Advanced Peak Phase of ENSO under Global Warming

Xiao-Tong Zheng aFrontier Science Center for Deep Ocean Multispheres and Earth System and Physical Oceanography Laboratory, Ocean University of China, Qingdao, China
bLaoshan Laboratory, Qingdao, China

Search for other papers by Xiao-Tong Zheng in
Current site
Google Scholar
PubMed
Close
https://orcid.org/0000-0001-6335-1383
,
Chang Hui aFrontier Science Center for Deep Ocean Multispheres and Earth System and Physical Oceanography Laboratory, Ocean University of China, Qingdao, China

Search for other papers by Chang Hui in
Current site
Google Scholar
PubMed
Close
,
Zi-Wen Han aFrontier Science Center for Deep Ocean Multispheres and Earth System and Physical Oceanography Laboratory, Ocean University of China, Qingdao, China

Search for other papers by Zi-Wen Han in
Current site
Google Scholar
PubMed
Close
, and
Yue Wu bLaoshan Laboratory, Qingdao, China
cCAS Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China
dCollege of Marine Science, University of Chinese Academy of Sciences, Qingdao, China

Search for other papers by Yue Wu in
Current site
Google Scholar
PubMed
Close
Full access

Abstract

El Niño–Southern Oscillation (ENSO) is the leading mode of interannual ocean–atmosphere coupling in the tropical Pacific, greatly influencing the global climate system. Seasonal phase locking, which means that ENSO events usually peak in boreal winter, is a distinctive feature of ENSO. In model future projections, the ENSO sea surface temperature (SST) amplitude in winter shows no significant change with a large intermodel spread. However, whether and how ENSO phase locking will respond to global warming are not fully understood. In this study, using Community Earth System Model Large Ensemble (CESM-LE) projections, we found that the seasonality of ENSO events, especially its peak phase, has advanced under global warming. This phenomenon corresponds to the seasonal difference in the changes in the ENSO SST amplitude with an enhanced (weakened) amplitude from boreal summer to autumn (winter). Mixed layer ocean heat budget analysis revealed that the advanced ENSO seasonality is due to intensified positive meridional advective and thermocline feedback during the ENSO developing phase and intensified negative thermal damping during the ENSO peak phase. Furthermore, the seasonal variation in the mean El Niño–like SST warming in the tropical Pacific favors a weakened zonal advective feedback in boreal autumn–winter and earlier decay of ENSO. The advance of the ENSO peak phase is also found in most CMIP5/6 models that simulate the seasonal phase locking of ENSO well in the present climate. Thus, even though the amplitude response in the winter shows no model consensus, ENSO also significantly changes during different stages under global warming.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xiao-Tong Zheng, zhengxt@ouc.edu.cn

Abstract

El Niño–Southern Oscillation (ENSO) is the leading mode of interannual ocean–atmosphere coupling in the tropical Pacific, greatly influencing the global climate system. Seasonal phase locking, which means that ENSO events usually peak in boreal winter, is a distinctive feature of ENSO. In model future projections, the ENSO sea surface temperature (SST) amplitude in winter shows no significant change with a large intermodel spread. However, whether and how ENSO phase locking will respond to global warming are not fully understood. In this study, using Community Earth System Model Large Ensemble (CESM-LE) projections, we found that the seasonality of ENSO events, especially its peak phase, has advanced under global warming. This phenomenon corresponds to the seasonal difference in the changes in the ENSO SST amplitude with an enhanced (weakened) amplitude from boreal summer to autumn (winter). Mixed layer ocean heat budget analysis revealed that the advanced ENSO seasonality is due to intensified positive meridional advective and thermocline feedback during the ENSO developing phase and intensified negative thermal damping during the ENSO peak phase. Furthermore, the seasonal variation in the mean El Niño–like SST warming in the tropical Pacific favors a weakened zonal advective feedback in boreal autumn–winter and earlier decay of ENSO. The advance of the ENSO peak phase is also found in most CMIP5/6 models that simulate the seasonal phase locking of ENSO well in the present climate. Thus, even though the amplitude response in the winter shows no model consensus, ENSO also significantly changes during different stages under global warming.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Xiao-Tong Zheng, zhengxt@ouc.edu.cn

1. Introduction

El Niño–Southern Oscillation (ENSO) is the leading ocean–atmosphere coupled mode in the tropical Pacific and greatly impacts the global climate (Philander 1990). During its positive phase, i.e., El Niño events, the sea surface temperature (SST) in the central-eastern equatorial Pacific is above the climatology, inducing an eastward shift in atmospheric convection and a weakened Walker circulation. The weakened equatorial trades in turn suppress the upwelling in the eastern equatorial Pacific and further warm the SST. Conversely, the La Niña events are characterized by the opposite oceanic and atmospheric conditions. This coupled loop among the SST, wind, and thermocline is referred to as Bjerknes feedback (Bjerknes 1969) and is the main mechanism of ENSO development. With anomalous atmospheric circulation, the SST anomaly (SSTA) in the central-eastern equatorial Pacific can generate remote impacts on the entire tropics and mid–high latitudes (Horel and Wallace 1981; Klein et al. 1999; Wang et al. 2000).

ENSO exhibits significant seasonality. Generally, ENSO events initiate in boreal spring, develop in summer–autumn, peak in winter, and decay in the following spring (Rasmusson and Carpenter 1982). This seasonal phase locking is a fundamental feature of ENSO. The main mechanism of ENSO phase locking is the seasonal evolution of ocean–atmosphere coupled instability (Philander et al. 1984; Li 1997; Galanti and Tziperman 2000; Stein et al. 2014; Chen and Jin 2020), which is related to the seasonal cycle of the mean state of the tropical Pacific. The annual cycle of the central-eastern equatorial Pacific SST climatology features as the warmest (coldest) SST in boreal spring (autumn), which is related to the seasonal shift in the intertropical convergence zone (ITCZ) and corresponding cross-equatorial wind (Xie 1994). In the boreal spring, the ITCZ is closest to the equator, resulting in convection being more sensitive to SSTA and favoring a rapid growth of the initial perturbation of ENSO (Philander 1983; Philander et al. 1984; Tziperman et al. 1997). The thermocline in the eastern equatorial Pacific is the shallowest with the strongest upwelling during boreal summer to autumn, intensifying thermocline feedback (Galanti et al. 2002). In addition, the zonal SST gradient is the largest during summer and autumn, favoring the strongest zonal advective feedback (Yan and Wu 2007). These enhanced oceanic dynamics contribute to the rapid development of ENSO in these two seasons and cause it to reach its peak in boreal winter.

In contrast, during the following spring, strong convection favors the decay of ENSO through negative cloud–shortwave–SST feedback (Dommenget and Yu 2016). Based on the Bjerknes stability index (BJ index; Jin et al. 2006), which quantitively represents ocean–atmosphere coupling instability, previous studies have attributed ENSO phase locking to seasonal variations in the BJ index and, therein, different ocean–atmosphere coupled feedbacks (Stein et al. 2010, 2014; Wengel et al. 2018). In other words, with the seasonal cycle of the mean state, the seasonal variations in ocean–atmosphere feedback shape the seasonal phase locking of ENSO.

How ENSO responds to a warming climate has been an important issue in recent decades (Timmermann et al. 1999; Yeh et al. 2009; Collins et al. 2010; Cai et al. 2015, 2021). Under global warming, the oceanic and atmospheric mean states in the tropical Pacific will undergo a series of significant changes, regulating the coupled ocean–atmosphere feedbacks that change the characteristics of ENSO. For instance, the dry stability of the tropical troposphere will increase (Held and Soden 2006), weakening the zonal wind feedback and the intensity of ENSO (Meehl et al. 1993; Knutson and Manabe 1995; Huang et al. 2017). In contrast, as the absorbed heat accumulates in the surface layer, the stratification of the upper ocean will increase, shoaling the thermocline and strengthening the thermocline–SST feedback, which tends to intensify ENSO activity (Timmermann et al. 1999; Cai et al. 2018). Recent studies have focused on the effect of mean SST warming in the tropical Pacific, which shows an El Niño–like pattern due to weakened Walker circulation (Vecchi and Soden 2007; Xie et al. 2010). Specifically, the enhanced warming in the central-eastern equatorial Pacific implies that the local SST is closer to the convective threshold, intensifying the convective-SST feedback of ENSO (Johnson and Xie 2010; Power et al. 2013; Zheng et al. 2016). This intensified ENSO-induced convection further strengthens the teleconnection of ENSO with extratropical climate (Zhou et al. 2014; Yeh et al. 2018). In addition, the weakened Walker circulation can also regulate the occurrence of different types of El Niño events (Yeh et al. 2009).

Most of the abovementioned studies on the responses to global warming have focused only on the change in ENSO during its peak phase (i.e., boreal winter). However, few studies have paid attention to the changes in ENSO during other stages, especially its seasonality. Does the seasonal phase locking of ENSO change in a warmer climate? Will the seasonal changes in the tropical Pacific mean state contribute to the changes in ENSO phase locking? These questions are not understood. A recent study reported that the seasonal phase locking of the Indian Ocean dipole (IOD) will advance under global warming due to strengthened thermocline feedback during boreal summer and weakened zonal wind feedback during autumn in the equatorial Indian Ocean (Zheng et al. 2021). Being closely interactive with the IOD, ENSO is also projected to peak earlier in a warmer climate (see Fig. 13 in Zheng et al. 2021). As seasonal evolution is an important feature of ENSO events and impacts remote climates (e.g., the Asian summer monsoon) during both the developing and decaying phases, it is necessary to understand the response of ENSO seasonality to global warming.

The present study investigates the change in seasonal phase locking of ENSO events in a warmer climate based on future projections of state-of-the-art climate models. We found that ENSO tends to peak approximately 1 month earlier during the end of the twenty-first century in high-emission scenario projections. This change can be induced by an increased SST amplitude of ENSO in boreal summer and a decreased amplitude during autumn and winter, leading to an advance in ENSO seasonal evolution. In boreal summer, dynamical feedbacks, including zonal, meridional advective, and thermocline feedbacks, are intensified, resulting in an accelerated growth of ENSO. In contrast, during boreal autumn and winter, the enhanced thermal damping effect contributes to the earlier decay of ENSO.

The rest of this paper is organized as follows. Section 2 describes the data and methods utilized in the study. In section 3, the future changes in ENSO phase locking are presented. Section 4 presents the mixed layer heat budget analysis. Changes in the seasonality of the tropical Pacific mean state and their impacts on ENSO phase locking are investigated in section 5. Section 6 is a summary with discussion.

2. Data and methods

a. Model outputs and reanalysis dataset

This study mainly analyzes the output from the Community Earth System Model Large Ensemble (CESM-LE), which is a set of climate model simulations intended to advance the understanding of climate change in the presence of internal variability (Kay et al. 2015). This large ensemble experiment has been used to eliminate the interference of internal variability and extract the response of ENSO to greenhouse warming (Maher et al. 2018; Zheng et al. 2018; Zheng 2019; Cai et al. 2020; Ng et al. 2021). The CESM-LE project includes a 40-member ensemble of CESM1 simulations under historical radiative forcing from 1920 to 2005 and under the representative concentration pathway 8.5 (RCP8.5) forcing scenario from 2006 to 2100, with slightly different initial conditions for each member. Given that the last five ensemble members have systematically larger climate sensitivity, we use only the first 35 members in this study. The ensemble mean results, which mostly removed the effect of internal variability, can represent the response of ENSO to radiative forcing.

In addition, we analyzed the multimodel ensemble from the phases 5 and 6 of Coupled Model Intercomparison Project (CMIP5, Taylor et al. 2012; CMIP6, Eyring et al. 2016) under historical simulation and RCP8.5 (SSP585 for CMIP6) scenario. Given that some CMIP models cannot simulate ENSO phase locking behavior (Ham et al. 2013; Ham and Kug 2014; Chen and Jin 2021; Liao et al. 2021; Chen and Jin 2022), we use only the models with ENSO peaking in November–January (NDJ, according to the monthly standard deviations (STDs) of the Niño-3.4 SSTA) from 1970 to 1999 in historical simulations. Only the first member of each CMIP model is utilized.

