Seasonal variability in the inorganic ocean
carbon cycle in the Northwest Pacificevaluated using a biogeochemical andcarbon model coupled with an operational
ocean model
Miho Ishizu, Yasumasa Miyazawa,
Tomohiko Tsunoda & Xinyu Guo

Climatic Change
An Interdisciplinary, International
Journal Devoted to the Description,
Causes and Implications of Climatic
ChangeISSN 0165-0009
Climatic Change
DOI 10.1007/s10584-020-02779-2

1 23

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Climatic Change
https://doi.org/10.1007/s10584-020-02779-2

Seasonal variability in the inorganic ocean carbon
cycle in the Northwest Pacific evaluated using
a biogeochemical and carbon model coupledwith an operational ocean model
Miho Ishizu 1

1

2& Yasumasa Miyazawa & Tomohiko Tsunoda & Xinyu Guo

1,3

Received: 9 January 2020 / Accepted: 25 June 2020/# The Author(s) 2020

Abstract

Here, we investigate the seasonal variability in the dissolved inorganic carbon (DIC) cycle in
the Northwest Pacific using a high-resolution biogeochemical and carbon model coupledwith an operational ocean model. Results show that the contribution to DIC from air–sea CO2
exchange is generally offset by vertical mixing at the surface at all latitudes, with someseasonal variation. Biological processes in subarctic regions are evident at the surface,whereas in the subtropical region these processes take place within the euphotic layer andthen DIC consumption deepens southward with latitude. Such latitudinal differences inbiological processes lead to marked horizontal and vertical contrasts in the distribution of
DIC, with modulation by horizontal and vertical advection–diffusion processes.
Keywords Biogeochemical model . Inorganic carbon cycle . NPZDC . Northwest Pacific . Ocean
acidification . JCOPE . pH . Aragonite saturation

1 Introduction
The atmospheric partial pressure of CO2 (pCO2) has been increasing at a rate of ~ 1.8 ppm byvolume (ppmv) per year in recent decades as a result of human activities such as fossil-fuelburning, deforestation, and cement production (Takahashi et al. 2009; IPCC 2013). In the preindustrial era, the ocean was generally a net source of CO2 emissions to the atmospherebecause of the mineralization of land-derived organic matter in addition to that produced by in* Miho Ishizu
mishizu@jamstec.go.jp

1

Japan Agency for Marine-Earth Science and Technology, Yokohama-shi, Kanagawa, Japan

2

The Ocean Policy Research Institute of the Sasakawa Peace Foundation, Tokyo, Japan

3

Center for Marine Environmental Studies, Ehime University, Matsuyama, JapanClimatic Change

situ production, and CaCO3 precipitation (Mackenzie et al. 2004). Rising atmospheric CO2
concentrations caused by fossil-fuel combustion and land-use changes (Mackenzie et al. 2004;
Bauer et al. 2013; IPCC 2013; IGBP, IOC, SCOR 2013) reversed the direction of the air–sea
CO2 flux, leading the global ocean to become a net sink of anthropogenic CO2. The present
thickness of the upper thermocline, where large amounts of anthropogenic CO2 emissions are
stored, is estimated to be of the order of a few hundred meters (Mackenzie et al. 2004). The
oceanic coastal zone changed from being a source to a sink during the industrial era(Mackenzie et al. 2004; Bauer et al. 2013). Several estimates of CO2 sinks and sources inocean provinces (Cai et al. 2006) and/or spatially explicit typology (Laurelle et al. 2010)
showed that marginal seas in the tropics are sources of CO2, whereas those in temperateregions and at high latitudes act as sinks (Cai et al. 2006; Laurelle et al. 2010).
Data-based estimates of variability and trends in ocean CO2 uptake are limited by the short recordof observations. Although high-quality measurements of CO2 in surface waters and air commenced
in the early 1960s, the amount of available information is still limited (Wanninkhof et al. 2013). The
principal observational approaches for estimating sea–air fluxes of CO2 are to measure ΔpCO2 fromships (Takahashi et al. 2009; Nakaoka et al. 2013) and moorings (Sutton et al. 2017), and apply aparameterization using a function of wind speed (Wanninkhof et al. 2013). Other approaches rely on
simulations made by ocean biochemistry general circulation models (OBGCMs) with parameterization of biogeochemical processes and total dissolved inorganic carbon (DIC) measurements in theocean interior, and/or atmospheric data. However, gaps remain in the understanding of ocean CO2
uptake, especially the spatiotemporal variability of the seasonal inorganic/organic carbon cycle,
because CO2 concentrations and other related oceanic variables are difficult to observe simultaneously, frequently, and widely. The seasonal variability in pCO2 shows differences at a local scale(Takahashi et al. 2009; Sutton et al. 2017). Model estimates of temporal trend detection (Keller et al.
2014; Lovenduski et al. 2015) show the influence of both decadal/interannual and seasonalvariabilities and suggest that the time of emergence of a trend signal is basically around 10 years
in the surface but the tropical area needs more time.
The detailed processes that generate variation in the DIC of the ocean interior are still uncertain.
Several studies have proposed possible mechanisms for the oceanic annual carbon cycle (Palmer
and Totterdell 2001; Takahashi et al. 2002; Xiu and Chai 2013). Palmer and Totterdell (2001)
discussed physical and biological mechanisms that contribute to the global annual mean carbon
cycle using an ecosystem model without considering the contribution of air–sea CO2 exchange.
They reported that the effects of vertical mixing were largely offset by biological processes in thelatitudinal range of 25−60° N over ocean surfaces and that the effects of advection were mostly
offset by biological processes at latitudes of < 20° N. Takahashi et al. (2009) focused on the relative
importance of temperature and biological effects to the global seasonal cycle of air–sea CO2
exchange by evaluating monthly climatological maps of air–sea CO2 flux and pCO2. Xiu andChai (2013) investigated the seasonal and decadal variability of the upper-ocean carbon cycle in the
North Pacific using a physical–biogeochemical model. Their results showed that the seasonalvariability in pCO2 and CO2 flux in the North Pacific followed the change in sea surface temperature
closely, with high and low values in summer and winter, respectively, and that surface pCO2
variations at the modeled sites correspond to well-known observational monitoring points controlled
primarily by anthropogenic CO2 and modulated by decadal variations.
The Japan Coastal Ocean Predictability Experiment (JCOPE; http://www.jamstec.go.
jp/jcope/) is an operational eddy-resolving physical ocean model for the Northwest Pacific,
the Japan Sea, the Okhotsk Sea, and the East China Sea (Miyazawa et al. 2009, 2014). Ishizu
et al. (2019) recently developed a biogeochemical and carbon model coupled with the JCOPE

