A quasi-global eddying ocean hindcast simulation using a
new version of our model, called OFES2 (Ocean General Circulation Model for
the Earth Simulator version 2), was conducted to overcome several issues with
unrealistic properties in its previous version, OFES. This paper
describes the model and the simulated oceanic fields in OFES2 compared with
OFES and also observed data. OFES2 includes a sea-ice model and a tidal
mixing scheme, is forced by a newly created surface atmospheric dataset
called JRA55-do, and simulated the oceanic fields from 1958 to 2016. We found
several improvements in OFES2 over OFES: smaller biases in the global sea
surface temperature and sea surface salinity as well as the water mass properties
in the Indonesian and Arabian seas. The time series of the Niño3.4 and
Indian Ocean Dipole (IOD) indexes are somewhat better in OFES2 than in OFES.
Unlike the previous version, OFES2 reproduces more realistic anomalously low
sea surface temperatures during a positive IOD event. One possible cause of
these improvements in El Niño and IOD events is the replacement of the
atmospheric dataset. On the other hand, several issues remained unrealistic,
such as the pathways of the Kuroshio and Gulf Stream and the unrealistic
spreading of salty Mediterranean overflow. Given the worldwide use of the
previous version and the improvements presented here, the output from
OFES2 will be useful in studying various oceanic phenomena with broad
spatiotemporal scales.
Introduction
The global ocean includes phenomena with various spatial scales. Basin-scale
circulations occur over thousands of kilometers, while oceanic fronts,
western boundary currents, and the Antarctic Circumpolar Current (ACC) have
widths of approximately or less than 100 km. Mesoscale eddies, ubiquitous
around these currents and in the ocean interior, have a spatial scale of a
few tens of kilometers in the subarctic ocean to a few hundred kilometers in
the subtropics (Chelton et al., 1998). The location and strength of oceanic
fronts, currents, and mesoscale eddies also change over time (e.g., Sasaki
and Schneider, 2011; Qiu and Chen, 2010; Zhai et al., 2008).
Observations are crucial for understanding the ocean, but their data
coverage and resolution are limited. Since the 2000s, gridded hydrographic
products based on Argo float observations (e.g., Roemmich et al., 2009;
Hosoda et al., 2008) have been able to capture global ocean properties at a
resolution of approximately 300 km. However, such a spatial resolution is
not adequate to resolve narrow currents, mesoscale eddies, or frontal
structures. Satellite observations can provide high-resolution data on
sea surface height (SSH) and temperature (SST), for example, but are limited to
surface measurements. Global eddying simulations have therefore become a
useful and convenient tool for understanding the ocean. Computational power
has increased exponentially, and over the past decades, several research groups
have been conducting global eddying ocean simulations at horizontal
resolutions of approximately 10 km using the Parallel Ocean Program (POP;
Maltrud and McClean, 2005), the Hybrid Coordinate Ocean Model (HYCOM;
Chassignet et al., 2006), the Max Planck Institute ocean model (MPIOM;
Jungclaus et al., 2013), and the Ocean General Circulation Model (OGCM) for
the Earth Simulator (OFES; Masumoto et al., 2004). The realistic long-term
hindcast global eddying ocean simulation outputs from OFES have been widely
used in the community
(http://www.jamstec.go.jp/res/ress/sasaki/ofes_publication.html, last access: 15 July 2020).
The outputs from global eddying ocean simulations have provided
unprecedented information about oceanic phenomena on wide spatiotemporal
scales in areas where observational data are limited. These simulations
create a significant amount of data, which are very informative because the
data exhibit oceanic phenomena from around the globe on
mesoscales to basin scales and their variations from intraseasonal to
decadal timescales. Sharing simulation outputs among the community is
crucial, and such use of OFES (Sasaki et al., 2008) has led to research
achievements in various topics (see details in Masumoto, 2010), such as
oceanic phenomena from intraseasonal (e.g., Hu et al., 2018) to decadal
variations (e.g., Taguchi et al., 2017) and mesoscale eddies (e.g., Aoki et
al., 2016). However, numerical models are not perfect. Model deficiencies and
biases exist, and the usage of simulation outputs in the community has led
to findings of where these limitations exist and their possible causes. One
of the major problems of OFES seems to be its surface wind stress field.
Kutsuwada et al. (2019) showed that the thermocline depth in the subtropical
northwestern Pacific was too shallow due to unrealistic wind stress. Another
problem is the lack of tidally induced vertical mixing. Masumoto et al. (2008) found unrealistic water properties within the Indonesian seas, where
tidally induced vertical mixing is considered significant (Ffield and
Gordon, 1996). Another problem is the lack of sea ice, because of which the
sea surface salinity in OFES was strongly restored to monthly climatological
observations.
This paper highlights how an updated OFES improved the hindcast simulation
outputs. The updated model was forced by surface forcing based on
3-hourly atmospheric reanalysis data at a finer horizontal resolution. A
tidal mixing scheme and a sea-ice model were added, and we call the standard
hindcast simulation using this new version OFES2 (Fig. 1). Section 2
describes OFES2, Sect. 3 examines its simulated mean oceanic fields, and
Sect. 4 examines the time variability based on climate indexes of El
Niño and the Indian Ocean Dipole (IOD). We will further examine the
IOD events and highlight the simulated SST distribution around the eastern
pole of the IOD. A summary and discussion are provided in Sect. 5.
An example of monthly averaged surface current speeds (cm s-1) in OFES2.
Descriptions of OFES2 compared with OFES
OFES2 is an update of a quasi-global eddying hindcast simulation: OFES
(Sasaki et al., 2008). It is based on the Modular Ocean Model (MOM) version 3
(Pacanowski and Griffies, 1999) and utilizes the latitude and longitude grid
system. The horizontal resolution of 0.1∘ remains the same as that
in OFES, but the model setup and parameterization are altered to reduce the
model biases that exist in OFES. The model configuration of OFES2 will be
described first, and the differences from OFES will be described next.
The domain extends from 76∘ S to 76∘ N without polar
regions. The horizontal resolution is 0.1∘, and the number of
vertical levels is 105 with a maximum depth of 7500 m. The thickness of
each layer within the upper 100 m is 5 m. The thickness gradually increases,
and there are 55 levels within the upper 500 m. We constructed the bottom
topography with partial bottom cells (Adcroft et al., 1997) using the
bathymetry dataset ETOPO1 (Amante and Eakins, 2009). Although the model
domain does not include the polar regions, a sea-ice model (Komori et al.,
2005) was internally implemented into OFES2 to simulate the Antarctic and
Subarctic oceans, including the Sea of Okhotsk, more realistically. The
sea-ice model employs two-category, zero-layer thermodynamics (Hibler, 1979)
and elastic–viscous–plastic rheology (Hunke and Dukowicz, 2002).
A biharmonic operator is used for horizontal mixing to suppress
computational noise with a viscosity of 27×109 m4 s-1 and a diffusivity of 9×109 m4 s-1. The
drag coefficient is 2.5×10-3 (nondimensional) for linear
bottom drag. For vertical mixing, we added diffusivities from the tidal
mixing scheme developed by Jayne and St. Laurent (2001) and St. Laurent et
al. (2002) to those estimated from the mixed layer vertical mixing scheme of
a statistical closure model (Noh and Kim, 1999). In the tidal mixing scheme,
the three-dimensional diffusivities are estimated from the energy flux at
the ocean bottom and the local buoyancy frequency with the parameters of
dissipation efficiency, mixing efficiency, and vertical scale. These
parameters are the same as those used by St. Laurent et al. (2002). We used
constant barotropic tidal currents of K1 and M2 as the largest diurnal and
semidiurnal tidal components in the FES2012 finite-element tide model
(Carrère et al., 2012) and the bottom topographic slopes instead of
roughness to estimate the energy flux at the ocean bottom (Tanaka et al.,
2007). The simulated vertical diffusivities are large over rough bottom
topographies and in areas with large tidal motions (Fig. 2a). The
diffusivities exponentially decay in the upward direction (e.g., along
10∘ N in Fig. 2b). The distributions of vertical diffusivities in
Fig. 2a and b are similar to those of St. Laurent et al. (2002; see their Figs. 1
and 2). The diffusivities do not change much over time because the tidal
flow used to estimate the energy flux is assumed to be constant, and
therefore the diffusivities change in time only through changes in the
local stratification.
Daily mean vertical diffusivity (log10 m2 s-1) on
1 December 2016 estimated by the tidal mixing scheme (a) vertically
averaged from the surface to the bottom and (b) in the vertical section along 10∘ N.
We used the 3-hourly atmospheric surface dataset JRA55-do v08 (Tsujino et
al., 2018) to estimate surface fluxes in OFES2. This dataset is based on the
JRA55 atmospheric reanalysis at a horizontal resolution of approximately 55 km (Kobayashi et al., 2015). Momentum and heat fluxes are calculated with
the bulk formulas proposed by Large and Yeager (2004). Note that we used the
relative wind speed considering the surface current to estimate the surface
momentum flux. We also included the effects of river runoff at river mouths
as additional freshwater flux using a monthly mean climatological river
runoff dataset from Coordinated Ocean–Ice Reference Experiments (CORE)
version 2 (Large and Yeager, 2004). The sea surface salinity (SSS) is
restored to monthly climatological values of the WOA13 v2 observations
(Zweng et al., 2013) with a 15 d timescale to avoid unrealistic salinity fields.
