Introduction
In response to the polar amplification of global climate change, air
temperature in the lower atmosphere is increasing twice as fast in the Arctic
as in temperate regions. By the end of the century, model projections suggest
an average increase in the surface air temperature of 3.7 ∘C
relative to 1981–2000 (ACIA report, 2005). In response to Arctic warming,
plankton production and the biogeochemistry of the Arctic Ocean (AO) are
rapidly evolving. Changes in phytoplankton communities (Li et al., 2009) as
well as their phenology in spring (Kahru et al., 2011) and autumn (Ardyna et
al., 2014) are being observed. Overall, the AO tends to be more productive
(Bélanger et al., 2013) and is taking up more atmospheric carbon dioxide
(1996–2007; Manizza et al., 2013). In the long term, model projections
suggest an increase in spatially integrated primary production (PP) by the
end of the twenty-first century (Vancoppenolle et al., 2013).
The AO is the basin most influenced by continental freshwater. It receives
10 % of the freshwater that flows into the global ocean, but represents
only 1 % of the global ocean volume (Opshal et al., 1999). Circum-Arctic
rivers are potentially a significant source of inorganic nutrients and
organic matter for shelf seas (Le Fouest et al., 2013; Tank et al., 2012).
10 % of the global riverine inputs of organic carbon are conveyed into
the AO (Rachold et al., 2004). This fraction is projected to increase in the
near future due to the accelerated thawing of permafrost (Frey et al., 2007).
This pool of organic matter enters the carbon cycle, but little is known
about its fate and pathways within the plankton ecosystem in Arctic waters
prior to being exported into the Atlantic Ocean.
Bacterioplankton is a major biological component involved in the degradation
and mineralization of dissolved organic matter in Arctic waters
(Ortega-Retuerta et al., 2012a). It can significantly affect the fate and
distribution of organic matter within the entire water column (Bendsten et
al., 2002) as well as the microbial food web activity through the
assimilation of remineralized nitrogen. However, the contribution of Arctic
bacterioplankton to plankton production in the context of Arctic warming
remains unknown. Despite the fact that the AO basin now acts as a sink for
atmospheric carbon dioxide (1996–2007; Manizza et al., 2013), the balance
between autotrophy and heterotrophy may change in the future based on
observations of enhanced stratification of the water column (Li et al.,
2009), increased sea temperature (Timmermans et al., 2014; Steele et al.,
2008), which acts as a key driver of Arctic bacterioplankton metabolism
(Piontek et al., 2014; Bendsten et al., 2002), and changes in the riverine
inputs of nutrients due to an increase in freshwater discharge (Shiklomanov
and Lammers, 2011). In near-shore AO waters, riverine inputs already sustain
part of the bacterial activity (e.g. Vallières et al., 2008).
Using a relatively simple biogeochemical modelling approach, Tank et
al. (2012) shed light on the potential impact of riverine nutrient inputs on
the PP of the AO. In the present study, we propose building on the static
view provided by the work of Tank et al. (2012) by explicitly modelling the
effect of the interactions between riverine dissolved organic nitrogen (RDON)
and bacterioplankton. The objective is to use a pan-Arctic ocean–sea ice
coupled model to quantify the contribution of usable RDON processed by marine
bacterioplankton to the production of both bacterioplankton and phytoplankton
in a scenario of melting sea ice over the period 1998–2011.
Material and methods
The physical model
We used the MIT general circulation model (MITgcm) (Marshall et al., 1997)
coupled with a sea-ice model. The model is configured on a “cubed-sphere”
grid encompassing the Arctic domain with open boundaries at
≈ 55∘ N in the Atlantic and Pacific sectors. Prescribed
boundary conditions for potential temperature, salinity, flow, and
sea-surface elevation are provided from previous integrations of a global
configuration of the same model (Menemenlis et al., 2005). The grid has a
variable horizontal resolution with an average mesh of ∼ 18 km. The
mesh resolves major Arctic straits, including many of the channels of the
Canadian Archipelago. The sea-ice and fluid dynamics equations are solved on
the same horizontal mesh. The 50-level vertical grid is height based, varying
from 10 m thick near the surface to ∼ 450 m at a depth of
∼ 6 km. Bathymetry is derived from the US National Geophysical Data
Center (NGDC) two-minute global relief data set (ETOPO2), which uses the
International Bathymetric Chart of the Arctic Ocean (IBCAO) product for
Arctic bathymetry (Jakobsson et al., 2008). The ETOPO2 data are smoothed to
the model's horizontal mesh and mapped to the ocean's vertical levels using a
“lopped cell” strategy (Adcroft et al., 1997), which permits an accurate
representation of the ocean bottom boundary.
