Kawase et al.
Progress in Earth and Planetary Science(2023) 10:7
https://doi.org/10.1186/s40645-023-00536-4

RESEARCH ARTICLE

Progress in Earth andPlanetary Science

Open AccessHistorical regional climate changes in Japan
in winter as assessed by a 5‑km regional climate
model with a land surface process
Hiroaki Kawase1* , Shin Fukui1, Masaya Nosaka1, Shun‑ichi I. Watanabe1, Keishi Otomo2, Akihiko Murata1,
Kazuyo Murazaki1 and Tosiyuki Nakaegawa1

Abstract
We investigate historical regional climate changes in Japan from 1959 to 2020, analyzing a high-resolution dynamical
downscaling forced by the Japanese 55-year Reanalysis (JRA-55). One-year continuous simulations are conducted by
the non-hydrostatic regional climate model with a land surface model that includes the snow accumulation process,which enables us to evaluate the seasonal variation of snow cover in all of Japan. Our simulation reproduces interan‑nual variations of the annual mean surface air temperature and annual total precipitation, and it shows rapid warming
since around 1980. The annual maximum snow depth and annual maximum daily snowfall show significant decreas‑
ing trends at lower elevations on the Japan Sea sides of eastern and western Japan. Areas at higher elevations ineastern Japan show no trend in the maximum snow depth and a significant increasing trend in the maximum daily
snowfall. In northern Japan, altitudinal dependencies in snow depth and snowfall changes are smaller on the Japan
Sea side than on the Pacific Ocean side: the Japan Sea side shows insignificant changes in snow depth at all eleva‑
tions, while the Pacific Ocean side shows decreasing and significant increasing trends at lower and higher elevations,
respectively. The total number of snow-covered days is decreasing at most elevations in all regions, while the rate ofdecrease is smaller at higher elevations and latitudes. Composite analyses of annual maximum daily snowfall eventsat one prefectural city facing the Japan Sea in central Japan indicate that heavy daily snowfall occurs when theJapan Polar air mass Convergence Zone appears over the Japan Sea and the snowfall amounts show increasing anddecreasing trends over the mountainous and coastal areas, respectively, due to historical warming and moistening.
Our results are basically consistent with previous studies that have focused on future changes in snow depth andsnowfall.
Keywords Dynamical downscaling, Regional climate model, JRA-55, Global warming, Extreme snowfall, Snow coverchange*Correspondence:
Hiroaki Kawasehkawase@mri-jma.go.jp
1
Meteorological Research Institute, Japan Meteorological Agency, 1‑1
Nagamine, Tsukuba, Ibaraki 305‑0052, Japan
2
University of Tsukuba, Tenoudai 1‑1‑1, Tsukuba, Ibaraki 305‑8573, Japan

1 Introduction
According to the Intergovernmental Panel on Climate
Change’s Sixth Assessment Report (AR6) (IPCC 2021),
it is unequivocal that human influence has warmed theatmosphere, ocean, and land. The 20-year-mean global
surface temperature from 2001 to 2020 was 0.99 °C
higher than that from 1850 to 1900 (IPCC 2021). In
Japan, the temperature increased by 1.28 °C/100 years
from 1898 to 2021 (JMA 2021), and the three recent
years, 2019–2021, are the warmest since 1898. In urban© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/.

Kawase et al. Progress in Earth and Planetary Science(2023) 10:7

areas, a much larger temperature rise has been observed
because of the urban heat island (e.g., Fujibe et al. 1995;
Fujibe 2009; Adachi et al. 2012).
Global warming can change the precipitation extremes.
Fujibe et al. (2016) pointed out that hourly precipitation
extreme values have increased at a rate to mean temperature that is slightly lower than the Clausius–Clapeyron(CC) relation rate of 6–7%/K. However, the increasingrate of water vapor due to atmospheric warming occurs
in ideal conditions when the atmosphere is saturated andthe synoptic-scale condition is fixed. When synopticscale and mesoscale water vapor convergences occur, theincreasing rate of water vapor get more than the CC relation rate. Lenderink and Meijgaard (2008) demonstrated
that hourly precipitation extremes increase at a rate ofabout 14% per K over Europe; this is known as a super ordouble CC relationship (Berg and Haerter 2013; Loriaux
et al. 2013; Lenderink et al. 2017; Hatsuzuka et al. 2021).
In winter, snowfall and snow cover are strongly influenced by global warming. The IPCC (2021) reported thathuman-induced global warming has very likely contributed to the decrease in spring snow cover in the Northern
Hemisphere since 1950. Rapid decreases in the annual
maximum snow depth are observed at the meteorologicalstations in central and western Japan (JMA 2021). At low
elevations in Japan, snow cover was strongly influenced
by historical warming (e.g., Suzuki 2006; Ishii and Suzuki
2011; Kawase et al. 2012). In contrast, snow reductions
were not observed at high-elevation meteorological stations (Suzuki 2013). Interannual variations of annual
maximum snow depth differ between low elevations andhigh elevations (Yamaguchi et al. 2011). However, a lackof continuous observations makes it difficult to evaluatechanges in snow cover over all of Japan, especially at highelevations.
Global warming promotes the conversion of snowfall
to rainfall, resulting in a decrease in snowfall. On theother hand, the oceanic warming due to global warming increases water vapor in the atmosphere, contributing to an increase in snowfall if the temperature is still
lower than 0 °C. Therefore, global warming has a paradox
between the snowfall decrease due to warming and thesnowfall increase due to moistening. In future climate
conditions, snowfall is projected to largely decrease in
the coastal areas of the Japan Sea (Inoue and Yokoyama
2003; Hara et al. 2008; Kawase et al. 2021), while snowfall is projected to increase over mountainous areas incentral Japan and inland areas in northern Japan during
mid-winter (Kawase et al. 2021). Changes in atmosphericcirculation, e.g., East Asian winter monsoon and extratropical cyclone tracks, can also change winter precipitation in Japan (e.g., Hori and Ueda 2011, Kawase et al.
2015).