Since the polar regions are not simulated, the temperature and salinity are
restored at all depths to the monthly climatological values from the same
WOA13 v2 observations (Locarnini et al., 2013; Zweng et al., 2013) within a
distance of 3∘ from the northern and southern boundaries of the
model domain. The restoring timescale linearly increases from 1 d at the
boundary to infinity at the inner end of the restoring band. Additionally,
the temperature and salinity near the straits of Gibraltar, Hormuz, and Bab el Mandeb are restored to observations at all depths since the horizontal
resolution of the model is inadequate to capture dynamics within these
straits (Fig. 3). The Strait of Gibraltar is where the Mediterranean Sea
connects to the Atlantic Ocean, and the straits of Hormuz and Bab el Mandeb
are where the Persian Gulf and the Red Sea are connected to the Indian
Ocean, respectively.
Timescales for restoring the temperature and salinity in and near
the Straits of Gibraltar, Hormuz, and Bab el Mandeb. Red, yellow, light
blue, and blue represent timescales of 1, 5, 10, and 30 d, respectively.
OFES (Sasaki et al., 2008) after 50 years of spin-up integration under
climatological forcing (Masumoto et al., 2004) has been integrated from 1950
to the present. OFES2 was integrated from 1958 to 2016 and started with the
temperature and salinity fields of OFES from 1 January 1958. Table 1 is the
list of the updates for OFES2 compared to OFES. The maximum depth of OFES2
is increased to 7500 from 6065 m. The surface fluxes are now based on
3-hourly data rather than daily data to capture the diurnal cycle. Momentum
fluxes are based on a bulk formula using the relative wind speed rather than
that estimated in the reanalysis. The distribution of momentum flux curl in
OFES2 differs greatly from that in OFES (Fig. S1 in the Supplement). The mixed layer mixing
scheme is updated by replacing the K-profile parameterization (KPP) scheme based on an empirical approach
(Large et al., 1994) by a statistical closure model (Noh and Kim, 1999). A
tidal mixing scheme and a sea-ice model are newly included. The river runoff
is also added as additional freshwater flux. SSS is restored with a 15 d
timescale rather than a 6 d timescale for the topmost 5 m layer: a
150 d timescale and a 60 d timescale, respectively, for a 50 m mixed
layer. The timescale was relaxed compared to OFES, wherein neither sea ice nor
river runoff was used.
Descriptions of the quasi-global eddying hindcast simulations of
OFES2 and OFES.
OFES2OFESDomain76∘ S–76∘ N75∘ S–75∘ NHorizontal resolution0.1∘0.1∘Number of vertical levels10554Maximum depth7500 m6065 mBathymetry dataETOPO1OCCAM 30'Sea-ice modelKomori et al. (2005)–Horizontal mixing schemeBiharmonicBiharmonicVertical mixing schemeNoh and Kim (1999)KPP (Large et al., 1994)Tidal mixing schemeSt. Laurent et al. (2002)–SSS Restoring15 d to WOA136 d to WOA98Northern–southern artificial boundaryT & S restoring within 3∘ from the boundaryT & S restoring within 3∘ from the boundaryImportant narrow channelsStraits of Gibraltar, Hormuz, and Bab el Mandeb–Atmospheric forcingJRA55-do (3-hourly, 55 km × 55 km)NCEP (daily, 2.5∘×2.5∘)River runoffCORE2 (monthly climatology)–Bulk formulaLarge and Yeager (2004)Rosati and Miyakoda (1988)Momentum fluxBulk formula using the relative wind speedMomentum flux in NCEP (daily)Hindcast period1958–20161950–2017Initial conditionT & S of OFES on 1 Jan 1958OFES climatological runOutputsDaily mean every 3 d until 1989Snapshot every 3 d from 1980Daily mean from 1990Monthly meanMonthly meanMean oceanic fields
We next discuss improvements in the mean oceanic fields in OFES2 from OFES
by comparing those to the observations. The mean temperature and salinity
fields at a horizontal resolution of 0.25∘ averaged over 2005–2012
from the World Ocean Atlas 2013 version 2 (WOA13; Locarnini et al., 2013;
Zweng et al., 2013) are used, which include a large number of Argo float
observations. During this period, both OFES2 and OFES were well spun up.
Satellite-observed SSH over 1993–2016 from AVISO is used to examine the
simulated oceanic circulations and SSH variations in both OFES2 and OFES. To
see how the sea-ice model works in OFES2, the climatological data for sea-ice
cover averaged over 2005–2012 from HadISST version 1 (Rayner et al., 2003)
are compared with the data in OFES2.
Global oceanic fieldsSea surface temperature and salinity
Figure 4a and c show the 8-year mean SST and SSS biases averaged over
2005–2012 in OFES2 against WOA13. For SST (Fig. 4a), the bias is less than
1 ∘C in most parts of the globe. Weak cold biases broadly spread
over the subtropical Pacific and Indian oceans as well as the Arctic Ocean, and weak
warm biases spread over the subarctic Pacific, the subarctic Atlantic, and
the Southern Ocean. We also found prominent biases in several regions. Warm
biases (>1∘C) appear in the South Pacific
(170∘–130∘ W and 55∘ S) and to the north of
the Kuroshio Extension (140∘–170∘ E and 35∘–40∘ N). In the North Atlantic, along the Gulf Stream and the
North Atlantic Current, and in the Labrador and Norwegian seas, several large
warm and cold biases (magnitudes larger than 1 ∘C) are present.
One possible cause of these biases is the unrealistic current pathway of the
Gulf Stream. The Gulf Stream in OFES2 does not turn to the north at
approximately 40∘ W, which we will examine more in detail in the
next section.
SST bias (∘C) in (a) OFES2 and (b) OFES averaged over
2005–2012 against WOA13. Panels (c) and (d) are the same as (a) and (b),
respectively, but the SSS bias is shown instead (psu). SSS differences in
(e) long-term WOA13 and (f) WOA98 from WOA13 averaged over 2005–2012. The
contour lines are superimposed at an interval of 1 ∘C for SST and
0.2 psu for SSS, but zero contour lines are omitted.
The mean SSS biases in OFES2 (Fig. 4c) are smaller than 0.2 psu in most
regions. This feature is partly due to the restoring surface boundary
condition, but several large biases (larger than 0.2 psu) exist
sporadically. The salty bias (>0.4 psu) in the North Atlantic
(30∘ W and 50∘ N) likely comes from the unrealistic Gulf
Stream pathway, similar to the SST bias mentioned above. The salty bias
(>0.4 psu) also appears to the north of South America and in the
northern part of the Bay of Bengal. Each salty bias surrounds a fresh bias.
One reason for these large salty biases is probably the underestimation of
river runoff from the Amazon and Ganges–Brahmaputra rivers, respectively.
The impacts of physical processes near the river mouth, such as horizontal
and vertical mixing, coastal circulation, and tidal mixing, should also be
included to mitigate the biases. In addition, there are large salty and
fresh biases in the Chukchi Sea as well as large salty biases in the Nordic and
Labrador seas and along the coast of Greenland. These SSS biases are
possibly attributed to unrealistic sea-ice distribution in the Chukchi Sea
(Fig. 9g) and unrealistic circulations due to the artificial northern
boundary.
Figure 4b and d show the 8-year mean SST and SSS biases averaged over
2005–2012 in OFES against WOA13. The SST biases are much smaller in OFES2
(Fig. 4a) than in OFES (Fig. 4b). Cold (warm) SST biases with large
amplitudes appear in the equatorial and subtropical regions (high-latitude
regions) in both hemispheres in OFES. The centers of the cold biases
(<-1∘C) zonally spread along 15∘ N and
15∘ S in the Pacific Ocean and the northwestern and southeastern
Indian Ocean. Patches of warm biases (>1∘C) exist in
the Antarctic Ocean to the south of the ACC. Prominent warm biases
(>1∘C) appear in the northwestern Pacific, the Sea of
Okhotsk, and along the west coasts of South America and southern Africa. The
prominent warm biases along the west coasts in OFES are presumably
associated with unrealistic coastal currents and upwelling, which are driven
by unrealistic wind stresses near the coasts in the NCEP reanalysis (Fig. S1). The reductions of these biases in OFES2 are likely a result of using
the bulk formula (Large and Yeager, 2004) and the atmospheric surface data
(JRA55-do) optimized to drive OGCMs (Tsujino et al., 2018). Additionally,
the implementation of a sea-ice model in OFES2 may contribute to the
reduction of the warm biases in the Arctic Ocean and the Sea of Okhotsk.
The mean SSS biases in OFES2 (Fig. 4c) are also very reduced compared to
those in OFES (Fig. 4d), especially in the tropical and subtropical regions.
These bias reductions are also likely due to the bulk formula and
atmospheric data used in OFES2. We notice that the global distribution of
the biases in OFES (Fig. 4d), prominent in the Arctic Ocean, is quite similar
to the difference between WOA98 (Conkright et al., 1998) and WOA13 averaged
over 2005–2012 (Fig. 4f). This similarity suggests that the SSS fields in
OFES are restored too much toward WOA98. In contrast, the global distribution of
the SSS biases in OFES2 (Fig. 4c) does not resemble the difference between
long-term mean WOA13 and WOA13 over 2005–2012 (Fig. 4e). The weak restoring
in OFES2 does not greatly constrain the simulated SSS. Therefore, the SSS
bias in OFES2 (Fig. 4c) comes from something other than the restoring, such
as the unrealistic pathways of Kuroshio and the Gulf Stream and the unrealistic
sea-ice distribution in the Chukchi Sea as mentioned above.
Sea surface height and its variability
Figure 5 shows the average and standard deviation of the sea surface height
(SSH) over 1993–2016 in OFES2, OFES, and AVISO. The large-scale distribution
of the mean SSH in OFES2 (Fig. 5a) agrees well with that in AVISO (Fig. 5c),
suggesting that OFES2 reproduces the global ocean circulations well. The SSH
variability (Fig. 5d) is large around the Gulf Stream, the Kuroshio, and the
ACC, which also resembles that in AVISO (Fig. 5f). This large variability is
mostly due to high activities of mesoscale eddies and shifts in frontal
positions (e.g., Chelton et al., 2007).
