In the format provided by the authors and unedited.

LETTERSPUBLISHED ONLINE: 12 JUNE 2017 | DOI: 10.1038/NCLIMATE3304

Distinct global warming rates tied to multipleocean surface temperature changesShuai-Lei Yao1,2, Jing-Jia Luo3*, Gang Huang1,2,4* and Pengfei Wang1,5

The globally averaged surface temperature has shown distinctmulti-decadal fluctuations since 19001–4, characterized bytwo weak slowdowns in the mid-twentieth century andearly twenty-first century and two strong accelerations inthe early and late twentieth century. While the recentglobal warming (GW) hiatus has been particularly ascribedto the eastern Pacific cooling5,6, causes of the cooling inthe mid-twentieth century and distinct intensity di�erencesbetween the slowdowns and accelerations remain unclear7,8.Here, our model experiments with multiple ocean sea surfacetemperature (SST) forcing reveal that, although the PacificSSTs play essential roles in the GW rates, SST changes in otherbasins also exert vital influences. The mid-twentieth-centurycooling results from the SST cooling in the tropical Pacific andAtlantic,which is partly o�set by theSouthernOceanwarming.During the recent hiatus, the tropical Pacific-induced strongcooling is largely compensated by warming e�ects of otheroceans. In contrast, during the acceleration periods, ubiquitousSST warming across all the oceans acts jointly to exaggeratethe GW. Multi-model simulations with separated radiativeforcing suggest diverse causes of the SST changes in multipleoceans during the GW acceleration and slowdown periods.Our results highlight the importance of multiple oceans on themulti-decadal GW rates.

The Paris Climate Conference in December 2015 has agreed topursue efforts to limit the global warming (GW) to no more than1.5 ◦C above pre-industrial levels by 2100 (ref. 9). The observedannual-mean globally averaged surface temperature (GST) has alr-eady risen about 0.89 ◦C (0.69–1.08 ◦C, 90% confidence interval)10during the period 1900–2012 (Fig. 1 and Supplementary Table 1),leaving room for just another 0.5–0.6 ◦C warming relative to todayfor the rest of the twenty-first century. While the early twenty-first-century GW hiatus indicates that the relentlessly increasinggreenhouse gases (GHGs) may not always boost the GST warmingsimultaneously6, it is yet unclear whether the recent hiatus has surelyended and how strongly GW will resume in the next decades. Inaddition, it is found that the GW rate since 1900 has experienced apronounced multi-decadal fluctuation, characterized by two accel-erations in the early and late twentieth century and two slowdownsduring the mid-twentieth century and the early twenty-first cen-tury1,2 (Fig. 1). Correspondingly, the globally averaged land surfaceair temperature shows similar multi-decadal modulations. The dis-tinctive multi-decadal fluctuation undoubtedly affects the estima-tion of centennial mean GW rate11,12. Improved understanding ofthese GW accelerations and slowdowns could help provide a betterestimate of the future GW rate for the Paris Climate Agreement.

The estimated rates of the multi-decadal GW accelerations andslowdowns are subject to the time interval selection13, which isoften based on an arbitrary decision14. Here, the GW accelerationand slowdown periods are objectively determined according tothree thresholds: a maximum magnitude of statistically significanttrends in observed GST, relatively low uncertainty among differentobservations (represented by error bars in Supplementary Fig. 1),and a high percentage of global areas where the merged landair temperature and ocean surface temperature has a statisticallysignificant trend (Supplementary Fig. 1 and Methods). Ourapproach identifies two periods with maximum accelerations ofthe GW during 1908–1945 and 1975–1998 and two periods withmaximum slowdowns during 1940–1976 and 2001–2012 (Fig. 1).

It is found that observed GST trends during the two accelerationperiods are statistically significant across four different observa-tional data sets, whereas those during the two slowdown periodsare less significant (Supplementary Table 1), especially for the earlytwenty-first-century slowdown. The latest slowdown does not showa consistently negative GW rate among the different observationsand is statistically insignificant (Supplementary Table 1), proba-bly owing to a relatively short period for the trend estimation.Despite this, the recent GW slowdown has received tremendousattention and has been examined extensively5,6,8. The magnitudes ofthe GW rates during the two slowdown periods are considerablysmaller than those during the two acceleration periods. This dif-ference has been explained as being due to the naturally occurringdecadal/multi-decadal climate variability halting the steadyGWrateattributed to the continuously increasing GHGs emissions15. Forinstance, the recent slowdown has been tied to the equatorial Pacificsea surface temperature (SST) cooling5,6, which is underpinned bythe large impacts of Interdecadal Pacific Oscillation (IPO) on theGST16,17. The IPO’s impacts are similar but not identical to thoseexerted by El Niño18.