The ocean model's hydrography is initialized with observations taken from the
Polar Science Center Hydrographic Climatology (PHC) 3.0 database (Steele et
al., 2001). Initial sea-ice
distributions are taken from the pan-Arctic Ice-Ocean Modeling and
Assimilation System data sets (Zhang and Rothrock, 2003). Atmospheric
forcings (10 m surface winds, 2 m air temperature and humidity, and
downward longwave and shortwave radiation) are taken from the 6-hourly data
sets of the Japanese 25-year ReAnalysis (JRA-25; Onogi et al., 2007). Monthly
mean estuarine fluxes of freshwater are based on the Arctic Runoff database
(Lammers et al., 2001; Shiklomanov et al., 2000). The sea-ice component of
the coupled model follows the viscous-plastic rheology formulation of
Hibler (1979) with momentum equations solved implicitly on a C-grid (Arakawa
and Lamb, 1977) using a procedure based on Zhang and Hibler (1997). Fluxes of
momentum into ice due to the overlying atmospheric winds and momentum fluxes
between sea ice and the ocean are calculated by solving for the momentum
balance at each surface grid column (Hibler and Bryan, 1987). This model
configuration was previously used to study the Arctic freshwater budget
(Condron et al., 2009). Modelling studies of Condron et al. (2009) compared
to observations by Serreze et al. (2006) concluded that this model
configuration is able to realistically represent the freshwater budget of the
AO, including the import and export of freshwater from the Bering and Fram
straits and from the Canadian Archipelago.
The riverine DON (RDON) discharge
To realistically represent the RDON flux in the AO in our biogeochemical
model, we follow the approach adopted by Manizza et al. (2009), which is
based on seasonally explicit regression relationships. These relationships
use co-variations between water yield and dissolved organic carbon (DOC)
concentrations in circum-Arctic rivers to define riverine DOC (RDOC) monthly
averaged fluxes for 10 regions in the pan-Arctic domain. These regions are
the Barents Sea, Kara Sea, Laptev Sea, East Siberian Sea, Chukchi Sea, Bering
Strait, Beaufort Sea, Canadian Archipelago, Hudson Bay, and Hudson Strait
using published watershed areas and seasonal water runoff (Lammers et al.,
2001). The approach uses empirical relationships quantifying the co-variation
between discharge and RDOC to scale the Lammers et al. (2001) discharge
estimates into estimates of RDOC export. Estimates of RDOC export for
December–March, April–July, and August–November were divided into monthly
bins according to measured distributions of RDOC export for those months in
Arctic rivers. For each season, [RDOC]–discharge relationships were
developed. North American and Eurasian rivers were considered separately.
Data from the Yukon, Mackenzie, and Kuparuk rivers were used to define a
runoff–[RDOC] relationship for drainage areas in North America, and data
from the Ob', Yenisey, and Lena rivers were used to define a runoff–[RDOC]
relationship for drainage areas in Eurasia. RDOC for the Yenisey, Ob', Lena,
and Mackenzie were collected as part of the Pan-Arctic River Transport of
Nutrients, Organic Matter, and Suspended Sediments (PARTNERS) project
(McClelland et al., 2008). RDOC concentrations for the Kuparuk River were
collected as part of the NSF Study of the Northern Alaska Coastal System
(SNACS, http://www.arcus.org/arcss/snacs/index.php). In all cases,
discharge data were acquired from ArcticRIMS (http://rims.unh.edu/).
Recent sampling efforts on these rivers have provided exceptional seasonal
coverage (McClelland et al., 2008) and the total annual discharge of RDOC in
the model is 37.7 TgC yr-1, which is consistent with the estimate of
Raymond et al. (2007). To initialize the model, we used the three-dimensional
RDOC field obtained from the 3-decade integration of the model by Manizza et
al. (2009). After that time, RDOC distributions are relatively steady,
because the flushing time for tracers through the surface waters of the basin
is of the order of a decade. RDOC was converted into nitrogen currency (RDON)
using a molar C : N ratio of 40:1 (Tank et al., 2012; Köhler et al.,
2003). We assume that 15 % of the RDON entering the model at river grid
cells is usable by bacterioplankton (e.g. Wickland et al., 2012).
The biogeochemical model
We couple to the MITgcm physical model a biogeochemical model that explicitly
represents the plankton ecosystem dynamics. The biogeochemical model is
improved from previous applications in sub-Arctic (Le Fouest et al., 2005,
2006) and Arctic waters (Le Fouest et al., 2011, 2013b). In the model,
nitrogen is the currency and it includes 10 compartments (i.e. nine in the
pelagic domain + RDON that couples the marine and terrestrial cycling of
nitrogen), chosen according to the ecosystem structure observed in the AO.
