1

Collapsed upwelling weakens ENSO under sustained warming beyond the 21st2

century3

Qihua Peng1, Shang-Ping Xie1*, and Clara Deser2

4

Affiliations:

5

1

6

92093, USA

7

2

8

*Correspondence author. Email: sxie@ucsd.edu

9Abstract: The El Niño Southern Oscillation (ENSO) in a warming climate has been studied

10

extensively, but the response beyond 2100 has received little attention. Here, using long-term

11

model simulations we find that while ENSO variability exhibits diverse changes in the short term,

12

there is a robust reduction in ENSO variability by 2300. Continued warming beyond 2100

13

pushes sea surface temperature above the convective threshold over the eastern Pacific, causing

14

collapsed mean equatorial upwelling with intensified deep convection. We show that the15

weakened thermocline feedback due to collapsed upwelling and increased thermal expansion

16

coefficient, along with enhanced thermodynamic damping, are crucial to reducing ENSO

17

amplitude under sustained warming. Our results suggest a threshold behavior in the tropical

18

Pacific, where a convective atmosphere over the eastern equatorial Pacific causes dramatic shifts

19

in ENSO variability. This threshold is not crossed under low emissions scenarios.

Scripps Institution of Oceanography, University of California San Diego, La Jolla, California

National Center for Atmospheric Research, Boulder, Colorado, USA

1

20

The El Niño–Southern Oscillation (ENSO) is the dominant mode of interannual climate

21

variability, characterized by large variations in sea surface temperature (SST) over the tropical

22

Pacific. Potential future changes in ENSO variability due to greenhouse gas increase could have23

far-reaching impacts on extreme weather, ecosystems, and socioeconomic conditions around the24

world1,2. The projected ENSO SST response to global warming remains under debate. While25

early research based on Coupled Model Intercomparison Project (CMIP) phases 3 and 5

26

indicates a lack of consensus among climate models regarding the ENSO SST response to global

27

warming3-6, some studies suggest an emerging consensus among models in CMIP6 for increased

28

ENSO SST variability in the 21st century7-9. Complicating the matter are recent studies

29

suggesting a reduction in ENSO SST variability under strong CO2 forcing (e.g., 4xCO2)10,11.

30

These disagreements are likely linked to scenario differences, model generation7, and the mean

31

warming patterns6.

32

The projected change in ENSO SST amplitude is time-varying, with an increasing trend

33

before 2040 and a decreasing trend thereafter12. This underscores the necessity to investigate

34

ENSO responses across various stages of global warming. Most studies have so far focused on

35

the transient ENSO responses in the 21st century or under idealized CO2 forcings. Nevertheless,36

how ENSO will respond to sustained high warming beyond the 21st century has received little

37

attention. Under high emission scenarios (RCP8.5 and SSP585), global temperatures are38

expected to continue rising beyond 2100, with much greater warming magnitudes than in the 21st

39

century. This potentially leads to distinct changes in ENSO variability, involving different

40

physical mechanisms. A recent study10 shows a robust decrease in ENSO amplitude under global

41

warming on millennial horizons, contrasting with the diverse ENSO responses in the 21st century.

42

However, limited outputs available within the Long Run Model Intercomparison Project43(LongRunMIP)13 hinder a rigorous examination of detailed air-sea processes, particularly the44role of dynamic adjustments10. Several explanations—some mutually conflicting—have been

45

proposed to explain the reduced ENSO variability, ranging from a longitudinal variation in the46

surface47

damping10. Nevertheless, the key ocean-atmospheric processes causing the reduction in ENSO

48

variability under sustained global warming remain unclear.

warming

rate

acrossthe

Indo-Pacificbasin12 toenhanced

thermodynamic

49

The present study aims to gain insights for a more comprehensive understanding of the50

coupled dynamics of ENSO change under sustained global warming. Based on climate model2

51

simulations extended to 2300, we find a consistent reduction in ENSO variability under high52

CO2 emissions during 2241-2290 when compared to the present-day climate. Our results

53

highlight that collapsed upwelling and increased thermal expansion coefficient weaken the54

thermocline feedback, which, in conjunction with enhanced thermodynamic damping, are crucial

55

to reducing ENSO SST variability under sustained warming. Importantly, we reveal that these56

dynamic and thermodynamic adjustments are closely linked to the transition of the equatorial

57

cold tongue into a warm and convective mean state.

58

Suppressed ENSO variability

59

We analyze 16 available climate models with extension runs to 2299 or 2300 under high-60

emission scenarios (RCP8.5 and SSP585) from CMIP5 and CMIP6. In these extension runs,

61

anthropogenic radiative forcing continues to increase beyond 2100, reaching slightly above 1262

W/m2 in 2250 and subsequently leveling off14 (see details in Methods). The climate models

63

reasonably simulate the present-day ENSO with large SST variance in the central and eastern

64

Pacific Ocean as in observations (Fig. 1d). We investigate the ENSO response to substantial

65

global warming by contrasting its monthly SSTA standard deviation (STD) in the present-day

66(1941-1990) with that of the 23rd century (2241-2290) in each climate model.

67

Fifteen out of sixteen models see a robust decrease in Niño 3.4 SST variability during 2241-

68

2290 (Fig. 1a), statistically significant above the 95% confidence level from a bootstrap test

69

(Extended Data Fig. 1a). The only exception is MPI-ESM-LR. As an earlier CMIP5 model, MPI-

70

ESM-LR shows limited skill in simulating present-day El Niño with two warming centers in the71eastern and western Pacific, contrasting with other models (Extended Data Fig. 2). Hereafter, we

72

exclude this model from further analysis. For the remaining 15 models, the multi-model73

ensemble (MME) mean decrease in Niño 3.4 amplitude is 41%±18%. Specifically, the ENSO

74

amplitude initially undergoes a slight increase up to ~2020 (Fig. 1b) and then gradually decreases

75

toward the current level in the early 2100s, with considerable inter-model differences (Extended

76

Data Fig. 1b)12,15. Around 2120, the ENSO amplitude undergoes a rapid decrease, and by 2300,

77

there is a consistent decrease in ENSO amplitude with high inter-model consensus (Figs. 1a and781b). The SST variance pattern over the tropical Pacific Ocean does not change much, which can

79

be represented by the Nino 3.4 index (Figs. 1e and 1f). Note that our assessment of ENSO

80

variability change employs monthly SST STD, with potential impacts from ENSO seasonality3

81change. The reduced ENSO amplitude is evident across almost all models when ENSO

82

seasonality change is considered (Extended Data Fig. 3). The decreased ENSO variability on

83

multi-century horizons is also evident in ACCESS-ESM1-5 large ensemble experiments: all the84

ensemble members project reduced ENSO variations during 2241-2290, with an ensemble mean

85

decrease of ~16% (Extended Data Fig. 4a).