Consistent with previous findings5,6, SST warming (cooling)occurs in the tropical Pacific during the acceleration (slow-down) periods (Supplementary Fig. 2). The SST change differ-ences between the slowdown and acceleration periods bear a closeresemblance to the pattern of the cold phase of the IPO19 or aLa Niña-like decadal/multi-decadal variability20. This indicates thepossible importance of internal climate variability in modulatingGW5,16. However, notable differences in global SST changes existamong the acceleration and slowdown periods (Fig. 2). While theSST shows a similar warming in the tropical Pacific between thetwo acceleration periods (Fig. 2a,c), the equatorial central-easternPacific cooling during 2001–2012 is far stronger than that during1940–1976 (Fig. 2b,d). Its magnitude is also greater than those

1State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, ChineseAcademy of Sciences, Beijing 100029, China. 2University of Chinese Academy of Sciences, Beijing 100049, China. 3Australian Bureau of Meteorology,Melbourne, Victoria 3008, Australia. 4Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Scienceand Technology, Qingdao 266237, China. 5Center for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences,Beijing 100029, China. *e-mail: J.luo@bom.gov.au; hg@mail.iap.ac.cn

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© 2017 Macmillan Publishers Limited, part of Springer Nature. All rights reserved.

LETTERSPUBLISHED ONLINE: 12 JUNE 2017 | DOI: 10.1038/NCLIMATE3304

Distinct global warming rates tied to multipleocean surface temperature changesShuai-Lei Yao1,2, Jing-Jia Luo3*, Gang Huang1,2,4* and Pengfei Wang1,5

The globally averaged surface temperature has shown distinctmulti-decadal fluctuations since 19001–4, characterized bytwo weak slowdowns in the mid-twentieth century andearly twenty-first century and two strong accelerations inthe early and late twentieth century. While the recentglobal warming (GW) hiatus has been particularly ascribedto the eastern Pacific cooling5,6, causes of the cooling inthe mid-twentieth century and distinct intensity di�erencesbetween the slowdowns and accelerations remain unclear7,8.Here, our model experiments with multiple ocean sea surfacetemperature (SST) forcing reveal that, although the PacificSSTs play essential roles in the GW rates, SST changes in otherbasins also exert vital influences. The mid-twentieth-centurycooling results from the SST cooling in the tropical Pacific andAtlantic,which is partly o�set by theSouthernOceanwarming.During the recent hiatus, the tropical Pacific-induced strongcooling is largely compensated by warming e�ects of otheroceans. In contrast, during the acceleration periods, ubiquitousSST warming across all the oceans acts jointly to exaggeratethe GW. Multi-model simulations with separated radiativeforcing suggest diverse causes of the SST changes in multipleoceans during the GW acceleration and slowdown periods.Our results highlight the importance of multiple oceans on themulti-decadal GW rates.

The Paris Climate Conference in December 2015 has agreed topursue efforts to limit the global warming (GW) to no more than1.5 ◦C above pre-industrial levels by 2100 (ref. 9). The observedannual-mean globally averaged surface temperature (GST) has alr-eady risen about 0.89 ◦C (0.69–1.08 ◦C, 90% confidence interval)10during the period 1900–2012 (Fig. 1 and Supplementary Table 1),leaving room for just another 0.5–0.6 ◦C warming relative to todayfor the rest of the twenty-first century. While the early twenty-first-century GW hiatus indicates that the relentlessly increasinggreenhouse gases (GHGs) may not always boost the GST warmingsimultaneously6, it is yet unclear whether the recent hiatus has surelyended and how strongly GW will resume in the next decades. Inaddition, it is found that the GW rate since 1900 has experienced apronounced multi-decadal fluctuation, characterized by two accel-erations in the early and late twentieth century and two slowdownsduring the mid-twentieth century and the early twenty-first cen-tury1,2 (Fig. 1). Correspondingly, the globally averaged land surfaceair temperature shows similar multi-decadal modulations. The dis-tinctive multi-decadal fluctuation undoubtedly affects the estima-tion of centennial mean GW rate11,12. Improved understanding ofthese GW accelerations and slowdowns could help provide a betterestimate of the future GW rate for the Paris Climate Agreement.

The estimated rates of the multi-decadal GW accelerations andslowdowns are subject to the time interval selection13, which isoften based on an arbitrary decision14. Here, the GW accelerationand slowdown periods are objectively determined according tothree thresholds: a maximum magnitude of statistically significanttrends in observed GST, relatively low uncertainty among differentobservations (represented by error bars in Supplementary Fig. 1),and a high percentage of global areas where the merged landair temperature and ocean surface temperature has a statisticallysignificant trend (Supplementary Fig. 1 and Methods). Ourapproach identifies two periods with maximum accelerations ofthe GW during 1908–1945 and 1975–1998 and two periods withmaximum slowdowns during 1940–1976 and 2001–2012 (Fig. 1).

