The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index; 17 of the 37 sources its claims cite were checked for support.
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.
Overview
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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.
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Claims the paper makes
26 Own result · 9 No full text of the sources · 5 Supported so far · 5 Partly supported · 4 Own interpretation · 3 Supported · 3 Own method · 1 No basis given · accuracy not yet measured
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“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.”
What the sources say
- Not checked — no full text of the sourcecites [9] Cai, W. et al. ENSO and greenhouse warming. Nat. Clim. Change 5, 849–859 (2015). source
- Supportedcites [10] Cai, W. et al. Increased variability of eastern Pacific El Niño under greenhouse warming. Nature 564, 201–206 (2018). source
The main text reports that this CMIP5-only study selected models able to simulate the two distinct ENSO regimes and found a robust future increase in eastern-Pacific ENSO SST variance. The CMIP5–CMIP6 combination in the citing sentence is not attributed specifically to this paper. (LLM, conf=0.90)
plaincorrelational- Asserted: passes — The authors put it forward as true.
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The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Under greenhouse warming, an increased air-sea coupling arising from an enhanced upper equatorial ocean stratification underpins the increase in ENSO variability 10 .”
What the sources say
- Partly supportedcites [10] Cai, W. et al. Increased variability of eastern Pacific El Niño under greenhouse warming. Nature 564, 201–206 (2018). sourceExperimental — accuracy not yet measured
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)
plaindirect- Asserted: passes — The authors put it forward as true.
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- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“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).”
What the sources say
- Partly supportedcites [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). sourceExperimental — accuracy not yet measured
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)
- Supportedcites [6] Yang, Y. et al. Greenhouse warming intensifies north tropical Atlantic climate variability. Sci. Adv. 7, eabg9690 (2021). source
In Results, under “Increased ENSO variability” (page unavailable), the paper reports increased Niño3.4 variability under warming in 12 of 16 selected CMIP6 models and 16 of all 23 models. Materials and Methods, “Observed and CMIP6 data” (page unavailable), confirms that the study uses CMIP6 models. The comparison with the IPCC AR6 statement is the citing authors’ framing, not a statement attributed to this source. (LLM, conf=0.90)
- Not checked — no full text of the sourcecites [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
- Supportedcites [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
Citation 8 is attached to the identification of CMIP6, not to the claim that the IPCC AR6 statement conflicts with studies 5–7. The paper’s title identifies it as an overview of the Coupled Model Intercomparison Project Phase 6, and Section 2 describes modelling centres contributing to CMIP6. No page number is available in our copy. (LLM, conf=0.93)
plain- Asserted: passes — The authors put it forward as true.
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- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“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 .”
What the sources say
- Not checked — no full text of the sourcecites [9] Cai, W. et al. ENSO and greenhouse warming. Nat. Clim. Change 5, 849–859 (2015). source
- Partly supportedcites [10] Cai, W. et al. Increased variability of eastern Pacific El Niño under greenhouse warming. Nature 564, 201–206 (2018). sourceExperimental — accuracy not yet measured
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)
plain- Asserted: passes — The authors put it forward as true.
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Every source the paper cites for this claim was checked and supports it.
In the paper
“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 .”
What the sources say
- Supportedcites [10] Cai, W. et al. Increased variability of eastern Pacific El Niño under greenhouse warming. Nature 564, 201–206 (2018). source
The main text reports that 17 models with a reasonable nonlinear relationship were selected to assess eastern Pacific (EP) ENSO change, and that 15 of those 17 project increased E-index variance. The Diagnosis of nonlinear Bjerknes feedback section says their nonlinear response is close to the observed value. The consensus reported here concerns EP ENSO SST variability. (LLM, conf=0.92)
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Models generally underestimate this feedback 10 .”
What the sources say
- Supportedcites [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 that most of the 34 models have a lower-than-observed measure of ENSO nonlinearity, and that the 17 non-selected models show a weaker nonlinear Bjerknes feedback. The Methods section reports that the selected models' response is close to the observed value, so the claim is supported as a generalization, not as a statement about every model. (LLM, conf=0.90)
plain- Asserted: passes — The authors put it forward as true.