(a, b, c) Mean SSH (cm) and (d, e, f) its standard deviation
(log10 cm) averaged over 1993–2016 from (a, d) OFES2, (b, e) OFES, and (c, f) AVISO observations. The SSH in OFES2 and OFES was offset by adding 50 cm.
However, there are regional differences in the mean SSH distribution and its
standard deviation in OFES2 from those in AVISO. The mean SSH contours along
the Gulf Stream extend northeastward across the Atlantic in OFES2 (Fig. 5a),
while a sharp northern turn is observed at approximately 40∘ W in
AVISO (Fig. 5c). The SSH variability is large along the simulated Gulf
Stream (Fig. 5d). The zonal extension of the mean SSH contours along with
the Azores Current at approximately 33∘ N in the northeastern
Atlantic (Fig. 5c) and large SSH variability accompanying this current (Fig. 5f) are recognizable in AVISO but not in OFES2 (Fig. 5a and d). For
the Kuroshio in OFES2, the SSH variability is too large along the southern
coast of Japan. This large variability is due to the unrealistic detachment
of the Kuroshio from Kyushu. Around subtropical countercurrents in the North
Pacific and the southern Indian Ocean as well as in most regions away from the strong
currents, the SSH variability is slightly smaller in OFES2 than in AVISO. We
discuss these issues in Sect. 5.
Compared to OFES (Fig. 5b), the mean SSH in OFES2 (Fig. 5a) shows
improvements. In the northern and southern subtropical gyres of the Pacific,
the SSH contours are oriented more in the north–south direction in OFES
(Fig. 5b) than in OFES2 and AVISO (Fig. 5a and c). In contrast, the
improvement in the subtropical gyres of the Atlantic and Indian oceans is
limited. One possible cause of this improvement in the SSH field in OFES2
is the replacement of atmospheric wind driving OFES2 by JRA55-do. The
overall amplitude of SSH variability around strong currents, such as the Gulf
Stream, the Kuroshio, and the ACC, is similar to that of AVISO (Fig. 5f) in
OFES2 (Fig. 5d), whereas it is somewhat larger in OFES (Fig. 5e). The
northwestward extension of high SSH variability emanating from the southern
tip of South Africa, which represents the propagation of Agulhas rings, is
too distinct in OFES due to unrealistically long-lived rings. This problem
is solved in OFES2. These reductions of SSH variability in OFES2 are possibly
due to the eddy-killing effect in the estimation of the surface momentum flux using
relative wind (e.g., Renault et al. 2017, 2019a).
Impact of tidal mixing on water mass property
Internal tides enhance vertical mixing, especially above rough bottom
topography. Previous studies have suggested that the Indonesian seas are
regions where such mixing significantly impacts the water mass properties
(e.g., Ffield and Gordon, 1996). Koch-Larrouy et al. (2007) demonstrated how
the inclusion of a local tidal mixing scheme can improve the subsurface
water mass in the Indonesian seas and the eastern Indian Ocean. As mentioned
in the Introduction, unrealistic water mass properties in the subsurface of
Indonesian seas were one of the major biases recognized in OFES (Masumoto et
al., 2008), which was one of the motivations to add a tidal mixing scheme in OFES2.
A comparison of subsurface salinity biases in the Indonesian seas shows
significant improvement in OFES2 (Fig. 6a and d) from OFES (Fig. 6b and e). The saltier bias at a depth of 135 m is large (>0.5 psu) in
the northern Banda Sea in OFES but is greatly reduced in OFES2. To the south
of the Sunda Islands, the saltier biases are prominent both at depths of 135 m (>0.2 psu) and 325 m (>0.5 psu) in OFES but are
greatly reduced in OFES2. The remaining salty biases in OFES2 may be
partially due to a lack of nonlocal tidal mixing (e.g., Nagai et al., 2017), as
discussed in Sasaki et al. (2018). This result supports the importance of
tidal mixing in the water mass transformation in the Indonesian seas.
Salinity biases (a, b, d, e) against WOA13 (c, f) in OFES2 (a, d) and
OFES (b, e) at 135 m (a, b, c) and at 325 m (d, e, f). All fields are
averaged over 2005–2012, and the units are practical salinity units (psu).
The Kuril Strait between the North Pacific and the Sea of Okhotsk is another
location where previous studies (e.g., Nakamura et al., 2006) have suggested the
importance of tidal mixing in the water mass properties of the North Pacific
Intermediate Water (NPIW). The vertical section of salinity along
165∘ E in WOA13 shows this subsurface low-salinity water, which
OFES reproduces well and OFES2 does a little better (Fig. S2). This result
suggests that tidal mixing does not affect the properties of NPIW much,
which supports the results using an eddy-permitting model by Tanaka et al. (2010). The vertical diffusivity of 0.02 m2 s-1 used in the strait
at all depths in an OGCM in Nakamura et al. (2006) was probably too large.
Vertical sections of mean salinity along (a–c) 13∘ N and
(d–f) 65∘ E in the Arabian Sea and (g–i) 36∘ N in the
eastern Atlantic Ocean averaged over 2005–2012: (a, d, g) OFES2, (b, e, h) OFES, and (c, f, i) WOA13.
Vertical profile of (a) temperature (∘C) and (b) salinity (psu) at 137∘ E averaged from 8 to
12∘ N and over 2005–2012. (c) Longitudinal distributions of the
wind stress curl (10-8 N m-3) along 10∘ N (averaged from
8 to 12∘ N and over 2005–2012). The red, blue, and
black curves are OFES2 driven by JRA55-do, OFES driven by the NCEP reanalysis,
and the WOA13 observations, respectively.
Salty outflows from marginal seas
OFES could not accurately simulate high-salinity outflows from the
Mediterranean Sea, the Persian Gulf, or the Red Sea to the open ocean. To
represent the impacts of these outflows in OFES2, we restored temperature
and salinity near the straits (Sect. 2). Proper representations of these
outflows are considered important for simulating not only the subsurface but
also the surface properties (e.g., Jia, 2000; Prasad et al., 2001;
Sofianos and Johns, 2002).
Vertical sections of salinity averaged over 2005–2012 (Fig. 7) exhibit the
salty outflows at the subsurface in the Arabian Sea and the Atlantic Ocean.
For the Arabian Sea, the basic influence of the outflow appears to be
captured in OFES2. The longitudinal section of mean salinity crossing the
mouth of the Red Sea shows that OFES2 (Fig. 7a) mostly reproduces the
eastward extent of salty water (>35.5 psu) from 46∘ E
at approximately 700 m of depth in WOA13 (Fig. 7c). This feature represents the
salty outflow from the Red Sea. The eastward extension (>35.5 psu), however, reaches too far to 70∘ E, and its depth of 700 m is
too stable over the basin compared to that in WOA13. OFES2 (Fig. 7d) also
generally demonstrates the southward spreading of salty outflow from the
Persian Gulf: salty water (>35.5 psu) spreads southward from
25∘ N above 1000 m in WOA13 (Fig. 7f). However, the high-salinity
core (>35.5 psu) at a depth of 800 m is slightly too distinct
and deep in OFES2 (Fig. 7d).
Sea-ice concentrations (%) in the Antarctic Ocean in (a, b) March and (c, d) September in (a, c) OFES2 and (b, d) HadISST averaged over
2005–2012. Similarly, the sea-ice concentrations in the Arctic Ocean in (e, f) March and (g, h) September in (e, g) OFES2 and (f, h) HadISST. The gray
areas are out of the model domain in OFES2 (a, c, e, g).
In contrast, we found that OFES2 does not reproduce the salty outflow
from the Mediterranean Sea into the Atlantic Ocean well, even with the restoration
of temperature and salinity near the Strait of Gibraltar. A zonal vertical
section of salinity along 36∘ N in the eastern Atlantic Ocean in
WOA13 (Fig. 7i) exhibits the westward extension of salty water (>35.8 psu) to 25∘ W at approximately 1100 m of depth and a thick layer
with an almost constant salinity of 35.7 psu over 500–1100 m depths to the west
of 26∘ W. However, the westward extension of high salinity is weak
in OFES2 (Fig. 7g). This high salinity (>36.0 psu) remains to
the east of 9∘ W at depths over 1000–1500 m, where OFES2 restores
salinity to the observation (Fig. 3). It is not clear why the salty water
does not spread westward much in OFES2, but this phenomenon is possibly
connected to the bias found in the mid-ocean surface circulation in the
North Atlantic (Fig. 5a and c). Entrainment of surface water to the
Mediterranean outflow near the Strait of Gibraltar is suggested as the
mechanism driving the Azores Current (Jia, 2000; Kida et al., 2008)
and the northward turn of the Gulf Stream (Jia, 2000).
The temperature and salinity restoration at the straits resulted in
significant improvements in the Arabian Sea from OFES. OFES2 reproduces the
salty outflow from the Red Sea well (Fig. 7a) but OFES does not: there is no
salty water at the subsurface along 13∘ N in the Arabian Sea (Fig. 7b). OFES2 also greatly improved the salty outflow from the Persian Gulf
(Fig. 7d) from OFES (Fig. 7e). The meridional section along 65∘ E
shows that the salty subsurface outflow is much fresher by 0.3–0.5 psu in
OFES (Fig. 7e) than in WOA13 (Fig. 7f), and its depth of 1000 m is deeper
than in WOA13 (800 m). For the Mediterranean outflow, the improvement in
OFES2 from OFES is marginal. Both OFES2 (Fig. 7g) and OFES (Fig. 7h) cannot
reproduce the westward extent of the salty outflow from the Strait of
Gibraltar found in WOA13 (Fig. 7i).
