Characterization of Ocean Productivity Using a New Physical-Biological Coupled Ocean Model

Global Environmental Change in the Ocean and on Land, Eds., M. Shiyomi et al., pp. 1–44.
© by TERRAPUB, 2004.
Characterization of Ocean Productivity Using
a New Physical-Biological Coupled Ocean Model
Kisaburo NAKATA1, Toshimasa DOI 2, Koichi TAGUCHI2 and Shigeaki A OKI3
1
Department of Marine Science and Technology, University of Tokai,
3-20-1 Shimizu-Orido, Shizuoka 424-8610, Japan
2
Science and Technology Department, Chuden CTI Co., Ltd.,
1-27-2 Meieki-Minami, Nakamura Ward, Nagoya 450-0003, Japan
3
Institute of environmental management technology, National Institute of Advanced
Industrial Science and Technology, 16-3 Onogawa, Tsukuba 305-8569, Japan
Abstract. A lower-trophic marine ecosystem model that takes into account
both the grazing food web and the microbial food web has been developed to
investigate the ocean carbon cycle. The ecosystem model was coupled to an
oceanic general circulation model and a simulation was performed to examine
the temporal and spatial distribution of primary production in the world ocean.
Monthly-mean-based observations were used to force the ocean ecosystem in
order to simulate seasonal variation in the model compartments. Numerical
results revealed that the total amount of annual net primary production reaches
nearly 61.2 GtC, showing fair agreement with the recent estimates based on the
satellite image analysis. The annual flux of particulate organic carbon toward
the subsurface layer, viz., the export flux, was evaluated as 5.5 GtC. The model
results reproduced the general tendency that the regions of low latitude are
characterized by high primary productivity as well as low export flux, and
suggested a dominant role for the microbial food web in the oceanic carbon
cycle.
Keywords: global ocean carbon cycle, coupled physical and biological model,
microbial food web, net primary production, export flux
1. INTRODUCTION
The ocean is expected to play an important role in absorbing the increasing
amounts of atmospheric anthropogenic carbon dioxide released following the
industrial revolution that started at the end of the 18th century (e.g., Gruber, 1999;
Rayner et al., 1999; Le Quéré et al., 2000; Orr et al., 2001; Sabine et al., 2002;
Takahashi et al., 2002). In order to assess the ocean’s role quantitatively as a
reservoir of anthropogenic carbon dioxide, a detailed knowledge of the carbon
cycle mechanism over the world ocean is required. Processes associated with the
carbon cycle in the ocean have physical, chemical and biological origins and
interact mutually. It is recognized that the carbon cycle in an oceanic ecosystem
depends strongly on physical processes. A typical example of a physical process
is vertical mixing: it can enhance phytoplankton growth by mixing nutrients into
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the surface layer from below, or it can suppress photosynthesis by carrying
phytoplankton down away from the euphotic zone. Whether or not increased
vertical mixing acts positively on the aquatic biota depends on a delicate balance
among various conditions such as nutrient concentration and light intensity. The
relation between changes in the oceanic ecosystem and in the mixed layer depth
(MLD hereafter) have been discussed based on modeling studies (e.g., Venrick
et al., 1987; Polovina et al., 1995). Processes occurring within the ML should be
represented in numerical models in terms of their physical and biological aspects.
Therefore most of the model studies that attempt to examine the ecosystem
structure include the carbon and nutrient cycles in combination with the physical
processes. Historically, Bacastow and Maier-Reimer (1990) introduced an oceanic
general circulation model (OGCM) into their study to reproduce the distribution
of tracers in the deep layer, and then coupled it to an oceanic ecosystem model.
Their model was the first attempt to approach the global ocean carbon cycle
(GOCC hereafter), but was so simplified that the relation between MLD and
biological activities was only poorly resolved. Improvements were made in
subsequent studies (e.g., Bacastow and Maier-Reimer, 1991; Najjar et al., 1992;
Anderson and Sarmiento, 1995; Yamanaka and Tajika, 1996; Fasham et al., 1993;
Sarmiento et al., 1993; Six and Maier-Reimer, 1996). In particular, Fasham et al.
(1993) and Sarmiento et al. (1993) incorporated ecological constituents such as
phytoplankton and zooplankton into an OGCM as explicit state variables, assuming
that the planktonic variables are susceptible to vertical mixing, which means that
the relation between MLD and the ecosystem could be represented much better
than before. Their model was applied to the North Atlantic and showed good
agreement with the satellite coastal zone color scanner (CZCS) data. Although
there were many points for improvement, their model demonstrated great potential
for examining the structure of the marine ecosystem. Six and Maier-Reimer
(1996) also developed an ecosystem model coupled to an OGCM. They showed
that the incorporation of ecosystem dynamics can improve tracer distributions by
reducing the magnitude of an undesired subsurface nutrient maximum, i.e.,
nutrient trapping, in the equatorial region. Kawamiya et al. (2000) applied an
ecosystem model of the same kind to study the nitrogen cycle in the North Pacific.
Their model qualitatively reproduced the basin-wide distributions of the state
variables, but revealed some discrepancies in comparison with the observational
data. For example, chlorophyll concentration at the sea surface remained lower
in the subpolar region and higher in the subtropical and equatorial regions than
represented by CZCS observations.
Nowadays, estimates of net primary production (NPP hereafter) are available
on a global scale, not only through the satellite images of chlorophyll concentration
but also through the numerical algorithms developed to describe the relationship
between phytoplankton abundance and photosynthetic activity as a function of
environmental parameters such as surface temperature, solar irradiance and MLD
(Platt et al., 1991; Longhurst et al., 1995; Antoine et al., 1996; Behrenfeld and
Falkowski, 1997). Recently, Buesseler (1998) showed that a large part of the
world ocean is characterized by low export of particulate organic carbon (POC)
Characterization of Ocean Productivity Using a New Physical-Biological
3
against primary production (the export/production ratio stays below 0.05–0.1),
and that the sites with high export values are overwhelmingly characterized by
food webs dominated by large-size phytoplankton, diatoms in particular. He
suggested that incorporation of different food web structures such as a diatomdominated system or a microbial food web (MFW hereafter) into the carbon cycle
model is crucial in explaining the decoupling between production and particulate
export in the surface ocean.
Our present study stems from the modeling work that aims ultimately at
developing predictive and reliable tools capable of investigating the responses of
an oceanic ecosystem to anticipated changes in the global environment under the
condition of increasing anthropogenic carbon dioxide. In order to gain a better
insight into the behavior of carbon dioxide in the ocean, our conventional
ecosystem model based only on the classical grazing food web, referred usually
to as the PZDN (phytoplankton, zooplankton, detritus, nutrient) model or averaged
plankton model, was improved to take MFW into account by defining size
structure in the plankton system and coupling it to the OGCM. This is very
important because a feature of the global ocean ecosystem is the significant
regional differences in dominant species or size of plankton and biological
productivity, and hence in food web structure, which has a direct effect on carbon
and nutrient cycles. In this paper, GOCC is studied using this new physicalbiological coupled ocean model. It should be emphasized that no artificial
adjustment or regional calibration for the model parameters so that they may
reproduce global characteristics of the ecosystem has been implemented here.
This also deserves attention because, as mentioned above, most of the numerical
ecosystem models to date, no matter how they consider the MFW structure, have
been developed and calibrated for the purpose of regional or site-specific
application. The discussions in this paper emphasize primarily how reasonably
the new model can estimate NPP compared to the conventional PZDN model,
then addresses export POC flux against NPP in terms of export ratio, and finally
considering the relative role of MFW in drawing the model results together.
2. MODEL DESCRIPTIONS
A 3-D time-dependent coupled physical and biological model is introduced
to simulate the seasonal pattern of GOCC. The physical part depends on a wellestablished OGCM described by a set of geohydrodynamic equations on a
spherical coordinate system, whereas the biological counterpart comprises a
plankton-base lower-trophic marine ecosystem model that takes MFW into
account to explain regional characteristics in NPP and export POC flux in the
world ocean.
2.1 Physical model
The physical model used is Version 2 of the GFDL Modular Ocean Model
(MOM2; Pacanowski, 1996). MOM2 is a 3-D, z-coordinate, primitive-equation
OGCM that employs the hydrostatic and Bousinesq approximations as well as the
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rigid-lid assumption for the sea surface elevation.