86

We investigate the Time-of-Emergence (ToE) of the reduction in ENSO variability (see87

Methods). Our results reveal that a weakened ENSO variability would start to emerge above the88background noise around the 2120s, with a median ToE of 2124 (interquartile range: 2059−2189).

89

However, the ToE exhibits sizeable inter-model differences. Eight models projected reduced

90

ENSO variability in the 21st century and five of them show weakened ENSO variability

91

emerging above the background noise before 2100. This inter-model difference may be closely

92

linked to model generation. Specifically, the ToE from CMIP5 models (median value: 2068)

93

generally precedes that from CMIP6 models (median value: 2168) (Figs. 1b and 1c). This much

94

earlier (later) ToE in CMIP5 (CMIP6) would lead to more (fewer) models exhibiting reduced

95

ENSO variabilities in the 21st century. This systematic ToE difference seems related to the recent

96

finding that more models project increased ENSO variability in the 21st century in CMIP6 than97

CMIP5 ensemble7-9. More research is needed to elucidate the underlying physical processes.

98

Mean state changes

99

The response of ENSO variability to global warming is closely linked to changes in the100

mean state4,16,17. In the current climate, the prevailing easterly winds in the equatorial Pacific101

Ocean shoal the thermocline and upwell cold water, forming the familiar equatorial “cold tongue”

102

in SST. Consequently, deep convection in the eastern tropical Pacific is strongly suppressed,

103

accompanied by strong trade winds (Fig. 2a) and intense upwelling.

104

All the sixteen climate models project an El Niño-like warming pattern during 2241-2290

105(Fig. 2e and Extended Data Fig. 1c), similar to that in the 21st century18,19 but with much larger

106

amplitudes. Atmospheric convective instability in the eastern Pacific is measured by the local

107

SST deviation from the tropical mean over 20°S–20°N6, which is taken as the convective

108

threshold20. The MME relative SST in the eastern Pacific remains negative through the 21st109

century (Fig. 3a), indicating suppressed deep convection. However, the El Niño-like warming

110

pattern causes the relative SST to increase and ultimately turn positive around the mid-22nd4

111

century. In the eastern Pacific, when SST exceeds the convective threshold, deep convection

112

develops (Figs. 2b and 3c). This causes gradual intrusion of rainfall and westerly wind anomalies

113

into the eastern Pacific (Figs. 2b and 2d). The westerly wind changes effectively reduce the114

equatorial upwelling (Fig. 2d) and further warm up the upper ocean in the eastern Pacific (Fig.

1153g), forming a positive feedback. Consequently, there is a notable reduction in the trade winds

116

and mean equatorial upwelling during 2241-2290 (Fig. 3f). The strongly relaxed trade winds

117

cause the background upwelling to weaken by a factor of four in the central equatorial Pacific118(Fig. 2d), a change large enough to be called a collapse. Notably, even under sustained warming,119

the trade winds and upwelling do not vanish entirely (Fig. 3f) due to the meridional advection of120

easterly momentum to the equator during much of the year by the climatological cross-equatorial

121

winds (Extended Data Fig. 1d)21. These mean state changes lead to drastic shifts in the eastern122

tropical Pacific from a dynamic perspective: the cold tongue with its suppressed convection

123

transitions to a basin-wide warm and convective state with distinct air-sea interactions. The

124

strong ocean warming and collapsed upwelling at the equator weaken ENSO variability as

125shown below.

126

Physical mechanisms

127

To investigate physical processes underlying the robust reduction in ENSO variability, we

128

quantify the relative importance of each dynamical and thermodynamic feedback for the change

129

between the present day (1941-1990) and future (2241-2290). Here we mainly focus on ten

130

models that produce positive Niño3 skewness as observed (Methods; Supplementary Table 1).

131

The thermocline (TH) feedback dominates the present-day ENSO (Fig. 4a), consistent with

132

observations22,23. The reduced ENSO variability during 2241-2290 is primarily due to the133

reduced thermocline feedback and enhanced thermodynamic damping (TD) (Fig. 4c) with high134inter-model consistency (Extended Data Fig. 5).

135

Thermocline feedback refers to the effect of thermocline displacements on SST variability

136

in the presence of background upwelling. Various definitions of the thermocline can lead to137

different, sometimes mutually contradicting, conclusions regarding changes in thermocline

138

depth10,24,25 and ENSO feedbacks in a warming climate. We sidestep this issue and use sea level139

anomaly (SLA) instead to evaluate the thermocline feedback across different time periods in

140

model projections. The SLA is equivalent to vertically integrated subsurface temperature5

141

disturbances due mostly to thermocline displacements and its zonal gradient is nearly in balance142with the zonal wind on the interannual timescales. The weakened thermocline feedback is143dominated by the mean upwelling term (w) and the subsurface temperature response term (αh)

144(Fig. 4d). Regarding the former, the strong El Niño-like warming pattern during 2241-2290 leads

145

to a substantially weakened equatorial upwelling, which limits the vertical advection of

146

subsurface temperature anomalies into the surface layer and thus reduces the SST response to147

thermocline displacements. Indeed, models with a larger reduction in the mean trade winds

148

feature a stronger reduction in ENSO variability (Fig. 2f), with an inter-model correlation of -149

0.61, statistically significant above the 95% confidence level. This supports that the reduced

150

ENSO variability during 2241-2290 is closely linked to the weakened mean equatorial upwelling.