The average SST in the Niño-3.4 (5°S–5°N, 120°–170°W) region is referred to as the ENSO index. To enhance the precision of our analysis and focus on changes in ENSO which has an interannual time scale, we first perform a 3-month to 9-yr bandpass filter to remove the climate variabilities that do not align with the time scale of ENSO (e.g., Madden–Julian oscillation and Pacific decadal oscillation). In addition, given the dominant role of ENSO in tropical Pacific climate variabilities, the results without any filter are similar to those with such a bandpass filter (figures not shown). To examine the change in ENSO under global warming, we compare the Niño-3.4 SST amplitude and related dynamic and thermodynamic processes during ENSO developing and decay years between 1970–99 and 2060–89. Ocean variables are horizontally regridded to 1° × 1° and vertically interpolated to 10-m intervals from 5 to 205 m before further analysis.

To evaluate the ENSO phase locking and related feedback simulations in the models, we also analyze a global ocean model assimilation product, Estimating the Circulation and Climate of the Ocean, version 4.3 (ECCO v4.3), which provides ocean temperature and three-dimensional currents (Forget et al. 2015; Fukumori et al. 2017).

b. Mixed layer heat budget

To explore the mechanisms of the changes in ENSO seasonal phase locking, we use the mixed layer ocean heat budget for the mixed layer temperature in the tropical Pacific (Li et al. 2002; Su et al. 2010; Chen et al. 2015). The tendency equation of the mixed layer temperature anomaly can be written as follows:
Tt=Qnetρ0cpHm(uT¯x+u¯Tx+uTx)(υT¯y+υ¯Ty+υTy)(wbTTb¯Hm+wb¯THmwb¯TbHm+wbTTbHm)+R,
where T is the mixed layer vertical mean temperature, Qnet is the surface net heat flux (downward is positive), which includes net shortwave (SW) and longwave (LW) radiation, surface latent, and sensible heat fluxes (SHFs); ρ0 is the density of seawater (1025 kg m−3); cp is the specific heat of seawater (3850 J kg−1 K−1); Hm is the mixed layer depth; u and υ are the zonal and meridional mixed layer vertical mean ocean current velocities, respectively; wb and Tb are the vertical velocity and ocean temperature at the bottom of the mixed layer; and R is the residual term. The bar and prime denote the monthly climatology and interannual anomaly, respectively.

It is worth noting that the Hm in this study is different from the fixed depth adopted in previous studies (Su et al. 2010; Chen et al. 2015). Under global warming, absorbed heat accumulates in the surface layer, leading to a shallower mixed layer and shoaling thermocline, which can further intensify the oceanic dynamics associated with ENSO (Cai et al. 2018). Thus, the fixed Hm may underestimate these processes related to ENSO changes under global warming. Here, we assume that the interannual variance in the mixed layer temperature is approximately vertically uniform and thus define Hm as the depth at which the local vertical gradient of the ocean temperature variance reaches its maximum. We calculate the annual-mean climatological Hm on every grid during 1970–99 and 2060–89 for each member. This newly defined Hm can reflect the shoaling of the mixed layer and its effect on ENSO activity under global warming (see the appendix for more details).

ENSO events are identified when the December Niño-3.4 SSTA exceeds a threshold of 0.5°C for a minimum of 5 consecutive months. A mixed layer heat budget analysis was performed for composite ENSO events. It should be noted that there is an asymmetry in SST amplitude between El Niño and La Niña events (Burgers and Stephenson 1999; An and Jin 2004). To evaluate whether the ENSO composites adequately represent the response features of both El Niño and La Niña events, we have examined changes in the dynamic and thermodynamic feedback during the two types of events, which show similar results (figures not shown). Therefore, the anomalies during La Niña events are multiplied by −1 and composited with the anomalies during El Niño events to represent the anomalies during composite ENSO events. The detailed mechanisms of ENSO asymmetry that potentially contribute to the discrepancy in response of the El Niño and La Niña events to global warming are beyond the scope of the present study.

3. Changes in ENSO phase locking under global warming

Compared with the observation, the CESM-LE can simulate ENSO seasonal phase locking well, as the interannual variability in the Niño-3.4 SSTA reaches its maximum in NDJ (Figs. 1a,c). Under global warming, the variability in the Niño-3.4 SSTA tends to peak 1 month earlier in individual models, as more members project a November ENSO peak, while fewer project a January peak (Fig. 1c). This seasonal advance in the ENSO peak phase can also be found in the CMIP models (Figs. 1b,d), although the peak month is more diverse among the models after global warming (Fig. 1d). Among the 35 members in CESM-LE, 17 members project a seasonal advance of ENSO phase locking, while only 4 members project a delayed peak phase. The results are similar for the 34 CMIP models, with 16 showing an advanced ENSO peak and 8 showing a delayed one. The specific models with seasonal advance and decay of ENSO phase locking are identified in Table 1. Additionally, to examine the sensitivity of our findings to the duration of the analysis period, we further use other periods of 50 and 80 years based on CESM-LE and CMIP models, which show similar results to those obtained from the 30-yr period (figures not shown). This indicates that the advance of seasonal phase locking is a significant response of ENSO to global warming.

Fig. 1.
Fig. 1.

Monthly STDs of Niño-3.4 SST anomalies (K) in the (a) CESM-LE and (b) CMIP models during 1970–99 (blue lines) and 2060–89 (red lines) normalized by the STD during the whole 360 months of each period for each member or model. The thick lines denote the ensemble mean, and the thin lines denote each member or model. (c),(d) The probability of the peak month of the monthly STD for each ensemble. Seasonal mean changes in the standard deviation of Niño-3.4 SST anomalies (K) between 1970–99 and 2060–89 normalized by the STD during the whole 360 months in the (e) CESM-LE and (f) CMIP models. The bars denote the ensemble mean, and the box-and-whisker plots show the 10th, 25th, 50th, 75th, and 90th percentiles.

Citation: Journal of Climate 37, 20; 10.1175/JCLI-D-24-0002.1

Table 1.

The 19 CMIP5 models and 15 CMIP6 models used in this study.

Table 1.

The advance of ENSO phase locking also indicates that the seasonal difference in the ENSO amplitude changes between boreal autumn and winter (Figs. 1e,f). Although a large intermodel spread exists, the ensemble mean of the normalized ENSO amplitude during boreal autumn increases but decreases during winter in both the CESM-LE and the CMIP ensembles. In this study, we emphasize the seasonality of changes in SST variability to concentrate on the timing of the ENSO peak, so the SST interannual variability has been normalized by its STD during the whole 360 months of each period for each member or model, to compare the relative changes in different seasons, and to reduce the influences of the systematic spread from different models. Without normalization, the CESM-LE models show an increase in the SST amplitude in both boreal autumn and winter (Figs. S1a,c,e in the online supplemental material), particularly during the earlier months of the ENSO development. The CMIP models show similar results, but the increased SST variability in winter can only be discovered during the comparison on a centennial time scale (Figs. S1b,d,f), which is consistent with previous studies (Cai et al. 2022; Huang et al. 2023).

In the following section, we investigate the mechanisms underlying this advance in seasonal phase locking under global warming based on mixed layer heat budget analysis.

4. Mixed layer heat budget analysis

ENSO phase locking originates from the seasonality of ocean–atmosphere coupling in the equatorial Pacific, which dominates the rates of ENSO development and decay. Therefore, the seasonal advance of ENSO phase locking may result from seasonal differences in the response of ocean–atmosphere coupling to global warming.

Based on Eq. (1), we first evaluate the simulated seasonal evolution of ENSO ocean–atmosphere coupling. In observations (Fig. 2a), the tendency of the Niño-3.4 mixed layer temperature anomaly turns from positive to negative around December, when the Niño-3.4 SSTA reaches its peak. The development of ENSO mainly results from zonal advective feedback (term 2), meridional advective feedback (term 6), Ekman feedback (term 8), and thermocline feedback (term 10), while the thermal damping effect of the surface net heat flux (term 1) and the upwelling (term 9) are the major negative feedbacks. Following the temporal evolution of the SSTA, the thermal damping effect is largest during the peak phase of the ENSO. Besides, zonal and meridional advective feedback and the thermocline feedback turn negative during the decay year, reflecting the dynamic mechanisms of the ENSO cycle (Fig. 2a). Compared with the observations, CESM-LE and CMIP models can simulate the features of the abovementioned processes during the development and decay phases of ENSO (Fig. 2b), although some feedbacks are underestimated (e.g., the thermal damping and the thermocline feedback).

Fig. 2.
Fig. 2.

Mixed layer heat budget for the ENSO composite in (a) ECCO v4.3 during 1992–2015, (b) CESM-LE, and (c) CMIP during 1970–99. The terms 1–12 denote the 12 terms on the right-hand side of Eq. (1), and the term 13 denotes the temperature tendency. The formulas for the 12 terms are as follows: term 1: Qnet/(ρ0cpHm), term 2: u(T¯/x), term 3: u¯(T/x), term 4: u(T/x), term 5: υ(T¯/y), term 6: υ¯(T/y), term 7: υ(T/y), term 8: wb(TTb¯/Hm), term 9: wb¯(T/Hm), term 10: wb¯(Tb/Hm), term 11: wb[(TTb)/Hm], term 12: R, and term 13: T/t.

Citation: Journal of Climate 37, 20; 10.1175/JCLI-D-24-0002.1

The tendency of the Niño-3.4 mixed layer temperature (term 13, Tt), which represents the evolution of ENSO, is projected to change under global warming (Fig. 3a). In particular, the temperature tendency increases from April to September of the developing year [Apr(0)–Sep(0)] and decreases from October to the following March [Oct(0)–Mar(1)], corresponding to the earlier peak in ENSO under global warming. Then, we decompose Tt into zonal Du, meridional Dυ, and vertical Dw advection; thermal damping Q; and residual R terms. It is clear that the increased Tt in Apr(0)–Sep(0) is mostly contributed by strengthened dynamical advection, i.e., Dυ, Dw, and Du (Fig. 3b). In contrast, the decreased Tt in Oct(0)–Mar(1) is mainly due to the strengthened thermal damping term. Additionally, the decreased Du also plays an important role.

Fig. 3.
Fig. 3.

(a) The temporal evolution of the tendency of the mixed layer temperature anomalies (K month−1) in the Niño-3.4 region for composite ENSO events during 1970–99 (blue line) and 2060–89 (red line) in the CESM-LE, where the light red and light blue shadings denote the two periods, i.e., from April to September of the developing year [Apr(0)–Sep(0)] and from October of the developing year to March of the decay year [Oct(0)–Mar(1)], which are referred to in the following five subplots as pink and blue bars. (b)–(f) Six-month mean changes in each term of the mixed layer heat budget in the Niño-3.4 region for composite ENSO events from 1970–99 to 2060–89. Changes in the mixed layer temperature anomaly tendency [Tt, i.e., T/t in Eq. (1)] are decomposed into changes in zonal Du, meridional Dυ, and vertical Dw heat advection; heat exchange with atmosphere Q, and residual R in (b). Changes in zonal heat advection D are decomposed into changes in u(T¯/x), u¯(T/x), and u(T/x) in (c). Changes in meridional heat advection Dυ are decomposed into changes in υ(T¯/y), υ¯(T/y), and υ(T/y) in (d). The partial differential operators are simplified as subscripts. Changes in vertical heat advection Dw are decomposed into changes in wb(TTb¯/Hm), wb¯(T/Hm), wb¯(Tb/Hm), and wb[(TTb)/Hm] in (e). Changes in Q are decomposed into changes in SW radiation, LW radiation, LHF, and SHF in (f).