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For evaluating the historical changes in precipitationand snow cover in Japan, a historical climate simulation
with a high-resolution regional climate model is one useful method. Kayaba et al. (2016) conducted a dynamical
regional downscaling of the Japanese 55-year Reanalysis (JRA-55) (Kobayashi et al. 2015), named DSJRA-55,
which covers all of Japan for the 55 years from 1958 to2012 with a 5-km grid spacing. However, it lacks continuity over time because its initial and boundary conditions are renewed every 6 h. The land surface model for
DSJRA-55 includes no snow accumulation process. Thus,
DSJRA-55 cannot evaluate historical changes in snow
cover and related variables. To evaluate the regional
characteristics of extreme snowfall in Japan, Kawase
et al. (2018) conducted 5-km simulations using a regional
climate model including a land surface process basedon JRA-55 and historical experiments of the databasefor Policy Decision-making for Future climate change(d4PDF) (Mizuta et al. 2017). However, their simulations
were conducted from 1981 to 2010 and did not cover thenorthern part of Japan, Hokkaido, and the southwesternisland, Okinawa.
In this study, we conducted a dynamical downscaling of JRA-55 using a 5-km regional climate model witha snow accumulation process covering all of Japan from1958 to 2020. In the dynamical downscaling, time integrations were continuously performed without frequent
reinitializations. The downscaling data enable us to evaluate long-term changes in annual maximum snow depth
in the whole of Japan in addition to changes in air temperature and precipitation. The purpose of this study is toinvestigate historical changes in winter precipitation andsnow cover using the 5-km regional climate model. Our
experimental designs are shown in Sect. 2. We describe
the reproducibility of historical changes in regional climate in Japan—including snowfall and snow depth—ascompared with in situ observations and DSJRA-55. We
also evaluate the altitudinal dependency of historicalsnow cover changes in each region, which has not been
evaluated because of a lack of snow observations at highelevations. We show an example of the analysis of thehistorical trend in heavy daily snowfall and discuss thetrends of background conditions on heavy snowfall daysin Sect. 4. Section 5 summarizes our results.

2 Methods/experimental design
A dynamical downscaling of the JRA-55 was conductedto produce a regional climate dataset over Japan with agrid spacing of 5 km from September 1958 to August2021. We use the non-hydrostatic regional climate model(NHRCM, Sasaki et al. 2008) developed by the Meteorological Research Institute (MRI) of the Japan Meteorological Agency (JMA). The double nesting was applied here

Kawase et al. Progress in Earth and Planetary Science(2023) 10:7

by downscaling from JRA-55 to a horizontal grid spacingof 20 km covering the Far East and further by downscaling from the result to a horizontal grid spacing of 5 km
covering Japan (hereafter, referred to as DS20km and
DS5km, respectively) (Fig. 1). The model tops are set to
22 km with 50 terrain-following layers in NHRCMs withboth horizontal grid spacings.
For representing physical processes, the NHRCM
includes the Kain–Fritsch cumulus parameterization(Kain and Fritsch 1990; Kain 2004; Nakano et al. 2012),
the bulk cloud microphysics scheme predicting the mixing ratios of five hydrometeor species and the number
concentrations of three ice-phase hydrometeor species(Ikawa and Saito 1991); the Mellor–Yamada–Nakanishi–Niino level-2.5 turbulence closure model (Nakanishiand Niino 2004), the radiation schemes for clear skies(Yabu et al. 2005) and clouds (Kitagawa 2000); and theimproved MJ-SiB (Hirai et al. 2007) (iSiB), which is aland surface model with a snow accumulation sub-model
based on the simple biosphere model (Sellers et al. 1986)
with surface bulk coefficients provided by Beljaars andHoltslag (1991).
Spectral nudging (von Storch et al. 2000; Nakano
et al. 2012) is applied, in addition to forcing from lateralboundaries. The components for spectral nudging are
longer than 800 km in wavelengths with heights above
2 km in the DS20km, while they are longer than 500 km
in wavelength with heights above 7 km in the DS5km.
The land use is derived from the global land cover characterization (GLCC), which is fixed throughout theperiod. Grids where the sea surface temperature is lessthan 271 K are assumed to be covered with sea ice. The
concentrations of greenhouse gases (GHGs)—specifically ­CO2, ­CH4, and ­N2O—are updated every year. Notethat GHG concentrations are based on representative
concentration pathway 8.5 (RCP8.5) from 2005. The ­CO2
concentrations in RCP8.5 agree most closely with historical ones—within 1% from 2005 to 2020 (Schwalm et al.
2020). The specifications of the numerical settings are
summarized in Table 1.
To cover the long term, the dynamical downscaling is split into streams. Each stream is set to cover 1
year—from September to the following August—with
a continuous time integration. Each year, the DS20km
and DS5km are initialized at 12 UTC on July 20 and 21,
respectively. Our experiments have spin-up durations ofapproximately 40 days. To distinguish it from DSJRA-55(Kayaba et al. 2016), our simulation was called regional
climate downscaling using JRA-55 (RCDSJRA-55), which
includes DS20km and DS5km.
DS5km is compared with in situ long-term observations operated by the JMA from 1959 to 2020. We used
15, 51, and 30 stations for air temperature, precipitation,

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and snow depth, respectively. The locations of the stations are shown in Fig. 1b. We also used AMeDAS
information after 1976 to evaluating the climatologicaltemperature and precipitation biases. The AMeDAS network has approximately 1300 points that observe precipitation in Japan (figure not shown). Approximately
two-thirds of AMeDAS stations observe surface airtemperature and wind. For comparing with the observations, the grid points near the observational stations are
extracted from DS5km. DS5km is also compared withDSJRA-55. Climatology is defined as the 30-year averages
from 1991 to 2020 or from 1981 to 2010 because of thelimitation of DSJRA-55. In the analysis of snowfall andsnow cover, annual values are calculated from September
to December of the preceding year and from January toAugust of the target year, i.e., the annual maximum snow
depth in 1962 means the maximum snow depth from
September 1961 to August 1962, which is the same as the
JMA’s definition.

