDB40030000, 2019YFC1509100)
- Centre for Southern Hemisphere Oceans Research
- Joint Research Centre
- National Key Research and Development Program of China (2019YFC1509100)
- 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 › Environmental Science › Global and Planetary Change › Climate Change and Environmental Impact
- Keywords
- ENSO, sea surface temperature variability, IPCC climate scenarios, climate change projections
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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.
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