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Abstract and Figures

Over the past decade, anomalously hot summers and persistent droughts frequented over the western United States (wUS), the condition similar to the 1950s and 1960s. While atmospheric internal variability is important for mid-latitude interannual climate variability, it has been suggested that anthropogenic external forcing and multidecadal modes of variability in sea surface temperature, namely, the Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO), also affect the occurrence of droughts and hot summers. In this study, 100-member ensemble simulations for 1951–2010 by an atmospheric general circulation model were used to explore relative contributions of anthropogenic warming, atmospheric internal variability, and atmospheric response to PDO and AMO to the decadal anomalies over the wUS. By comparing historical and sensitivity simulations driven by observed sea surface temperature, sea ice, historical forcing agents, and non-warming counterfactual climate forcing, we found
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DOI 10.1007/s00382-016-3350-x
Clim Dyn (2017) 49:403–417
Forced response and internal variability of summer climate
over western North America
Youichi Kamae1,2 · Hideo Shiogama3 · Yukiko Imada4 · Masato Mori5 · Osamu Arakawa1 ·
Ryo Mizuta4 · Kohei Yoshida4 · Chiharu Takahashi5 · Miki Arai5 · Masayoshi Ishii4 ·
Masahiro Watanabe5 · Masahide Kimoto5 · Shang‑Ping Xie2 · Hiroaki Ueda1
Received: 26 March 2016 / Accepted: 7 September 2016 / Published online: 16 September 2016
© Springer-Verlag Berlin Heidelberg 2016
large portions of recent increases in mean temperature and
frequency of hot summers (66 and 82 %) over the wUS
can be attributed to the anthropogenic global warming. In
contrast, multidecadal change in the wUS precipitation is
explained by a combination of the negative PDO and the
positive AMO after the 2000s. Diagnostics using a linear
baroclinic model indicate that AMO- and PDO-related
diabatic heating anomalies over the tropics contribute to
the anomalous atmospheric circulation associated with
the droughts and hot summers over wUS on multidec-
adal timescale. Those anomalies are not robust during the
periods when PDO and AMO are in phase. The prolonged
PDO–AMO antiphase period since the late twentieth cen-
tury resulted in the substantial component of multidecadal
anomalies in temperature and precipitation over the wUS.
Keywords Global warming hiatus · PDO · AMO · Hot
summers · Linear baroclinic model
1 Introduction
Since the late twentieth century, mean temperature and fre-
quency of warm extremes have both remarkably increased
over land (e.g. Hansen et al. 2012; Perkins et al. 2012).
Anthropogenic influences including human-induced green-
house gases emissions play an essential role in the observed
climate change during the recent six decades (e.g. Jones
et al. 2013; IPCC 2013). In addition, intrinsic variability
in the climate system also influences decadal-to-centennial
climate trends particularly during the winter season (Hawk-
ins and Sutton 2009; Deser et al. 2012). Since the end of
the twentieth century, substantial decadal-to-multidecadal
variations (DMV) in the rate of global-mean temperature
increase have been observed. Particularly, temperature and
Abstract Over the past decade, anomalously hot sum-
mers and persistent droughts frequented over the western
United States (wUS), the condition similar to the 1950s and
1960s. While atmospheric internal variability is important
for mid-latitude interannual climate variability, it has been
suggested that anthropogenic external forcing and multi-
decadal modes of variability in sea surface temperature,
namely, the Pacific Decadal Oscillation (PDO) and Atlantic
Multidecadal Oscillation (AMO), also affect the occurrence
of droughts and hot summers. In this study, 100-member
ensemble simulations for 1951–2010 by an atmospheric
general circulation model were used to explore relative
contributions of anthropogenic warming, atmospheric inter-
nal variability, and atmospheric response to PDO and AMO
to the decadal anomalies over the wUS. By comparing his-
torical and sensitivity simulations driven by observed sea
surface temperature, sea ice, historical forcing agents, and
non-warming counterfactual climate forcing, we found that
Electronic supplementary material The online version of this
article (doi:10.1007/s00382-016-3350-x) contains supplementary
material, which is available to authorized users.
* Youichi Kamae
kamae.yoichi.fw@u.tsukuba.ac.jp
1 Faculty of Life and Environmental Sciences, University
of Tsukuba, 1-1-1 Tennoudai, Tsukuba, Ibaraki 305-8506,
Japan
2 Scripps Institution of Oceanography, University of California
San Diego, La Jolla, CA, USA
3 Center for Global Environmental Research, National Institute
for Environmental Studies, Tsukuba, Ibaraki, Japan
4 Meteorological Research Institute, Tsukuba, Ibaraki, Japan
5 Atmosphere and Ocean Research Institute, University
of Tokyo, Kashiwa, Chiba, Japan
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... Error bars in Fig. 5b are generally large, indicating the great importance of atmospheric internal variability under the fixed radiative forcing and SST boundary conditions in the d4PDF simulations. Kamae et al. (2017a) showed that the relative importance of atmospheric internal variability compared with the forced atmospheric response to global SST perturbation is larger over the mid-and high-latitudes than the tropics (Figs. 10a, c of Kamae et al. 2017a). ...
... Kamae et al. (2017a) showed that the relative importance of atmospheric internal variability compared with the forced atmospheric response to global SST perturbation is larger over the mid-and high-latitudes than the tropics (Figs. 10a, c of Kamae et al. 2017a). The d4PDF ensemble mean can be considered a forced response to fixed boundary conditions because the effects of atmospheric internal variability cancel out one another (Kamae et al. 2017a, b;Ueda et al. 2018). ...
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... Here we use the 30-member ensemble simulations to quantify the total variability and its two components: SST-forced variability and internal variability, following Mei et al. (2014Mei et al. ( , 2015Mei et al. ( , 2019 and Kamae et al. (2017c). Specifically, the total variability T at each grid point is calculated as: ...
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