Geophysical Research Letters
Supporting Information for
Recent slowdown of tropical upper-tropospheric warming associated with Pacific
climate variability
Youichi Kamae1, Hideo Shiogama1, Masahiro Watanabe2, Masayoshi Ishii3, Hiroaki
Ueda4 and Masahide Kimoto2
1Center
for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Japan
2Atmosphere
and Ocean Research Institute, University of Tokyo, Kashiwa, Japan
3Meteorological
4Graduate
Research Institute, Tsukuba, Japan
School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan
Contents of this file
Text S1
Figures S1 to S8
Table S1
Introduction
Text and figures below provide additional discussion and supporting materials to
substantiate the results and discussion presented in the main text.
1
Text S1.
Upper tropospheric temperature variations in the other dataset
To examine upper-tropospheric radiosonde temperature, we used three datasets
(RAOBCORE, RICH obs, and RICH τ) in this study (Section 2.1). Upper-tropospheric
temperatures are also provided as other datasets. Hadley Centre provides atmospheric
temperature dataset HadAT2 [Thorne et al., 2005] ranging from 1958 to the present with
a spatial resolution of 5° latitude and 10° longitude. HadAT2 employed a neighborcomposite-based identification of breakpoint. More details of locations of stations,
breakpoint identification and adjustment were provided in Thorne et al. [2005]. We only
used the grids within the RICH-based mask (Figure S1) to compare the uppertropospheric temperature with the three datasets used in this paper. We also compared
ERA-Interim [Dee et al., 2011] with the radiosonde-based upper-tropospheric
temperature.
As for the MSU/AMSU-based estimate of the upper-tropospheric temperature (T24),
we showed a spatial pattern of T24 derived from NOAA/STAR [Zou et al., 2006, 2009] in
Figure 2e. We also examined other MSU datasets provided by the University of Alabama
in Huntsville (UAH version 5.6; Christy et al., 2003) and the Remote Sensing System
(RSS version 3.3; Mears and Wentz, 2009).
Figure S2 compares four radiosonde and ERA-Interim TTUT. All the data are
anomalies relative to 1968–1990 averages. For ERA-Interim, temperature anomaly TE*
is calculated as follows:
̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅
̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅
𝑇𝐸 ∗ = 𝑇𝐸 − ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅
𝑇𝐸1979−1990 + 𝑇𝑅
1979−1990 − 𝑇𝑅1968−1990
(1)
TE is ERA-Interim temperature, TR is RAOBCORE 1.5 temperature, and bars represent
averages for the given periods. All the temperature anomalies shown in Figure S2 are
𝑇 − ̅̅̅̅̅̅̅̅̅̅̅̅̅
𝑇1968−1990, but TE* for ERA-Interim. All the temperatures show good agreement in
the interannual variabilities and long-term trends. Figure S3 shows long-term (1968–
2011) and short-term (1997–2011) TTUT trends. All the radiosonde-based TTUT have
comparable long-term (ensemble-means of RICH obs and RICH τ are 0.245 and 0.239 K
decade-1, and RAOBCORE 1.5 and HadAT2 are 0.184 and 0.172 K decade-1,
respectively) and short-term trends (0.162, 0.128, 0.121, 0.130 K decade-1 for RICH obs,
RICH τ, RAOBCORE 1.5 and HadAT2, respectively). However, some differences among
the datasets are also found. For example, long-term trend in HadAT2 and short-term
trend in ERA-Interim (0.071 K decade-1) show smaller trends than the others,
respectively. A relatively smaller long-term trend in HadAT2 than the others was detailed
in Thorne et al. [2011]. Some assessments based on realistic validation experiments
suggested that HadAT likely exhibits too little tropospheric warming over the satellite era
over the tropics [McCarthy et al., 2008; Titchner et al., 2009].