Climatic Change(JCOPE_EC). This model generally reproduces the observed seasonal variability ofchlorophyll-a (Chl-a), dissolved inorganic nitrogen (DIN), phosphorus (DIP), and inorganiccarbon (DIC), and total alkalinity (ALK), but includes monthly climatological damping for
DIN, DIP, DIC, and ALK (Ishizu et al. 2019). The damping forcibly constrains the calculated
biological parameters (DIN, DIP, DIC, and ALK) around the monthly climatological values
with a timescale of 30 days (Ishizu et al. 2019), meaning that those authors were unable todiscuss the mechanism of the inorganic carbon cycle. In this study, we therefore performed asimulation using JCOPE_EC without any climatological damping and examined the physicaland biological mechanisms represented by the model dynamics, focusing on the critical rolesof horizontal and vertical advection–diffusion processes in generating seasonal variation in
DIC.
We present the results from JCOPE_EC (without climatological damping) and discuss themechanisms responsible for the simulated seasonal inorganic carbon cycle for the Northwest
Pacific. Details of the model configuration are given in Section 2, model accuracy is describedin Section 3, the processes involved in the inorganic carbon cycle in the model are discussed in
Section 4, and the conclusions of the study are provided in Section 5.

2 Model and data
2.1 Model configuration
The JCOPE_EC (Ishizu et al. 2019) is an off-line tracer model driven by physical processes
simulated by an operational eddy-resolving ocean general circulation model (JCOPE2M;
Miyazawa et al. 2017) based on the Princeton Ocean Model with a generalized sigma
coordinate (Mellor 2001). The model is a three-dimensional high-resolution regional model
covering the Northwest Pacific (108–180° E, 10.5−62° N) with a horizontal resolution of 1/12°(4.4–9.1 km) and 46 vertical active levels. The model structure is the same as that described by
Ishizu et al. (2019), but our model differs in that the governing equations for DIN, DIP, DIC,
and ALK (equations 4, 5, 17, and 18 of Ishizu et al. 2019) remove climatological damping.
We determined the biogeochemical model parameters using multi-optimized operations(Ishizu et al. 2019) separately for subarctic and subtropical regions (Table 1). These biogeochemical parameters are the maximum growth rate from photosynthesis (Vmax), the phytoplankton mortality rate at 0 °C (MP), the phytoplankton respiration rate (R), the maximum
grazing rate of zooplankton (Gz), the zooplankton mortality rate (M)z, the decomposition rate
(VPN), and the optimum light intensity for phytoplankton (Iopt). In addition to the parametersdescribed in Ishizu et al. (2019), we introduced latitudinal changes in Vmax, MP, R, Mz, VPN, andIopt according to the results of several sensitivity experiments as follows:
V max ¼ 0:5V 0max tanh ðLat−Latbnd:vmax Þ=Latslp þ 1 þ V 1max
ð1ÞM P ¼ 0:5M 0P tanh ðLat−Latbnd Þ=Latslp þ 1 þ M 1P

ð2Þ



R ¼ 0:5R0 tanh ðLat−Latbnd Þ=Latslp þ 1 þ R1

ð3Þ

Climatic ChangeM Z ¼ 0:5M 0Z tanh ðLat−Latbnd Þ=Latslp þ 1 þ M 1Z

ð4Þ



V PN ¼ 0:5V 0PN tanh −ðLat−Latbnd Þ=Latslp þ 1 þ V 1PN

ð5Þ



I opt ¼ 0:5I 0opt tanh −ðLat−Latbnd Þ=Latslp þ 1 þ I 1opt

ð6Þ

where Vmax, MP, R, Mz, VPN, and Iopt change latitudinally from 0.28 to 0.97 day−1, 0.054 to
0.155 (mmol Nm−1) m−3 day−1, 0.0011 to 0.00256 day−1, 0.044 to 0.12 (°C)−1, 0.0954 to
0.47 day−1, and from 20 to 100 W m−2, respectively; Vmax0, R0, MP0, Mz0, VPN0, Iopt0, Vmax1, R1,
MP1, Mz1, VPN1, and Iopt1 are the tunable parameters (Table 1); Latbnd.vmax, Latbnd, and Latslp are
the coefficients representing the values at latitudinal boundaries and the latitudinal slopes forthese parameters, respectively (Table 1 and Eqs. 1, 3, 4, 5, and 6).
The first version of JCOPE_EC had a model bias resulting in a large decrease in ALK insummer due to anomalously high CaCO3 production with a large increase in Chl-a during
summer in subarctic regions (Ishizu et al. 2019). To suppress this large decrease in ALK in thismodel, we set the CaCO3 to non-photosynthetic POC production ratio to a much smaller value(0.00035) in the version of the model used here (Table 1). This improvement allows the model
to well represent the seasonal variability of ALK in our target region (Section 3).
The model was driven by forcing from daily oceanic (JCOPE2M) and six-hourly atmospheric (NCEP/NCAR) reanalysis data for a 1-year period (2015). The initial concentrations ofphytoplankton were set to 0.1 and 0.0 mmol N m−3 for depths above and below 150 m,
respectively. The initial zooplankton concentrations were set to 10% of the phytoplankton
concentration. The initial detritus concentration was set to 0.0 mmol N m−3. The variables
DIN, DIP, and DIC were initialized using the climatology for January, and ALK was
initialized using the annual climatology (Ishizu et al. 2019).

2.2 Model validation
To validate phytoplankton concentrations in the model, MODIS-Aqua Ocean Color Data for
Chl-a from 2015 were used, as downloaded from the website of the Physical Oceanography
Distributed Active Archive Center (PODAAC). To validate the model results for DIN, DIP,
and DIC, we used monthly climatological DIN, DIP, and DIC data (World Ocean Atlas 2013
(WOA13); Yasunaka et al. 2013) and Japan Meteorological Agency (JMA) observational datafor 2015, as in Ishizu et al. (2019). There are no applicable monthly climatological datasets for
ALK in our target region (Goyet et al. 2000; Key et al. 2004; Takatani et al. 2014, Takahashi
et al. 2014). We therefore used ALK observational data obtained by JMA in 2015 forcomparison with model results (Section 3).

