Towards understanding the robust strengthening of ENSO and extreme El Niño events with global warming in CMIP6

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Heede, Alexey V. Fedorov This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1731142/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Jun, 2023 Read the published version in Climate Dynamics → Version 1 posted 9 You are reading this latest preprint version Abstract The El Niño Southern Oscillation (ENSO) has profound implications for weather patterns across the globe, and yet there is no consensus on its response to global warming. Several modelling studies suggest a stronger ENSO in global warming scenarios, while other studies find ENSO weakening. Using a broad range of models of the Coupled Model Intercomparison Project phase 6 (CMIP6) and four different types of warming experiments, here we show that the majority of the models predict a stronger ENSO by century-end in Shared Social Pathway (SSP) experiments, and in idealized 1pctCO 2 and abrupt 4xCO 2 experiments. Several models, however, predict no change or ENSO weakening, especially in the idealized experiments. For most models the strongest forcing (abrupt-4xCO 2 ) is not associated with the strongest ENSO response, while differences between the models are much greater than between warming scenarios. For the long-term (1000 years) response the models disagree even on the sign of change. Furthermore, changes in ENSO SST variability appear to be poorly correlated with the tropical mean state change, while changes in ENSO rainfall variability correlate well with changes in the mean state and, especially, ENSO SST variability. Evaluating changes in the Bjerknes Stability Index for a subset of models we find it to be a poor predictor for ENSO strengthening as this index typically suggests greater stability with warming. We hypothesize that changes to ENSO stability are offset by increases in atmospheric noise or/and potential nonlinear effects. Thus, a robust inter-model mechanism is still lacking to explain a stronger ENSO simulated with global warming, and caution should be exercised when considering ENSO changes based on a single model or warming scenario. El Niño ENSO CMIP6 Climate Change extreme events Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction The El Niño Southern Oscillation (ENSO) is the strongest interannual oscillation in Earth’s climate, with dramatic impacts on weather patterns and extreme events in the tropics and beyond (Cai et al. 2021 ; McPhaden, Santoso, and Cai 2020 ). El Niño conditions are characterized by warm central and/or eastern Pacific sea surface temperature (SST) anomalies, reduced easterly trade winds and the corresponding slope of the ocean thermocline, and eastward shift of atmospheric convection. El Niño can lead to flooding in South America but drought and forest fires in areas bordering the western Pacific (Ropelewski and Halpert 1987). In addition, due to its effect on atmospheric planetary waves, ENSO affects a range of whether phenomena outside the tropical Pacific via teleconnections, and the signature of ENSO has been observed on all seven continents (Yeh et al. 2018 ). Whether ENSO is already changing or will change with future warming, and, in particular, whether this change will lead to stronger and/or more frequent El Nino events is of great importance for timely implementation of societal adaptation and mitigation strategies. However, despite the importance of ENSO in Earth’s climate, much uncertainty still remains concerning these questions. It has long been known that ENSO depends on changes in the mean state tropical Pacific (Fedorov and Philander 2000, 2021; Fedorov et al. 2020; An and Jin 2020, Jin et al. 2006 ; DiNezio et al. 2012 ). There is broad consensus models participating in the Coupled Model Intercomparison Project, phase 6 (CMIP6) that the Walker cell will slow down with global warming, leading to enhanced warming of the eastern and central equatorial Pacific by century-end (e.g. Heede and Fedorov 2021). This enhanced warming of the eastern Pacific can be understood in terms of both atmospheric and oceanic mechanisms acting to reduce equatorial trade winds and eastern Pacific upwelling (U. K. Heede, Fedorov, and Burls 2020 ; 2021; Vecchi et al. 2006 ). Despite this consensus on future mean state warming in the equatorial Pacific, and the known links between mean state and ENSO characteristics, there is no robust consensus among climate models concerning the response of ENSO to global warming. A number of papers have suggested stronger ENSO in future climates in CMIP5 models, in particular based on precipitation metrics (Huang and Xie 2015; Cai et al. 2015), or based on metrics that isolate certain models (Cai et al. 2018 ). The tendency for ENSO to be stronger in future warming scenarios has also been reported for subsets of CMIP6 models (Fredriksen et al. 2020 , 6; Cai et al. 2021 ; Brown et al. 2020 ). Cai et al. 2018 suggests two mechanisms that drive stronger eastern Pacific El Nino events: a stronger coupling between wind stress and SST in the eastern Pacific, and a stronger coupling between ocean feedbacks and wind stress due to enhanced upper ocean stratification. Opposing Cai’s et al. findings, several recent studies have argued that ENSO strength may decrease with warming (Christian Wengel et al. 2021 ; Callahan et al. 2021 ; Kohyama, Hartmann, and Battisti 2018 ). These studies typically rely on abrupt CO 2 quadrupling experiments and argue that increased thermodynamic damping and decreased ocean feedbacks among other mechanisms lead to a more stable ENSO with warming. Kohyama et al. ( 2018 ) in particular argues that enhanced ocean stratification leads to a stiffer thermocline and a decrease in ENSO non-linearity in contrast with the findings of Cai et al. ( 2018 ). These disagreements highlight that changes in different feedbacks due to changes in the mean state and ocean-atmosphere coupling can partially compensate each other leading to unanticipated results. The goal of this study is to attempt to reconcile these divergent views of how ENSO responds to warming as well as provide more detailed insight into how ENSO changes within the CMIP6 models than previous studies have done (i.e. Fredriksen et al. 2020 ; Cai et al. 2022 ), using a broader range of models and climate scenarios. As one set of studies finding a stronger ENSO primarily rely on the 21st century SSP scenarios, while another set of studies finding a weaker ENSO uses the abrupt CO 2 quadrupling experiments, we provide an overview of how models respond across these idealized and realistic forcing experiments to elucidate any uncertainty associated with a given experiment-type. Furthermore, we aim to explore how the various mechanisms proposed for a stronger or weaker ENSO may manifest across different models and document how ENSO changes might be linked to the tropical Pacific mean state. Finally, to explore whether the transient response differs from the long-term response in the abrupt-4xCO 2 experiments as proposed by Callahan et al. ( 2021 ), we include three 1000-year simulations. To our knowledge, no study has compared ENSO changes across a broad range of idealized and realistic forcing scenarios in CMIP6 models, as well as attempted to link those changes to tropical mean state changes in a comprehensive manner. 2. Methods 2.1 CMIP6 archive We analyze two types of experiments from the Climate Model Intercomparison Project Phase 6 (CMIP6) archive. The first type consists of two hypothetical or idealized CO 2 -only experiments: abrupt-4xCO 2 rise and 1pctCO 2 gradual CO 2 rise (1% per year) where CO 2 -increases are relative to a pre-industrial level of 280 ppm. The second type has full-forcing experiments, including two future scenarios, SSP5-8.5 which is a high-emission scenario and SSP1-2.6, in which emissions peak and decline in the 21st century (O’Neill et al. 2016 ). We use a total of 20 CMIP6 models in our overview analysis (see Table 1 ), utilizing surface temperature, column precipitation flux, and zonal wind stress. The criterion for including a given model into the analysis is whether it has surface temperature data available for at least three ensemble members for the SSP5-8.5 scenario. Some models do not have all datasets available for all experiments, and models are excluded whenever data is not available. For the idealized warming experiments (4xCO 2 and 1pctCO 2 ), where ensemble members are generally not available, we use a single ensemble member, and for the SSP scenarios we use the ensemble-mean results based on three members. We include 85, 150 and 200 years of simulation for the SSP experiments, the idealized experiments and the piControl experiment respectively. Table 1 Overview of models used and their associated marker. 2.2 Metrics We define the amplitude of ENSO SST variability as the standard deviation of band-passed temperature anomalies in the frequency range between 1.5 to 7 years from which the climatology was subtracted for the central-east Pacific (180–280ºE). This region is chosen to capture ENSO changes across both the central and eastern Pacific following Ferrett and Collins (2019). We further define the mean state zonal Pacific gradient as the difference between time mean anomalies of the western Pacific (120 o -180 o E) and the central-eastern Pacific (180 o -280 o E). While the issue of Central Pacific (CP) versus Eastern Pacific (EP) El Niño events has been discussed as a potential culprit in using set boxes in other studies (Cai et al. 2018 ), we find that the estimated ENSO changes are not very sensitive to the chosen box, and that most models show a consistent change in standard deviation across both the central and eastern equatorial Pacific. Similarly, we define the strength of ENSO rainfall variability as the standard deviation of band-passed precipitation anomalies in the frequency range between 1.5 to 7 years from which the climatology is subtracted for the central-east Pacific (180–280ºE). We define extreme El Niño events as events with warm temperature anomalies in the central-east Pacific (180–280ºE), exceeding 2 standard deviations. While 2.5 standard deviations are sometimes used to define an extreme event (Yu and Fedorov 2020), we chose 2 to get robust statistics as most models have few events of 2.5 standard deviation magnitude in timeframe of 85 to 200 years. All changes are computed relative to the piControl experiment. While some studies compare SSP scenarios to the historical period (i.e. Cai et al. 2022 ), we compare all experiment types to the piControl experiment in order to facilitate a fair comparison between idealized experiments and future warming scenarios. For the majority of models, the strength of ENSO in the piControl experiment is similar to the historical experiment. 