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- Supported
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“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.”
What the sources say
- Supportedcites [6] Yang, Y. et al. Greenhouse warming intensifies north tropical Atlantic climate variability. Sci. Adv. 7, eabg9690 (2021). source
The Results section describes ENSO-related precipitation changes within the Pacific and shows that ENSO variability influences climate variability in the north tropical Atlantic. The Discussion links that remote variability to droughts, floods, and extreme Atlantic hurricanes. Together, these passages support the broad claim about climate variability and extremes within and outside the Pacific; page numbers are unavailable in our copy. (LLM, conf=0.87)
- Not checked — no full text of the sourcecites [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
- Supportedcites [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
The Introduction says that the location of ENSO sea-surface temperature anomalies has major consequences for impacts in South America and other regions of the globe. Section 3, “Mechanism for ENSO impact” (page unavailable), describes ENSO effects on rainfall and temperature through local forcing and remote teleconnections, and notes that extreme-event frequency is generally more affected in ENSO years. (LLM, conf=0.88)
plaindirect- Asserted: passes — The authors put it forward as true.
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- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“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] .”
What the sources say
- Supportedcites [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
The Introduction (page unavailable) describes strong decadal and interdecadal ENSO variability and says internal variability arises from the climate system’s chaotic nature. Section 4 (page unavailable) finds that internal variability on 30-year timescales exceeds observed changes, which therefore cannot be attributed to forcing. This supports the claim that variability can obscure a greenhouse-forced signal in short analysis periods. (LLM, conf=0.91)
- Not checked — no full text of the sourcecites [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
- Partly supportedcites [16] Cai, W. et al. Butterfly effect and a self-modulating El Niño response to global warming. Nature 585, 68–73 (2020). sourceExperimental — accuracy not yet measured
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)
hedgeddirect- Asserted: passes — The authors put it forward as true.
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- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
plaindirect- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A similar increase (15.7%) is obtained when inter-model differences in representing ENSO amplitude are removed (Extended Data Fig. 2).”
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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- Probably not a substantial claim. 8
- It may state two separate claims as one. 8
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Besides some improvements in the representation of the tropical Pacific climate 20 , more CMIP6 models simulate realistic ENSO nonlinear feedbacks 7 .”
What the sources say
- Partly supportedcites [20] Planton, Y. et al. Evaluating climate models with the CLIVAR 2020 ENSO metrics package. Bull. Am. Meteorol. Soc. 102, E193–E217 (2021). sourceExperimental — accuracy not yet measured
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)
- Not checked — no full text of the sourcecites [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
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Partly supported
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“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 .”
What the sources say
- Not checked — no full text of the sourcecites [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
- Supportedcites [10] Cai, W. et al. Increased variability of eastern Pacific El Niño under greenhouse warming. Nature 564, 201–206 (2018). source
Split into 2 atoms — 2 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
Every source the paper cites for this claim was checked and supports it.
In the paper
“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.”
What the sources say
- Supportedcites [23] Beobide-Arsuaga, G. et al. Uncertainty of ENSO-amplitude projections in CMIP5 and CMIP6 models. Clim. Dyn. 56, 3875–3888 (2021). source
Section 2.1 (Data) states that the study analyzed 36 CMIP5 and 20 CMIP6 models. Section 6 (Summary and discussion) reports that the models highly disagree on future ENSO amplitude change, with projections ranging from decreases to increases. (LLM, conf=0.94)
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Supported
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“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 .”
What the sources say
- Not checked — no full text of the sourcecites [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
- Supportedcites [10] Cai, W. et al. Increased variability of eastern Pacific El Niño under greenhouse warming. Nature 564, 201–206 (2018). source
In the main text (page not supplied), the paper says that parameter α connects the E-index and C-index skewness values and measures ENSO diversity, encapsulating nonlinear feedback. Figure 1’s caption likewise describes α as a measure of the contrast between eastern- and central-Pacific ENSO regimes and their skewness. (LLM, conf=0.92)
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“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 .”