It is found that observed GST trends during the two accelerationperiods are statistically significant across four different observa-tional data sets, whereas those during the two slowdown periodsare less significant (Supplementary Table 1), especially for the earlytwenty-first-century slowdown. The latest slowdown does not showa consistently negative GW rate among the different observationsand is statistically insignificant (Supplementary Table 1), proba-bly owing to a relatively short period for the trend estimation.Despite this, the recent GW slowdown has received tremendousattention and has been examined extensively5,6,8. The magnitudes ofthe GW rates during the two slowdown periods are considerablysmaller than those during the two acceleration periods. This dif-ference has been explained as being due to the naturally occurringdecadal/multi-decadal climate variability halting the steadyGWrateattributed to the continuously increasing GHGs emissions15. Forinstance, the recent slowdown has been tied to the equatorial Pacificsea surface temperature (SST) cooling5,6, which is underpinned bythe large impacts of Interdecadal Pacific Oscillation (IPO) on theGST16,17. The IPO’s impacts are similar but not identical to thoseexerted by El Niño18.

Consistent with previous findings5,6, SST warming (cooling)occurs in the tropical Pacific during the acceleration (slow-down) periods (Supplementary Fig. 2). The SST change differ-ences between the slowdown and acceleration periods bear a closeresemblance to the pattern of the cold phase of the IPO19 or aLa Niña-like decadal/multi-decadal variability20. This indicates thepossible importance of internal climate variability in modulatingGW5,16. However, notable differences in global SST changes existamong the acceleration and slowdown periods (Fig. 2). While theSST shows a similar warming in the tropical Pacific between thetwo acceleration periods (Fig. 2a,c), the equatorial central-easternPacific cooling during 2001–2012 is far stronger than that during1940–1976 (Fig. 2b,d). Its magnitude is also greater than those

1State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, ChineseAcademy of Sciences, Beijing 100029, China. 2University of Chinese Academy of Sciences, Beijing 100049, China. 3Australian Bureau of Meteorology,Melbourne, Victoria 3008, Australia. 4Laboratory for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Scienceand Technology, Qingdao 266237, China. 5Center for Monsoon System Research, Institute of Atmospheric Physics, Chinese Academy of Sciences,Beijing 100029, China. *e-mail: J.luo@bom.gov.au; hg@mail.iap.ac.cn

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SUPPLEMENTARY INFORMATIONDOI: 10.1038/NCLIMATE3304

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2

The nonlinear effects on the global warming rates in the AGCM simulations 22

The linear summation of the individual ocean basins’ effects is not equal to the 23

global SST effect because of the intrinsic nonlinearity of the climate system. The 24

nonlinear effects will always exist no matter whether AGCM or coupled model 25

approach is used. In this study, the nonlinear effects during the global warming 26

accelerations and slowdowns only account for small fractions of the global SST 27

effects and/or are smaller than the impacts of individual oceans. For the 28

early-20th

-century warming period (1908-1945), the global SST-forced GST trend is 29

~0.146°C per decade (it is close to the observed values of ~0.143°C per decade). And 30

the sum of the individual ocean basins’ effects is ~0.167°C per decade (Fig. 3). The 31

difference between them (i.e., nonlinear effect) is only approximately -0.021°C per 32

decade, acting to reduce the total trend by ~14%. For the late 20th

century rapid 33

warming during 1975-1998, the global SST-forced GST trend is ~0.157°C per decade 34

(the observed value is ~0.175°C per decade), and the sum of individual ocean basins’ 35

effects is ~0.185°C per decade. The nonlinear effect accounts for ~18% of the total 36

trend. Similarly, for the mid-20th

-century cooling period of 1940-1976 and the recent 37

slowdown period of 2001-2012, the nonlinear effects are also small. For the 38

1940-1976 periods, the global SST-forced trend is approximately -0.029°C per decade 39

(the observed trend is roughly -0.033°C per decade) and the sum of individual ocean 40

basins’ effects is around -0.03°C per decade. For the period 2001-2012, the global 41

SST-forced trend is about -0.007°C per decade (the observed trend is ~0.028°C per 42

decade). The sum of individual ocean basins’ effects is ~0.003°C per decade. The 43

3

nonlinear effect is smaller than the impacts of individual oceans. These results suggest 44

that the summed contributions of individual ocean basins’ effects are close to the 45

global SST effects and hence the nonlinear effect does not change the main results. 46

The scaled impacts of individual ocean basins on the global warming rates based 47

on the corresponding area percentage of individual oceans are also investigated. For 48

the period 1908-1945, the global SST-forced trend of ~0.21°C per decade is close to 49

the summed effects of individual oceans of ~0.17°C per decade. For the rapid 50

warming period 1975-1998, the effect of the global SST changes on the global 51

warming rate is ~0.22°C per decade, similar to the sum of individual oceans’ SST 52

effects of ~0.19°C per decade. For the period 1940-1976, the global SST-induced 53

warming rate is approximately -0.04°C per decade, close to the linear additivity of 54

individual oceans of about -0.03°C per decade. Similar results are also obtained for 55

the recent slowdown from 2001-2012. The global SST produce a warming rate of 56

about -0.01°C per decade and the summed warming rates of individual oceans are 57