2.3 Bjerknes Stability Index To evaluate possible links between mean state changes and ENSO response to warming, we use the Bjerknes Stability Index (Jin, Kim, and Bejarano 2006 ). The Bjerknes Index (BJ index, in units of yr − 1 ) assumes that ENSO dynamics are controlled by the recharge oscillator physics where perturbations to SST in the eastern Pacific are either amplified or dampened through a series of feedbacks: $$BJ=CD+TD+ZA+ED+TC$$ Here, CD is damping by the mean currents given by: $$CD=-\left(\frac{⟨\stackrel{-}{u}⟩}{{L}_{x}}+\frac{⟨-2y\stackrel{-}{v}⟩}{{{L}_{y}}^{2}}+\frac{⟨H\left(\stackrel{-}{w}\right)\stackrel{-}{w}⟩}{{H}_{m}}\right)$$ Angle brackets indicate the area average for the central-eastern Pacific (180–280ºE) and bars indicate a time-mean over the time length of the experiment. \(\stackrel{-}{u},\stackrel{-}{v}\) and \(\stackrel{-}{w}\) are the mean zonal, meridional and vertical velocities in the mixed layer. \({L}_{x}\) and \({L}_{y}\) are the zonal and meridional scale lengths. \({H}_{m}\) is the depth of the ocean mixed layer (50 m), and \(H\left(\stackrel{-}{w}\right)\) is the Heaviside step function, which ensures that only positive values of vertical velocity are taken into account. TD is thermodynamic damping given by: $$TD=-\alpha$$ Here, α is the linear regression coefficient from mixed layer temperature to net energy flux \({E}_{net}\) , from the ocean to the atmosphere. \({E}_{net}\) is given by: $${E}_{net}={E}_{incoming\_shortwave}+{E}_{incoming\_longewave}-{E}_{outgoing\_shortwave}-{E}_{outgoing\_longewave}- {E}_{latent\_heat}-{E}_{sensible\_heat}$$ \(\alpha\) is further multiplied by \(\frac{1}{\rho {C}_{p}{H}_{m}}\) where \(\rho\) is sea water density and \({C}_{p}\) is specific heat capacity of seawater and thus has units of time − 1 . ZA, EK and TC correspond to the zonal advection, Ekman and thermocline feedbacks respectively, given by: \(ZA={\mu }_{a}{\beta }_{u}⟨-\frac{\partial \stackrel{-}{T}}{\partial x}⟩\) \(\) \(EK={\mu }_{a}{\beta }_{w}⟨-\frac{\partial \stackrel{-}{T}}{\partial z}⟩\) \(\) \(TC={\mu }_{a}^{*}{\beta }_{h}⟨-\frac{\stackrel{-}{w}}{{H}_{m}}⟩\) Where \({\mu }_{a}\) and \({\mu }_{a}^{*}\) are linear regression coefficients between wind stress and SST anomalies. \({\beta }_{u}\) , \({\beta }_{w}\) , and \({\beta }_{h}\) are the linear correlation coefficients between wind stress and zonal current, vertical current and thermocline anomalies respectively. \(\frac{\partial \stackrel{-}{T}}{\partial x}\) is the mean state zonal temperature gradient and \(\frac{\partial \stackrel{-}{T}}{\partial z}\) is the mean vertical stratification. \(\stackrel{-}{w}\) is the mean vertical velocity. All regression coefficients are for the central-east equatorial Pacific (180–280ºE), except \({\mu }_{a}^{*}\) which is calculated for the entire Pacific equatorial region (130–280ºE). Following Kim et al. ( 2014 ) and Ferrett and Collins (2019), we use temperature at 50 m depth as a proxy for thermocline anomalies. In a supplementary analysis, we then repeat Bjerknes Index calculation, but for the Niño3 region (210–280ºE) to illustrate how the relative role of damping and feedback terms change when a different range of averaging is used. The equatorial region is defined between 5ºS and 5ºN. 2.4 Noise Finally, an important contribution to ENSO dynamics comes from atmospheric noise, which can drive a damped oscillation even when the BJ-index is negative. Here we define atmospheric noise in the tropics following Philip and Oldenborgh (2009) as: $$ϵ\left(x,y,t\right)= {\tau }_{x}\left(x,y,t\right)-\sum _{i=1}^{2}{{A}_{1}\left(x,y\right)}_{i}{{T}^{\text{'}}}_{i}\left(t\right)$$ Where \(ϵ\left(x,y,t\right)\) denotes stochastic forcing by random wind stress variations, \({{T}^{\text{'}}}_{i}\left(t\right)\) is SST anomalies averaged over standard regions and \({{A}_{1}\left(x,y\right)}_{i}\) is the domain-wide wind stress patterns corresponding to the SST anomalies of the separate regions i = 1, 2. Here we use i = 1 as the Niño3 region, and i = 2 as the Niño4 region. We then average \(ϵ\) over the equatorial Pacific region (130–280ºE) and then take the standard deviation over time to estimate the noise magnitude. 3. Results 3.1 SST, rainfall and extreme event changes across multiple warming experiments We find all models show an enhanced ENSO SST variability in the SSP5-8.5 scenario relative to the piControl experiment (in the ensemble-mean sense). Most models show a similar or slightly smaller increase of ENSO amplitude in the SSP1-2.6 scenario, yet the differences between the two scenarios are small compared to differences between the models. ENSO responses in the 1pctCO 2 scenario is also correlated to the SSP5-8.5 response, yet is more muted. The abrupt-4xCO 2 scenario on average yields a poorer correlation with the SSP5-8.5 response and also has the largest spread among the experiments. In fact, 6 models in the 4xCO 2 scenario show a reduction in ENSO activity, with CESM2 having the most dramatic reduction (Fig. 1 ). By contrast, 2 models (ACCESS-ESM5-1 and FIO-2) show an increase that is larger than any ensemble member change in the SSP scenarios. Changes in extreme El Niño events, defined through their Niño3 SST index, are expectedly linked to changes in ENSO SST amplitude, and there is a steep slope between ENSO amplitude and extreme events, such that a 25% increase in ENSO SST amplitude leads on average to an about 100% increase in the frequency of extreme El Niño events (Fig. 2 ). The strongest correlation between extreme events and ENSO SST is found in the abrupt-4xCO 2 experiments. While the response of ENSO SST variability is highly dependent on both the experiment and model used, there is a universal increase in ENSO rainfall variability across all models and all experiments. In contrast to ENSO SST amplitude, the increase in ENSO precipitation variability is largest, on average, in the abrupt-4xCO 2 experiment (Fig. 3 ). The next question is whether and how these ENSO changes are related to changes in the mean state of the tropical Pacific and other factors. We find that the correlation between changes in ENSO SST amplitude and in the mean state zonal SST gradient is weak (Fig. 4 ) and not statistically significant at the 99% level, while the correlation between changes in ENSO rainfall variability and in the mean zonal SST gradient is generally higher (Fig. 5 a, 5 b, 5 c). Yet, the highest correlation is found between changes in variability of ENSO rainfall and SST (Fig. 5 d, 5 e, 5 f). We also investigated whether changes in ENSO rainfall variability might be linked to the eastern equatorial Pacific warming, mean tropical warming and mean rainfall, but did not find any statistically significant correlations. 3.2 Changes to the Bjerknes Stability Index, thermodynamic damping and noise To investigate the connection between mean state changes and ENSO SST response in a more quantitative manner, we compute the Bjerknes Stability Index (BJ Index) for a subset on models with available data. First, we compute this index using the central-eastern equatorial Pacific (180–280ºE) as the region of averaging following (Kim et al. 2014 ). This analysis reveals that all models in the subset show an increase in mean state stability with warming. For 5 out of 7 models, an increase in thermodynamical damping α dominates the changes, driving a decrease in the Bjerknes Index, and hence increasing ENSO stability (Fig. 6 ). For two models (MIROC6 and MIROC-ES2L), the increase in α is more modest and a decrease in the thermocline feedback is a dominating term in the change of the Bjerknes Index with warming. For all models, the decrease in the Bjerknes Index is higher for abrupt-4xCO 2 than for SSP585, suggesting that a rapid strong increase in radiative forcing stabilizes the system in the linear stability sense. While changes to the damping terms are robust in response the warming, there is no consensus in changes to the feedback terms. The 5 out of 7 of models show an increase in the Ekman feedback, 4 out of 7 show an increase in the thermocline feedback while all models show only small changes in the zonal advection feedback. We next recompute the Bjerknes Index but for the smaller Niño3 region (Supplementary Fig. 1). In this case, the computed Bjerknes Index is generally more negative, due to smaller positive feedback strengths compared with the full central-eastern Pacific region. For the global warming simulations, 5 out of 7 models show a more stable Niño3 Bjerknes Index, which is similar to the central-eastern Pacific Bjerknes Index. However, two models (MIROC6 and MIROC-ES2L) show a more unstable Bjerknes Index with warming for the Niño3 region, driven by an increase in the zonal advection and thermocline feedbacks. This could indicate that changes in the mean state of these two models lead to a more unstable Niño3 region, which might be able to explain a stronger ENSO SST in these two particular models. Even for these two models the use of the Bjerknes Index for explaining ENSO strengthening is problematic, given the strong sensitivity of the results to the choice of the averaging region. Next, to examine potential changes in non-linearities that could counteract increased linear stability, we calculate changes to wind stress-SST coupling ( \({\mu }_{a}\) ) and thermodynamic damping ( \(\alpha\) ) in three different ranges of temperature anomalies. We find that the thermodynamic damping \(\alpha\) , while increasing in the central range of temperature anomalies (-1 o C to 1 o C), decreases in the CanESM5, CESM2, CNRM-CM6-1, HadGEM3-CG31-LL for SST anomalies above 1 o C (Supplementary Fig. 2). This suggests that changes in \(\alpha\) , which dominate the Bjerknes Index, may be overestimated in some models because the damping becomes less efficient for larger temperature anomalies for some models. The coupling coefficient \({\mu }_{a}\) becomes stronger with warming in the majority of models in the central SST anomaly range (-1 o C to 1 o C), while for the IPSL-CM6A-LR, MIROC6 and MIROC-ES2L, \({\mu }_{a}\) reduces for temperature anomalies below − 1 o C in warming experiments (Supplementary Fig. 3). One however has to be careful in interpreting this result as the correlations may become very small for the high and low SST ranges. To further investigate the discrepancy between changes in the Bjerknes Index and ENSO response, we also analyze changes to atmospheric noise among the models for which wind stress data was available. Atmospheric noise, including westerly wind bursts that occur frequently in the tropical Pacific, is believed to play an important role in sustaining ENSO and especially extreme El Niño events (Puy et al. 2019 ; Yu and Fedorov 2020; Fedorov 2002 ). We find that on average, the correlation between change in atmospheric noise and change in ENSO (Fig. 7 ) is significant and higher than between change in mean state and ENSO SST amplitude, indicating that atmospheric noise could play an important role in ENSO changes with warming, despite some outliers in the abrupt-4xCO 2 experiment, and potentially explain the general strengthening of ENSO with warming. However, quantifying this relationship is difficult because (1) the computations of the Bjerknes index and noise amplitude is sensitive to the choice of the averaging regions and (2) the question of causality is difficult to resolve as the noise can increase as a result of strengthening of ENSO. 3.3 Long-term warming experiments Lastly, we investigate three 1000-year simulations for the abrupt-4xCO 2 experiment for three models (ACCESS-ESM1-5, CESM2 and IPSL-CM6A-LR). We find