This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit: http://www.elsevier.com/copyright
Transcript
This article appeared in a journal published by Elsevier. The attachedcopy is furnished to the author for internal non-commercial researchand education use, including for instruction at the authors institution
and sharing with colleagues.
Other uses, including reproduction and distribution, or selling orlicensing copies, or posting to personal, institutional or third party
websites are prohibited.
In most cases authors are permitted to post their version of thearticle (e.g. in Word or Tex form) to their personal website orinstitutional repository. Authors requiring further information
regarding Elsevier’s archiving and manuscript policies areencouraged to visit:
On the role of tides and strong wind events in promoting summer primaryproduction in the Barents Sea
Vincent Le Fouest a,n, Clare Postlethwaite b, Miguel Angel Morales Maqueda b, Simon Belanger c,Marcel Babin d,e
a The Scottish Association for Marine Science, Dunstaffnage Marine Laboratory, Oban PA37 1QA, United Kingdomb National Oceanography Centre, Joseph Proudman Building, 6 Brownlow Street, Liverpool L3 5DA, United Kingdomc Universite du Quebec �a Rimouski, Departement de biologie, chimie et geographie, 300, allee des Ursulines, Rimouski, Quebec, Canada G5L 3A1d Takuvik Joint International Laboratory, Universite Laval (Canada) & Centre National de la Recherche Scientifique (France), Departement de Biologie, 1045, Avenue de la Medecine,
Universite Laval, Quebec (Quebec), G1V 0A6, Canadae Laboratoire d’Oceanographie de Villefranche, BP 8, Centre National de la Recherche Scientifique & Universite Pierre et Marie Curie (Paris VI), 06238 Villefranche-sur-Mer Cedex,
France
a r t i c l e i n f o
Article history:
Received 31 January 2011
Received in revised form
18 August 2011
Accepted 30 August 2011Available online 12 September 2011
Keywords:
Physical-biological modelling
Phytoplankton primary production
Physical forcing
Barents Sea
a b s t r a c t
Tides and wind-driven mixing play a major role in promoting post-bloom productivity in subarctic
shelf seas. Whether this is also true in the high Arctic remains unknown. This question is particularly
relevant in a context of increasing Arctic Ocean stratification in response to global climatic change. We
have used a three-dimensional ocean-sea ice-plankton ecosystem model to assess the contribution of
tides and strong wind events to summer (June–August 2001) primary production in the Barents Sea.
Tides are responsible for 20% (60% locally) of the post-bloom primary production above Svalbard Bank
and east of the Kola Peninsula. By contrast, more than 9% of the primary production is due to winds
faster than 8 m s�1 in the central Barents Sea. Locally, this contribution reaches 25%. In the marginal ice
zone, both tides and wind events have only a limited effect on primary production (o2%). Removing
tides or winds faster than 8 m s�1 promotes a regime more sustained by regenerated production with a
f-ratio (i.e. the proportion of nitrate-based ‘‘new’’ primary production in the total primary production)
that decreases by up to 26% (east of the Kola Peninsula) or 35% (central Barents Sea), respectively. When
integrated over all Barents Sea sub-regions, tides and strong wind events account, respectively, for 6.8%
(1.55 Tg C; 1 Tg C¼1012 g C) and 4.1% (0.93 Tg C) of the post-bloom primary production (22.6 Tg C).
To put this in context, this contribution to summer primary production is equivalent to the spring
bloom integrated over the Svalbard area. Tides and winds are significant drivers of summer plankton
productivity in the Barents Sea.
& 2011 Elsevier Ltd. All rights reserved.
1. Introduction
The Barents Sea is a large shelf sea covering about 1.4 million km2
of the Arctic Ocean. Its northern part is seasonally ice-covered(Loeng, 1991) while the southern waters are kept ice-free bythe inflow of warm and nutrient-rich Atlantic waters. It is amajor contributor to total Arctic primary production to which itcontributes about 40% (Sakshaug, 2004), and it supports impor-tant stocks of commercially valuable planktivorous and piscivor-ous fishes such as capelin, herring and cod (Hamre, 1994). A goodknowledge of the main drivers responsible for its productivity isthus essential.