3 Results
3.1 Climatological biases and historical variations of air
temperature and precipitation

Figure 2 shows biases of surface air temperature andprecipitation in DS5km to the AMeDAS observation.
Note that the air temperature biases derived from thedifference of surface heights between AMeDAS and thenearest model grid point were modified using the dry
adiabatic lapse rate. Annual surface air temperatures
show small biases in most parts of Japan, while DJF surface air temperatures have cold biases, especially in eastern and northern Japan (Fig. 2a, b). The cold and warm
biases in cold and warm seasons, respectively, are almost
canceled out (Fig. 3a, b). Kayaba et al. (2016) showed thatDSJRA-55 had similar temperature biases in eastern andnorthern Japan, i.e., cold temperature biases in January
and warm temperature biases in August.
The annual total precipitation shows small biases atmost stations, with the exception of inland stations incentral Japan (Fig. 2d). The positive biases at the inland
stations in the central parts of Japan are derived fromthe overestimation of summer precipitation (figure notshown). The total DJF precipitation, on the other hand,shows large positive biases over the inland areas along
the Pacific Ocean and negative biases over the coastal
areas along the Japan Sea (Fig. 2e). Note that the amountof winter precipitation along the Pacific Ocean is muchsmaller than that along the Japan Sea (figure not shown).
Figure 3 shows the climatology and biases of themonthly mean surface air temperature and precipitation.
DS5km shows cold and warm biases in cold and warm
seasons, respectively, while DSJRA-55 shows relatively
small warm biases in all seasons (Fig. 3a, b). DifferentKawase et al. Progress in Earth and Planetary Science(2023) 10:7

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Fig. 1 Model domains, locations of observational stations, Japanese regions, and topography. a The gray-shaded region is the first domain of
the NHRCM with 20-km grid spacing. The inner region shows the second domain of the NHRCM with 5-km grid spacing. The gray and black
gradation in the second domain shows the topography. b Locations of in situ stations. Black, red, and green circles represent stations measuring
precipitation, air temperature, and snow depth, respectively. Purple, light blue, and red squares represent stations measuring both precipitation and
air temperature, both precipitation and snow depth, and all three variables, respectively. c Japanese regions with their naming of a combination of
NJ/EJ/WJ and JS/PO: The former denotes northern, eastern, and western Japan, and the latter denotes the Japan Sea and Pacific Ocean sides. These
regions are based on those delineated by Murata et al. (2015). d Topography in Japan except for southwestern islands

Kawase et al. Progress in Earth and Planetary Science(2023) 10:7

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Table 1 Specifications of the numerical model
20 km NHRCM
Grid numberTime interval

5 km NHRCM

281 × 281 × 50

527 × 804 × 50

60 s

20 s

Initial and lateral boundary condition

JRA-55 (Kobayashi et al. 2015)

20 km NHRCM

Initial dateJuly 20

July 21

Target years

From September 1958 to August 2020 First 40 days (July 21–August 31) are spin-up durations for
NHRCM with 5 km grid spacing

Topography

GTOPO30 (grid mean)

Microphysics

Prognostic variables: mixing ratio (water vapor, cloud water, cloud ice, snow, and graupel) and number
concentration (cloud ice, snow, graupel) (Ikawa and Saito 1991)

Boundary layer

MYNN level 2.5 (Nakanishi and Niino 2004)

Cumulus convection

KF scheme (Kain and Fritsch 1990; Kain 2004; Nakano et al. 2012)

Radiation

Clear sky (Yabu et al. 2005) and cloud (Kitagawa 2000)

Land surface

Improved MJ-SiB (Hirai et al. 2007)

Wave number boundary of spectral nudging Wave length: longer than 800 km
Height: over 2 km above sea level

Wave length:
longer than
500 kmHeight: over
7 km above sea
level(von Storch et al. 2000; Nakano et al. 2021)

surface models between DS5km and DSJRA-55 can produce the difference in surface air temperature biases. On
the other hand, both DS5km and DSJRA-55 show similar seasonal variation in precipitation biases (Fig. 3c, d)and DS5km shows smaller and larger precipitation biases
in warm and cold seasons, respectively, than those in
DSJRA-55. DS5km can reproduce large amounts of precipitation from June to September better than those of
DSJRA-55. A short spin-up duration in DSJRA-55 may
result in the underestimation of precipitation in warm
seasons.
The grid-mean topography is smoother than the realistic topography. The mountain heights in the model
are lower than the actual mountain heights, resulting in
the underestimation of topographical precipitation on
the windward side of the mountains. Variables in theregional climate model, such as precipitation, air temperature, and wind, are the averaged values in each grid
point, which is different from the variables observed
just at the observation site; this also causes the model
biases. Moreover, in winter, convective precipitation
develops over the Japan Sea, which is developed by thewarm Japan Sea and the cold air mass from the continent. In this situation, the planetary boundary layer(PBL), cumulus convection, and microphysics scheme
are important for reproducing the winter precipitation
over the Japan Sea side. The overestimation of winterprecipitation can be derived from these parameterization schemes in the NHRCM. Moreover, the horizontal