151

Equally important for the thermocline feedback change is the robust decrease in the eastern

152

Pacific subsurface temperature sensitivity to a given SLA (or αh) in a warming climate (Figs. 4c153

and 5) as the thermal expansion coefficient (α) of water increases with temperature26, from154

2.1x10-4 at present to 2.7x10-4 °C-1 in 2300 (Extended Data Figs. 6b). There is a tendency for155

models that with larger α increase to generate a greater reduction in ENSO variability, and vice156

versa, and this relationship is statistically significant above the 95% confidence level (Extended

157

Data Fig. 6a). Specifically, the zonal momentum balance dictates that the SLA tilt response to158

wind stress anomalies (βh) changes little (Fig. 4d), but with a larger thermal expansion

159

coefficient in a warming ocean, the same SLA (or steric height) corresponds to a smaller160

subsurface temperature anomaly (Extended Data Fig. 6d), leading to a reduction in αh in the 23rd

161

century (Fig. 4d). Indeed, subsurface temperature responses to a given SLA of an El Niño during162

2241-2290 are notably smaller than at present (Fig. 5 and Extended Data Fig. 6c). Together, the163

collapsed mean upwelling and the increased thermal expansion coefficient weaken the164thermocline feedback in a warmer climate, leading to a substantial decrease in ENSO SST

165

variability.

166

The enhanced thermodynamic damping (TD) is another important mechanism for inhibiting

167

ENSO variability (Fig. 4c)10. SST variability in the eastern Pacific is associated with more heat168

loss to the atmosphere during 2241-2290 than 1941-1990, primarily due to stronger damping

169

effects of shortwave radiation and latent heat flux (Fig. 4e and Extended Data Fig. 7). These heat

170

flux changes are due to the background El Niño-like warming pattern under sustained global
6

171

warming: the faster central-eastern Pacific warming pushes the background SST above the172

convective threshold. Consequently, relatively small central and eastern Pacific SST anomalies

173

during 2241-2290 lead to large deep convection and rainfall anomalies6,27. For El Niño events

174

during 2241-2290, the increased convective response to central and eastern Pacific SST warming

175

strongly reduces downward shortwave radiation and cools SST there (Extended Data Figs. 7c176

and 7d). Moreover, saturation vapor pressure increases with temperature (the Clausius-Clapeyron177equation), and so does the evaporative damping for ENSO (Extended Data Figs. 7b).

178

In the present-day climate, the thermocline feedback in models with negative Niño3

179

skewness is only ~30% of that in models with positive skewness (Extended Data Fig. 8d) and180

observations12. Consequently, the change in thermocline feedback is also smaller in these models

181with negative skewness, where the reduced ENSO amplitude primarily results from the enhanced

182

thermodynamic damping (Extended Data Fig. 8f). This highlights the need for realistic

183

simulation of nonlinear ENSO processes.

184

A threshold behavior for rapid reduction in ENSO variability

185

Both the weakened thermocline feedback and enhanced thermodynamic damping are

186

intricately connected to the transition of the eastern Pacific from a cold, non-convective state to a

187

warm, convective state. Consequently, in relative SST space, ENSO variance drops precipitously

188

when the eastern Pacific enters a convective state (Fig. 3b). ENSO dynamics also undergo drastic

189changes, from a thermocline feedback dominant regime at present to an Ekman feedback

190

dominant regime during 2241-2290 (Figs. 4a, b). Therefore, we suggest that the convective191

threshold, as represented by relative SST and precipitation in the cold tongue region, is a192

potential key point at which ENSO and equatorial Pacific climate undergo dramatic shifts. The

193

following comparison with more moderate warming scenarios corroborates such a convective

194

threshold behavior.

195

The first example is diverse projected changes in the 21st century: CMIP3 and CMIP5

196

models disagree on the sign of projected ENSO change3-6 but recent studies suggest an inter-197

model consensus on increased ENSO SST variability among CMIP6 models7-9. Beyond the 21st198

century, both the CMIP5 and CMIP6 ensembles consistently project a substantial reduction in

199

ENSO SST amplitude under SSP585/RCP8.5. While anthropogenic warming in the 21st-century

200

results in a weakening of mean equatorial upwelling and a warming of the eastern Pacific upper7

201

ocean, the magnitudes of these changes are relatively small (Figs. 3f and 3g). The resulting

202

ENSO variability reduction might counteract other ENSO-amplifying effects such as increased

203

air-sea coupling7, or it could be too subtle to distinguish from the sizeable ensemble spread204

originating from inter-model differences or internal variability. This leads to diverse ENSO

205

responses among different models in the 21st century in a combined CMIP5-CMIP6 ensemble

206(Extended Data Fig. 1b). However, beyond the 21st century, the eastern Pacific mean SST

207continues to rise and exceeds the convective threshold (Fig. 3a). The resultant reduced

208

thermocline feedback and enhanced thermodynamic damping dominate over other processes,

209

ultimately leading to a robust decrease in ENSO amplitude in the 23rd century.

210

Another example is the distinct ENSO responses under moderate warming scenarios. We

211compare extension runs under SSP126/RCP2.6 and SSP245/RCP4.5 (see details in Methods).

212

There is a lack of inter-model consensus on changes in ENSO SST variability by 2300 under213

either the SSP126/RCP2.6 (Extended Data Figs. 4b and 9a) or SSP245/RCP4.5 (Extended Data

214

Fig. 10a) scenario. These results indicate that the ENSO variance change beyond the 21st century

215

is scenario-dependent: the robust reduction in ENSO amplitude is only apparent in high CO2

216

emission scenarios. The MME-mean relative SST in the eastern Pacific under low emission

217

scenarios remains negative through the 23rd century (Fig. 3a and Extended Data Fig. 10c),

218

indicating continued suppression of convective activity in the eastern Pacific. This implies that219

the limited warming under SSP126/RCP2.6 or SSP245/RCP4.5 is generally insufficient to220

trigger the convective threshold behavior, preventing a dramatic shift in the eastern Pacific mean

221

state. The deep convection in the eastern Pacific is still suppressed during 2241-2290 (Extended

222

Data Figs. 9b, and 10b), together with persistent easterly trade winds (Fig. 3e and Extended Data

223

Fig. 10d). These moderate mean-state perturbations lead to small changes in equatorial mean

224

upwelling (Fig. 3f) and upper ocean temperature (Fig. 3h), with relatively small impacts on the225

ocean-atmosphere coupling processes (Extended Data Fig. 9d). Consequently, the resultant

226

ENSO changes fail to surpass other factors, such as inter-model differences or internal variability.

227

This leads to divergent projections of ENSO amplitude changes under low-emission scenarios.