What the sources say
- Not checked — no full text of the sourcecites [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
- Not checked — no full text of the sourcecites [3] Cai, W. et al. Increasing frequency of extreme El Niño events due to greenhouse warming. Nat. Clim. Change 4, 111–116 (2014). source
- Supportedcites [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
The “Robust underpinning process” section says warming reduces the meridional surface-temperature gradient, making extreme El Niño easier to induce even if temperature variability does not change. The “Additional information” section links that reduced gradient to convection and extraordinary eastern equatorial Pacific rainfall, supporting the paraphrase’s rainfall-response point. Our copy provides no page numbers for these passages. (LLM, conf=0.85)
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“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).”
What the sources say
- Not checked — no full text of the sourcecites [18] Austin, P. C. & Tu, J. V. Bootstrap methods for developing predictive models. Am. Stat. 58, 131–137 (2004). source
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The multi-model ensemble mean increase (11.7%) is statistically significant.”
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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).”
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“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).”
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“Even on centennial timescales, ENSO variability is influenced by internal climate fluctuations 17 .”
What the sources say
- Not checked — no full text of the sourcecites [17] Li, J. et al. Interdecadal modulation of El Niño amplitude during the past millennium. Nat. Clim. Change 1, 114–118 (2011). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
plaindirect- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“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.”
hedged- Asserted: passes — The authors put it forward as true.
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- Load-bearing: fails — The paper’s conclusions may not depend on it.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“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.”
What the sources say
- Not checked — no full text of the sourcecites [22] Wengel, C. et al. Future high-resolution El Niño/Southern Oscillation dynamics. Nat. Clim. Change 11, 758–765 (2021). source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“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”
What the sources say
- Not checked — no full text of the sourcecites [21] Callahan, C. W. et al. Robust decrease in El Niño/Southern Oscillation amplitude under long-term warming. Nat. Clim. Change 11, 752–757 (2021). source
- Not checked — no full text of the sourcecites [22] Wengel, C. et al. Future high-resolution El Niño/Southern Oscillation dynamics. Nat. Clim. Change 11, 758–765 (2021). source
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
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.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Increased ENSO sea surface temperature variability under four IPCC emission scenarios
“50 years of the current climate of 1965-2015 and the future climate of 2050-2099”
§ CMIP6 models.
“between the 50-year future climate of 2050-2099 and current climate of 1965-2014 (Fig. 2).”
Citations
Cites
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Increasing frequency of extreme El Niño events due to greenhouse warming.
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Self-citationThe impact of global warming on the tropical Pacific Ocean and El Niño
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Self-citationClimate impacts of the El Niño–Southern Oscillation on South America
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Self-citationChanging El Niño–Southern Oscillation in a warming climate
Cai, W., Santoso, A., Collins, M., Dewitte, B., Karamperidou, C., Kug, J.-S., Lengaigne, M., McPhaden, M. J., Stuecker, M. F., Taschetto, A. S., et al. (2021) · Nature Reviews Earth & Environment · doi:10.1038/s43017-021-00199-z · Cited by 702
Self-citationIncreased variability of eastern Pacific El Niño under greenhouse warming
Cai, W., Wang, G., Dewitte, B., Wu, L., Santoso, A., Takahashi, K., Yang, Y., Carréric, A., & McPhaden, M. J. (2018) · Nature · doi:10.1038/s41586-018-0776-9 · Cited by 642
Self-citationBootstrap Methods for Developing Predictive Models
Austin, P. C., & Tu, J. V. (2004) · The American Statistician · doi:10.1198/0003130043277 · Cited by 622
Research works printed but not linked: 1
- IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, in the press).
