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Supplementary online materials
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Watanabe, M., Y. Kamae, M. Yoshimori, A. Oka, M. Sato, M. Ishii, T. Mochizuki, and
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M. Kimoto (2013), Strengthening of ocean heat uptake efficiency associated with the
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recent climate hiatus.
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Internal variability associated with the hiatus period in MIROC5
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The nature of the internal variability associated with the hiatus period is examined using
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the piControl run. The hiatus accompanies a negative peak in global-mean SST and the
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decrease in HC300, which leads the deeper-layer heat content decrease by about 10–20 years
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(Fig. S4a). The SST anomaly pattern associated with the internally generated hiatus in
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MIROC5 (Fig. S4b) exhibits some similarities to Fig. 4a, and the regression of the ocean
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meridional streamfunction at the same lag indicates that tropical cooling is caused by a
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strengthening of equatorial upwelling above 500 m (Fig. S4c). A similar lagged relationship
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between HC300 and HC1500 is seen in observations and MIROC5 historical simulations when
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anomalies are detrended (Fig. S5). However, HC300 and HC1500 anomalies are not oscillatory
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and the former can only be a predictor with a lead time of around four years. Furthermore, the
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model HC300 anomalies exhibit decadal fluctuations roughly in phase for 11 members and
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similar to those in observational data. This suggests that the observed variability in HC300 is
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controlled partly by external forcing such as volcanic eruptions.
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Two-box EBM
The global-mean energy balance equations are written as
dTs
  (Ts  Td )  Ts  F (t ) ,
dt
dT
Cd d   (Ts  Td ) ,
dt
Cs
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where Ts and Td are surface and deep ocean temperature anomalies, and Cs and Cd are the
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heat capacities of the respective layers. The climate feedback parameter is denoted as , and
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heat uptake is represented by heat exchange between the two layers, defined by the efficiency
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coefficient  A realistic solution of Ts can be obtained by integrating the above set of
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equations by prescribing a time-dependent forcing, F(t), which includes both natural
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variability and anthropogenic changes. In the present analysis, we assume that Ts is equivalent
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to SATg, that Td anomaly is much smaller than Ts anomaly (cf. Fig. S2), and that the tendency
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term for Ts is small for timescales longer than a decade. These assumptions lead to the
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approximated form of Eq. (1), where Ts is rewritten as T.
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Table S1
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origin are grouped into the same row.
List of the CMIP3 and CMIP5 models used in this study. Models with the same
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CMIP3 model name
CMIP5 model name
Country
ACCESS1-3
Australia
BCC-CSM1
China
CCCma CGCM3.1(T47) CanCM4
Canada
CCCma CGCM3.1(T63) CanESM2
Canada
NCAR CCSM3
NCAR CCSM4
NCAR PCM
USA
USA
CNRM-CM3
CNRM-CM5
CSIRO Mk3.0
CSIRO Mk-3.6
CSIRO Mk3.5
France
Australia
Australia
FGOALS-g1
FGOALS-g2
China
GFDL-CM2.0
GFDL-CM3
USA
GFDL-CM2.1
GFDL-ESM2G
USA
GFDL-ESM2M
USA
GISS-E2-R
USA
GISS-E-R
GISS-E-H
USA
GISS-AOM
USA
HadCM3
HadCM3
UK
HadGEM1
HadGEM2-CC
UK
INGV-CMCC
Italy
INM-CM3
INM-CM4
Russia
IPSL-CM4
IPSL-CM5A-LR
France
IPSL-CM5A-MR
France
MIROC3.2med
MIROC5
Japan
MIROC3.2hi
MIROC4h
Japan
ECHAM5/MPI-OM
MPI-ESM-LR
ECHO-G
MRI-CGCM2.3.2
Germany
Germany
MRI-CGCM3
Japan
NorESM1-M
Norway
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Fig. S1 Histogram (bars) and probability density functions (PDFs, curves) for 15-year linear
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trends (K per 15 years) in SATg during 1986–2000 (blue) and 1996–2010 (red) based on 22
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CMIP5 models. The vertical lines indicate the trends in HadCRUT4, with the value presented
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at the top. The fractions of models exhibiting a trend lower than the observations are shown in
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parentheses. It is shown that the observation lies in the middle of the GCM ensemble for
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1986–2000, but is shifted toward cooling for 1996–2010. The PDF (extrapolated from
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histogram at the tail) suggests that 20.9% of models can produce the hiatus for 1996–2010,
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but the histogram shows that none of the models actually simulates this.
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Fig. S2 Zonal-mean ocean temperature anomaly in 2001–2010 (shading) imposed on the
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climatological temperature (contours). (a) Observations by Ishii and Kimoto (2009) and (b)
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the 11-member ensemble average of the MIROC5 historical and RCP4.5 simulations. The
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color scale is different for the Northern (right) and Southern (left) Hemispheres.
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Fig. S3 Schematic showing the procedure for the statistical correction to the model ensemble.
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Given that the ensemble members indicated by ‘×’ marks show a quasi-linear relationship
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between SATg and a variable x (HC1500 anomaly taken as an example in the plot), the model
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ensemble mean (solid square) is modified by using the regression slope representing the
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natural internal variability and the difference from observations (circle). The corrected
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ensemble mean (grey square) that is close to the observation supports that the difference in x
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between model and observations is well explained by the internal variability.
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Fig. S4 Hiatus internally generated in a long control simulation. (a) Lagged regression with
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global-mean ocean heat content (HC) anomaly for the global-mean anomalies in SST (pink),
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HC300 (red), HC1500 (green), and the heat content below 1500 m (HCbtm, blue) using the
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500-year preindustrial run by MIROC5. The regressed values represent anomalies per −1
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HC anomaly, and circles indicate the regression significant at the 95% level. The thin black
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curve denotes the HC autoregression. The period when the SST regression shows significant
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negative anomalies corresponds to the hiatus period. All the anomalies are smoothed with an
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11-year running mean. (b)−(c) Regressed anomalies in SST and the zonal-mean meridional
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streamfunction at 4 years prior to the negative peak of the HC anomaly. Values significant at
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the 95% levels are stippled. Gray contours in (c) indicate the climatological mean
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streamfunction (intervals of 10 Sv, negative contours dashed).
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Fig. S5 Detrended anomalies of HC300 (red) and HC1500 (blue) for 1971–2010. (a)
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Observations and (b) 11 members of the MIROC5 historical and RCP4.5 simulations. The
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annual-mean anomalies are smoothed with a 3-year running mean. Triangles in (a) indicate
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the El Chichon and Pinatubo eruptions. (c)–(d) Lagged correlation between the HC300 and
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HC1500 time series shown in (a)–(b) (green), imposed on their autocorrelation functions (red
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and blue). Dashed lines indicate the 95% significance level. The shading in (d) denotes the
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ensemble spread.
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Fig. S6 As in Fig. 4(a), but for individual CMIP5 models. Grey and black dots represent
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results in the 1pct experiments during the first 30 years and the remaining 110 years,
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respectively. The estimated values of  for these two periods are shown at the top of each
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panel, indicating the decline of  in all models.
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