3 Results
3.1 Accuracy of modeled Chl-a, DIN, DIC, and ALK
Model results presented here are slightly less accurate than those of the model with theclimatological conditions described by Ishizu et al. (2019), except for ALK. However, theClimatic Change
Table 1 Biogeochemical parameters in JCOPE_EC without the climatological condition. An asterisk in thevalue column signifies that latitudinal differences are the biogeochemical parameters adopted latitudinal differences from Eqs. 1–6
SymbolDefinition

Ecosystem model
For phytoplankton
Vmax
Growth rate for phytoplankton
Vopt0
Vopt1
lat
Boundary for latitudinal
bnd.vmaxdifferences in growth rate forphytoplanktonA
Affinity coefficient of basic
cellular physiology
MP
Phytoplankton mortality rate at
0 °C
Threshold of phytoplankton
Pmin
mortality
R
Phytoplankton respiration rate at
0 °C
Ropt0
Ropt1
CPT
Temperature coefficient forphotosynthesis
CRPT
Temperature coefficient forphytoplankton respiration
CMPT
Temperature coefficient forphytoplankton mortality
Iopt
Optimum light intensity for
phytoplankton
Iopt0
latbndBoundary for latitudinaldifferences
Slope for latitudinal differences
latslpcdom
Light dissipation coefficient of sea
water
Latslp_dom Slope for latitudinal differences
for cdom
For zooplankton
Maximum grazing rate ofGZ
zooplankton at 0 °C
λIvlev constant
MZ
Zooplankton mortality rate at 0 °C
MZ0
MZ1
βz
Growth efficiency of zooplankton
αzAssimilation efficiency ofzooplanktonP*
Zooplankton threshold value forgrazing on phytoplankton
CGZT
Temperature coefficient for
zooplankton grazing
CMPT
Temperature coefficient for
zooplankton mortality

Value

Units

Referenced
valuesIshizu et al.
2019*0.28–0.97
0.690
0.625
40.0

day−1

0.04926.75

mmol N m−1 day−1*0.054–0.155(mmol N m−1) m−3 day−1 0.04

0.0587(mmol N m−1) m−3*0.0011–0.00256 day−1

0.03170.001470.001850.0392°C−1

0.0519°C−1

0.0693°C−1*20–120

W m−2*20–120m−1*0.015–0.045

100.0
45.0
4.0
0.015
1.5

0.423

day−1

1.4
*0.044–0.12
0.07600.08250.3
0.7(mmol N m−3)−1°C−10.0430(mmol N m−1) m−3

0.0390°C−1

0.0693°C−1

0.05

Climatic Change
Table 1 (continued)
SymbolDefinition

For diatoms
WD
Singing velocity of detritus
VPN
Decomposition rate at 0 °C
(DET → DIN)
VPN0
VPN1
CλDT
Temperature coefficient fordecomposition
Carbon cycle model
RP:N
Stoichiometry of nitrogen tophosphorus
Molar elemental ratios
RC:P
RCaCO3 :POC CaCO3 over nonphotosynthetical
POC production ratio
Alkalinity over
RALK:Nnonphotosynthetical N
production ratio
DCaCO3
CaCO3 remineralization e-folding
depth

Value

Units

6.7
*0.0954–0.28

m day−1
day−1

0.18530.1876
0.0693°C−1

Referenced
valuesIshizu et al.
2019

0.05

16.0
112.0
0.00035

0.035

0.001

3500.0m

model used here still simulates observed conditions well, capturing the basic seasonal variations of DIN, DIP, DIC, and ALK in comparison with the climatological and in situ data
(Figs. 1, 2, 3, 4, and 5). Although the satellite data in the subarctic region exhibit
distinctive double peaks in spring and autumn (Fig. 5(a)), the concentration of Chla given by the model shows only a single peak in summer in the subarctic region(Figs. 1 and 5(a, d)). Ishizu et al. (2019) suggested that this single peak in summer iscaused by a lack of iron restriction (Tsuda et al. 2003) of the model. The seasonalvariabilities in DIN and DIC in the model outputs are represented well (Fig. 2(g, h)and the 50° N line in Fig. 5(e)) compared to the DIN and DIC climatology (Figs. 2(c,d) and 5(c−e)). It is difficult to judge whether the distribution of ALK is accurate, butmodeled ALK concentrations are generally constant throughout the year (Fig. 4(d−g))
and are consistent with JMA observations (Fig. 4(a−c)).
The reproducibility of the model is poorer than that of Ishizu et al. (2019) for DIN and DIC(Table 2), but the reproducibility of ALK is much improved, especially in the subarctic region,
the Kuroshio Extension, and the Japan Sea. The correlation between observed ALK andsimulated ALK is low and negative (R = − 0.24) in the Japan Sea because the simulated
ALK values there have near-uniform values with depth (not shown).

3.2 Ocean acidification indices pHin situ, pH25, and Ωarg
The ocean acidification indices pHin situ, pH25, and aragonite saturation (Ωarg) were calculated
from model results for temperature, salinity, DIC, and ALK. The pHin situ values change
throughout the year (Figs. 6 and 7). The summer pHin situ values in the subarctic region(150–175° E, 50−60° N) are slightly higher (8.05–8.10) than those shown in Fig. 9 of Ishizu
et al. (2019); 7.95–8.00; Figs. 6(a−d) and 7a). Summer pHin situ values for regions north of 35°

Climatic Change
MODIS AQUA Chlorophyll-a60(a) January(b) April(c) July(d) October(f) April(g) July(h) October

Latitude (N)

50
40
30
20

MODEL Chlorophyll-a60(e) January

Latitude (N)