Citation: Journal of Climate 37, 20; 10.1175/JCLI-D-24-0002.1

Specifically, in the developing phase, the increased Du mainly comes from the strengthened advective effect of anomalous zonal currents on the climatological zonal temperature gradient [u(T¯/x)], which is commonly known as zonal advective feedback (Fig. 3c). The strengthened Dυ mainly comes from the strengthened advection of mean meridional currents on the anomalous meridional temperature gradient [υ¯(T/y)], which is referred to as meridional advective feedback (Fig. 3d). The strengthened Dw mainly comes from the enhanced vertical heat transport of the anomalous vertical temperature gradient by the mean upwelling [wb¯(Tb/Hm)], i.e., thermocline feedback (Fig. 3e). The dynamic damping of upwelling [wb¯(T/Hm)] is enhanced under global warming, primarily due to the increased amplitude of ENSO (Cai et al. 2023; Heede and Fedorov 2023), which partially offsets the effect of advective feedbacks. Overall, the strengthened dynamic feedback intensifies the Bjerknes feedback in the developing phase, leading to an increase in the ENSO development.

We further decompose the changes in zonal and meridional advective and thermocline feedbacks {u(T¯/x),υ¯(T/y),wb¯[(TTb)/Hm]} to examine the relative contributions of changes in the mean state and interannual variability under global warming. Following previous studies (Chen et al. 2015; Wang et al. 2019), the change in each feedback can be decomposed based on the following equation:
d(A×B)=d(A)×B+A×d(B)+d(A)×d(B),
where d(X) denotes the changes under global warming, i.e., the difference between 2060 and 2089 and 1970–99, A denotes the climatological or anomalous oceanic velocity, while B denotes the climatological or anomalous temperature gradient.

Figure 4 shows the decomposed results based on Eq. (2). In the ENSO developing phase, the strengthened zonal advective feedback is mainly contributed by the intensified anomalous zonal currents, which are closely related to the stronger anomalous westerly winds inspired by warmer SSTAs. Changes in the climatological zonal temperature gradient (T¯/x) have a negligible effect in the developing phase [Apr(0)–Sep(0)] but dominate the weakened Du in the peak phase [Oct(0)–Mar(1)] (Fig. 4a). This change is associated with the El Niño–like mean warming pattern in the tropical Pacific (Power et al. 2013; Zhou et al 2014; Zheng et al. 2018), which intensifies anomalous convection and eastward currents u′ in the equatorial Pacific but reduces the zonal temperature gradient between the warm pool and cold tongue (i.e., T¯/x).

Fig. 4.
Fig. 4.

Changes (K month−1) in (a) zonal advective feedback, (b) meridional advective feedback, and (c) thermocline feedback and the contributions of mean state change and anomaly change for Apr(0)–Sep(0) (pink bars) and Oct(0)–Mar(1) (blue bars) during composite ENSO events from 1970–99 to 2060–89 in the CESM-LE.

Citation: Journal of Climate 37, 20; 10.1175/JCLI-D-24-0002.1

For strengthened meridional advective feedback, changes in the mean meridional current (υ¯) and anomalous meridional temperature gradient (T/y) have comparable contributions (Fig. 4b). The strengthened thermocline feedback mainly results from the enhanced anomalous temperature at the bottom of the mixed layer (Tb/Hm), which may be related to the shoaling of the mixed layer and the strengthening of subsurface temperature anomalies (Fig. 4c, Timmermann et al. 1999; Cai et al. 2018).

The decreased temperature tendency in the ENSO peak phase [Oct(0)–Mar(1)], which is dominated by the damping effect of surface net heat flux and upwelling (Fig. 3b), is also crucial for changes in the ENSO seasonal phase locking. Further analysis reveals that the changes in shortwave radiation and latent heat flux (LHF) contribute most to the enhanced thermal and dynamic damping effects on ENSO (Fig. 3f). The former is related to enhanced ENSO-induced convection and cloud–SST negative feedback (Ramanathan and Collins 1991; Ying et al. 2018; Zheng et al. 2019), while the latter is due to the increased sensitivity of saturation vapor pressure to SST anomalies following the Clausius–Clapeyron relationship in a warmer climate (Held and Soden 2006).

Overall, the seasonal changes in ENSO phase locking can be attributed to strengthened ocean–atmosphere dynamic feedback during the developing phase [Apr(0)–Sep(0)] and enhanced thermal damping (mainly shortwave radiation and latent heat feedback) during the peak phase [Oct(0)–Mar(1)]. It should be noted that the seasonal variation in the response of ENSO thermal damping is much smaller than that in the intensification of positive dynamic feedbacks, suggesting that the seasonal variation in the response of positive Bjerknes feedback to global warming is essential for the seasonal advance of the ENSO peak phase.

We can find a similar response of ENSO seasonal evolution (i.e., the advance of the ENSO peak phase) under global warming in the CMIP5/6 models (Fig. 1b), meaning that ENSO develops (decays) faster during its developing (mature) phase (Figs. 1b and 5a). In the CMIP5/6 models, the accelerated evolution of ENSO during its developing phase can also be attributed to the intensification of the meridional and vertical dynamical terms (Fig. 5b), among which the meridional advective [υ¯(T/y)] and thermocline {wb¯[(TTb)/Hm]} feedbacks play dominant roles (Figs. 5d,e). In addition, the strengthened thermal damping effect, which mainly originates from shortwave radiation and latent heat flux terms, helps to terminate ENSO more rapidly in a warmer climate (Fig. 5f). However, the zonal advection term in the CMIP models, especially the anomalous temperature advection term [u¯(T/x)], decreases throughout the development of ENSO, even though the zonal advective feedback [u(T¯/x)] still contributes somewhat to the seasonal advance of the ENSO peak phase (Figs. 5b,c).

Fig. 5.
Fig. 5.

As in Fig. 3, but for the CMIP models.

Citation: Journal of Climate 37, 20; 10.1175/JCLI-D-24-0002.1

5. Changes in the seasonality of the tropical Pacific mean state and their impacts on ENSO phase locking

The previous section showed that several robust mean state changes in the tropical Pacific can regulate ENSO development and decay under global warming. For instance, the El Niño–like warming pattern intensifies ENSO-induced atmospheric anomalies (Zhou et al. 2014), strengthening the dynamic feedback during the developing phase of ENSO. In addition, shoaling of the mixed layer and thermocline can strengthen thermocline feedback. On the other hand, the El Niño–like warming pattern also contributes to changes in the zonal advective and thermal damping effects of ENSO, which are more prominent during the ENSO peak phase. Given that the seasonal evolutions of ocean–atmosphere dynamic and thermodynamic feedbacks, which shape the seasonal phase locking of ENSO, are closely connected to the seasonality of the tropical Pacific mean state, this section further investigates the changes in the seasonality of the tropical Pacific mean state, as well as their impacts on the advance of the ENSO peak phase under global warming.

In the observations, the climatological SST in the eastern equatorial Pacific is warmest in March and coldest in October, with westward propagation (Fig. S2a, Xie 1996). Both the CESM-LE and CMIP models can simulate this SST seasonality well across the equatorial Pacific (Figs. S2b,c). Previous studies have reported that SST seasonality is strengthened under global warming based on a single-model simulation (Timmermann et al. 2004; Xie et al. 2010), as the strongest climatological upwelling in boreal autumn effectively reduces SST warming via the ocean dynamic thermostat mechanism (Clement et al. 1996). In this study, however, the SST warming along the eastern coast reaches a maximum (minimum) in October (February) with westward propagation, indicating a weakened seasonality of the climatological SST (Fig. 6a). The maximum warming in October may be related to the local positive low cloud-SST feedback (Fig. 6c), because the climatological eastern equatorial Pacific SST is coldest with a stable atmospheric boundary layer in autumn (Fig. 6b). The El Niño–like warming pattern reduces the atmospheric stability there, decreasing (increasing) the local low cloud amount (shortwave radiation) and further intensifying the SST warming in boreal autumn (Figs. 6b,c). In contrast, the El Niño–like warming pattern increases convection over the eastern equatorial Pacific (Fig. 6d), suppressing the SST warming during boreal spring. Additionally, the enhanced warming during boreal autumn reduces the climatological trade winds and latent heat flux due to evaporation (Figs. 6e,f). Similarly, the CMIP multimodel ensemble (MME) shows a weakened seasonality of mean SST with a smaller magnitude (Fig. S2e).

Fig. 6.
Fig. 6.

Ensemble mean changes (shading) in the equatorial Pacific (averaged in 5°S–5°N) monthly climatological (a) SST (K), (b) low cloud fraction, (c) surface net SW radiation flux (W m−2), (d) rainfall (mm day−1), (e) surface zonal wind stress (TAUX; dyn cm−2), and (f) surface downward latent heat flux (W m−2) from 1970–99 to 2060–89 in CEMS-LE, with the climatology in 1970–99 (contours).

Citation: Journal of Climate 37, 20; 10.1175/JCLI-D-24-0002.1

The seasonality of the mean SST in the equatorial Pacific is essential for shaping the seasonal evolution of the seasonal phase locking of ENSO (Yan and Wu 2007; Ham et al. 2013; Liao et al. 2021). Thus, the seasonality of the mean state change potentially influences ENSO seasonal phase locking under global warming. In the CESM-LE, the change in the zonal SST gradient in the equatorial Pacific shows obvious seasonal differences (Fig. 7c), which is nearly unchanged from May to August but decreases from September to January. Specifically, the SST warming shows little zonal gradient in the central Pacific during the developing phase of ENSO (Fig. 7a). In contrast, during the peak phase, the El Niño–like warming pattern is more pronounced (Fig. 7b), indicating a reduced zonal SST gradient and thus weakened zonal advective feedback (Fig. 4a). Thus, the zonal advective feedback strengthens due to the increase in anomalous zonal current during the developing phase but weakens due to the decrease in the mean SST gradient during the peak phase, in favor of the changes in ENSO phase locking under global warming. In addition, the more pronounced El Niño–like warming pattern during the peak phase leads to stronger thermal damping than that during the developing phase, resulting in an acceleration of ENSO decay.

Fig. 7.
Fig. 7.

Multimember ensemble mean changes (K) in the tropical Pacific climatological SST for the (a) April–September mean and (b) October–March mean in the CESM-LE from 1970–99 to 2060–89. The blue rectangles represent the Niño-3.4 region. (c) The climatological SST zonal gradient between the Niño-3 and Niño-4 regions during 1970–99 (blue lines) and 2060–89 (red lines) in the CESM-LE. The thick lines denote the ensemble mean, and the thin lines denote each member.

Citation: Journal of Climate 37, 20; 10.1175/JCLI-D-24-0002.1

We also found a significant change in meridional ocean circulation in the tropical Pacific, which may influence the meridional advective feedback of ENSO (Fig. 4b). In response to global warming, the shallow meridional overturning in the equatorial Pacific weakens (Figs. 8a,b) due to weakened atmospheric circulation (Vecchi and Soden 2007), resulting in a narrower meridional structure of ENSO (Chen et al. 2015). Thus, the anomalous meridional temperature gradient (T/y) will increase during ENSO events, implying an increase in meridional advective feedback (Fig. 4b). In addition, changes in the mixed layer vertical mean meridional current (υ¯) also contribute to the strengthened feedback (Figs. 4b and 8c). In a warmer climate, the increased vertical stratification shoals the upper ocean current system. The accumulation of momentum in the upper layer leads to an acceleration of the surface currents (Peng et al. 2022). On the other hand, the mixed layer is shallower under global warming. Considering that the upper ocean moves faster than the deeper ocean, the average mixed layer velocity will increase. It is worth noting that this response shows substantial seasonal variation under global warming. During the developing phase of ENSO, the poleward meridional currents are accelerated on both sides of the equator, indicating that ENSO signals on the equator will be advected poleward by stronger climatological meridional currents and enhanced meridional advective feedback. In contrast, during the peak phase, the meridional currents slow down north of the equator but still accelerate south of the equator, leading to a smaller increase in meridional advective feedback (Fig. 4b).

Fig. 8.
Fig. 8.