228

Discussion

229

Diverse changes in ENSO amplitude were projected among CMIP3 and CMIP53-6 models,230

although a tendency for increased ENSO variability in the 21st century emerged in CMIP68

231

models7-9. We show a robust decrease in ENSO SST variation under sustained large global

232

warming in the 23rd century10,12. The ocean-atmospheric processes that contribute to this ENSO

233

variance reduction are summarized in Fig. 6. As the eastern Pacific Ocean surpasses the234

convective threshold under sustained global warming, it transitions from low SSTs with

235

suppressed convection in present climate to a basin-wide warm and convective state,236

accompanied by collapsed equatorial upwelling. This change, together with the increased

237

thermal expansion coefficient, efficiently weakens the thermocline feedback. Furthermore, a238

convective eastern Pacific strongly enhances the thermodynamic damping: Relatively small SST

239

anomalies in the eastern Pacific during El Niño events in the 23rd century lead to large

240

convective rainfall anomalies, causing a strong reduction in downward shortwave radiation and241

cooling SST. Thus, both the dynamic and thermodynamic adjustments, which are closely linked

242

to mean state shifts to a convective eastern Pacific, act to reduce ENSO amplitude under243

sustained warming. These results highlight dramatical shifts in ENSO variability and equatorial

244

Pacific climate cross the convective threshold. Only substantial warming under high emission

245

scenarios beyond the 21st century can raise eastern Pacific SST above the convective threshold.

246

This explains why the projected decrease in ENSO variability does not emerge in the 21st century

247

or under low-emission scenarios (e.g., SSP126 or SSP245). These results could provide insights

248

for a more comprehensive understanding of ENSO dynamics toward more reliable projections of

249

ENSO change in warming climate.

9

250

Acknowledgements

251

Q.P. and S.-P.X. are supported by the National Science Foundation (AGS 1637450). The

252

National Center for Atmospheric Research (NCAR) is sponsored by the National Science

253

Foundation under Cooperative Agreement 1852977. We would like to acknowledge high-254

performance computing support from Cheyenne (doi:10.5065/D6RX99HX) provided by NCAR's

255

Computational and Information Systems Laboratory, sponsored by the National Science

256

Foundation. We thank M.T. Luongo for his help with grammar corrections and suggestions.

257

Author contributions

258

Q.P. and S.P.X. designed the study. Q.P., S.P.X., and C. D. carried out the analysis. Q.P. wrote the259

first draft. S.P.X. and C. D. contributed to writing and editing the manuscript.

260

Competing interests

261

The authors declare no competing interests.

262

Additional information

263

Correspondence and requests for materials should be addressed to Shang-Ping Xie.

10

264
265

Fig. 1 | ENSO amplitude changes under sustained global warming. (a) The difference in Niño

266

3.4 SST standard deviation (STD; °C) between the 23rd (2241-2290) and 20th (1941-1990)

267

centuries under the SSP585/RCP8.5 scenario. (b) The 50-year running mean ENSO amplitude268

change (°C) relative to 1850-1900 under SSP585/RCP8.5. The red line represents the MME

269mean and the color shadings indicate one inter-member standard deviation; grey (blue) lines

270

indicate individual model from CMIP5 (CMIP6), respectively; the x-axis represents the ending

271

year of each 50-year time window. (c) ToE of the reduced ENSO variability (grey bars); black272

bar indicates the median value and the error bar represents the interquartile range (n=15). The

273

SST STD for (d) present-day (1941-1990), (e) future (2241-2290) period, and (f) their difference.

274

The stippling indicates a significant difference at the 95% confidence level from the bootstrap

275

test.

11

276
277

Fig. 2 | Mean state changes in the tropical Pacific. (Left panels) Annual mean SST (°C, color

278

shading), rainfall (contours with an interval of 3 mm/day), and wind stress (N/m2, vectors) for (a)279

present-day (1941-1990), (c) future (2241-2290) and (e) their difference (future minus present-

280day). Longitude–time evolution at the equator of (b) rainfall (mm/day, color shading), and zonal

281

SST gradient for each grid point (Here we computed 𝑑𝑆𝑆𝑇/𝑑𝑥 at each grid box along the282

equator; contours with an interval of 0.005 °C/°, positive in black, and negative in grey; negative

283

values indicate cooler temperatures in the east); (d) mean equatorial upwelling at 60 m derived

284

from the models with direct vertical velocity outputs (w; 10-5 m/s, color shading; see methods)285

and zonal wind stress (Taux; contours with an interval of 0.01 N/m2; positive in black and286

negative in gray), all meridionally averaged in 2°S–2°N. (f) Scatter plots of Niño 3.4 averaged

287∆Taux (10-2N/m2, vectors) and SST STD changes from SSP585/RCP8.5 extended simulations.

12

288
289

Fig. 3 | Eastern equatorial Pacific mean state changes. (a) The 50-year running mean relative290

SST (°C; defined as deviation from the tropical mean over 20°S–20°N) averaged over the eastern

291

Pacific (EP) region (170°W–90°W, 5°S–5°N) from the historical (black), SSP585/RCP8.5 (red)292

and SSP126/RCP2.6 (blue) simulations. (b) Relationship between EP relative SST (°C) and the
13

293

Niño 3.4 STD change during 1850-2300 from historical and SSP585/RCP8.5 runs; the triangle

294

represents the relative SST in 2100. The dashed blue (black) line indicates the EP relative SST in

295

the 23rd century under the SSP126/RCP2.6 (SSP245/RCP4.5) scenario. (c), (d) Same as (a) but

296

for EP rainfall changes and zonal SST gradient, respectively. Here zonal SST gradient change is297

defined as the SST change difference (°C) between the eastern (120°W-170°W, 5°S–5°N) and298

western (150°E-170°W, 5°S–5°N) boxes, with positive values indicating an El Niño-like299

warming pattern. The evolution of (e) Taux (10-2 N/m2), (f) mean upwelling (10-6 m/s, derived

300

from models with direct vertical velocity outputs; see methods), and ocean temperature (color

301

shading) with the 14°C isotherm (black line) under (g) SSP585/RCP8.5 and (h) SSP126/RCP2.6

302

in the eastern equatorial Pacific (EEP, 170°W–90°W, 2°S–2°N). The colored shadings

303

superimposed indicate the spread of one standard deviation among inter-members. The x-axis

304

indicates the ending year of each 50-year time window.

305

14

306
307

Fig. 4 | ENSO-related air-sea feedback changes. The MME (n=10) ENSO-related air-sea

308

feedbacks (yr-1) (bars; see Methods) in the eastern Pacific (5°S–5°N, 90°W–170°W) for (a) the309

present-day (1941-1990), (b) future (2241-2290), and (c) their difference. TH, EK, ZAF, TD, and310

DD represent the thermocline feedback, Ekman feedback, zonal advective feedback,311

thermodynamic damping, and dynamic damping, respectively. (d) The contribution of mean

312

upwelling (w), the subsurface temperature response to SLA (Kelvin waves) (ah) in the eastern

313

Pacific Ocean, the zonal SLA slope response to anomalous equatorial wind forcing (βh), and the314

equatorial wind response to eastern SSTA forcing (µa) in reducing the TH feedback, respectively.