The computational domain covers the global ocean, including the Pacific,
the Atlantic and the Indian Ocean up to 80° north and south, using a spherical grid
system with a resolution set at 2°. Under the bathymetric condition built in using
the Etopo5 database, the water column up to 5,500 m is divided into 30 layers the
thickness of which starts from the minimum value of 10 m at the surface layer,
then increasing gradually to reach 500 m at the bottom layer. The seasonal
condition of the sea-surface wind stress depends on the monthly mean database
provided by Hellerman and Rosenstein (1983). Regarding heat and salt flux
conditions at the sea surface, the monthly mean objective analysis data from
World Ocean Atlas 1998 (WOA98; Levitus, 1999) are utilized to restore the
computed surface water temperature and salinity fields in 10 days every month.
The MOM2 simulation also employs the WOA98 T-S database for temperature
and salinity in layers deeper than 4,000 m, assuming a slower restoration
parameter of 1,000 days.
Regarding the vertical turbulent mixing process, the Pacanowski-Philander
scheme is introduced into seasonal variation in the coefficient of eddy diffusivity.
Note that the MOM2 simulation does not directly output MLD itself, and it is not
MLD but the eddy diffusion coefficient that the biological model inputs through
the coupling procedure. In this regard, following Kawamiya et al. (2000),
background viscosity and diffusivity are chosen as 1.0 cm2s –1 and 0.3 cm2s –1,
respectively. As for the diffusion process, the simulation considers the effect of
isopycnal diffusion and adopts a coefficient of 2 × 107 cm2s –1. The horizontal
eddy viscosity and diffusivity are assumed to be constant throughout the simulation
period with the respective values set at 8 × 108 cm2s–1 and 3 × 106 cm2s –1. Needless
to say, all the settings are realized within the functions of the present MOM2.
The time integration of the basic primitive equations lasted over 1,300 years
from the stationary initial condition with spatial patterns of temperature and
salinity estimated from the WOA98 annual mean data. Different time steps were
selected to save computational time: 1 hour for the equations of motion and 12
hours for the transport equations of heat and salt. During simulation, the temporal
variation in kinetic energy over the entire domain was monitored every time step
along with those in temperature and salinity. While sluggish variations continued
even at the final stage, the annual variation over the last 100 years stayed within
0.2% for kinetic energy and around 0.5% for both temperature and salinity,
permitting us to judge that the simulation almost reproduced a steady-state annual
cycle. After the hydrodynamic calculation, the estimated seasonal flow field was
recorded in a computer disk file every 5 days throughout a year, along with
temperature, salinity and vertical eddy diffusivity, and then introduced into the
biological model.
2.2 Biological model
Figure 1 presents a schematic view of the biological model in this study,
which features the introduction of the MFW structure into our conventional
Characterization of Ocean Productivity Using a New Physical-Biological
5
Surface
Zooplankton
Mesograzers
Phytoplankton
Grazing
Diatoms
Grazing
Diel Migration
Dinoflagellates
Micrograzers
Sinking
Mortality
Extracellular Release
Phytoflagellates
Heteroflagellates
Natural Mortality
Feeding
Picophytoplankton
Grazing
Feeding Feeding
Detritus
Uptake
Respiration
Excretion
Biogenic Particulate Silica
Degradation
Sinking
Dissolved Organic Matter
Bacteria
Up take
Sinking
Excretion
Respiration
Uptake
CO2 Exchange
with the air
Dissolution
Uptake
Carbonates
Silicate
Phosphate
Ammonium
Uptake
Nitrate
Nitrite
Nitrification
Nitrification
Nutrients
Euphotic Layer
Lower Layer
Advection Diffusion
Fig. 1. Schematic view of the ocean ecosystem model considering MFW structure.
ecosystem model based on the grazing food web. The MFW in the model
comprises three functional groups: the primary producers, the consumers, and the
decomposers. According to Baretta-Bekker et al. (1997) and Taylor et al. (1993),
the biological model defines four phytoplankton categories for primary producers:
diatoms, dinoflagellates, autotrophic nanoflagellates (phytoflagellates), and
picophytoplankton (picoalgae); three zooplankton categories for consumers:
mesograzers, micrograzers and heterotrophic nanoflagellates (heteroflagellates);
and the bacteria compartment as decomposers. Mathematical formulations of
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Table 1. Mathematical expressions of biochemical processes in the global ocean ecosystem model.
Characterization of Ocean Productivity Using a New Physical-Biological
Table 1. (continued).
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Table 2. Biological parameters for the global ocean ecosystem model.
Characterization of Ocean Productivity Using a New Physical-Biological
9
Table 2. (continued).
biological processes for these functional groups are presented in Table 1. It
should be stated that the present model assigns the phytoplankton categories to
each zooplankton category as grazing target simply based on their cell size. Those
selected as grazing target are diatoms and dinoflagellates for mesograzers,
phytoflagellates for micrograzers, and picoalgae for heteroflagellates. In addition,
the model assumes that all the zooplankton categories consume both bacteria and
detritus.
It should also be noted that the present model assumes the existence of
thermal optima for growth process of phytoplankton and describes the maximal
growth rate of each category by the equation presented by Kawamata (1997) for
the growth response of sea urchin (see Table 1). This is an attempt to cope with
shortcomings of the conventional model employing the familiar exponential
response to temperature (Eppley, 1972): estimates of primary production always
tended to attain a maximum around the Equator according to the spatial gradient
of surface water temperature (Nakata et al., 1995; Kawamiya et al., 2000), which
is obviously in conflict with the satellite images, which demonstrate enhanced
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primary productivity in high latitudes. The model also adopts the growth pattern
with thermal optima for every zooplankton category.
Following the ecological simulation of the North Pacific by Nakata et al.
(1995), all the state variables of the ecosystem model were initialized using
observational data from the Northwest Pacific Carbon Cycle Study (NOPACCS)
obtained during 1990–1996. The data in the vertical section along the 175°E
meridian were deployed in the longitudinal direction to set up their distributions
all over the ocean. Only for the nutrient compartments, were the WOA98 annual
mean data utilized to initialize the distributions of phosphate, nitrate and silicate.
The condition of the sea surface light intensity depends both on the daily estimate
of global solar radiation by astronomical calculation and on the monthly
observations of cloud cover (the 1994 Atlas of Surface Marine Data; da Silva et
al., 1994). The model also considers diurnal variation in the sea surface light
intensity: a cubic sinusoidal curve is employed, following Ikushima (1967), to
approximate the temporal change from sunrise to sunset.
After coupling with the annual time series of physical parameters obtained
from the preceding hydrodynamic simulation by MOM2, the biological model
was run over 10 years with a time step of 3 hours. The physiological rate
coefficients and parameters, so-called “biological parameters”, are summarized
in Table 2. Their choice depends largely on the literature (e.g., Baretta-Bekker et
al. (1997) for phytoplankton growth and Taylor et al. (1993) for zooplankton
growth), except for some characteristic coefficients, such as natural mortality
rates of bacteria and plankton, which are usually regarded as fitting or tuning
parameters. Note here that the model assumes fixed stoichiometry in every
90 N
60
30
Eq.
30
60
90 S
0
30E
60E
-50 -40
90E
-30
120E 150E
-20 -10
180
0
150W 120W 90W
10
20
30
40
60W
30W
0
50 ( Sv )
Fig. 2. Annual mean horizontal circulations obtained from the OGCM. Values of volume transport
are in Sv (10 6 m 3s –1).
Characterization of Ocean Productivity Using a New Physical-Biological
11
organic compartment except phytoplankton. For phytoplankton, according to
Caperon and Meyer (1972), Lehman et al. (1975) and other researchers, a cell
quota model was embedded into all categories to formulate their nutrient dynamics
and distinguish them from the photosynthetic growth processes. This allows each
plankton category to sustain growth through intracellular nutrient reserves
without directly responding to variation in ambient nutrient concentration. In this
connection, only the stoichiometry of phytoplankton varied temporally according
to the cell quota size. Special attention was paid to the photosynthesis-light
response of phytoplankton so that the simulated vertical profile of primary
productivity coincides with the observation: the model evaluates the response by
the classical Steele equation (1962), which introduces a light optimum for
photosynthesis, and considers the effect of shelf-shading by the Riley equation
(1956).