Cited by
Sorted by citations
Increased occurrences of consecutive La Niña events under global warming
Geng, T., Jia, F., Cai, W., Wu, L., Gan, B., Jing, Z., Li, S., & McPhaden, M. J. (2023) · Nature · doi:10.1038/s41586-023-06236-9 · Cited by 289
Self-citationAnthropogenic impacts on twentieth-century ENSO variability changes
Cai, W., Ng, B., Geng, T., Jia, F., Wu, L., Wang, G., Liu, Y., Gan, B., Yang, K., Santoso, A., et al. (2023) · Nature Reviews Earth & Environment · doi:10.1038/s43017-023-00427-8 · Cited by 176
Self-citationPersistent effect of El Niño on global economic growth
Callahan, C. W., & Mankin, J. (2023) · Science · doi:10.1126/science.adf2983 · Cited by 125
Southern Ocean warming and its climatic impacts
Cai, W., Gao, L., Luo, Y., Li, X., Zheng, X., Zhang, X., Cheng, X., Jia, F., Purich, A., Santoso, A., et al. (2023) · 中国科学通报：英文版 · doi:10.1016/j.scib.2023.03.049 · Cited by 90
Self-citationProjected ENSO Teleconnection Changes in CMIP6
McGregor, S., Cassou, C., Kosaka, Y., & Phillips, A. S. (2022) · Geophysical Research Letters · doi:10.1029/2021gl097511 · Cited by 99
“, 2021), but significant when the entire 21st century is contrasted against the 20th century (Cai et al., 2022).”
BackgroundMaher, N., Wills, R. C. J., DiNezio, P., Klavans, J. M., Milinski, S., Sanchez, S. C., Stevenson, S. L., Stuecker, M. F., & Wu, X. (2023) · Earth System Dynamics · doi:10.5194/esd-14-413-2023 · Cited by 102
Geng, T., Cai, W., Wu, L., Santoso, A., Wang, G., Jing, Z., Gan, B., Yang, Y., Li, S., Wang, S., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33930-5 · Cited by 107
Self-citationTen new insights in climate science 2024
Schaeffer, R., Schipper, E. L. F., Ospina, D., Mirazo, P., Alencar, A. A. C., Anvari, M., Artaxo, P. E., Biresselioğlu, M. E., Blome, T., Boeckmann, M., et al. (2025) · One Earth · doi:10.1016/j.oneear.2025.101285 · Cited by 82
“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
Lopez, H., Lee, S., Kim, D., Wittenberg, A. T., & Yeh, S. (2022) · Nature Communications · doi:10.1038/s41467-022-29519-7 · Cited by 78
Nonlinear El Niño impacts on the global economy under climate change
Liu, Y., Cai, W., Lin, X., Li, Z., & Zhang, Y. (2023) · Nature Communications · doi:10.1038/s41467-023-41551-9 · Cited by 82
“All four emission scenarios simulate an increase in future ENSO variability with a strong inter-model consensus as reported by previous studies 28,29 (Fig.”
Self-citationFuture Southern Ocean warming linked to projected ENSO variability
Wang, G., Cai, W., Santoso, A., et al. (2022) · Nature Climate Change · doi:10.1038/s41558-022-01398-2 · Cited by 60
Self-citationMore frequent central Pacific El Niño and stronger eastern pacific El Niño in a warmer climate
Shin, N.-Y., Kug, J., Stuecker, M. F., Jin, F., Timmermann, A., & Kim, G.-I. (2022) · npj Climate and Atmospheric Science · doi:10.1038/s41612-022-00324-9 · Cited by 78
Asian-Australian summer monsoons linkage to ENSO strengthened by global warming
Xu, C., Wang, S., Borhara, K., Buckley, B. M., Tan, N., Zhao, Y., An, W., Sano, M., Nakatsuka, T., & Guo, Z. (2023) · npj Climate and Atmospheric Science · doi:10.1038/s41612-023-00341-2 · Cited by 60
Hydrologic Extremes in a Changing Climate: a Review of Extremes in East Africa
Taye, M. T., & Dyer, E. L. E. (2024) · Current Climate Change Reports · doi:10.1007/s40641-024-00193-9 · Cited by 75
“Some studies have shown an increase in ENSO SST variability, but the strength of the consensus on this increasing change among studies may be sensitive to the model generations used and to the length of periods used in the analysis [68].”