315(e) The relative importance of each heat flux component in modulating the TD feedback, which316

is represented by the regression coefficient of the heat flux (W/m²) with respect to Niño 3.4 SST

317(°C) anomalies. (f) The contributions of vertical stratification (dT/dz), upwelling response to318

anomalous equatorial wind forcing (βw), and μa in enhancing the EK feedback. The dots indicate

319

results from the ten individual models with positive Niño 3 skewness (see Methods).

15

320
321

Fig. 5 | Changes in subsurface temperature anomalies due to the increased thermal

322

expansion coefficient. Regression of ocean temperature (°C) anomalies onto EP (90°W–170°W,

323

5°S–5°N) SLA variability (mm) for (a) present day (1941-1990), (b) future (2241-2290), and (c)324their difference. Contour lines in (c) denote the present-day regression coefficient (at 0.1°C/mm

325intervals; positive in black and negative in grey). All these results are derived from the ten

326

models exhibiting positive Niño 3 skewness (see Methods). The stippling denotes differences327

that are significant at the 95% confidence level from the bootstrap test.

16

328
329

Fig. 6 | Schematic diagram for the reduced ENSO variability under sustained global

330

warming. Blue (red) color represents the physical processes that inhibit (amplify) ENSO

331

variability. The size of the circles indicates the strength of their influence on ENSO variability.

332

Sustained greenhouse warming induces an El Nino-like warming and pushes eastern Pacific SST

333

beyond the convective threshold. On the one hand, this induces the collapse of equatorial mean

334

upwelling, and the strong upper ocean warming with increased thermal expansion coefficient.

335

These mean state changes largely reduce the thermocline (TH) feedback during 2241-2290. On

336

the other hand, the convective eastern Pacific mean state strongly enhances the thermodynamic

337

damping. During 2241-2300, relatively small SST warming perturbations of ENSO could induce338

large rainfall responses, which reduces downward shortwave radiation (deep convective cloud339

feedback) and cools SST there. Evaporative damping also enhances due to strong background340

SST warming. Taken together, the reduced TH feedback and enhanced thermodynamic damping

341

work together and efficiently reduce ENSO variability. Despite the enhanced Ekman (EK)342

feedback due to increased vertical stratification amplifying ENSO variation, its contribution is343

relatively small. Consequently, we find a net decrease in ENSO variability under sustained

344

warming.

17

345
346

References

347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18

McPhaden, M. J., Zebiak, S. E. & Glantz, M. H. ENSO as an integrating concept in earthscience. science 314, 1740-1745 (2006).
Philander, S. G. El Niño, La Niña, and the southern oscillation. International geophysics
series 46, X-289 (1989).
Collins, M. et al. The impact of global warming on the tropical Pacific ocean and El Nino.
Nature Geoscience 3, 391-397, doi:10.1038/Ngeo868 (2010).
Latif, M. & Keenlyside, N. S. El Niño/Southern Oscillation response to global warming.
Proceedings of the National Academy of Sciences 106, 20578-20583 (2009).
Stevenson, S. Significant changes to ENSO strength and impacts in the twenty‐first
century: Results from CMIP5. Geophysical Research Letters 39 (2012).
Zheng, X.-T., Xie, S.-P., Lv, L.-H. & Zhou, Z.-Q. Intermodel Uncertainty in ENSO
Amplitude Change Tied to Pacific Ocean Warming Pattern. Journal of Climate 29, 72657279, doi:10.1175/jcli-d-16-0039.1 (2016).
Cai, W. et al. Increased ENSO sea surface temperature variability under four IPCC
emission scenarios. Nature Climate Change 12, 228-231, doi:10.1038/s41558-02201282-z (2022).
Fredriksen, H. B., Berner, J., Subramanian, A. C. & Capotondi, A. How does El Niño–
Southern Oscillation change under global warming—A first look at CMIP6. GeophysicalResearch Letters 47, e2020GL090640 (2020).
Yun, K.-S. et al. Increasing ENSO–rainfall variability due to changes in future tropicaltemperature–rainfall relationship. Communications Earth & Environment 2, 43 (2021).
Callahan, C. W. et al. Robust decrease in El Niño/Southern Oscillation amplitude underlong-term warming. Nature Climate Change 11, 752-757, doi:10.1038/s41558-02101099-2 (2021).
Wengel, C. et al. Future high-resolution El Niño/Southern Oscillation dynamics. Nature
Climate Change 11, 758-765, doi:10.1038/s41558-021-01132-4 (2021).
Kim, S. T. et al. Response of El Niño sea surface temperature variability to greenhousewarming. Nature Climate Change 4, 786-790, doi:10.1038/nclimate2326 (2014).
Rugenstein, M. et al. LongRunMIP: motivation and design for a large collection ofmillennial-length AOGCM simulations. Bulletin of the American Meteorological Society
100, 2551-2570 (2019).
van Vuuren, D. P. et al. The representative concentration pathways: an overview. Climatic
Change 109, 5-31, doi:10.1007/s10584-011-0148-z (2011).
Maher, N. et al. The future of the El Niño-Southern Oscillation: Using large ensembles toilluminate time-varying responses and inter-model differences. Earth System Dynamics
Discussions 2022, 1-28 (2022).
Collins, M. The El Niño–Southern Oscillation in the second Hadley Centre coupledmodel and its response to greenhouse warming. Journal of Climate 13, 1299-1312 (2000).
Timmermann, A. et al. Increased El Niño frequency in a climate model forced by future
greenhouse warming. Nature 398, 694-697 (1999).
Heede, U. K. & Fedorov, A. V. Towards understanding the robust strengthening of ENSO
and more frequent extreme El Niño events in CMIP6 global warming simulations.
Climate Dynamics 61, 3047-3060, doi:10.1007/s00382-023-06856-x (2023).
18