~0.003°C per decade. The difference between them is smaller than the impacts of 58

individual oceans. These results also demonstrate that the nonlinear effects do not 59

change the main conclusions. 60

61

The consistency between observed and simulated land SAT trend patterns 62

During the two global warming acceleration periods, the simulated land SAT trend 63

patterns forced by the observed global SST trend are in broadly good agreement with 64

the observed patterns, with the areas where the sign of observed trends is correctly 65

4

reproduced reaching ~87% of global lands (Supplementary Fig. 5a, b), albeit with a 66

slight reduced magnitude over Eurasia. The tropical Pacific SST changes also induce 67

wide impacts on global lands, which reproduces the correct sign trends over ~75% of 68

global lands (Supplementary Fig. 5a, c), except some areas of North and South 69

America where spurious cooling occurs. Similarly, the summed effects of other 70

oceans are also important, with ~71% of global lands having the correct sign trends 71

(Supplementary Fig. 5a, d). 72

For the mid-20th

-century cooling periods, the simulated land surface air 73

temperature trend is in relatively good agreement with the observed except local 74

discrepancies (Supplementary Fig. 6a, b), and the areas of the correct sign trends 75

reach ~52% of global lands. The effects of the tropical Pacific-induced cooling and 76

the summed effects of the North Pacific Ocean and North Atlantic Ocean cooling are 77

similar (Supplementary Fig. 6c, d), with the areas of the correct sign trends in the both 78

cases approaching ~51% of global lands. The summed effects of the SST warming in 79

other oceans are also important (Supplementary Fig. 6e), with ~55% of global lands 80

having the correct sign trends. 81

For the recent slowdown period, the model reasonably well captures the broad 82

characteristics of observed land surface temperature trends (Supplementary Fig. 7a, b), 83

but with large discrepancies in some parts of Eurasia and Antarctic. The sign of the 84

observed surface air temperature trends over ~51% of global lands is correctly 85

reproduced. The cooling in the tropical lands and high northern continental regions 86

induced by the tropical Pacific SST cooling is largely compensated by the summed 87

5

effects of the SST warming in other oceans (Supplementary Fig. 7c, d). The areas 88

where the simulated trends have the same sign as the observed due to the tropical 89

Pacific SST cooling and the other ocean SST warming reaches ~47% and ~60% of 90

global lands, respectively. These results suggest distinct roles of individual oceans in 91

causing the multi-decadal global warming rates. 92

In addition, during the two warming acceleration periods, continental warming is 93

generally larger than the observed SST changes (the ratios of global mean land SAT 94

trend to SST trend are approximately 1.0 and 1.8, respectively). For the two 95

slowdown periods, the ratios of the cooling over global lands to the observed global 96

mean SST trends are about 0.5 and 2.3, respectively. The average ratio of the global 97

mean land SAT trend to SST trend during the two global warming accelerations and 98

two slowdowns is ~1.4. This is in good agreement with previous studies (Dommenget 99

2009), suggesting that the thermodynamic mechanisms may play a dominant role in 100

land SAT changes. 101

102

Evaluating relative impacts of nonlinear SST effect, direct radiative forcing and 103

model bias on the land SAT trends 104

In this study, the nonlinear SST effect is defined as the difference between the 105

global SST-forced land SAT trend and the sum of the individual ocean basins’ effects. 106

The AGCM experiments forced by the SST trends only do not include the direct 107

response of the land SAT to historical variations in external radiative forcing, which 108

has been found to be important to the historical SAT trend (Deser and Phillips 2009; 109

6

Andrews 2014; Kamae et al. 2014). A model experiment suggests a land SAT 110

warming rate of ~0.07°C per decade with the forcing of the observed CO2 trend 111

during 1958-2012 (see Methods). The direct forcing effect due to CO2 increase during 112

each of the global warming acceleration and slowdown periods is then roughly 113

estimated based on the ratio of the CO2 trend during individual periods relative to that 114

during 1958-2012. CO2 concentrations prior to 1958 are adopted from those used for 115

CMIP5 historical runs. The model bias, which usually originates from errors in model 116

physics and numerical schemes, insufficient resolutions and other factors, is estimated 117

by the difference between the observed land SAT trend and the sum of the global 118

SST-forced land SAT trend and the direct forcing effect. The respective roles played 119

by the three residual terms during the two global warming accelerations and the two 120

slowdowns are summarized in Supplementary Table 3. 121

The estimated nonlinear SST effect has the opposite sign of the global SST-forced 122

land SAT trend during each of the global warming acceleration and slowdown periods, 123

suggesting that the linear sum of the individual ocean basins’ effects, to some extent, 124

tends to overestimate the impacts of global SST changes. In contrast, the estimated 125

model biases for the global warming accelerations and slowdowns are always 126

negative, suggesting that the AGCM tends to overestimate (underestimate) the 127

multi-decadal global warming (cooling) rates. The estimated direct forcing effect due 128

to CO2 increase has persistently increased from the first epoch (i.e., 1908-1945) to the 129

last decade (i.e., 2001-2012), consistent with the increased trend of carbon dioxide 130

emissions. The large direct forcing effects during the late-20th

-century rapid warming 131

7

period and during 2001-2012 may contribute to the discrepancies between the 132

observed and simulated GST and land SAT trends (see Fig. 3) (Deser and Phillips 133