MIROC6 Large Ensemble (MIROC6-LE): experimental design and initial analyses
Shiogama, H., Tatebe, H., Hayashi, M., Abe, M., Arai, M., Koyama, H., Imada, Y., Kosaka, Y., Ogura, T., & Watanabe, M. (2023) · Earth System Dynamics · doi:10.5194/esd-14-1107-2023 · Cited by 61
The Indian Ocean Dipole in a warming world
Wang, G., Cai, W., Santoso, A., Abram, N. J., Ng, B., Yang, K., Geng, T., Doi, T., Du, Y., Izumo, T., et al. (2024) · Nature Reviews Earth & Environment · doi:10.1038/s43017-024-00573-7 · Cited by 87
“Given that most models project an increase of CP-and EP-ENSO variability under green-house warming 120,124,125 , it is possible that projected increases in IOD variability are forced by changes in ENSO–IOD coupling.”
Self-citationFrequent but Predictable Droughts in East Africa Driven by a Walker Circulation Intensification
Funk, C. C., Fink, A. H., Harrison, L., Segele, Z., Endris, H. S., Galu, G., Korecha, D., & Nicholson, S. E. (2023) · Earth s Future · doi:10.1029/2022ef003454 · Cited by 60
“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).”
BackgroundResultLai, J., Qi, S., Chen, J., Guo, J., Wu, H., & Chen, Y. (2025) · Ecological Indicators · doi:10.1016/j.ecolind.2025.113236 · Cited by 55
Suppressed Atlantic Niño/Niña variability under greenhouse warming
Yang, Y., Wu, L., Cai, W., Jia, F., Ng, B., Wang, G., & Geng, T. (2022) · Nature Climate Change · doi:10.1038/s41558-022-01444-z · Cited by 53
Self-citationAntarctic shelf ocean warming and sea ice melt affected by projected El Niño changes
Cai, W., Jia, F., Li, S., et al. (2023) · Nature Climate Change · doi:10.1038/s41558-023-01610-x · Cited by 50
“%) models generate an increased ENSO amplitude with a multimodel mean increase of ~11.4%, consistent with a recent finding 25 .”
ResultInfluentialSelf-citationChan, F. K. S., Lü, X., Li, J., Lai, Y., Luo, M., Chen, Y. D., Wang, D., Li, N., Chen, W., Zhu, Y., et al. (2022) · Ocean & Coastal Management · doi:10.1016/j.ocecoaman.2023.107015 · Cited by 51
Oxyfuel Combustion Makes Carbon Capture More Efficient
Talei, S., Fózer, D., Varbanov, P. S., Szanyi, Á., & Mizsey, P. (2024) · ACS Omega · doi:10.1021/acsomega.3c05034 · Cited by 47
Marine heatwaves and cold-spells in global coral reef zones
Yao, Y., & Wang, C. (2022) · Progress In Oceanography · doi:10.1016/j.pocean.2022.102920 · Cited by 47
Bioenergy with carbon capture and storage technology to achieve net zero emissions–A review
Sher, F., Hameed, S., Smječanin, N., Wang, B., Hai, I. U., Rashid, T., Teoh, Y. H., & Yildiz, M. J. (2024) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2024.115229 · Cited by 50
Hydrologic risk from consecutive dry and wet extremes at the global scale
Rashid, M. M., & Wahl, T. (2022) · Environmental Research Communications · doi:10.1088/2515-7620/ac77de · Cited by 44
Hysteresis of the El Niño–Southern Oscillation to CO 2 forcing
Liu, C., An, S., Jin, F., Shin, J., Kug, J., Zhang, W., Stuecker, M. F., Yuan, X., Xue, A., Geng, X., et al. (2023) · Science Advances · doi:10.1126/sciadv.adh8442 · Cited by 49
Vialard, J., Jin, F., McPhaden, M. J., Fedorov, A. V., Cai, W., An, S., Dommenget, D., Fang, X., Stuecker, M. F., Wang, C., et al. (2025) · Reviews of Geophysics · doi:10.1029/2024rg000843 · Cited by 61
Self-citationArctic sea ice–air interactions weaken El Niño–Southern Oscillation
Deng, J., & Dai, A. (2024) · Science Advances · doi:10.1126/sciadv.adk3990 · Cited by 47
“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.”