50
40
30
20

110 120130 140150 160 170

110 120130 140150 160

170

110

120

130

140 150160 170

110

120

130

140 150160 170

Longitude (E)mg m-3Fig. 1 Surface distribution of monthly mean chlorophyll-a (Chl-a) concentrations from MODIS_Aqua data andfrom model outputs (this study) for January (a, e), April (b, f), July (c, g), and October (d, h). Conversion fromphytoplankton to Chl-a values was performed using a weight ratio of carbon to chlorophyll-a of C:Chl-a = 40:1
(Li et al. 2010; Stelmakh and Gorbunova 2018) and a ratio of C:N = 106:16 (Redfield et al. 1963)

N decreased by 0.05 from winter to summer (Figs. 6(a−d) and 7a). The general seasonalvariability is consistent with the previous model version used by Ishizu et al. (2019).
Horizontal distributions of pH25 exhibit consistent seasonal variability across all latitudes(Figs. 6(e−h) and 7b) but with different amplitudes. The pH25 values at higher latitudes arelower than at lower latitudes, with an increase of 0.15 in summer. Their amplitudes gradually
decrease southward. The pH25 values in the subtropical region south of 20° N are generally
constant throughout the year.
Climatology
Latitude (N)60(a) January(b) April(c) July(d) October(f) April(g) July(h) October

50
40
30
20

Model outputs
60(e) January

Latitude (N)

50
40
30
20

110

120

130

140 150160 170

110

120

130

140 150160 170

110

120

130

140 150160 170

110

120

130

140 150160 170

Longitude (E)µmol kg-1

Surface monthly distributions of dissolved inorganic nitrate (DIN: μmol kg−1) for January (a, e), March

Fig. 2(b, f), July (c, g), and October (d, h) from climatology from WOA13 and Yasunaka et al. (2014) and model
outputs, respectively

Climatic Change

Climatology(a) January60(b) April(c) July(d) October(f) April(g) July(h) October

Latitude (N)

50
40
30
20

Model outputs
60(e) January

Latitude (N)

50
40
30
20

110

120

130

140 150160 170

110

120

130

140 150160 170

110

120

130

140 150160 170

110

120

130

140 150

Longitude (E)160 170µmol kg-1

Fig. 3 Surface monthly distributions of dissolved inorganic carbon (DIC: μmol kg−1) for January (a, e), April (b,f), July (c, g), and October (d, h) from climatology and model outputs, respectively. The climatology was
obtained by combining the datasets of Goyet et al. (2009), Key et al. (2014), and Yasunaka et al. (2014) (seeSection 3.1 for details)

The seasonal pattern of Ωarg (Fig. 6(i–l)) is similar to that of pH25 (Fig. 6(e–h)). The Ωarg
values in the same area of pH25 north of 35° N become 0.5–1.0 larger in summer and in
autumn. The summer increase in Ωarg is less evident in the south.
Correlation coefficients for modeled pHin situ, pH25, and Ωarg are higher than those reported
by Ishizu et al. (2019) because the accuracy of the modeled ALK concentrations is much
improved compared to them.
JMA observation data
60(a) Spring(b) Summer(c) Autumn

MAR to MAY

JUN to AUG

SEP to NOV(e) April(f) July(g) October

Latitude (N)

50
40
30
20

Model outputs
60(d) January

Latitude (N)

50
40
30
20

110

120

130

140 150160 170

110

120

130

140 150160 170

110

120

130

140 150160 170

110

120

130

140 150160 170

Longitude (E)µmol kg-1

Fig. 4 Plots of JMA observation values of total alkalinity averaged above 20 m depth in spring (a; March toMay), summer (b; June to August), and autumn (c; September to November). Surface monthly distributions of
total alkalinity (ALK: μmol kg−1) for January (d), April (e), July (f), and October (g) in model outputs. All figure
panels use the same color bar to denote values

Climatic Change

20N

Chl_a (mili mol m-3)

3.0

30N

40N

50N

MODIS Aqua chlorophyll-a data in 165E Line

Model outputs of surface chlorophyll-a data in 165E Line

(a)

(b)

2.0

1.0

0.0

DIN (µmol kg-1)

30

Surface monthly DIN climatology in 165E Line

Model outputs of surface DIN in 165E Line

(c)

(d)

Surface monthly DIC climatology in 165E Line

Model outputs of surface DIC in 165E Line

(e)

(f)

25
20
15
10
5

0

DIC ((µmol kg-1)

2150
2100

2050
2000
1950

J

F M A M

J

J

A S

O N

D
2400

Model outputs of surface ALK in 165E Line(g)

ALK (µmol kg-1)

2350
2300
2250
2200
2150
2100

J

F M A M

J

J

A

S

O N

D

Month
Fig. 5 Time series of surface chlorophyll-a, DIN, DIC, and ALK data along 165° E from model outputs. Purple,
blue, green, and red colors depict data for 20° N, 30° N, 40° N, and 50° N, respectively

4 Discussion
4.1 Processes affecting the seasonal inorganic carbon cycle
To identify processes affecting the inorganic carbon cycle, we examined the physical andbiological mechanisms underlying the seasonal inorganic carbon cycle in the Northwest
Pacific using the model results. We separately evaluate each process included in the governing
equation as follows:Climatic Change
Table 2 Correlation coefficients (R) in chlorophyll-a (Chl-a), DIN, DIC, alkalinity, pH 25, and Ωarg between
observed data and model outputs in 2015 for each region (Fig. 1). The left and middle values in a column indicate
R and the 95% confidence interval for R, respectively. Values in brackets indicate p values relative to asignificance level of 0.05. Chl-a values are expressed by using the common logarithm, log10(Chl-a). Italic andbold values indicate the areas of lower and higher and correlation coefficients, respectively, compared with thosegiven by Ishizu et al. (2019)
Parameter

Subtropical region

Chlorophyll-a 0.67; 0.63 < R < 0.70
(0.07)
DIN
0.61; 0.57 < R < 0.65
(0.04)
DIC
0.69; 0.64 < R < 0.74
(0.09)
Alkalinity
0.39; 0.31 < R < 0.47
(0.09)
pH25
0.81; 0.77 < R < 0.84
(0.09)
Ωarg
0.99; 0.994 < R < 0.995(0.09)

60

Subarctic region

Kuroshio extension

Japan Sea

0.83; 0.79 < R < 0.87
(0.07)
0.87; 0.83 < R < 0.90
(0.12)
0.87; 0.81 < R < 0.91
(0.20)
0.88; 0.83 < R < 0.92
(0.20)
0.88; 0.82 < R < 0.92
(0.20)
0.98; 0.978 < R < 0.986(0.20)

