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The high temperature event in July 2018 caused record-breaking human damage throughout Japan. Large-ensemble historical simulations with a high-resolution atmospheric general circulation model showed that the occurrence rate of this event under the condition of external forcings in July 2018 was approximately 20%. This high probability was a result of the high-pressure systems both in the upper and lower troposphere in July 2018. The event attribution approach based on the large-ensemble simulations with and without human-induced climate change indicated the following: (1) The event would never have happened without anthropogenic global warming. (2) The strength of the two-tiered high-pressure systems was also at an extreme level and at least doubled the level of event probability, which was independent of global warming. Moreover, a set of the large-ensemble dynamically downscaled outputs revealed that the mean annual occurrence of extremely hot days in Japan will be expected to increase by 1.8 times under a global warming level of 2°C above pre-industrial levels.
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8SOLA, 2019, Vol. 15A, 8−12, doi:10.2151/sola.15A-002
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Attribution 4.0 International (CC BY 4.0) license (
The high temperature event in July 2018 caused record-break-
ing human damage throughout Japan. Large-ensemble historical
simulations with a high-resolution atmospheric general circulation
model showed that the occurrence rate of this event under the
condition of external forcings in July 2018 was approximately
20%. This high probability was a result of the high-pressure
systems both in the upper and lower troposphere in July 2018.
The event attribution approach based on the large-ensemble
simulations with and without human-induced climate change
indicated the following: (1) The event would never have happened
without anthropogenic global warming. (2) The strength of the
two-tiered high-pressure systems was also at an extreme level and
at least doubled the level of event probability, which was inde-
pendent of global warming. Moreover, a set of the large-ensemble
dynamically downscaled outputs revealed that the mean annual
occurrence of extremely hot days in Japan will be expected to
increase by 1.8 times under a global warming level of 2°C above
pre-industrial levels.
(Citation: Imada, Y., M. Watanabe, H. Kawase, H. Shiogama,
and M. Arai, 2019: The July 2018 high temperature event in Japan
could not have happened without human-induced global warming.
SOLA, 15A, 8−12, doi:10.2151/sola.15A-002.)
1. Introduction
In July 2018, Japan experienced extremely high temperature.
The accumulated number of weather stations reporting extremely
high daily maximum temperatures (exceeding 35°C) was more
than 6000 based on the 927 stations. This heat event caused
damage to human health with 1032 deaths during this period (based
on the statistical summary provided by the Japanese Ministry
of Health, Labor and Welfare). Human-induced global warming
probably contributed to the high temperatures in Japan and other
East Asian regions in recent decades (Imada et al. 2014; Imada
et al. 2017). Moreover, two seasonal high-pressure systems,
namely the North Pacic subtropical high (NPSH) in the lower
troposphere and Tibetan high in the upper troposphere often
cause warm climate in Japan (Imada et al. 2014). This two-tiered
high-pressure system (double-High) was also visible in July 2018
(Figs. 1a and b). Shimpo et al. (2019) stated that the expansion of
the Tibetan High to Japan was attributable to the repeated mean-
dering of the subtropical jet stream (STJ), and expansion of the
NPSH was attributable to the meridional dipole pressure pattern
(the Pacic-Japan (PJ) pattern; Nitta 1987; Kosaka and Nakamura
2010) associated with enhanced convective activity around the
Philippines. The meandering of the polar front jet (PFJ) also
contributed to the development of the double-High in July 2018.
However, experts do not have any answers on whether the dou-
ble-High in 2018 was extreme compared to other such historical
events. It is even more difcult to determine the extent to which
human-induced global warming contributed to this event.
Attributing changes in the probability of a specic extreme
event to possible causes is called event attribution (EA; Stott
2016). A set of large-ensemble model simulations under condi-
tions with and without human-induced climate change is often
utilized as an effective tool of EA. In this study, we quantitatively
estimated the contribution of both human-induced global warming
and the double-High toward the July 2018 high temperature event
in Japan using high-resolution large-ensemble historical and non-
warming simulations, the so-called d4PDF (Mizuta et al. 2017).
We also estimated the occurrence of extremely hot days at each
grid points in Japan owing to global warming in the near future.