Multimember ensemble mean changes in the zonal mean meridional velocity (m s−1; shading) over 120°–170°W in the CESM-LE from 1970–99 to 2060–89 for (a) April–September and (b) October–March with the monthly climatology (contours) during 1970–99. The dashed (solid) black line denotes the annual-mean mixed layer depth during 1970–99 (2060–89). (c) Ensemble mean changes (m s−1; shading) in the zonal mean mixed layer vertical mean meridional current over 120°–170°W in the CESM-LE from 1970–99 to 2060–89, with the monthly climatology (contours) during 1970–99.

Citation: Journal of Climate 37, 20; 10.1175/JCLI-D-24-0002.1

The changes in the climatological upper ocean vertical temperature structure in the equatorial Pacific also have seasonal differences (Fig. 9). Under global warming, the upper ocean stratification intensifies as the upper ocean warms faster than the deeper ocean. Accompanying the rapid warming of the upper ocean, the weakened upwelling suppresses the propagation of the downward warming signal, resulting in an intensification of the vertical temperature gradient and strengthening of thermocline feedback. As the climatological thermocline depth during the developing phase (Fig. 9a) is shallower than that during the peak phase (Fig. 9b), the shoaling of thermocline depth results in more strengthening of thermocline feedback, favoring a faster evolution of ENSO in the developing phase (Fig. 4c).

Fig. 9.
Fig. 9.

Ensemble mean changes in the equatorial (averaged in 5°S–5°N) Pacific upper 200-m climatological ocean temperature (K) for (a) April–September and (b) October–March from 1970–99 to 2060–89 in the CESM-LE. The dashed (solid) black lines denote the climatological thermocline depth for each 6-month period during 1970–99 (2060–89). The dashed (solid) light gray lines denote the annual-mean mixed layer depth during 1970–99 (2060–89), used for the heat budget analysis.

Citation: Journal of Climate 37, 20; 10.1175/JCLI-D-24-0002.1

Overall, in the CESM-LE, changes in the seasonality of the tropical Pacific mean state are favorable for changes in ENSO phase locking under global warming, intensifying dynamic feedbacks more during the ENSO developing phase. In addition, a more pronounced El Niño–like warming pattern during the peak phase would be more favorable for the thermal damping of ENSO and the advance of ENSO seasonal phase locking (Fig. 3f).

6. Summary and discussion

This study investigated the response of ENSO phase locking to global warming based on 35-member CESM-LE and 34 CMIP5/6 models that can reproduce ENSO phase locking in the present climate well. In the MME and most members (models), the peak of ENSO emerges 1 month earlier in a warmer climate. The advance of ENSO phase locking means an increased (decreased) amplitude of SST during the developing (peak) phase of ENSO, indicating faster development and earlier decay.

We conducted a mixed layer heat budget analysis to reveal the underlying mechanism for the advance of ENSO phase locking. The results showed that the increased Niño-3.4 temperature tendency in the developing phase is attributed to strengthened positive ocean–atmosphere dynamic feedback, especially meridional advective feedback and thermocline feedback. The decrease in the temperature tendency in the peak phase mainly results from enhanced negative thermodynamic feedback. Additionally, in the CESM-LE, the strengthened zonal advective feedback during the developing phase and weakened feedback during the peak phase are also favorable for the seasonal advance of ENSO phase locking.

The MME changes are robust in this study. However, large intermodel uncertainties exist in the CMIP models compared with those in the CESM-LE (Figs. 1a,b). Among the 34 selected CMIP models, 16(8) models project the advance (delay) of the ENSO peak. In the models with advanced (delayed) ENSO phase locking, the largest amplitude of the Niño-3.4 SSTA appears approximately 2 (1) months earlier (later) in a warmer climate (Figs. 10a,c). We then divide the models into three categories based on their projection of the ENSO phase locking (i.e., advanced, delayed, and unchanged). We first examine whether the intermodel uncertainty in future projections is associated with the spread in simulating the historical ENSO dynamics. We compare the Niño-3.4 temperature tendency and relevant feedbacks during ENSO evolution between the ECCO datasets and the historical simulations from the individual CMIP models (Fig. S3). However, there are no significant differences between the three categories, implying that the capacity to reproduce the historical evolution of ENSO may not be the key indicator of the reliability in future projections. The sources of intermodel diversity in simulating the response of ENSO phase locking to global warming still need further investigation.

Fig. 10.
Fig. 10.

Monthly standard deviations of Niño-3.4 SST anomalies during 1970–99 (blue lines) and 2060–89 (red lines) for CMIP models with (a) advanced, (c) delayed, and (e) unchanged phase locking of ENSO. The thick lines denote the multimodel ensemble mean, and the thin lines denote each model. (b),(d),(f) As in Fig. 3a, but for the three CMIP categories.

Citation: Journal of Climate 37, 20; 10.1175/JCLI-D-24-0002.1

We note that the Niño-3.4 temperature tendency increases during the developing phase in all categories of the models (Figs. 10b,d,f). During the peak phase, in contrast, the temperature tendency decreases only in the models with an advanced ENSO peak but remains stable in the models with a delayed and unchanged ENSO peak. Therefore, changes in the Niño-3.4 temperature tendency during its peak phase may be responsible for the shift in ENSO phase locking.

Based on the heat budget analysis of the CMIP models, we identified six terms that [i.e., u(T¯/x), u¯(T/x), υ¯(T/y), wb¯(T/Hm), wb¯(Tb/Hm), and Qnet/(ρ0cpHm)] significantly contribute to the changes in the temperature tendency (Fig. 5). The intermodel spreads in the six terms potentially contribute to the distinct changes in ENSO evolution in the three categories of models and thus impact phase locking. Given that thermodynamic and dynamic processes are closely associated with SST anomalies during ENSO, we attempt to explain the intermodel uncertainty in the six heat budget terms from the perspective of T′ (Fig. 11). The results show that the changes in dynamic feedback are strongly regulated by the changes in T′ (Figs. 11a–d). A warmer T′ corresponds to stronger positive dynamical feedback, decelerating the decay of ENSO. This could potentially explain the differences between the advanced and other two categories. In contrast, the damping terms exhibit negative correlations with T′ (Figs. 11e,f), which partially counteract the effects of dynamic feedback and accelerate the decay of ENSO. The complex interactions between these terms may be the underlying cause of the intermodel spread in the changes in ENSO phase locking. Further investigations are needed to understand the specific physical mechanisms involved.

Fig. 11.
Fig. 11.

Scatterplots of changes in the anomalies of the Niño-3.4 mixed layer vertical mean ocean temperature (K) and changes in the (a) zonal advective feedback, (b) zonal anomalous temperature advection, (c) meridional advective feedback, (d) thermocline feedback, (e) upwelling damping, and (f) thermal damping term Q for Oct(0)–Mar(1) during ENSO events from 1970–99 to 2060–89 among 34 CMIP models. The red (blue) circles denote the 16(8) models with an advanced (delayed) ENSO peak phase, based on the peak time of the monthly standard deviation of the Niño-3.4 SST anomalies. The gray circles denote the 10 models with no shift in the ENSO peak phase. The black dashed line denotes the linear regression.

Citation: Journal of Climate 37, 20; 10.1175/JCLI-D-24-0002.1

The responses of the seasonal cycle in the tropical Pacific to global warming play important roles in the shift in ENSO phase locking. The seasonal cycle of climatological SST in the equatorial Pacific is weakened under global warming, indicating the most pronounced El Niño–like warming from boreal autumn to winter. This change means that the weakened zonal SST gradient in the central-eastern equatorial Pacific reduces the zonal advective feedback during the ENSO peak phase. In addition, the seasonal changes in the mean state intensify the meridional advective and thermocline feedbacks more efficiently during the developing phase than during the peak phase. It is worth noting that the seasonal cycles of the mean states in the equatorial Pacific do not exhibit a significant phase shift under global warming (Fig. 7). Therefore, the advance of the ENSO peak phase cannot be simply attributed to changes in the mean state seasonal cycle but comes from the different adjustments of ENSO-related ocean–atmosphere feedbacks during different seasons under global warming.

The above analysis is based mainly on a dynamic diagnosis of the mixed layer heat budget. However, the physical framework related to the ENSO phase-locking advance has not been well established. In particular, we cannot fully understand why ENSO-related dynamic feedbacks are more enhanced in the development phase than in the peak phase. In addition, the advance of ENSO phase locking can be explained by ENSO cycle theories. For example, the strengthening of oceanic stratification accelerates oceanic wave propagation (Li et al. 2020), and therefore, from the perspective of delayed oscillator theory (Battisti and Hirst 1989), ENSO will develop and decay more rapidly under global warming. Alternatively, shoaling of the oceanic mixed layer under global warming will enhance the response of ocean circulation to wind stress (Peng et al. 2022). Thus, we can also understand the advance of ENSO phase locking from the perspective of the recharge oscillator (Jin 1997). In the future, we will examine the advance of ENSO phase locking from the perspective of ENSO cycle theory.

Acknowledgments.

This work is supported by the National Natural Science Foundation of China (41975092 and 42230405), the National Key R&D Program of China (2023YFF0805100), the Shandong Natural Science Foundation Project (ZR2019ZD12), and Taishan Scholars Project of Shandong Province (tsqn202306095).

Data availability statement.

The CESM-LE outputs were obtained from data sets available to the community | Community Earth System Model (ucar.edu). The CMIP5 outputs were obtained from CMIP5 Data Search | CMIP5 | ESGF-CoG (llnl.gov). The CMIP6 outputs were obtained from cmip6 – Home | ESGF-CoG (llnl.gov). The ECCO datasets were provided by Index of/OceanProjects/ECCO/ECCOv4/Release3/ (utexas.edu).

APPENDIX

Definition of the Mixed Layer Depth

The ocean mixed layer constitutes a fundamental component of ocean dynamics and ocean–atmosphere interactions and plays an important role in the ENSO cycle. In heat budget analysis, the mixed layer depth Hm has conventionally been regarded as a constant with no spatial pattern and does not change under global warming, such as a fixed value of 50 m. However, the depth and characteristics of the mixed layer can vary both spatially and temporally, depending on factors such as location, season, and atmospheric conditions. Specifically, there are pronounced zonal differences in Hm in the equatorial Pacific, which are associated with the mean state ocean currents and atmospheric circulations. Moreover, the response of ocean temperature to global warming is not vertically uniform. Generally, the upper ocean warms faster due to heat accumulation, leading to enhanced stratification and a shoaling of the mixed layer. Conducting heat budget analysis with a fixed mixed layer depth may result in substantial biases. For instance, the vertical heat exchange at the bottom of the mixed layer will be greatly underestimated if the Hm used is not accurate.

Mixing in the ocean mixed layer homogenizes properties such as temperature and salinity, ensuring vertically uniform conditions within this layer, including interannual variances in ocean temperature (Fig. A1). In this study, we define Hm as the depth at which the local vertical gradient of the interannual variance in ocean temperature reaches its maximum. This particular definition of Hm is applicable to the heat budget processes of the ENSO cycle, given its inherently interannual time scale. Besides, the new Hm captures the shoaling of the ocean mixed layer owing to the changes in the ocean vertical structure under global warming (Fig. A1). We calculate the annual-mean climatological Hm on every grid during 1970–99 and 2060–89 for each member to perform heat budget analysis. To maintain the necessary mass balance of the water volume and ensure the integrity and continuity of the heat budget analysis, Hm is treated as a temporally constant value without seasonal variance during both 1970–99 and 2060–89 periods.

Fig. A1.
Fig. A1.

Annual-mean ocean temperature (contour; K) and its interannual variance (shading; K) during (a) 1970–99 and (b) 2060–89. The black solid denotes the mixed layer depth, defined as the depth where the local vertical gradient of the interannual variance in the ocean temperature reaches its maximum. The gray solid line denotes the mixed layer depth, defined as the depth where the temperature is 0.5 K cooler than the surface temperature. The black dashed line denotes the thermocline depth.