In the past few decades, the upper Arctic Ocean has seen anincrease in surface air temperature and precipitation (ACIA,2005), a larger freshwater discharge from Eurasian and North-American rivers (Peterson et al., 2002; Shiklomanov, 2010) and adecline in both the extent and thickness of the sea ice cover(Wadhams and Davis, 2000; Stroeve et al., 2005). In response tothese changes, the summer stratification has become stronger,causing a decrease of nutrient stocks in the photic zone andpromoting the growth of small phytoplankton at the expense oflarger phytoplankton (Li et al., 2009).
In open waters, summertime mixing mediated by winds andtides can weaken this stratification and trigger synoptic pulses ofnew primary production through the input into the photic zoneof new nutrients from below the pycnocline. When sea ice ispresent, the action of tides and winds on sea ice can enhance lightabsorption through the newly created and nutrient-rich openwater and promote primary production. However, our knowledge
Contents lists available at SciVerse ScienceDirect
journal homepage: www.elsevier.com/locate/csr
Continental Shelf Research
0278-4343/$ - see front matter & 2011 Elsevier Ltd. All rights reserved.
doi:10.1016/j.csr.2011.08.013
n Corresponding author. Present address: Laboratoire d’Oceanographie de Ville-
franche, BP 8, CNRS & Univ. Pierre et Marie Curie (Paris VI), 06238 Villefranche-
of the contribution of mixing events to primary production in thehigh Arctic is very limited. In their modelling study focused on theBarents Sea, Wassmann et al. (2006) have linked winds associatedto passing low-pressure systems to pulsed primary productionevents in open waters. With respect to tidal mixing, while it is ofprime importance for the productivity of subarctic shelf seas (e.g.Le Fouest et al., 2005), its impact on Arctic shelf productivity hasreceived little attention (e.g. Wassmann et al., 2006, 2010). Tidesare generally not included in the forcing of coupled physical–biological models (e.g. Walsh et al., 2005; Popova et al., 2010;Zhang et al., 2010). This study aims to quantify the contribution oftides and synoptic wind events to summer primary production inthe Barents Sea, one of the most productive shelf seas of the Arctic(Hunt and Megrey, 2005).
2. Material and methods
2.1. The 3-D physical–biological coupled model
The physical model is fully detailed in Postlethwaite et al.(2011) hence a concise description is given in this section.We have used the three-dimensional (3-D), barotropic-baroclinic,sigma-coordinate shelf sea model POLCOMS (Holt and James,2001) coupled to the dynamic-thermodynamic Los Alamos sea-ice model (CICE v3.14; Hunke and Lipscomb, 2004). The domainreaches from 01E to 1101E and from 641N to 841N, encompassingthe Barents and Kara seas (Fig. 1). For this application, bothmodels are constructed on a polar stereographic grid, whichallows each grid box to cover approximately the same area witha resolution of 25 km in both the x and y direction. The oceanmodel has 30 vertical sigma levels. Vertical mixing is parameter-ized according to a one equation variant of the Mellor andYamada (1974) level 2.5 turbulence closure scheme. Initial andboundary conditions for ocean temperature, salinity and veloci-ties are from the US Naval Research Laboratory Naval CoastalOcean Model (NCOM, Barron et al., 2006) and from the Polar IcePrediction System team (Woert et al., 2004) for ice concentration
and thickness. Sea surface elevations and velocities for tidalforcing at the open boundaries were sourced from the globalanalysis of Egbert and Erofeeva (2002). The surface meteorologi-cal forcing is derived from the ERA-40 reanalysis of the EuropeanCentre for Medium-Range Weather Forecasts (Uppala et al.,2005), and includes six hourly fields of atmospheric temperature,pressure, humidity, easterly and northerly wind components andcloud cover. A monthly climatology of freshwater discharge fromthe Ob’ (1954–1999) and Yenisey (1955–1999) rivers constructedfrom GRDC (2003) is prescribed at these river points.