2. Observational data set and large ensemble simu-
The data for tropospheric temperatures and geopotential
heights was obtained from the 55-year Japanese reanalysis (JRA-
55) dataset (Kobayashi et al. 2015). For the accumulated number
of stations reporting extremely high temperature, we used the data
source provided by JMA which is based on the automated meteo-
rological data acquisition system (AMeDAS; approximately 927
stations at intervals of approximately 21 km) maintained by the
Our large-ensemble pairs of global and regional climate model
simulations are part of the database for policy decision-making for
future climate change (d4PDF) (Mizuta et al. 2017). The global
climate simulations were conducted using the atmospheric general
circulation model (MRI-AGCM3.2) developed by the Meteoro-
logical Research Institute, with a grid spacing of approximately
60 km (Mizuta et al. 2012). The regional climate simulations
were conducted using the nonhydrostatic regional climate model
(NHRCM) with a 20 km grid spacing (Sasaki et al. 2008). The
regional climate simulation covered the East Asian region.
The historical simulations (HIST) were forced by the histor-
ical sea surface temperature (SST) and sea ice thickness/concen-
tration based on COBE-SST2 (Hirahara et al. 2014) and historical
anthropogenic and natural forcing agents such as greenhouse
gases and solar irradiance; the RCP4.5 emission scenario was
used for the period after 2006, comprising a 100-member ensem-
ble with different initial conditions and SST perturbations from
1951 to the present. The non-warming simulations (non-W) were
forced by the historical natural forcing agents and counterfactual
“natural” SST and sea ice estimated by removing the warming
trends observed in the 20th century. The anthropogenic forcings
were xed at a value of 1850 in the non-W (for more details, refer
to Shiogama et al. 2016). In the ofcial d4PDF, the HIST/non-W
datasets of MRI-AGCM3.1 (60km resolution) cover the period
from 1951 to 2010. For the regional simulations, only the HIST
ensemble of NHRCM (20 km resolution) is available. Thus, we
extended the calculations to July 2018 and added the downscaled
calculations as a quasi-real-time product.
In this paper, the model analyses presented in Sections 3 and 4
are based on the 60 km global model. The 20 km regional climate
simulations are used in Section 5 to estimate the simulated total
area of locations experiencing extremely hot days.
The July 2018 High Temperature Event in Japan Could Not Have Happened
without Human-Induced Global Warming
Yukiko Imada1, Masahiro Watanabe2, Hiroaki Kawase1,
Hideo Shiogama3, and Miki Arai2
1Meteorological Research Institute, Japan Meteorological Agency, Ibaraki, Japan
2Atmosphere and Ocean Research Institute, University of Tokyo, Chiba, Japan
3National Institute for Environmental Studies, Ibaraki, Japan
Corresponding author: Yukiko Imada, Meteorological Research Institute,
Japan Meteorological Agency, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052,
Japan. E-mail:
9Imada et al., Event Attribution of the July 2018 High Temperature Event
dominant intrinsic mode from JRA-55. Here, the area mean inside
each analysis region was removed from Z200 and Z850 to obtain
a relative circulation eld. The rst principle mode (SVD1) shows
the double-High structure (Figs. 2b and 2d). The Z850 eld shows
the typical PJ pattern (Fig. 2b), and the Z200 eld shows the
Rossby wave trains along the STJ and the PFJ causing meandering
of the jet streams. The patterns in Figs. 2b and 2d well represent
the anomaly patterns in July 2018 (Figs. 1a and 1b), indicating
that the SVD1 can capture the combined mode of the PJ pattern
and the Rossby wave trains along the STJ and the PFJ. The
expansion coefcient of July 2018 for Z200 (Z850) was the fourth
highest (the second highest), indicating that the circulation aspect
was also extreme compared to the historical record in JRA-55
(black line in Figs. 2a and 2c), although we have experienced the
same level of double-High events a few times in the past. The
scatter plot of the two expansion coefcients of JRA-55 (black
cross marks in Fig. 2e) also conrms the extremeness of the
double-High in July 2018. Hereafter, we used the two expansion
coefcients as the index of the double-High. The long-term
trend of the double-High index remains at the same level unlike
3. Historical variations in temperature and the two-
tiered high-pressure system
Figure 1e shows historical changes in the lower-troposphere
(850 hPa) temperature (T850) in July averaged over Japan
(130°E−147°E, 30°N−43°N) based on JRA-55, HIST, and non-W.
Here, we used T850 to dene the temperature index in place of
land surface temperature because, in AGCM simulations forced
by the prescribed SST, variance of surface temperature could be
underestimated on small islands due to a direct impact of the sur-
rounding SST. The JRA-55 time series shows unprecedented high
temperatures in July 2018. The HIST ensemble adequately covers
the historical temperature changes over Japan and the ensemble
mean eld reproduces record high temperatures in July 2018.