0.80; 0.75 < R < 0.84
(0.12)
0.84; 0.81 < R < 0.87
(0.10)
0.72; 0.79 < R < 0.89
(0.17)
0.63; 0.51 < R < 0.72
(0.17)
0.89; 0.92 < R < 0.95
(0.17)
0.99; 0.987 < R < 0.991(0.17)

0.92; 0.89 < R < 0.94 (0.15)
0.94; 0.92 < R < 0.95
(0.11)
0.95; 0.92 < R < 0.97 (0.26)− 0.24; − 0.47 < R < 0.02
(0.26)
0.94; 0.90 < R < 0.96 (0.26)
0.99; 0.985 < R < 0.993(0.26)

pHinsitu(a) January(b) April(c) July(d) October(f) April(g) July(h) October(k) July(l) October

Latitude (N)

50
40
30
20

pH25
60

(e) January

Latitude (N)

50
40
30
20

60

Aragonite saturation (:arg)(i) January(j) April

Latitude (N)

50
40
30
20

110

120

130

140 150160 170

110

120

130

140 150160 170

110

120

130

140 150160 170

110

120

130

140 150160 170

Longitude (E)Fig. 6 Surface monthly distributions of pHin situ, pH25, and aragonite saturation (Ωarg) for January (a, e, i, m),
March (b, f, j, n), July (c, g, k, o), and October (d, h, l, p) from model outputs, respectively

Climatic Change

20N
8.2

30N

40N

50N

Model outputs of surface pHinsitu in 165E Line(a)

PHinsitu

8.1
8.0
7.9

7.8
Model outputs of surface pH25 in 165E Line

8.1 (b)

PH25

8.0
7.9
7.8
7.7
7.6
4.5

Model outputs of surface :arg in 165E Line

(c)

4.0

:arg

3.5
3.0
2.5
2.0
1.5

1.0

J

F M A M

J

J

A S

O N

D

Month
Fig. 7 Time series of surface pHin situ (a), pH25 (b), and Ωarg (c) along 165° E from model outputs. Purple, blue,
green, and red colors depict data for 20° N, 30° N, 40° N, and 50° N, respectively











∂½DIC
∂½DIC
∂½DIC
∂½DIC
∂½DIC
∂½DIC
¼
þ
þ
þ
þ
ð7Þ
∂t
∂t
∂t
∂t
∂t
∂t
A
xy difz dif
Bio
air−sea

where the subscripts A, xy_dif, z_dif, Bio, and air–sea represent the time derivatives of DIC
induced by advection, horizontal diffusion (i.e., horizontal mixing), vertical mixing, biologicalprocesses, and air–sea CO2 exchange (positive values indicate a transfer of CO2 from air tosea), respectively. Note that we refer to these as “DIC variation terms” here, and the DIC
variations induced by air–sea CO2 exchange are included only for the top (surface) level andnot for the levels below it. The total DIC variation term on the left side of Eq. (7) is asummation of all the terms on the right-hand side of the equation.

Climatic Change

Air–sea CO2 exchange shows negative values (CO2 emission to the atmosphere) in
winter north of 35° N (Fig. 8(a)) and positive values (CO2 absorption from theatmosphere) south of 35° N, and vice versa in summer for these regions. The
subarctic region releases CO2 to the atmosphere in winter north of 35° N (Fig.
8(a)), and absorbs CO2 from the atmosphere in summer north of 35° N (Fig. 8(c)).
In contrast, the subtropical region south of 35° N intensely absorbs CO2 south of theKuroshio Extension in winter (Fig. 8(a)). A weak release of CO2 from the ocean tothe atmosphere occurs south of 40° N in summer. The areas of strongest release arelocated in the northeast of the Kuril Islands in the Oyashio region and in the Okhotsk
Sea in autumn–winter (Fig. 8(a, d)). (Further details on air–sea CO2 exchange
represented in the model are described in the Appendix.)
Horizontal distributions and monthly mean balances of the DIC variation terms inthe surface layer induced by all subprocesses (Figs. 8 and 9(a−d)) indicate that theair–sea CO2 exchange shown in Fig. 8(a−d) is generally offset by vertical mixing.
Biological processes, however, made a subordinate contribution to the total DIC
variation (Figs. 8 and 9(c, d, g, h, j, k, m)). Negative biological process values
indicate consumption of DIC through photosynthesis. Biological process contributions
vary spatially and with depth (Figs. 8 and 9; Appendix Figs. 16–18). At the surface
level, the DIC consumption induced by biological processes is high in the subarctic
region around 50° N throughout the year (Figs. 8(m−p) and 9(a−d)). The peakconsumption of DIC moves deeper beneath the surface southward (Fig. 9(d, e–g, j,m); Appendix Figs. 17 and 18). The highest DIC consumption occurs at 50–100 m
depth at 30–40° N (Fig. 9(g, j, k); Appendix Figs. 16(m–p) and 17(m–p)) and at
200 m depth in the subtropical region south of 30° N (Fig. 9(m); Appendix Fig.
18(m–p)). The contributions from biological processes at the surface and at 200 m
depth are of opposite sign (Fig. 8(m–p); Appendix Fig. 18(m–p)). Instead of thecontributions of the vertical mixing or biological processes below the surface, we seethat advection processes are relatively contributed to the total DIC variation (Fig. 9(i–k, n, m)).
The relative contribution of biological processes compared with the other terms at 165° E iscalculated as
relative contribution¼

ΔDICBio 100% ð8Þ
jΔDICA j þ jΔDICxy dif j þ jΔDICz dif j þ jΔDICBio j þ jΔDICair−sea j

as shown in Fig. 10. The highest DIC consumption occurs above 100 m depth north of 40° N
throughout the year, gradually deepening in the range 30–40° N, and spreading vertically at
100–350 m depth in the subtropical region at latitudes south of 30° N. These patterns of DIC
consumption/production may be caused by the latitudinal difference of the Chl-a maximum
depth (Ishizu et al. 2019; Sauzede et al. 2015). Vertical distributions of Chl-a along 165° E
from Ishizu et al. (2019) and global ocean Chl-a data from Sauzede et al. (2015) indicate that
the Chl-a maximum deepens southward; the Chl-a maximum is located in the surface layer insubarctic regions and at ~ 150 m depth in subtropical regions. The highest Chl-a consumption
in the subtropical region from our results (Fig. 10) occurs at greater depths (200–500 m), but
the magnitude is relatively low and makes a negligible contribution to the total variation in
DIC (Fig. 9(n−p)).