that even after such a long time ENSO response does not converge in these models. Specifically, CESM2 shows a drastic reduction in ENSO strength, such that at the end of the simulation its amplitude reaches only about 1/3 of that in the control. In ACCESS-ESM1-5 ENSO remains stronger than the control throughout the simulation period (Fig. 8 ), while in IPSL-CM6A-LR ENSO does not change much over the first 600 years, but then strengthens. Discussion and conclusions We have investigated the ENSO response to warming in four different scenarios with realistic and idealized radiative forcing across 20 models. We found that most of the models show enhanced ENSO SST variability in all warming scenarios considered. Moreover, in a high-forcing scenario SSP5-8.5, each model shows a stronger ensemble mean ENSO. Yet, there are large inter-model differences, which play a greater role than scenario uncertainty. The majority of models do show a generally consistent response across different types of warming experiments indicating that the observed response is indeed a result of radiative forcing, and the spread among models is primarily caused by factors other than natural variability. Interestingly, for the majority of models, gradual CO 2 -increase SSP scenarios on average show a stronger ENSO response to warming than the abrupt-4xCO2 scenario. In fact, roughly 40% of the models considered show a weakening or no change of ENSO SST variability in this scenario, which implies that caution should be taken when relying on such experiments for making projections of ENSO future changes. Despite this uncertainty in ENSO SST response, ENSO rainfall variability in the tropics increases universally across all experiments and models, which has important consequences for adaptation and mitigation as changes to floods and droughts may cause more damage than changes to SST itself and as ENSO remote teleconnections depend of latent heat release. The fact that rainfall variability increases robustly with warming is expected given that a warmer atmosphere can hold more water following the Clausius-Clapeyron relation and eastern Pacific warming can reduce the convection barrier for El Niño events (Yun et al. 2021 ). However, we find that changes in the background state alone cannot fully account for the modeled change in ENSO rainfall variability as changes in SST variability play a critical role as well. This highlights the need to better understand the response of ENSO SST variability in order to improve predictions for ENSO rainfall response. Extreme events can change drastically given a small change in ENSO SST amplitude and models with a relatively modest change in ENSO SST variability may show up to a doubling of the number of extreme El Niño events. This close connection between extreme events and ENSO SST amplitude is evident even in a relatively short timeframe of 150 years. The fact that the change in ENSO SST variability is closely connected to an increase in extreme events, which, like increases in ENSO rainfall, has important consequences for adaptation and mitigation as extreme events are often associated with more damage for society than a change in mean conditions (Trenberth 2012 ). Given the sensitivity of extreme events to small changes in ENSO SST, it is crucial to improve projections of ENSO SST amplitude with warming across models and warming experiments. In attempt to explain the robust strengthening of ENSO in warming scenarios we compute changes in the Bjerknes Stability Index but find it to be a poor predictor for changes in ENSO in a small subset of models (7), which is similar to findings in other studies using the Bjerknes Index for individual models (Manucharyan and Fedorov 2014; Ferrett and Collins 2019). Like Callahan et al. ( 2021 ), we find the increased thermodynamic damping to be the most important term in the Bjerknes Index for 5 out of 7 models, yielding a more stable Bjerknes index those models. However, the fact that all models show a stronger ENSO SST variability in the SSP585 scenario points to other effects counteracting this stability increase. For example, here we show that the thermodynamic damping may be overestimated in the models due to a nonlinear relationship between SST and surface energy fluxes. We show that the coupling between SST and surface heat fluxes decrease in warming experiments above a threshold of 1 o C SST anomalies in 4 out of 7 models, which could explain why ENSO SST amplitude can increase despite a stronger thermodynamic damping in a linear sense. MIROC6 and MIROC-ES2L show a decrease in the Bjerknes Stability Index for the Niño3 region as opposed to other models for which the Bjerknes Index is calculated. These models, together with EC-Earth3 and EC-Earth3-Veg, are outliers among the 20 models analyzed in that they show a drastic increase in ENSO amplitude across experiments. Thus, a decrease in stability for the Niño3 region, driven by increases in feedbacks in the eastern Pacific, may explain why some models have a drastic increase in ENSO amplitude, but it cannot explain the robust increase in ENSO across models. We suggest here that atmospheric noise, including westerly wind bursts, may play a crucial role in driving changes to ENSO, which is supported by a strong correlation between changes to noise and ENSO SST variability as well as other studies (C. Wengel et al. 2018 ; Lopez et al. 2022 ), but questions remain whether this noise is in fact driving a stronger ENSO, as ENSO itself can also generate more noise (Kug et al. 2008 ; Eisenman, Yu, and Tziperman 2005). This result highlights a problematic gap in our understanding on what drives changes in ENSO in response to CO 2 in GCMs, as we cannot understand changes in ENSO in terms of a simple linear Bjerknes stability framework that links those changes to changes in the tropical mean state. This is exemplified by the fact that mean state changes are larger for the abrupt-4xCO 2 scenario, and yet by century-end ENSO amplitude increases more in the SSP585 scenario. In fact, 5 or 6 models in this scenario actually show a weaker ENSO. It is feasible that more comprehensive linear stability analyses, computing the full leading eigen modes of the system (e.g. Sévellec and Fedorov 2013 or Fedorov and Philander 2001) could provide more consistent results, but this is yet to be done for coupled GCMs. Overall, our results point towards a robust increase in ENSO activity in the SSP5-8.5 and SSP1-2.6 scenarios, yet a common mechanism to explain these changes is lacking. Models that show a drastic increase in ENSO amplitude, such as the MIROC6 and MIROC-ES2L models may be driven by stability changes over the Nino3 region in combination with a small change or no change in thermodynamic damping. On the contrary, for models with a moderate change in ENSO, the linear Bjerknes Index decreases – primarily because of increased thermodynamic damping, leading to a more stable system. This suggests that atmospheric noise and/or nonlinear changes may drive a stronger ENSO for these models. On the other hand, for CESM2, which showed a weaker ENSO with 4xCO 2 , the Bjerknes Index did show the largest reduction, driven by increased thermodynamic damping and a decrease in the thermocline feedback. Yet, the same model shows an increase in ENSO, albeit small, for SSP5-8.5 and SSP1-2.6 relative to piControl. ENSO amplitude on average also increases for high CO 2 scenarios, such as the 1pctCO 2 or abrupt-4xCO 2 scenario, although the change is less robust across models. Furthermore, in contrast to Wenger et al. (2021) we did not find evidence that models converge to a weaker ENSO in the abrupt 4xCO 2 experiments over longer time-scales. In fact, ENSO remains stronger than the control in several models considered even after 1000 years of computation. On the whole, the abrupt 4xCO 2 scenario produces the broadest spread of ENSO projections, and as such may not be the most reliable indicator of changes to come. These findings highlight that despite the robust strengthening of ENSO by century-end in a broad range of models and warming scenarios of CMIP6, there is still a large uncertainty in ENSO future response to global warming, which should be addressed by evaluating ENSO drivers across multiple warming experiments in multiple models. Declarations Funding U.K.H. is supported by a NASA FINESST Fellowship (80NSSC20K1634). A.V.F. is supported by grants from NASA (80NSSC21K0558) and NOAA (NA20OAR4310377). Additional funding is provided by the ARCHANGE project (ANR-18-MPGA-0001, France). We also acknowledge a generous gift to Yale University from T. Sandoz. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript Competing interests The authors declare no competing interests. Data Availability CMIP6 data are available at https://esgf-node.llnl.gov/search/cmip6/. Code used for the analysis is available per request and at Gibhub upon publication. Author contributions U.K.H. and A.V.F. contributed equally to designing the research. U.K.H. performed the data analysis and, together with A.V.F., interpreted the results. U.K.H. wrote the manuscript and edited it together with A.V.F. References Brown, Josephine R., Chris M. Brierley, Soon-Il An, Maria-Vittoria Guarino, Samantha Stevenson, Charles J. R. Williams, Qiong Zhang, et al. 2020. ‘Comparison of Past and Future Simulations of ENSO in CMIP5/PMIP3 and CMIP6/PMIP4 Models’. Preprint. Climate Modelling/Modelling only/Pleistocene. https://doi.org/10.5194/cp-2019-155. Cai, Wenju, Benjamin Ng, Guojian Wang, Agus Santoso, Lixin Wu, and Kai Yang. 2022. ‘Increased ENSO Sea Surface Temperature Variability under Four IPCC Emission Scenarios’. Nature Climate Change 12 (3): 228–31. https://doi.org/10.1038/s41558-022-01282-z. 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Kug, Jong-Seong, Fei-Fei Jin, K. P. Sooraj, and In-Sik Kang. 2008. ‘State-Dependent Atmospheric Noise Associated with ENSO’. Geophysical Research Letters 35 (5). https://doi.org/10.1029/2007GL032017. Lopez, Hosmay, Sang-Ki Lee, Dongmin Kim, Andrew T. Wittenberg, and Sang-Wook Yeh. 2022. ‘Projections of Faster Onset and Slower Decay of El Niño in the 21st Century’. Nature Communications 13 (1): 1915. https://doi.org/10.1038/s41467-022-29519-7. Manucharyan, Georgy E., and Alexey V. Fedorov. 2014. ‘Robust ENSO across a Wide Range of Climates’. Journal of Climate 27 (15): 5836–50. https://doi.org/10.1175/JCLI-D-13-00759.1. McPhaden, Michael J., Agus Santoso, and Wenju Cai. 2020. El Niño Southern Oscillation in a Changing Climate . Vol. 253. John Wiley & Sons. O’Neill, Brian C., Claudia Tebaldi, Detlef P. van Vuuren, Veronika Eyring, Pierre Friedlingstein, George Hurtt, Reto Knutti, et al. 2016. ‘The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6’. 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Fedorov. 2013. ‘The Leading, Interdecadal Eigenmode of the Atlantic Meridional Overturning Circulation in a Realistic Ocean Model’. Journal of Climate 26 (7): 2160–83. https://doi.org/10.1175/JCLI-D-11-00023.1. Trenberth, Kevin E. 2012. ‘Framing the Way to Relate Climate Extremes to Climate Change’. Climatic Change 115 (2): 283–90. https://doi.org/10.1007/s10584-012-0441-5. Vecchi, Gabriel A., Brian J. Soden, Andrew T. Wittenberg, Isaac M. Held, Ants Leetmaa, and Matthew J. Harrison. 