Phytoplankton is size-fractionated into large (> 5 µm)
and small (< 5 µm) phytoplankton (LP and SP,
respectively). These two compartments encompass the major phytoplankton
groups relevant for plankton dynamics and biogeochemistry in the Arctic
waters (e.g. Li et al., 2009; Coupel et al., 2012). The two zooplankton
compartments represent large (LZ, mainly copepods) and small (SZ,
protozooplankton) organisms. Dissolved inorganic nutrients are nitrate
(NO3) and ammonium (NH4). Detrital (i.e. produced by the
biogeochemical model components) particulate and dissolved organic nitrogen
(dPON and dDON, respectively) close the nitrogen cycle. Bacterioplankton
(BACT) are explicitly represented following the model of Fasham et
al. (1990). They grow on NH4, dDON, and on the usable fraction of RDON
(see the Appendix for details). LP and SP growth depends on light, NO3
and NH4 availability according to Liebig's law of the minimum. LZ graze
on LP and SZ, whereas SZ graze on SP and BACT. Fecal pellets and LP basal
mortality fuel the dPON pool. The dDON pool is made of unassimilated nitrogen
resulting from SZ grazing, SP, SZ and BACT basal mortality and dPON
fragmentation. BACT release, LZ excretion and unassimilated nitrogen
resulting from SZ grazing are the sources of NH4 in the model. NH4
is converted into NO3 through the nitrification process. For
phytoplankton, nitrogen is converted into carbon using the Redfield carbon to
nitrogen (C : N) molar ratio (106:16; Redfield et al., 1963) and into Chl
using variable C : Chl mass ratios computed according to a modified version
of the phytoplankton photoacclimation model of Cloern et al. (1995). The
plankton biogeochemical model (Fig. 1) is fully detailed in the Appendix.
Differential equations are given in Table 1, whereas biological parameters
are given in Table 2.
Differential equations for the 10-component biogeochemical model:
nitrate (NO3), ammonium (NH4), large and small phytoplankton (LP
and SP, respectively), large and small zooplankton (LZ and SZ, respectively),
bacterioplankton (BACT), detrital particulate and dissolved organic nitrogen
(dPON and dDON, respectively), and usable riverine dissolved organic nitrogen
(RDON).
∂NO3∂t=-∇⋅uNO3-K⋅∇NO3+nitrif-limNO3LPμLPLP-limNO3SPμSPSP
∂NH4∂t=-∇⋅uNH4-K⋅∇NH4-limNH4LPμLPLP-limNH4SPμSPSP-nitrif-UbactNH4BACT1-geBACT+egSZ(1-geSZ)GSZSZ+exLZLZ
∂LP∂t=-∇⋅uLP-K⋅∇LP+μLPLP-GLZpfLPLZ-mLPLP+∂∂zsedlpLP
∂SP∂t=-∇⋅uSP-K⋅∇SP+μSPSP-GSZpfSPSZ-mSPSP
∂LZ∂t=-∇⋅uLZ-K⋅∇LZ+assimLZGLZLZ-mLZLZ2-exLZLZ
∂SZ∂t=-∇⋅uSZ-K⋅∇SZ+assimSZGSZSZ-mSZSZ2-GLZ(1-pfLP)LZ
∂BACT∂t=-∇⋅uBACT-K⋅∇BACT+UbactNH4BACTge+UbactdDONlBACTgeBACT-mBACTBACT-GSZ1-pfSPSZ
∂dPON∂t=-∇⋅udPON-K⋅∇dPON+(1-assimLZ)GLZLZ+mLZLZ2+mLPLP+∂∂zseddpondPON-fgdPON
∂dDON∂t=-∇⋅udDON-K⋅∇dDON+fgdPON+mSZSZ2+mSPSP+mBACTBACT+(1-egSZ)(1-geSZ)GSZSZ-UbactDONlBACTpfDONl1-geBACT
∂RDON∂t=-∇⋅uRDON-K⋅∇RDON-UbactDONlBACT1-pfDONl1/ratioCN1-geBACT
Conceptual diagram of the biogeochemical model. The 10 state
variables are nitrate (NO3), ammonium (NH4), large
(> 5 µm) and small (< 5 µm)
phytoplankton, large zooplankton, protozooplankton, bacterioplankton,
detrital particulate and dissolved organic nitrogen (dPON and dDON,
respectively), and usable riverine dissolved organic nitrogen (RDON). Green,
red and blue arrows represent nutrient uptake, grazing, and nitrogen
recycling, respectively.
Nitrate data used for the model initialization are from the World Ocean Atlas
2005 (National Oceanographic Data Centre, 2006). LP and SP are assigned a
constant field over the model grid (0.2 and 0.002 mmol N m-3 in the
top eight layers and below, respectively; e.g. Sherr et al., 2003; Ducklow,
1999, Taniguchi, 1999). The same conditions are imposed for BACT (e.g. Sherr
et al., 2003; Ducklow, 1999). LZ and SZ are assigned a constant field over
the model grid (0.1 and 0.001 mmol N m-3 in the top eight layers and
below, respectively; e.g. Sherr et al., 2003, Taniguchi, 1999). The same
conditions are imposed a priori for dPON. A value of 1 mmol N m-3 of
NH4 (e.g. Kristiansen et al., 1994) and dDON (e.g. Charria et al., 2008)
is imposed at each grid cell. Boundary conditions at the North Atlantic and
North Pacific sectors are data from the World Ocean Atlas 2005 (NODC, 2006)
for NO3, and null for the remaining nine biogeochemical tracers. Apart
from RDON, there are no riverine inputs for the remaining nine biogeochemical
tracers.
