Nishikawa et al. Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-020-00399-z(2021) 8:7

Progress in Earth andPlanetary Science

METHODOLOGYOpen Access

Development of high-resolution future
ocean regional projection datasets forcoastal applications in Japan
Shiro Nishikawa1*, Tsuyoshi Wakamatsu2,3, Hiroshi Ishizaki1, Kei Sakamoto4, Yusuke Tanaka1, Hiroyuki Tsujino4,
Goro Yamanaka4, Masafumi Kamachi1 and Yoichi Ishikawa1

Abstract
In this study, we developed two high-resolution future ocean regional projection datasets for coastal applications inJapan, in which we made use of dynamical downscaling via regional ocean models with atmospheric forcing fromtwo climate models (i.e., MIROC5 and MRI-CGCM3) participating in Coupled Model Intercomparison Project Phase 5(CMIP5) under historical, representative concentration pathway (RCP) 2.6, and RCP8.5 scenarios. The first dataset wasan eddy-resolving 10-km resolution product covering the North Pacific Ocean area and ranging continuously from1981 to 2100, in which the Kuroshio current and mesoscale structures were reasonably resolved. The second
dataset was a 2-km resolution product covering the regional domain surrounding Japan and comprising 10–15-yeartime slices, in which the coastal geometry and current structure were resolved even more realistically. An importantfeature of these datasets was the availability of reference datasets based on atmospheric and oceanic reanalysis
data for cross-validation during the historical run period. Using these reference datasets, biases of regional surface
thermal properties and the Kuroshio states during the historical run period were evaluated, which constituteimportant information for users of the datasets. In these downscaled datasets, the future surface thermal responses
were generally consistent with those of their original data. Utilizing the high-resolution property of the downscaleddata, possible future impact analyses regarding coastal phenomena such as strait throughflows, coastal sea levelvariability, and the Kuroshio intrusion phenomenon into bays (“Kyucho” phenomenon) were demonstrated and theimportant role of the Kuroshio state representation was indicated, which had proved difficult to analyze using the
low-resolution projection data. Given these properties, the present datasets would be useful in climate change
adaptation studies regarding the Japanese coastal region.
Keywords: Ocean future projection, Dynamical downscaling, Ocean model, CMIP5

1 Introduction
The North Pacific Ocean (NPO) will experience a significant transition by the end of the twenty-first century,in accordance with the current trend of global climatechange, including substantial oceanographic warming(IPCC 2013, 2014; Alexander et al. 2018). At present,
NPO coastal communities are facing challenges in the* Correspondence: snishika@jamstec.go.jp
1
Japan Agency for Marine-Earth Science and Technology, 3173-25
Showa-machi, Kanazawa-ku, Yokohama 236-0001, Japan
Full list of author information is available at the end of the article

establishment of adaptation plans to mitigate the impact
of climate change on their socioeconomic activities. The
current best available resource for projecting future
ocean states is the Coupled Model Intercomparison Project Phase 5 (CMIP5) model ensemble dataset (Taylor
et al. 2012), which has been extensively used to assess
future projections of the coastal marine environment.
However, this approach faces problems in fulfilling thecurrent demands for coastal applications. Since a
coupled earth system model produces CMIP5 data, thedata resolutions are kept relatively coarse, and additional© The Author(s). 2021 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 the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence 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 this licence, visit http://creativecommons.org/licenses/by/4.0/.

(2021) 8:7

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downscaling efforts, whether dynamical, statistical, or
hybrid, are required for coastal applications. For instance, the Kuroshio representations in CMIP5 models
are usually too broad to measure the detailed impacts oncoastal communities, and not all key variables are available for assessment due to limited storage space. In several studies, ocean downscaling has been performed
using climate model projection products (i.e., CMIP)
that focus on the western North Pacific region (e.g., Satoet al. 2006; Liu et al. 2016). However, their resolutions
were eddy-permitting (about 30 km) and insufficient forassessing the future climate change impacts on the Kuroshio
current or coastal problems in the NPO. High-resolution
ocean downscaling using CMIP products for regional/coastal
applications has been achieved for world ocean regions, suchas the Northwest Atlantic Ocean (e.g., Alexander et al. 2020;
see their introduction for further references), the northwest
European shelf regions (e.g., Hermans et al. 2020; see theirintroduction for additional references), and several othercoastal regions (e.g., Sun et al. 2012; Hermann et al. 2016;
Toste et al. 2017; Xiu et al. 2018). However, such a highresolution dynamical downscaling approach seems to haverarely been used for the NPO.
The Social Implementation Program on Climate
Change Adaptation Technology (SI-CAT) project was
conducted between 2016 and 2020, supported by the
Ministry of Education, Culture, Sports, Science andTechnology, Japan (SI-CAT 2020). One of the primarytasks of the project was to develop reliable technologies
and high-resolution future ocean projection information
to assist the local government in Japan in developing climate change adaptation plans. At the beginning of the
project, the ocean future projection task team conducteda series of surveys on the parameters of future oceanstate projections needed in developing adaptation plans.
The surveys suggested that high-resolution sea level, sea
surface temperature, and surface current speed, of theorder of a 2-km horizontal resolution, are the importantparameters for assessing local climate change impacts,
e.g., coastal environment conservation, coastal disasterprevention, and fisheries (SI-CAT 2020). Ocean boundary variables for a coastal-scale ocean circulation andwave model, for the further refinement of the projected
ocean state, have also been requested for the assessmentof coastal erosion and storm surge (e.g., Troselj et al.
2018). Therefore, in an effort to satisfy these demands,
we designed dynamical downscaling experiments thatutilized CMIP5 products to advance our future projections of the coastal ocean state around Japan.
The ensemble projected future ocean states would be
regenerated by driving eddy-resolving regional ocean
models with ensemble atmospheric forcing from CMIP5.
Our approach was unique in the following ways: (i) reference ocean states, generated from the same ocean

models forced by an atmospheric reanalysis forcing,were provided for evaluation of the models; (ii) thedownscaled ocean states were validated against a highresolution ocean state reanalysis; and (iii) a set of CMIP5
models were selected based on the availability of threehourly atmospheric components in the historical, representative concentration pathways (RCP) 2.6, and RCP8.5
periods for historical and future projections. In light ofthe background outlined above, we developed highresolution future ocean projection datasets under the SICAT project, collectively termed the future ocean regional projection (FORP) dataset.
This paper describes the development of this dataset(including the ocean model system, atmospheric forcings, experimental designs, and the dataset specifications) and briefly outlines the models’ performance andvalidation, focusing on the mean surface thermal properties and the Kuroshio and coastal current structures
around Japan. Section 2 reports the methods and procedures, and Section 3 validates the products. Section 4summarizes and discusses the findings, conveys the primary concluding points, and identifies the remaining
tasks for future research.