grid spacing of 5 km is too coarse to resolve the cumulus convections over the Japan Sea, which results in lessprecipitation over coastal and more precipitation overmountainous areas along the Japan Sea (Kawase et al.
2019). The fact that the mountain ranges in the model
are lower than the actual ones also causes negative andpositive biases of winter precipitation over mountainous areas of the Japan Sea and the Pacific Ocean sides,
respectively (Ishizaki et al. 2012; Kawase et al. 2015).
DS5km well reproduces the interannual variationsof surface air temperature from 1959 to 2020 observedby the 15 in situ stations as with DSJRA-55 (Fig. 4a).
Correlation coefficients between DS5km (DSJRA-55)
and observations are 0.981 (0.965) and 0.992 (0.970)
in annual and 5-year running mean temperatures,
respectively. The recent rapid warming since about
1980 is well reproduced by DS5km and DSJRA-55. Linear trends of the annual mean temperature are 1.25 °C
and 0.92 °C per 60 years for the observations and the
DS5km, respectively, with a 99% confidence level. As
with the annual mean temperature, the annual and5-year running mean total precipitation shows highcorrelations with DS5km (0.877 and 0.822) and withDSJRA-55 (0.936 and 0.919), respectively. Since theDS5km underestimates the annual total precipitation in
the 1970s, the correlation is lower than that of DSJRA55 (Fig. 4b). Annual total precipitation shows increasing trends in DS5km and the observation, althoughboth are statistically insignificant.

Kawase et al. Progress in Earth and Planetary Science(2023) 10:7

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Fig. 2 a, b Surface air temperature and c, d precipitation biases based on AMeDAS observations averaged between 1991 and 2020; a annual and b
DJF mean air temperature biases; c annual and d DJF total precipitation biases shown as percentages

DS5km is also highly capable of simulating interannual variations of precipitation extremes, such as daily
precipitation exceeding 100 mm/day (hereafter, referred
to as nPR100) and annual maximum daily precipitation(hereafter, referred to as Rx1d) (Fig. 4c–d). The correlation coefficient of nPR100 between DS5km (DSJRA-55)
and the observation is 0.638 (0.758) for annual values
and 0.649 (0.695) for 5-year running mean values. Boththe observation and DS5km show increasing trends(0.34 and 0.23 days/60 years) with 95% and 90% confidence levels, respectively (Fig. 4c). Rx1d also shows asimilar interannual variation between the observation
and DS5km and DSJRA-55. The observed Rx1d shows
an increasing trend (10.57 mm/60 year) with a 90%
confidence level, while the trends simulated by DS5km
and DSJRA-55 are not statistically significant (Fig. 4d).
The extreme daily precipitation in Japan mainly occurs
in summer. Since this study focuses on the historical chances in winter precipitation and snow cover inJapan, the detailed analyses of historical changes inextreme precipitation in summer would be the futurestudies.

Kawase et al. Progress in Earth and Planetary Science(2023) 10:7

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Fig. 3 Monthly mean surface air temperature and monthly precipitation; a mean surface air temperature and their biases; c, d total precipitationand their biases; a, c black and orange lines represent the averages of AMeDAS and DS5km, respectively, between 1991 and 2020. Blue and green
lines represent the averages of DS5km and DSJRA-55, respectively, between 1981 and 2010. b, d Orange, blue, and green lines represent the biases
in DS5km (1991–2020), DS5km (1981–2010), and DSJRA-55 (1981–2010), respectively(See figure on next page.)
Fig. 4 Interannual variations of air temperature and precipitation in all of Japan from 1959 to 2020. a Surface air temperature, b annual total
precipitation, c annual frequency of daily precipitation exceeding 100 mm/day (nPR100), and d annual maximum daily precipitation (Rx1d).
Thick and thin lines represent annual and 5-year means, respectively. Black and gray, red and light red, and blue and light blue lines represent
observations, DS5km, and DSJRA-55, respectively. The temperature and precipitation are averaged from 15 and 51 stations shown in Fig. 1b or grid
points near the stations, respectively

3.2 Historical changes in annual maximum snow depth
and daily snowfall

As compared to the DSJRA-55, the biggest advantage of
the DS5km is in evaluating historical snow cover witha 5-km grid spacing for all of Japan. First, we compare
the reproducibility in annual maximum snow depth(hereafter, referred to as SDmax) in DS5km with in situ
observations by the JMA. Figure 5 shows the timeseries of SDmax anomalies relative to 1991–2020 on
the Japan Sea side, which has large amounts of snowfall. Note that most stations observing snow depth overthe long term are located at low elevations (Fig. 1b).
As with the previous figures, the grid points in DS5km

Kawase et al. Progress in Earth and Planetary ScienceFig. 4 (See legend on previous page.)(2023) 10:7

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Kawase et al. Progress in Earth and Planetary Science(2023) 10:7

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Fig. 5 Time series of SDmax anomalies over the Japan Sea coast from 1962 to 2020. a Northern Japan, b eastern Japan, and c western Japan.
The reference climate is 1991–2020. Thick and thin lines represent the annual and 5-year means, respectively. Black and red lines represent theobservation and DS5km, respectively