Biogeochemical model parameters.
Symbol
Description
Value
Units
Nutrients
nitrifmax
Maximum NH4 nitrification rate
0.05
d-1
KnitrifN
Half-saturation constant for NH4 nitrification
0.07
mmolNm-3
Phytoplankton
kw
Light attenuation coefficient due to water
0.04
m-1
knonchl
Light attenuation coefficient due to nonchlorophyllous matter
0.05
m-1
KNO3LP
Half-saturation constant for NO3 use by LP
1
mmolNm-3
KNO3SP
Half-saturation constant for NO3 use by SP
1
mmolNm-3
KNH4LP
Half-saturation constant for NH4 use by LP
0.5
mmolNm-3
KNH4SP
Half-saturation constant for NH4 use by SP
0.1
mmolNm-3
KELP
Photoacclimation parameter
8
Ein m-2d-1
KESP
Photoacclimation parameter
4
Ein m-2d-1
ChlCmaxLP
Maximum Chl to C ratio for LP
0.0125
gg-1
ChlCmaxSP
Maximum Chl to C ratio for SP
0.07
gg-1
μmaxLP
Maximum growth rate for LP
1.4
d-1
μmaxSP
Maximum growth rate for SP
1.4
d-1
αSP
Initial slope of the photosynthesis–irradiance curve
5.5
mgC(mgChl)-1(Einm-2d-1)-1
αLP
Initial slope of the photosynthesis–irradiance curve
7.5
mgC(mgChl)-1(Einm-2d-1)-1
sed_lp
LP sinking rate
2
m-1
mLP
LP basal mortality
0.05
d-1
mSP
SP basal mortality
0.05
d-1
Zooplankton
GLZmax
Maximum grazing rate for LZ
0.3
d-1
λ
Ivlev constant for LZ
0.5
(mmolNm-3)-1
GSZmax
Maximum grazing rate for SZ
1
d-1
KG
Half-saturation constant for SZ grazing
0.8
mmolNm-3
assimLZ
LZ assimilation efficiency
70
%
geSZ
SZ growth efficiency
30
%
egSZ
dDON egestion by SZ
40
%
exLZ
NH4 excretion by LZ
0.05
d-1
mSZ
SZ mortality
0.05
(mmolNm-3)-1
mLZ
LZ mortality
0.2
(mmolNm-3)-1
Bacterioplankton
Ubactmax
Maximum growth rate
1
d-1
KNH4BACT
Half-saturation constant for NH4 uptake
0.1
mmolNm-3
KDONlBACT
Half-saturation constant for DONl uptake
0.1
mmolNm-3
geBACT
Growth efficiency
20
%
mBACT
Basal mortality
0.05
d-1
Detritus
sed_dpon
dPON sinking rate
100
md-1 (mmolNm-3)-1
fg
dPON fragmentation
0.05
d-1
Coupled model integrations
The model is spun up by repeating the January 1980–December 1989 decade
twice. It is thereafter initialized with the physical and biogeochemical
fields obtained from 31 December 1989 to run the 1990–2011 time period. Two
simulations are then carried out: without usable RDON removal by
bacterioplankton (our control run, hereafter CTRL run) and with usable RDON
removal by bacterioplankton (hereafter RIV run). The difference between the
two simulations provides information on the potential impact of the
interactions between bacterioplankton and usable RDON on bacterioplankton
production (BP), nutrient regeneration, and ultimately primary production
(PP) in the Arctic basin.
Results
Primary production
Shelf seas influenced least by riverine inputs of RDON show comparable
simulated annual rates of total PP in the CTRL and RIV runs (Fig. 2). In the
Barents Sea, simulated PP averaged over 1998–2011 reaches up to
∼ 80 gC m-2 yr-1, in line with previous remote sensing
estimates (up to 70–80 gC m-2 yr-1 on average over 1998–2010;
Bélanger et al., 2013). In the central Chukchi Sea, simulated PP lies
between 50 and 80 gC m-2 yr-1, in agreement with the observed
range (15–80 gC m-2 yr-1 on average over 1998–2007;
Bélanger et al., 2013).
Mean annual ocean primary production (gC m-2) over 1998–2011
(a) without RDON removal by bacterioplankton (CTRL run) and
(b) with RDON removal by bacterioplankton (RIV run), and
(c) the absolute difference (gC m-2; RIV run – CTRL run).
The largest differences in total PP between the two runs are found in the
river-influenced Eurasian seas (East Siberian Sea, Laptev Sea, and Kara Sea;
Fig. 2). In the CTRL run, maximum simulated PP rates reach
∼ 30 gC m-2 yr-1, which is more than 3-fold lower than
satellite-derived and in situ estimates that can exceed
100 gC m-2 yr-1 (Bélanger et al., 2013; Codispoti et al.,
2013; Sakshaug, 2004). In contrast, PP rates simulated in the RIV run
(80–90 gC m-2 yr-1) show a better agreement with observations.