2006. ‘Weakening of Tropical Pacific Atmospheric Circulation Due to Anthropogenic Forcing’. Nature 441 (7089): 73–76. Wengel, C., D. Dommenget, M. Latif, T. Bayr, and A. Vijayeta. 2018. ‘What Controls ENSO-Amplitude Diversity in Climate Models?’ Geophysical Research Letters 45 (4): 1989–96. https://doi.org/10.1002/2017GL076849. Wengel, Christian, Sun-Seon Lee, Malte F. Stuecker, Axel Timmermann, Jung-Eun Chu, and Fabian Schloesser. 2021. ‘Future High-Resolution El Niño/Southern Oscillation Dynamics’. 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Additional Declarations No competing interests reported. Supplementary Files Supplementary.docx Cite Share Download PDF Status: Published Journal Publication published 25 Jun, 2023 Read the published version in Climate Dynamics → Version 1 posted Editorial decision: Major revision 29 Sep, 2022 Reviews received at journal 19 Sep, 2022 Reviewers agreed at journal 01 Sep, 2022 Reviews received at journal 03 Aug, 2022 Reviewers agreed at journal 06 Jul, 2022 Reviewers invited by journal 04 Jul, 2022 Editor assigned by journal 11 Jun, 2022 Submission checks completed at journal 10 Jun, 2022 First submitted to journal 06 Jun, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1731142","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":112649900,"identity":"6b0cfb66-f53f-47ce-882c-9ad2c78f907b","order_by":0,"name":"Ulla K. Heede","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYDACduYGBsYGIOMA8wEGhgKQUAIBLcyMMC1sQKUGpGnhMSBOi8FhxsYPjDts5PiO93yT+GFgw8DPnmNASEuzBOOZNGPJM2e3SfYYpDFI9rwhqKVBgrHtcOKGG7nbJHgMDjMY3CDClh+Mbf/rN9x/80zyj8F/BnsitLQBbTmQYHCDh02ax+AAg4EEAS2SQC0WiW3JhjOB/rGWMUjmkTjzrACvFr7jzYdvfGyzk+c7fvjhzTcVdnL87ckb8GoBgwQIxSIBJHgIK0cCzB9IUj4KRsEoGAUjBgAAGe1I+VBp16gAAAAASUVORK5CYII=","orcid":"","institution":"Yale University","correspondingAuthor":true,"prefix":"","firstName":"Ulla","middleName":"K.","lastName":"Heede","suffix":""},{"id":112649901,"identity":"8e92bc62-ba10-4679-89d8-51368b13e2df","order_by":1,"name":"Alexey V. Fedorov","email":"","orcid":"","institution":"Yale University","correspondingAuthor":false,"prefix":"","firstName":"Alexey","middleName":"V.","lastName":"Fedorov","suffix":""}],"badges":[],"createdAt":"2022-06-06 16:59:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1731142/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1731142/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00382-023-06856-x","type":"published","date":"2023-06-25T21:17:33+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":22796417,"identity":"94179b65-82ba-4cf5-a9c8-6c9c0b039eac","added_by":"auto","created_at":"2022-06-17 22:10:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":113477,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eOverview of ENSO changes in CMIP6 models across different experiments. \u003c/em\u003e\u003c/strong\u003e\u003cem\u003e(a) A barplot showing the time-mean amplitude of ENSO SST variability (Methods) in individual models for the piControl, abrupt-4xCO2, 1pctCO2, SSP585 and SSP126 experiments, respectively. Thin errorbars for SSP experiments indicate the maximum and minimum values across three ensemble members. (b), (c) and (d) Changes in ENSO SST amplitude in the 1pctCO2, abrupt4xCO2 and SSP126 scenarios, respectively, versus the SSP585 scenario. These changes are calculated relative to the piControl time-mean ENSO SST amplitude. Each marker+color combination signifies the same model across all plots as given by Table 1. The corresponding correlations and p-values are shown at the top left corners of the bottom panels. The high correlations suggest a general consistency of ENSO response across different types of warming experiments.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-1731142/v1/248fa5a8364f7501e598ed27.png"},{"id":22795495,"identity":"5aa995f7-5157-42ca-919b-493f37eda4ec","added_by":"auto","created_at":"2022-06-17 22:00:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":115375,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eOverview of changes in extreme El Nino events. \u003c/em\u003e\u003c/strong\u003e\u003cem\u003e(a) A barplot showing the time-mean frequency of extreme ENSO SST events (Methods) for the piControl, abrupt-4xCO2, 1pctCO2, SSP585 and SSP126 experiments, respectively. Thin errorbars for SSP-experiments indicate the maximum and minimum values across three ensemble members. (b), (c) and (d) Relative changes (%) in the frequency of extreme events versus changes in ENSO SST amplitude in the 1pctCO2, abrupt4xCO2 and SSP585 scenarios, respectively. Note that changes are calculated relative to the piControl time-mean ENSO SST variability. Each marker+color combination signifies the same model across all plots as given in Table 1. Note the robust increase in the frequency of extreme El Nino event in most models.\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-1731142/v1/d787a0af0969c2f768c41c07.png"},{"id":22795979,"identity":"6a170897-13cc-4271-a722-c1e255f84318","added_by":"auto","created_at":"2022-06-17 22:05:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":68264,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eOverview of ENSO precipitation changes. \u003c/em\u003e\u003c/strong\u003e\u003cem\u003eA barplot showing the time-mean individual model ENSO rainfall variability (see methods) for the piControl, abrupt-4xCO2, 1pctCO2, SSP585 and SSP126 experiments respectively. Thin errorbars for SSP-experiments indicate the maximum and minimum values across three ensemble members.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-1731142/v1/9cffbb0f208171222323c674.png"},{"id":22796940,"identity":"6c578eb7-3d7a-4d9d-b5dd-0cf3ab28b02b","added_by":"auto","created_at":"2022-06-17 22:15:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":56546,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLinking ENSO SST variability changes to changes in the mean state east-west SST gradient. \u003c/em\u003e\u003c/strong\u003e\u003cem\u003e(a), (b), (c) Changes in ENSO SST variability amplitude versus changes in the zonal SST mean gradient\u0026nbsp;for the 1pctCO2, abrupt-4xCO2 and SSP585 scenarios. Each marker+color combination signifies the same model across all plots as given by Table 1. The corresponding correlations and p-values are shown at the top of each panel. Negative values on the horizontal axes indicate the SST gradient weakening. Across the models, the link between ENSO SST variability and the mean zonal gradient appears to be weak, even though there is a general tendency towards stronger ENSO when this gradient relaxes in the 1pctCO2 and SSP585 scenarios.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-1731142/v1/b7f8065cc8380302bcc1bd36.png"},{"id":22795981,"identity":"b5bd040d-4cc6-4e71-97b0-85ac13397a2f","added_by":"auto","created_at":"2022-06-17 22:05:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":116805,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLinking ENSO rainfall variability changes to changes in SST mean state gradient and variability. \u003c/em\u003e\u003c/strong\u003e\u003cem\u003e(a), (b), (c) Changes in ENSO rainfall variability versus changes in the zonal SST mean gradient\u003c/em\u003e\u003cstrong\u003e\u003cem\u003e \u003c/em\u003e\u003c/strong\u003e\u003cem\u003efor the 1pctCO2, abrupt-4xCO2 and SSP585 scenarios. (d), (e), (f) The same but versus changes in ENSO SST variability amplitude. Each marker+color combination signifies the same model across all plots as given by Table 1. The corresponding correlations and p-values are shown at the top of each panel.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-1731142/v1/b0d823fea625e370a977772a.png"},{"id":22795501,"identity":"9d7b0180-02f6-463f-9146-1924fbee1c26","added_by":"auto","created_at":"2022-06-17 22:00:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":147045,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eThe Bjerknes stability Index and its contributing terms for a subset of models. \u003c/em\u003e\u003c/strong\u003e\u003cem\u003ePanel (a) shows the changes in ENSO SST amplitude for a subset of models for the piControl, abrupt-4xCO2 and the SSP585 experiments. (b),(c),(d) show each of the major damping terms in the Bjerknes Index, and (e), (f) (g) show the three positive feedback terms for those same experiments. (h) shows the noise and (i) shows the overall Bjerknes Index value. All Bjerknes values are in units of year\u003c/em\u003e\u003csup\u003e\u003cem\u003e-1\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e. Noise is in the units of N m\u003c/em\u003e\u003csup\u003e\u003cem\u003e-2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e and ENSO SST is in the units of \u003c/em\u003e\u003csup\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sup\u003e\u003cem\u003eC. The Bjerknes index is computed for the region 180\u003c/em\u003e\u003csup\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e-280\u003c/em\u003e\u003csup\u003e\u003cem\u003eo\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e E (c.f. Supplementary Fig. 1 using the Niño3 region).\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-1731142/v1/4572b6ac8272f8d51a96415b.png"},{"id":22795498,"identity":"35d58859-b6b7-458e-9f10-d6b1e418d092","added_by":"auto","created_at":"2022-06-17 22:00:15","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":124000,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eOverview of changes atmospheric noise in the tropical Pacific. \u003c/em\u003e\u003c/strong\u003e\u003cem\u003e(a)\u003c/em\u003e\u003cstrong\u003e\u003cem\u003e \u003c/em\u003e\u003c/strong\u003e\u003cem\u003eA barplot showing the time-mean amplitude of model atmospheric noise (Methods) for the piControl, abrupt-4xCO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e, 1pctCO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e, and SSP585 experiments, respectively. Thin errorbars for SSP-experiments indicate the maximum and minimum values across three ensemble members. (b), (c) and (d) shows changes in noise amplitude versus changes in ENSO SST amplitude in the abrupt-4xCO2, 1pctCO2, and SSP585 experiments, respectively. The corresponding correlations and p-values are shown at the top of each panel.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-1731142/v1/bf02cfe85b2ddeec1e9bfe61.png"},{"id":22795977,"identity":"756cecdb-685e-42b5-bfb7-7460ee5fc8b5","added_by":"auto","created_at":"2022-06-17 22:05:15","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":28208,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSimulated long-term changes in ENSO. \u003c/em\u003e\u003c/strong\u003e\u003cem\u003eThe plot shows\u003c/em\u003e\u003cstrong\u003e\u003cem\u003e \u003c/em\u003e\u003c/strong\u003e\u003cem\u003eENSO SST amplitude estimated for consecutive 200-year time intervals of 1000-year abrupt-4xCO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e simulations using three different climate models.