Citation: Journal of Climate 37, 20; 10.1175/JCLI-D-24-0002.1

To verify the applicability of the definition and the robustness of the results, we also performed the heat budget using another Hm (defined as the depth where the temperature is 0.5 K cooler than the surface). The results are similar (Fig. S4).

REFERENCES

  • An, S. I., and F. F. Jin, 2004: Nonlinearity and asymmetry of ENSO. J. Climate, 17, 23992412, https://doi.org/10.1175/1520-0442(2004)017<2399:NAAOE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Battisti, D. S., and A. C. Hirst, 1989: Interannual variability in a tropical atmosphere–ocean model: Influence of the basic state, ocean geometry and nonlinearity. J. Atmos. Sci., 46, 16871712, https://doi.org/10.1175/1520-0469(1989)046<1687:IVIATA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bjerknes, J., 1969: Atmospheric teleconnections from the equatorial Pacific. Mon. Wea. Rev., 97, 163172, https://doi.org/10.1175/1520-0493(1969)097<0163:ATFTEP>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Burgers, G., and D. B. Stephenson, 1999: The “normality” of El Niño. Geophys. Res. Lett., 26, 10271030, https://doi.org/10.1029/1999GL900161.

    • Search Google Scholar
    • Export Citation
  • Cai, W., and Coauthors, 2015: ENSO and greenhouse warming. Nat. Climate Change, 5, 849859, https://doi.org/10.1038/nclimate2743.

  • Cai, W., and Coauthors, 2018: Increased variability of eastern Pacific El Niño under greenhouse warming. Nature, 564, 201206, https://doi.org/10.1038/s41586-018-0776-9.

    • Search Google Scholar
    • Export Citation
  • Cai, W., B. Ng, T. Geng, L. Wu, A. Santoso, and M. J. McPhaden, 2020: Butterfly effect and a self-modulating El Niño response to global warming. Nature, 585, 6873, https://doi.org/10.1038/s41586-020-2641-x.

    • Search Google Scholar
    • Export Citation
  • Cai, W., and Coauthors, 2021: Changing El Niño–Southern Oscillation in a warming climate. Nat. Rev. Earth Environ., 2, 628644, https://doi.org/10.1038/s43017-021-00199-z.

    • Search Google Scholar
    • Export Citation
  • Cai, W., B. Ng, G. Wang, A. Santoso, L. Wu, and K. Yang, 2022: Increased ENSO sea surface temperature variability under four IPCC emission scenarios. Nat. Climate Change, 12, 228231, https://doi.org/10.1038/s41558-022-01282-z.

    • Search Google Scholar
    • Export Citation
  • Cai, W., and Coauthors, 2023: Anthropogenic impacts on twentieth-century ENSO variability changes. Nat. Rev. Earth Environ., 4, 407418, https://doi.org/10.1038/s43017-023-00427-8.

    • Search Google Scholar
    • Export Citation
  • Chen, H.-C., and F.-F. Jin, 2020: Fundamental behavior of ENSO phase locking. J. Climate, 33, 19531968, https://doi.org/10.1175/JCLI-D-19-0264.1.

    • Search Google Scholar
    • Export Citation
  • Chen, H.-C., and F.-F. Jin, 2021: Simulations of ENSO phase-locking in CMIP5 and CMIP6. J. Climate, 34, 51355149, https://doi.org/10.1175/JCLI-D-20-0874.1.

    • Search Google Scholar
    • Export Citation
  • Chen, H.-C., and F.-F. Jin, 2022: Dynamics of ENSO phase-locking and its biases in climate models. Geophys. Res. Lett., 49, e2021GL097603, https://doi.org/10.1029/2021GL097603.

    • Search Google Scholar
    • Export Citation
  • Chen, L., T. Li, and Y. Yu, 2015: Causes of strengthening and weakening of ENSO amplitude under global warming in four CMIP5 models. J. Climate, 28, 32503274, https://doi.org/10.1175/JCLI-D-14-00439.1.

    • Search Google Scholar
    • Export Citation
  • Clement, A. C., R. Seager, M. A. Cane, and S. E. Zebiak, 1996: An ocean dynamical thermostat. J. Climate, 9, 21902196, https://doi.org/10.1175/1520-0442(1996)009<2190:AODT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Collins, M., and Coauthors, 2010: The impact of global warming on the tropical Pacific Ocean and El Niño. Nat. Geosci., 3, 391397, https://doi.org/10.1038/ngeo868.

    • Search Google Scholar
    • Export Citation
  • Dommenget, D., and Y. Yu, 2016: The seasonally changing cloud feedbacks contribution to the ENSO seasonal phase-locking. Climate Dyn., 47, 36613672, https://doi.org/10.1007/s00382-016-3034-6.

    • Search Google Scholar
    • Export Citation
  • Eyring, V., S. Bony, G. A. Meehl, C. A. Senior, B. Stevens, R. J. Stouffer, and K. E. Taylor, 2016: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev., 9, 19371958, https://doi.org/10.5194/gmd-9-1937-2016.

    • Search Google Scholar
    • Export Citation
  • Forget, G., J.-M. Campin, P. Heimbach, C. N. Hill, R. M. Ponte, and C. Wunsch, 2015: ECCO version 4: An integrated framework for non-linear inverse modeling and global ocean state estimation. Geosci. Model Dev., 8, 30713104, https://doi.org/10.5194/gmd-8-3071-2015.

    • Search Google Scholar
    • Export Citation
  • Fukumori, I., O. Wang, I. Fenty, G. Forget, P. Heimbach, and R. M. Ponte, 2017: ECCO version 4 release 3. MIT DSpace Rep., 10 pp., http://hdl.handle.net/1721.1/110380.

  • Galanti, E., and E. Tziperman, 2000: ENSO’s phase locking to the seasonal cycle in the fast-SST, fast-wave, and mixed-mode regimes. J. Atmos. Sci., 57, 29362950, https://doi.org/10.1175/1520-0469(2000)057<2936:ESPLTT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Galanti, E., E. Tziperman, M. Harrison, A. Rosati, R. Giering, and Z. Sirkes, 2002: The equatorial thermocline outcropping—A seasonal control on the tropical Pacific Ocean–atmosphere instability strength. J. Climate, 15, 27212739, https://doi.org/10.1175/1520-0442(2002)015<2721:TETOAS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ham, Y.-G., and J.-S. Kug, 2014: ENSO phase-locking to the boreal winter in CMIP3 and CMIP5 models. Climate Dyn., 43, 305318, https://doi.org/10.1007/s00382-014-2064-1.

    • Search Google Scholar
    • Export Citation
  • Ham, Y.-G., J.-S. Kug, D. Kim, Y.-H. Kim, and D.-H. Kim, 2013: What controls phase-locking of ENSO to boreal winter in coupled GCMs? Climate Dyn., 40, 15511568, https://doi.org/10.1007/s00382-012-1420-2.

    • Search Google Scholar
    • Export Citation
  • Heede, U. K., and A. V. Fedorov, 2023: Towards understanding the robust strengthening of ENSO and more frequent extreme El Niño events in CMIP6 global warming simulations. Climate Dyn., 61, 30473060, https://doi.org/10.1007/s00382-023-06856-x.

    • Search Google Scholar
    • Export Citation
  • Held, I. M., and B. J. Soden, 2006: Robust responses of the hydrological cycle to global warming. J. Climate, 19, 56865699, https://doi.org/10.1175/JCLI3990.1.

    • Search Google Scholar
    • Export Citation
  • Horel, J. D., and J. M. Wallace, 1981: Planetary-scale atmospheric phenomena associated with the Southern Oscillation. Mon. Wea. Rev., 109, 813829, https://doi.org/10.1175/1520-0493(1981)109<0813:PSAPAW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Huang, P., D. Chen, and J. Ying, 2017: Weakening of the tropical atmospheric circulation response to local sea surface temperature anomalies under global warming. J. Climate, 30, 81498158, https://doi.org/10.1175/JCLI-D-17-0171.1.

    • Search Google Scholar
    • Export Citation
  • Huang, P., X.-T. Zheng, X. Li, K. Hu, and Z.-Q. Zhou, 2023: More complex interactions: Continuing progress in understanding the dynamics of regional climate change under a warming climate. Innovation, 4, 100398, https://doi.org/10.1016/j.xinn.2023.100398.

    • Search Google Scholar
    • Export Citation
  • Jin, F.-F., 1997: An equatorial ocean recharge paradigm for ENSO. Part I: Conceptual model. J. Atmos. Sci., 54, 811829, https://doi.org/10.1175/1520-0469(1997)054<0811:AEORPF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Jin, F.-F., S. T. Kim, and L. Bejarano, 2006: A coupled-stability index for ENSO. Geophys. Res. Lett., 33, L23708, https://doi.org/10.1029/2006GL027221.

    • Search Google Scholar
    • Export Citation
  • Johnson, N. C., and S.-P. Xie, 2010: Changes in the sea surface temperature threshold for tropical convection. Nat. Geosci., 3, 842845, https://doi.org/10.1038/ngeo1008.

    • Search Google Scholar
    • Export Citation
  • Kay, J. E., and Coauthors, 2015: The Community Earth System Model (CESM) large ensemble project: A community resource for studying climate change in the presence of internal climate variability. Bull. Amer. Meteor. Soc., 96, 13331349, https://doi.org/10.1175/BAMS-D-13-00255.1.

    • Search Google Scholar
    • Export Citation
  • Klein, S. A., B. J. Soden, and N.-C. Lau, 1999: Remote sea surface temperature variations during ENSO: Evidence for a tropical atmospheric bridge. J. Climate, 12, 917932, https://doi.org/10.1175/1520-0442(1999)012<0917:RSSTVD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Knutson, T. R., and S. Manabe, 1995: Time-mean response over the tropical Pacific to increased CO2 in a coupled ocean-atmosphere model. J. Climate, 8, 21812199, https://doi.org/10.1175/1520-0442(1995)008<2181:TMROTT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Li, S., and Coauthors, 2020: The Pacific Decadal Oscillation less predictable under greenhouse warming. Nat. Climate Change, 10, 3034, https://doi.org/10.1038/s41558-019-0663-x.

    • Search Google Scholar
    • Export Citation
  • Li, T., 1997: Phase transition of the El Niño–Southern Oscillation: A stationary SST mode. J. Atmos. Sci., 54, 28722887, https://doi.org/10.1175/1520-0469(1997)054<2872:PTOTEN>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Li, T., Y. Zhang, E. Lu, and D. Wang, 2002: Relative role of dynamic and thermodynamic processes in the development of the Indian Ocean dipole: An OGCM diagnosis. Geophys. Res. Lett., 29, 2110, https://doi.org/10.1029/2002GL015789.

    • Search Google Scholar
    • Export Citation
  • Liao, H., C. Wang, and Z. Song, 2021: ENSO phase-locking biases from the CMIP5 to CMIP6 models and a possible explanation. Deep-Sea Res. II, 189–190, 104943, https://doi.org/10.1016/j.dsr2.2021.104943.

    • Search Google Scholar
    • Export Citation
  • Maher, N., D. Matei, S. Milinski, and J. Marotzke, 2018: ENSO change in climate projections: Forced response or internal variability? Geophys. Res. Lett., 45, 11 39011 398, https://doi.org/10.1029/2018GL079764.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., G. W. Branstator, and W. M. Washington, 1993: Tropical Pacific interannual variability and CO2 climate change. J. Climate, 6, 4263, https://doi.org/10.1175/1520-0442(1993)006<0042:TPIVAC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ng, B., W. Cai, T. Cowan, and D. Bi, 2021: Impacts of low-frequency internal climate variability and greenhouse warming on El Niño–Southern Oscillation. J. Climate, 34, 22052218, https://doi.org/10.1175/JCLI-D-20-0232.1.