(a) Monthly Niño3.4 index defined as SSTAs (∘C) at
165–145∘ W and 5∘ S–5∘ N in the
eastern topical Pacific. (b) The monthly DMI (∘C) defined as
the difference between the SSTAs (∘C) at the (c) eastern
(90–110∘ E, 10∘ S–0∘) and (d)
western (50–70∘ E and 10∘ S–10∘ N) poles (Saji et al., 1999) from OFES2 (red curve), OFES
(blue curve), and HadISST version 1 (black curve;
http://www.cpc.ncep.noaa.gov/data/indices/ last access: 15 July 2020 and
http://www.jamstec.go.jp/aplinfo/sintexf/iod/dipole_mode_index.html, last access: 15 July 2020).
(a) RMS amplitude (∘C) of the Niño3.4 index
and the DMI for OFES2, OFES, and HadISST as well as their correlations between
OFES2 and HadISST and between OFES and HadISST. (b) Same as (a) but the
eastern and western pole DMIs.
(a)OFES2OFESHadISSTRMS amplitude of the Niño3.4 index (∘)0.950.930.89Correlation with the Niño3.4 in HadISST0.9630.880–RMS amplitude of the DMI (∘)0.520.380.32Correlation with the DMI in HadISST0.7140.659–(b)OFES2OFESHadISSTRMS amplitude of the eastern pole DMI(∘)0.430.330.33Correlation with the eastern pole DMI in HadISST0.7130.749–RMS amplitude of the western pole DMI (∘)0.310.410.33Correlation with the western pole DMI in HadISST0.8470.751–Subsurface field in the subtropical North Pacific
The subsurface water properties are sensitive to the wind stress product
used. Kutsuwada et al. (2019) showed that wind stress products affect the
simulated oceanic fields in an OGCM not only at the surface but also in the
subsurface. In the subtropical Pacific along 10∘ N, where the
subsurface bias is large in OFES (Fig. 4 of Kutsuwada et al., 2019), they
found that the use of QuikSCAT wind stress (Kutsuwada, 1998) in another
version of OFES, called OFES QSCAT (Sasaki et al., 2006), improves the
subsurface water properties compared to OFES, which uses wind stress from
the NCEP reanalysis (Kalnay et al., 1996).
The vertical profile of the mean temperatures in the subtropical western
Pacific in OFES2 (red curve) mostly overlaps that in WOA13 (black
curve) (Fig. 8a). The maximum difference occurs at 280 m and is less than 1 ∘C. This region is characterized by a subsurface salinity maximum
(e.g., Nakano et al., 2005). Its depth agrees between OFES2 and WOA13 (Fig. 8b), and its peak salinity value differs a bit by 0.2 psu.
We found that the temperature and salinity biases were significantly reduced in
OFES2 from OFES. In the thermocline between 50 and 350 m depths, the
temperature is much lower in OFES (Fig. 8a, blue curve) than in WOA13 (black
curve). The maximum difference is approximately 6 ∘C at a depth
of approximately 150 m. The depth of the salinity maximum is much shallower
in OFES (approximately 100 m of depth) than in WOA13 (approximately 140 m
of depth) (Fig. 8b). The maximum difference in salinity between OFES and WOA13
is large (∼0.4 psu). These biases are very similar to those
found by Kutsuwada et al. (2019) in their comparison between OFES QSCAT and
OFES (their Fig. 5). As Kutsuwada et al. (2019) suggested, these large
biases in OFES possibly come from wind stress. The wind stress curl used in
OFES along 10∘ N (blue curve in Fig. 8c) is relatively strong,
which results in the anomalously shallow thermocline via Ekman
upwelling that is too large. The wind stress curl in OFES2 (red curve in Fig. 8c) estimated by
using 10 m wind in JRA55-do is comparable in amplitudes and variations to
the satellite observations (red curve in Fig. 3c of Kutsuwada et al., 2019).
The similarity between the wind stress curl in OFES2 and the satellite
observations comes from modifications of 10 m wind in JRA55-do using
satellite observations (Tsujino et al., 2018).
SST (∘C) in a region including the IOD eastern pole
(90–110∘ E and 10∘ S–0∘) in the
mature month of (a–d) the 1997 positive IOD event (November 1997) and (e–h)
the 2010 negative IOD event (September 2010). (a, e) OFES2, (b, f) OFES, (c, g) satellite observations of AVHRR version 4.1 (Casey et al., 2010) and
AMSR-E version 7 (Wentz and Meissner, 2007), and (d, h) HadISST v1
(Rayner et al., 2003). The vectors in (a, b, e, f) are the surface wind
stress (N m-2) in the models, which are plotted at a 1∘×1∘ resolution. The thick vectors denote wind stress
magnitudes stronger than 0.05 N m-2.
Sea-ice distribution in OFES2
We implemented a sea-ice model in OFES2, which is not present in OFES. The
domain of OFES2 excludes a large central part of the Arctic Sea and the
southernmost parts of the Ross Sea and the Weddell Sea. Figure 9 shows the
distribution of monthly climatological sea-ice cover in the polar regions
averaged over 2005–2012 compared to the observations from HadISST. The
sea-ice cover around Antarctica in March is realistic in OFES2 (Fig. 9a).
The simulated sea ice covers most areas of the Weddell Sea, as found in
HadISST (Fig. 9b). A small amount of sea ice remains along most
of the coastline of East Antarctica in HadISST, whereas
OFES2 misses the observed sea-ice cover near the coast from 90
to 180∘ E. The sea-ice cover greatly expands in September compared
to March in HadISST (Fig. 9d), and OFES2 reproduces this sea-ice
distribution very well (Fig. 9c). Off the coast of Victoria Land between
180 and 150∘ E and along
the southern boundary of the model domain (76∘ S) in the Ross Sea
(160∘ E–150∘ W), the sea-ice concentration is somewhat
lower in OFES2 than in HadISST.
Improvements in OFES2 over OFES and new or remaining issues in
OFES2.
Improvements in OFES2 over OFESNew or remaining issues in OFES2SST (3.1.1)Suppressed cold biases in the equatorial and subtropical regionsWarm biases in the South Pacific and to the north of the Kuroshio ExtensionSuppressed warm biases in the high-latitude regions, the Antarctic Ocean, the Sea of Okhotsk, and along the west coasts of South America and southern AfricaWarm and cold biases along the Gulf StreamSSS (3.1.1)Suppressed large biases by relatively weak SSS restoringSalty biases in the North Atlantic, the northern part of the Bay of Bengal, and to the north of South AmericaMean SSH (3.1.2)More realistic gyres in the subtropical North and South PacificUnrealistic pathways of the Gulf Stream and KuroshioSuppressed propagations of Agulhas rings that were too distinctNo Azores CurrentSSH variability (3.1.2)Suppressed variability that was too large along the strong currentsSlightly small in the regions away from the strong currentsWater property (3.2, 3.3, 3.4)Suppressed biases in the subsurfaces of the Indonesian seas, the Arabian Sea (salty outflows from the Persian Gulf and Red Sea), and the subtropical western PacificLack of nonlocal tidal mixing in the Indonesian seasUnrealistic subsurface in the northeastern subtropical Atlantic Ocean (salty outflow from the Mediterranean Sea)El Niño and IOD (4)Slightly higher correlations of the indexes with observationsMore realistic SST near Sumatra and Java during the IOD events
In the Arctic region, the observed sea ice covers the Chukchi Sea in March
and seeps into the Bering Sea through the Bering Strait (Fig. 9f). OFES2
reproduces this feature well (Fig. 9e). However, the simulated sea ice
spreads too far southward into marginal seas: the Baltic Sea, the Gulf of
Saint Lawrence, and the Sea of Okhotsk. In September, unrealistic sea-ice
cover spreads in the Chukchi Sea (Fig. 9g), which does not exist in HadISST
(Fig. 9h). This discrepancy is possibly due to the artificial northern
boundary in OFES2, which blocks the sea-ice outflow through the Fram Strait
(Kwok et al., 2004).
Observations show a multi-decadal decreasing trend in summer to fall sea-ice
cover in the Arctic region (compare Fig. S3h with Fig. 9h). However, OFES2
fails to capture this trend, probably because of the limited domain, which
does not cover most of the Arctic Sea. In the Antarctic region, no
comparable trend exists in either OFES2 or observations (Figs. 9a–d, S3a–d).
Monthly AMO index defined as SSTAs (∘C) at
0∘ S–70∘ N in the eastern topical Pacific in the Kaplan
SST (black curve; Kaplan et al., 1998;
https://psl.noaa.gov/data/timeseries/AMO/, last access: 15 July 2020), OFES2 (red curve), and OFES
(blue curve).
Interannual variationsNiño3.4 and Indian Ocean Dipole mode indexes
We examine the monthly time series of indexes for El Niño and IOD events
to determine how well OFES2 reproduces these variations over 1968–2016 (Fig. 10 and Table 2), excluding the initial 10 years to avoid potential impacts
of the initial conditions. HadISST version 1 (Rayner et al., 2003) is used
as the reference because it covers the whole analysis period. In HadISST,
however, the anomalous SST in the eastern pole during the IOD events, which
is discussed in Sect. 4.2, appears to be obscure.