The increase in the 1998–2011 averaged annual PP in the RIV run relative to
the CTRL run is due to the increase in NH4-supported PP (Fig. 3d, e
and f). In contrast, overall, new PP remains unaffected by the bacterial use
of RDON (Fig. 3a, b and c). In the Kara Sea, Laptev Sea, East Siberian Sea,
and Beaufort Sea, simulated new PP is mostly
< 20 gC m-2 yr-1, in agreement with previously
estimated rates (< 17 gC m-2 yr-1; Sakshaug, 2004). New
PP rates simulated by the model in the more productive areas are also in line
with Sakshaug's estimated rates. In the Chukchi Sea, new PP generally lies in
the 10–30 gC m-2 yr-1 range and reaches
> 100 gC m-2 yr-1 at the sea opening
(5–160 gC m-2 yr-1; Sakshaug, 2004). Simulated new PP is up to
∼ 70 gC m-2 yr-1 in the Barents Sea, close to the value
given by Sakshaug (up to 100 gC m-2 yr-1; 2004). In the
Greenland and Labrador seas, the simulated new PP rates are ∼ 50 and
∼ 30 gC m-2 yr-1, respectively
(40–45 gC m-2 yr-1; Sakshaug, 2004).
Mean annual new primary production (gC m-2; upper panels) and
NH4-supported primary production (gC m-2; lower panels) over
1998–2011 simulated in the CTRL run (left panels a and d)
and the RIV run (middle panels b and e). Right panels
(c and f) provide the absolute difference (gC m-2;
RIV run – CTRL run).
Direct estimates of NH4-supported PP based on measurements are rare in
Arctic coastal waters. Nevertheless, they can be crudely derived by
subtracting new PP from total PP estimated by Sakshaug (2004). In the
Eurasian and North American shelves, NH4-supported PP in the CTRL run is
< 10 gC m-2 yr-1 (Fig. 3d) overall. This is low
relative to the rates derived from Sakshaug's data, which would range from
∼ 25 to ∼ 40 gC m-2 yr-1. By contrast, in the RIV
run, rates simulated in offshore shelf waters are
∼ 10–30 gC m-2 yr-1. However, closer to the coast, local
rates reach 40–50 (Laptev Sea) and 70–80 gC m-2 yr-1 (Kara
Sea; Fig. 3e).
Averaged over 1998–2011, the total PP simulated by the model and integrated
over the whole AO is 662 ± 91 TgC yr-1 in the CTRL run and
717 ± 95 TgC yr-1 in the RIV run. These values are within the
range of previously reported rates based on remote sensing or in situ data
(385–1008 TgC yr-1, Bélanger et al., 2013; Codispoti et al.,
2013; Hill et al., 2013; Arrigo and van Dijken, 2011). Between the two model
runs, the annual total PP increased by ∼ 8 %, on average, between
1998 and 2011. In September–October, when the simulated sea-ice
concentration reaches its seasonal minimum, the annual total PP increase is
more than twice this value (∼ 18 %, on average).
Bacterioplankton activity
The PP increase is tightly linked to a higher bacterioplankton activity that
promotes RDON recycling into nutrients usable by both phytoplankton and
bacterioplankton. The bacterioplankton biomass (BB), integrated between the
sea surface and 50 m and averaged over April–June (spring) and
July–September (summer), is shown in Fig. 4. As for PP, the Barents and
Chukchi seas show comparable levels of BB in CTRL and RIV runs. In the
Chukchi Sea, the BB simulated in spring (< 100–250 mgC m-2;
Fig. 4a and b) overlaps with the measured range (222–358 mgC m-2;
Kirchman et al., 2009). It is similar in summer, when simulated
(∼ 100 mgC m-2 to > 800 mgC m-2; Fig. 4d
and e) and measured BB levels (250–507 mgC m-2; Kirchman et al.,
2009; Steward et al., 1996) are higher than in spring. In the Barents Sea,
the simulated BB increases from < 100 mgC m-2 in spring to
< 250 mgC m-2 in summer, falling within the measured range
(from ∼ 80 mgC m-2 in spring to ∼ 400 mgC m-2 in
summer, on average; Sturluson et al., 2008). In the highly river-influenced
shelf seas, the two runs show notable differences in their simulated BB
(Fig. 4c and f). In the central part of the Kara Sea, influenced by the Ob'
and Yenisey river plumes, BB measured in late summer along a south–north
transect from the Yama Peninsula to the Novaya Zemlya island is reported to
range from ∼ 0.1 to 7 mgC m-3 (Sazhin et al., 2010). For the
same time period and along a comparable transect, simulated values of BB are
< 2 mgC m-2 in the CTRL run. However, in the RIV run, BB
increases up to ∼ 6–7 mgC m-3 to match the values measured by
Sazhin et al. (2010).