BackgroundInfluentialMultiscale causes of the 2022 Yangtze mega-flash drought under climate change
Yuan, X., Wang, Y., Zhou, S., Li, H., & Li, C. (2024) · Science China Earth Sciences · doi:10.1007/s11430-024-1356-x · Cited by 43
“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
Peng, Q., Xie, S.-P., & Deser, C. (2024) · Nature Climate Change · doi:10.1038/s41558-024-02061-8 · Cited by 46
“The first example is diverse projected changes in the twenty-first century: CMIP3 and CMIP5 models disagree on the sign of projected ENSO change 3–6 , but recent studies suggest an inter-model consensus on increased ENSO SST variability among CMIP6 models 7–9 .”
InfluentialHu, Z., Fan, H., Liu, Y., et al. (2024) · Quaternary Science Reviews · doi:10.1016/j.quascirev.2023.108498 · Cited by 38
Geng, T., Cai, W., Jia, F., & Wu, L. (2024) · Nature Communications · doi:10.1038/s41467-024-50156-9 · Cited by 47
“To examine ENSO response to persistent greenhouse warming beyondthe 21stcentury,wetakenineCMIP5models 15 andeight CMIP6 models 39 that are available so far (Supplementary Table 1), forced under historicalanthropogenicandnaturalforcingsto2005 forCMIP5 and 2014 for CMIP6, respectively, and thereafter…”
BackgroundSelf-citationPo–Chedley, S., Fasullo, J., Siler, N., Labe, Z. M., Barnes, E. A., Bonfils, C., & Santer, B. D. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2209431119 · Cited by 35
Global expansion of marine protected areas and the redistribution of fishing effort
McDonald, G. G., Bone, J., Costello, C., Englander, G., & Raynor, J. L. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2400592121 · Cited by 34
Exploring climate stabilisation at different global warming levels in ACCESS-ESM-1.5
King, A. D., Ziehn, T., Chamberlain, M. A., Borowiak, A. R., Brown, J. R., Cassidy, L. J., Dittus, A. J., Grose, M., Maher, N., Paik, S., et al. (2024) · Earth System Dynamics · doi:10.5194/esd-15-1353-2024 · Cited by 44
Wang, P., Yang, Y., Xue, D., Qu, Y., Tang, J., Leung, L.-Y. R., & Liao, H. (2022) · Journal of Climate · doi:10.1175/jcli-d-22-0279.1 · Cited by 37
Heede, U. K., & Fedorov, A. V. (2023) · Climate Dynamics · doi:10.1007/s00382-023-06856-x · Cited by 40
de Perez, E. C., Harrison, L., Berse, K. B., Easton‐Calabria, E., Marunye, J., Marake, M. V., Murshed, S. B., Shampa, S., & Zauisomue, E.-H. (2022) · Weather and Climate Extremes · doi:10.1016/j.wace.2022.100508 · Cited by 39
Climate impacts of the El Niño–Southern Oscillation in Africa
Cai, W., Reason, C. J. C., Mohíno, E., Rodríguez‐Fonseca, B., Malherbe, J., Santoso, A., Li, X., Chikoore, H., Nnamchi, H. C., McPhaden, M. J., et al. (2025) · Nature Reviews Earth & Environment · doi:10.1038/s43017-025-00705-7 · Cited by 49
Self-citationLatinne, A., & Morand, S. (2022) · Viruses · doi:10.3390/v14051100 · Cited by 28
Exogenous moisture deficit fuels drought risks across China
Wang, G., Zhang, Q., POKHREL, Y. N., Farinotti, D., Wang, J., Singh, V. P., & Xu, C. (2023) · npj Climate and Atmospheric Science · doi:10.1038/s41612-023-00543-8 · Cited by 26
Self-citationOcean stratification in a warming climate
Cheng, L., Li, G., Long, S.-M., Li, Y., von Schuckmann, K., Trenberth, K. E., Mann, M. E., Abraham, J., Du, Y., Cheng, X., et al. (2025) · Nature Reviews Earth & Environment · doi:10.1038/s43017-025-00715-5 · Cited by 56
“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 33
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 28
Dhage, L., & Widlansky, M. J. (2022) · Earth s Future · doi:10.1029/2021ef002524 · Cited by 26
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
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 37
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 27
“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
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
Enhanced North Pacific Victoria mode in a warming climate
Ji, K., Yu, J., Li, J., Hu, Z., Tseng, Y., Shi, J., Zhao, Y., Sun, C. J., & Ding, R. (2024) · npj Climate and Atmospheric Science · doi:10.1038/s41612-024-00599-0 · Cited by 28
“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., Delworth, T. L., & Cooke, W. F. (2022) · Frontiers in Climate · doi:10.3389/fclim.2022.941055 · Cited by 22