Nishikawa et al. Progress in Earth and Planetary Science

2 Methods
In order to create high-resolution future ocean projection datasets for application in Japan, we developed two
regional ocean models with different horizontal resolution and conducted a series of numerical experiments
for future ocean state projections for the period 1981 to
2100. The future projections were produced via the dynamical downscaling of the selected global climate projection datasets from CMIP5, namely MIROC5(Watanabe et al. 2010) and MRI-CGCM3 (Yukimoto
et al. 2012). As a result, the large-scale climate projections from CMIP5 were dynamically extrapolated using
the regional ocean models to an ocean mesoscale ofO(10 km) and a coastal scale of O(2 km). Crossvalidation data for the ocean projection products during
the historical run period were also provided by utilizing
an ocean reanalysis data and performing reference simulations under an atmospheric reanalysis forcing. The
outline of the dataset generation was as follows. First,
the 10-km ensemble data of historical and future oceanstate projections were generated from the outer regional
ocean model utilizing the constructed atmospheric forcings. Then, the 2-km downscaled data were generated
from the inner regional ocean model via the one-way(off-line) nesting method for several time slices from thehistorical and future terms.
2.1 Regional ocean models and reference data

The regional ocean models comprised the NPO modelwith a 10-km horizontal resolution (NP10) and the(2021) 8:7

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nested ocean model surrounding Japan with a 2-km
horizontal resolution (JPN02). Figure 1 presents themodel domains. Both models were developed based on
the ocean general circulation model, i.e., the Meteorological Research Institute Community Ocean Model version 4 (MRI.COMv4; Tsujino et al. 2017). Table 1
presents the detailed configurations of each model. The
bathymetry of the JPN02 model was based on the JTOPO30v2 data provided by the Marine Information Research Center, and several detailed settings (e.g., physicalschemes and parameters) of the JPN02 model were similar to those described by Sakamoto et al. (2016).
We adopted a four-dimensional variational (4D-VAR)
ocean reanalysis dataset for the western North Pacific(FORA-WNP30) (Usui et al. 2017) as a reference dataset.
This was an open dataset of ocean reanalysis covering
the years 1982–2014. Various observational data, including temperature-salinity profiles from ship and float databases, as well as satellite-based data on sea surface
temperature (SST) and sea level anomalies, were synthesized using the 4D-VAR method. For the atmosphericreanalysis data, the Japanese 55-year Reanalysis (JRA-55)(Kobayashi et al. 2015) was used for the surface boundary condition to produce FORA-WNP30 and to conductreference runs of the NP10 and JPN02 (see the following
subsections). The good reproducibility of the ocean statein the western North Pacific, especially regarding theKuroshio and Oyashio currents and surface thermal
properties around Japan, has been confirmed (Usui et al.
2017). Therefore, we used this ocean reanalysis dataset as areference state to validate the present ocean model results.

Note that FORA-WNP30 shared its ocean model component MRI.COM and vertical grid coordinates with the NP10
and JPN02 configurations, and its horizontal resolution was
basically the same as that of the NP10 (Table 1).

Nishikawa et al. Progress in Earth and Planetary Science

2.2 Construction of the atmospheric forcings

The atmospheric forcings, used for the surface boundary
condition of the regional ocean models, were constructed from the JRA-55 atmospheric reanalysis data
and the MIROC5 and MRI-CGCM3 (hereafter, CMIP5)
products. The historical scenarios were chosen for the
CMIP5 control runs, which were derived from the observed natural and anthropogenic atmospheric compositions and covered the period from 1960 to 2005. For the
CMIP5 climate projection runs, two RCP scenarios(Moss et al. 2010; Vuuren et al. 2011), namely RCP2.6
and RCP8.5, were chosen, covering the period from2006 to 2100. The key considerations in the selection of
the MIROC5 and MRI-CGCM3 among the CMIP5
model ensembles were as follows: (i) the representationin the NPO was considered and carefully calibrated, as
they were developed in Japan (e.g., Watanabe et al. 2010;
Yukimoto et al. 2012), and (ii) the eight surface variables
with three-hourly temporal resolution (Table 2) werecomplete and available for the historical, RCP2.6, and
RCP8.5 scenarios to construct the forcing data. The
three-hourly resolution was chosen so that the upper
ocean momentum flux and mixed layer dynamics would
be well-represented in the ocean model integrations. Allof the atmospheric variables of MIROC5 and MRICGCM3 were downloaded from the Earth System Grid

Fig. 1 Model domains of a NP10 and b JPN02. In (a), the gray box indicates the FORA-WNP30 domain, and the inner redbox indicates theJPN02 domain

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Table 1 Configurations of the regional ocean model system (NP10, JPN02) and the ocean reanalysis (FORA-WNP30, Usui et al. 2017)
NP10

JPN02

Use

Reginal ocean model (outer)

Regional ocean model (inner)

Reference data

Domain100° E–75° W, 15° S–70° N (Fig. 1a)

122.6° E–150° E, 23.7° N–47.5° N (Fig. 1b)

117° E–160° W, 15° N–65° N (grayrectangle in Fig. 1a)Model code

MRI.COMv4 (Tsujino et al. 2017)

MRI.COMv4 (Tsujino et al. 2017)

MRI.COM ver2.4 (Tsujino et al. 2010)

Resolution(hor.)

1/10° × 1/10° (approx. 10 km)

1/30° × 1/50° (approx. 2 km)

1/10° × 1/10°

Resolution(vert.)

1 m (surface)–600 m (bottom)

Same as the NP10

Same as the NP10

Grid size

1852 × 852 (hor.), 54 (vert.)

826 × 1194 (hor.), 54 (vert.)

673 × 442 (hor.), 54 (vert.)

Atmos.
forcing

JRA-55, CMIP5 (3-hourly)

JRA-55, CMIP5 (3-hourly)

JRA-55 (3-hourly)

Lateralboundary
condition

WOA monthly T/S climatology, kinematicallyno flux

One-way nesting with the NP10

WOA monthly T/S climatology

Surface
restoring

WOA monthly SSS

Climatological monthly SSS from the
NP10

WOA monthly SSS

River input

N/A

JRA55-do (Suzuki et al. 2018) based data

N/A

Physicalprocesses

Included: Sea-ice, fresh water in terms of salt
flux. Not included: tides, river input, thermal
expansion.

Included: Sea-ice, fresh water in terms ofsalt flux, river input. Not included: tides,
thermal expansion.

Included: Sea-ice, fresh water in terms ofsalt flux. Not included: tides, river input,
thermal expansion.