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-1731142/v1/fb075a1ca918b8844e245ce9.png"},{"id":44732863,"identity":"2c2a1e50-98a3-4c3f-9dbd-2d3ea66da92c","added_by":"auto","created_at":"2023-10-16 22:00:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1252625,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1731142/v1/1998c9b8-c7fb-4331-a6e3-58d1747cb1eb.pdf"},{"id":22795503,"identity":"621c96f6-2b77-4fb2-ad57-6340e70598dc","added_by":"auto","created_at":"2022-06-17 22:00:17","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":43606647,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-1731142/v1/900981530dcbee264e7f7b12.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Towards understanding the robust strengthening of ENSO and extreme El Niño events with global warming in CMIP6","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe El Ni\u0026ntilde;o Southern Oscillation (ENSO) is the strongest interannual oscillation in Earth\u0026rsquo;s climate, with dramatic impacts on weather patterns and extreme events in the tropics and beyond (Cai et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; McPhaden, Santoso, and Cai \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). El Ni\u0026ntilde;o conditions are characterized by warm central and/or eastern Pacific sea surface temperature (SST) anomalies, reduced easterly trade winds and the corresponding slope of the ocean thermocline, and eastward shift of atmospheric convection. El Ni\u0026ntilde;o can lead to flooding in South America but drought and forest fires in areas bordering the western Pacific (Ropelewski and Halpert 1987). In addition, due to its effect on atmospheric planetary waves, ENSO affects a range of whether phenomena outside the tropical Pacific via teleconnections, and the signature of ENSO has been observed on all seven continents (Yeh et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhether ENSO is already changing or will change with future warming, and, in particular, whether this change will lead to stronger and/or more frequent El Nino events is of great importance for timely implementation of societal adaptation and mitigation strategies. However, despite the importance of ENSO in Earth\u0026rsquo;s climate, much uncertainty still remains concerning these questions.\u003c/p\u003e \u003cp\u003eIt has long been known that ENSO depends on changes in the mean state tropical Pacific (Fedorov and Philander 2000, 2021; Fedorov et al. 2020; An and Jin 2020, Jin et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; DiNezio et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). There is broad consensus models participating in the Coupled Model Intercomparison Project, phase 6 (CMIP6) that the Walker cell will slow down with global warming, leading to enhanced warming of the eastern and central equatorial Pacific by century-end (e.g. Heede and Fedorov 2021). This enhanced warming of the eastern Pacific can be understood in terms of both atmospheric and oceanic mechanisms acting to reduce equatorial trade winds and eastern Pacific upwelling (U. K. Heede, Fedorov, and Burls \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; 2021; Vecchi et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Despite this consensus on future mean state warming in the equatorial Pacific, and the known links between mean state and ENSO characteristics, there is no robust consensus among climate models concerning the response of ENSO to global warming. A number of papers have suggested stronger ENSO in future climates in CMIP5 models, in particular based on precipitation metrics (Huang and Xie 2015; Cai et al. 2015), or based on metrics that isolate certain models (Cai et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The tendency for ENSO to be stronger in future warming scenarios has also been reported for subsets of CMIP6 models (Fredriksen et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, 6; Cai et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Brown et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Cai et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e suggests two mechanisms that drive stronger eastern Pacific El Nino events: a stronger coupling between wind stress and SST in the eastern Pacific, and a stronger coupling between ocean feedbacks and wind stress due to enhanced upper ocean stratification.\u003c/p\u003e \u003cp\u003eOpposing Cai\u0026rsquo;s et al. findings, several recent studies have argued that ENSO strength may decrease with warming (Christian Wengel et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Callahan et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kohyama, Hartmann, and Battisti \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These studies typically rely on abrupt CO\u003csub\u003e2\u003c/sub\u003e quadrupling experiments and argue that increased thermodynamic damping and decreased ocean feedbacks among other mechanisms lead to a more stable ENSO with warming. Kohyama et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) in particular argues that enhanced ocean stratification leads to a stiffer thermocline and a decrease in ENSO non-linearity in contrast with the findings of Cai et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These disagreements highlight that changes in different feedbacks due to changes in the mean state and ocean-atmosphere coupling can partially compensate each other leading to unanticipated results.\u003c/p\u003e \u003cp\u003eThe goal of this study is to attempt to reconcile these divergent views of how ENSO responds to warming as well as provide more detailed insight into how ENSO changes within the CMIP6 models than previous studies have done (i.e. Fredriksen et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cai et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), using a broader range of models and climate scenarios. As one set of studies finding a stronger ENSO primarily rely on the 21st century SSP scenarios, while another set of studies finding a weaker ENSO uses the abrupt CO\u003csub\u003e2\u003c/sub\u003e quadrupling experiments, we provide an overview of how models respond across these idealized and realistic forcing experiments to elucidate any uncertainty associated with a given experiment-type.\u003c/p\u003e \u003cp\u003eFurthermore, we aim to explore how the various mechanisms proposed for a stronger or weaker ENSO may manifest across different models and document how ENSO changes might be linked to the tropical Pacific mean state. Finally, to explore whether the transient response differs from the long-term response in the abrupt-4xCO\u003csub\u003e2\u003c/sub\u003e experiments as proposed by Callahan et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), we include three 1000-year simulations. To our knowledge, no study has compared ENSO changes across a broad range of idealized and realistic forcing scenarios in CMIP6 models, as well as attempted to link those changes to tropical mean state changes in a comprehensive manner.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003e2.1 CMIP6 archive\u003c/h2\u003e\n \u003cp\u003eWe analyze two types of experiments from the Climate Model Intercomparison Project Phase 6 (CMIP6) archive. The first type consists of two hypothetical or idealized CO\u003csub\u003e2\u003c/sub\u003e-only experiments: abrupt-4xCO\u003csub\u003e2\u003c/sub\u003e rise and 1pctCO\u003csub\u003e2\u003c/sub\u003e gradual CO\u003csub\u003e2\u003c/sub\u003e rise (1% per year) where CO\u003csub\u003e2\u003c/sub\u003e-increases are relative to a pre-industrial level of 280 ppm. The second type has full-forcing experiments, including two future scenarios, SSP5-8.5 which is a high-emission scenario and SSP1-2.6, in which emissions peak and decline in the 21st century (O\u0026rsquo;Neill et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eWe use a total of 20 CMIP6 models in our overview analysis (see Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), utilizing surface temperature, column precipitation flux, and zonal wind stress. The criterion for including a given model into the analysis is whether it has surface temperature data available for at least three ensemble members for the SSP5-8.5 scenario. Some models do not have all datasets available for all experiments, and models are excluded whenever data is not available. For the idealized warming experiments (4xCO\u003csub\u003e2\u003c/sub\u003e and 1pctCO\u003csub\u003e2\u003c/sub\u003e), where ensemble members are generally not available, we use a single ensemble member, and for the SSP scenarios we use the ensemble-mean results based on three members. We include 85, 150 and 200 years of simulation for the SSP experiments, the idealized experiments and the piControl experiment respectively.\u003c/p\u003e\n \u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003e\u003cem\u003eTable 1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;Overview of models used and their associated marker.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003e\u003cem\u003e\u003cimg 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\"\u003e\u003c/em\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec4\"\u003e\n \u003ch2\u003e2.2 Metrics\u003c/h2\u003e\n \u003cp\u003eWe define the amplitude of ENSO SST variability as the standard deviation of band-passed temperature anomalies in the frequency range between 1.5 to 7 years from which the climatology was subtracted for the central-east Pacific (180\u0026ndash;280\u0026ordm;E). This region is chosen to capture ENSO changes across both the central and eastern Pacific following Ferrett and Collins (2019). We further define the mean state zonal Pacific gradient as the difference between time mean anomalies of the western Pacific (120\u003csup\u003eo\u003c/sup\u003e-180\u003csup\u003eo\u003c/sup\u003e E) and the central-eastern Pacific (180\u003csup\u003eo\u003c/sup\u003e -280\u003csup\u003eo\u003c/sup\u003e E). While the issue of Central Pacific (CP) versus Eastern Pacific (EP) El Ni\u0026ntilde;o events has been discussed as a potential culprit in using set boxes in other studies (Cai et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), we find that the estimated ENSO changes are not very sensitive to the chosen box, and that most models show a consistent change in standard deviation across both the central and eastern equatorial Pacific.\u003c/p\u003e\n \u003cp\u003eSimilarly, we define the strength of ENSO rainfall variability as the standard deviation of band-passed precipitation anomalies in the frequency range between 1.5 to 7 years from which the climatology is subtracted for the central-east Pacific (180\u0026ndash;280\u0026ordm;E). We define extreme El Ni\u0026ntilde;o events as events with warm temperature anomalies in the central-east Pacific (180\u0026ndash;280\u0026ordm;E), exceeding 2 standard deviations. While 2.5 standard deviations are sometimes used to define an extreme event (Yu and Fedorov 2020), we chose 2 to get robust statistics as most models have few events of 2.5 standard deviation magnitude in timeframe of 85 to 200 years. All changes are computed relative to the piControl experiment. While some studies compare SSP scenarios to the historical period (i.e. Cai et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), we compare all experiment types to the piControl experiment in order to facilitate a fair comparison between idealized experiments and future warming scenarios. For the majority of models, the strength of ENSO in the piControl experiment is similar to the historical experiment.