390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409

19
20
21
22
23
24
25
26
27

Xie, S. P. et al. Global Warming Pattern Formation: Sea Surface Temperature andRainfall. Journal of Climate 23, 966-986, doi:10.1175/2009jcli3329.1 (2010).
Johnson, N. C. & Xie, S.-P. Changes in the sea surface temperature threshold for tropicalconvection. Nature Geoscience 3, 842-845 (2010).
Okumura, Y. & Xie, S.-P. Interaction of the Atlantic equatorial cold tongue and theAfrican monsoon. Journal of Climate 17, 3589-3602 (2004).
Lubbecke, J. F. & McPhaden, M. J. Assessing the Twenty-First-Century Shift in ENSO
Variability in Terms of the Bjerknes Stability Index*. Journal of Climate 27, 2577-2587,
doi:10.1175/Jcli-D-13-00438.1 (2014).
Peng, Q. et al. Eastern Pacific Wind Effect on the Evolution of El Niño: Implications for
ENSO Diversity. Journal of Climate 33, 3197-3212, doi:10.1175/jcli-d-19-0435.1 (2020).
Crespo, L. R. et al. Weakening of the Atlantic Niño variability under global warming.
Nature Climate Change 12, 822-827, doi:10.1038/s41558-022-01453-y (2022).
Yang, Y. et al. Suppressed Atlantic Niño/Niña variability under greenhouse warming.
Nature Climate Change 12, 814-821, doi:10.1038/s41558-022-01444-z (2022).
Widlansky, M. J., Long, X. & Schloesser, F. Increase in sea level variability with ocean
warming associated with the nonlinear thermal expansion of seawater. Communications
Earth & Environment 1, 9 (2020).
Cai, W. et al. Increasing frequency of extreme El Niño events due to greenhouse warming.
Nature Climate Change 4, 111-116, doi:10.1038/nclimate2100 (2014).

19

410

Methods

411

CMIP5 and CMIP6 models

412

We analyze 16 climate models with extended simulations to 2299 or 2300 from CMIP5 and413

CMIP6 based on data availability: the historical runs for the period 1850-2005 (1850-2014),

414

RCP8.5 (SSP585) scenario runs for the period 2006-2300 (2015-2300) from 8 (8) CMIP5415(CMIP6) models. For the extension of RCP8.5/SSP585, anthropogenic radiative forcing

416

continues to increase beyond the year 2100, reaching a level slightly above 12 W/m2 by 2250,

417

after which it stabilizes and maintains a consistent value14,28. This facilitates the multi-model

418

analyses of ENSO changes under long-term anthropogenic warming. We subtract a 10-year419

running mean to remove decadal and longer variability in each variable to investigate ENSO

420

variability. We choose 50 years in the twentieth (1941–1990) and twenty-third (2241–2290)

421

centuries to represent present-day and future climates, respectively. The ENSO variation is422

represented by the monthly STD of the Niño 3.4 index during these two periods. To reveal the423time variation of ENSO amplitude, we compute the running 50-year STD of the Niño 3.4 index

424

for each model. We also analyze 14 (9) climate model outputs under SSP126/RCP2.6

425(SSP245/RCP4.5) based on data availability to assess the long-term ENSO response under426

different emission scenarios. Under the RCP2.6/SSP126 extension scenario, radiative forcing

427

reaches 2.6 W/m2 in 2100 and then slowly declines, stabilizing at around 2.0 W/m2 beyond 2200.

428

For RCP4.5, radiative forcing is held constant at 2100 levels of around 4.5 W/m2 beyond

429

210014,28. For each model, only one member-run (r1i1p1 or r1i1p2) is analyzed in this study. We

430

also analyze the outputs from ACCESS-ESM1-5 large ensembles with extended simulations to

431

2300 and compare them with the above climate outputs. Each of the ten members is integrated

432

forward under the historical (1850-2014) and future emission scenarios (SSP585 and SSP126 for433

2015-2300) with different initial conditions.

434

Ocean-atmosphere feedbacks

435

ENSO variations in the eastern equatorial Pacific are controlled by a series of positive and436

negative ocean-atmosphere feedbacks. Based on a heat budget analysis, the growth rate of ENSO

437

SST anomalies can be cast as29

438

𝑅! = 𝜇" 𝛽# 〈

( )'
(
$%('
%!

$*+,

$*+,

〈.
( 〉

〈$34 6," 〉#2%$𝑎# 〉+𝜇" 𝛽' 〈 *- 〉+𝜇" 𝛽. 〈 */ 〉 − 𝑇𝐷 − ( 2 " +
20

〈'
( 〉

+ %" )
!

(1)

439

here we are mainly focused on the changes in each ocean-atmosphere feedback term under long-440

term global warming. The terms on the right-hand side of equation (1) represent thermocline

441

feedback (TH), Ekman feedback (EK), zonal advective feedback (ZA), thermodynamic damping

442(TD), and dynamic damping (DD), respectively. T and Hm represent mixed layer temperature and443

depth, where Hm is 50 m in this study. u and v indicate the mixed layer zonal and meridional444

velocities, respectively. w is the vertical velocity around the base of the mixed layer (60 m). The

445

symbol ⟨.⟩ denotes the volume average in the eastern Pacific region (90°W –170°W, 5°S–5°N),

446

and the overbar represents the monthly climatological mean value. µa is the equatorial (130°E –447

90°W, 5°S–5°N) wind response to eastern Pacific SSTA forcing. βu, βw, and βh represent the448

responses of central equatorial (160°E –150°W, 5°S–5°N) zonal currents, eastern Pacific449

upwelling, and zonal SLA slope to anomalous equatorial wind forcing, respectively. ah describes

450

the subsurface (80 m) temperature response to SLA (Kelvin waves) in the eastern Pacific Ocean.

451

All these responses are estimated from linear regression between different variables. The

452

function H(x), also known as the Heaviside step function, is used to account for only the vertical453

advection upstream. Lx and Ly are the zonal and meridional extent of the eastern equatorial box,454

and y is the distance from the equator.

455

Prior to the diagnostic analysis, we first calculate the Niño 3 SST skewness to assess the456

model’s capability to simulate realistic ENSO nonlinearity and feedbacks8,30,31. It should be457

noted that we utilize monthly rather than seasonal-mean November-January (NDJ) anomalies at458

the peak of ENSO for the skewness calculation, resulting in relatively small skewness values in459

our study. Four out of the sixteen models simulate negative Niño 3 skewness, contrasting with460

observations. For instance, the ACCESS-ESM1-5 and its large ensemble experiments display

461

strong negative Niño 3 skewness, suggesting the limited capabilities of this model in simulating

462

ENSO nonlinearity and feedbacks. The remaining models successfully simulate positive Niño 3463

skewness, aligning with observations. Six models provide direct outputs of vertical velocity

464(Supplementary Table 1). For the other models, we indirectly calculate the vertical velocity using

465

upward ocean mass transport. However, due to the absence of both vertical velocity and vertical

466

mass transport output, the GISS-E2-H model was excluded from the diagnostic analysis.