The ocean-sea ice model is coupled on-line to a mass-based(mmol N m�3) plankton ecosystem model that has been exten-sively compared to observations in the subarctic Gulf ofSt. Lawrence, Canada (Le Fouest et al., 2005, 2006; Mei et al.,2010) and successfully applied to ecosystem simulations inHudson Bay in the Canadian Arctic (Sibert et al., 2011). Theplankton ecosystem model fully detailed in Le Fouest et al.(2005) can be schematized as follows: the export at depth ofbiogenic matter is mediated by the herbivorous food web (nitrate,large phytoplankton (45 mm), mesozooplankton and detritalparticulate organic nitrogen) while the microbial food web(ammonium, small phytoplankton (o5 mm), microzooplankton,detrital dissolved organic nitrogen) is responsible for nutrientrecycling within the euphotic zone. The growth of large and smallphytoplankton depends on light, nitrate and ammonium avail-ability according to the Liebig’s Law of minimum. Mesozooplank-ton graze on both large phytoplankton and microzooplankton,whereas microzooplankton graze on small phytoplankton only.Faecal pellets and large phytoplankton mortality constitute thedetrital particulate organic nitrogen (PON) pool. The detritaldissolved organic nitrogen (DON) pool is made of unassimilatedgrazed phytoplankton by microzooplankton, small phytoplanktonand microzooplankton mortality and detrital PON fragmentationmediated by bacteria. Detrital DON recycling, mesozooplanktonexcretion and unassimilated phytoplankton grazed by microzoo-plankton are the sources of ammonium in the model. Nitrogen isconverted into carbon using the Redfield C:N molar ratio (106:16)and into chlorophyll-a (Chl-a) using a fixed C:Chl mass ratio of 55.In this study, the only change relative to the version of Le Fouestet al. (2005) concerns the half-saturation parameter for phyto-plankton light use that was set to 7 mol photons m�2 d�1 to liewithin the range of values reported for the Arctic Ocean (Rey,1991). The partial differential equation used to compute theevolution of a simulated biological scalar (here C) is as follows:
@C
@tþu
@C
@xþv
@C
@yþw
@C
@z¼
@
@xKx@C
@x
� �þ@
@yKy@C
@y
� �þ@
@zKz@C
@z
� �þsources�sinks,
where t is time, (x, y, z) are the spatial coordinates, (u, v, w) arethe ocean current components in the x, y, z directions, respec-tively, Kx and Ky are the horizontal eddy diffusivities, and Kz is thevertical eddy diffusion coefficient. The transfer functions thatconstrain the sources and sinks of the biological scalars arerelated to phytoplankton growth, zooplankton grazing and detri-tal organic matter remineralization (see Table 1 and Appendix Ain Le Fouest et al., 2005, for details). At each time step, thetransport of each biological variable is performed by the advec-tion-diffusion routine of the physical model while the sink andsource terms are explicitly computed afterwards. The initialchemical and biological conditions were defined according toobservations (Table 1). Boundary conditions were also requiredfor the state variables of the biological model. These took thesame concentrations as described above for the initial conditions,except for nitrate for which the World Ocean Atlas 2005 (NationalOceanographic Data Centre, 2006) monthly climatology wasprescribed at the open boundaries of the domain to reflectseasonality. The biological state variables were set to zero in
Fig. 1. Map of the Barents and Kara seas. The transects indicate the location of the
Kola section (blue line) and of the section shown in Fig. 3 (red line). The 200 m
isobath is shown (black line). (For interpretation of the references to colour in this
figure legend, the reader is referred to the web version of this article.)
V. Le Fouest et al. / Continental Shelf Research 31 (2011) 1869–18791870
Author's personal copy
the two inflowing rivers as their contribution to the Kara Seaproductivity is expected to be minor (e.g. Dittmar and Kattner,2003). Both the initial and the boundary conditions for thebiological component of the model were spatially uniform. The3-D coupled model was run from 1 September 2000 to 31August 2001.
2.2. Observation data
Temperature data measured along the Kola section (http://www.pinro.ru) were used to compare with the simulated fields.The section is located in the central part of the Barents Sea ata longitude of 331300E (Fig. 2). It crosses the waters of theMurmansk Coastal Current (691300N–701300N, stations 1–3),the Murmansk Current (701300N–721300N, stations 3–7) and thecentral branch of the North Cape Current (73–741N, stations8–10). Observational data were available as vertical means (0–50 m and 50–200 m). Similar averages were calculated from themodel data.