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec5\"\u003e\n \u003ch2\u003e2.3 Bjerknes Stability Index\u003c/h2\u003e\n \u003cp\u003eTo evaluate possible links between mean state changes and ENSO response to warming, we use the Bjerknes Stability Index (Jin, Kim, and Bejarano \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e). The Bjerknes Index (BJ index, in units of yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) assumes that ENSO dynamics are controlled by the recharge oscillator physics where perturbations to SST in the eastern Pacific are either amplified or dampened through a series of feedbacks:\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equa\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$BJ=CD+TD+ZA+ED+TC$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eHere, CD is damping by the mean currents given by:\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equb\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$CD=-\\left(\\frac{\u0026lang;\\stackrel{-}{u}\u0026rang;}{{L}_{x}}+\\frac{\u0026lang;-2y\\stackrel{-}{v}\u0026rang;}{{{L}_{y}}^{2}}+\\frac{\u0026lang;H\\left(\\stackrel{-}{w}\\right)\\stackrel{-}{w}\u0026rang;}{{H}_{m}}\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eAngle brackets indicate the area average for the central-eastern Pacific (180\u0026ndash;280\u0026ordm;E) and bars indicate a time-mean over the time length of the experiment. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\stackrel{-}{u},\\stackrel{-}{v}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\stackrel{-}{w}\\)\u003c/span\u003e\u003c/span\u003eare the mean zonal, meridional and vertical velocities in the mixed layer. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({L}_{x}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({L}_{y}\\)\u003c/span\u003e\u003c/span\u003e are the zonal and meridional scale lengths. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({H}_{m}\\)\u003c/span\u003e\u003c/span\u003e is the depth of the ocean mixed layer (50 m), and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(H\\left(\\stackrel{-}{w}\\right)\\)\u003c/span\u003e\u003c/span\u003e is the Heaviside step function, which ensures that only positive values of vertical velocity are taken into account.\u003c/p\u003e\n \u003cp\u003eTD is thermodynamic damping given by:\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equc\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e$$TD=-\\alpha$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eHere, \u0026alpha; is the linear regression coefficient from mixed layer temperature to net energy flux\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({E}_{net}\\)\u003c/span\u003e\u003c/span\u003e, from the ocean to the atmosphere. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({E}_{net}\\)\u003c/span\u003e\u003c/span\u003e is given by:\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equd\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e$${E}_{net}={E}_{incoming\\_shortwave}+{E}_{incoming\\_longewave}-{E}_{outgoing\\_shortwave}-{E}_{outgoing\\_longewave}- {E}_{latent\\_heat}-{E}_{sensible\\_heat}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\alpha\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e is further multiplied by \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{1}{\\rho {C}_{p}{H}_{m}}\\)\u003c/span\u003e\u003c/span\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\rho\\)\u003c/span\u003e\u003c/span\u003e is sea water density and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({C}_{p}\\)\u003c/span\u003e\u003c/span\u003e is specific heat capacity of seawater and thus has units of time\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eZA, EK and TC correspond to the zonal advection, Ekman and thermocline feedbacks respectively, given by:\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(ZA={\\mu }_{a}{\\beta }_{u}\u0026lang;-\\frac{\\partial \\stackrel{-}{T}}{\\partial x}\u0026rang;\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(EK={\\mu }_{a}{\\beta }_{w}\u0026lang;-\\frac{\\partial \\stackrel{-}{T}}{\\partial z}\u0026rang;\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(TC={\\mu }_{a}^{*}{\\beta }_{h}\u0026lang;-\\frac{\\stackrel{-}{w}}{{H}_{m}}\u0026rang;\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\mu }_{a}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\mu }_{a}^{*}\\)\u003c/span\u003e\u003c/span\u003eare linear regression coefficients between wind stress and SST anomalies. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{u}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{w}\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{h}\\)\u003c/span\u003e\u003c/span\u003e are the linear correlation coefficients between wind stress and zonal current, vertical current and thermocline anomalies respectively. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{\\partial \\stackrel{-}{T}}{\\partial x}\\)\u003c/span\u003e\u003c/span\u003e is the mean state zonal temperature gradient and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{\\partial \\stackrel{-}{T}}{\\partial z}\\)\u003c/span\u003e\u003c/span\u003e is the mean vertical stratification. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\stackrel{-}{w}\\)\u003c/span\u003e\u003c/span\u003e is the mean vertical velocity. All regression coefficients are for the central-east equatorial Pacific (180\u0026ndash;280\u0026ordm;E), except \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\mu }_{a}^{*}\\)\u003c/span\u003e\u003c/span\u003e which is calculated for the entire Pacific equatorial region (130\u0026ndash;280\u0026ordm;E). Following Kim et al. (\u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Ferrett and Collins (2019), we use temperature at 50 m depth as a proxy for thermocline anomalies. In a supplementary analysis, we then repeat Bjerknes Index calculation, but for the Ni\u0026ntilde;o3 region (210\u0026ndash;280\u0026ordm;E) to illustrate how the relative role of damping and feedback terms change when a different range of averaging is used. The equatorial region is defined between 5\u0026ordm;S and 5\u0026ordm;N.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec6\"\u003e\n \u003ch2\u003e2.4 Noise\u003c/h2\u003e\n \u003cp\u003eFinally, an important contribution to ENSO dynamics comes from atmospheric noise, which can drive a damped oscillation even when the BJ-index is negative. Here we define atmospheric noise in the tropics following Philip and Oldenborgh (2009) as:\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Eque\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e$$ϵ\\left(x,y,t\\right)= {\\tau }_{x}\\left(x,y,t\\right)-\\sum _{i=1}^{2}{{A}_{1}\\left(x,y\\right)}_{i}{{T}^{\\text{\u0026apos;}}}_{i}\\left(t\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(ϵ\\left(x,y,t\\right)\\)\u003c/span\u003e\u003c/span\u003edenotes stochastic forcing by random wind stress variations,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{T}^{\\text{\u0026apos;}}}_{i}\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e is SST anomalies averaged over standard regions and\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{A}_{1}\\left(x,y\\right)}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the domain-wide wind stress patterns corresponding to the SST anomalies of the separate regions i\u0026thinsp;=\u0026thinsp;1, 2. Here we use i\u0026thinsp;=\u0026thinsp;1 as the Ni\u0026ntilde;o3 region, and i\u0026thinsp;=\u0026thinsp;2 as the Ni\u0026ntilde;o4 region. We then average \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(ϵ\\)\u003c/span\u003e\u003c/span\u003e over the equatorial Pacific region (130\u0026ndash;280\u0026ordm;E) and then take the standard deviation over time to estimate the noise magnitude.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv class=\"Section2\" id=\"Sec8\"\u003e\n \u003ch2\u003e3.1 SST, rainfall and extreme event changes across multiple warming experiments\u003c/h2\u003e\n \u003cp\u003eWe find all models show an enhanced ENSO SST variability in the SSP5-8.5 scenario relative to the piControl experiment (in the ensemble-mean sense). Most models show a similar or slightly smaller increase of ENSO amplitude in the SSP1-2.6 scenario, yet the differences between the two scenarios are small compared to differences between the models. ENSO responses in the 1pctCO\u003csub\u003e2\u003c/sub\u003e scenario is also correlated to the SSP5-8.5 response, yet is more muted.\u003c/p\u003e\n \u003cp\u003eThe abrupt-4xCO\u003csub\u003e2\u003c/sub\u003e scenario on average yields a poorer correlation with the SSP5-8.5 response and also has the largest spread among the experiments. In fact, 6 models in the 4xCO\u003csub\u003e2\u003c/sub\u003e scenario show a reduction in ENSO activity, with CESM2 having the most dramatic reduction (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). By contrast, 2 models (ACCESS-ESM5-1 and FIO-2) show an increase that is larger than any ensemble member change in the SSP scenarios.\u003c/p\u003e\n \u003cp\u003eChanges in extreme El Ni\u0026ntilde;o events, defined through their Ni\u0026ntilde;o3 SST index, are expectedly linked to changes in ENSO SST amplitude, and there is a steep slope between ENSO amplitude and extreme events, such that a 25% increase in ENSO SST amplitude leads on average to an about 100% increase in the frequency of extreme El Ni\u0026ntilde;o events (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The strongest correlation between extreme events and ENSO SST is found in the abrupt-4xCO\u003csub\u003e2\u003c/sub\u003e experiments.\u003c/p\u003e\n \u003cp\u003eWhile the response of ENSO SST variability is highly dependent on both the experiment and model used, there is a universal increase in ENSO rainfall variability across all models and all experiments. In contrast to ENSO SST amplitude, the increase in ENSO precipitation variability is largest, on average, in the abrupt-4xCO\u003csub\u003e2\u003c/sub\u003e experiment (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe next question is whether and how these ENSO changes are related to changes in the mean state of the tropical Pacific and other factors. We find that the correlation between changes in ENSO SST amplitude and in the mean state zonal SST gradient is weak (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) and not statistically significant at the 99% level, while the correlation between changes in ENSO rainfall variability and in the mean zonal SST gradient is generally higher (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea, \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb, \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec). Yet, the highest correlation is found between changes in variability of ENSO rainfall and SST (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ed, \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ee, \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ef). We also investigated whether changes in ENSO rainfall variability might be linked to the eastern equatorial Pacific warming, mean tropical warming and mean rainfall, but did not find any statistically significant correlations.