To identify the double-High events from the historical record,
we performed a singular value decomposition (SVD) analysis
using the upper- (200 hPa) and lower- (850 hPa) tropospheric geo-
potential heights (hereafter, Z200 and Z850, respectively) around
Japan (See rectangles in Figs. 2b and 2d) to obtain a concurrent
Fig. 1. Anomalies in July 2018 for (a) Z850 [m] according to JRA-55, (b) Z200 [m] according to JRA-55, (c) Z850 [m] according to HIST, and (d) Z200
[m] according to HIST. 100-member ensemble-mean anomalies are shown in (c) and (d). (e) Time series of T850 [K] averaged in 130°E−147°E and 30°N−
43°N. Black: JRA-55; red: ensemble mean of HIST; blue: ensemble mean of non-W. The shadings indicate the range of all members.
10 SOLA, 2019, Vol. 15A, 8−12, doi:10.2151/sola.15A-002
the warming trend presented in Fig. 1e. Thus, the double-High
condition appears to be natural variability and not affected by the
human-induced climate change at this stage.
The double-High indices for the model simulations were
estimated by projecting each member onto each singular vector
obtained from the JRA-55 records. The obtained indices are
plotted in Figs. 2a, 2c, and 2e. The ensemble means of the Z200
and Z850 indices both for HIST and non-W in July 2018 also
show positive peaks (Figs. 2a and 2c). The scatter plot of the July
2018 samples (squares in Fig. 2e) also shifts to the rst quadrant.
These features are consistent with the fact that the ensemble-mean
spatial anomaly patterns in Figs. 1c and 1d show the development
of a double-High structure similar to that in the reanalysis dataset
over Japan in July 2018 although the wave trains along the STJ
and the PFJ are not clear in the model. Moreover, the magnitude
of the ensemble-mean signals is smaller than the amplitude of
anomalies in the reanalysis. This implies that the double-High
pressure system in July 2018 was not purely because of the sto-
chastic atmospheric internal variability but was partly forced by
the SST, sea ice, or any other external forcing. The weak La Niña
condition in July 2018 could have enhanced convection around
the Philippine Sea and the development of the NPSH (Kosaka
et al. 2013). The La Niña event in the preceding winter could also
have a role in developing the PJ pattern in the summer through the
tropical Indian Ocean warming (Xie et al. 2009; Hu et al. 2011).
On the other hand, the source of the Rossby-wave propagations
along the STJ and the PFJ are not easy to detect. These points will
be discussed in another paper.
Again, the difference in the double-High indices between
HIST and non-W is unclear (the red and blue lines in Figs. 2a and
2c), indicating that the impact of anthropogenic climate change
on the double-High event is little. Note that, the results do not
change when the singular vectors obtained from the SVD analysis
of HIST and non-W are used to dene the double-High index
although the contribution rates of SVD1 based on the simulations
are slightly smaller than that based on JRA-55.
Fig. 2. SVD1 expansion coefcients normalized by each standard deviation of JRA-55 for (a) Z200 and (c) Z850 from the SVD analysis based on JRA-
55 (non-dimensional). Shadings indicate the range of normalized expansion coefcients for simulations estimated by projecting each member onto each
singular vector obtained from the JRA-55 records. (b) and (d) Z200 and Z850 anomaly patterns of JRA-55 regressed onto each expansion coefcient ([m],
black lines of a and c), respectively. The stippling in (b) and (d) indicates the area exceeding 99% signicance level by t-test. (e) Scatter plot of the SVD1
expansion coefcient for Z850 against the coefcient for Z200. The red (blue) squares denote the coefcients of HIST (non-W) in July 2018, and the dark
yellow (light-blue) circles denote the coefcients of HIST (non-W) from 1958 to 2017. The black cross marks denote the coefcients of JRA-55 from 1958
to 2018 (The value of 2018 is highlighted).
11Imada et al., Event Attribution of the July 2018 High Temperature Event
4. Event Attribution study
In this section, we focus on the event probability in July
2018. Figure 3 shows the probability density functions (PDFs) of
the T850 indices dened in Fig. 1. The difference in the curves
for HIST (red) and non-W (blue) indicates the impact of human
activity. The probability of exceeding the level of the July 2018
temperature (T850 = 18.5°C) is 19.9% (a condence interval
is 15.5−24.5% by 10th and 90th percentiles of 5000 bootstrap
random sampling) for HIST, while it is 3.31 × 10−5% (1.13 × 10−5
5.69 × 10−5%) for non-W. This result indicates that the warm event
during July 2018 would never have happened without anthro-
pogenic global warming. Moreover, when we considered only
the subsample of HIST with a double-High system where both
the expansion coefcients are positive (the rst quadrant of Fig.