2009; Andrews 2014; Kamae et al. 2014). However, the direct forcing effect alone 134

cannot explain the multi-decadal difference between the global warming acceleration 135

and following slowdown. If neglecting the model bias and considering the effect of all 136

external radiative forcing over the past century, including greenhouse gases, aerosols 137

(Rosenfeld et al. 2014), and other historical forcing agents, the net effect of direct 138

forcing could be roughly estimated as the difference between the observed mean 139

global warming rate and the global SST-forced mean rate during 1908-2012. While 140

this century-mean direct forcing effect accounts for a considerable portion of the 141

global warming rates during the acceleration and slowdown periods, in general it 142

could not explain the multi-decadal accelerations and slowdowns that can be viewed 143

as the departures from the century-mean global warming rate. 144

145

Use of the AGCM approach for quantifying the global impacts of individual 146

oceans' SST changes 147

Previous studies have suggested that: 1) A statistically-derived northern 148

hemispheric internal multi-decadal variability may contribute to the early-2000s 149

global warming hiatus (Steinman et al. 2015); 2) the internal variability originating 150

from the tropical Pacific alone may induce the recent global warming hiatus (Kosaka 151

and Xie 2013, 2016); 3) heat uptake in multiple ocean basins may be responsible for 152

the global warming hiatus (Chen and Tung 2014; Drijfhout et al. 2014), which does 153

8

not clearly elucidate the respective roles of individual oceans in global impacts. In this 154

study, we provide a novel perspective for understanding the relative roles of 155

individual ocean basin’s SSTs in the multi-decadal global warming rates. The results 156

highlight that, it is the net impact of multiple ocean SST changes that plays a main 157

driver for the global warming accelerations and slowdowns. This may provide a novel 158

implication for a better assessment of global warming rates for the Paris Climate 159

Agreement, compared to that based on increasing GHG emissions alone and/or a 160

single ocean SST change alone. 161

It is worth noting that SST changes in individual oceans may affect one another 162

because of trans-basin interactions (Luo et al. 2012; McGregor et al. 2014; Chikamoto 163

et al. 2015; Li et al. 2015). In this regard, ocean-atmosphere coupled models that can 164

properly simulate the trans-basin influences could provide a desirable tool to explore 165

the global impacts of the individual ocean SST on the GST (Meehl et al. 2011; 166

Kosaka and Xie 2013, 2016). However, a prerequisite for such a coupled model 167

approach is that the prescribed SST trends in individual oceans need to be free of the 168

inter-basin influences. Otherwise, the summed contributions of individual basin SSTs 169

to the GST will not match up with that of the global SST. But exact contributions of 170

the trans-basin interactions to the individual basin SST changes are not yet clear. 171

Given that the observed SST changes in individual oceans have already contained a 172

mix of the internally-generated and externally-forced changes (e.g. the consequence 173

of trans-basin influences through atmosphere-ocean interactions), the conventional 174

stand-alone AGCM approach reveals the impacts that are directly forced by individual 175

9

basin SST trends in a straightforward way. Additionally, compared to the coupled 176

model approach, the AGCM experiment helps avoid potential impacts of coupled 177

models’ biases in simulating the climate mean-states, trans-basin teleconnections, and 178

air-sea interactions, etc. It also helps avoid the influences of ocean’s initial condition 179

errors that may considerably affect the simulations of the multi-decadal global 180

warming rates. The AGCM approach remains a useful tool to probe global and 181

regional SST’s impacts on weather-climate. However, it should be noted that the 182

AGCM approach also has some known deficiencies. For instance, prescribing 183

observed SST variations corresponds to an infinite ocean capacity, leading to 184

unrealistic changes in heat fluxes to the atmosphere, especially in the higher latitudes 185

(Dommenget 2009). The AGCM simulations forced by time-varying SST also 186

produce spurious downward surface energy fluxes due to a missing interactive ocean 187

(Deser and Phillips 2009). In addition, due to the weak influence of extra-tropical SST 188

on the atmosphere (Barsugli and Battisti 1998; Sutton and Mathieu 2002; Tyrrell et al. 189

2015), large uncertainties may exist for the extra-tropical SST forcing experiments. 190

Further studies are warranted to examine the impacts of model errors and 191

extra-tropical air-sea interactions. 192

193

194

195

196

197

10

Supplemental References 198

199

Andrews, T., 2014: Using an AGCM to diagnose historical effective radiative forcing 200

and mechanisms of recent decadal climate change. J. Clim., 27, 1193-1209. 201

Barsugli, J. J., and D. S. Battisti, 1998: The Basic Effects of Atmosphere-Ocean 202