The depth-integrated (0–50 m) bacterioplankton production (BP) simulated in
both the CTRL and RIV runs in summer in the Chukchi Sea
(< 280 mgC m-2 d-1) is consistent with measurements
reported for the same season (5–301 mgC m-2 d-1; Kirchman et
al., 2009; Rich et al., 1997; Steward et al., 1996). In the Beaufort Sea,
influenced by the Mackenzie River plume, simulated BP is lower than
∼ 6 mgC m-2 d-1 in the CTRL run, which is far below
measurements made within the area (25–68 mgC m-2 d-1;
Ortega-Retuerta et al., 2012a; Vallières et al., 2008). By contrast, in
the RIV run, simulated BP (< 30 mgC m-2 d-1) approaches
the lower range of observations. Similarly, BP simulated in the CTRL run for
the Kara Sea (< 30 mgC m-2 d-1) does not exceed the
first mid-range of measurements given by Meon and Amon (2004;
12–79 mgC m-2 d-1). In the RIV run, the simulated BP
(∼ 4–90 mgC m-2 d-1) overlaps the measured range
(12–79 mgC m-2 d-1; Meon and Amon, 2004) to reach up to
120 mgC m-2 d-1 locally. This result is consistent with
enrichment experiments conducted with surface oceanic water sampled in the
Beaufort Sea that showed a 43 % increase in BP when Mackenzie River water
was included in samples (see Ortega-Retuerta et al., 2012a).
Seasonal climatology of the 0–50 m integrated bacterial biomass
(mmolN m-2) for spring (upper panels) and summer (lower panels) over
the 1998–2011 period simulated in the CTRL run (left panels a and
d) and in the RIV run (middle panels b and e).
Right panels (c and f) provide the absolute difference
(gC m-2; RIV run – CTRL run).
Averaged over 1998–2011, the total BP simulated by the model and integrated
over the whole AO is, on average, 26 % higher in the RIV run
(68 ± 9 TgC yr-1) than in the CTRL run
(54 ± 8 TgC yr-1). Bacterioplankton recycle RDON into nutrients
that can be used by both phytoplankton and bacterioplankton, hence promoting
their growth. In addition, bacterioplankton and small phytoplankton are
grazed by microzooplankton that, in turn, are grazed by mesozooplankton. More
organic matter is channelled towards the upper trophic levels, a flow that
also contributes to fuelling the dDON and NH4 pools through recycling.
By enabling the removal of RDON by bacterioplankton in the biogeochemical
model, the biomass of microzooplankton and mesozooplankton, averaged over
1998–2011, increased by ∼ 16.1 and 43.6 %, respectively.
The bacterioplankton production versus primary production ratio (BP : PP)
The BP : PP ratio is computed over the AO shelf, delimited here by the
200 m isobaths, for ice-free waters (i.e. with less than 30 % ice
cover). On average for the 1998–2011 period, the simulated BP : PP ratio
is 0.19 ± 0.02 in the CTRL run and 0.21 ± 0.01 in the RIV run.
These values lie within the range observed in open (0.02; Kirchman et al.,
2009) and coastal (0.37–0.43; Ortega-Retuerta et al., 2012a; Garneau et al.,
2008) waters. When looking at the temporal evolution of BP : PP in the RIV
run (Fig. 5), the model simulates a significant increase in PP (r= 0.57,
p < 0.05) and BP (r= 0.63, p < 0.05) between 1998
and 2011, with a production maximum simulated in 2007, the year showing the
higher sea-ice minimum. However, there is no evidence of a significant
temporal trend of BP : PP (r= -0.09, p > 0.05) over
1998–2011. This result suggests that, with a constant annual flux of RDON
into the coastal AO, the significant increase in simulated BP in the model is
not high enough to promote a higher contribution of heterotrophy with respect
to autotrophy within the upper water column.
Time course of primary production (PP, TgC yr-1) (top panel),
bacterioplankton production (BP, TgC yr-1) (middle panel), and the
BP : PP ratio in the ice-free shelves (see text for details) of the Arctic
Ocean domain (> 66.5∘ N) simulated in the RIV run. The
dashed straight lines represent the linear trend computed over the 1998–2011
period.
Discussion
The coupled model suggests that NH4 produced from the remineralization
of RDON by the microbial food web contributed ∼ 8 % to annual
pan-Arctic PP over the 1998–2011 period. This is about twice the value given
in the study by Tank et al. (2012) that, in addition to RDON, accounted for
the contribution of riverine inorganic nutrients as well as of the
photochemical transformation of RDON into NH4. In our coupled model, the
uptake of RDON by marine bacterioplankton and its subsequent recycling into
reduced nitrogen is the sink term that shapes, with ocean transport, the
spatial and temporal distribution of RDON. The photoammonification process is
not parameterized but, if so, it would fuel the stock of NH4 available
for phytoplankton and bacterioplankton use, particularly in summer (e.g. Le
Fouest et al., 2013; Xie et al., 2012). The RDON contribution to plankton production simulated by the
coupled model can thus be considered as a minimum estimate.