2e) in the SVD analysis presented in Section 3, the probability
increased to 24.6% (20.0−29.4%) as shown in Fig. 3 with the
magenta-dashed curve). On the other hand, when we considered
only the subsample without a double-High system where both or
one of the expansion coefcients are negative (the second, third,
and forth quadrant of Fig. 2e), the probability reduced to 12.2%
(7.62−17.1%) as shown with the orange-dashed curve in Fig.
3. Note that, a case “with” a double-High dened here does not
necessarily correspond to a case as strong as the double-High in
July 2018 but includes weaker double-High systems. Therefore,
the double-High structure also had an important role in at least
doubling the risk of the 2018 high temperature event, which is
independent of anthropogenic impacts.
5. Occurrence of extremely hot days at each grid
points in Japan
Using large-ensemble high-resolution (20 km mesh) regional
products, we can estimate the impact of human-induced global
warming to regional-scale extreme events. As an example, Fig. 4a
presents the difference in the number of extremely hot days in July
2018 between HIST and non-W at each grid point of the regional
model. Here, the JMA dened the threshold for extremely high
temperatures as 35.0°C, which is approximately the 99-percentile
value of the total daily maximum temperature observations during
2010−2017. For the model simulations, we used the 99-percentile
value of HIST in the same period (32.9°C) as the threshold
because the simulated grid-mean temperature underestimated the
temperature observed at the sites. Figure 4a shows a pronounced
increase in the extremely hot days in the populated areas of Japan
during July 2018 due to the anthropogenic climate change. Further
details of the regional aspects of the July 2018 warm event will be
presented in another paper.
The large-ensemble regional products are also available to
project near-future changes in regional extremes. We presented
the density of the scatter plot of the mean annual occurrence of
extremely high temperatures (vertical axis) relative to the global
annual-mean surface air temperature (horizontal axis) using
the HIST and non-W simulations (Fig. 4b). The mean annual
occurrence (unit: days/year/grid) was estimated as accumulated
fraction of grid points experiencing an extremely high daily
maximum temperature within each year among all the grid points
in Japan (1350 grid points) throughout each year. For reference,
mean annual occurrence estimated from the AMeDAS sites (total
927 sites) and global-mean surface temperature provided by the
JMA were also plotted for the period after 2010. The white curve
indicates the cumulative distribution function (CDF) of the daily
maximum temperatures in Japan for all the grid points and days of
non-W from 1951 to 2017 in assuming that the PDF of daily max-
Fig. 4. (a) Difference in the number of extremely hot days in July 2018 between HIST and non-W at each grid point of the regional model [days/month].
Ensemble-mean values are plotted. (b) Density of the scatter plot [counts/bin] of the mean annual occurrence [days per year] of extremely high daily max-
imum temperature per the total number of grid points of Japan against the global annual-mean surface air temperature [K]. We divided the plot area into
1750 bins; each bin is dened as a space with 0.06 K (horizontal) and 0.2 days/year/grid (vertical). Warm-color (cold-color) contours are results from all
members and years (1958−2017; the results of 2018 are not plotted because the simulations ended at July 2018) from the HIST (non-W) run with an inter-
val of 20 (40) counts per bin starting at 1. For reference, the mean annual occurrence estimated from the AMeDAS sites after 2010 are also plotted, indicat-
ed by open-black squares. The white curve indicates the CDF estimated from surface temperatures in Japan for all grid points and days of the non-W run
from 1951 to 2017, and the gray curves indicate 5000 CDFs generated by the bootstrap resampling.
Fig. 3. PDFs for T850 [K] over Japan. The red- (blue-) solid line is the
result of the July 2018 ensemble simulations of HIST (non-W). The
magenta-dashed and orange-dashed line was estimated from the members
with and without the double-High system in the HIST run, respectively.
12 SOLA, 2019, Vol. 15A, 8−12, doi:10.2151/sola.15A-002
imum temperature for all grid points of Japan shifts in a positive
direction in proportion to the global-mean surface temperature
with a xed shape. The CDF curve well represents the combined
plots for HIST and non-W, indicating an average level of the mean
annual occurrence of extremely high temperatures per year at each
warming stage. Since the last decade, the warming level across
the world has been at approximately 1°C. Thus, in recent years,
it is not surprising that the mean annual occurrence of high tem-
peratures is equal to almost 2.7 days per year per grid. Again, the
extremely high temperature event in 2018 was an extraordinary
situation in which extreme temperatures were experienced almost
seven days per year per sites of AMeDAS on average.
Using the CDF curve shown in Fig. 4b, we could project near-
future trends regarding the mean annual occurrence of high
temperatures. Under a global warming level of 1.5°C (2°C), the
average days that could experience high temperatures is estimated
to be approximately 3.6 (4.8) days/year/grid; such a fraction was
observed only a few times in the past.