3. NUMERICAL RESULTS
3.1 Hydrodynamic aspect
1) Flow field
The simulated annual mean horizontal circulation in the uppermost layer is
presented in Fig. 2. General features of the surface circulation are reasonably well
reproduced: the Kuroshio and the Oyashio Currents along the western boundary
in the North Pacific, the Gulf Stream in the North Atlantic and the strongly
divergent westward flow along the Equator. But the model result also has some
shortcomings: e.g., the latitude where the Kuroshio separates deviated further
north from the observed location, the Equatorial Counter Current disappeared in
the western equatorial region, etc. These discrepancies seem to be attributed to
the coarse grid resolution of 2°. While not presented in the figure, the model’s
90 N
60
30
Eq.
30
60
90 S
0
30E
60E
90E
120E 150E
180
150W 120W 90W
60W
30W
Fig. 3. Horizontal distributions of the annual mean MLD. Values are in m.
0
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K. NAKATA et al.
Month
(a)
Mixed Layer Depth (m)
0
I
❊
L
❅
L
I
I
❅
❘
N
M
❈
100
200
300
400
500
OWS-P
OWS-I
600
Fig. 4. Seasonal variations in the estimated MLD in comparison with observations. (a) simulated
annual patterns at OWS-P and OWS-I; (b) observations given by Fasham (1995) based on the results
of Frost (1987) and Levitus (1982); (c) simulated annual pattern at BATS; and (d) annual pattern
derived from field data by Spitz et al. (2001).
seasonal pattern shows that the surface circulation strengthens generally during
winter both in the Northern and the Southern Hemispheres. Regional characteristics
in the estimated volume transport are summarized as follows: in the subtropical
gyre of the North Pacific including the Kuroshio Current, the volume transport
varies from 55 Sv (Sv = 106 m3s –1) in winter to 35 Sv in summer. The circulation
in the subtropical North Atlantic, including the Gulf Stream, also strengthens in
winter, where the volume transport ranges from 20 to 30 Sv. The current passing
through Indonesia is characterized by a westward transport toward the Indian
Ocean throughout the year, with a volume that fluctuates periodically from 16 to
20 Sv, reaching its maximum in April and November and minimum in February
and August. Despite a large volume transport, in the range 110–140 Sv, the
Antarctic Circumpolar Current does not show a clear seasonal variation. The
volume transport through Drake Passage amounts to 120 Sv. These estimates
compare favorably with the reported values: e.g., approximately 50 Sv for the
Kuroshio extension estimated from CTD measurements and mooring current
observations (Joyce, 1987; Schmitz, 1987), 140 Sv for Drake Passage, and 16 Sv
Characterization of Ocean Productivity Using a New Physical-Biological
13
Model Years
(c)
1
2
3
4
5
Mixed Layer Depth (m)
0
50
100
150
200
250
300
Fig. 4. (continued).
for the eastward current toward the Indian Ocean, both estimated from the
geostrophic current based on hydrographic data (Ganachaud and Wunsch, 2000),
and 135 Sv for Drake Passage reported by Sloyan and Rintoul (2001).
2) Mixed layer depth
To visualize the vertical turbulent mixing process, seasonal variation in
MLD is examined based on the simulated eddy diffusion coefficients. Since the
condition of the ML in the surface layer strongly affects nutrient transport from
subsurface layers, regions where the ML develops deep into the water column are
generally characterized by high plankton productivity, and therefore by enhanced
new production, utilizing nutrients from the lower layers. Figure 3 presents the
resulting annual mean distribution, where, following Kawamiya et al. (2000), the
MLD is tentatively defined as the thickness of a water column with vertical eddy
diffusivity exceeding 10 cm2s–1. Incidentally, there is little difference from the
MLD estimate if one chooses the diffusivity threshold at 5 cm2s–1. The annual
mean MLD attains approximately 150 m in the western basin of the North Pacific,
in contrast to 30 m in the northeastern basin, and 20 m in the Equatorial Pacific,
except for the central zone with a value less than 50 m, showing a general
tendency analogous to the result reported by Kawamiya et al. (2000). The MLD
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K. NAKATA et al.
90 N
(a)
60
30
Eq.
30
60
90 S
0
30E
60E
90E
120E 150E
180
150W 120W 90W
60W
30W
0
60E
90E
120E 150E
180
150W 120W 90W
60W
30W
0
90 N
(b)
60
30
Eq.
30
60
90 S
0
30E
Fig. 5. Annual mean phytoplankton abundance in the surface layer in comparison between (a) the
model and (b) the objective analysis data. The simulated total phytoplankton carbon biomass was
converted into chlorophyll-a concentration (mgm–3) assuming a constant C/Chl-a ratio of 50. The
objective analysis data represent the annual mean chlorophyll-a distribution from the WOA98
database.
of the Atlantic is estimated at 200–300 m in high latitudes and 30 m in low
latitudes. Along the Circumpolar Current, it attains a high value ranging from 300
to 500 m. The annual variation is characterized by pronounced vertical mixing
both in the western North Atlantic and in the region around Greenland-Iceland:
the MLD increases up to 250–300 m from March to May and exceeds 500 m from
winter to spring, respectively.
Panel (a) of Fig. 4 illustrates the estimated seasonal variation in MLD for the
two observational sites, OWS-P (50°N, 145°W) and OWS-I (59°N, 19°W), which
are well known for their contrasting temporal variability in MLD: OWS-P has no
distinct ML throughout the year, while OWS-I has strong vertical mixing in the
Characterization of Ocean Productivity Using a New Physical-Biological
15
90 N
(a)
60
30
Eq.
30
60
90 S
0
30E
60E
90E
120E 150E
180
150W 120W 90W
60W
30W
0
Fig. 6. Annual mean zooplankton abundance in the surface layer in comparison between (a) the
model and (b) observation. The simulated total zooplankton carbon biomass was converted into
nitrogen stock (mmolNm–3) according to the C/N ratio of each category. The observation data are
those compiled by Bogorov et al. (1968).
first half of the year. This feature can be clearly seen in the model result: at OWSI, the ML develops from early January to late May with a thickness reaching
almost 550 m, while at OWS-P, it is restricted within a thin surface layer, the
maximum thickness of which attains only 70 m. Panel (b) presents the seasonal
variation in MLD at these two sites, which Fasham (1995) studied based on the
data by Frost (1987) and Levitus (1982). On comparing the model result with this
study, it was found that the difference in MLD between the two sites coincides
well, but the timing when the spring ML declines has a delay of about a month.
The other two panels of Fig. 4 give the seasonal variation in the estimated MLD
at the Bermuda Atlantic Times-Series site (BATS), located in the subtropical
zone (31.7°N, 64.2°W), in comparison with the estimate by Spitz et al. (2001) and
Steinberg et al. (2001). Compared with the observations, their studies suggested
that the ML deepens gradually after September to reach its maximum of about 200
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K. NAKATA et al.
90 N
60
30
Eq.
30
60
90 S
0
30E
60E
90E
120E 150E
180
150W 120W 90W
60W
30W
0
Fig. 7. Annual mean bacterial stock in the surface layer obtained from the model. Values are in
mgCm–3.
m from January to March. The tendency is well explained by our present
simulation, except that the strengthened vertical mixing continues until late May.
Putting these speculations together, it turns out that the model explains the
seasonal variation in ML reasonably well: the MLD coincides well with the
observations except for the onset of mixing in the spring season.
3.2 Ecological aspect
1) Horizontal pattern of biological constituents
Panel (a) of Fig. 5 illustrates the simulated annual mean total abundance of
phytoplankton in the surface layer in terms of chlorophyll-a concentration.
Compared with the map from the WOA98 objective analysis data (panel (b) of the
figure), the ecosystem model displays a general tendency that the concentration
stays high both in the subpolar and the equatorial upwelling regions (~0.8 mgChlam–3), and low in the subtropical region (less than 0.1–0.2 mgChl-am –3). It should
be noted, however, as a discrepancy of the model, that the simulation gives values
rather higher than the map from WOA98 data both in the equatorial zone and in
the Antarctic Ocean. This may be attributed to the constant C/Chl-a stoichiometry
assumed in this study since the model reproduced the general feature of chlorophylla distribution, which will be discussed later in further detail.