Federation server (https://esgf.llnl.gov/) and interpolatedwith the standard 320 × 160 Gaussian grid. The JRA-55
forcing data utilized a 640 × 320 Gaussian grid. The atmospheric variables were then used to calculate theocean driving surface fluxes via bulk formulas within
MRI.COMv4, in which the SST from the ocean model
was used (see Tsujino et al. 2017 for details). Note thatthe surface freshwater flux was converted to a salinity
flux in the models; therefore, over the model integrations, the total volume within the model domain wasconserved.
Following the spatial interpolation into the standard
grid, the CMIP5 (MIROC5 and MRI-CGCM3) atmospheric variables were slightly tuned against the JRA-55

Table 2 Atmospheric variables used for constructing the model
forcing from CMIP5 (MIROC5 and MRI-CGCM3)
Variableshuss

Near-surface specific humidity

pr

Precipitationpsl

Sea level pressure

rlds

Surface downwelling longwave radiation

rsds

Surface downwelling shortwave radiationtas

Air temperatureuas

Eastward near-surface wind speedvasNorthward near-surface wind speed

FORA-WNP30

data. We compared the variability of each surface variable (Table 2) from the CMIP5 data with that from theJRA-55 and found that the CMIP5 data included muchlarger variability in the short-term (daily) frequency
range, which induced computational instability in theocean model integration, specifically in the sea ice module. To reduce this unrealistically large short-term variability, we applied temporal smoothing with a five-point
periodic moving window to the CMIP5 data at each
three-hourly time step across 5 days. Note that thissmoothing retained the diurnal variation.
The river input effect was implemented in the JPN02
model as follows: The freshwater flux along the Japan
coast was based on the daily mean river runoff data fromthe reanalysis-based JRA55-do dataset (Suzuki et al. 2018).
The daily mean gridded runoff data from JRA55-do with ahorizontal resolution of 0.25° were merged with the
CMIP5 freshwater flux data. In the historical run period,
the daily mean runoff data were simply incorporated into
the freshwater flux. In the RCP scenario run period, thenewly constructed future river runoff data were merged,
which were constructed from the JRA55-do river runoff
and CMIP5 precipitation data. First, the CMIP5 precipitation data were averaged for a region surrounding Japan(123.5° E–148.75° E, 25° N–46.75° N). Assuming that the
future river runoff change roughly corresponds to the precipitation change integrated within the region surrounding
Japan, the following ratio was computed. The ratio of theannual mean domain-averaged precipitation of each year
to the 20-year (1996–2015) mean domain-averaged

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precipitation was calculated for each year in each CMIP5
case. The yearly time series of the precipitation change ratio was then multiplied by the 20-year (1996–2015) mean
daily JRA55-do river runoff data to derive the future river
runoff data in the RCP run period for each CMIP5 case.
(Note that this was only an assumption for the future runand not based on any detailed relationship within the historical run period.)

future projection-based (RCP2.6 and RCP8.5) atmospheric forcings. The NP10 model was also integrated
with the JRA-55 atmospheric reanalysis forcing from1958 to 2015 for cross-validation purposes. Bothmodel integrations forced by the CMIP5 historical
run-based atmospheric forcings and JRA-55 atmospheric forcing began from a static state, initializedwith temperature and salinity from the World Ocean
Atlas (WOA) monthly climatology (Levitus and Boyer
1994; Levitus et al. 1994). The first 20 years (1958/
1960–1980) were considered the models’ spin-up
period and were excluded from the FORP datasets.
For the boundary conditions, there was no kinematic
flux at the lateral boundaries. The temperature andsalinity within the 2° band along the lateral boundaries
were restored to the values of the WOA monthly climatology. In all experiments with the NP10 model(Table 3), the model settings (Table 1), lateral boundary condition, and initial condition of the historicalrun were the same; the only difference was the surface
forcing. This 10-km dataset from the NP10 simulations was termed FORP-NP10.

Nishikawa et al. Progress in Earth and Planetary Science

2.3 Evaluation of the ensemble atmospheric forcing

To characterize the differences among the selected
CMIP5 forcings in terms of their impact on ocean circulation, the Sverdrup transport stream function (STSF)
was calculated from the monthly mean wind stress foreach CMIP5 and JRA-55 forcing. After applying decadal
averaging to the STSF, the time series of the maximum
STSF, minimum STSF, and latitude of zero STSF measured at 160° E were plotted (Fig. 2). From basic winddriven ocean circulation theory (e.g., Pedlosky 1996), themaximum STSF corresponds to the strength of the subtropical gyre, the minimum STSF corresponds to thestrength of the subpolar gyre, and the latitude of zero
STSF reflects the border of the subtropical and subpolar
gyres (e.g., Aoki and Kutsuwada 2008). During the JRA55 run period (1981–2015), the maximum STSFs of both
CMIP5 forcings had clear offsets from that in the JRA55 (Fig. 2a). MIROC5 had an offset of about + 2 Sv (= 1
06 m3 s−1), and MRI-CGCM3 had an offset of about + 4
to + 5 Sv. Offsets could also be found in the minimum
STSF: MIROC5 had an offset of about + 25 Sv, and
MRI-CGCM3 had one of about + 15 Sv (Fig. 2b). This
indicated that both of the CMIP5 atmospheric forcing
data had a bias toward generating stronger subtropicalgyres and weaker subpolar gyres than the JRA55-driven
ocean circulation according to linear wind-driven ocean
circulation theory. The latitudes of zero STSF of theCMIP5 forcing data were all around 40° N during theJRA-55 run period, i.e., offset by about 3° south compared to that of the JRA-55. This suggests that the
MIROC5/MRI-CGCM3-forcing-driven ocean circulation
had an equatorward-shifted bias regarding the boundaryof the subtropical and subpolar gyres.
2.4 Experimental design of the NP10 future projection
simulation

Table 3 presents the periods of the historical and future projection experiments with the NP10 model.
The historical and future ocean state projections weregenerated as follows: To start with, the NP10 ocean
model was integrated with CMIP5 historical atmospheric forcings from 1960 to 2005. Following the end
condition of the historical run, it was further integrated by switching the atmospheric forcing from thehistorical-run-based forcings to the CMIP5 ensemble

2.5 Experimental design of the JPN02 downscaling
simulation

We employed time slice and dynamical downscaling
methods to produce higher resolution (2 km) downscaled historical and future projection products. Notethat the JPN02 ocean model required large computational and storage resources as well as CPU hours. The
selected time slice periods are listed in Table 4, composed of 11 slices with 10- or 15-year integration periods(with a total of 120 years). The initial condition of eachtime slice was derived from the corresponding restart fileof the parent model (NP10) integrations. The spin-up
period of each time slice with the JPN02 model was
about 1 month without any nudging processes. The oneway (off-line) nesting method (see Tsujino et al. 2017 fordetails) was used for the dynamical downscaling fromthe NP10 to the JPN02. The daily mean outputs fromthe parent model (NP10) were used for the lateralboundaries of the child model (JPN02). The 10-year(1996–2005) downscaling period from the historical runs
was incorporated by the MIROC5, MRI-CGCM3, andJRA-55 forced cases (Table 4). However, the time slice
periods of the downscaling for the future projection
were only covered by the MRI-CGCM3 forced case forboth the RCP2.6 and RCP8.5 runs (Table 4) due to limitations in computational resources. Surface forcing wascommon between the parent and child models for eachdownscaling experiment. On the basis of these procedures, we constructed a higher-resolution downscaling
dataset using the FORP-NP10 and the JPN02 model,
which we termed FORP-JPN02.

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Fig. 2 (See legend on next page.)

2.6 Basic specifications of the FORP datasets

The FORP-NP10 and FORP-JPN02 datasets included
the following variables: sea surface height (SSH), potential temperature, salinity, zonal velocity, and meridional velocity. The spatial domains of these datasetscorresponded to those of the respective ocean models.
The time resolution of each dataset was the monthly
mean for FORP-NP10 and the daily mean for FORPJPN02. The file formats for the distribution were
netCDF. Table 5 summarizes these basic specificationsof the datasets.