Thermal Coupling on Midlatitude Variability*. Journal of the Atmospheric 203

Sciences, 55, 477-493. 204

Chen, X., and K.-K. Tung, 2014: Varying planetary heat sink led to global-warming 205

slowdown and acceleration. Science, 345, 897-903. 206

Chikamoto, Y., and Coauthors, 2015: Skilful multi-year predictions of tropical 207

trans-basin climate variability. Nature communications, 6, 6869. 208

Deser, C., and A. S. Phillips, 2009: Atmospheric circulation trends, 1950-2000: The 209

relative roles of sea surface temperature forcing and direct atmospheric radiative 210

forcing. J. Clim., 22, 396-413. 211

Dommenget, D., 2009: The ocean's role in continental climate variability and change. 212

J. Clim., 22, 4939-4952. 213

Drijfhout, S., A. Blaker, S. Josey, A. Nurser, B. Sinha, and M. Balmaseda, 2014: 214

Surface warming hiatus caused by increased heat uptake across multiple ocean 215

basins. Geophys. Res. Lett., 41, 7868-7874. 216

Kamae, Y., H. Shiogama, M. Watanabe, and M. Kimoto, 2014: Attributing the 217

increase in Northern Hemisphere hot summers since the late 20th century. 218

Geophys. Res. Lett., 41, 5192-5199. 219

Kosaka, Y., and S.-P. Xie, 2013: Recent global-warming hiatus tied to equatorial 220

Pacific surface cooling. Nature, 501, 403-407. 221

——, 2016: The tropical Pacific as a key pacemaker of the variable rates of global 222

warming. Nature. Geosci., 9, 669-673. 223

Li, X., S.-P. Xie, S. T. Gille, and C. Yoo, 2015: Atlantic-induced pan-tropical climate 224

change over the past three decades. Nature Clim. Change., 6, 275-279. 225

Luo, J.-J., W. Sasaki, and Y. Masumoto, 2012: Indian Ocean warming modulates 226

Pacific climate change. Proc. Natl. Acad. Sci., 109, 18701-18706. 227

11

McGregor, S., A. Timmermann, M. F. Stuecker, M. H. England, M. Merrifield, F.-F. 228

Jin, and Y. Chikamoto, 2014: Recent Walker circulation strengthening and 229

Pacific cooling amplified by Atlantic warming. Nature Clim. Change., 4, 230

888-892 231

Meehl, G. A., J. M. Arblaster, J. T. Fasullo, A. Hu, and K. E. Trenberth, 2011: 232

Model-based evidence of deep-ocean heat uptake during surface-temperature 233

hiatus periods. Nature Clim. Change, 1, 360-364. 234

Rosenfeld, D., S. Sherwood, R. Wood, and L. Donner, 2014: Climate effects of 235

aerosol-cloud interactions. Science, 343, 379-380. 236

Steinman, B. A., M. E. Mann, and S. K. Miller, 2015: Atlantic and Pacific 237

multidecadal oscillations and Northern Hemisphere temperatures. Science, 347, 238

988-991. 239

Sutton, R., and P. P. Mathieu, 2002: Response of the atmosphere–ocean mixed‐layer 240

system to anomalous ocean heat‐flux convergence. Quarterly Journal of the 241

Royal Meteorological Society, 128, 1259-1275. 242

Tyrrell, N. L., D. Dommenget, C. Frauen, S. Wales, and M. Rezny, 2015: The 243

influence of global sea surface temperature variability on the large-scale land 244

surface temperature. Climate. Dyn., 44, 2159-2176. 245

246

247

248

12

249

250

Supplementary Figure 1 | Trends with increasing time intervals in observed GST 251

(black dots) and percentages of grid boxes in observed MLOST where the trend 252

values are statistically significant at 95% confidence level (red dots). The error 253

bars represent one standard deviation of the differences among the four observed GST 254

trends. The cyan dots indicate GST trend values are statistically significant at 95% 255

confidence level. Vertical dashed purple lines denote the optimal ending years of the 256

periods that are used to calculate the trends for individual GW accelerations and 257

slowdowns in the main text. 258

259

13

260 261

Supplementary Figure 2 | Observed changes in annual-mean merged land air 262

temperature and ocean surface temperature (MLOST). (a) Composite MLOST 263

trends of the early-20th

-century warming and the late-20th

-century rapid warming 264

periods. (b) As in (a) but for averaged MLOST trends of the mid-20th

-century cooling 265

and the early-21st-century slowdown periods. (c) Composite trend differences between 266

the two slowdown periods and the two warming acceleration periods. White areas 267

represent missing values. Stippling indicates regions exceeding 95% confidence level 268

based on a two-sided Student’s t-test. 269

270

14

271

272

Supplementary Figure 3 | Selected regions where sea surface temperature trend 273

forcing is specified in the atmospheric model experiments. Seven regions are 274

selected, including the global ocean, tropical Pacific Ocean (TPO; blue), North Pacific 275