The simulated annual mean total biomass of zooplankton in the uppermost
layer is shown in Fig. 6(a), along with the surface observation compiled by
Bogorov et al. (1968) in the North Pacific (panel (b)). Here the model results in
carbon units are converted into nitrogen stock using the weight C/N ratio of 6.0
(see Table 2) for the sake of comparison. A similar tendency to phytoplankton is
found in the distribution: zooplankton in the model tends to concentrate more
densely in the subpolar and the equatorial regions than in the subtropical region.
Characterization of Ocean Productivity Using a New Physical-Biological
17
Fig. 8. Seasonal variation in bacterial stock at BATS in comparison between (a) the model and (b)
observation. The model result was converted into nitrogen stock (mmolNm–3) using the C/N
composition.
While the spatial pattern seems to be reproduced reasonably well, the nitrogen
biomass becomes a little higher, especially in the northern basin: e.g., 0.3–0.5
mmolNm–3 in the Bering Sea in contrast to the estimate given by Bogorov et al.
(0.1–0.3 mmolNm–3). Another estimate for the same region given by Sugimoto
and Tadokoro (1997) suggests that the biomass stays around 0.2–0.3
mmolNm–3.
Figure 7 illustrates the simulated annual mean carbon biomass of bacteria in
the uppermost layer. High values are found around the equatorial region. In the
Pacific, measurements are available for comparison through the 1991–1992
NOPACCS cruise. At high latitudes (44–45°N), the cumulated biomass over the
upper water column up to 200 m resulted in about 400 mgCm–3 for both the model
and the observed values. In the subtropical and the equatorial regions, however,
the model considerably overestimates the biomass: the model result shows high
value of almost 1,000 mgCm–3 compared with observations ranging from 300 to
400 mgCm –3. As another observation, Spitz et al. (2001) reported the bacterial
biomass at BATS in the North Atlantic. As presented in Fig. 8, the nitrogen
biomass observed there during 1988–1993 varied from 0.15 to 0.2 mmolNm–3.
Applying the C/N composition (see Table 2), the corresponding model value
comes to 0.05–0.2 mmolNm–3. Despite differences in the biomass level in the
latter half of the year, the model result broadly follows the observed annual
variation.
2) Seasonal pattern of plankton biomass
Figure 9 shows the simulated seasonal variations in phytoplankton and
zooplankton abundance at OWS-P and OWS-I, both averaged over the surface
water column up to 50 m. Thorough discussions have concluded that the OWSP site is not characterized by phytoplankton blooming, whereas a typical strong
spring bloom occurs at OWS-I.
At OWS-P, the model result seems to follow the regionalism satisfactorily,
while it introduces a slight bloom in the spring season (panel (a) of Fig. 9). The
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K. NAKATA et al.
Fig. 9. Simulated seasonal variations in phytoplankton and zooplankton biomass in comparison
between the two sites: (a) OWS-P and (b) OWS-I. Values are in mgCm–3. The model results are
averaged over the surface water column up to 50 m.
total phytoplankton biomass reaches a maximum of around 50 mgCm–3 in March
and May. Following phytoplankton, zooplankton also reaches a maximum in
May. The model puts the peak value at about 60 mgCm–3, higher than that of
phytoplankton, suggesting that zooplankton exerts strong grazing pressure there.
For phytoplankton at this site, the seasonal variation is characterized by the
annual dominance of smaller-size categories, i.e., dinoflagellates, phytoflagellates
and picoalgae. The contribution of these categories to the total phytoplankton
biomass amounts to 80% throughout the year. Diatoms dominate only from
March to July. For zooplankton, about 50% of the total biomass is shared by
Characterization of Ocean Productivity Using a New Physical-Biological
19
Fig. 9. (continued).
micrograzers and heteroflagellates. Accordingly, at OWS-P, the model result
suggests that MFW plays a dominant role in the carbon and nutrient cycles. Figure
10 illustrates the observed seasonal variations in chlorophyll-a concentration
(Anderson et al., 1977) and zooplankton abundance (Fulton, 1983) at this site.
Note that zooplankton is expressed as wet body weight, which can be converted
into carbon biomass using the carbon content of about 6%. In addition, the
observed chlorophyll-a is considered here to represent the total phytoplankton of
the model. It becomes clear from the comparison that the model slightly
overestimates both chlorophyll-a concentration and zooplankton biomass, although
it is in agreement with the general opinion that typical spring blooming does not
20
K. NAKATA et al.
Surface Chlorophyll-a (mg m-3 )
2.0
1959-1970
(a)
1.6
1.2
0.8
0.4
0.0
J
F
M A M J J A S
Month
O N D
Zooplankton Biomass (mg m-3 )
200
(b)
150
100
50
0
J
F
M A M J J A S O N
Month
5 day means for 20 years combined
D
Fig. 10. Seasonal variations in (a) chlorophyll-a concentration and (b) zooplankton biomass,
observed at OWS-P by Anderson et al., (1977) and by Fulton (1983), respectively.
occur at this site. A lack of iron is sometimes referred to as one possible
explanation for the HNLC (High-Nutrient Low-Chlorophyll) characteristic in the
North Pacific (e.g., Martin and Fitzwater, 1988). The effect of such a micronutrient,
however, remains beyond the scope of our present model. If this is a question of
severe regulation of phytoplankton growth at OWS-P, it may be natural that the
model overestimated the abundance.
At OWS-I, in marked contrast to OWS-P, the model reproduced the intense
phytoplankton blooming at the end of May (panel (b) of Fig. 9), which seems to
be consistent with the ecological studies by Fasham et al. (1993) and Hurtt and
Armstrong (1999). The bloom is mainly attributed to diatoms with peak biomass
exceeding 70 mgCm–3. The total phytoplankton abundance attains a maximum of
around 150 mgCm–3 in late May, which is followed by the total zooplankton,
which peaks at about 120 mgCm–3 early in June. Since the model regards only
physical vertical mixing as a primary factor in the control of phytoplankton
Characterization of Ocean Productivity Using a New Physical-Biological
0
0.9
Depth (m)
0.5
100
0.3
0.7 0.9
0.5
21
0.1
0.5
0.7
0.3
0.2
0.1
200
(a)
300
45N 40 35 30 25 20 15 10
Latitude
5
0
5
10 15S
Apr. - Jun., 1994
0
Depth (m)
50
0.4
0.1
0.5 0.40.3
1.4
0.5
0.4
0.6
0.5
100
0.1 0.2
0.3
150
0.1
0.2
0.3
0.1
0.3
0.2
0.2
0.2
0.3
0.2
0.1
200
250
(b)
300
48 45
N
40
35
30
25
20 15
Latitude
Depth (m)
0
10
10
50
0
5
10
15
S
10
50
90
70
100
5
70
50
30
30
20
10
200
(c)
300
45N 40 35 30 25 20 15 10
Latitude
5
0
5
10 15S
Apr. - Jun., 1994
0
Depth (m)
50
90
70
50
70
50
30
30
30
30
100
150
200
10
10
250
10
10
300
48 45
N
40
35
30
25
20 15
Latitude
10
5
0
5
(d)
10
15
S
Fig. 11. Vertical sections of chlorophyll-a and POC along the 175°E meridian. (a) Simulated total
chlorophyll-a concentration (in mgm–3) as the three-month average from April to June; (b) chlorophylla concentration (in mgm–3) observed during April–June in the 1994 NOPACCS cruise; (c) simulated
POC concentration (in mgCm –3) as the three-month average from April to June; and (d) observed
POC concentration (in mgCm–3) from the 1994 NOPACCS cruise during April–June.
22
K. NAKATA et al.
Fig. 12. Sensitivity of vertical chlorophyll-a profile to the choice of C/Chl-a ratio of phytoplankton.