3 Results
3.1 Representation of the general ocean state and future
projections around Japan

Figure 3 displays the 10-year (1996–2005) mean maps ofthe ocean heat content (OHC) around Japan calculated
from the NP10 and JPN02 historical results (JRA-55,
MIROC5, and MRI-CGCM3), compared with the ocean
reanalysis data FORA-WNP30 (Fig. 3a) and the original
CMIP5 ocean products (Fig. 3b, c). Here, the OHC wasderived by averaging the temperature of the upper 0–
700 m layer. As temperature dominates density in thisregion, the OHC contours closely reflect the surface
flows. On the basis of this relationship, the Kuroshio andOyashio (the western boundary currents of the NPO)and Tsushima and Tsugaru coastal currents (see thenext subsection for details) around Japan are represented
by the OHC frontal structures in the OHC map, as observed in the 10-km ocean reanalysis data (Fig. 3a).
These mean surface current properties were found to be
fairly common among the reanalysis and high-resolution
simulation results of the NP10 and JPN02 (Fig. 3a, d–i).
Thus, the NP10 and JPN02 ocean models were regarded
as having the ability to reproduce climatological surface
thermal and kinematical ocean states under given reasonably accurate atmospheric forcing conditions. In contrast, the original MIROC5 and MRI-CGCM3 (hereafter,
original CMIP5) ocean products (Fig. 3b, c) did not represent such details of the western boundary currents andcoastal currents around Japan due to their coarse model
resolutions.

Nishikawa et al. Progress in Earth and Planetary Science(2021) 8:7

Page 7 of 22(See figure on previous page.)
Fig. 2 Time series of the 10-year mean STSF (Sv) at 160° E, calculated from wind stress of two CMIP5 (MIROC5 (black lines) and MRI-CGCM3(redlines)) and the JRA-55 (blue lines) atmospheric data. For the CMIP5 cases, the solid lines denote RCP2.6 and the dashed lines RCP8.5 where thehistorical data (1981–2005) were included. a Maximum STSF. b Minimum STSF. c Latitude of zero transport. Note that the last value of JRA-55 ineach panel is a 5-year (2011–2015) mean

In Fig. 4, the differences (biases) in the area mean
OHCs from the reanalysis data (FORA-WNP30) were
evaluated for the four rectangular regions presented inFig. 3d, namely the East China Sea (ECS) region, theNorth Pacific subtropical gyre region along the Kuroshio(KUR), the mixed water region (MWR) north of theKuroshio, and the Sea of Japan (SOJ) region. As is clear
from the OHC maps (Fig. 3b, c), the original CMIP5
products (red and blue bars) exhibited relatively large regional biases, e.g., a cold bias in the ECS and KUR (i.e.,
the North Pacific subtropical gyre) for both the MIROC5
and MRI-CGCM3 and a significant warm bias over the
SOJ for the MIROC5. These biases were effectively reduced in the corresponding NP10 and JPN02 downscaling
results (orange and light blue bars). The biases of the
NP10 and JPN02 results over the ECS and KUR were generally small, within ± 0.5°C and the standard deviation(SD) range of the reanalysis (the error bars in Fig. 4), except for those in the ECS for the JPN02 cases. Relatively
large biases were found in the northern regions (MWR
and SOJ): in the MWR, + 2 °C and − 1 °C for the JRA-55
and MRI-CGCM3 forced cases respectively, and in the
SOJ, + 1 °C to + 1.5 °C for the MIROC5 and JRA-55 forced
cases, which were beyond the SD ranges of the reanalysis.
These surface thermal biases would prove important for
FORP data users to assess future ocean regional impacts.
In Fig. 5, the vertical sections at 145.5° E of the potential
density and zonal velocities are compared to examine theoffshore thermal and current structures in the NPO. The
eastward flow with a meridional density gradient at 40°
N–42° N, as shown in the ocean reanalysis (Fig. 5a), corresponded to the extension of the Oyashio current (or subarctic front) (e.g., Yasuda 2003; Kida et al. 2016). The
position and vertical structure were generally wellreproduced in the NP10 and JPN02 results (Fig. 5d–g),apart from the MIROC5 and MRI-CGCM3 forced resultsof the JPN02 where the positions stood at 38° N–40° N(Fig. 5h, i). The relationship between the relative strengths
for three NP10 cases (JRA-55 > MRI-CGCM3 > MIROC5)

may be explained by the STSFs presented in Fig. 2b. However, defining and evaluating the Oyashio is a difficult
problem and this topic, based on the FORP data, is discussed in Nishikawa et al. (2020). The eastward flow with astrong meridional density gradient at 32° N–37° N (Fig. 5a)corresponded to the Kuroshio Extension (KE) (e.g., Qiu et al.
2006). Among the reanalysis and simulation results (Fig. 5a,d–i), the basic KE jet structure (the thermal wind relationship)
was essentially consistent. The KE axis position in the
MIROC5 and MRI-CGCM3 forced results (32.5° N, in Fig. 5e,f, h, i) was further to the south than in the reanalysis and theJRA-55 forced results (35° N–36° N, in Fig. 5a, d, g). This apparent bias in the KE latitudes in the MIROC5 and MRICGCM3 forced products was attributed to the bias in thewind stress distribution in their original atmosphere products.
The latitude of zero STSF at 160° E (Fig. 2c), calculated fromthe wind data of JRA-55 and MIROC5/MRI-CGCM3, indicated that the boundary of the subtropical and subpolar gyres
expected in the MIROC5/MRI-CGCM3 forcings was locatedapproximately 3° further south than that expected in the JRA55 forcing. This difference could affect the evaluation of the
future ocean state in the coastal region around Japan, as is discussed below. Note that the resolution of the original CMIP5
products (Fig. 5b, c) was insufficient for resolving the Kuroshio
and Oyashio jet structures.
Figure 6 presents the time series of the 10-year mean
Kuroshio transports over the historical and RCP2.6/
RCP8.5 run periods (i.e., 1981 to 2100) from the three
NP10 results (MIROC5, MRI-CGCM3, and JRA-55).
Here, based on the work of Imawaki et al. (2001), theKuroshio transport was approximated from the volumetransport stream function between Shikoku Island (34°N, 134° E) and an offshore point (26° N, 140° E). The offshore point for the NP10 results was located slightly eastof Imawaki et al.’s (2001) observation line after analyzing
the long-term variance in the transport stream function
to minimize the influence of the Kuroshio recirculation.
During the historical run period (1981–2005), the meantransports of three NP10 results were generally close to

Table 3 Periods of historical and future projection experiments by the NP10
Forcing (CMIP5, JRA-55)

Historical (JRA-55 hindcast)

RCP2.6

RCP8.5

MIROC51960–2005

2006–2100

2006–2100

MRI-CGCM3

1960–2005

2006–2100

2006–2100

JRA-551958–2015

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Table 4 Periods of time-sliced downscaling experiments by the JPN02
Forcings (CMIP5, JRA-55)