Kawase et al. Progress in Earth and Planetary Science(2023) 10:7

near the snow observational stations are extracted andaveraged in each region.
The observed SDmax shows decreasing trends in all
regions. Eastern and western Japan shows large decreasing trends (− 15.0% and − 19.0%/10 years, respectively).
The decreasing trend in northern Japan (− 4.6%/10 years)
is smaller than those in other regions. All decreasing
trends are statistically significant with a 95% or 99% confidence level. DS5km also shows decreasing trends ineastern and western Japan (− 10.0% and − 9.2%/10 years,
respectively) with a 95% confidence level, although thedecreasing trends are smaller than the observed ones. On
the other hand, the decreasing trend is quite small andstatistically insignificant in northern Japan. The difference between the SDmax before 1990 and after 1991 isrelatively smaller in DS5km than that in the observation,
resulting in the smaller decreasing trends in DS5km.
Figure 6a shows the interannual variation of the annual
maximum daily snowfall (hereafter, referred to as Sx1d)
at 30 stations shown in Fig. 1. Here, daily snowfall is
calculated by hourly increments of snow depth, whichis the same definition used by the JMA. The Sx1d simulated by DS5km is smaller than the observation. On theother hand, the annual variation has a high correlation
coefficient (0.768) between the observation and DS5km.
DS5km shows decreasing trends, which are opposite to
the trends of the Rx1d (Fig. 4d), while they are statistically insignificant. Note that the JMA measured daily
snowfall using a snow board before around 1997–2004,
which was different from the current method. Since thechange in measurement influences a long-term trend ofdaily snowfall, we did not discuss the long-term trendsof observed daily snowfall. Figure 6b–d shows the annual
variations of the Sx1d anomaly in each region as withSDmax in Fig. 5. The correlation of annual variations in
the Sx1d anomaly is higher in western Japan and lower
in northern Japan between the observation and DS5km,
which could be derived from the lower and higher frequencies of snowfall in northern Japan and westernJapan, respectively. Generally, the heavy snowfall on
Japan Sea coast in western Japan is brought by the Japan
Polar air mass Convergence Zone (JPCZ), which is generated by a convergence of northwesterly winds circumventing the Changbai Mountains on Korean Peninsula.
Our results show that the reproducibility of heavy snowfall events derived from the JPCZ would be better.
3.3 Altitudinal dependency of historical snow coverchanges

Snowfall and snow depth are strongly influenced by air
temperature, especially at approximately 0 °C, meaning that elevation is an important factor when considering historical changes in snowfall and snow depth.

Page 10 of 19

Climatology of SDmax exceeds 150 cm over the mountainous areas in eastern and northern Japan (Fig. 7a).
Table 2 summarizes climatology of SDmax at eachelevation range in each region. Above 1000 m, SDmax
exceeds 300 cm in northern Japan and exceeds 250 cm
in eastern Japan. Both decreasing and increasing trends
are simulated by DS5km (Fig. 7b), while most trends
are statistically insignificant in each grid except forwestern Japan. We evaluated the annual variations ofregional mean SDmax from low elevations (0–250 m)
to high elevations (1000–1500 m or over 1500 m).
SDmax shows similar variations at all elevations on
the Japan Sea side of northern Japan, and no significant trends were detected (Fig. 8a). On the other hand,on the Japan Sea side of eastern Japan, SDmax showsa decreasing trend more clearly at lower elevations, aswith observations (Fig. 5), and no trend or increasing
trends at higher elevations (Fig. 8c). In western Japan,
all elevations show decreasing trends in SDmax, with
a 95% confidence level. As stated in previous studies (Suzuki 2006; Ishii and Suzuki 2011; Kawase et al.
2012), SDmax can be largely decreased by historicalwarming at the lower elevations in eastern and westernJapan where the surface air temperature is around 0 °C
even in winter.
Short-term heavy snowfalls bring risks of traffic hindrances, the isolation of villages through road closures,
collapse of houses, and surface avalanches in mountainous areas. Compared with SDmax, the altitudinal
dependency of Sx1d is smaller (Fig. 7c). Sx1d widely
exceeds 20 cm in most areas of northern Japan and insome parts of eastern and western Japan, especially theareas facing the Japan Sea. Note that Sx1d over 20 cm
is also found in the mountainous areas of western Japanfacing Pacific Ocean. Heavy daily snowfall occurs overthe mountainous areas in eastern Japan and the northernpart of Japan (Hokkaido). Long-term trends of Sx1d aresimilar to those of SDmax (Fig. 7d). Increasing trends arefound more widely in eastern and western Japan. As withSDmax, most trends are statistically insignificant in eachgrid, except for the coastal areas along the Japan Sea inwestern Japan. Figure 9 shows the interannual variations
and trends in Sx1d. In the Japan Sea side of northern
Japan, all elevations show increasing trends, especiallyat higher elevations above 1000 m (1.40%/10 years),
although they are not statistically significant (Table 2).
The Pacific Ocean side of northern Japan has large
interannual variations of Sx1d, and the variations differ among the elevations (Fig. 9b). As with the Japan Sea
side, all elevations except for 0–250 m show increasing
trends of Sx1d, and elevations higher than 1500 m show
a significant increasing trend with a 90% confidence level(Table 2).

Kawase et al. Progress in Earth and Planetary Science(2023) 10:7

Fig. 6 a Same as Fig. 4, but Sx1d without an observed trend; b–d same as Fig. 5, but Sx1d without observed trends

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Fig. 7 Climatology in 1991–2020 and linear trends from 1959 to 2020. a, c, d Climatology of SDmax, Sx1d, and snow-covered days, respectively;b, d, f linear trends of SDmax, Sx1d, and snow-covered days, respectively. Gray and white shadings represent decreasing and increasing trends,
respectively, which are statistically insignificant with a 95% confidence levelKawase et al. Progress in Earth and Planetary Science(2023) 10:7

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Table 2 SDmax and Sx1d climatology and linear trends
Elevation(m)SDmax climatology (cm) (1991–2020)
NJ–JP

NJ–PO

0–250

89.5

59.7

49.6

13.1

22.9

25.0

26.1

9.3

250–500

142.6

93.8

103.7

31.6

28.6

31.1

36.9

19.4

500–1000

230.5

157.7

161.1

54.7

36.3

37.2

40.6

25.4

1000–1500

326.6

320.9

275.4

41.8

43.3

44.4

45.6

26.9

667.2

443.0

63.4

50.6

1500–

EJ–JS

Sx1d climatology (cm) (1991–2020)
WJ–JS

SDmax trend [%/10 years]
0–250
250–500
500–1000
1000–1500
1500–

NJ–JP

− 1.02− 8.70***− 11.25***0.11− 0.36− 0.20− 0.98− 5.90**− 0.37− 0.461.00

2.40**

EJ–JS

WJ–JS

Sx1d trend [%/10 years]− 0.92− 0.34

NJ–PO− 3.770.04

1.10− 0.32− 2.24− 6.97***

0.52

0.26

0.79

1.40

0.96

1.46*

− 2.42*

2.23*

1.39

− 7.48**0.42− 7.62**0.12− 0.18− 3.43**
0.12

One, two, and three asterisks mean a statistical significance with 90%, 95%, and 99% confidence levels, respectively