The simulated total phytoplankton carbon biomass at the two sites (20°N, 175°E) and (30°N, 175°E)
was converted into chlorophyll-a concentration under two different conditions of C/Chl-a ratio: (a)
constant at 50, and (b) varies with depth according to (c) the scheme proposed by Taylor et al. (1997).
blooming, the sharp contrast between the two ecosystems, OWS-P and OWS-I,
is inferred here from the seasonal pattern of estimated MLD. At OWS-P, where
the ML does not deepen, the reproductive mechanism involved in MFW will
maintain the phytoplankton abundance and consequently bring high biomass to
zooplankton in the surface layer, even during winter, due to the availability of
sufficient food. This leads to the intense grazing pressure in spring that restrains
phytoplankton bloom. At OWS-I, on the other hand, the highly developing ML
Characterization of Ocean Productivity Using a New Physical-Biological
23
will reduce zooplankton biomass in the surface layer to a considerable extent
during winter and provoke the spring phytoplankton bloom by supplying nutrients
from the subsurface layer. In any case, it seems significant that the model explains
the ecological feature for both sites based primarily on the MLD characteristics
(see Fig. 4).
3) Vertical profiles
Vertical sections of the simulated chlorophyll-a and POC concentrations
along the 175°E meridian are presented in Fig. 11 in comparison with the field
data obtained during the 1994 NOPACCS cruise during April–June (Nat. Inst. of
AIST, 1994). Both of the model results represent an average over the same threemonth period. For chlorophyll-a, the simulated distribution does not agree well
with the observation: the model overestimates the concentration as a whole. The
greatest discrepancy is that, in the observations, the low chlorophyll-a area lies
over a wide range from 5 to 30°N, whereas in the model it is restricted within a
narrow interval of 18–28°N. Moreover, the observed depth of the chlorophyll
maximum reaches about 130 m, but the model result stays at most at 50 m. On the
other hand, POC concentration, expressed as the sum of carbon stocks of
phytoplankton, zooplankton, bacteria and detritus, seems to be reproduced better
than chlorophyll-a, while the level also stays little higher than observation. The
spatial pattern, however, seems reasonable in that the model satisfactorily
reproduced the situation where high the POC zone lay both in high latitudes and
in the equatorial region, and in the vertical direction the concentration decreased
to 10 mgCm –3 at a depth of around 200 m.
In connection with the issue of the vertical location of chlorophyll-a maximum,
an attempt was made to examine the sensitivity of the model to the choice of C/
Chl-a ratio of phytoplankton. The present model regards the ratio as constant (C/
Chl-a = 50; see Table 2); but Taylor et al. (1997) suggested that the spatial
variability in the ratio in the vertical direction significantly affects the depth of
the chlorophyll maximum. Following this view, a depth-dependent C/Chl-a
scheme (Fig. 12 (c); after Taylor et al., 1997) was applied to the simulated vertical
profile of phytoplankton carbon biomass to convert into chlorophyll-a
concentration, and the result was compared with the original case using a constant
C/Chl-a ratio. As shown in the panels (a): constant ratio and (b): variable ratio of
Fig. 12, the location of the chlorophyll maximum deepens in the case of a variable
ratio than in the original. The results suggest that realistic profiles of C/Chl-a ratio
would be required in order to bring the vertical profiles of the simulated
chlorophyll-a concentration much closer to the observed values.
4) Primary production
Figure 13 illustrates the horizontal distribution of simulated NPP over the
global ocean for four seasons (March, June, September and December). The
satellite image observations (SeaWiFS) in 2000 are presented in Fig. 14 after
Ishizaka and Kameda (2003). The model results show that the general pattern is
in fair agreement with observation. Above all, the model satisfactorily reproduces
the seasonal pattern in high latitudes characterized by high NPP (spring bloom in
the Northern Hemisphere and autumn bloom in the Southern Hemisphere), as
180
150W 120W 90W
60W
30W
0
0
100
200
300
400
600
700
800 ( mgCm-2 day -1 )
100
0
200
30E
(d) Dec.
200
30E
60E
60E
300
90E
300
90E
500
400
500
120E 150E
400
120E 150E
180
180
700
600
700
60W
30W
0
30W
0
800 ( mgCm-2 day -1 )
60W
800 ( mgCm-2 day -1 )
150W 120W 90W
600
150W 120W 90W
Fig. 13. Horizontal distributions of the simulated net primary production (NPP) during (a) March,
(b) June, (c) September, and (d) December.
500
90 S
30W
90 S
60W
60
90 N
60
150W 120W 90W
0
(c) Sep.
100
30
180
800 ( mgCm-2 day -1 )
30
120E 150E
700
Eq.
90E
600
Eq.
60E
500
30
30E
400
30
0
300
60
(b) Jun.
200
60
90 N
100
90 S
120E 150E
90 S
90E
60
60
60E
30
30
30E
Eq.
Eq.
0
30
30
90 N
60
(a) Mar.
60
90 N
24
K. NAKATA et al.
(d) Dec.
(b) Jun.
Fig. 14. Horizontal NPP maps estimated from the satellite data (SeaWiFS; provided by Ishizaka and
Kameda, 2003). Seasonal variations are shown here for comparison with the model: (a) March,
(b) June, (c) September and (d) December in 2000.
(c) Sep.
(a) Mar.
Characterization of Ocean Productivity Using a New Physical-Biological
25
26
K. NAKATA et al.
Fig. 15. Comparison of NPP at OWS-P between (a) the model and (b) observation. Data during
1961–1967 are taken from Miller et al. (1984).
well as the existence of high NPP area in the eastern Equatorial Pacific (offPeruvian upwelling zone). High NPP in high latitudes has a close relation to the
variation in MLD: enhanced vertical mixing contributes to the supply of nutrients
to the surface layer. In contrast, continuous nutrient supply due to upwelling
process brings high primary productivity in the eastern equatorial zone. A closer
look at the figure, however, reveals some discrepancies. The NPP is overestimated
in the Kuroshio extension, the eastern Equatorial Pacific, and the southern Pacific
sector. Despite reproducing low NPP waters in the subtropics, the model
overestimates the magnitude compared with the satellite data. Moreover, a low
NPP region that is not found in the satellite image appears in the eastern North
Pacific (around 40°N, 140°W). Here the NPP values estimated by the present
model are compared with some observations. Figure 15 gives a comparison at
OWS-P with the observation during 1961–1967 (Miller et al., 1984). At this site,
the model overestimates NPP by about 10 mgCm–2hr–1 throughout the year. In
Characterization of Ocean Productivity Using a New Physical-Biological
Model Result
ALOHA Observation (1990-1993)
800
-1
NPP (mgCm day )
27
-2
600
400
200
0
J
F
M
A
M
J
6
J
7
A
S
O
N
D
Month
Fig. 16. Simulated and observed monthly NPP at ALOHA. The observed data were obtained during
1990–1993, compiled by Karl et al. (1996).
Fig. 17. Comparison of NPP at BATS between (a) the model and (b) observation taken from Spitz
et al. (2001).
addition, the timing of the NPP peak is slightly different in both cases, being May
in the simulation vs. June according to observation. This may be attributed to
reproducibility in the timing of phytoplankton growth since the model predicts
the maximum biomass for the total phytoplankton during March–May.
Another comparison at the station ALOHA (A Long-term Oligotrophic
Habitat Assessment; 22.75°N, 158°W) is given in Fig. 16. The observed NPP
represents the annual average of the data from 1991 to 1993 compiled by Karl et
al. (1996). At this site, while the model result remains a little higher than
observation, both the seasonal pattern and the magnitude are in good agreement.
Finally, comparison of NPP at BATS is given in Fig. 17. Here the observation
refers to the modeling study by Spitz et al. (2001). At this location, the NPP peak
28
K. NAKATA et al.
0.25
0.025
0.020
-1
Global Ocean
-1
NPP (GtCday )
0.20
0.15
0.10
Export Flux (GtCday )
NPP
Export Flux
0.015
Southern
Hemisphere
0.010
0.05
0.005
Northern
Hemisphere
0.00
J
F
M
A
M 6J 7J A
Month
S
O
N
D
0.000
Fig. 18. Seasonal variations in the simulated gross NPP and export flux. The export flux stands for
sinking POC flux across a plane at a depth of 300 m.
takes place in April in the observation, but July in the present model. The delay
of the NPP peak at BATS seems to be closely related to the behavior of MLD. In
fact, as seen in the previous section (Figs. 4(c) and (d)), the observed MLD
deepens during winter and shallows drastically around March, whereas the
estimated MLD shallows near the end of May, which is a few months later. In this
way the comparisons with observation have clarified the problems with the
present model. It is encouraging, however, that, without any regional tuning, the
model could simulate the general feature of NPP in the global ocean in terms both
of spatial distribution and seasonal variation.