467

Consequently, we diagnose ENSO feedbacks from ten (four) models (Supplementary Table 1)468

with positive (negative) skewness in this study.

469

Impacts of thermal expansion coefficient
21

470

The thermal expansion coefficient is defined as32
7 *8

𝛼 = − 8 *95 :,<

471

(2)

472

where ρ is the density, θ is the potential temperature, S is the salinity, and p is the pressure. Here473

we calculate the thermal expansion coefficient ( 𝛼 ) using the Gibbs Sea Water (GSW)

474

Oceanographic Toolbox based on the International Thermodynamic Equation of Seawater—2010

475(TEOS-10)32.

476

The subsurface temperature anomaly response to a given SLA (or steric height) anomaly

477

could be influenced by the thermal expansion coefficient changes. To evaluate the impacts of the478

thermal expansion coefficient changes, we initially compute steric height anomalies using ocean479

temperature and salinity data during 1850-1860. Subsequently, we maintain the salinity and480

interannual ocean temperature anomalies at their 1850-1860 values, introducing only the481

background ocean temperature with a 10-year sliding average from 1850 to 2300. We then482

calculate the steric height and the resultant regression coefficient between subsurface483

temperature (upper 500 m) and steric height over a 10-year moving window (Extended Data Fig.

484

6d). This approach ensures that any changes in the regression coefficient are the results of the485

background ocean temperature and thus thermal expansion coefficient changes.

486

ToE of reduced ENSO SST variability

487

We employ a signal-to-noise ratio (SNR) method to quantify the time of emergence (ToE)488

of ENSO SST variability changes33. First, we calculate the monthly SST anomalies by489

subtracting the climatological monthly value for each model. Then we filter out low-frequency

490

signals longer than 10 years and calculate the ENSO amplitude change over a 50-year moving

491

window, with a one-year shift forward starting from 1850 to 2300. This results in an evolution of492

50-year running variability from 1900 to 2300, with the ending year of each 50-year time493

window being recorded. The unforced internal variability in ENSO SST variation, defined as the494

standard deviation of the 50-year ENSO amplitude change based on the last 500 years of the495

piControl run, is considered the background noise. We then calculate future changes in ENSO

496

SST variability by subtracting the present-day (1941-1990) ENSO variation, which we refer to as497

the signal. We define the ToE of reduced ENSO SST variability as the year when the SNR of the498

Niño 3.4 index falls below a threshold (-1.0) and remains below the threshold (SNR<-1)

499

thereafter. The ToE is recorded as the last year of the sliding window of emergence. It should be
22

500

noted that the ToE of the HadGEM2-ES and MRI-ESM2-0 models exceeds 2300 and thus cannot

501

be evaluated with the available data. For simplicity and to minimize its impact on the results, we502

assign a ToE value of 2300 to these two models when calculating the median value of ToE.

503

Importantly, such an assignment does not impact the final median and interquartile range of the504

ToE presented in this study.

505

Bootstrap test

506

We test the significance of our results with the bootstrap test34. To examine whether the507

multi-model mean decrease in ENSO variation is statistically significant, we average 16508

randomly resampled models from all the 16 climate models used in this study, allowing for the509

possibility of selecting the same model multiple times. This process is repeated 10,000 times to510

calculate mean and standard deviation values for both the twentieth and twenty-first centuries. If

511

the difference in mean values between the two periods is greater than the sum of the standard

512

deviation values, the change is considered statistically significant at the 95% confidence level 22.

513

Data availability

514

All data supporting the findings of this study are openly available. The CMIP6 data can be found515

at https://esgf-data.dkrz.de/search/cmip6-dkrz/. The CMIP5 data can be found at https://esgf-

516

node.llnl.gov/search/cmip5/.

517

Code availability

518

The code is publicly available at https://zenodo.org/records/11416550 (ref. 35).

519

Methods references

520
521
522
523
524
525
526
527
528

28
29
30
31

O'Neill, B. C. et al. The Scenario Model Intercomparison Project (ScenarioMIP) for
CMIP6. Geoscientific Model Development 9, 3461-3482, doi:10.5194/gmd-9-3461-2016(2016).
Kim, S. T. & Jin, F.-F. An ENSO stability analysis. Part I: results from a hybrid coupledmodel. Climate Dynamics 36, 1593-1607, doi:10.1007/s00382-010-0796-0 (2010).
Cai, W. J. et al. Increased variability of eastern Pacific El Nino under greenhousewarming. Nature 564, 201-+, doi:10.1038/s41586-018-0776-9 (2018).
Kohyama, T., Hartmann, D. L. & Battisti, D. S. Weakening of nonlinear ENSO underglobal warming. Geophysical Research Letters 45, 8557-8567 (2018).
23

529
530
531
532
533
534
535

32

536
537

35

33
34

Commission, I. O. The International thermodynamic equation of seawater, 2010:
calculation and use of thermodynamic properties. (2010).
Geng, T. et al. Emergence of changing Central-Pacific and Eastern-Pacific El NinoSouthern Oscillation in a warming climate. Nat Commun 13, 6616, doi:10.1038/s41467022-33930-5 (2022).
Cai, W. et al. Changing El Niño–Southern Oscillation in a warming climate. Nature
Reviews Earth & Environment 2, 628-644, doi:10.1038/s43017-021-00199-z (2021).
Peng, Q. Collapsed upwelling weakens ENSO under sustained warming beyond the 21st
century. Zenodo https://zenodo.org/records/11416550 (2024).