Historical (JRA-55 hindcast)

MIROC5

1996–2005

MRI-CGCM3

1996–2005

JRA-55

1996–2005, 2006–2015

RCP2.6

RCP8.52086–2100

2006–2015, 2016–2025, 2026–2035, 2036–2045, 2046–2055, 2086–2100

the value observed (42 Sv) by Imawaki et al. (2001).
The transports of the JRA-55 and MIROC5 forced results were at a similar level, approximately 38 Sv,
whereas that of the MRI-CGCM3 forced result was
about 42 Sv. The discrepancies among the models are
consistent with the analysis of the maximum STSF at
160° E (Fig. 2a). In the RCP8.5 cases from the
MIROC5 and MRI-CGCM3 forced results (the dashedlines in Fig. 6), a gradual reduction in the Kuroshiotransport was observed, whereas in the RCP2.6 cases(black and red solid lines in Fig. 6), no clear trends
were apparent. These future Kuroshio trends have notbeen noted in past studies on future projections of theKuroshio, although the intensification and/or northward shifting trend of the future KE has been previously noted (e.g., Sakamoto et al. 2005; Sato et al.
2006; Li et al. 2017). However, a detailed investigation
into this is beyond the scope of this paper, but it is aninteresting future research topic.
The transitions (1981–2100) of the OHC against the
1986–2005 mean are depicted in Fig. 7 for the NP10 results (Fig. 7a) and the original CMIP5 ocean products(Fig. 7b). While the difference among the scenarios andmodels was relatively small in the near future term
(2030–2050), it expanded after 2070. These general
OHC transition properties (tendency and variability)
were found to be similar between the NP10 and their
original results.
Figure 8 presents the OHC changes from 1996–2005
to 2086–2095 for the four rectangular regions presented
in Fig. 3d, with the NP10, JPN02, and their original
products, where the error bars denote the range from
RCP2.6 to RCP8.5 and the box bars indicate their means.

In the RCP8.5 cases (the top of the error bars), the OHC
changes range from + 1.5 to + 2.0 °C on the ECS and
KUR, and they reach + 2.0 °C to + 3.5 °C on the SOJ and
MWR. In the RCP2.6 cases (the bottom of the errorbars), the OHC changes were relatively uniform among
the regions and within + 0.5 °C to + 1.0 °C except on the
SOJ for the MIROC5 cases (+ 1.5 °C). In general, the
MIROC5 cases indicated greater warming than the MRICGCM3 ones, and the differences between the MIROC5
and MRI-CGCM3 were somewhat larger over the marginal seas (SOJ and ECS). There appeared to be no significant gaps between the downscaling and their parent/
original results for the same atmospheric forcing cases,suggesting that the difference between the dynamical
downscaling and non-downscaling cases may not be statistically significant with respect to the regional OHC future response.
In summary, the MIROC5 and MRI-CGCM3 forced
results of the NP10 and JPN02 reasonably represented
large-scale upper ocean thermal states around Japan,
with some improved regional biases in the historicalterm and consistent future responses with their originals. Because of the high horizontal resolutions ofO(10 km) and O(2 km), they reproduced the important current systems, including the Kuroshio and Oyashio currents, which were not well-resolved in theoriginal CMIP5 data of O(100 km). However, it should
be noted that the latitudinal positions of the Kuroshio
and Oyashio tended to be southward biased, which
can presumably be explained by the latitudinal positions of mid-latitude westerly, which were also biased
southward in the original MIROC5 and MRI-CGCM3
atmospheres.

Table 5 Outline of the basic specifications of the FORP datasets (FORP-NP10 and FORP-JPN02)
FORP-NP10

FORP-JPN02

Main variables

Sea surface height, potential temperature, salinity, zonal
velocity, and meridional velocity

Sea surface height, potential temperature, salinity, zonal
velocity, and meridional velocity

Time resolution

Monthly mean

Daily mean

FormatNetCDFNetCDF

Period(see Table 3)(see Table 4)

Domain andresolution(As the NP10, see Table 1)(As the JPN02, see Table 1)

Nishikawa et al. Progress in Earth and Planetary Science(2021) 8:7

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Fig. 3 The 10-year (1996–2005) mean maps of the OHC (°C) derived by averaging the surface 0–700 m layer temperature. a FORA-WNP30. The
original b MIROC5 and c MRI-CGCM3 ocean products. The NP10 results are shown in d JRA-55, e MIROC5, and f MRI-CGCM3. The JPN02 results
are shown in g JRA-55, h MIROC5, and i MRI-CGCM3. The thick red line overlaid on (a) is the meridional cross section plotted in Fig. 5. The boxes
overlaid on (d) are for deriving the area mean OHC analyses plotted in Figs. 4 and 8. The original MIROC5 and MRI-CGCM3 cases of (b) and (c)are regridded to the standard WOA grid

3.2 Coastal ocean representation and future projections
around Japan

Strait throughflows (e.g., the Straits of Tsushima, Tsugaru, and Soya around the Japan Sea) are important
coastal oceanographic metrics around Japan (e.g., Han
et al. 2016). Since the original CMIP5 ocean productswith O(100 km) resolution cannot sufficiently represent

the details of these straits, it is necessary to employ thedownscaling approach. Figure 9 presents the verticalcross-sections of the potential density and zonal velocity
at 129.45° E across the Tsushima Strait. As is shown in
the ocean reanalysis data (Fig. 9a), the Tsushima Strait
has a shallow bottom (less than 200 m) and is divided by
Tsushima Island, where the Tsushima Warm Current

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Fig. 4 OHC differences (biases) from the ocean reanalysis (FORA-WNP30) values for four rectangular regions (shown in Fig. 3d), comparing the
NP10 and JPN02 results (JRA-55, MIROC5, MRI-CGCM3) and the original CMIP5 results (MIROC5 and MRI-CGCM3) for the historical term (1996–2005). The error bar in each region denotes the standard deviation of the monthly OHC for the term 1996–2005 from the reanalysis

flows eastward with lighter (warmer) surface water overdenser (colder) bottom water (e.g., Isobe et al. 2002).
The JPN02 results (Fig. 9e–g) represent this current
structure well because the bottom topography of the
Tsushima Strait would be reasonably accurate. The
current strengths of the southern branch in the NP10 results (Fig. 9b–d) were weaker by more than 10 cm s−1
than those of the reanalysis and JPN02 results. Figure 10
presents the vertical cross-sections at 140.55° E acrossthe Tsugaru Strait, where the Tsugaru Warm Current
flows eastward, as is shown in Fig. 10a. The width of the
Tsugaru Strait at the narrowest position is about 20 km.
Therefore, the 10-km resolution is apparently insufficient to represent the Tsugaru Warm Current structure.
In fact, the current in the 10-km reanalysis and NP10 results (Fig. 10a–d) appeared to be too coarse against theobserved structure (e.g., Ito et al. 2003), whereas theJPN02 results (Fig. 10e–g) represented the finer flow
structure in the strait. Similar improvements in the strait
bathymetry and current structure from the NP10 to theJPN02 were also apparent for the Soya Strait (142° E,
45.4° N–46° N) that has a width of 40 km (not shown).
Figure 11a compares the 10-year (1996–2005) meantransport through the Tsushima, Tsugaru, and Soya
Straits among the reanalysis, NP10, and JPN02 results.
For observational references of the transport throughthese straits (black bars), we utilized 2.65 Sv from