Fig. 8 Annual variations and linear trends of SDmax anomalies relative to 1991–2000 at 0–250 m (blue line), 250–500 m (light blue line), 500–
1000 m (green line), 1000–1500 m (orange line), and over 1500 m (brown line). The straight lines represent linear trends

Sx1d of eastern Japan has a clear elevation dependency, which is similar to those of SDmax (Fig. 9c). In
contrast to SDmax, the decreasing trend at the lowest
elevations is insignificant, and higher elevations show
significant increasing trends with 90% and 95% confidence levels (Table 2). These results are consistent
with the enhancement of daily snowfall extremes in

the mountainous areas due to global warming (Kawase
et al. 2016, 2021). In western Japan, the decreasing
trends of Sx1d are significant at elevations lower than
1000 m, but the decreasing trends are smaller thanthose of SDmax. At elevations higher than 1000 m, theSx1d shows insignificant decreasing trends, although
SDmax shows a significant decreasing trend.

Kawase et al. Progress in Earth and Planetary Science(2023) 10:7

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3.4 Synoptic conditions of heavy snowfall eventson the Japan Sea and Pacific Ocean sides of northern
Japan, Hokkaido

the other hand, heavy snowfall occurs on the Japan Sea
side of northern Japan when cold air breaks out. Figure 10 shows the composites of precipitation, surface
wind, sea-level pressure (SLP), and surface air temperature in Sx1d events at Rumoi (Fig. 10a, b) and at Obihiro (Fig. 10c, d) in Hokkaido. The composite of heavy
snowfall events in Rumoi shows that the northerly
wind prevails and a cold air advection occurs northeast of Rumoi. The daily mean air temperature is lower
than − 6 °C over most parts of Hokkaido. On the other
hand, the composite of heavy snowfall events in Obihiro shows that the southeasterly prevails and warm air
advection occurs southeast of Obihiro. The daily mean
air temperature is higher than − 6 °C with the exceptionof high elevations. Large amounts of precipitation are
widely simulated over the eastern parts of Hokkaido.
These results are consistent with those of Inatsu et al.
(2021), and the air temperature on the Pacific Oceanside is relatively higher than that on the Japan Sea side
when heavy snowfall occurs. Historical global warming (Fig. 4a) promotes the conversion from snowfall
to rainfall if the air temperature is around 0 °C. The airtemperature at lower elevations is close to 0 °C whensnowfall occurs at low elevations in NJ–PO, while the
air temperature is much lower than 0 °C at high elevations (Fig. 10d). Therefore, the NJ–PO shows the large

Fig. 9 Same as in Fig. 8, but Sx1d

Generally, the total snowfall on the Pacific Ocean sideof Japan is much less than that on the Japan Sea side.
The mean SDmax in EJ–PO and WJ–PO is 8.6 cm and5.2 cm, respectively, at elevations of 0 to 250 m, which
are much less than those in EJ–JP and WJ–JP (49.6 and13.1 cm, respectively) (Table 2). On the other hand, thePacific Ocean side of northern Japan has plenty of snow
depth as compared with EJ–PO and WJ–PO at lower
elevations (Table 2) and is comparable to that on theJapan Sea side at higher elevations. Sudden heavy snowfall can collapse wooden and unmanaged houses becauseof the snow load, which mainly occurs in regions with
infrequent snowfall, such as on the Pacific Ocean side.
It is noteworthy that the trends and variations in SDmax
and Sx1d strongly depend on the elevation on the Pacific
Ocean sides of northern Japan (Figs. 8b, 9b, respectively),in contrast to that on the Japan Sea side (Figs. 8a, 9a,
respectively).
On the Pacific Ocean side of northern Japan, extratropical cyclones passing around northern Japan bringheavy snowfall (Inatsu et al. 2021). In this synoptic
condition, warm and moist air masses are transported
from the warm Pacific Ocean to northern Japan. On

Kawase et al. Progress in Earth and Planetary Science(2023) 10:7

Page 15 of 19

Fig. 10 Composites of precipitation, surface wind, SLP, and surface air temperature when Sx1d is calculated in DS5km at Rumoi and Obihiro. a, c
Precipitation, surface wind, and SLP; b, d surface air temperature and surface wind; a, b Rumoi and c, d Obihiro. Cross marks represent the locationsof Rumoi and Obihiro

elevation dependency of SDmax as compared with the
NJ–JS.
3.5 Historical changes in snow‑covered days

Finally, we investigate changes in the number of days
with snow cover. Here, we define a snow-covered day as
one in which the maximum daily snow depth, as calculated by hourly snow depth output of the model, exceeds

1 cm. Most areas in northern Japan and mountainous
areas in eastern Japan are covered with snow for morethan 100 days (Fig. 7e). Table 3 shows the climatology ofsnow-covered days from 1961 to 1990 and from 1991 to2020 at four or five elevation ranges in each region. The
climatological number of snow-covered days increases
at higher latitudes and elevations. In contrast to SDmax
and Sx1d, the number of snow-covered days has been

Table 3 Climatology of snow-covered days at each elevation. Round blanks show ratios of 1991–2020 relative to 1961–1990
Elevation(m)NJ–JS
1961–1990