6. Discussion and conclusion
This paper presented a quantitative estimate of the contribu-
tion of human-induced climate change to the extremely high tem-
perature experienced in Japan in July 2018 using a large-ensemble
database, the so-called d4PDF. By comparing the event probabil-
ities between the historical (realistic) and non-warming (without
human impact) ensemble sets, we concluded that the warm event
in July 2018 would never have happened without human-induced
climate change.
Furthermore, this is an initial attempt to quantify not only the
human-induced effect but also the contribution of specic natural
variabilities. In the July 2018 case, the double-High system was
the main natural variability, which is independent of anthropo-
genic effects. We separated the 2018 simulations into members
with and without double-High development and found that the
probability of extremely high temperatures at least doubled owing
to the existence of the double-High pressure system in July 2018.
The July 2018 high temperature event caused serious damage
to human lives in Japan. We have the responsibility of reporting
to what extent this disastrous event was attributable to human
activity to provide concrete results and alert people to take all
possible steps to minimize future damage associated with anthro-
pogenic global warming. To emphasize this fact, we also projected
near-future trends of the mean annual occurrence of extremely hot
days in Japan. Our results suggested that the extremely high tem-
peratures experienced only a few times in the past could become a
usual situation with warming levels of 1.5 or 2°C in the next few
Note that, model dependency and uncertainty in the boundary
conditions of non-W are big issues in an EA research. There is
an international multi-model intercomparison project for EA to
address these issues (Stone 2013). Although the EA simulations of
d4PDF has a higher resolution and a longer-term coverage of peri-
ods than the other state-of-the-art EA databases in the world, the
weak point is in consideration of the uncertainties. Huge compu-
tational costs limit the goal. The results in this paper appears to be
the best estimates within our current capabilities. The remaining
issues will be explored in the future.
This is a prompt report, and we skipped the discussion regard-
ing how the development of the double-High system (that is, the
PJ pattern and the Rossby wave-like patterns) in July 2018 was
forced by large-scale factors. The specic SST in 2018 possibly
had a role in forming the double-High system. Further discussions
regarding this issue will be reported in future papers.
We are grateful to M. Mori, and C. Takahashi for their coop-
eration. This work was supported by the TOUGOU program of
the Japanese Ministry of Education, Culture, Sports, Science and
Technology (MEXT). This study utilized the d4PDF, which was
produced using the Earth Simulator as “Strategic Project with
Special Support” of JAMSTEC under corporations among the
programs of SOUSEI, TOUGOU, and SI-CAT, which all were
sponsored by the MEXT, Japan. This study was partly supported
by Japan Science and Technology Agency (JST) and Japan Society
for the Promotion of Science (JSPS) Grants-in-Aid for Scientic
Research (KAKENHI) Grant Numbers 18K19951 and 18K03749.
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Manuscript received 22 February 2019, accepted 23 April 2019
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Citizens in many countries are now experiencing record-smashing heatwaves that were intensified due to anthropogenic climate change. Whether today’s most impactful heatwaves could have occurred in a pre-industrial climate, traditionally a central focus of attribution research, is fast becoming an obsolete question. The next frontier for attribution science is to inform adaptation decision-making in the face of unprecedented future heat.
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Climate change is one of the greatest long-term challenges faced by humanity. In the projection of climate change impacts, scenarios based on assumptions regarding future conditions are commonly used. Shared socio-economic pathways (SSPs) are widely employed as socio-economic scenarios for global-scale predictions. The SSPs provide future projections of population and gross domestic products. However, SSPs are not suitable for detailed assessments for a country such as Japan, as they include only global regional data. The S-18 project aims at a nationally unified projection of climate change impacts across multiple sectors in Japan. In contribution to this, based on the previous study for Japan SSPs, we established common socio-economic scenarios designated as Japan SSP1, Japan SSP5, and status quo. Japan SSP1 and Japan SSP5 are based on qualitative links to global SSPs. Japan SSP1 foresees sustainable society with low-carbon emission, while Japan SSP5 envisions a society dependent on fossil fuels, emitting large amounts of greenhouse gases. The status-quo scenario assumes no future change based on the current conditions in Japan. Moreover, we provided a common dataset of population and land-use under these scenarios. Population data were obtained from existing population projections, and land-use data were estimated according to population changes and current land-use classifications. Here, the dataset prepared for the S-18 project is detailed and possibilities for its improvement discussed.