Fukudome et al. (2010), 1.5 Sv from Ito et al. (2003), and0.91 Sv from Ohshima et al. (2017), respectively. For the
Tsushima and Soya Strait transports, while the reanalysis
and JRA-55 forced results were closer to the observation,
the MIROC5 and MRI-CGCM3 forced results underestimated the transports for both straits. These underestimations would partly come from the surface winds ofthe MIROC5 and MRI-CGCM3 atmospheres (e.g., Tsujino et al. 2008). It was also found that the underestimated transports in the NP10 were improved in the
JPN02 results, probably due to the more realistic representation of the strait bathymetry. For the Tsugaru Straittransport, the three NP10 results indicated the overestimation. The JPN02 results again showed closer transports to the observation, as they included improved
horizontal resolution for the Tsugaru Strait. Figure 11b
depicts the future impact on the transports for the MRICGCM3 forced results of the JPN02. The RCP8.5 result(2086–2095) suggests increases (+ 0.15 to + 0.2 Sv) fromthe historical result (1996–2005) for the transports
through the straits, whereas the changes in the RCP2.6
case were smaller. The future increases in the strait transports in the RCP8.5 case appeared to be opposite to the
future decrease in the Kuroshio transport (Fig. 6). Thesetransports (the Kuroshio and the three straits around
Japan) should be closely affected by future changes in thesurface winds (Fig. 2). Investigation of the details of theseNishikawa et al. Progress in Earth and Planetary Science(2021) 8:7

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Fig. 5 The 10-year (1996–2005) mean potential density (kg m−3) cross-sections along 145.5° E from 30° N to 43° N (see the thick red line in Fig.
3a). a FORA-WNP30. The original b MIROC5 and c MRI-CGCM3 ocean products. The NP10 results are shown in d JRA-55, e MIROC5, and f MRICGCM3. The JPN02 results are shown in g JRA-55, h MIROC5, and i MRI-CGCM3. The contours in (a) and (d)–(i) denote the zonal velocity (solid,
eastward; dotted, westward) and the contour interval (CI) is 5 cm s−1

relationships, however, is beyond the scope of this paper
but would be an interesting future research topic.
The reproducibility and variability of the coastal sea
level, or SSH, are important targets of coastal

applications in the Japanese coastal region. Figure 12
shows the SSH variance from the coastal tide gauge observation data (1997–2005) of the Japan Meteorological
Agency (e.g., Sakamoto et al. 2016, 2019), which is theNishikawa et al. Progress in Earth and Planetary Science(2021) 8:7

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Fig. 6 Time series of the 10-year mean net Kuroshio transport (Sv), estimated from the transport stream function between (26° N, 140° E) and (34°N, 134° E) from the NP10 results. Note that the last value of JRA-55 is the 5-year (2011–2015) mean

daily mean data, excluding tidal variation. Using thisdata, we validated the coastal SSH variance in Japan for
the JPN02 results. Figure 13 shows the ratio of the SSH
variance from the ocean reanalysis and three JPN02 results against the observation value (Fig. 12). Figure 13a
and b indicates that the SSH variances in the reanalysis

and JRA-55 forced result were close to that of the observation (more than 0.7 in many instances, except for
Hokkaido Island). In contrast, the MIROC5 and MRICGCM3 forced results (Fig. 13c, d) were generally
smaller, i.e., less than 0.6 in most cases, except for Kyushu and part of Shikoku Island. The relatively small

Fig. 7 a Transitions (1981–2100) of the OHC anomalies (°C) against the 1986–2005 mean for the area surrounding Japan (123° E–150° E, 24° N–
47° N) from the NP10 results. Black, MIROC5; red, MRI-CGCM3; thick solid line, historical + RCP8.5; thin dotted line, RCP2.6. b The same as (a), butusing the corresponding original CMIP5 ocean products

Nishikawa et al. Progress in Earth and Planetary Science(2021) 8:7

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Fig. 8 OHC changes for four rectangular regions (shown in Fig. 3d) from the 1996–2005 mean to the 2086–2095 mean, comparing the NP10,
JPN02, and their original products. The box bars denote the average of RCP2.6 and RCP8.5, and the error bars the range from RCP2.6 to RCP8.5.
Note that the OHCs for the ECS region are derived from the surface 0–300 m layer temperature (the others are derived from the surface 0–700
m layer)

amplitude of the SSH variance in the MIROC5 and
MRI-CGCM3 forced results could be partly due to thecoarse spatial resolution of their atmospheric forcings(see the Section 2).
The SSH variances were higher along the southerncoast of Japan, as is shown in Fig. 12, through the effectof the Kuroshio current. However, the higher SSH variances in this coastal area were found to be weak in the
MIROC5 and MRI-CGCM3 forced results (Fig. 13c, d)due to the southward shift (bias) of the Kuroshio position (Figs. 3 and 5). Figure 14 shows the SSH varianceat seven points from the southern coast of Japan denoted in Fig. 12, comparing three terms from the MRICGCM3 forced cases and the observation. The apparent
eastward reduction and bias of the SSH variance in thehistorical term (light blue bars) indicate the influence ofthe southward-shifted position of the Kuroshio. Thisbias (the weaker SSH variability) would be important fordata users interested in its application to the southeastern coast of Japan. Future changes in the SSH varianceat the coastal points are indicated by comparing the light
blue (historical), blue (RCP2.6), and red (RCP8.5) bars in
Fig. 14. In the RCP8.5, the SSH variances were increasedon the eastern side against those in the historical term,probably because of the future northward shift of theKuroshio (Fig. 2). In the RCP2.6, the SSH variances weredecreased on the western side, which could also relate to
the local change in the Kuroshio path in the future term.
As an example of a coastal phenomenon, we examined
the Kuroshio intrusion phenomenon into the bays along
the southern coast of Japan. Figure 15a–f presents thetime series of the daily mean velocity and temperature ata coastal point P (139.21° E, 35.09° N) in Sagami Bay(e.g., Fig. 15g) comparing the MRI-CGCM3 forced results from the JPN02 and NP10, and the ocean reanalysis. The observations (e.g., Matsuyama et al. 1999)
revealed that an occasional stormy current (called“Kyucho”) occurred in Sagami Bay due to the intrusion of the Kuroshio warm water from the east, as indicated by a sudden increase in the southward flow
and temperature at point P. In the JPN02 result, thered arrows in Fig. 15a, d corresponded to thisphenomenon, with the increased velocity being close
to Matsuyama et al.’s (1999) observed value of over
50 cm s−1. Figure 15g presents the velocity map on
the date of the sudden increase. The anticlockwise
coastal flow in Sagami Bay was consistent with Matsuyama et al.’s (1999) description. Although such sudden increase was also found in the 10-km reanalysis
and NP10 results (the red arrows in Fig. 15b, c, e, f),
it seemed to be much weaker than the observed value
and should not have been reflected by the realNishikawa et al. Progress in Earth and Planetary Science(2021) 8:7