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec9\"\u003e\n \u003ch2\u003e3.2 Changes to the Bjerknes Stability Index, thermodynamic damping and noise\u003c/h2\u003e\n \u003cp\u003eTo investigate the connection between mean state changes and ENSO SST response in a more quantitative manner, we compute the Bjerknes Stability Index (BJ Index) for a subset on models with available data. First, we compute this index using the central-eastern equatorial Pacific (180\u0026ndash;280\u0026ordm;E) as the region of averaging following (Kim et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). This analysis reveals that all models in the subset show an increase in mean state stability with warming. For 5 out of 7 models, an increase in thermodynamical damping \u0026alpha; dominates the changes, driving a decrease in the Bjerknes Index, and hence increasing ENSO stability (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). For two models (MIROC6 and MIROC-ES2L), the increase in \u0026alpha; is more modest and a decrease in the thermocline feedback is a dominating term in the change of the Bjerknes Index with warming. For all models, the decrease in the Bjerknes Index is higher for abrupt-4xCO\u003csub\u003e2\u003c/sub\u003e than for SSP585, suggesting that a rapid strong increase in radiative forcing stabilizes the system in the linear stability sense. While changes to the damping terms are robust in response the warming, there is no consensus in changes to the feedback terms. The 5 out of 7 of models show an increase in the Ekman feedback, 4 out of 7 show an increase in the thermocline feedback while all models show only small changes in the zonal advection feedback.\u003c/p\u003e\n \u003cp\u003eWe next recompute the Bjerknes Index but for the smaller Ni\u0026ntilde;o3 region (Supplementary Fig.\u0026nbsp;1). In this case, the computed Bjerknes Index is generally more negative, due to smaller positive feedback strengths compared with the full central-eastern Pacific region. For the global warming simulations, 5 out of 7 models show a more stable Ni\u0026ntilde;o3 Bjerknes Index, which is similar to the central-eastern Pacific Bjerknes Index. However, two models (MIROC6 and MIROC-ES2L) show a more unstable Bjerknes Index with warming for the Ni\u0026ntilde;o3 region, driven by an increase in the zonal advection and thermocline feedbacks. This could indicate that changes in the mean state of these two models lead to a more unstable Ni\u0026ntilde;o3 region, which might be able to explain a stronger ENSO SST in these two particular models. Even for these two models the use of the Bjerknes Index for explaining ENSO strengthening is problematic, given the strong sensitivity of the results to the choice of the averaging region.\u003c/p\u003e\n \u003cp\u003eNext, to examine potential changes in non-linearities that could counteract increased linear stability, we calculate changes to wind stress-SST coupling (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\mu }_{a}\\)\u003c/span\u003e\u003c/span\u003e) and thermodynamic damping (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\alpha\\)\u003c/span\u003e\u003c/span\u003e) in three different ranges of temperature anomalies. We find that the thermodynamic damping \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\alpha\\)\u003c/span\u003e\u003c/span\u003e, while increasing in the central range of temperature anomalies (-1 \u003csup\u003eo\u003c/sup\u003eC to 1 \u003csup\u003eo\u003c/sup\u003eC), decreases in the CanESM5, CESM2, CNRM-CM6-1, HadGEM3-CG31-LL for SST anomalies above 1 \u003csup\u003eo\u003c/sup\u003eC (Supplementary Fig. 2). This suggests that changes in \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\alpha\\)\u003c/span\u003e\u003c/span\u003e, which dominate the Bjerknes Index, may be overestimated in some models because the damping becomes less efficient for larger temperature anomalies for some models. The coupling coefficient \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\mu }_{a}\\)\u003c/span\u003e\u003c/span\u003e becomes stronger with warming in the majority of models in the central SST anomaly range (-1 \u003csup\u003eo\u003c/sup\u003eC to 1 \u003csup\u003eo\u003c/sup\u003eC), while for the IPSL-CM6A-LR, MIROC6 and MIROC-ES2L, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\mu }_{a}\\)\u003c/span\u003e\u003c/span\u003e reduces for temperature anomalies below \u0026minus;\u0026thinsp;1 \u003csup\u003eo\u003c/sup\u003eC in warming experiments (Supplementary Fig.\u0026nbsp;3). One however has to be careful in interpreting this result as the correlations may become very small for the high and low SST ranges.\u003c/p\u003e\n \u003cp\u003eTo further investigate the discrepancy between changes in the Bjerknes Index and ENSO response, we also analyze changes to atmospheric noise among the models for which wind stress data was available. Atmospheric noise, including westerly wind bursts that occur frequently in the tropical Pacific, is believed to play an important role in sustaining ENSO and especially extreme El Ni\u0026ntilde;o events (Puy et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yu and Fedorov 2020; Fedorov \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e). We find that on average, the correlation between change in atmospheric noise and change in ENSO (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e) is significant and higher than between change in mean state and ENSO SST amplitude, indicating that atmospheric noise could play an important role in ENSO changes with warming, despite some outliers in the abrupt-4xCO\u003csub\u003e2\u003c/sub\u003e experiment, and potentially explain the general strengthening of ENSO with warming. However, quantifying this relationship is difficult because (1) the computations of the Bjerknes index and noise amplitude is sensitive to the choice of the averaging regions and (2) the question of causality is difficult to resolve as the noise can increase as a result of strengthening of ENSO.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec10\"\u003e\n \u003ch2\u003e3.3 Long-term warming experiments\u003c/h2\u003e\n \u003cp\u003eLastly, we investigate three 1000-year simulations for the abrupt-4xCO\u003csub\u003e2\u003c/sub\u003e experiment for three models (ACCESS-ESM1-5, CESM2 and IPSL-CM6A-LR). We find that even after such a long time ENSO response does not converge in these models. Specifically, CESM2 shows a drastic reduction in ENSO strength, such that at the end of the simulation its amplitude reaches only about 1/3 of that in the control. In ACCESS-ESM1-5 ENSO remains stronger than the control throughout the simulation period (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e), while in IPSL-CM6A-LR ENSO does not change much over the first 600 years, but then strengthens.\u003c/p\u003e"},{"header":"Discussion and conclusions","content":"\u003cp\u003eWe have investigated the ENSO response to warming in four different scenarios with realistic and idealized radiative forcing across 20 models. We found that most of the models show enhanced ENSO SST variability in all warming scenarios considered. Moreover, in a high-forcing scenario SSP5-8.5, each model shows a stronger ensemble mean ENSO. Yet, there are large inter-model differences, which play a greater role than scenario uncertainty. The majority of models do show a generally consistent response across different types of warming experiments indicating that the observed response is indeed a result of radiative forcing, and the spread among models is primarily caused by factors other than natural variability.\u003c/p\u003e\n\u003cp\u003eInterestingly, for the majority of models, gradual CO\u003csub\u003e2\u003c/sub\u003e-increase SSP scenarios on average show a stronger ENSO response to warming than the abrupt-4xCO2 scenario. In fact, roughly 40% of the models considered show a weakening or no change of ENSO SST variability in this scenario, which implies that caution should be taken when relying on such experiments for making projections of ENSO future changes.\u003c/p\u003e\n\u003cp\u003eDespite this uncertainty in ENSO SST response, ENSO rainfall variability in the tropics increases universally across all experiments and models, which has important consequences for adaptation and mitigation as changes to floods and droughts may cause more damage than changes to SST itself and as ENSO remote teleconnections depend of latent heat release. The fact that rainfall variability increases robustly with warming is expected given that a warmer atmosphere can hold more water following the Clausius-Clapeyron relation and eastern Pacific warming can reduce the convection barrier for El Ni\u0026ntilde;o events (Yun et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, we find that changes in the background state alone cannot fully account for the modeled change in ENSO rainfall variability as changes in SST variability play a critical role as well. This highlights the need to better understand the response of ENSO SST variability in order to improve predictions for ENSO rainfall response.\u003c/p\u003e\n\u003cp\u003eExtreme events can change drastically given a small change in ENSO SST amplitude and models with a relatively modest change in ENSO SST variability may show up to a doubling of the number of extreme El Ni\u0026ntilde;o events. This close connection between extreme events and ENSO SST amplitude is evident even in a relatively short timeframe of 150 years. The fact that the change in ENSO SST variability is closely connected to an increase in extreme events, which, like increases in ENSO rainfall, has important consequences for adaptation and mitigation as extreme events are often associated with more damage for society than a change in mean conditions (Trenberth \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). Given the sensitivity of extreme events to small changes in ENSO SST, it is crucial to improve projections of ENSO SST amplitude with warming across models and warming experiments.\u003c/p\u003e\n\u003cp\u003eIn attempt to explain the robust strengthening of ENSO in warming scenarios we compute changes in the Bjerknes Stability Index but find it to be a poor predictor for changes in ENSO in a small subset of models (7), which is similar to findings in other studies using the Bjerknes Index for individual models (Manucharyan and Fedorov 2014; Ferrett and Collins 2019). Like Callahan et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), we find the increased thermodynamic damping to be the most important term in the Bjerknes Index for 5 out of 7 models, yielding a more stable Bjerknes index those models. However, the fact that all models show a stronger ENSO SST variability in the SSP585 scenario points to other effects counteracting this stability increase. For example, here we show that the thermodynamic damping may be overestimated in the models due to a nonlinear relationship between SST and surface energy fluxes. We show that the coupling between SST and surface heat fluxes decrease in warming experiments above a threshold of 1\u003csup\u003eo\u003c/sup\u003e C SST anomalies in 4 out of 7 models, which could explain why ENSO SST amplitude can increase despite a stronger thermodynamic damping in a linear sense.