NJ–PO
1991–2020

1961–1990

EJ–JS
1991–2020

1961–1990

WJ–JS
1991–2020

1961–1990

1991–2020

0–250

139.4

133.3 (0.96)

113.2

107.5 (0.95)

71.7

58.6 (0.82)

20.9

14.2 (0.68)

250–500

158.9

154.5 (0.97)

134.5

130.6 (0.97)

108.4

98.3 (0.91)

51.7

41.4 (0.80)

500–1000

178.7

174.9 (0.98)

164.9

161.7 (0.98)

134.3

128.3 (0.96)

76.8

66.0 (0.86)

1000–1500

202.5

198.8 (0.98)

220.2

218.5 (0.99)

179.6

176.5 (0.98)

102.2

85.8 (0.84)

350.2

349.7 (1.00)

219.0

216.9 (0.99)

1500–

Kawase et al. Progress in Earth and Planetary Science(2023) 10:7

Page 16 of 19

reduced in most regions in Japan (Fig. 8f and Table 3).
In northern Japan, the decrease in snow-covered days infrom 1991 to 2020 is less than 5% relative to 1961–1990 at
all elevations. In contrast, snow-covered days decreased
by 18% and 32% at 0–250 m in eastern and westernJapan, respectively. In western Japan, snow-covered days
decreased by 16% even at elevations higher than 1000 m.
Our results indicate that rapid decreases in snow-covered
days have already occurred in eastern and western Japan.

4 Discussion
4.1 Trends of background atmospheric conditions
during heavy daily snowfall events over coastal areas
along the Japan Sea in central Japan

Here, we will show the example of the composite analysis of heavy daily snowfall events on the Japan Sea coastin central Japan, where the heavy snowfall is frequency
brought by the JPCZ. Fukui City is a prefectural capital
located on this area and has experienced heavy snowfall in early February 2018. The 3-day total snowfall was
129 cm, and the maximum snow depth was 147 cm,
which was the greatest snow depth since 1981. Figure 11
shows composites of precipitation, surface wind, and SLP
when Sx1d events occur at a grid point near the Fukui
observational station. The northeastern wind converges
to the east of the Korean Peninsula, and the precipitation
band, elongating from northwest to southeast over thewestern part of the Japan Sea, corresponds to the JPCZ. A
large amount of precipitation is calculated around Fukui,
most of which is snowfall over land (figure not shown).
Figure 12 shows linear trends of daily precipitation,
snowfall, and rainfall from 1959 to 2020 when the Sx1d is
calculated. Here, snowfall in Fig. 12 is directly calculatedusing the physical schemes in the NHRCM. Precipitation
increases over most regions, although the statistically significant increases are limited in the mountainous areas

Fig. 12 Linear trends of a precipitation, b snowfall, and c rainfall
when Sx1d is calculated by DS5km. White and black dots show
statistically significant grids with 90 and 95% confidence levels,
respectively. A cross mark represent the location of Fukui(Fig. 12a). Snowfall, on the other hand, increases overthe inland and mountainous areas as with the precipitation, while it decreases over the ocean and coastal areas(Fig. 12b). Rainfall increases over the ocean, which shows
statistically significance at the 90% confidence level. Since
the composite of air temperature shows above and below
0 °C over the ocean and land, respectively (Fig. 13a), theconversion from snowfall to rainfall has been accelerated due to significant warming (Fig. 13b). Precipitable water vapor exceeds 6 mm around the coastal areasand shows the increasing trends over both the landand ocean (Fig. 13c, d). The increasing trend is statistically significant with a 90% confidence level around the
JPCZ. Although this composite analysis only focuses on
the Sx1d at the Fukui station, this result is partly consistent with decreases and increases in the Sx1d at lower
elevations, i.e., coastal areas, and higher elevations, i.e.,
mountainous areas, respectively (Figs. 7d and 9c) due tohistorical warming.
4.2 Factors causing biases of long‑term trends

Fig. 11 Same as in Fig. 10a and c, but for Fukui

Kobayashi et al. (2015) already pointed out that the
global mean temperature in the lower tropospherein JRA-55 showed interannual variations very similar to those in observational data (HadAT2) and comparable with the other reanalysis data, NCEP/NCAR

Kawase et al. Progress in Earth and Planetary Science(2023) 10:7

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Fig. 13 Composites of surface air temperature and precipitable water vapor and their trends when Sx1d is calculated by DS5km. a Surface airtemperature and b its trend; c precipitable water vapor and d its trend. White and black dots show statistically significant grids with 90 and 95%
confidence levels, respectively

reanalysis and ERA-40. DS5km can be compareddirectly with the station data in Japan; it indicates thatthe interannual variations of surface air temperatureare quite similar to the observation, even before 1980,
in all of Japan (Fig. 4a). The long-term trend is, however, underestimated by DS5km. It is well known thatthe reproducibility of the reanalysis data itself depends
on assimilated observations, especially satellite data.
Actually, the quality of JRA-55 has gradually changed
from the 1960s to the 2010s (Kobayashi et al. 2015). The
underestimation of long-term trends can be caused byan innovation in the satellite data assimilated in JRA55 around the late 1990s. Our experimental design,
excluding land-use changes, such as urbanization anddeforestation, can also underestimate the temperaturerise in Japan. After 1980, the variation and warming
trend are quite similar to those observed. The interannual variations and trends in annual total precipitation
are well reproduced by DS5km after the 1980s (Fig. 4b).
On the other hand, DS5km has cold temperature biases
and positive precipitation biases in the mountainous
area in eastern and northern Japan in winter (Figs. 2a,3b), respectively, which can cause the overestimationof snow accumulation. Moreover, positive snow coverbiases can lead to cold bias around the surface. It isdifficult to widely evaluate the reproducibility of simulated snow cover because of a lack of observationaldata.
The annual mean temperature, total precipitation, andmaximum snow depth in DS5km can reproduce bothinterannual and decadal variations (Figs. 4 and 5). The El
Niño-Southern Oscillation (ENSO) is a typical interannual variation that influences seasonal temperature andprecipitation in Japan. Imada et al. (2021) pointed outthat various flavors of ENSO affect summer heavy rainfall in each region of Japan in different ways. Ueda et al.
(2014) indicated that the tropical heating relevant to the
ENSO cycle, i.e., an enhancement of convection in thevicinity of the Philippines, influenced heavy snowfall on
the Japan Sea side via the teleconnection. These pointsof view can be related to the interannual variations ofprecipitation (Fig. 4) and snow depth (Fig. 5). Generally,
reanalysis data are considered reliable for interannual
and shorter-timescale variations, while the availability of
longer-timescale variations such as decadal variability is
uncertain. Our comparison of DS5km and the site observation suggests that JRA-55 can correctly capture thedecadal variations around Japan. On the other hand, thereproducibility of interannual and decadal variations ofprecipitation extreme indices is worse than that of otherKawase et al. Progress in Earth and Planetary Science(2023) 10:7