Figure S5 shows TTUT anomaly averaged over the CP grids (black boxes in Figures
2, 3, S4 and S8) in four radiosonde datasets compared with HadISST anomaly averaged
over the Nino3.4 region. All the CP TTUT from four datasets show slight cooling
(RAOBCORE 1.5 and RICH τ) or muted change (RICH obs and HadAT2) during the
recent decades (Figures 2c and S5) despite the tropical-mean warming (Figures S2 and
2
S3). As for the interannual variability, CP TTUT shows a high correlation with the Nino3.4
SST (Figures S5 and S8). Correlation coefficients of CP TTUT in RAOBCORE 1.5, RICH
obs, HadAT2 with the Nino3.4 SST are 0.84, 0.76, 0.67, respectively. We should note
that 1) the HadAT2 has a lower correlation with the Nino3.4 SST and 2) the HadAT2
shows warmer condition than the other three temperature data in the recent years. The
large uncertainty among the different radiosonde temperature datasets was also noted in
previous studies [e.g., Santer et al., 2008; Thorne et al., 2011; Seidel et al., 2012]. In
addition to the radiosonde datasets, T24 anomalies derived from three datasets (NOAA,
UAH and RSS) also show some differences among them. For example, amplitudes of
cooling in the subtropical twin CP areas (Figures 2e and S6) are different among the
three (maximum cooling in the subtropical CP areas are 0.0, −0.4 and −0.2 K in
NOAA/STAR, UAH and RSS, respectively). However, all the datasets show clear
zonally-asymmetric T24 anomaly patterns. We can conclude that the recent negative
phase of the PDO (Figures 3b and S8) contributes significantly to the recent TTUT (and
T24) anomalies.
Comparison between the recent TTUT in the observations and CMIP5 multi-model
ensemble mean
The CMIP5 multi-model mean shows a less frequent variability (Figure 1a) and a
larger long-term trend in TTUT than the observation (Figure S3; 0.354 and 0.184 K per
decade for 1968–2011 in the CMIP5 multi-model mean and RAOBCORE 1.5,
respectively). Because the CMIP5 ensemble was not designed to simulate the observed
natural variability of SST, less reproducibility in interannual variability of TTUT was
expected (except for the volcanic events). In addition, the internal climate variabilities in
the individual realizations are smoothed out, resulting in the less frequent variability
when the multi-model realizations are averaged (Figure 1a).
Both the observed and the AGCM-simulated short-term changes (2001–2011 minus
1991–2000) in TTUT and T24 show the zonally-asymmetric patterns (Figures 2c, 2d, 2e,
2f, and S6). In contrast, the TTUT and T24 anomalies derived from the CMIP5 multi-model
mean (Figures S4b and S4c) show warming throughout the tropics, contributing to the
tropical-mean warming bias during this period (Figure 1a). The observation–model gap
in CP TTUT (Figures 2c and S4b) associated with the tropical Pacific cooling (Figure 3b)
as a part of the natural variability (Figure 3d) is one of the largest contributors to the TTUT
gap during the recent years (Figure 1a).
We further examined the contribution from the natural SST variability to the TTUT
discord between the observation and CMIP5 multi-model mean. Figure S7 shows the
time series of TTUT anomaly simulated in CMIP5, and the Nat and Anthro effects
estimated from the AGCM runs (Section 2.2). Compared with the CMIP5 multi-model
mean, RICH τ shows higher frequency variability associated with ENSO and less
warming during these 15 years (Figures 1a, 1c, S1 and S3). Two time series obtained
from the difference between the ensemble means of RICH τ and CMIP5 (blue line in
Figure S7a) and the Nat effect, estimated by the AGCM runs (blue line in Figure S7b),
show similar interannual variations and long-term cooling trends. Because of the large
influences of the ENSO in the TTUT time series (Figure S8), the interannual variations of
the two show high correlation (R between the two detrended time series is 0.74). In
addition, the long-term cooling trend owing to the Nat effect (−0.040  0.006 K decade-1
with 95% confidence interval during 1968–2011) accounts for 37.4  5.6% of that of the
“RICH τ minus CMIP5 mean” (−0.107 K decade-1) in contrast to the continuous warming
3
owing to the Anthro effect (0.292  0.011 K decade-1). These results indicate that the
CMIP5 multi-model mean cannot reproduce accurately the recent variations and longterm trend of TTUT because of the large influence of tropical Pacific SST (Figures 3b, 4
and S8) on the recent variability in TTUT. Note that volcanic eruptions (before 2005) were
implemented as a natural forcing factor (Figure S7b) while this forcing was implemented
in the CMIP5 AOGCM runs. As suggested by Haywood et al. [2014] and Santer et al.
[2014], recent volcanic forcings have substantial contributions on the decadal-scale
tropospheric temperature variations. The relative importance of the internal SST
variability on the observed long-term TTUT trend, compared with that of the volcanic
forcing, should be further examined by other methodologies.
We should also note that the difference in CP TTUT is insufficient to explain all the
tropical-wide discrepancy between the CMIP5 multi-model mean and observations
(Figures 2 and S4). Natural SST variation (Figure 3d) contributes to tropical-wide cooling
in the upper troposphere (Figure 3c) and part of this can be ascribed to tropical-wide
free-tropospheric warming/cooling in response to SST perturbations in the tropics
[Kamae et al., 2014], often referred to as the “weak temperature gradient” [Sobel et al.,
2001].