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 22
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
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
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
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 26
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
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 24
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
“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 23
Mohsin, M., Xuhua, H., Sarfraz, M., et al. (2023) · Sustainable Development · doi:10.1002/sd.2734 · Cited by 17
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
Self-citationSolaimani, K., Ahmadi, S. B., & Shokrian, F. (2024) · Ecological Indicators · doi:10.1016/j.ecolind.2024.111629 · Cited by 16
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
“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
Zhang, H., Ye, X., Zhu, P., et al. (2022) · Remote Sensing · doi:10.3390/rs14071596 · Cited by 14
Liu, J., Yu, J., Lin, C., et al. (2024) · Earth system science data · doi:10.5194/essd-16-4949-2024 · Cited by 13
“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
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
“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
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
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
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 15
“…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
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
Global climate-driven sea surface temperature and chlorophyll dynamics
Venegas, R. M., Rivas, D., & Treml, E. A. (2024) · Marine Environmental Research · doi:10.1016/j.marenvres.2024.106856 · Cited by 18
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
“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 18
“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
“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 12
“…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 12
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
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
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
Self-citationSartori, D., Scatena, G., Vrinceanu, C., & Gaion, A. (2023) · Marine Pollution Bulletin · doi:10.1016/j.marpolbul.2023.115274 · Cited by 9
Anthropogenic climate change drives non-stationary phytoplankton internal variability
Elsworth, G. W., Lovenduski, N. S., Krumhardt, K. M., Marchitto, T. M., & Schlunegger, S. (2023) · Biogeosciences · doi:10.5194/bg-20-4477-2023 · Cited by 12
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
Huang, P., Chen, Y., Li, J., & Yan, H. (2024) · npj Climate and Atmospheric Science · doi:10.1038/s41612-024-00695-1 · Cited by 11
“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 12
“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
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
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
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
“StudiesshowthatENSOSSTvariabilitywillbeenhancedunderthe plausibleemissionscenarioofCoupledModelIntercomparisonProject-6(CMIP6)emission scenarios(Caietal.2022).”
Estimating Uncertainty in Simulated ENSO Statistics
Planton, Y., Lee, J., Wittenberg, A. T., Gleckler, P. J., Guilyardi, É., McGregor, S., & McPhaden, M. J. (2024) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2023ms004147 · Cited by 10
Pennington, L. K., Sheth, S. N., Franks, S. J., et al. (2026) · American Journal of Botany · doi:10.1002/ajb2.70214 · Cited by 8
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
“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
“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. R., Henley, B. J., Risbey, J. S., Ramesh, N., Lieber, R., & Sharmila, S. (2024) · Environmental Research Letters · doi:10.1088/1748-9326/ad78db · Cited by 16
“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
Metadata
- 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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