Climatic Change
DIC variation term induced by air-sea CO2 exchange(a) Jan(b) Apr(c) Jul60°(d) Oct[mmol C m-3
day-1]

50°
40°
30°
20°

DIC variation term induced by advection effect(e) Jan(f) Apr60°(g) Jul(h) Oct(k) Jul(l) Oct

DIC variation term induced by biological processes(m) Jan(n) Apr(o) Jul(p) Oct

50°
40°
30°
20°

DIC variation term induced by vertical mixing(i) Jan(j) Apr

60°
50°
40°
30°
20°

60°
50°
40°
30°
20°

Total DIC time variation term(q) Jan(r) Apr60°(s) Jul(t) Oct

110° 120° 130° 140° 150° 160° 170°

110° 120° 130° 140° 150° 160° 170°

50°
40°
30°
20°

110° 120° 130° 140° 150° 160° 170°

110° 120° 130° 140° 150° 160° 170°

Fig. 8 Surface horizontal distributions of monthly mean DIC variation terms generated by air–sea CO2 exchange
to the ocean (a–d), advection (e–h), vertical mixing (i–l), biological processes (m–p), and total DIC time variation
terms (q–t) at the surface (0 m depth) for January, April, July, and October, respectively

4.2 Mechanisms of the seasonal inorganic carbon cycle
The relative contributions of terms in the governing equation of DIC (Figs. 8 and 9; Appendix
Figs. 16–18) suggest possible mechanisms for the seasonal carbon cycle, as follows. CO2(DIC) is absorbed from the atmosphere to the ocean during winter south of 40° N. The
corresponding absorbed volume of DIC is conveyed from the surface to the subsurface byvertical mixing (Figs. 8(i, j) and 9(a−c)). In summer and autumn, CO2 (DIC) is released to theatmosphere by air–sea interactions, and vertical mixing generally offsets carbon emissions

Climatic Change

Fig. 9 Monthly mean balances of DIC variation terms (mmol C m−3 day−1) along 165° E for 20° N, 30° N, 40°
N, and 50° N at depths of 0, 50, 100, and 200 m. The monthly mean values were calculated within a range of fivegrid cells (22.0–45.5 km) from the target location. Colored lines indicate monthly means of the processes of DIC
variation terms (ΔDIC) induced by advection, horizontal mixing, vertical mixing, biological processes, and air–sea exchange process, and total DIC time variation (from Eq. 7). Positive and negative values indicate an increase
and decrease in each DIC time variation term, respectively. The DIC variation term influenced by air–sea CO2
exchange is considered only for the surface (a–d), not below it (e–p), where the green line representing zero isgiven as a reference for the other terms(Figs. 8(c, d) and 9(a−c)). In the subarctic region, this interaction is opposite that in thesubtropical region (Figs. 8(a–k) and 9(a−d)), with a negative contribution from biologicalprocesses (i.e., a sink) throughout the year (Figs. 8(m−p) and 9(a−d)). Below 50 m depth, theadvection term becomes more prominent in the subtropical region and in the Kuroshio0(a) Jan(b) Apr(c) Jul(d) Oct[%]

Depth(m)

100
200
300
400
500

15

20

25

30

35

Latitude (N)

40

45

50 15

20

25

30

35

Latitude (N)

40

45

50 15

20

25

30

35

Latitude (N)

40

45

50 15

20

25

30

35

40

45

50

Latitude (N)Fig. 10 Vertical sections showing the relative contribution of biological processes to total DIC time variation
along 165° E (from Eq. 7) for January, April, July, and October. Positive and negative percentages indicate theproduction and consumption of DIC induced by biological processes, respectively

Climatic Change

Extension region, connecting with total DIC time variation (Fig. 9(i–k, n, m)). Working
against the small negative contribution of biological processes below 200 m depth in ourmodel (Fig. 9(n−p)) is advection, which can produce an increase or decrease in DIC in thesubtropical region (Fig. 9(n)).
The modeled carbon cycle also includes latitudinal differences in total DIC time variation,
as shown in Figs. 12 and 13(a). Differences in the contribution from biological processes are
related to the total DIC time variation both horizontally and vertically (Figs. 12 and 13(a)). A
distinctive area of DIC decrease exists near 0–200 m depth, and deepens southward from thesubarctic region to the subtropical region (Figs. 11(a−d) and 12b). A comparison between theannual mean DIC time variation and the Chl-a maximum depth (Ishizu et al. 2019; Fig. 12)shows some similarity above 200 m depth. Advection dominates the trend in annual mean DIC
time variation in the subtropical region south of 30° N below 200 m depth (Fig. 9(n)). These
results suggest that the positive and negative patterns of annual mean DIC time variation arecaused by ocean currents (Fig. 9(n)).
The density range of this distinctive zone of contrast is comparable with that of the North
Pacific Ocean Central Mode Water (CMW; σθ = 26.0–26.5 kg m−3; Oka and Suga 2005; Oka
and Qui 2012; Fig. 12b). Those waters are formed by winter ventilation around thermocline
fronts, including the Kuroshio Extension front, the Kurhoshio Bifurcation front, and thesubarctic front, and are then spread by advection (Oka and Suga 2005; Oka and Qui 2012).
We therefore suggest that the positive and negative contrast in the temporal DIC variations
depends on uptake in the ventilation areas north of 40° N and could be transported byadvection (Fig. 12). A sensitivity experiment was performed in which the maximum depthof Chl-a was decreased by adjusting the biological parameters related to photosynthesis (Iopt),
to check whether advection affects the contrast in DIC time variation below the surface layer.
Features of the DIC time variation south of 25° N were almost identical in the base andsensitivity experiments (not shown), supporting the dominant role of advection in the subtropical region.