4. DISCUSSIONS
4.1 NPP and export flux of POC
1) Gross NPP
Figure 18 presents the seasonal variation in the simulated total NPP over the
global ocean (gross NPP hereafter). It can be seen that the gross NPP attains a
maximum during December–January and a minimum in June. Looking into the
regional difference, the NPP in the Northern Hemisphere reaches a peak in June
and a trough during December–January, whereas in the Southern Hemisphere the
situation is the direct opposite with the maximum NPP during June. This
demonstrates that the NPP in the Southern Hemisphere characterizes the seasonal
pattern of the gross NPP. From the horizontal map of simulated annual mean NPP
(panel (a) of Fig. 19), the annual amount of the gross NPP is estimated at
approximately 61.2 GtC. This is somewhat higher than the value reported by
Longhurst et al. (1995) based on satellite data. They evaluated the annual gross
Characterization of Ocean Productivity Using a New Physical-Biological
29
90 N
(a)
60
30
Eq.
30
60
90 S
0
30E
100
60E
200
90E
300
120E 150E
400
500
180
150W 120W 90W
600
700
60W
30W
0
800 ( mgCm-2 day -1 )
90 N
(b)
60
30
Eq.
30
60
90 S
0
20
30E
60E
30
90E
40
120E 150E
50
60
180
150W 120W 90W
70
80
60W
30W
0
90 ( mgCm-2 day -1 )
90 N
(c)
60
30
Eq.
30
60
90 S
0
30E
0.05
60E
90E
0.10
120E 150E
0.15
180
150W 120W 90W
0.20
0.25
60W
30W
0
0.30
Fig. 19. Horizontal distributions of the annual mean (a) NPP, (b) export flux, and (c) export ratio
(export/production ratio) resulting from the present model considering MFW.
30
K. NAKATA et al.
Table 3. Historical estimates of annual gross NPP compiled by Suzuki (1997).
NPP as 45–51 GtC. Historically, Fleming (1957) made a primary production map
of the world ocean based on the data by Steemann (1955) and the production map
that Sverdrup (1955) estimated from the distribution of upwelling area. Ryther
(1969) used this map to calculate the annual gross NPP as 20 GtC. KoblentzMishke et al. (1970) compiled over 7,000 data collected in the 1960s and
concluded that the annual gross NPP reaches 23 GtC. Platt and Subba Rao (1975)
also estimated it at 31.1 GtC. They divided the global ocean into five basins (the
Pacific, the Atlantic, the Indian, the Arctic and the Antarctic Oceans) and
calculated the NPP for each ocean basin. Martin et al. (1987) reevaluated the
result of the 14C method to conclude that the annual NPP reaches 51 GtC. Thus
the estimates of the annual gross NPP have increased gradually (see, Table 3).
Recent estimates by satellite data have a tendency to fall in a range of 50–60 GtC
(Longhurst et al., 1995; Antoine et al., 1996; Behrenfeld and Falkowski, 1997;
Ishizaka and Kameda, 2003). The model value in this study agrees fairly with the
recent estimates.
2) Export flux
Also shown in Fig. 18 is the simulated seasonal variation in export POC flux,
which is defined here as the sum of sinking carbon fluxes of diatoms,
dinoflagellates, detritus and bacteria across a plane at a depth of 300 m. The
choice of this value is just a measure of the euphotic depth where primary
production takes place, while the depth varies considerably with water area. The
model result shows that the export flux becomes the highest in February and
lowest in July. From the horizontal map of annual mean export flux (panel (b) of
Fig. 19), the annual gross flux amounts to 5.5 GtC. Therefore, about 9% of the
gross NPP (61.2 GtC) corresponds to the carbon flux being transported to the
subsurface layer as sinking particles. To make a comparison between the two
hemispheres, the NPP per unit area results in 482 and 490 mgCm–2day –1 in the
Northern and Southern Hemispheres, respectively, indicating that the two are
Characterization of Ocean Productivity Using a New Physical-Biological
31
(a) 0.15
Global Ocean
Northern Hemisphere
Southern Hemisphere
0.14
0.13
Export ratio
0.12
0.11
0.10
0.09
0.08
0.07
0.06
0.05
J✵
✴
(b)
F✶
✵
M
✷
✶
A
✸
✷
M
J ✻J
✺
Month
✹
✸
✹
✺
A
S
✼
✻
O
✽
✼
N
✵✴
✽
✵✵
✵✴
D
✵✶
✵✵
✵✶
0
20
40
MLD (m)
60
80
100
120
140
Global Ocean
Northern Hemisphere
Southern Hemisphere
160
180
200
J
F
M
A
M
J J
Month
A
S
O
N
D
Fig. 20. Simulated seasonal variations in (a) the global and the hemispherical export ratio, in
comparison with (b) the corresponding MLD profiles.
almost identical. The export flux, on the other hand, estimated at 39.7
mgCm –2day–1 in the Northern Hemisphere against 46.2 mgCm –2day –1 in the
Southern Hemisphere, suggests that the sinking process of POC in the south
dominates over the north. This implies that a much larger quantity of organic
particulates is transported to the subsurface layer in the Southern Hemisphere
because of the strengthened MLD throughout the year. Apart from the relative
importance between the two hemispheres, however, it should be noted that the
magnitude of export flux depends strongly on how one chooses the POC sinking
rate. The flux values are therefore left unexamined here since the export fluxes
in the present model are estimates only, based on a conventional value for sinking
rate of detritus.
3) Export ratio
As shown in panel (a) of Fig. 20, the export ratio, defined as the ratio of gross
export flux to gross NPP, stays at around 0.09 with slight double peaks in April
32
K. NAKATA et al.
(a)
Surface
Zooplankton
25.0
34.1
Mesograzers
Phytoplankton
83.6
4.5
21.5
11.8
28.1
10.6
Diatoms
4.9
28.2
Grazing
8.5
35.4 12.7
23.3
4.5
6.7
19.6
Phytoflagellates
17.5
37.3
36.6
3.7
32.9
8.2
23.4
Heteroflagellates
9.1
19.3
Dinoflagellates
Micrograzers
57.5
24.8
Grazing
10.7
9.4
3.5
Picophytoplankton
Mortality
12.5
37.8
21.5
Grazing
Natural Mortality
20.7
19.0
6.3
Extracellular
Release
Detritus
Feeding
134.0
8.3
Feeding
Bacteria
58.2
Respiration
Excretion
2.5
22.4
20.5
Dissolved Organic Matter
221.8
Up take
100.0
Respiration
CO2 Exchange
with the air
18.0
Degradation
Mortality
12.5
Sinking
Up take
9.6
77.9
Carbonates
386.1 x 103
9.6
Sinking
Euphotic Layer
Advection Diffusion
Lower Layer
Fig. 21. Annual carbon budget obtained from the MFW model. Fluxes and standing stocks are
normalized by the respective regional values of NPP and the total phytoplankton biomass. (a) Highlatitude area in the North Pacific (around OWS-P); and (b) subtropical area in the North Pacific
(around ALOHA).
and October. Regionally, the Northern Hemisphere shows a high value during
October–January against the Southern Hemisphere during March–May. In order
to interpret the situation, the seasonal pattern of the estimated global mean MLD
is illustrated in panel (b). From the comparison, a close relation comes to light
between the peak of export ratio and the timing when the MLD begins to deepen.
In the situation where ML develops, a rapid increase in the large-size phytoplankton
such as diatoms enhances the production of POC, and hence introduces a large
quantity of the sinking flux. It is worth noting, however, that this does not directly
explain the behavior of export ratio. Indeed, the export ratio varies seasonally, out
Characterization of Ocean Productivity Using a New Physical-Biological
(b)
33
Surface
Zooplankton
13.7
10.7
Mesograzers
Phytoplankton
28.7
5.1
8.4
3.5
25.5
Diatoms
4.1
24.1
Grazing
15.9 24.6 6.7
2.7
39.9
32.1
6.9
20.7
23.3
5.1
20.6
Phytoflagellates
Heteroflagellates
16.7
23.9
17.0 25.9
9.7
Dinoflagellates
Micrograzers
34.0
23.3
Grazing
2.9
4.5
11.5
Mortality
6.3
4.0
Picophytoplankton
37.6
19.4
Grazing
Natural Mortality
19.2
24.6
6.7
Extracellular
Release
Detritus
Feeding
126.6
52.8
Feeding
Bacteria
115.1
Respiration
Excretion
10.2
91.9
Dissolved Organic Matter
30.5
44.5
Up take
100.0
Respiration
CO2 Exchange
with the air
20.3
Degradation
Mortality
31.6
Sinking
Up take
5.9
62.5
Carbonates
405.3 x 103
5.9
Sinking
Euphotic Layer
Advection Diffusion
Lower Layer
Fig. 21. (continued).
of phase with the NPP, differing by almost half a year in the timing of peak (see
Fig. 18).