    • Search Google Scholar
    • Export Citation
  • Peng, Q., S.-P. Xie, D. Wang, R. X. Huang, G. Chen, Y. Shu, J.-R. Shi, and W. Liu, 2022: Surface warming–induced global acceleration of upper ocean currents. Sci. Adv., 8, eabj8394, https://doi.org/10.1126/sciadv.abj8394.

    • Search Google Scholar
    • Export Citation
  • Philander, S. G., 1990: El Niño, La Niña and the Southern Oscillation. Academic Press, 293 pp.

  • Philander, S. G. H., 1983: El Niño Southern Oscillation phenomena. Nature, 302, 295301, https://doi.org/10.1038/302295a0.

  • Philander, S. G. H., T. Yamagata, and R. C. Pacanowski, 1984: Unstable air-sea interactions in the tropics. J. Atmos. Sci., 41, 604613, https://doi.org/10.1175/1520-0469(1984)041<0604:UASIIT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Power, S., F. Delage, C. Chung, G. Kociuba, and K. Keay, 2013: Robust twenty-first-century projections of El Niño and related precipitation variability. Nature, 502, 541545, https://doi.org/10.1038/nature12580.

    • Search Google Scholar
    • Export Citation
  • Ramanathan, V., and W. Collins, 1991: Thermodynamic regulation of ocean warming by cirrus clouds deduced from observations of the 1987 El Niño. Nature, 351, 2732, https://doi.org/10.1038/351027a0.

    • Search Google Scholar
    • Export Citation
  • Rasmusson, E. M., and T. H. Carpenter, 1982: Variations in tropical sea surface temperature and surface wind fields associated with the Southern Oscillation/El Niño. Mon. Wea. Rev., 110, 354384, https://doi.org/10.1175/1520-0493(1982)110<0354:VITSST>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Stein, K., N. Schneider, A. Timmermann, and F.-F. Jin, 2010: Seasonal synchronization of ENSO events in a linear stochastic model. J. Climate, 23, 56295643, https://doi.org/10.1175/2010JCLI3292.1.

    • Search Google Scholar
    • Export Citation
  • Stein, K., A. Timmermann, N. Schneider, F.-F. Jin, and M. F. Stuecker, 2014: ENSO seasonal synchronization theory. J. Climate, 27, 52855310, https://doi.org/10.1175/JCLI-D-13-00525.1.

    • Search Google Scholar
    • Export Citation
  • Su, J., R. Zhang, T. Li, X. Rong, J.-S. Kug, and C.-C. Hong, 2010: Causes of the El Niño and La Niña amplitude asymmetry in the equatorial eastern Pacific. J. Climate, 23, 605617, https://doi.org/10.1175/2009JCLI2894.1.

    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485498, https://doi.org/10.1175/BAMS-D-11-00094.1.

    • Search Google Scholar
    • Export Citation
  • Timmermann, A., J. Oberhuber, A. Bacher, M. Esch, M. Latif, and E. Roeckner, 1999: Increased El Niño frequency in a climate model forced by future greenhouse warming. Nature, 398, 694697, https://doi.org/10.1038/19505.

    • Search Google Scholar
    • Export Citation
  • Timmermann, A., F.-F. Jin, and M. Collins, 2004: Intensification of the annual cycle in the tropical Pacific due to greenhouse warming. Geophys. Res. Lett., 31, L12208, https://doi.org/10.1029/2004GL019442.

    • Search Google Scholar
    • Export Citation
  • Tziperman, E., S. E. Zebiak, and M. A. Cane, 1997: Mechanisms of seasonal – ENSO interaction. J. Atmos. Sci., 54, 6171, https://doi.org/10.1175/1520-0469(1997)054<0061:MOSEI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Vecchi, G. A., and B. J. Soden, 2007: Global warming and the weakening of the tropical circulation. J. Climate, 20, 43164340, https://doi.org/10.1175/JCLI4258.1.

    • Search Google Scholar
    • Export Citation
  • Wang, B., R. Wu, and X. Fu, 2000: Pacific–East Asian teleconnection: How does ENSO affect East Asian climate? J. Climate, 13, 15171536, https://doi.org/10.1175/1520-0442(2000)013<1517:PEATHD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wang, Y., Y. Luo, J. Lu, and F. Liu, 2019: Changes in ENSO amplitude under climate warming and cooling. Climate Dyn., 52, 18711882, https://doi.org/10.1007/s00382-018-4224-1.

    • Search Google Scholar
    • Export Citation
  • Wengel, C., M. Latif, W. Park, J. Harlaß, and T. Bayr, 2018: Seasonal ENSO phase locking in the Kiel Climate Model: The importance of the equatorial cold sea surface temperature bias. Climate Dyn., 50, 901919, https://doi.org/10.1007/s00382-017-3648-3.

    • Search Google Scholar
    • Export Citation
  • Xie, S.-P., 1994: On the genesis of the equatorial annual cycle. J. Climate, 7, 20082013, https://doi.org/10.1175/1520-0442(1994)007<2008:OTGOTE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Xie, S.-P., 1996: Westward propagation of latitudinal asymmetry in a coupled ocean–atmosphere model. J. Atmos. Sci., 53, 32363250, https://doi.org/10.1175/1520-0469(1996)053<3236:WPOLAI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Xie, S.-P., C. Deser, G. A. Vecchi, J. Ma, H. Teng, and A. T. Wittenberg, 2010: Global warming pattern formation: Sea surface temperature and rainfall. J. Climate, 23, 966986, https://doi.org/10.1175/2009JCLI3329.1.

    • Search Google Scholar
    • Export Citation
  • Yan, B., and R. Wu, 2007: Relative roles of different components of the basic state in the phase locking of El Niño mature phases. J. Climate, 20, 42674277, https://doi.org/10.1175/JCLI4242.1.

    • Search Google Scholar
    • Export Citation
  • Yeh, S.-W., J.-S. Kug, B. Dewitte, M.-H. Kwon, B. P. Kirtman, and F.-F. Jin, 2009: El Niño in a changing climate. Nature, 461, 511514, https://doi.org/10.1038/nature08316.

    • Search Google Scholar
    • Export Citation
  • Yeh, S.-W., and Coauthors, 2018: ENSO atmospheric teleconnections and their response to greenhouse gas forcing. Rev. Geophys., 56, 185206, https://doi.org/10.1002/2017RG000568.

    • Search Google Scholar
    • Export Citation
  • Ying, J., P. Huang, T. Lian, and H. Tan, 2018: Understanding the effect of an excessive cold tongue bias on projecting the tropical Pacific SST warming pattern in CMIP5 models. Climate Dyn., 52, 18051818, https://doi.org/10.1007/s00382-018-4219-y.

    • Search Google Scholar
    • Export Citation
  • Zheng, X.-T., 2019: Indo-Pacific climate modes in warming climate: Consensus and uncertainty across model projections. Curr. Climate Change Rep., 5, 308321, https://doi.org/10.1007/s40641-019-00152-9.

    • Search Google Scholar
    • Export Citation
  • Zheng, X.-T., S.-P. Xie, L.-H. Lv, and Z.-Q. Zhou, 2016: Intermodel uncertainty in ENSO amplitude change tied to Pacific Ocean warming pattern. J. Climate, 29, 72657279, https://doi.org/10.1175/JCLI-D-16-0039.1.

    • Search Google Scholar
    • Export Citation
  • Zheng, X.-T., C. Hui, and S.-W. Yeh, 2018: Response of ENSO amplitude to global warming in CESM large ensemble: Uncertainty due to internal variability. Climate Dyn., 50, 40194035, https://doi.org/10.1007/s00382-017-3859-7.

    • Search Google Scholar
    • Export Citation
  • Zheng, X.-T., C. Hui, S.-P. Xie, W. Cai, and S.-M. Long, 2019: Intensification of El Niño rainfall variability over the tropical Pacific in the slow oceanic response to global warming. Geophys. Res. Lett., 46, 22532260, https://doi.org/10.1029/2018GL081414.

    • Search Google Scholar
    • Export Citation
  • Zheng, X.-T., J. Lu, and C. Hui, 2021: Response of seasonal phase locking of Indian Ocean dipole to global warming. Climate Dyn., 57, 27372751, https://doi.org/10.1007/s00382-021-05834-5.

    • Search Google Scholar
    • Export Citation
  • Zhou, Z.-Q., S.-P. Xie, X.-T. Zheng, Q. Liu, and H. Wang, 2014: Global warming–induced changes in El Niño teleconnections over the North Pacific and North America. J. Climate, 27, 90509064, https://doi.org/10.1175/JCLI-D-14-00254.1.

    • Search Google Scholar
    • Export Citation

Supplementary Materials

Save
  • An, S. I., and F. F. Jin, 2004: Nonlinearity and asymmetry of ENSO. J. Climate, 17, 23992412, https://doi.org/10.1175/1520-0442(2004)017<2399:NAAOE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Battisti, D. S., and A. C. Hirst, 1989: Interannual variability in a tropical atmosphere–ocean model: Influence of the basic state, ocean geometry and nonlinearity. J. Atmos. Sci., 46, 16871712, https://doi.org/10.1175/1520-0469(1989)046<1687:IVIATA>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bjerknes, J., 1969: Atmospheric teleconnections from the equatorial Pacific. Mon. Wea. Rev., 97, 163172, https://doi.org/10.1175/1520-0493(1969)097<0163:ATFTEP>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Burgers, G., and D. B. Stephenson, 1999: The “normality” of El Niño. Geophys. Res. Lett., 26, 10271030, https://doi.org/10.1029/1999GL900161.

    • Search Google Scholar
    • Export Citation
  • Cai, W., and Coauthors, 2015: ENSO and greenhouse warming. Nat. Climate Change, 5, 849859, https://doi.org/10.1038/nclimate2743.

  • Cai, W., and Coauthors, 2018: Increased variability of eastern Pacific El Niño under greenhouse warming. Nature, 564, 201206, https://doi.org/10.1038/s41586-018-0776-9.

    • Search Google Scholar
    • Export Citation
  • Cai, W., B. Ng, T. Geng, L. Wu, A. Santoso, and M. J. McPhaden, 2020: Butterfly effect and a self-modulating El Niño response to global warming. Nature, 585, 6873, https://doi.org/10.1038/s41586-020-2641-x.

    • Search Google Scholar
    • Export Citation
  • Cai, W., and Coauthors, 2021: Changing El Niño–Southern Oscillation in a warming climate. Nat. Rev. Earth Environ., 2, 628644, https://doi.org/10.1038/s43017-021-00199-z.

    • Search Google Scholar
    • Export Citation
  • Cai, W., B. Ng, G. Wang, A. Santoso, L. Wu, and K. Yang, 2022: Increased ENSO sea surface temperature variability under four IPCC emission scenarios. Nat. Climate Change, 12, 228231, https://doi.org/10.1038/s41558-022-01282-z.

    • Search Google Scholar
    • Export Citation
  • Cai, W., and Coauthors, 2023: Anthropogenic impacts on twentieth-century ENSO variability changes. Nat. Rev. Earth Environ., 4, 407418, https://doi.org/10.1038/s43017-023-00427-8.

    • Search Google Scholar
    • Export Citation
  • Chen, H.-C., and F.-F. Jin, 2020: Fundamental behavior of ENSO phase locking. J. Climate, 33, 19531968, https://doi.org/10.1175/JCLI-D-19-0264.1.

    • Search Google Scholar
    • Export Citation
  • Chen, H.-C., and F.-F. Jin, 2021: Simulations of ENSO phase-locking in CMIP5 and CMIP6. J. Climate, 34, 51355149, https://doi.org/10.1175/JCLI-D-20-0874.1.

    • Search Google Scholar
    • Export Citation
  • Chen, H.-C., and F.-F. Jin, 2022: Dynamics of ENSO phase-locking and its biases in climate models. Geophys. Res. Lett., 49, e2021GL097603, https://doi.org/10.1029/2021GL097603.