Annual mean of the DIC time variation

Latitude (N)

60 (a) 0 m depth(b) 50 m depth(c) 100 m depth(d) 200 m depth

50
40
30
20

110

120

130

140 150160 170

110

120

130

140 150

160

170

110

120

130

140 150

160

170

110

120

130

Longitude (E)Latitude (N)60 (e) 300 m depth(f) 400 m depth

140 150160

170[mmol C m-3
day-1](g) 500 m depth

50
40
30
20
110

120

130

140 150

160

170

110

120

130

140 150

160

170

110

120

130

140 150

160

170

Longitude (E)Fig. 11 Horizontal distributions of the annual mean DIC time variation at depths of 0 m (a), 50 m (b), 100 m (c),200 m (d), 300 m (e), 400 m (f), and 500 m (g). Positive and negative values indicate an increase and decrease ineach DIC time variation term, respectively

Climatic Change(a) Annual mean of DIC time variation[mg m-3]Depth(m)(b) Annual mean of chlorophyll-a with density contours[mmol C m-3
day-1]

Latude (° N)
Fig. 12 Vertical distributions of the annual mean of DIC time variation (a; mmol C m−3 day−1) and ofchlorophyll-a (b; mg m−3) along 165° E. Vertical distributions of colored chlorophyll-a in b are also shownwith contours of potential density, σθ (kg m−3)

4.3 Comparison with previous work on the marginal seas
The balance of the monthly mean DIC time variation terms in the East China and Japan seas
(Fig. 13(a, b)) shows that air–sea CO2 exchange is generally offset by vertical mixing. Thesebalances differ from the simulation results for the Yellow and East China Seas of Luo et al.
(2015), which indicates that the contributions of vertical mixing, advection, and biologicalprocesses to the inorganic carbon cycle largely offset each other on the continental shelves andvary seasonally. The difference between the results of Luo et al. (2015) and those presented
here may be due to missing processes in our model that may be required to represent local
variability in these regions (e.g., the DIC input from river discharge), although the reproducibilities for the Japan and East China seas are high, with high correlation coefficients forseveral variables, including Chl-a, DIN, DIC, pH25, and Ωarg (Table 2).
The reproducibility for the Okhotsk Sea could not be evaluated because of a lack ofobservational data. Variations in DIC in Okhotsk Sea simulated by our model (Fig. 13(c))
are similar to those for the subarctic region of the Northwest Pacific, where the contributions of
air–sea CO2 exchange and vertical mixing are generally offset by each other (Fig. 13(c)) andbiological processes make a subordinate contribution.
The relatively small contribution of biological processes in the DIC cycle inmarginal seas can be explained by the latitude-dependent functions of biologicalClimatic Change

Fig. 13 The same as in Fig. 9 but for the monthly mean balance of each DIC term in the surface layer (at 0 m
depth) in the East China Sea (30° N, 125° E), the Japan Sea (40° N, 135° E), and the Okhotsk Sea (55° N, 150°
E)parameters (Section 2), as we adjusted the biological parameters to focus mainly on
the Northwest Pacific in the subtropical region and on subarctic regions (Ishizu et al.
2019; Eqs. 1–6). Tittensor et al. (2010) showed that biological communities in themarginal seas are dissimilar to those of the Pacific at the same latitude, suggestingthat there is a limitation for the ability of the parameters optimizing method applied in
the present model (Section 2.1).

4.4 Comparison with a study based on observation data
Yasunaka et al. (2013) discussed the inorganic carbon cycle using surface DIC climatological
data, omitting physical processes such as advection and vertical mixing. Our results agree withthose of Yasunaka et al. (2013) in terms of the importance of biological processes from spring
to summer, especially in the subarctic region. However, in our model, biological processes area subordinate contributor to variation in DIC compared with both vertical mixing and air–sea
exchange throughout the year. The subsurface vertical structure of the biological contribution
varies with latitude.
Average daily net community production (NCP) values were estimated by Yasunaka et al.
(2013) from March to July as > 14 mmol m−2 C day−1 in the Kuroshio Extension region (140–170° E, 30–40° N) and 2–6 mmol C m−2 day−1 in the subarctic region. The corresponding
values of NCP in our model can be calculated as ∫0 ∂½DIC∂t dz, where MLD indicates themixed layer depth, which is defined as the depth at which the density is 0.125 kg m−3 greaterthan the density at the surface (Ohishi et al. 2019; Suga et al. 2004; Ohno et al. 2004). The
modeled NCP (Fig. 14) shows positive values in the subtropical region of less than
2 mmol C m−2 day −1 and negative values in the subarctic region of less than −4 mmol C m−2 day−1, respectively (Fig. 14), which are smaller than their respective estimatedvalues (Yasunaka et al. 2013). In addition, our model predicts that the distinctive positive/
negative (maximum/minimum) values spread sparsely and weakly only at the boundary
between the Kuroshio Extension and the subarctic region. Such differences between ourmodel and that of Yasunaka et al. (2013) can be explained by the horizontal and verticaladvection/diffusion processes represented in our model, which should be considered for better
understanding of the inorganic carbon cycle in our target regions.
MLD

Climatic Change

Latitude (N)[mmol C /m2
day-1]

Longitude (E)Fig. 14 Daily net community production (NCP) from March to July, estimated from our model outputs