With the aim of investigating how export ratio differs with choice of the
control depth in the surface ocean, NPP and export flux were calculated for
several locations and the resulting ratios were compared with the standard
estimate choosing the depth at 300 m. The result shows that the annual mean
global export ratio diminishes to about 13% and 6% of NPP when the control
depth is set at 200 and 500 m, respectively. For deeper locations, the value is
reduced to nearly 3% and 2% for the cases of 1,000 and 2,000 m, respectively.
Thus the model seems to simulate reasonably well the situation where the organic
particulates produced in the euphotic layer are decomposed and scavenged while
34
K. NAKATA et al.
0.25
PZDN model (Case 1)
PZDN model (Case 2)
MFW model
Export ratio
0.20
0.15
0.10
0.05
0.00
J✵
F✶
M
✷
A
✸
M
✹
J✻ A
✺J
✼
Month
S✽
O
✵✴
N
✵✵
D
✵✶
Fig. 22. Comparison of the simulated seasonal variations in export ratio among the three model runs.
The conventional PZDN model was run for two different cases of plankton categorization in order
to examine relative importance of the MFW modeling.
sinking to the subsurface layer. Although the discussion is limited to the surface
ocean for the time being, the carbon cycle throughout the global ocean including
subsurface and lower layers should be investigated in our future studies.
The 234Th method is well known as a valuable tool for tracing scavenging
processes over time-scales of days to weeks, taking advantage of the isotope’s
short half-life. The method has been widely applied in the past decade to quantify
the sinking flux of POC from the upper ocean (e.g., Matsumoto, 1975; Coale and
Bruland, 1985; Buesseler et al., 1992; Buesseler, 1998). Buesseler (1998)
estimated the relationship between primary production and POC flux derived
from the 234Th technique for some typical water areas in the Equatorial Pacific,
together with BATS, off Greenland and Antarctic Polar Front, etc. He suggests
that most of the areas are characterized by low POC export fluxes compared to
NPP, with export ratio less than 0.05–0.1, and that areas of high export ratio
appear in high latitudes. The horizontal distribution of the annual mean export
ratio is presented in the panel (c) of Fig. 19 to examine the relation between NPP
and export flux resulting from the model. The simulated export ratio in the
subpolar region fluctuates seasonally, the value ranging from 0.1 to 0.35. In the
subtropical and the equatorial regions, on the other hand, the ratio is evaluated as
0.1 or below throughout the year. Both the magnitude and spatial pattern of the
simulated export ratio agree well with the estimate given by Buesseler (1998).
Comparing the simulated spatial distribution between the export ratio and the
NPP (panels (a) and (b) of Fig. 19), it turns out that the region of high export ratio
corresponds to that of high NPP in high latitudes. However, in the subtropical and
the equatorial regions there are many areas featuring high NPP and low export
ratio. This means that, in low latitudes, a large part of the NPP is decomposed in
180
150W 120W 90W
60W
30W
0
60E
90E
120E 150E
500
180
700
60W
30W
0
800 ( mgCm-2 day -1 )
150W 120W 90W
600
90 N
200
300
400
500
600
700
800 ( mgCm-2 day -1 )
Fig. 23. Horizontal distributions of the annual mean NPP resulting from two
comparison runs, (a) Case 1 and (b) Case 2, of the PZDN model.
100
90 S
60
30
Eq.
0
0
(b)
(a)
0.05
30E
0.05
30E
60E
60E
0.10
90E
0.10
90E
0.15
120E 150E
0.15
120E 150E
180
180
0.25
0.20
0.25
150W 120W 90W
0.20
150W 120W 90W
30W
30W
0.30
60W
0.30
60W
0
0
Fig. 24. Horizontal distributions of the annual mean export ratio
resulting from two comparison runs, (a) Case 1 and (b) Case 2, of the
PZDN model.
90 S
60
30
Eq.
30
30E
400
30
0
300
60
(b)
200
60
90 N
100
90 S
120E 150E
90 S
90E
60
60
60E
30
30
30E
Eq.
Eq.
0
30
30
90 N
60
(a)
60
90 N
Characterization of Ocean Productivity Using a New Physical-Biological
35
36
K. NAKATA et al.
700
-1
NPP (mgCm day )
600
-2
500
400
300
200
100
❖❈M✤❧n❝❞❦✤✬❇r❞✤✵✭
❖❈M✤❧n❝❞❦✤✬❇r❞✤✶✭
L❊
(a)
0
-70 60
-60 50
-50 40
-40 30
-30 20
-20 10
-10 Eq.
0
70S
10
10
20
20
30
30
40 50N
50
40
Latitude
-2
-1
Export Flux (mgCm day )
100
❖❈M✤❧n❝❞❦✤✬❇r❞✤✵✭
❖❈M✤❧n❝❞❦✤✬❇r❞✤✶✭
L❊
90
80
70
60
50
40
30
20
10
(b)
0
-70 60
-60 50
-50 40
-40 30
-30 20
-20 10
-10 Eq.
0
70S
10
10
20
20
30
30
40
40
50
50N
Latitude
0.40
❖❈M✤❧n❝❞❦✤✬❇r❞✤✵✭
❖❈M✤❧n❝❞❦✤✬❇r❞✤✶✭
L❊
0.35
Export ratio
0.30
0.25
0.20
0.15
0.10
0.05
(c)
0.00
-70 60
-60 50
-50 40
-40 30
-30 20
-20 10
-10 Eq.
0
70S
10
20
30
30
40
40
50
50N
Latitude
Fig. 25. Meridional distributions of the annual mean (a) NPP, (b) export flux and (c) export ratio
resulting from the MFW model and the PZDN model. The conventional PZDN model was run for
two cases and compared with the MFW model. Each map stands for the zonal average over an interval
from 180° to 150°W.
Characterization of Ocean Productivity Using a New Physical-Biological
37
the euphotic layer, and therefore the sinking flux becomes much smaller than in
high latitudes. This suggests the relative importance of MFW in regions of low
latitude.
4.2 Carbon budgets
With a view to gaining a better insight into the food web structure, the annual
carbon budgets from the model are compared in Fig. 21 between a region of high
latitude (50–60°N) and a subtropical region (20–30°N). In each diagram, fluxes
and standing stocks are normalized by the total NPP and the total phytoplankton
biomass. Note that the flux from carbonates to phytoplankton is equivalent to the
NPP. While little difference is seen in the phytoplankton abundance between the
two, other compartments show the following characteristics: for zooplankton,
meso- and micrograzers tend to dominate in high latitudes; bacteria concentrate
in the subtropics; labile DOC therefore tends to concentrate in the high-latitude
region with smaller consumption by bacteria; detritus also inclines toward the
high latitude zone, directly reflecting the difference in sinking flux between the
two. In these diagrams, biological processes with carbon flux exceeding 20% of
NPP are drawn with a bold line. In high latitudes, the carbon flow is mainly
characterized by the formation and consumption of detritus as well as respiration
by zooplankton. In the subtropics, on the other hand, the flux is not characterized
by zooplankton processes, but by the formation of detritus and consumption
processes associated with bacteria. The NPP of diatoms also remains small
compared with the high-latitude region. Moreover, in response to the dominant
contribution by bacteria, detritus is rapidly decomposed to cause a small settling
flux of particulate matter.