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Fig. 9 The 10-year (1996–2005) mean potential density (kg m−3) cross-sections along 129.45° E from 33.3° N to 35.7° N, which is across the
Tsushima Strait and denoted by the thick black line in the top-right panel (the surface density map from the JRA-55 forced result of the JPN02). a
FORA-WNP30. The NP10 results are shown in b JRA-55, c MIROC5, and d MRI-CGCM3. The JPN02 results are shown in e JRA-55, f MIROC5, and g
MRI-CGCM3. The contours denote the zonal velocity (solid, eastward; dotted, westward), and the CI is 5 cm s−1“Kyucho” mechanism. In fact, the corresponding velocity maps at the event times (Fig. 15h, i) imply thatthe lack of horizontal resolution and unrealistic
coastal geometry led to insufficient representations of
the Kuroshio warm water intrusion into Sagami Bay.

Note that the apparent close timing of the increase
between these data (Fig. 15a–c) is not essential, as
the “Kyucho” is a stochastic event.
The sudden increase at P in Sagami Bay for the historical (1996–2005) and future RCP2.6/RCP8.5 (2086–

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Fig. 10 The same as Fig. 9, but for the cross-sections along 140.55° E from 41.1° N to 41.9° N, which is across the Tsugaru Strait and is denotedby the thick black line in the top-right panel (the surface density map from the JRA-55 forced result of the JPN02). The CI of the zonal velocity inpanels (a)–(g) is 10 cm s−12095) runs was statistically evaluated in Table 6, where
the 10-year frequencies of sudden meridional velocity increase were counted based on the threshold velocity
values. Given the SD of the meridional velocity at P, weused the threshold meridional velocity value of − 30 cm
s−1 for the JPN02 results and − 13 cm s−1 for the NP10
result and reanalysis. Consequently, the 10-year

frequency of the occurrence in the historical term
(1996–2005) for the JPN02 result was 38, and the frequency was increased by 10–20% in both future projection cases (42 for RCP8.5 and 46 for RCP2.6, in 2086–
2095) accompanied by the 7–10% increases in the velocity variabilities (SD in Table 6). The future change inthe Kuroshio path and KE latitude in these RCP cases

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Fig. 11 a The 10-year (1996–2005) mean transports (Sv) through the Tsushima, Tsugaru, and Soya Straits from the JPN02, NP10, and FORAWNP30 (colored bars). The observational reference values (black bars) are 2.65 Sv from Fukudome et al. (2010) for the Tsushima Strait, 1.5 Sv from
Ito et al. (2003) for the Tsugaru Strait, and 0.91 Sv from Ohshima et al. (2017) for the Soya Strait. b Same as (a), but comparing the historical(1996–2005), RCP2.6 (2086–2095), and RCP8.5 (2086–2095) cases of the MRI-CGCM3 forced results of the JPN02

may have affected these “Kyucho” frequency and intensity increases; however, a more detailed analysis
into the future Kuroshio impacts would be needed toclarify the relationship with this coastal event. Notethat in the 10-km products, the velocity variances andevent frequencies were much weaker and the mean
velocities were opposite to the JPN02 cases (Table 6),
implying that the “Kyucho” phenomenon was notproperly represented with the 10-km horizontal
resolution.

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Fig. 12 Variances (cm2) in the sea level along the coast of Japan denoted by colored circles, from the coastal tide gauge observation data (1997–2005) by the Japan Meteorological Agency (JMA), which is the daily mean data, excluding tidal variation. The calculation of variance was basedon a daily anomaly from the daily climatology (and so the effect of the seasonal cycle was excluded). The seven points denoted by the arrows
are used in Fig. 14

In summary, the JPN02 was used to reasonably represent the coastal currents and finer-scale structures(e.g., strait throughflows and transports around Japanand the Kuroshio intrusion phenomenon in the Japanese coastal region) over the historical term due to theO(2 km) resolution with finer coastal geometry. The
coastal sea level variance in the MIROC5 and MRICGCM3 forced results of the JPN02 was smaller thanthat of the observation and the JRA-55 forced results,
especially along the southern coast of Japan, in which
the effect of the Kuroshio bias was large. Possible future changes in the coastal flows (the strait throughflows and “Kyucho” phenomenon) and SSH variancewere examined, and it was suggested that these werestrongly affected by the larger-scale Kuroshio properties (e.g., the axis position). These are important
points to be taken into account by users of the FORP
data. Note that the future impact analyses using the
JPN02 (Figs. 11 and 14 and Table 6) were based on
only one model (i.e., the MRI-CGCM3) projection
data, and so multi-model ensemble downscaling projections with the JPN02 are needed to achieve greater
scientific validity in future research.

4 Discussion and Conclusions
In this study, we developed the FORP datasets via the dynamical downscaling using the MIROC5 and MRI-CGCM3(from CMIP5) atmospheric forcings, comprising the NPO
dataset with a 10-km resolution and the regional dataset surrounding Japan with a 2-km resolution, i.e., FORP-NP10 and
FORP-JPN02. The datasets were to provide a set of likely future NPO and Japanese coastal ocean states within the scopeof the SI-CAT project, i.e., to assist stakeholders in developing an adaptation plan for the Japanese coast. For crossvalidation purposes, the FORP datasets included historicalexperiments using the JRA-55 atmospheric reanalysis forcing
and shared some basic specifications with the ocean reanalysis data FORA-WNP30. The usability of these reference data
for evaluating various biases of the model ocean states is an
important feature of these datasets.
Both the FORP-NP10 and FORP-JPN02 data were
found to reproduce the primary flow structures around
Japan, in particular the Kuroshio and Oyashio currents
with their frontal and jet structures, which had proved
difficult to evaluate using the low-resolution products of
the original CMIP5 models (Figs. 3 and 5). The Kuroshiotransports were reasonable, and the KE positions

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Fig. 13 Ratio of the 10-year (1996–2005) mean SSH variance against the observed value (Fig. 12) along the coast of Japan for a FORA-WNP30
and the JPN02 results of b JRA-55, c MIROC5, and d MRI-CGCM3, which are denoted by colored circles. The calculation of variance was based ona daily anomaly from the daily climatology (thus, the effect of the seasonal cycle was excluded)

exhibited a southward-shifted bias (about 3°) in the historical run results (Figs. 3, 5, and 6) consistent with the
STSF analysis of each set of atmospheric data (Fig. 2).
The FORP-JPN02 data further represented the finer flow
structures and transports along the coasts, including thestrait throughflows around the Japan Sea (Figs. 9, 10 and 11).
The Kuroshio water intrusion into the Japanese coastal region (such as the “Kyucho” phenomenon) was an example ofthe improved coastal representation in the FORP-JPN02(Fig. 15), which was difficult to represent at O(100 km) toO(10 km) resolutions. The SSH variance along the Japanese
coast from the MIROC5 and MRI-CGCM3 forced productsof the FORP-JPN02 was smaller than that from the tidegauge observation (Figs. 12, 13, and 14), possibly due to thecoarse resolution of their atmospheric forcings. The FORP
data reproduced the surface thermal structure with reducing
the temperature biases observed in their original (CMIP5)
data, while there were some substantial biases in the