CMIP5 multi-model ensemble and MIROC5 11-member initial condition ensemble
In this study, two AOGCM ensembles are examined: single-member, 25-model
ensemble (CMIP5 multi-model ensemble) and 11-member, single model ensemble
(MIROC5 11-member ensemble). To quantify the role of internal climate variability in the
TTUT variation, we used MIROC5 11-member ensemble. This is because the uncertainty
in the CMIP5 multi-model ensemble contains both the internal climate variability and
other factors, including different climate sensitivity [Andrews et al., 2012], fast
adjustment [Kamae and Watanabe, 2012], and representation of control climate among
the different models. In Section 3.2, we used MIROC5 11-member ensemble because
these realizations were derived from single AOGCM and the uncertainty can be
attributed to the internal climate variability [e.g., Watanabe et al., 2013]. Note that
ensemble-mean of MIROC5 shows a smaller long-term increase in TTUT than CMIP5
multi-model mean (Figures 1b and S3). This is consistent with the fact that the MIROC5
has a relatively lower climate sensitivity than the CMIP5 multi-model ensemble mean
[Andrews et al., 2012].
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5
Figure S1. CMIP5 25-model mean of zonal-mean temperature anomaly in (a) 1991–
2011 and (b) 2070–2099 relative to 1968–1990. (c) Ensemble means of TTUT anomaly
(relative to 1968–1990 mean) in RICH τ (red) and CMIP5 25 models (black line) and its 1
standard deviation (gray shading). TTUT is defined by 200–300 hPa mean temperature
averaged over tropical grid points (shaded in black) in 20°S–20°N. The tropical grid
points are derived by a criterion that 95% of the data are available during 1981–2000
and 2001–2011 in RICH/RAOBCORE dataset.
6
Figure S2. Similar to Figure 1a, but for comparison of the radiosonde datasets and
reanalysis. ERA-Interim (gray), HadAT2 (red), RAOBCORE 1.5 (blue), RICH obs (green),
and RICH τ (black).
7
Figure S3. Scatter plot of the linear trends of TTUT (K decade-1) during 1968–2011
(horizontal axis) and 1997–2011 (vertical axis). The linear trends and their uncertainty
ranges (lines are minimum-maximum ranges and boxes represent 25–75% ranges) are
identical to Figures 1b and 1c, but with HadAT2 and ERA-Interim. Purple horizontal line
represents linear trend of ERA-Interim for 1997–2011 (linear trend for 1968–2011 is not
available). Gray diagonal line is an 1:1 line.
8
Figure S4. Similar to Figures 2b, d and f, but for CMIP5 25-model mean.
9
Figure S5. Time series of SST anomaly averaged over 170°W–120°W, 5°S–5°N (red)
and TTUT in three-grid (black boxes in Figures 2, 3, S4 and S8) mean over CP. HadAT2
(black), RAOBCORE 1.5 (blue), RICH obs (green) and RICH τ (orange).
10
Figure S6. Similar to Figure 2e, but for (a) UAH version 5.6 and (b) RSS version 3.3.
11
Figure S7. (a) Similar to Figure 1a, but for CMIP5 25-model mean (red) and the
difference between RICH τ and CMIP5 25-model mean (blue). The difference is
calculated by subtracting CMIP5 time series from the ensemble mean of RICH τ.
Shading represents 1 standard deviation. (b) Anthro effect (red) and Nat effect (blue;
Section 2.2).
12
Figure S8. (a) Correlation coefficient of SST (HadISST) with ensemble-mean of CP
three-grid mean TTUT in RICH τ. (b) Similar to Figure S5, but for SST averaged over
170°W–120°W, 5°S–5°N (red) and ensemble-mean of TTUT in three grid points (gray)
and their average (black) from RICH τ.
13
Table S1. List of the CMIP5 models used in Figures 1, S1, S3, S4 and S7.
Model name
Country
ACCESS1.0
Australia
ACCESS1.3
Australia
BCC-CSM1.1
China
BNU-ESM
China
CCSM4
USA
CESM1(CAM5)
USA
CMCC-CM
Italy
CMCC-CMS
Italy
CNRM-CM5
France
CSIRO-Mk3-6-0
Australia
CanESM2
Canada
EC-EARTH
Europe
FGOALS-g2
China
FGOALS-s2
China
GFDL-CM3
USA
GISS-E2-R
USA
HadGEM2-ES
UK
INM-CM4
Russia
IPSL-CM5A-LR
France
IPSL-CM5A-MR
France
MIROC-ESM
Japan
MIROC5
Japan
MPI-ESM-LR
Germany
MRI-CGCM3
Japan
NorESM1-M
Norway
14