From the total input of RDON, only a fraction is directly usable by the
plankton (e.g. Wickland et al., 2012). The fraction that enters the coupled
model by the 10 river source points is set to 15 % of the total RDON
input according to a study by Wickland et al. (2012), which suggests that
about 15 % of the total RDON pool can be degraded within less than
1 month. This value was chosen based on annual averages calculated from
measurements or from model outputs for the Mackenzie River, Yukon River,
Kolyma River, Lena River, Yenisey River, and Ob' River (e.g. Wickland et al.,
2012). Note, however, that the average values given in Wickland et al. (2012)
vary between seasons and rivers. They are lowest in the Lena River (8 %)
and highest in the Ob' River (19 %). Maximum values as high as 24 %
of usable RDON are reported for the Ob' River. Sensitivity analyses with
different parameterizations of the usable RDON fraction set amongst rivers
and seasons would hence be informative on the amplitude of the PP and BP
response to spatial and temporal variations of the usable RDON flux. To be
robust, they should be combined with sensitivity analyses of the freshwater
discharge to better constrain the RDON flux. In the Mackenzie River, strong
inter-annual variations in terms of peak of discharge and maximum spring flow
were observed in the last four decades (Yang et al., 2015). Nevertheless, the
use a constant fraction of usable RDON as preformed in the present study
provides a first-order estimation of its contribution to BP and PP that is
consistent with the current state of knowledge about the RDON inputs. In
addition to the usable RDON flux into coastal oceans, autochthonous sources
of DONl (usable RDON + dDON) are important in fuelling BP. Despite improved
BP estimates simulated in the RIV run, the rates remain within the lower
range of the observations. It can result from unresolved sources of DONl
within the model such as ice-edge and under-ice phytoplankton blooms (Arrigo
et al., 2012; Perrette et al., 2011), and from missing biological processes
like sloppy mesozooplankton feeding and viral lysis.
In the biogeochemical model, the usable RDON, dDON, and NH4 produced by
the plankton components are taken up by bacterioplankton to build up biomass.
The synthesis of cell proteins requires at least carbon and nitrogen.
Bacterioplankton obtain all their carbon and some of their nitrogen from DONl
(usable RDON + dDON). The simulated NH4 uptake supplements their
nitrogen requirements. The growth function is formulated using the Fasham et
al. (1990) model. It assumes, in a balanced growth situation, where N and C
assimilation occurs simultaneously and where bacterioplankton have fixed
stoichiometry, that the ratio of NH4 uptake to DONl uptake is constant
(0.6; see Appendix A) to ensure that the biomass of the required C : N
ratio is produced from DONl with a given C : N ratio. If there is not
enough NH4 available, the uptake rate of both DONl and NH4 decreases,
allowing both N and energy limitation. In Arctic waters, the inhibition of
DOC uptake by bacterioplankton under inorganic nitrogen limitation was shown
by Thingstad et al. (2008). However, as DONl in the model is made a proxy of
DOC, the C : N ratio of the substrate is assumed constant. As a
consequence, any explicit stoichiometric treatment of the simulated
bacterioplankton metabolism is precluded as well as any stoichiometric
coupling between DOC and inorganic nutrients (e.g. Thingstad et al., 2008).
In addition, the implicit treatment of DOC in the model implies that all of
the DOC required for growth is in N-containing forms. Hence it assumes that
bacterioplankton cannot be N-limited in substrate. However, N-limitation of
bacterioplankton production was observed in summer in surface waters of the
Beaufort Sea (Ortega-Retuerta et al., 2012b). This pattern contrasts with the
organic carbon limitation observed in the Yenisei and Mackenzie river plumes
and the adjacent Kara and Beaufort seas (Meon and Amon, 2004; Vallières
et al., 2008), hence highlighting the difficulty in drawing a general pattern
on the AO scale. Nevertheless, making the C : N ratio of substrates of
terrigeneous and marine origin vary in a realistic way in biogeochemical
models would further be required. Single explicit pools of DOC and DON
represented as two different state variables, as well as a distinction
between readily usable molecules (turnover within days) and more complex ones
(turnover within a month) would also make the model more realistic. The
parameterization of variable C : N ratios is not trivial as it requires
large in situ data sets (see Letscher et al., 2015) and, in Arctic
river-influenced shelf seas, a good knowledge of the characteristics of the
terrigenous dissolved organic matter flowing into the coastal ocean (e.g.
Mann et al., 2012). Appropriate values for the maximum uptake rates and
half-saturation constants may not be easily obtained from existing data in
the Arctic. As a result, the coupled model that is used in the present study
is an interesting compromise relative to more complex (in terms of number of
biological equations and parameters) models of bacterioplankton growth
applied to shelf waters (e.g. Auger et al., 2011; Anderson and Williams,
1998).