Ocean (NPO; magenta), tropical Atlantic Ocean (TAO; purple), North Atlantic Ocean 276

(NAO; cyan), tropical Indian Ocean (TIO; green) and Southern Ocean (SOC; orange). 277

Each number in parenthesis corresponds to area percentage of individual oceans in the 278

global area. 279

280

15

281

282

Supplementary Figure 4 | Simulated global near-surface air temperature (SAT) 283

trends during two warming acceleration and two slowdown periods. These are 284

produced by the ECHAM5 model forced with the observed global SST trend during 285

(a) the early 20th

-century warming period, (b) the mid-20th

-century cooling period, (c) 286

the late 20th

-century rapid warming period, and (d) the early 21st-century slowdown 287

period, respectively. The model's results are based on six-member ensemble mean 288

(Methods) and stippling denotes the area where at least 67% of the six members 289

produce the same sign trends. 290

16

291

292

Supplementary Figure 5 | Observed and simulated trends of global land 293

near-surface air temperature (SAT) trends during the two GW acceleration 294

periods. (a) Composite land SAT trends in observations. Stippling denotes the same 295

sign trends for two GW acceleration periods. White areas in land indicate missing 296

values. (b) Composite land SAT trends produced by ECHAM5 model with the 297

observed global (Glb) SST trend forcing. (c) As in (b), but for the model results with 298

the observed tropical Pacific Ocean (TPO) SST trend forcing. (d) As in (b), but for 299

the summed contributions of the tropical Indian Ocean (TIO), tropical Atlantic Ocean 300

(TAO), North Pacific Ocean (NPO), North Atlantic Ocean (NAO) and Southern 301

Ocean (SOC) SST trend forcing. The model's results are based on six-member 302

ensemble mean (Methods) and stippling denotes the statistically significant trends (i.e., 303

at least 67% of six members produce the same sign trends). 304

17

305

306

Supplementary Figure 6 | Observed and simulated global land near-surface air 307

temperature (SAT) trends during the mid-20th

century cooling period. (a) 308

Observed land SAT trends. Stippling indicates 5% significance according to a 309

two-sided Student’s t-test. (b) Simulated land SAT trends produced by ECHAM5 310

model with the observed global (Glb) SST trend forcing. (c) As in (b), but for the 311

model results with observed tropical Pacific Ocean (TPO) SST cooling trend forcing. 312

(d) As in (b), but for the summed contributions of the North Atlantic Ocean (NAO) 313

and North Pacific Ocean (NPO) SST cooling trends. (e) As in (b), but for the summed 314

impacts of the warm SST trends in the tropical Indian Ocean (TIO), Southern Ocean 315

(SOC) and tropical Atlantic Ocean (TAO). The model's results are based on 316

six-member ensemble mean (Methods) and stippling represents the statistically 317

significant trends (i.e., at least 67% of six members produce the same sign trends). 318

18

319 320

Supplementary Figure 7 | Observed and simulated global land near-surface air 321

temperature (SAT) trends during the early 21st century slowdown period. (a) 322

Observed land SAT trends. Stippling indicates 5% significance according to a 323

two-sided Student’s t-test. (b) Simulated land SAT trends produced by ECHAM5 324

model with the observed global (Glb) SST trend forcing. (c) As in (b), but for the 325

observed tropical Pacific Ocean (TPO) SST cooling trend forcing. (d) As in (b), but 326

for the summed contribution of the tropical Indian Ocean (TIO), tropical Atlantic 327

Ocean (TAO), North Pacific Ocean (NPO), North Atlantic Ocean (NAO) and 328

Southern Ocean (SOC) SST trend forcing. The model's results are based on 329

six-member ensemble mean (Methods) and stippling represents the statistically 330

significant trends (i.e., at least 67% of six members produce the same sign trends). 331

19

332

333

Supplementary Figure 8 | Sea surface temperature (SST) trend patterns. (left 334

panels) SST trend differences between the observations and CMIP5 multi-model 335

ensemble mean historical simulations, and (right panels) SST trends produced by the 336

multi-model ensemble mean that represents models' response to external forcing for 337

the period 1908-1945 (a, b), 1975-1998 (c, d), 1940-1976 (e, f), and 2001-2012 (g, h). 338

Stippling denotes 5% significance based on a two sided Student’s t-test. 339

340

341

20

Supplementary Table 1 | Linear trends of observed annual-mean 342

globally-averaged surface temperature (GST). The data sets are from HadCRUT4, 343

NASA GISS, NOAA NCDC, ISTI and multi-observation ensemble mean (MOM). 344

Trends are estimated using the Sen’s slope method (blue) and the least-squares 345

regression method (red). The Mann-Kendall test is performed for the statistical 346

significance of Theil-Sen trends. Student’s t-test is applied for the significance of 347

linear regression trends. The significant trends at 90% and 95% confidence level are 348

indicated by one asterisk (*) and two asterisks (**), respectively. 349

Linear Trend

GST 1908-1945

(ºC/10yr)