Page 18 of 19

variables. Further experimental designs and simulations
are needed to improve them.

appropriately, long-term regional reanalyses would bedesired in the future.

5 Conclusions
We conducted a high-resolution dynamical downscaling using NHRCM with 5-km grid spacing forced by theJRA-55 (DS5km). DS5km well reproduced the climatology of the annual mean surface air temperature andannual total precipitation, while air temperature has cold
and warm biases in cold and warm seasons, respectively.
The DS5km also reproduced recent rapid warming andinterannual variations of annual mean air temperature,annual total precipitation, and annual number of heavy
precipitation days. The skills of DS5km for simulating air
temperature and precipitation are comparable to that of
the DSJRA-55. Composite analyses of annual maximum
daily snowfall events at Fukui City indicate that heavy
daily snowfall is brought about by the JPCZ, and thesnowfall amounts show increasing and decreasing trends
over mountains and coastal areas, respectively, due tohistorical warming and moistening.
As compared with DSJRA-55, DS5km has an important
advantage in calculating snow depth for all of Japan with
5-km grid spacing. The annual maximum snow depth andannual maximum daily snowfall show significant decreasing trends in eastern and western Japan, while they show
increasing trends at higher elevations in northern andeastern Japan. The Pacific Ocean side of northern Japan
has a clear elevation dependency of annual maximum
snow depth and annual maximum daily snowfall, while
the Japan Sea side has no elevation dependency. Longterm dynamical downscaling with a land surface modelis useful for evaluating historical changes in snow depth,
not only in the plains but also in mountainous areas.
In our experiment, the land uses are fixed from 1958
to 2020. The impact of historical changes in land use will
be studied in the future. Also, our dynamical downscaling still has some problems in representing the extreme
precipitation. The grid spacing of 5 km is insufficient tosimulate deep convections explicitly and resolve complex terrain over Japan, which causes failures in representing heavy precipitations, such as senjo-kousuitai
and snowfalls accompanied by winter monsoons. An
enhancement of the model’s resolutions is necessary. In
addition, long-term continuous dynamical downscaling
sometimes fails to simulate synoptic-scale environmental conditions, such as typhoon tracks, which results inbiases for precipitation and wind extremes. Fukui et al.
(2018) showed that a regional reanalysis, assimilating theobservations covering long term only, reduces the errorsof conventional dynamical downscaling. To evaluate past
local climate variations, including extreme states, more

Abbreviations
AMeDAS	Automated Meteorological Data Acquisition System
DSJRA-55	Dynamical regional downscaling using the JRA-55 reanalysis
DS5km	Dynamical regional downscaling using the NHRCM with 5-km
grid spacing
DS20km	Dynamical regional downscaling using the NHRCM with 20-km
grid spacing
ENSO	El Niño–Southern Oscillation
JRA-55	Japanese 55-year reanalysis
NHRCM	Non-hydrostatic regional climate modelnPR100	Annual frequency of daily precipitation exceeding 100 mm/day
RCDSJRA-55	Regional climate downscaling using the JRA-55
reanalysis
aRx1d	Annual maximum daily precipitation
SDmax	Annual maximum snow depth
Sx1d	Annual maximum daily snowfall
Acknowledgements
We thank two anonymous reviewers for their useful comments andsuggestions. We thank Dr. Yukiko Imada for discussing interannual anddecadal variations in JRA-55. This research was supported by the Integrated
Research Program for Advancing Climate Models (TOUGOU) Grant Number
JPMXD0717935561 from the Ministry of Education, Culture, Sports, Scienceand Technology (MEXT), Japan, MEXT-Program for the advanced studies ofclimate change projection (SENTAN) Grant Number JPMXD0722680734, and
JSPS KAKENHI Grant Number 19H01377 and 19H05697.
Author contributions
HK proposed the topics, conducted the numerical simulations, and analyzed
the output of the simulations. SF established the experimental design andconducted the numerical simulations. MN and SW analyzed the simulation
results. KO conducted the numerical simulation and analyzed the simulation
results. AM checked the numerical design. TN and KM supported the gather‑ing and analyzing of observational data. All authors read and approved thefinal manuscript.
Funding
This research was supported by the Integrated Research Program for Advanc‑ing Climate Models (TOUGOU) Grant Number JPMXD0717935561 from the
Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan,
MEXT-Program for the advanced studies of climate change projection (SEN‑
TAN) Grant Number JPMXD0722680734, and JSPS KAKENHI Grant Number
19H01377 and 19H05697.
Availability of data and materials
Data sharing is not applicable to this article as no datasets were generated oranalyzed during the current study. Please contact author for data requests.

Declarations
Competing interests
The authors declare that they have no competing interest.
Received: 21 June 2022 Accepted: 12 January 2023

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