In the model, bacterioplankton compete with phytoplankton for the NH4
remineralized from the usable RDON and dDON pools. This competition for a
nutrient resource acts as a bottom-up control of the simulated phytoplankton
and bacterioplankton production and, finally, of the BP : PP ratio. For
bacterioplankton, the maximum growth rate is temperature normalized. At
10 ∘C, which corresponds to a sea surface temperature within the
upper range of observations over the shelves in summer, it takes a value of 3
d-1. Hence the NH4 uptake efficiency (α= maximum growth
rate/half-saturation constant for uptake) can reach
30 m3 mmol N-1 d-1, which is about 2 times higher than for
small phytoplankton (14 m3 mmol N-1 d-1). In their study,
Lignell et al. (2013) report values of α that are about 10 times
higher for bacterioplankton than for small phytoplankton, hence a larger
difference compared to the model. Nevertheless, the difference in α
in the model is comparable to that estimated for Isefjord at the entrance of
the Baltic Sea (see Lignell et al., 2013). Although the model
parameterization can be improved in that respect, the model is in fair
agreement with the theoretical and empirical results showing that smaller
cells are more efficient in nutrient uptake than larger ones (Lignell et al.,
2013). In contrast to bacterioplankton, phytoplankton uptake of inorganic
nutrients is also limited by light. In the model, the diffuse attenuation of
the incident light caused by the pool of coloured dissolved organic matter
(0.05 m-1) is set as constant in the model. This results in the light
attenuation in the water column being the same in river plumes as in open and
clearer waters. However, river plumes transfer to the coastal marine
environment large amounts of optically active coloured dissolved organic
matter of terrigenous origin that strongly attenuate the incident light
propagation with depth. As the model does not account for the stronger light
attenuation in river plumes, it may overestimate the simulated phytoplankton
growth on NH4 recycled from RDON by bacterioplankton and underestimate
the BP in river plumes. As a consequence, the spatial and temporal evolution
of the simulated BP : PP ratio can be impacted on shelves. In addition, the
ability of Arctic phytoplankton to assimilate low molecular weight DON
compounds (50 % of total nitrogen assimilated annually; see Simpson et
al., 2013) is likely to also play an important role in the
phytoplankton–bacterioplankton competition on shelves. A more accurate
representation of the simulated underwater light field and uptake of
nutrients in river plumes in the coupled model will certainly improve its
ability to simulate the competition for nutrients between phytoplankton and
bacterioplankton, and hence predict the temporal evolution of the BP : PP
ratio within Arctic waters.
Conclusions
A pan-Arctic physical–biogeochemical model was used to quantify the
contribution of usable dissolved organic nitrogen drained by the major
pan-Arctic rivers to marine bacterioplankton and phytoplankton production in
a scenario of melting sea ice (1998–2011). By accounting for the removal of
RDON by bacterioplankton in the coupled model, the ability to predict PP and
BP in river-influenced shelves is improved. The key points of the study are
that
on average between 1998 and 2011, the removal of usable RDON by bacterioplankton is responsible for an increase of ∼ 26 % in the annual BP, and an increase of ∼ 8 % in the total annual PP;
recycled ammonium is responsible for the total PP increase; total summertime PP is increased by ∼ 18 %, on average, over
1998–2011; and that
the processing of usable RDON by bacterioplankton promotes a higher annual BP and PP, but there is no significant temporal trend in the BP : PP ratio
over 1998–2011 on the ice-free shelves; this suggests no significant
evolution in the balance between autotrophy and heterotrophy in the last
decade, with a constant annual flux of RDON into the coastal ocean.
The effect of the predicted warming on the Arctic watersheds is linked to a
potential regional increase in RDON inputs into the AO shelf by 32–53 %
before the end of the century (Frey et al., 2007). Combined with the
accelerated sea-ice decline (Comiso et al., 2008) and an increase in seawater
temperature on Arctic shelves (Timmermans et al., 2014), this new
biogeochemical and physical setting might exacerbate the competing effect for
resources between autotrophs and heterotrophs as sea ice recedes in summer.
As a consequence, the metabolic state of the AO shelves could be altered.
Nevertheless, to obtain robust predictions of the response of the microbial
food web functioning and mass fluxes, coupled models would require
improvements in parameterized land–ocean fluxes in terms of spatial and
temporal variability of freshwater discharge and nutrient fluxes. In their
study combining in situ data sets and modelling, Holmes et al. (2011) show
that annual fluxes of RDOC in the Lena River, estimated between 1999 and
2008, can vary by about a factor of 2. Such variations accentuate the
significance of considering the short-term and inter-annual variability of
the continental fluxes into the coastal ocean when deriving temporal trends
in plankton production and investigating potential changes in trends related
to the Arctic warming. Finally, model predictions of future trajectories of
PP (e.g. Vancoppenolle et al., 2013) would probably benefit from considering
riverine nutrient fluxes as important drivers of PP on Arctic shelves in
future decades. However, models that are
mechanistically more robust and allow for flexible stoichiometry and
N-limitation of bacterial substrate uptake are probably needed for
forecasting AO ecosystem responses to climate change scenarios.