1940-1976

(ºC/10yr)

1975-1998

(ºC/10yr)

2001-2012

(ºC/10yr)

1900-2012

(ºC/100yr)

HadCRUT4 0.15**

(0. 145**

)

-0.036**

(-0. 035**

)

0.186**

(0.196**

)

-0.001

(-0.006)

0.749**

(0.739**)

GISS 0.143**

(0.138**

)

-0.028

(-0.025*)

0.179**

(0.187**

)

0.055

(0.052)

0.857**

(0.841**)

NCDC 0.130**

(0.144**

)

-0.028

(-0.035**)

0.131**

(0.140**

)

0.054

(0.028)

0.736**

(0.707**)

ISTI 0.150**

(0.145**

)

-0.032*

(-0.027)

0.168**

(0.179**

)

0.034

(0.039)

0.85**

(0.831**)

MOM 0.149**

(0.143**

)

-0.033*

(-0.030**

)

0.163**

(0.175**

)

0.029

(0.028)

0.797**

(0.78**)

350

351

352

353

354

355

356

357

358

359

360

361

362

363

364

365

366

367

368

21

Supplementary Table 2 | List of World Climate Research Programme CMIP5 369

models for each experiment. Four scenario experiments are selected, including 370

historical simulations, historicalGHG simulations (only greenhouse gas forcing), 371

historicalNat simulations (only natural forcing) and historicalMisc simulations (only 372

anthropogenic aerosol forcing). For each model used here, the model acronym is 373

listed in conjunction with the number of available members. 374

375

Model historical historicalGHG

G

historicalNat historicalMisc

ACCESS1-0 1

ACCESS1-3 1 bcc-csm1-1 3 1 1

bcc-csm1-1-m 3 BNU-ESM 1 CanESM2 5 5 5 5 CCSM4 6

CESM1-BGC 1 CESM1-CAM5 3 CMCC-CESM 1

CMCC-CM 1 CMCC-CMS 1 CNRM-CM5 10 6 6

CSIRO-Mk3-6-0 10 5 5 5 EC-EARTH 5 FGOALS-g2 1 FGOALS-s2 3

FIO-ESM 3 GFDL-CM2.1 10 GFDL-CM3 5

GFDL-ESM2G 1 GFDL-ESM2M 1

GISS-E2-H 5 5 5 5 GISS-E2-H-CC 1

GISS-E2-R 6 5 5 5 GISS-E2-R-CC 1

HadCM3 10 HadGEM2-AO 1 HadGEM2-CC 1 HadGEM2-ES 4 4 4

INM-CM4 1 IPSL-CM5A-LR 6 3 3 IPSL-CM5A-MR 3 3 IPSL-CM5B-LR 1

MIROC5 5 MIROC-ESM 3

MIROC-ESM-CHEM 1 MPI-ESM-LR 3 MPI-ESM-MR 3 MRI-CGCM3 3 NorESM1-M 3 1 1 1 NorEM1-ME 1

376

377

378

379

380

22

Supplementary Table 3 | Linear trends of observed and simulated land surface 381

air temperature (land SAT). The observed (Obs) land SAT trends are estimated 382

based on the ensemble mean of four observations. The simulated land SAT trends 383

include those forced by the observed SST changes in the global ocean (Glb), tropical 384

Pacific Ocean (TPO), tropical Indian Ocean (TIO), tropical Atlantic Ocean (TAO), 385

North Pacific Ocean (NPO), North Atlantic Ocean (NAO), and Southern Ocean 386

(SOC), respectively. The model’s simulations are based on six-member ensemble 387

mean for each experiment (Methods). Trends are estimated using the least-squares 388

regression method. The nonlinear SST effect (Nonlinear) is defined as the difference 389

between the global SST-forced land SAT trend and the sum of the individual ocean 390

basins’ effects. The direct forcing effect (Direct) is estimated based on the simulated 391

land SAT trend with the observed CO2 trend forcing during 1958-2012 (Methods), 392

which is then scaled by the CO2 trends for each period. The model bias (Bias) is 393

estimated by the difference between the observed land SAT trend and the sum of the 394

global SST-forced land SAT trend and the direct forcing effect. 395

396

Linear Trend

Land SAT 1908-1945

(ºC/10yr)

1940-1976

(ºC/10yr)

1975-1998

(ºC/10yr)

2001-2012

(ºC/10yr)

Obs

Glb

0.147

0.175

-0.020

-0.051

0.265

0.199

0.087

0.010

TPO

TIO

0.078

0.023

-0.037

0.004

0.109

0.039

-0.031

0.004

TAO

NPO

0.046

0.017

0.001

-0.008

0.033

0.005

0.076

0.002

NAO

SOC

0.015

0.019

-0.026

0.001

0.046

0.044

0.003

0.005

Nonlinear

Direct

-0.023

0.014

0.014

0.038

-0.077

0.076

-0.049

0.096

Bias -0.042 -0.007 -0.010 -0.019

397