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Southeast Asia is one of the world’s regions most vulnerable to climate change impacts with low-lying land, more severe floods and droughts, larger populations, higher dependency on agriculture for the economic sector, and low resilience of communities. Therefore, a study on how future climate change will affect this region has been conducted, and the results are provided in this paper. Projected surface temperatures and total precipitation from the baseline period of 2013 up to 2100 for Southeast Asia were investigated using the Global Climate Model (GCM) and the Weather Research Forecast (WRF) v3.9.1.1 modelling systems under RCP4.5 and RCP8.5 future climate scenarios. The results showed that future temperatures were projected to increase under both climate scenarios RCP4.5 and RCP8.5; however, precipitation was projected to decrease. The temperature was projected to increase by 0.93C and 2.50C under RCP4.5 and 8.5. Meanwhile, precipitation greatly varied under the RCP4.5 and RCP8.5 climate scenarios in both monsoonal seasons. We conclude that the change in climate variables, particularly the temperature and precipitation, could potentially increase the vulnerability of this region.
Geographical distributions of trends in climate indices with statistical significance in Japan are investigated at each of the 51 observation stations for the recent 120 years. We employed a comprehensive set of the 70 climate indices on an annual time scale to detect climate changes and to facilitate climate information for a variety of sectors, which are developed by expert team on sector‐specific indices in the World Meteorological Organization. The rising trends have regional differences such as the annual mean of daily maximum and minimum surface air temperatures in the Kanto‐Koushin region. The statistically significant increasing trends in 3 daily heavy precipitation‐relevant climate indices: R95p, R99p, and RX1day: are seen in Hokuriku, Shikoku, the western part of Northern and Southern Kyusyu regions, and isolated stations in the other regions. Rotational empirical orthogonal function analysis of R99p revealed different features of statistically significant modes suggesting the trends are attributed to changes in different atmospheric phenomena as well as those of warm spell duration index. The number of the stations with statistically significant trends varies with an index. Indies relevant to daily minimum surface air temperatures have statistically significant trends at most of the 51 stations. Consecutive dry days are the most sensitive to climate changes for the 120 years among the indices relevant to precipitation in this analysis. The indices relevant to heavy precipitation are not always been most sensitive to climate change detections in Japan. The geographical distributions of the percentage of the number of indices with statistically significant changes to the total number of the stations provide information about stations sensitive to climate changes. The highest sensitivity is seen at Kumamoto. These results directly provide detection of climate changes and show the potentials of which index is sensitive to ongoing climate changes from the viewpoint of geographical distributions.
Extreme event attribution aims to elucidate the link between global climate change, extreme weather events, and the harms experienced on the ground by people, property, and nature. It therefore allows the disentangling of different drivers of extreme weather from human-induced climate change and hence provides valuable information to adapt to climate change and to assess loss and damage. However, providing such assessments systematically is currently out of reach. This is due to limitations in attribution science, including the capacity for studying different types of events, as well as the geographical heterogeneity of both climate and impact data availability. Here, we review current knowledge of the influences of climate change on five different extreme weather hazards (extreme temperatures, heavy rainfall, drought, wildfire, tropical cyclones), the impacts of recent extreme weather events of each type, and thus the degree to which various impacts are attributable to climate change. For instance, heat extremes have increased in likelihood and intensity worldwide due to climate change, with tens of thousands of deaths directly attributable. This is likely a significant underestimate due to the limited availability of impact information in lower- and middle-income countries. Meanwhile, tropical cyclone rainfall and storm surge height have increased for individual events and across all basins. In the North Atlantic basin, climate change amplified the rainfall of events that, combined, caused half a trillion USD in damages. At the same time, severe droughts in many parts of the world are not attributable to climate change. To advance our understanding of present-day extreme weather impacts due to climate change developments on several levels are required. These include improving the recording of extreme weather impacts around the world, improving the coverage of attribution studies across different events and regions, and using attribution studies to explore the contributions of both climate and non-climate drivers of impacts.
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An extreme rainfall event occurred over western Japan and the adjacent Tokai region mainly in early July, named “the Heavy Rain Event of July 2018”, which caused widespread havoc. It was followed by heat wave that persisted in many regions over Japan in setting the highest temperature on record since 1946 over eastern Japan as the July and summertime means. The rain event was attributable to two extremely moist airflows of tropical origins confluent persistently into western Japan and large-scale ascent along the stationary Baiu front. The heat wave was attributable to the enhanced surface North Pacific Subtropical High and upper-tropospheric Tibetan High, with a prominent barotropic anticyclonic anomaly around the Korean Peninsula. The consecutive occurrence of these extreme events was related to persistent meandering of the upper-level subtropical jet, indicating remote influence from the upstream. The heat wave can also be influenced by enhanced summertime convective activity around the Philippines and possibly by extremely anomalous warmth over the Northern Hemisphere midlatitude in July 2018. The global warming can also influence not only the heat wave but also the rain event, consistent with a long-term increasing trend in intensity of extreme precipitation observed over Japan.