The variation in the Niño3.4 index is very similar between OFES2 and
HadISST (Fig. 10a). The correlation of the index is very high (0.963), and
its root mean square (RMS) amplitude is slightly larger in OFES2 (0.95 ∘C) than in
HadISST (0.89 ∘C). For IOD, the Dipole Mode Index (DMI) time
series is also similar between OFES2 and HadISST (Fig. 10b). The correlation
of the DMI between OFES2 and HadISST is high (0.714), but its RMS amplitude
is considerably larger in OFES2 (0.52 ∘C) than in HadISST (0.32 ∘C).
In OFES, the indexes of El Niño and IOD events are also similar to those
in HadISST (see Table 2 for the correlations and RMS amplitudes), with
somewhat lower correlations than in OFES2. A possible cause of these high
correlations in OFES2 is the replacement of the atmospheric dataset by JRA55-do
to estimate surface fluxes because usually SST in the ocean models is
strongly constrained to the atmospheric data via the surface flux. The RMS
amplitudes in OFES (0.93 ∘C for Niño3.4 index and 0.38 ∘C for DMI) are comparable to those of HadISST. The reason why
the DMI RMS amplitude is larger in OFES2 (0.52 ∘C) than in OFES
or the HadISST (0.32 ∘C) is the variations of the SST anomaly (SSTA)
simulated in the eastern pole of the IOD. The SSTA is colder (warmer) in the
positive (negative) IOD years of 1982, 1983, 1994, 1997, and 2006 (1996,
1998, and 2010) in OFES2 than in OFES and HadISST (Fig. 10c). The amplitude
of SSTA variations is much larger in OFES2 (0.43 ∘C) than in OFES
(0.33 ∘C) and HadISST (0.33 ∘C). On the other hand,
OFES2 reproduces the time series of SSTA in the western pole well (Fig. 10d), with a correlation coefficient of 0.847 between OFES2 and HadISST
compared with 0.751 between OFES and HadISST. In OFES, the SSTA rises
greatly after 2005. The amplitude of SSTA variations is similar between
OFES2 (0.31 ∘C) and HadISST (0.33 ∘C), which is
relatively small compared to OFES (0.41 ∘C). In the next section,
we will closely examine this SST distribution around the eastern pole in a
typical positive and a typical negative IOD year.
Sea surface temperature around the eastern pole of the Indian Ocean Dipole
We examine a strong positive and a strong negative IOD event of 1997 and
2010, respectively, as typical cases. Satellite observations captured a low
SST (<26∘C) area to the southwest of Sumatra and Java
during the positive event (Fig. 11c). This anomaly is due to the coastal
upwelling induced by anomalous southeasterly wind. OFES2 (Fig. 11a)
reproduces this observed anomalously cold SST along the coast well, although
the SST near Java is too cold (<22∘C). During the
negative event, the satellite-observed SST was warm (∼30∘C) to the west of Sumatra (Fig. 11g). OFES2 (Fig. 11e) also
captures this warm SST well. This warming is presumably due to weak
upwelling from weak wind west of Sumatra (Fig. 11e). OFES2 also reproduces cold
and warm SST anomalies well at the eastern pole in other IOD events
(Fig. S4).
In contrast, HadISST in Fig. 11d (Fig. 11h) does not capture the cold (warm)
SST near the southwestern coast of Sumatra and Java in the selected typical
positive (negative) IOD event. Therefore, the DMI amplitude from HadISST is
likely to be smaller than the reality. In contrast, OISST v2 (Reynolds,
1988), covering a relatively short period from 1981 to the present,
reproduces the anomalous SST near the coast well in both the positive and
negative IOD events (Fig. S5), which is similar to the satellite
observations (Fig. 11c and g). The average amplitude of the DMI over
1981–2016 is 0.54 ∘C for OISST v2, which is comparable to 0.54 ∘C for OFES2. These results suggest that OFES2 reproduces
SST anomalies well near the southwestern coast of Sumatra and Java during IOD
events and exhibits both the variation and amplitude of the DMI well.
OFES (Fig. 11b) did not accurately reproduce the observed anomalously cold SST
(Fig. 11c) near Sumatra and Java during the mature positive IOD event in
1997. The SST in OFES remains unrealistically warm (>26∘C) to the southwest of Sumatra and Java. We attribute this fault
to the wind stress driving OFES. The strong southeasterly wind stress (thick
arrows, >0.05 N m-2) is located far offshore (Fig. 11b),
which cannot induce coastal upwelling with realistic strength. On the other
hand, the anomalously warm SST at the eastern pole during the negative IOD in
2010 is fairly realistic in OFES (Fig. 11f), although the SST in the entire
region is somewhat colder than from the satellite observations (Fig. 11g).
This cold SST bias seems consistent with the bias over the entire Indian Ocean
in the long-term mean in OFES (Fig. 4b). These features generally apply to
other IOD events (Fig. S4). The difference in the SST reproducibility at the
eastern pole between the positive and negative events in OFES probably comes
from the asymmetric property of the IOD events (e.g., Hong et al., 2008).
Summary and discussion
This paper describes a new version of our OGCM, which we call OFES2. OFES2
improves the atmospheric forcing to include the diurnal cycle and now
includes a tidal mixing scheme and a sea-ice model. We have presented how
well OFES2 simulates the mean oceanic features and interannual variations
such as El Niño and IOD events, which are generally improved compared to
OFES (Table 3).
OFES2 reproduces large-scale circulations and global distributions of
mesoscale eddy activity, SST, and SSS well, with significant improvements
found in the water mass properties in the subsurface in the subtropical
western Pacific and the Arabian and Indonesian seas over OFES. OFES2 also
represents the large SSH variability accompanying strong currents well, such as the
Gulf Stream and the Kuroshio, whereas SSH variability tends to be somewhat too large
in OFES. However, the SSH variability is slightly smaller in most regions in
OFES2 than in satellite observations. The surface momentum fluxes in OFES2
are estimated with a bulk formula by using the surface wind relative to the
simulated surface current. This method weakens mesoscale eddies, as Zhai and
Greatbatch (2007) and Renault et al. (2019a) suggested, which may be the
reason for the underestimation of SSH variability in OFES2. Taking into account
atmospheric responses to SST gradients, such as impacts on vertical mixing
in the atmospheric boundary layer (e.g., Wallace et al., 1989) and pressure
adjustment over SST fronts (e.g., Lindzen and Nigam, 1987), in OGCMs may be a
solution to overcome this issue. Renault et al. (2019b) also showed the
imprints of surface currents on surface atmospheric winds through surface
momentum flux in satellite observations and coupled model simulations.
The sensitivity of the coupling coefficients (Renault et al., 2020) is an
interesting subject for future studies.
The variations of the climate indexes Niño3.4 and DMI are also well
simulated in OFES2. The correlations of the monthly indexes between OFES2
and observations are slightly higher than for OFES. During a typical
positive IOD event, anomalous southeasterly wind near Sumatra and Java
induces cold SSTA via coastal upwelling. OFES2 reproduces this anomalous SST
distribution well during typical events, which is due to the realistic
surface winds of JRA55-do driving OFES2. Other various climate variations
are yet to be examined. As a preliminary exploration, we looked at the
Atlantic Multidecadal Oscillation (AMO; Enfield et al., 2001). The monthly
AMO index in OFES2 varies with the observation, with a correlation
coefficient of 0.90, which is much higher than 0.54 for OFES (Fig. 12).
There are several issues in OFES2 that remain unrealistic from OFES. For
example, parts of the pathways of the Kuroshio and Gulf Stream are
unrealistic, which created strong SST bias (Fig. 4a and b) and unrealistic
SSH variability (Fig. 5d and e) around these currents. We use wind
velocity relative to the surface current to estimate the surface momentum
fluxes and a deep maximum bottom depth (7500 m), as Tsujino et al. (2013)
and Kurogi et al. (2016) did to solve these issues for the Kuroshio.
Nevertheless, the simulated Kuroshio in OFES2 frequently makes an
unrealistic offshore excursion away from Kyushu. To simulate a realistic
Gulf Stream separation, the importance of the sub-grid parameterization
(Schoonover et al., 2016), adequate topographic resolution (Schoonover et al.,
2017), ageostrophic circulation, and frontogenesis (McWilliams et al., 2019)
was suggested. Chassignet and Xu (2017) also succeeded in simulating the
separation in a simulation at a horizontal resolution of 1/50∘.
In OFES2, the unrealistic pathway of the Gulf Stream contributes mainly to
the biases in SST, SSS, and SSH. Sensitivity experiments similar to
previous studies are needed to overcome this problem in OFES2.
The Azores Current was also not well simulated even with a restoring
condition to reproduce the impact of the salty Mediterranean outflow, which
we anticipated driving the Azores Current as suggested by Jia (2000).
An interesting result is that the Azores Current and the outflow do exist in
the 1960s, but the both abruptly start to decay in the 1970s and disappear
after the 1980s (see Figs. S6 and S7 for details). We have not yet found the
reason for this behavior.
The impacts of the Mediterranean outflow on deep meridional overturning were
also suggested by previous studies (e.g., Reid, 1979; McCartney and Mauitzen
2001). The overturning circulation in the Atlantic Ocean in OFES2 (Fig. S8)
appears realistic, but detailed analysis would be necessary to assess the
Atlantic circulations over the whole depths. The salty outflows into the
Arabian Sea and the water mass properties in the Indonesian seas are
improved in OFES2 with the restoring of temperature and salinity near the
straits and with the tidal mixing scheme, respectively. There are more
issues to investigate, like water mass properties in other regions, in the future.