    • Search Google Scholar
    • Export Citation
  • Chen, L., T. Li, and Y. Yu, 2015: Causes of strengthening and weakening of ENSO amplitude under global warming in four CMIP5 models. J. Climate, 28, 32503274, https://doi.org/10.1175/JCLI-D-14-00439.1.

    • Search Google Scholar
    • Export Citation
  • Clement, A. C., R. Seager, M. A. Cane, and S. E. Zebiak, 1996: An ocean dynamical thermostat. J. Climate, 9, 21902196, https://doi.org/10.1175/1520-0442(1996)009<2190:AODT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Collins, M., and Coauthors, 2010: The impact of global warming on the tropical Pacific Ocean and El Niño. Nat. Geosci., 3, 391397, https://doi.org/10.1038/ngeo868.

    • Search Google Scholar
    • Export Citation
  • Dommenget, D., and Y. Yu, 2016: The seasonally changing cloud feedbacks contribution to the ENSO seasonal phase-locking. Climate Dyn., 47, 36613672, https://doi.org/10.1007/s00382-016-3034-6.

    • Search Google Scholar
    • Export Citation
  • Eyring, V., S. Bony, G. A. Meehl, C. A. Senior, B. Stevens, R. J. Stouffer, and K. E. Taylor, 2016: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev., 9, 19371958, https://doi.org/10.5194/gmd-9-1937-2016.

    • Search Google Scholar
    • Export Citation
  • Forget, G., J.-M. Campin, P. Heimbach, C. N. Hill, R. M. Ponte, and C. Wunsch, 2015: ECCO version 4: An integrated framework for non-linear inverse modeling and global ocean state estimation. Geosci. Model Dev., 8, 30713104, https://doi.org/10.5194/gmd-8-3071-2015.

    • Search Google Scholar
    • Export Citation
  • Fukumori, I., O. Wang, I. Fenty, G. Forget, P. Heimbach, and R. M. Ponte, 2017: ECCO version 4 release 3. MIT DSpace Rep., 10 pp., http://hdl.handle.net/1721.1/110380.

  • Galanti, E., and E. Tziperman, 2000: ENSO’s phase locking to the seasonal cycle in the fast-SST, fast-wave, and mixed-mode regimes. J. Atmos. Sci., 57, 29362950, https://doi.org/10.1175/1520-0469(2000)057<2936:ESPLTT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Galanti, E., E. Tziperman, M. Harrison, A. Rosati, R. Giering, and Z. Sirkes, 2002: The equatorial thermocline outcropping—A seasonal control on the tropical Pacific Ocean–atmosphere instability strength. J. Climate, 15, 27212739, https://doi.org/10.1175/1520-0442(2002)015<2721:TETOAS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ham, Y.-G., and J.-S. Kug, 2014: ENSO phase-locking to the boreal winter in CMIP3 and CMIP5 models. Climate Dyn., 43, 305318, https://doi.org/10.1007/s00382-014-2064-1.

    • Search Google Scholar
    • Export Citation
  • Ham, Y.-G., J.-S. Kug, D. Kim, Y.-H. Kim, and D.-H. Kim, 2013: What controls phase-locking of ENSO to boreal winter in coupled GCMs? Climate Dyn., 40, 15511568, https://doi.org/10.1007/s00382-012-1420-2.

    • Search Google Scholar
    • Export Citation
  • Heede, U. K., and A. V. Fedorov, 2023: Towards understanding the robust strengthening of ENSO and more frequent extreme El Niño events in CMIP6 global warming simulations. Climate Dyn., 61, 30473060, https://doi.org/10.1007/s00382-023-06856-x.

    • Search Google Scholar
    • Export Citation
  • Held, I. M., and B. J. Soden, 2006: Robust responses of the hydrological cycle to global warming. J. Climate, 19, 56865699, https://doi.org/10.1175/JCLI3990.1.

    • Search Google Scholar
    • Export Citation
  • Horel, J. D., and J. M. Wallace, 1981: Planetary-scale atmospheric phenomena associated with the Southern Oscillation. Mon. Wea. Rev., 109, 813829, https://doi.org/10.1175/1520-0493(1981)109<0813:PSAPAW>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Huang, P., D. Chen, and J. Ying, 2017: Weakening of the tropical atmospheric circulation response to local sea surface temperature anomalies under global warming. J. Climate, 30, 81498158, https://doi.org/10.1175/JCLI-D-17-0171.1.

    • Search Google Scholar
    • Export Citation
  • Huang, P., X.-T. Zheng, X. Li, K. Hu, and Z.-Q. Zhou, 2023: More complex interactions: Continuing progress in understanding the dynamics of regional climate change under a warming climate. Innovation, 4, 100398, https://doi.org/10.1016/j.xinn.2023.100398.

    • Search Google Scholar
    • Export Citation
  • Jin, F.-F., 1997: An equatorial ocean recharge paradigm for ENSO. Part I: Conceptual model. J. Atmos. Sci., 54, 811829, https://doi.org/10.1175/1520-0469(1997)054<0811:AEORPF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Jin, F.-F., S. T. Kim, and L. Bejarano, 2006: A coupled-stability index for ENSO. Geophys. Res. Lett., 33, L23708, https://doi.org/10.1029/2006GL027221.

    • Search Google Scholar
    • Export Citation
  • Johnson, N. C., and S.-P. Xie, 2010: Changes in the sea surface temperature threshold for tropical convection. Nat. Geosci., 3, 842845, https://doi.org/10.1038/ngeo1008.

    • Search Google Scholar
    • Export Citation
  • Kay, J. E., and Coauthors, 2015: The Community Earth System Model (CESM) large ensemble project: A community resource for studying climate change in the presence of internal climate variability. Bull. Amer. Meteor. Soc., 96, 13331349, https://doi.org/10.1175/BAMS-D-13-00255.1.

    • Search Google Scholar
    • Export Citation
  • Klein, S. A., B. J. Soden, and N.-C. Lau, 1999: Remote sea surface temperature variations during ENSO: Evidence for a tropical atmospheric bridge. J. Climate, 12, 917932, https://doi.org/10.1175/1520-0442(1999)012<0917:RSSTVD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Knutson, T. R., and S. Manabe, 1995: Time-mean response over the tropical Pacific to increased CO2 in a coupled ocean-atmosphere model. J. Climate, 8, 21812199, https://doi.org/10.1175/1520-0442(1995)008<2181:TMROTT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Li, S., and Coauthors, 2020: The Pacific Decadal Oscillation less predictable under greenhouse warming. Nat. Climate Change, 10, 3034, https://doi.org/10.1038/s41558-019-0663-x.

    • Search Google Scholar
    • Export Citation
  • Li, T., 1997: Phase transition of the El Niño–Southern Oscillation: A stationary SST mode. J. Atmos. Sci., 54, 28722887, https://doi.org/10.1175/1520-0469(1997)054<2872:PTOTEN>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Li, T., Y. Zhang, E. Lu, and D. Wang, 2002: Relative role of dynamic and thermodynamic processes in the development of the Indian Ocean dipole: An OGCM diagnosis. Geophys. Res. Lett., 29, 2110, https://doi.org/10.1029/2002GL015789.

    • Search Google Scholar
    • Export Citation
  • Liao, H., C. Wang, and Z. Song, 2021: ENSO phase-locking biases from the CMIP5 to CMIP6 models and a possible explanation. Deep-Sea Res. II, 189–190, 104943, https://doi.org/10.1016/j.dsr2.2021.104943.

    • Search Google Scholar
    • Export Citation
  • Maher, N., D. Matei, S. Milinski, and J. Marotzke, 2018: ENSO change in climate projections: Forced response or internal variability? Geophys. Res. Lett., 45, 11 39011 398, https://doi.org/10.1029/2018GL079764.

    • Search Google Scholar
    • Export Citation
  • Meehl, G. A., G. W. Branstator, and W. M. Washington, 1993: Tropical Pacific interannual variability and CO2 climate change. J. Climate, 6, 4263, https://doi.org/10.1175/1520-0442(1993)006<0042:TPIVAC>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Ng, B., W. Cai, T. Cowan, and D. Bi, 2021: Impacts of low-frequency internal climate variability and greenhouse warming on El Niño–Southern Oscillation. J. Climate, 34, 22052218, https://doi.org/10.1175/JCLI-D-20-0232.1.

    • Search Google Scholar
    • Export Citation
  • Peng, Q., S.-P. Xie, D. Wang, R. X. Huang, G. Chen, Y. Shu, J.-R. Shi, and W. Liu, 2022: Surface warming–induced global acceleration of upper ocean currents. Sci. Adv., 8, eabj8394, https://doi.org/10.1126/sciadv.abj8394.

    • Search Google Scholar
    • Export Citation
  • Philander, S. G., 1990: El Niño, La Niña and the Southern Oscillation. Academic Press, 293 pp.

  • Philander, S. G. H., 1983: El Niño Southern Oscillation phenomena. Nature, 302, 295301, https://doi.org/10.1038/302295a0.

  • Philander, S. G. H., T. Yamagata, and R. C. Pacanowski, 1984: Unstable air-sea interactions in the tropics. J. Atmos. Sci., 41, 604613, https://doi.org/10.1175/1520-0469(1984)041<0604:UASIIT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Power, S., F. Delage, C. Chung, G. Kociuba, and K. Keay, 2013: Robust twenty-first-century projections of El Niño and related precipitation variability. Nature, 502, 541545, https://doi.org/10.1038/nature12580.

    • Search Google Scholar
    • Export Citation
  • Ramanathan, V., and W. Collins, 1991: Thermodynamic regulation of ocean warming by cirrus clouds deduced from observations of the 1987 El Niño. Nature, 351, 2732, https://doi.org/10.1038/351027a0.

    • Search Google Scholar
    • Export Citation
  • Rasmusson, E. M., and T. H. Carpenter, 1982: Variations in tropical sea surface temperature and surface wind fields associated with the Southern Oscillation/El Niño. Mon. Wea. Rev., 110, 354384, https://doi.org/10.1175/1520-0493(1982)110<0354:VITSST>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Stein, K., N. Schneider, A. Timmermann, and F.-F. Jin, 2010: Seasonal synchronization of ENSO events in a linear stochastic model. J. Climate, 23, 56295643, https://doi.org/10.1175/2010JCLI3292.1.

    • Search Google Scholar
    • Export Citation
  • Stein, K., A. Timmermann, N. Schneider, F.-F. Jin, and M. F. Stuecker, 2014: ENSO seasonal synchronization theory. J. Climate, 27, 52855310, https://doi.org/10.1175/JCLI-D-13-00525.1.

    • Search Google Scholar
    • Export Citation
  • Su, J., R. Zhang, T. Li, X. Rong, J.-S. Kug, and C.-C. Hong, 2010: Causes of the El Niño and La Niña amplitude asymmetry in the equatorial eastern Pacific. J. Climate, 23, 605617, https://doi.org/10.1175/2009JCLI2894.1.

    • Search Google Scholar
    • Export Citation
  • Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485498, https://doi.org/10.1175/BAMS-D-11-00094.1.

    • Search Google Scholar
    • Export Citation
  • Timmermann, A., J. Oberhuber, A. Bacher, M. Esch, M. Latif, and E. Roeckner, 1999: Increased El Niño frequency in a climate model forced by future greenhouse warming. Nature, 398, 694697, https://doi.org/10.1038/19505.

    • Search Google Scholar
    • Export Citation
  • Timmermann, A., F.-F. Jin, and M. Collins, 2004: Intensification of the annual cycle in the tropical Pacific due to greenhouse warming. Geophys. Res. Lett., 31, L12208, https://doi.org/10.1029/2004GL019442.

    • Search Google Scholar
    • Export Citation
  • Tziperman, E., S. E. Zebiak, and M. A. Cane, 1997: Mechanisms of seasonal – ENSO interaction. J. Atmos. Sci., 54, 6171, https://doi.org/10.1175/1520-0469(1997)054<0061:MOSEI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Vecchi, G. A., and B. J. Soden, 2007: Global warming and the weakening of the tropical circulation. J. Climate, 20, 43164340, https://doi.org/10.1175/JCLI4258.1.