\u003c/p\u003e\n\u003cp\u003eMIROC6 and MIROC-ES2L show a decrease in the Bjerknes Stability Index for the Ni\u0026ntilde;o3 region as opposed to other models for which the Bjerknes Index is calculated. These models, together with EC-Earth3 and EC-Earth3-Veg, are outliers among the 20 models analyzed in that they show a drastic increase in ENSO amplitude across experiments. Thus, a decrease in stability for the Ni\u0026ntilde;o3 region, driven by increases in feedbacks in the eastern Pacific, may explain why some models have a drastic increase in ENSO amplitude, but it cannot explain the robust increase in ENSO across models.\u003c/p\u003e\n\u003cp\u003eWe suggest here that atmospheric noise, including westerly wind bursts, may play a crucial role in driving changes to ENSO, which is supported by a strong correlation between changes to noise and ENSO SST variability as well as other studies (C. Wengel et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lopez et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), but questions remain whether this noise is in fact driving a stronger ENSO, as ENSO itself can also generate more noise (Kug et al. \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e; Eisenman, Yu, and Tziperman 2005).\u003c/p\u003e\n\u003cp\u003eThis result highlights a problematic gap in our understanding on what drives changes in ENSO in response to CO\u003csub\u003e2\u003c/sub\u003e in GCMs, as we cannot understand changes in ENSO in terms of a simple linear Bjerknes stability framework that links those changes to changes in the tropical mean state. This is exemplified by the fact that mean state changes are larger for the abrupt-4xCO\u003csub\u003e2\u003c/sub\u003e scenario, and yet by century-end ENSO amplitude increases more in the SSP585 scenario. In fact, 5 or 6 models in this scenario actually show a weaker ENSO. It is feasible that more comprehensive linear stability analyses, computing the full leading eigen modes of the system (e.g. S\u0026eacute;vellec and Fedorov 2013 or Fedorov and Philander 2001) could provide more consistent results, but this is yet to be done for coupled GCMs.\u003c/p\u003e\n\u003cp\u003eOverall, our results point towards a robust increase in ENSO activity in the SSP5-8.5 and SSP1-2.6 scenarios, yet a common mechanism to explain these changes is lacking. Models that show a drastic increase in ENSO amplitude, such as the MIROC6 and MIROC-ES2L models may be driven by stability changes over the Nino3 region in combination with a small change or no change in thermodynamic damping. On the contrary, for models with a moderate change in ENSO, the linear Bjerknes Index decreases \u0026ndash; primarily because of increased thermodynamic damping, leading to a more stable system. This suggests that atmospheric noise and/or nonlinear changes may drive a stronger ENSO for these models. On the other hand, for CESM2, which showed a weaker ENSO with 4xCO\u003csub\u003e2\u003c/sub\u003e, the Bjerknes Index did show the largest reduction, driven by increased thermodynamic damping and a decrease in the thermocline feedback. Yet, the same model shows an increase in ENSO, albeit small, for SSP5-8.5 and SSP1-2.6 relative to piControl.\u003c/p\u003e\n\u003cp\u003eENSO amplitude on average also increases for high CO\u003csub\u003e2\u003c/sub\u003e scenarios, such as the 1pctCO\u003csub\u003e2\u003c/sub\u003e or abrupt-4xCO\u003csub\u003e2\u003c/sub\u003e scenario, although the change is less robust across models. Furthermore, in contrast to Wenger et al. (2021) we did not find evidence that models converge to a weaker ENSO in the abrupt 4xCO\u003csub\u003e2\u003c/sub\u003e experiments over longer time-scales. In fact, ENSO remains stronger than the control in several models considered even after 1000 years of computation. On the whole, the abrupt 4xCO\u003csub\u003e2\u003c/sub\u003e scenario produces the broadest spread of ENSO projections, and as such may not be the most reliable indicator of changes to come. These findings highlight that despite the robust strengthening of ENSO by century-end in a broad range of models and warming scenarios of CMIP6, there is still a large uncertainty in ENSO future response to global warming, which should be addressed by evaluating ENSO drivers across multiple warming experiments in multiple models.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eU.K.H. is supported by a NASA FINESST Fellowship (80NSSC20K1634). A.V.F. is supported by grants from NASA (80NSSC21K0558) and NOAA (NA20OAR4310377). Additional funding is provided by the ARCHANGE project (ANR-18-MPGA-0001, France). We also acknowledge a generous gift to Yale University from T. Sandoz. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCMIP6 data are available at https://esgf-node.llnl.gov/search/cmip6/. Code used for the analysis is available per request and at Gibhub upon publication. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eU.K.H. and A.V.F. contributed equally to designing the research. U.K.H. performed the data analysis and, together with A.V.F., interpreted the results. U.K.H. wrote the manuscript and edited it together with A.V.F.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBrown, Josephine R., Chris M. Brierley, Soon-Il An, Maria-Vittoria Guarino, Samantha Stevenson, Charles J. R. Williams, Qiong Zhang, et al. 2020. \u0026lsquo;Comparison of Past and Future Simulations of ENSO in CMIP5/PMIP3 and CMIP6/PMIP4 Models\u0026rsquo;. Preprint. 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McPhaden, Dietmar Dommenget, Boris Dewitte, Matthew Collins, et al. 2018.\u0026nbsp;\u0026lsquo;ENSO Atmospheric Teleconnections and Their Response to Greenhouse Gas Forcing\u0026rsquo;. \u003cem\u003eReviews of Geophysics\u003c/em\u003e 56 (1): 185\u0026ndash;206. https://doi.org/10.1002/2017RG000568.\u003c/li\u003e\n \u003cli\u003eYu, Sungduk, and Alexey V. Fedorov. 2020. \u0026lsquo;The Role of Westerly Wind Bursts During Different Seasons Versus Ocean Heat Recharge in the Development of Extreme El Ni\u0026ntilde;o in Climate Models\u0026rsquo;. \u003cem\u003eGeophysical Research Letters\u003c/em\u003e 47 (16): e2020GL088381. https://doi.org/10.1029/2020GL088381.\u003c/li\u003e\n \u003cli\u003eYun, Kyung-Sook, June-Yi Lee, Axel Timmermann, Karl Stein, Malte F. Stuecker, John C. Fyfe, and Eui-Seok Chung. 2021. \u0026lsquo;Increasing ENSO\u0026ndash;Rainfall Variability Due to Changes in Future Tropical Temperature\u0026ndash;Rainfall Relationship\u0026rsquo;. \u003cem\u003eCommunications Earth \u0026amp; Environment\u003c/em\u003e 2 (1): 1\u0026ndash;7. https://doi.org/10.1038/s43247-021-00108-8.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"climate-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cldy","sideBox":"Learn more about [Climate Dynamics](https://www.springer.com/journal/382)","snPcode":"382","submissionUrl":"https://submission.nature.com/new-submission/382/3","title":"Climate Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"El Niño, ENSO, CMIP6, Climate Change, extreme events","lastPublishedDoi":"10.21203/rs.3.rs-1731142/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1731142/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe El Niño Southern Oscillation (ENSO) has profound implications for weather patterns across the globe, and yet there is no consensus on its response to global warming. Several modelling studies suggest a stronger ENSO in global warming scenarios, while other studies find ENSO weakening. Using a broad range of models of the Coupled Model Intercomparison Project phase 6 (CMIP6) and four different types of warming experiments, here we show that the majority of the models predict a stronger ENSO by century-end in Shared Social Pathway (SSP) experiments, and in idealized 1pctCO\u003csub\u003e2\u003c/sub\u003e and abrupt 4xCO\u003csub\u003e2\u003c/sub\u003e experiments. Several models, however, predict no change or ENSO weakening, especially in the idealized experiments. For most models the strongest forcing (abrupt-4xCO\u003csub\u003e2\u003c/sub\u003e) is not associated with the strongest ENSO response, while differences between the models are much greater than between warming scenarios. For the long-term (1000 years) response the models disagree even on the sign of change. Furthermore, changes in ENSO SST variability appear to be poorly correlated with the tropical mean state change, while changes in ENSO rainfall variability correlate well with changes in the mean state and, especially, ENSO SST variability. Evaluating changes in the Bjerknes Stability Index for a subset of models we find it to be a poor predictor for ENSO strengthening as this index typically suggests greater stability with warming. We hypothesize that changes to ENSO stability are offset by increases in atmospheric noise or/and potential nonlinear effects. Thus, a robust inter-model mechanism is still lacking to explain a stronger ENSO simulated with global warming, and caution should be exercised when considering ENSO changes based on a single model or warming scenario.\u003c/p\u003e","manuscriptTitle":"Towards understanding the robust strengthening of ENSO and extreme El Niño events with global warming in CMIP6","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-06-17 22:00:12","doi":"10.21203/rs.3.rs-1731142/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2022-09-29T15:44:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-09-19T08:42:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"825c6efc-8b5e-492c-abed-ca1848ad29e2","date":"2022-09-01T12:39:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-08-03T09:18:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3241ed9f-0f6b-4929-acda-a2e6cf45def0","date":"2022-07-06T15:30:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-07-04T15:41:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-06-11T10:34:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-06-10T13:16:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Climate Dynamics","date":"2022-06-06T16:48:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"climate-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cldy","sideBox":"Learn more about [Climate Dynamics](https://www.springer.com/journal/382)","snPcode":"382","submissionUrl":"https://submission.nature.com/new-submission/382/3","title":"Climate Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"76279ab7-4de7-482f-9178-ecd53e2601d4","owner":[],"postedDate":"June 17th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T21:45:08+00:00","versionOfRecord":{"articleIdentity":"rs-1731142","link":"https://doi.org/10.1007/s00382-023-06856-x","journal":{"identity":"climate-dynamics","isVorOnly":false,"title":"Climate Dynamics"},"publishedOn":"2023-06-25 21:17:33","publishedOnDateReadable":"June 25th, 2023"},"versionCreatedAt":"2022-06-17 22:00:12","video":"","vorDoi":"10.1007/s00382-023-06856-x","vorDoiUrl":"https://doi.org/10.1007/s00382-023-06856-x","workflowStages":[]},"version":"v1","identity":"rs-1731142","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1731142","identity":"rs-1731142","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}