Another issue in OFES2 is that the domain does not include the polar regions
at latitudes higher than 76∘. The sea-ice distribution is
unrealistic in the Arctic region (Fig. 9e and g), whose decreasing trend is
also not simulated. One possible reason for these defects is the existence
of the northern boundary in OFES2 as discussed in Sect. 3.5. The meridional overturning circulations over the globe and in the Atlantic
Ocean (Fig. S8) seem reasonable, as mentioned above. A century-scale
integration would be necessary to pursue this issue.
The latest supercomputer systems have made possible global eddying ocean
simulations with much less computational cost than before. Sensitivity
experiments are becoming more feasible. Sasaki et al. (2018) showed that the
inclusion of a tidal mixing scheme can result in an enhancement in the transport of Indonesian throughflow
due to the basin-scale SSH increase in the tropical Pacific Ocean.
While the direct impact of tidal mixing is local, its impact appears to
spread over a whole basin via Rossby and Kelvin waves (Furue et al., 2015).
Ensemble simulations are another way of utilizing computational power.
Nonaka et al. (2016) conducted a three-member ensemble simulation using OFES and
suggested the existence of intrinsic variations in the midlatitude ocean
currents. One future direction of global, multidecadal, eddying ocean
simulations is to obtain a large ensemble.
Global or basin-scale simulations capable of resolving oceanic submesoscales
with finer horizontal resolution (e.g., Sasaki et al., 2014; Qiu et al., 2018)
are also being pursued. However, it is still difficult to carry out these
simulations over many decades due to the huge demands on computational
resources and storage. The causes of model biases in eddying simulations
are still unresolved, and we still have much to learn from these
simulations. Our improved hindcast simulation will be useful for exploring
oceanic processes and for Lagrangian analyses of water mass properties
(e.g., Kida et al., 2019). We hope that OFES2 will serve as a valuable tool
for studying various oceanic features with wide spatiotemporal scales from
mesoscale to large-scale circulation and from intraseasonal to decadal
timescales.
Code and data availability
OFES and OFES2 are based on MOM3, which is available through
https://github.com/mom-ocean/MOM3 (last access: 15 July 2020, Pacanowski and Griffies, 1999). The code has been modified for
large-scale high-performance simulations and implementations of a sea-ice model
and tidal mixing scheme. The modification is copyrighted by the Japan Agency for
Marine-Earth Science and Technology (JAMSTEC). The modified code, scripts,
and input data to run OFES and OFES2 are available under a copyright
agreement. Monthly fields from OFES2 and OFES can be downloaded from
JAMSTEC OFES Dataset (10.17596/0002029, last access: 15 July 2020, Sasaki et al., 2008).
We thank Hiroyuki Tsujino for providing us with the earlier version of the
JRA55-do dataset before the official release of the latest version
(https://esgf-node.llnl.gov/search/input4mips/, last access: 15 July 2020, Tsujino et al., 2018). The river runoff dataset
from CORE version 2 was downloaded from
https://data1.gfdl.noaa.gov/nomads/forms/core/COREv2.html (last access: 15 July 2020, Large and Yeager, 2004). The ocean
bathymetry from ETOPO1 (10.7289/V5C8276M, Amante and Eakins, 2009) was used. WOA13 and
WOA98 are available at https://www.nodc.noaa.gov/OC5/woa13/ (last access: 15 July 2020, Locarnini et al., 2013; Zweng et al., 2013) and
https://www.esrl.noaa.gov/psd/data/gridded/data.nodc.woa98.html (last access: 15 July 2020, Conkright et al., 1998),
respectively. HadISST was downloaded from
https://www.metoffice.gov.uk/hadobs/hadisst/ (last access: 15 July 2020, Rayner et al., 2003). AMSR-E SST version 7 and AVHRR
SST version 4.1 were used through APDRC (http://apdrc.soest.hawaii.edu/, last access: 15 July 2020, Casey et al., 2010).
The AMSR data are produced by Remote Sensing Systems and were sponsored by
the NASA AMSR-E Science Team and the NASA Earth Science MEaSUREs Program.
AVHRR Pathfinder SST was made by GHRSST and the US National Oceanographic
Data Center. The AVISO SSH data and FES2012 tidal current speeds were
downloaded through AVISO (ftp://ftp-access.aviso.altimetry.fr, last access: 19 October 2019, Carrère et al., 2012). The monthly
Niño3.4 index, DMI, and the AMO index were downloaded from
http://www.cpc.ncep.noaa.gov/data/indices/ (last access: 15 July 2020, Reynolds, 1988),
http://www.jamstec.go.jp/aplinfo/sintexf/iod/dipole_mode_index.html (last access: 15 July 2020, Saji et al., 1999), and
https://psl.noaa.gov/data/timeseries/AMO (last access: 15 July 2020, Kaplan et al., 1998, respectively.
The supplement related to this article is available online at: https://doi.org/10.5194/gmd-13-3319-2020-supplement.
Author contributions
HS and SK implemented a tidal scheme, and NK implemented a
sea-ice model into OFES2. HS, SK, and RF wrote the
paper. HA, YM, TM, MN, YS, and
BT contributed model configurations and to writing the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
OFES and OFES2 simulations were conducted using the Earth Simulator under
the support of the Japan Agency for Marine-Earth Science and Technology
(JAMSTEC). We thank Takeshi Doi, who provided information about observational data to
examine IOD events.
Financial support
This research has been supported by the Japan Society for the Promotion of Science (JSPS) (KAKENHI grant nos. JP19H05701, JP17K05662, JP18H03731, JP20H01970, JP17K05663, and JP17K05665).
Review statement
This paper was edited by Qiang Wang and reviewed by two anonymous referees.
ReferencesAdcroft, A., Hill, C., and Marshall, J.: Representation of topography by
shaved cells in a height coordinate ocean model, Mon. Weather Rev., 125,
2293–2315, 10.1175/1520-0493(1997)125<2293:ROTBSC>2.0.CO;2, 1997.Amante, C. and Eakins, B. W.: ETOPO1 1 arc-minute global relief model:
procedures, data sources and analysis, NOAA Technical Memorandum NESDIS
NGDC-24, 19 pp., March 2009, available at: http://www.ngdc.noaa.gov/mgg/global/global.html (last access: 15 July 2020),
2009.Aoki, K., Kubokawa, A., Furue, R., and Sasaki, H.: Influence of eddy momentum
fluxes on the mean flow of the Kuroshio Extension in a 1/10∘ Ocean
General Circulation Model, J. Phys. Oceanogr., 46, 2769–2784,
10.1175/JPO-D-16-0021.1, 2016.
Carrère, L., Lyard, F., Cancet, M., Guillot, A., and Roblou, L.: FES
2012: a new global tidal model taking advantage of nearly 20 years of
altimetry, in: Proceedings of meeting “20 Years of Altimetry”, Venice,
2012.Chassignet, E. P. and Xu, X.: Impact of horizontal resolution
(1/12∘ to 1/50∘) on Gulf Stream separation,
penetration, and variability, J. Phys. Oceanogr., 47, 1999–2021,
10.1175/JPO-D-17-0031.1, 2017.Chassignet, E. P., Hurlburt, H. E., Smedstad, O. M., Halliwell, G. R.,
Wallcraft, A. J., Metzger, E. J., Blanton B. O., Lozano, C., Rao, D. B.,
Hogan, P. J., and Srinivasan, A.: Generalized vertical coordinates for
eddy-resolving global and coastal ocean forecasts, Oceanography, 19,
118–129, 10.5670/oceanog.2006.95, 2006.Casey, K. S., Brandon, T. B., Cornillon, P., and Evans, R.: The past,
present and future of the AVHRR Pathfinder SST program, in: Oceanography from
Space, edited by: Barale, V., Gower, J. F. R., and Alberotanza, L.,
Springer, 10.1007/978-90-481-8681-5_16, 2010.Chelton, D. B., deSzoeke, R. A., Schlax, M. G., El Naggar, K., and Siwertz,
N.: Geographical variability of the first baroclinic Rossby radius of
deformation, J. Phys. Oceanogr., 28, 433–460,
10.1175/1520-0485(1998)028<0433:GVOTFB>2.0.CO;2,
1998.Chelton, D. B., Schlax, M. G., Samelson, R. M., and de Szoeke, R. A.: Global
observations of large oceanic eddies, Geophys. Res. Lett., 34, L15606,
10.1029/2007GL030812, 2007.
Conkright, M. E., Levitus, S., O'Brien, T., Boyer, T. P., Stephens, C.,
Johnson, D., O. Baranova, Antonov, A., Gelfeld, R., Rochester, J., and
Forgy, C.: World Ocean Database 1998 Documentation and Quality Control,
National Oceanographic Data Center, Silver Spring, MD, 1998.Enfield, D. B., Mestas-Nunez, A. M., and Trimble, P. J.: The Atlantic
Multidecadal Oscillation and its relationship to rainfall and river flows in
the continental U.S., Geophys. Res. Lett., 28, 2077–2080, 10.1029/2000GL012745, 2001.Ffield, A. and Gordon, A. L.: Tidal mixing signatures in the Indonesian
Seas, J. Phys. Oceanogr., 26, 1924–1937,
10.1175/1520-0485(1996)026<1924:TMSITI>2.0.CO;2,
1996.Furue, R., Jia, Y., McCreary, J. P., Schneider, N., Richards, K. J.,
Müller, P., Cornuelle, B. D., Avellaneda, N. M., Stammer, D., Liu, C.,
and Köhld, A.: Impacts of regional mixing on the temperature structure
of the equatorial Pacific Ocean. Part 1: Vertically uniform vertical
diffusion, Ocean Modell., 91, 91–111, 10.1016/j.ocemod.2014.10.002,
2015.Hibler III, W. D.: A dynamic thermodynamic sea ice model, J. Phys.