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An unprecedentedly large ensemble of climate simulations with a 60 km atmospheric general circulation model and dynamical downscaling with a 20 km regional climate model have been performed to obtain probabilistic future projections of low-frequency local-scale events. The climate of the latter half of the 20th century, the climate 4 K warmer than the pre-industrial climate, and the climate of the latter half of the 20th century without historical trends associated with the anthropogenic effect are simulated respectively more than 5000 years. From large ensemble simulations, probabilistic future changes in extreme events are available directly without using any statistical models. The atmospheric models are highly skillful in representing localized extreme events such as heavy precipitation and tropical cyclones. Moreover, mean climate changes in the models are consistent with those in the CMIP5 model ensembles. Therefore, the results enable the assessment of probabilistic change in localized severe events that have large uncertainty from internal variability. The simulation outputs are open to the public as a database called “Database for Policy Decision-Making for Future Climate Change” (d4PDF), which is intended to be utilized for impact assessment studies and adaptation planning for global warming.
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We describe two unprecedented large (100-member), long-term (61-year) ensembles based on MRI-AGCM3.2, which were driven by historical and non-warming climate forcing. These ensembles comprise the “Database for Policy Decision making for Future climate change (d4PDF)”. We compare these ensembles to large ensembles based on another climate model, as well as to observed data, to investigate the influence of anthropogenic activities on historical changes in the numbers of record-breaking events, including: the annual coldest daily minimum temperature (TNn), the annual warmest daily maximum temperature (TXx) and the annual most intense daily precipitation event (Rx1day). These two climate model ensembles indicate that human activity has already had statistically significant impacts on the number of record-breaking extreme events worldwide mainly in the Northern Hemisphere land. Specifically, human activities have altered the likelihood that a wider area globally would suffer record-breaking TNn, TXx and Rx1day events than that observed over the 2001-2010 period by a factor of at least 0.6, 5.4 and 1.3, respectively. However, we also find that the estimated spatial patterns and amplitudes of anthropogenic impacts on the probabilities of record-breaking events are sensitive to the climate model and/or natural-world boundary conditions used in the attribution studies.
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Extreme weather and climate‐related events occur in a particular place, by definition, infrequently. It is therefore challenging to detect systematic changes in their occurrence given the relative shortness of observational records. However, there is a clear interest from outside the climate science community in the extent to which recent damaging extreme events can be linked to human‐induced climate change or natural climate variability. Event attribution studies seek to determine to what extent anthropogenic climate change has altered the probability or magnitude of particular events. They have shown clear evidence for human influence having increased the probability of many extremely warm seasonal temperatures and reduced the probability of extremely cold seasonal temperatures in many parts of the world. The evidence for human influence on the probability of extreme precipitation events, droughts, and storms is more mixed. Although the science of event attribution has developed rapidly in recent years, geographical coverage of events remains patchy and based on the interests and capabilities of individual research groups. The development of operational event attribution would allow a more timely and methodical production of attribution assessments than currently obtained on an ad hoc basis. For event attribution assessments to be most useful, remaining scientific uncertainties need to be robustly assessed and the results clearly communicated. This requires the continuing development of methodologies to assess the reliability of event attribution results and further work to understand the potential utility of event attribution for stakeholder groups and decision makers. WIREs Clim Change 2016, 7:23–41. doi: 10.1002/wcc.380 For further resources related to this article, please visit the WIREs website.
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Summertime atmospheric circulation over the midlatitude western North Pacific (WNP) is influenced by anomalous convective activity near the Philippines. This meridional teleconnection, observed in monthly anomalies and known as the Pacific-Japan (PJ) pattern, is characterized by zonally elongated cyclonic and anticyclonic anomalies around the enhanced convection center and to its northeast, respectively, in the lower troposphere, with an apparent poleward phase tilt with height. The authors' idealized two-layer linear model, whose basic state consists of a zonal subtropical jet and a pair of a monsoon system and a subtropical anticyclone, can simulate a PJ-like response against diabatic heating located between the pair. Each of the observed and simulated patterns can gain energy through barotropic and baroclinic conversions from the zonally varying baroclinic mean flow, in an efficiency comparable with that of energy generation due to the anomalous diabatic heating, indicating a characteristic of the pattern as a dry dynamical mode. In fact, the conversion efficiency is sensitive to the location of the anomaly pattern relative to the climatological-mean flow. Furthermore, the second-least damped mode identified in the idealized model bears certain resemblance with the observed PJ pattern, indicating its modal characteristics as well as a critical importance of these features in the mean field for the pattern. In addition to the PJ pattern, another meridional teleconnection pattern with high efficiency for its energy conversion is identified observationally in association with anomalous convection near the Bonin Islands. The anomalous circulation of the PJ pattern, in turn, can intensify the anomalous convective activity near the Philippines through enhancing evaporation and moisture convergence and dynamically inducing anomalous ascent. It is thus hypothesized that the PJ pattern can be regarded as a moist dynamical mode that sustains itself both via dry energy conversion and interaction with moist processes.