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Fig. 14 SSH variance (cm2) at seven coastal points along the southern coast of Japan shown in Fig. 12 comparing the historical (1996–2005),
RCP2.6 (2086–2095), and RCP8.5 (2086–2095) cases from the MRI-CGCM3 forced results of the JPN02 and the JMA observation (1997–2005)

marginal and northern mixed water regions in both the
FORP-NP10 and FORP-JPN02 products (Figs. 3 and 4). The
future climate change responses of the surface OHC around
Japan were generally consistent among the FORP-NP10,
FORP-JPN02, and their corresponding original data (Figs. 7
and 8).
Based on these assessments, the quality of the FORP
dataset is sufficient for use in climate change adaptation
studies for the Japanese coastal region. On the other
hand, FORP data users should be mindful of the various
biases noted above, i.e., regional OHC biases, smaller
variability biases in the coastal SSH, and the southernshift of the Kuroshio position. In particular, the effect of
the Kuroshio position bias is important because it would
strongly affect future impact analyses of the southerncoastal region of Japan (e.g., Fig. 14). Improving uponthis is a planned future task of ours.
Future changes in the mean sea levels along the coast
are an important target parameter with respect to climatechange adaptation issues in Japan. As the present ocean
models are volume-conservative and do not explicitly include volume change by thermal expansion and freshwater inputs (e.g., meltwater from glaciers and ice sheets),the future sea level changes in the model provide the local
effect of seawater redistribution via ocean circulation due
to momentum, heat, and salinity fluxes. The global effectsof thermal expansion and freshwater inputs should beassessed on the basis of the original CMIP5 global models
and other independent sources (e.g., IPCC SROCC; see

Oppenheimer et al. 2019). The sum of the local effects inthe present model and the global effects from externalsources can offer an approximate evaluation of future sea
level changes along the Japanese coast.
The ensemble of future projections in this study focused on two climate models (MIROC5 and MRICCGM3) from the CMIP5 and featured two scenarios(RCP2.6 and RCP8.5). However, an increase in the ensemble number using more CMIP5 atmospheric forcings
is required to evaluate the variance and uncertainty of future projections with greater reliability. In this study, thelateral boundary of the temperature/salinity in the NP10
model was restored to that of the WOA climatology. For
the RCP8.5 scenario runs (i.e., under strong global warming scenarios), this lateral boundary setting caused artificial cooling of the surface layer near the southern lateralboundaries of the NP10 model in the future projected
fields. Future information on ocean temperature/salinitychanges from each CMIP5 model should also be reflectedin the lateral boundaries of the NP10 to avoid this artificial cooling and extend the applicability of the FORPNP10 dataset. This is our next task, and a portion of thepreliminary results of this can be found in the work of
Nishikawa et al. (2020). The present JPN02 model did notinclude tidal and surface pressure effects, partly due tothe computational costs of long-term integration. As
these processes are important for further applications incoastal problems (e.g., Sakamoto et al. 2019), these effects
should be introduced in future studies.

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Fig. 15 Time series of the daily mean a–c meridional velocity (cm s−1) and d–f temperature (°C) at 27 m depth in 2003 at point P (139.21° E,
35.09° N) near the western coast of Sagami Bay (denoted in g–h). g–h Surface velocity (cm s−1) maps (at 27 m depth) on g July 13, 2003; hAugust 18, 2003; and i September 1, 2003, denoted by the red arrows in each upper panel (a–f). a, d, g Denoting the JPN02 results of MRICGCM3. b, e, h Denoting the NP10 results of MRI-CGCM3. c, f, i Drawn from the FORA-WNP30 data

Table 6 Statistics of the meridional velocity (cm s−1) and the sudden increase events (“Kyucho” events) at P in Sagami Bay. “Average”
and “SD” denote the 10-year average and standard deviation of the meridional velocity at P, respectively. The 10-year “Frequency” of the“Kyucho” events at P in each case was evaluated based on the “Threshold” velocity value (−30 cm s−1 or −13 cm s−1)
Model/Data

Cases and analysis terms

SD (cm s−1)

Average (cm s−1)

Frequency

Threshold (cm s−1)

JPN02

MRI-CGCM3 historical (1996-2005)

13.3

−1.51

38

−30

MRI-CGCM3 RCP2.6 (2086-2095)

14.7

−2.87

46

−30

MRI-CGCM3 RCP8.5 (2086-2095)

14.2

−1.77

42

−30

NP10

MRI-CGCM3 historical (1996-2005)

6.49

1.38

22

−13

Reanalysis (10 km)

FORA-WNP30 (1996-2005)

5.46

1.56

19

−13

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Acknowledgements
We are grateful to Dr. H. Igarashi for preparing the JRA-55 data, and K. Gotoand K. Sakauchi for their assistance in carrying out various numerical experiments on the supercomputer, Earth Simulator, at JAMSTEC. The constructive
comments from Dr. T. Suzuki and two anonymous reviewers greatly helped
in improving an early version of the manuscript. We also thank Enago (www.
enago.jp) for the English language review.

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Nishikawa et al. Progress in Earth and Planetary Science

Authors’ contributions
SN developed the model system, designed and conducted the experiments,
analyzed the data, and drafted the paper. TW developed the forcing data
and model system, designed the experiments, analyzed the data, anddrafted the paper. HI analyzed the data and produced some figures. KS
supported the development of the model system, analyzed the data andproduced some figures. YT supported the development of the forcing data.
HT supported the development of the model system. GY and MK supported
the model validation and the data analysis. YI proposed the topic and led
the SI-CAT project. All authors read and approved the final manuscript.
Funding
TW was supported partially by the Nansen Center (NERSC) basic funding.
This study was supported by the Social Implementation Program on Climate
Change Adaptation Technology (SI-CAT: grant no. JPMXD0715667163) andthe Integrated Research Program for Advancing Climate Models (TOUGOU:
grant no. JPMXD0717935561) sponsored by the Ministry of Education,
Culture, Sports, Science and Technology of Japan.
Availability of data and materials
The FORP datasets produced in this study are available from the DIAS
website (http://www.diasjp.net/en/). The ocean reanalysis dataset, FORAWNP30, is available from the following web site: http://www.godac.jamstec.
go.jp/fora/e/index.html.
Competing interests
The authors declare that they have no competing interest.
Author details
1
Japan Agency for Marine-Earth Science and Technology, 3173-25
Showa-machi, Kanazawa-ku, Yokohama 236-0001, Japan. 2The Nansen Center,
Bergen, Norway. 3Bjerknes Centre for Climate Research, Bergen, Norway.
4
Meteorological Research Institute, Japan Meteorological Agency, Tsukuba,
Japan.
Received: 5 January 2020 Accepted: 10 December 2020

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