Oceanogr., 9, 815–846, 10.1175/1520-0485(1979)009<0815:ADTSIM>2.0.CO;2, 1979.Hong, C., Li, T., LinHo, and Kug, J.: Asymmetry of the Indian Ocean Dipole.
Part I: Observational analysis, J. Climate, 21, 4834–4848,
10.1175/2008JCLI2222.1, 2008.Hosoda, S., Ohira, T., and Nakamura, T.: A monthly mean dataset of global
oceanic temperature and salinity derived from Argo float observations,
JAMSTEC Rep. Res. Develop., 8, 47–59,
10.5918/jamstecr.8.47, 2008.Hu, S., Sprintall, J., Guan, C., Sun, B., Wang, F., Yang, G., Jia, F., Wang,
J., Hu, D., and Chai, F.: Spatiotemporal features of intraseasonal oceanic
variability in the Philippine Sea from mooring observations and numerical
simulations, J. Geophys. Res.-Oceans, 123, 4874–4887,
10.1029/2017JC013653, 2018.Hunke, E. C. and Dukowicz, J. K.: The elastic–viscous–plastic sea ice
dynamics model in general orthogonal curvilinear coordinates on a
sphere – Incorporation of metric terms, Mon. Weather Rev., 130, 1848–1865,
10.1175/1520-0493(2002)130<1848:TEVPSI>2.0.CO;2,
2002.Jayne, S. R. and St. Laurent, L. C.: Parameterizing tidal dissipation over
rough topography, Geophys. Res. Lett., 28, 811–814,
10.1029/2000GL012044, 2001.Jia, Y.: Formation of an Azores Current due to Mediterranean overflow in a
modeling study of the North Atlantic, J. Phys. Oceanogr., 30, 2342–2358,
10.1175/1520-0485(2000)030<2342:FOAACD>2.0.CO;2,
2000.Jungclaus, J. H., Fischer, N., Haak, H., Lohmann, K., Marotzke, J., Matei,
D., Mikolajewicz, U., Notz, D., and Storch, J. S.: Characteristics of the
ocean simulations in MPIOM, the ocean component of the MPI-Earth system
model, J. Adv. Model. Earth Syst., 5, 422–446, 10.1002/jame.20023,
2013.Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L.,
Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M.,
Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang,
J., Leetmaa, A., Reynolds, R., Jenne, R., and Joseph, D.: The NCEP/NCAR
40-year reanalysis project, B. Am. Meteorol. Soc., 77, 437–471,
10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2,
1996.Kaplan, A., Cane, M., Kushnir, Y., Clement, A., Blumenthal, M., and
Rajagopalan, B.: Analyses of global sea surface temperature 1856–1991, J.
Geophys. Res., 103, 18567–18589, 10.1029/97JC01736, 1998.Kida, S., Price, J. F., and Yang, J.: The upper-oceanic response to
overflows: A mechanism for the Azores Current, J. Phys. Oceanogr., 38,
880–895, 10.1175/2007JPO3750.1, 2008.Kida, S., Richards, K. J., and Sasaki, H.: The fate of surface freshwater
entering the Indonesian Seas, J. Geophys. Res.-Oceans, 124, 3228–3245,
10.1029/2018JC014707, 2019.Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi,
K., Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K., and Takahashi, K.:
The JRA-55 reanalysis: General specifications and basic characteristics, J.
Meteorol. Soc. JPN Ser. II, 93, 5–48, 10.2151/jmsj.2015-001,
2015.Koch-Larrouy, A., Madec, G., Bouruet-Aubertot, P., Gerkema, T.,
Bessières, L., and Molcard, R.: On the transformation of Pacific Water
into Indonesian Throughflow Water by internal tidal mixing, Geophys. Res.
Lett., 34, L04604, 10.1029/2006GL028405, 2007.Komori, N., Takahashi, K., Komine, K., Motoi, T., Zhang, X., and Sagawa, G.:
Description of sea-ice component of Coupled Ocean Sea-Ice Model for the Earth Simulator (OIFES), J. Earth Simulator, 4, 31–45,
10.32131/jes.4.31, 2005.
Kurogi, M., Tanaka, Y., and Hasumi, H.: Effects of deep bottom topography on
the Kuroshio Extension studied by a nested-grid OGCM, CLVAR Exchanges, 69, 19–21, 2016.Kutsuwada, K.: Impact of wind/wind-stress field in the North Pacific
constructed by ADEOS/NSCAT data, J. Oceanogr., 54, 443–456,
10.1007/BF02742447, 1998.Kutsuwada, K., Kakiuchi, A., Sasai, Y., and Sasaki, H.: Wind-driven North
Pacific Tropical Gyre using high-resolution simulation outputs, J.
Oceanogr., 75, 81–93, 10.1007/s10872-018-0487-8, 2019.Kwok, R., Cunningham, G. F., and Pang, S. S.: Fram Strait sea ice outflow,
J. Geophys. Res., 109, C01009, 10.1029/2003JC001785, 2004.Large, W. and Yeager, S.: Diurnal to decadal global forcing for ocean and
sea-ice models: The data sets and flux climatologies, NCAR Technical Note
NCAR/TN-460+STR, 10.5065/D6KK98Q6, 2004.Large, W. G., McWilliams, J. C., and Doney, S. C.: Oceanic vertical mixing:
A review and a model with a nonlocal boundary layer parameterization, Rev.
Geophys., 32, 363–403, 10.1029/94RG01872, 1994.Lindzen, R. S. and Nigam, S.: On the role of sea surface temperature
gradients in forcing low-level winds and convergence in the tropics, J.
Atmos. Sci., 44, 2418–2436, 10.1175/1520-0469(1987)044<2418:OTROSS>2.0.CO;2, 1987.Locarnini, R. A., Mishonov, A. V., Antonov, J. I., Boyer, T. P., Garcia, H.
E., Baranova, O. K., Zweng, M. M., Paver, C. R., Reagan, J. R., Johnson, D.
R., Hamilton, M., and Seidov, D.: World Ocean Atlas 2013, Volume 1:
Temperature, edited by: Levitus, S., A. Mishonov Technical Ed., NOAA Atlas NESDIS
73, 40 pp., 10.7289/V55X26VD, 2013.Maltrud, M. E. and McClean, J. L.: An eddy resolving global 1/10∘
ocean simulation, Ocean Modell., 8, 31–54,
10.1016/j.ocemod.2003.12.001, 2005.Masumoto, Y.: Sharing the results of a high-resolution ocean general
circulation model under a multi-discipline framework – a review of OFES
activities, Ocean Dynam., 60, 633–652, 10.1007/s10236-010-0297-z,
2010.Masumoto, Y., Sasaki, H., Kagimoto, T., Komori, N., Ishida, A., Sasai, Y.,
Miyama, T., Motoi, T., Mitsudera, H., Takahashi, k., Sakuma, H., and
Yamagata, T.: A fifty-year eddy-resolving simulation of the world
ocean – Preliminary outcomes of OFES (OGCM for the Earth Simulator), J.
Earth Simulator, 1, 35–56, 10.32131/jes.1.35, 2004.Masumoto, Y., Morioka, Y., and Sasaki, H.: High-resolution Indian Ocean
simulations – Recent advances and issues from OFES-, in: Ocean Modeling in
an Eddying Regime, edited by: Hecht, M. W., Hasumi, H., Geophysical
Monograph Series, 177, AGU, Washington D.C., 165–175, 10.1029/177GM14,
2008.McCartney, M. S. and Mauritzen, C.: On the origin of the warm inflow to the
Nordic Seas, Prog. Oceanogr., 51, 125–214,
10.1016/S0079-6611(01)00084-2, 2001.McWilliams, J. C., Gula, J., and Molemaker, M. J.: The Gulf Stream North wall:
Ageostrophic circulation and frontogenesis, J. Phys. Oceanogr., 49,
893–916, 10.1175/JPO-D-18-0203.1, 2019.Nagai, T., Hibiya, T., and Bouruet-Aubertot, P.: Nonhydrostatic simulations
of tide-induced mixing in the Halmahera Sea: A possible role in the trans
formation of the Indonesian Throughflow waters, J. Geophys. Res.-Oceans,
122, 8933–8943, 10.1002/2017JC013381, 2017.Nakamura, T., Toyoda, T., Ishikawa, Y., and Awaji, T.: Enhanced ventilation
in the Okhotsk Sea through tidal mixing at the Kuril Straits, Deep Sea Res.
PT I, 53, 425–448, 10.1016/j.dsr.2005.12.006,
2006.Nakano, T., Kitamura, T., Sugimoto, S., Suga, T., and Kamachi, M.: Long-term
variations of North Pacific Tropical Water along the 137∘ E repeat
hydrographic section, J. Oceanogr., 71, 229–238,
10.1007/s10872-015-0279-3, 2005.Noh, Y. and Kim, H. J.: Simulations of temperature and turbulence structure
of the oceanic boundary layer with the improved near-surface process, J.
Geophys. Res., 104, 15621–15634, 10.1029/1999JC900068, 1999.Nonaka, M., Sasai, Y., Sasaki, H., Taguchi, B., and Nakamura, H.: How
potentially predictable are midlatitude ocean currents?, Sci. Rep.,
6, 20153, 10.1038/srep20153, 2016.Pacanowski, R. C. and Griffies, S. M.: The MOM3 manual. GFDL Ocean Group
Tech. Rep. 4, NOAA. Geophysical Fluid Dynamics Laboratory, Princeton, NJ.,
available at: https://mdl-mom5.herokuapp.com/web/docs/project/MOM3_manual.pdf (last access: 20 July 2020), 1999.Prasad, T. G., Ikeda, M., and Kumar, S. P.: Seasonal spreading of the Persian
Gulf Water mass in the Arabian Sea, J. Geophys. Res.-Oceans, 106,
17059–17071, 10.1029/2000JC000480, 2001.Qiu, B. and Chen, S.: Eddy-mean flow interaction in the decadally-modulating
Kuroshio Extension system, Deep Sea Res. Pt. II, 57,
1098–1110, 10.1016/j.dsr2.2008.11.036, 2010.Qiu, B., Chen, S., Klein, P., Wang, J., Torres, H., Fu, L., and Menemenlis,
D.: Seasonality in transition scale from balanced to unbalanced motions in
the world ocean, J. Phys. Oceanogr., 48, 591–605,
10.1175/JPO-D-17-0169.1, 2018.Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C. K., Alexander, L.