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A new version of the atmospheric general circulation model of the Meteorological Research Institute (MRI), with a horizontal grid size of about 20 km, has been developed. The previous version of the 20-km model, MRIAGCM3.1, which was developed from an operational numerical weather-prediction model, provided information on possible climate change induced by global warming, including future changes in tropical cyclones, the East Asian monsoon, extreme events, and blockings. For the new version, MRI-AGCM3.2, we have introduced various new parameterization schemes that improve the model climate. Using the new model, we performed a present-day climate experiment using observed sea surface temperature. The model shows improvements in simulating heavy monthly-mean precipitation around the tropical Western Pacific, the global distribution of tropical cyclones, the seasonal march of East Asian summer monsoon, and blockings in the Pacific. Improvements in the model climatologies were confirmed numerically using skill scores (e.g., Taylor’s skill score).
The 2016 extreme warmth across Asia would not have been possible without climate change. The 2015/16 El Niño also contributed to regional warm extremes over Southeast Asia and the Maritime Continent.
The Japan Meteorological Agency (JMA) conducted the second Japanese global atmospheric reanalysis, called the Japanese 55-year Reanalysis or JRA-55. It covers the period from 1958, when regular radiosonde observations began on a global basis. JRA-55 is the first comprehensive reanalysis that has covered the last half-century since the European Centre for Medium-Range Weather Forecasts 45-year Reanalysis (ERA-40), and is the first one to apply four-dimensional variational analysis to this period. The main objectives of JRA-55 were to address issues found in previous reanalyses and to produce a comprehensive atmospheric dataset suitable for studying multidecadal variability and climate change. This paper describes the observations, data assimilation system, and forecast model used to produce JRA-55 as well as the basic characteristics of the JRA-55 product. JRA-55 has been produced with the TL319 version of JMA’s operational data assimilation system as of December 2009, which was extensively improved since the Japanese 25-year Reanalysis (JRA-25). It also uses several newly available and improved past observations. The resulting reanalysis products are considerably better than the JRA-25 product. Two major problems of JRA-25 were a cold bias in the lower stratosphere, which has been diminished, and a dry bias in the Amazon basin, which has been mitigated. The temporal consistency of temperature analysis has also been considerably improved compared to previous reanalysis products. Our initial quality evaluation revealed problems such as a warm bias in the upper troposphere, large upward imbalance in the global mean net energy fluxes at the top of the atmosphere and at the surface, excessive precipitation over the tropics, and unrealistic trends in analyzed tropical cyclone strength. This paper also assesses the impacts of model biases and changes in the observing system, and mentions efforts to further investigate the representation of low-frequency variability and trends in JRA-55.
A new sea surface temperature (SST) analysis on a centennial time scale is presented. In this analysis, a daily SST field is constructed as a sum of a trend, interannual variations, and daily changes, using in situ SST and sea ice concentration observations. All SST values are accompanied with theory-based analysis errors as a measure of reliability. An improved equation is introduced to represent the ice-SST relationship, which is used to produce SST data from observed sea ice concentrations. Prior to the analysis, biases of individual SST measurement types are estimated for a homogenized long-term time series of global mean SST. Because metadata necessary for the bias correction are unavailable for many historical observational reports, the biases are determined so as to ensure consistency among existing SST and nighttime air temperature observations. The global mean SSTs with bias-corrected observations are in agreement with those of a previously published study, which adopted a different approach. Satellite observations are newly introduced for the purpose of reconstruction of SST variability over data-sparse regions. Moreover, uncertainty in areal means of the present and previous SST analyses is investigated using the theoretical analysis errors and estimated sampling errors. The result confirms the advantages of the present analysis, and it is helpful in understanding the reliability of SST for a specific area and time period.
Anthropogenic climate change played a significant role in increasing the probability of events such as the heat wave in Japan in 2013.