5 Conclusions
To understand the seasonal inorganic carbon cycle in the Northwest Pacific, we performed
simulations using a biogeochemical and carbon model coupled with an operational ocean
model. The reproducibility of the model was sufficient to evaluate processes related to theseasonal inorganic carbon cycle. We found that contributions to the inorganic carbon cyclefrom air–sea CO2 exchange generally offset those of vertical mixing at the surface in the
Northwest Pacific. Biological processes are a subordinate contributor to variation in DIC andshow a latitudinal dependence in the euphotic layer. Advection actively contributes to variationin DIC below the layer where biological processes contribute.
A schematic representation of the main inorganic carbon cycle in the Northwest Pacific isshown in Fig. 15. DIC is absorbed from the atmosphere during winter south of 40° N and isreleased to the atmosphere north of 40° N (Fig. 15a). The DIC introduced in the subtropicalregion is conveyed from the surface to the subsurface by vertical mixing, and the DIC releasedin the subarctic region is compensated for by vertical mixing from subsurface layers (Fig. 15).
In summer, the opposite pattern occurs because vertical mixing is weaker and the corresponding influence of mixing on variation in DIC decreases (Fig. 15b). Unlike the contribution fromseasonal air–sea CO2 exchange, the negative contribution induced by biological processes (i.e.,a sink) below the surface occurs throughout the year (Fig. 15) and becomes stronger to thesubarctic region. Below the surface, the contribution of advection is prominent in the subtropical and Kuroshio Extension regions. This contribution of advection connects with theincrease/decrease in DIC in the subtropical region (Fig. 15).
Ocean circulation (Tally et al. 1993; Suga and Hanawa 1995a; Yasuda et al. 1997; Qui andChen 2006) transports DIC-rich and DIC-poor water and redistributes them over time. The
horizontal pattern of long-term DIC trends varies with depth. The impacts of long-term trends
in ocean acidification indices (pH and aragonite saturation) also show horizontal and verticaldependencies. Future studies will use modeling experiments to evaluate decadal variations in
the NPZD and carbon cycle. Results of these experiments are expected to improve ourunderstanding of the variability in carbon, biological processes, and ocean acidification.

Climatic Change

Fig. 15 Schematic views of the main inorganic carbon cycle represented in our model outputs. The figure
represents the vertical distributions of the main inorganic carbon cycle with respect to longitude in the Northwest
Pacific in winter (a) and summer (b). Arrows indicate the movements of inorganic carbon due to air–sea CO2
exchange, to vertical mixing, to consumption of phytoplankton, and to advection

Acknowledgments We acknowledge the continuing support from the Sasakawa Peace Foundation of the Ocean
Policy Research Institute (OPRI-SPF). This study is a part of the Japan Coastal Ocean Predictability Experiment(JCOPE) sponsored by the Japan Agency for Marine–Earth Science and Technology (JAMSTEC). Commentsfrom two anonymous reviewers helped to improve earlier versions of the manuscript. World Ocean Atlas 2001
(WOA01) and 2013 (WOA13) data were downloaded from websites of the US National Oceanographic Data
Center (NODC): https://www.nodc.noaa.gov/OC5/WOA01/pr_woa01.html and https://www.nodc.noa.gov/OC5

Climatic Change/woa13/, respectively. Monthly climatological data for DIN, DIP, and DIC from Yasunaka et al. (2013) were
downloaded from http://soop.jp/index.html. In situ observational data from the Japan Meteorological Agency(JMA) were downloaded from https://www.data.jma.go.jp/kaiyou/db/vessel_obs/data-report/html/ship/ship.php.
Partial pressure data for carbon dioxide from the Ayasato observatory were downloaded from https://ds.data.jma.
go.jp/ghg/kanshi/obs/co2_monthave_ryo.html. Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua
Ocean Color Data for 2015 were downloaded from the Physical Oceanography Distributed Active Archive
Center (PODAAC) ftp site: ftp://podaac-ftp.jpl.nasa.gov/allData/modis/L3/aqua/4um/v2014.0/4km/daily/2015/.

Appendix
Figs. 16, 17, and 18, and 19

Fig. 16 Horizontal distributions of the monthly DIC variation term induced by advection (a–d), vertical mixing(e–h), biological processes (i–l), and the total DIC time variation term (m–p) at 50 m depth for January, April,
July, and October, respectively

Climatic Change

Fig. 17 Horizontal distributions of the monthly DIC variation term induced by air–sea CO2 exchange (a–d),advection (e–h), vertical mixing (i–l), biological processes (m–p), and the total DIC time variation term (q–t) at
100 m depth for January, April, July, and October, respectively

Climatic Change

Fig. 18 Horizontal distributions of the monthly DIC variation term induced by air–sea CO2 exchange (a–d),advection (e–h), vertical mixing (i–l), biological processes (m–p), and the total DIC time variation term (q–t) at
200 m depth for January, April, July, and October, respectively

Climatic Change
We calculated air–sea CO2 fluxes (Fig. 8(a–d)) as ρw V p k 0C PCO2 −Patm
CO2 (Kantha, 2004;
Ishizu et al. 2019), where ρw is the density of seawater (kg m−3); k0C is the solubility of CO2 inseawater (mol kg−1 atm−1; Weiss 1974); Vp is the piston velocity, which depends on windspeed (U 10 ) and the Schmidt number (S c ) and is expressed in units of m s -1

(V p ¼ 8:61  10−7 U 210 ðS c =660Þ−1=2 ; Wanninkhof, 1992); and PCO2 and Patm
CO2 are the partialpressures of CO2 in surface waters and the atmosphere (μatm), respectively.
Compared with the global air–sea CO2 fluxes evaluated on the basis of monthly climatological data (Takahashi et al. 2002, 2009; Yasunaka et al. 2013), our model provides a finer
spatiotemporal resolution of variability, with both strong and weak contrasts in air–sea CO2
fluxes in the target region (not shown). The seasonal changes in air–sea CO2 flux simulated byour model (Fig. 8(a−d); note that the values in Fig. 8(a−d) do not represent the actual air–sea
CO2 flux, but the contribution of air–sea CO2 exchange to the total DIC balance) are similar tothe seasonal climatology (Takahashi et al. 2002; Yasunaka et al. 2013), although the annual
mean air–sea CO2 flux (Fig. 19) indicates a smaller sink in the Kuroshio Extension region
between 30° N and 40° N, and the transition between sink and source areas is less distinct thanthat in the climatology (Takahashi et al. 2002; Yasunaka et al. 2013). For example, theclimatology (Yasunaka et al. 2013) shows an extensive sink area between 30° N and 40° N
of > 6 mmol m−2 C day−1, but our model predicts a mixture of sink and source areas in therange from − 5 to 5 mmol m−2 C day−1. One reason for the relatively weak sink areas predictedby our model may be uncertainty in the calculation of pCO2, as revealed by the MATLAB
program CO2sys.m (Orr and Epitalon 2015; Mackenzie et al. 2004). The calculation of pCO2
performed here gives higher pCO2 values in the ocean compared with the pCO2 climatology(Nakaoka et al. 2013). While et al. (2012) also referred to large estimation in the calculation of
pCO2.

Fig. 19 Annual mean air–sea CO2 flux in 2015

Climatic Change
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