24

538
539

Extended Data Fig. 1 | The projected ENSO variability and mean state changes. (a)540

Histograms of 10,000 realizations of the Bootstrap method for Niño 3.4 SST STD (°C) in the541

20th century (1941-1990, blue) and 23rd century (2241-2290, red). The blue and red lines indicate542

the mean values of the 10,000 realizations for each period. The grey shaded areas correspond to543

the respective one STD of the 10,000 realizations (see Method). (b) The difference in standard

544

deviation (STD; °C) of Niño 3.4 SST anomalies between the 21st-century SSP585/RCP8.5

545

scenario period (2041-2090) and the historical reference period (1941-1990). (c) The difference546

in equatorial Pacific zonal SST gradient between the 23rd (2241-2290) and 20th (1941-1990)

547

centuries under the SSP585/RCP8.5 scenario. Here, zonal SST gradient change is defined as the548

SST change difference between the eastern (120°W-170°W, 5°S–5°N) and western (150°E-

549

170°W, 5°S–5°N) boxes, with positive values indicating an El Niño-like warming pattern. (d)550

Latitude-time Hovmöller diagrams of the eastern Pacific (90°W-170°W) zonal mean monthly

551

climatological wind (vectors; m/s) and the −𝑣 *4 term (color shading; m/s2) during 2241-2290.

*.

25

552
553
554
555

Extended Data Fig. 2 | The ENSO simulations in the 16 climate models. The simulated
spatial pattern of present-day (1941-1990) SSTA STD (°C, color shading) from the 15 climate
models used in this study.
26

556
557

Extended Data Fig. 3 | The simulated seasonal cycle of ENSO amplitude. The simulated558

seasonal cycle of the Niño 3.4 SSTA STD from the 15 models with reduced ENSO variability

559

during the historical period (1941-1990) (blue line) and the 23rd-century (2241-2290) (red line)

560

under the SSP585/RCP8.5 scenario.

27

561
562

Extended Data Fig. 4 | Time variation of simulated ENSO amplitude from ACCESS-ESM1-563

5 large ensembles. The running 50-year ENSO amplitude change (°C) from ACCESS-ESM1-5

564

large ensembles during the historical period and under the (a) SSP585 and (b) SSP126 emission

565

scenarios. The ACCESS-ESM1-5 ensemble mean is shown as the thick red curve.

28

566
567

Extended Data Fig. 5 | ENSO-related air-sea feedback changes for each model. The strength568

of ENSO-related air-sea feedback changes (yr-1) (see Methods) in the eastern Pacific (5°S–5°N,

569

90°W–170°W) from each of the ten models with a positive Niño 3 skewness. TH, EK, ZAF, and570

TD represent thermocline feedback, Ekman feedback, zonal advective feedback, and571

thermodynamic damping, respectively. The CNRM-CM5, CCSM4, and GISS-E2-R belong to572

CMIP5, while the rest are from CMIP6.

29

573
574

Extended Data Fig. 6 | Impacts of thermal expansion coefficient changes. (a) Scatter plots of575

eastern Pacific (5°S–5°N, 90°W–170°W) averaged thermal expansion coefficient changes (Δα,576

10-4°C-1) and Niño 3.4 SST STD changes from SSP585/RCP8.5 extended simulations. The

577

running 10-year upper 500 m averaged (b) α (10-4°C-1) from the historical and SSP585/RCP8.5578

outputs. (c) Regression of upper 500 m ocean temperature (°C) against steric height (SH, mm)579

anomalies averaged in the eastern Pacific Ocean from CMIP6 models. (d) The impacts of Δα on580

the regression of upper 500 m ocean temperature against SH anomalies (see Methods). The

581

MME is shown as the thick red curve and the color shadings indicate one inter-member standard

582

deviation (n=10) above and below the MME. The regression coefficient is calculated over a 10-583

year moving window, with a one-year shift forward starting from 1850 to 2300. The results in584(b)-(d) are derived from the ten models exhibiting positive Niño 3 skewness (see Methods).

30

585
586

Extended Data Fig. 7 | Thermodynamic response changes to Niño 3.4 SST variability.

587

Projected changes in (a) Qnet (W/m2), (b) latent heat flux (W/m2), (c) shortwave radiation

588(W/m2), and (d) rainfall (mm/day) response to Niño 3.4 SST anomalies under SSP585 during

589

2241-2290 relative to the present-day (1941-1990). These responses are estimated using linear

590

regression between variables and Niño 3.4 SST anomalies from the ten climate models with591

positive Niño 3 skewness. The stippled areas denote signals that are significant at the 95%592

confidence level from the bootstrap test.

31

593
594

Extended Data Fig. 8 | ENSO-related air-sea feedback changes among models with positive595

and negative Niño 3 skewness. The MME (bars) ENSO-related air-sea feedback (yr-1) in the596eastern Pacific (5°S–5°N, 90°W–170°W) during the present-day (1941-1990) (left panels), future597(2241-2290) (middle panels), and their difference (right panels) across models with positive598(upper panels; n=10) and negative (lower panels; n=4) Niño 3 skewness (see methods). The dots

599

indicate individual model results.

32

600
601

Extended Data Fig. 9 | ENSO variation changes under SSP126/RCP2.6. (a) The MME (thick602

red curve) running 50-year ENSO variation change (°C) from the historical and SSP126/RCP2.6603

outputs; the color shadings indicate one inter-member standard deviation above and below the604

MME (n=14; Supplementary Table 1). (b) Future (2241-2290) mean state relative SST (°C, color

605

shading), rainfall (contours with an interval of 3 mm/day; positive in green), and wind stress606(N/m2, vectors) under SSP126/RCP2.6. (c) The Hovmöller diagram of the equatorial mean

607

upwelling at 60 m (w; 10-5 m/s, color shading; derived from models with direct vertical velocity

608

outputs), and the zonal wind stress (contours with an interval of 0.01 N/m2; positive in black and609

negative in gray). (d) The MME ENSO-related air-sea feedbacks (yr-1) during 2241-2290 under

610

SSP126/RCP2.6 (red bars) from seven climate models (Supplementary Table 1), along with the611

differences between these air-sea feedbacks and their present-day counterparts (blue bars); The

612

dots indicate individual model results.

33

613
614

Extended Data Fig. 10 | ENSO amplitude changes under SSP245/RCP4.5. (a) The running615

50-year ENSO variation change (°C) from the historical and SSP245/RCP4.5 outputs. (b) Future

616(2241-2290) annual mean relative SST (°C, color shading), rainfall (contours with an interval of617

3 mm/day; positive in green), and wind stress (N/m2, vectors) under SSP245/RCP4.5. The

618

running 50-year EP (c) relative SST (°C) and (d) zonal wind stress (10-2 N/m2) from the historical619

and SSP245/RCP4.5 outputs. The MME is shown as the thick curve, and the color shadings

620

indicate one inter-member standard deviation above and below the MME (n=7; Supplementary

621

Table 1).

34