Another feature in the carbon budget can be seen in the grazing and feeding
processes of zooplankton. The grazed amount of diatoms and dinoflagellates by
mesograzers almost doubles in the high-latitude region compared to the subtropical
region. The total grazing flux of zooplankton in the food web placing mesograzers
on the top also shows the same tendency: the value in the high-latitude region
becomes 2–3 times as high as in the subtropical region. On the other hand, the
feeding flux of bacteria by micrograzers and heteroflagellates in the subtropical
region is estimated to be about double that in the high-latitude region. These
findings corroborate the potential importance of MFW in low latitudes.
4.3 Importance of the MFW model
In order to elucidate the role of MFW in the simulation of GOCC, the
conventional PZDN model was run to compare the regional feature in NPP and
export flux with the present result. In the comparison run, two schemes were
provided for setting the plankton compartments; Case1: Both phyto- and
zooplankton represent the large-size single species group such as diatoms or
copepods that dominates generally in high latitudes. As biological parameters,
values of 2.2 day–1, 5 × 10–4 m 3mgC –1day–1 and 0.5 mday–1, respectively, are
given for the maximum growth rate at optimum water temperature (Gmax), natural
38
K. NAKATA et al.
mortality rate at 0°C ( δP), and sinking velocity (wP) of phytoplankton, and the
maximum grazing rate (R max) and the natural mortality rate (δ Z) of zooplankton
are chosen as 0.65 day–1 and 8 × 10–4 m3mgC –1day –1, respectively. Other
parameters remain unchanged (see, Table 2). Case2: Since the setting of Case1
alone does not explain the dominant contribution of small-size plankton in low
latitudes, here both phyto- and zooplankton are defined as an average compartment
over the respective MFW categories. The biological parameters are tentatively
chosen by the biomass-weighted geometric mean: G max = 2.8 day–1, δP = 3.5 ×
10 –3 m3mgC –1day–1, wP = 0.5 mday–1 for phytoplankton, and Rmax = 1.13 day–1,
δZ = 1.64 × 10–3 m3mgC –1day–1 for zooplankton. Note that carbon and nutrients
in this case turns over more rapidly than in Case1.
Figure 22 compares the seasonal variation in global mean export ratio among
the three model runs. Obviously the ratio from the PZDN model represents a sharp
contrast with the MFW model, showing that the values stay at higher level
throughout the year for both cases. Case1 of the PZDN model predicts that the
export ratio goes up to nearly 0.20 during January–February, which clearly
differs from the MFW model result, which has double peaks in April and October.
The seasonal pattern in Case2 appears to be an average of Case1 and the MFW
model. This suggests that the faster the ecosystem turns over, the smaller the
export ratio tends to be evaluated. Global distributions of annual mean NPP and
export ratio (based on the sinking flux at a depth of 300 m) resulting from the
comparison runs are presented in Figs. 23 and 24, respectively. Both NPP maps
show that the high production area distributes in high latitudes and the equatorial
upwelling region where the ML develops seasonally. They also reveal the
appearance of a longitudinal low NPP zone at around 30°N and S. The annual
gross NPP in these cases came to approximately 37 GtC (Case1) and 41 GtC
(Case2). The maps of export ratio reveal a general tendency that the value
becomes high in low NPP regions in the subtropical gyre where the export flux
dominates over NPP. The situation is much clearer in Case1: the ratio goes up
above 0.20, even exceeding 0.30 locally. Such characteristics are consistent with
the numerical study of the North Pacific by Kawamiya et al. (2000) with a typical
PZDN model.
For further investigation, numerical results from the three model runs were
averaged over an interval from 180° to 150°W and the meridional distributions
of the zonal-mean NPP, export flux and export ratio are compared in Fig. 25. Both
of the PZDN model cases seem to follow the same tendency, although one is more
pronounced than the other in the spatial pattern: NPP north and south of the 20°
latitude stays at a uniformly low level compared to the MFW model, and an area
of NPP minimum appears around 25°N and S, which is not observed in the MFW
model. Meridional export flux distribution shows little difference among the
three, except for slight falls in the PZDN model at around 25°N and S, corresponding
to the NPP minimum. In contrast to the MFW model, which estimates low export
ratio in the subtropical gyres, especially in the areas around the 25° latitudes, the
PZDN model estimates a very high value there. The situation is more evident in
Case1 than in Case2. In addition, values in high latitudes (above 35°) are
Characterization of Ocean Productivity Using a New Physical-Biological
39
estimated as considerably higher in the PZDN model than in the MFW model.
Accordingly, it turned out that the classical PZDN model alone does not reproduce
global features in the oceanic carbon and nutrient cycles. This attests to the
potential importance of the MFW model capable of simulating regional succession
from grazing food web to microbial food web.
4.4 Problem with coarse grid resolution
The new production associated with nutrients supplied from the lower layer
must be identical to the export flux. In our future work, we should perform a
detailed analysis of the seasonal variation in biological processes including new
production and other nutrient kinetics of primary importance, together with the
evaluation of NPP and export flux. For this purpose, there is an urgent need for
an improvement in oceanic flow field. Although the result of our biological model
seems reasonable as a whole, detailed inspection reveals that the reproducibility
of nutrient distribution around the Equator is not acceptable. The problem seems
to be closely related to the simulated flow field. In the hydrodynamic simulation,
for example, the Equatorial Counter Current could not be reproduced in the
western sector and the Equatorial Undercurrent remained significantly weak.
These shortcomings must be due to coarse spatial resolution. In this connection,
with the aim of examining the model’s sensitivity, another simulation with a finer
resolution of 1° was run and the resulting east-west flow field along the 180°E
longitude in the North Pacific was compared with the geostrophic circulation
diagram drawn by Wyrtki and Kilonsky (1984). Although still inadequate to
explain the intense Equatorial Undercurrent remaining within a narrow equatorial
zone, the flow field resulting from the finer resolution was a great improvement
over the present simulation. In the future, therefore, an improvement in the
simulation of flow field around the Equator is required to improve our discussions
of GOCC and nutrient dynamics.
5. SUMMARY AND CONCLUSIONS
The 3-D coupled physical and biological model considering processes
relevant to MFW has been developed to evaluate NPP in the world ocean. The
seasonal variation in flow field was estimated by the physical model together with
the vertical eddy diffusivity and incorporated into the biological model to
reproduce the seasonal variation in the ocean ecosystem. The numerical study
reproduced the general tendency of temporal and spatial variations in both
phytoplankton and zooplankton compartments, except for the equatorial region,
which reflects a deficiency in the OGCM associated with coarse grid resolution.
The annual gross NPP was evaluated as 61.2 GtC by the present simulation, which
agreed fairly well with recent estimates from satellite data. The annual gross
export flux of POC toward the subsurface layer was found to be 5.5 GtC. This
value shows that about 9% of the NPP is transported to the subsurface layer and
the remainder is respired by zooplankton and decomposed by bacteria in the
euphotic layer. The export ratio, defined by the ratio of export flux to NPP, turned
40
K. NAKATA et al.
out to stay high in the subpolar region, corresponding to the high NPP region. In
the subtropical and the equatorial regions, on the other hand, the export ratio
remained small at 0.1 or smaller throughout the year, no matter how high the NPP
value was. The mechanism was explained by a carbon budget analysis: the result
showed that a low export ratio in the low-latitude region may be closely related
to the enhanced decomposition through MFW.
Perhaps the most interesting finding of this study is that, in addition to the
conventional grazing food web modeling, the inclusion of MFW becomes critical
in gaining a better insight into the GOCC. Moreover, the present model reproduces
the general characteristics of the global ocean ecosystem without any local
calibration of the model parameters. Every parameter is left uniform throughout
the ocean, and the model can explain regional differences in the ecosystem solely
by considering seasonal variation in the forcing functions such as meteorological
parameters, flow field and vertical eddy diffusivity. Since our aim is to elucidate
carbon and nutrient cycles throughout the ocean, the present study must be
continued to improve our understanding of nutrient dynamics, new production
and export flux, as well as to improve in the reproducibility of the hydrodynamic
model.
Acknowledgements—This study was performed as part of the “GCMAPS program”
(Global carbon cycle and related mapping based on satellite imagery) promoted by the
Ministry of Education, Culture, Sports, Science and Technology of Japan. We thank Dr.
Trevor Platt, Bedford Institute of Oceanography, Dr. Katsumi Matumoto, Geological
Survey of Japan, and Dr. Shyoichiro Nakamoto, Advanced Earth Science and Technology
Organization, for their valuable comments.
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