V., Rowell, D. P., Kent, E. C., and Kaplan, A.: Global analyses of sea
surface temperature, sea ice, and night marine air temperature since the
late nineteenth century, J. Geophys. Res.-Atmos., 108, 4407,
10.1029/2002JD002670, 2003.Reid, J. L.: On the contribution of the Mediterranean Sea outflow to the
Norwegian-Greenland Sea, Deep Sea Res. A, 26,
1199–1223, 10.1016/0198-0149(79)90064-5, 1979.Renault, L., McWilliams, J. C., and Penven, P.: Modulation of the Agulhas
current retroflection and leakage by oceanic current interaction with the
atmosphere in coupled Simulations, J. Phys. Oceanogr., 47, 2077–2100,
10.1175/JPO-D-16-0168.1, 2017.Renault, L., Marchesiello, P., Masson, S., and McWilliams, J. C.: Remarkable
control of western boundary currents by eddy killing, a mechanical air-sea
coupling process, Geophys. Res. Lett., 46, 2743–2751,
10.1029/2018GL081211, 2019a.Renault, L., Masson, S., Oerder, V., Jullien, S., and Colas, F.:
Disentangling the mesoscale ocean-atmosphere interactions, J. Geophys. Res.-Oceans, 124, 2164–2178, 10.1029/2018JC014628, 2019b.Renault, L., Masson, S., Arsouze, T., Madec, G., and McWilliams, J. C.:
Recipes for how to force oceanic model dynamics, J. Adv. Mod. Ear. Sys., 12, e2019MS001715, 10.1029/2019MS001715, 2020.Reynolds, R. W.: A real-time global sea surface temperature analysis, J.
Climate, 1, 75–86, 10.1175/1520-0442(1988)001<0075:ARTGSS>2.0.CO;2, 1988.Roemmich, D., Johnson, G., Riser, S., Davis, R., Gilson, J., Owens, W. B.,
Garzoli, S. L., Schmid, C., and Ignaszewski, M.: The Argo program: Observing
the global ocean with profiling floats, Oceanography, 22, 34–43, 10.5670/oceanog.2009.36, 2009.Rosati, A. and Miyakoda, K.: A general circulation model for upper ocean
circulation, J. Phys. Oceanogr., 18, 1601–1626,
10.1175/1520-0485(1988)018<1601:AGCMFU>2.0.CO;2,
1988.Saji, N. H., Goswami, B. N., Vinayachandran, P. N., and Yamagata, T.: A
dipole mode in the tropical Indian Ocean, Nature, 401, 360–363,
10.1038/43854, 1999.Sasaki, H., Sasai, Y., Nonaka, M., Masumoto, Y., and Kawahara, S.: An
eddy-resolving simulation of the quasi-global ocean driven by
satellite-observed wind field: Preliminary outcomes from physical and
biological fields, J. Earth Simulator, 6, 35–49, 10.32131/jes.6.35,
2006.Sasaki, H., Nonaka, M., Masumoto, Y., Sasai, Y., Uehara, H., and Sakuma, H.:
An eddy-resolving hindcast simulation of the quasiglobal ocean from 1950 to
2003 on the Earth Simulator, edited by: Hamilton, K. and Ohfuchi, W., High
Resolution Numerical Modelling of the Atmosphere and Ocean, Springer, New
York, NY, 157–186, 10.1007/978-0-387-49791-4_10, 2008.Sasaki, H., Klein, P., Qiu, B., and Sasai, Y.: Impact of oceanic
scale-interactions on the seasonal modulation of ocean dynamics by the
atmosphere, Nat. Commun., 5, 5636, 10.1038/ncomms6636, 2014.Sasaki, H., Kida, S., Furue, R., Nonaka, M., and Masumoto, Y.: An increase
of the Indonesian Throughflow by internal tidal mixing in a high-resolution
quasi-global ocean simulation, Geophys. Res. Lett., 45, 8416–8424,
10.1029/2018GL078040, 2018.Sasaki, Y. N. and Schneider, N.: Decadal shifts of the Kuroshio Extension
jet: application of thin–jet theory, J. Phys. Oceanogr., 41, 979–993,
10.1175/2011JPO4550.1, 2011.Schoonover, J., Dewar, W., Wienders, N., Gula, J., McWilliams, J.C.,
Molemaker, M. J., Bates, S. C., Danabasoglu, G., and Yeager, S.: North
Atlantic barotropic vorticity balances in numerical models, J. Phys.
Oceanogr., 46, 289–303, 10.1175/JPO-D-15-0133.1, 2016.Schoonover, J., Dewar, W., Wienders, N., and Deremble, B.: Local
sensitivities of the Gulf Stream separation, J. Phys. Oceanogr., 47,
353–373, 10.1175/JPO-D-16-0195.1, 2017.Sofianos, S. S. and Johns, W. E.: An Oceanic General Circulation Model
(OGCM) investigation of the Red Sea circulation, 1, Exchange between the Red
Sea and the Indian Ocean, J. Geophys. Res.-Oceans, 107, 3196,
10.1029/2001JC001184, 2002.St. Laurent, L. C., Simmons, H. L., and Jayne, S. R.: Estimating tidally
driven mixing in the deep ocean, Geophys. Res. Lett., 29, 2106,
10.1029/2002GL015633, 2002.Taguchi, B., Schneider, N., Nonaka, M, and Sasaki, H.: Decadal variability
of upper-ocean heat content associated with meridional shifts of western
boundary current extensions in the North Pacific, J. Climate, 30, 6247–6264,
10.1175/JCLI-D-16-0779.1, 2017.Tanaka, Y., Hibiya, T., and Niwa, Y.: Estimates of tidal energy dissipation
and diapycnal diffusivity in the Kuril Straits using TOPEX/POSEIDON
altimeter data, J. Geophys. Res.-Oceans, 112, C10021,
10.1029/2007JC004172, 2007.Tanaka, Y., Hibiya, T., and Niwa, Y.: Assessment of the effects of tidal
mixing in the Kuril Straits on the formation of the North Pacific
Intermediate Water, J. Phys. Oceanogr., 40, 2569–2574,
10.1175/2010JPO4506.1, 2010.Tsujino, H., Nishikawa, S., Sakamoto, K., Usui, N., Nakano, H., and
Yamanaka, G.: Effects of large-scale wind on the Kuroshio path south of
Japan in a 60-year historical OGCM simulation, Clim. Dynam., 41, 2287–2318,
10.1007/s00382-012-1641-4, 2013.Tsujino, H., Urakawa, S., Nakano, H., Small, R. J., Kim, W. M., Yeager, S.
G., Danabasoglu, G., Suzuki, T., Bamber, J. L., Bentsen, M., Böning, C.
W., Bozec, A., Chassignet, E. P., Curchitser, E., Dias, F. B., Durack, P.
J., Griffies, S. M., Harada, Y., Ilicak, M., Josey, S. A., Kobayashi, C.,
Kobayashi, S., Komuro, Y., Large, W. G., Le Sommer, J., Marsland, S. J.,
Masina, S., Scheinert, M., Tomita, H., Valdivieso, M., and Yamazaki, D.:
JRA-55 based surface dataset for driving ocean–sea-ice models (JRA55-do),
Ocean Modell., 130, 79–139, 10.1016/j.ocemod.2018.07.002, 2018.Wallace, J. M., Michell, T. P., and Deser, C.: The influence of sea-surface
temperature on surface wind in the eastern equatorial Pacific: Seasonal and
interannual variability, J. Climate, 2, 1492–1499,
10.1175/1520-0442(1989)002<1492:TIOSST>2.0.CO;2,
1989.
Wentz, F. J. and Meissner, T.: Supplement 1: Algorithm theoretical basis
document for AMSR-E ocean algorithms, Remote Sensing Systems Tech. Rep.
051707, 6 pp., https://ghrc.nsstc.nasa.gov/opendap/tpw/doc/AMSR_Ocean_Algorithm_Version_2_Supplement_1.pdf (last access: 15 July 2020),
2007.Zhai, X. and Greatbatch, R. J.: Wind work in a model of the northwest
Atlantic Ocean, Geophys. Res. Lett., 34, L04606,
10.1029/2006GL028907, 2007.Zhai, X., Greatbatch, R. J., and Kohlmann, J. D.: On the seasonal
variability of eddy kinetic energy in the Gulf Stream region, Geophys. Res.
Lett., 35, L24609, 10.1029/2008GL036412, 2008.Zweng, M. M., Reagan, J. R., Antonov, J. I., Locarnini, R. A., Mishonov, A.
V., Boyer, T. P., Garcia, H. E., Baranova, O. K., Johnson, D. R., Seidov,
D., and Biddle, M. M.: World Ocean Atlas 2013, Volume 2: Salinity. S.
Levitus, Ed., A. Mishonov Technical Ed.; NOAA Atlas NESDIS 74, 39 pp.,
10.7289/V5251G4D, 2013.