    • Search Google Scholar
    • Export Citation
  • Wang, B., R. Wu, and X. Fu, 2000: Pacific–East Asian teleconnection: How does ENSO affect East Asian climate? J. Climate, 13, 15171536, https://doi.org/10.1175/1520-0442(2000)013<1517:PEATHD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wang, Y., Y. Luo, J. Lu, and F. Liu, 2019: Changes in ENSO amplitude under climate warming and cooling. Climate Dyn., 52, 18711882, https://doi.org/10.1007/s00382-018-4224-1.

    • Search Google Scholar
    • Export Citation
  • Wengel, C., M. Latif, W. Park, J. Harlaß, and T. Bayr, 2018: Seasonal ENSO phase locking in the Kiel Climate Model: The importance of the equatorial cold sea surface temperature bias. Climate Dyn., 50, 901919, https://doi.org/10.1007/s00382-017-3648-3.

    • Search Google Scholar
    • Export Citation
  • Xie, S.-P., 1994: On the genesis of the equatorial annual cycle. J. Climate, 7, 20082013, https://doi.org/10.1175/1520-0442(1994)007<2008:OTGOTE>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Xie, S.-P., 1996: Westward propagation of latitudinal asymmetry in a coupled ocean–atmosphere model. J. Atmos. Sci., 53, 32363250, https://doi.org/10.1175/1520-0469(1996)053<3236:WPOLAI>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Xie, S.-P., C. Deser, G. A. Vecchi, J. Ma, H. Teng, and A. T. Wittenberg, 2010: Global warming pattern formation: Sea surface temperature and rainfall. J. Climate, 23, 966986, https://doi.org/10.1175/2009JCLI3329.1.

    • Search Google Scholar
    • Export Citation
  • Yan, B., and R. Wu, 2007: Relative roles of different components of the basic state in the phase locking of El Niño mature phases. J. Climate, 20, 42674277, https://doi.org/10.1175/JCLI4242.1.

    • Search Google Scholar
    • Export Citation
  • Yeh, S.-W., J.-S. Kug, B. Dewitte, M.-H. Kwon, B. P. Kirtman, and F.-F. Jin, 2009: El Niño in a changing climate. Nature, 461, 511514, https://doi.org/10.1038/nature08316.

    • Search Google Scholar
    • Export Citation
  • Yeh, S.-W., and Coauthors, 2018: ENSO atmospheric teleconnections and their response to greenhouse gas forcing. Rev. Geophys., 56, 185206, https://doi.org/10.1002/2017RG000568.

    • Search Google Scholar
    • Export Citation
  • Ying, J., P. Huang, T. Lian, and H. Tan, 2018: Understanding the effect of an excessive cold tongue bias on projecting the tropical Pacific SST warming pattern in CMIP5 models. Climate Dyn., 52, 18051818, https://doi.org/10.1007/s00382-018-4219-y.

    • Search Google Scholar
    • Export Citation
  • Zheng, X.-T., 2019: Indo-Pacific climate modes in warming climate: Consensus and uncertainty across model projections. Curr. Climate Change Rep., 5, 308321, https://doi.org/10.1007/s40641-019-00152-9.

    • Search Google Scholar
    • Export Citation
  • Zheng, X.-T., S.-P. Xie, L.-H. Lv, and Z.-Q. Zhou, 2016: Intermodel uncertainty in ENSO amplitude change tied to Pacific Ocean warming pattern. J. Climate, 29, 72657279, https://doi.org/10.1175/JCLI-D-16-0039.1.

    • Search Google Scholar
    • Export Citation
  • Zheng, X.-T., C. Hui, and S.-W. Yeh, 2018: Response of ENSO amplitude to global warming in CESM large ensemble: Uncertainty due to internal variability. Climate Dyn., 50, 40194035, https://doi.org/10.1007/s00382-017-3859-7.

    • Search Google Scholar
    • Export Citation
  • Zheng, X.-T., C. Hui, S.-P. Xie, W. Cai, and S.-M. Long, 2019: Intensification of El Niño rainfall variability over the tropical Pacific in the slow oceanic response to global warming. Geophys. Res. Lett., 46, 22532260, https://doi.org/10.1029/2018GL081414.

    • Search Google Scholar
    • Export Citation
  • Zheng, X.-T., J. Lu, and C. Hui, 2021: Response of seasonal phase locking of Indian Ocean dipole to global warming. Climate Dyn., 57, 27372751, https://doi.org/10.1007/s00382-021-05834-5.

    • Search Google Scholar
    • Export Citation
  • Zhou, Z.-Q., S.-P. Xie, X.-T. Zheng, Q. Liu, and H. Wang, 2014: Global warming–induced changes in El Niño teleconnections over the North Pacific and North America. J. Climate, 27, 90509064, https://doi.org/10.1175/JCLI-D-14-00254.1.

    • Search Google Scholar
    • Export Citation
  • Fig. 1.

    Monthly STDs of Niño-3.4 SST anomalies (K) in the (a) CESM-LE and (b) CMIP models during 1970–99 (blue lines) and 2060–89 (red lines) normalized by the STD during the whole 360 months of each period for each member or model. The thick lines denote the ensemble mean, and the thin lines denote each member or model. (c),(d) The probability of the peak month of the monthly STD for each ensemble. Seasonal mean changes in the standard deviation of Niño-3.4 SST anomalies (K) between 1970–99 and 2060–89 normalized by the STD during the whole 360 months in the (e) CESM-LE and (f) CMIP models. The bars denote the ensemble mean, and the box-and-whisker plots show the 10th, 25th, 50th, 75th, and 90th percentiles.

  • Fig. 2.

    Mixed layer heat budget for the ENSO composite in (a) ECCO v4.3 during 1992–2015, (b) CESM-LE, and (c) CMIP during 1970–99. The terms 1–12 denote the 12 terms on the right-hand side of Eq. (1), and the term 13 denotes the temperature tendency. The formulas for the 12 terms are as follows: term 1: Qnet/(ρ0cpHm), term 2: u(T¯/x), term 3: u¯(T/x), term 4: u(T/x), term 5: υ(T¯/y), term 6: υ¯(T/y), term 7: υ(T/y), term 8: wb(TTb¯/Hm), term 9: wb¯(T/Hm), term 10: wb¯(Tb/Hm), term 11: wb[(TTb)/Hm], term 12: R, and term 13: T/t.

  • Fig. 3.

    (a) The temporal evolution of the tendency of the mixed layer temperature anomalies (K month−1) in the Niño-3.4 region for composite ENSO events during 1970–99 (blue line) and 2060–89 (red line) in the CESM-LE, where the light red and light blue shadings denote the two periods, i.e., from April to September of the developing year [Apr(0)–Sep(0)] and from October of the developing year to March of the decay year [Oct(0)–Mar(1)], which are referred to in the following five subplots as pink and blue bars. (b)–(f) Six-month mean changes in each term of the mixed layer heat budget in the Niño-3.4 region for composite ENSO events from 1970–99 to 2060–89. Changes in the mixed layer temperature anomaly tendency [Tt, i.e., T/t in Eq. (1)] are decomposed into changes in zonal Du, meridional Dυ, and vertical Dw heat advection; heat exchange with atmosphere Q, and residual R in (b). Changes in zonal heat advection D are decomposed into changes in u(T¯/x), u¯(T/x), and u(T/x) in (c). Changes in meridional heat advection Dυ are decomposed into changes in υ(T¯/y), υ¯(T/y), and υ(T/y) in (d). The partial differential operators are simplified as subscripts. Changes in vertical heat advection Dw are decomposed into changes in wb(TTb¯/Hm), wb¯(T/Hm), wb¯(Tb/Hm), and wb[(TTb)/Hm] in (e). Changes in Q are decomposed into changes in SW radiation, LW radiation, LHF, and SHF in (f).

  • Fig. 4.

    Changes (K month−1) in (a) zonal advective feedback, (b) meridional advective feedback, and (c) thermocline feedback and the contributions of mean state change and anomaly change for Apr(0)–Sep(0) (pink bars) and Oct(0)–Mar(1) (blue bars) during composite ENSO events from 1970–99 to 2060–89 in the CESM-LE.

  • Fig. 5.

    As in Fig. 3, but for the CMIP models.

  • Fig. 6.

    Ensemble mean changes (shading) in the equatorial Pacific (averaged in 5°S–5°N) monthly climatological (a) SST (K), (b) low cloud fraction, (c) surface net SW radiation flux (W m−2), (d) rainfall (mm day−1), (e) surface zonal wind stress (TAUX; dyn cm−2), and (f) surface downward latent heat flux (W m−2) from 1970–99 to 2060–89 in CEMS-LE, with the climatology in 1970–99 (contours).

  • Fig. 7.

    Multimember ensemble mean changes (K) in the tropical Pacific climatological SST for the (a) April–September mean and (b) October–March mean in the CESM-LE from 1970–99 to 2060–89. The blue rectangles represent the Niño-3.4 region. (c) The climatological SST zonal gradient between the Niño-3 and Niño-4 regions during 1970–99 (blue lines) and 2060–89 (red lines) in the CESM-LE. The thick lines denote the ensemble mean, and the thin lines denote each member.

  • Fig. 8.

    Multimember ensemble mean changes in the zonal mean meridional velocity (m s−1; shading) over 120°–170°W in the CESM-LE from 1970–99 to 2060–89 for (a) April–September and (b) October–March with the monthly climatology (contours) during 1970–99. The dashed (solid) black line denotes the annual-mean mixed layer depth during 1970–99 (2060–89). (c) Ensemble mean changes (m s−1; shading) in the zonal mean mixed layer vertical mean meridional current over 120°–170°W in the CESM-LE from 1970–99 to 2060–89, with the monthly climatology (contours) during 1970–99.

  • Fig. 9.

    Ensemble mean changes in the equatorial (averaged in 5°S–5°N) Pacific upper 200-m climatological ocean temperature (K) for (a) April–September and (b) October–March from 1970–99 to 2060–89 in the CESM-LE. The dashed (solid) black lines denote the climatological thermocline depth for each 6-month period during 1970–99 (2060–89). The dashed (solid) light gray lines denote the annual-mean mixed layer depth during 1970–99 (2060–89), used for the heat budget analysis.

  • Fig. 10.

    Monthly standard deviations of Niño-3.4 SST anomalies during 1970–99 (blue lines) and 2060–89 (red lines) for CMIP models with (a) advanced, (c) delayed, and (e) unchanged phase locking of ENSO. The thick lines denote the multimodel ensemble mean, and the thin lines denote each model. (b),(d),(f) As in Fig. 3a, but for the three CMIP categories.

  • Fig. 11.

    Scatterplots of changes in the anomalies of the Niño-3.4 mixed layer vertical mean ocean temperature (K) and changes in the (a) zonal advective feedback, (b) zonal anomalous temperature advection, (c) meridional advective feedback, (d) thermocline feedback, (e) upwelling damping, and (f) thermal damping term Q for Oct(0)–Mar(1) during ENSO events from 1970–99 to 2060–89 among 34 CMIP models. The red (blue) circles denote the 16(8) models with an advanced (delayed) ENSO peak phase, based on the peak time of the monthly standard deviation of the Niño-3.4 SST anomalies. The gray circles denote the 10 models with no shift in the ENSO peak phase. The black dashed line denotes the linear regression.

  • Fig. A1.

    Annual-mean ocean temperature (contour; K) and its interannual variance (shading; K) during (a) 1970–99 and (b) 2060–89. The black solid denotes the mixed layer depth, defined as the depth where the local vertical gradient of the interannual variance in the ocean temperature reaches its maximum. The gray solid line denotes the mixed layer depth, defined as the depth where the temperature is 0.5 K cooler than the surface temperature. The black dashed line denotes the thermocline depth.

All Time Past Year Past 30 Days
Abstract Views 4125 4125 0
Full Text Views 1934 1934 222
PDF Downloads 551 551 70