Time coincident remotely sensed primary production rateswere used in this study to track the seasonal phytoplankton cycle.The satellite-based primary production model description andits preliminary validation can be found in Babin and Belanger(in prep.). Briefly, the spectral incident downwelling irradiancejust beneath the sea surface (Ed[0� ,l]) was computed at 5 nmresolution every 3 h based on lookup-tables (EdLUT) generated usingan atmospheric radiative transfer code (Ricchiazzi et al., 1998). Input
parameters to the EdLUT are the solar zenith angle, the totalozone concentration, the cloud fraction over the pixel andthe cloud optical thickness. The latter three parameters wereobtained from the ISCCP (International Satellite Cloud Climatol-ogy Project; http://isccp.giss.nasa.gov/) (Zhang et al., 2004b).Merged ocean colour observations of MEdium Resolution ImagingSpectrometer (MERIS), MOderate Resolution Imaging Spectro-radiometer (MODIS) and Sea-viewing Wide Field-of-view Sensor(SeaWiFS) data from the Globcolour project (http://www.globcolour.info) (Maritorena and Siegel, 2005; Maritorena et al., 2010)were used to determine the phytoplankton biomass (i.e. Chl-asat)and the downwelling spectral irradiance at all water depths. Themonthly fully normalized spectral water-leaving radiances (Lwn)at 412, 443, 490, 510, 555 and 670 nm were used to computespectral inherent optical properties (IOPs), namely absorption andbackscattering coefficients, using a quasi-analytical algorithm(Lee et al., 2002). Spectral diffuse attenuation Kd(l) is computedfrom the IOPs using the model developed and validated by Leeet al. (2005a,b). In addition, Chl-asat estimated using the GSM01semi-analytical algorithm (Garver and Siegel, 1997; Maritorenaet al., 2002) rather than empirical algorithms was used tominimize the contamination by coloured dissolved organic matter(CDOM) and non-algal particles (NAP) in the Kara Sea. Chl-asat
was combined with the Arctic statistics of Matsuoka et al. (2007)to derive the phytoplankton absorption spectrum for the compu-tation of the photosynthetically usable radiation (PUR, sensus
Morel, 1978). Instantaneous and daily averaged PUR were then
Table 1Initial conditions of the biological model
State variable Depth Concentration(mmol N m�3)
Reference
Nitrate 0–40 m 0 Hegseth (1997), Luchetta et al. (2000),
Deeper than 40 m 12 Reigstad et al. (2002)
Ammonium 0–40 m 1 Kristiansen et al. (1994)
Deeper than 40 m 1
Large and small phytoplankton 0–40 m 0.2 Kristiansen et al. (1994)
Deeper than 40 m 0
Meso- and microzooplankton 0–40 m 0.4 Dvoresky and Dvoresky (2009a, 2009b),
Deeper than 40 m 0 Rat’kova and Wassmann (2002)
Detrital particulate organic nitrogen 0–40 m 0.005 Set a priori
Deeper than 40 m 0
Detrital dissolved organic nitrogen 0–40 m 0.05 Set a priori
Deeper than 40 m 0
Fig. 2. Simulated annual mean, depth-averaged (0–50 m) surface temperature (left panel) and salinity (right panel) with the 200 m isobath overlaid. The red line shows
the annual mean of the 30% of sea ice cover. Boxes on the right panel identify the sub-regions mentioned in Section 3.2: Svalbard Bank (box 1), the marginal ice zone
(box 2), the central Barents Sea (box 3) and the eastern Kola Peninsula (box 4).
V. Le Fouest et al. / Continental Shelf Research 31 (2011) 1869–1879 1871
Author's personal copy
calculated at all depths by propagating down in the water columnthe incident irradiance using a diffuse attenuation coefficient (Kd).The daily PUR was used to derive the saturation parameter(Ek(PUR)) using the model formulation established by Arrigoand Sullivan (1994) and parameterized by Arrigo et al. (1998)for Antarctic phytoplankton (see their Fig. 1). Remotely sensedprimary production (PPsat, mg C m�3 d�1) was calculated at tendepths (z) from surface to 0.1% of the incident PUR, and 3-hourlyof the day (t) as follows:
where Chl-asat was the remotely sensed Chl-a, the photosyntheticcapacity (Pb
max) set constant using a fixed representative value of2 mg C m�3 h�1 (mg Chl-a m�3)�1 (Harrisson and Platt, 1986;Rey, 1991) and PUR(z) was instantaneous rather than daily. PPsat
rates were depth-integrated and binned monthly and fields(originally pixels of 4.6�4.6 km) interpolated to the modelgrid. Note that PPsat data may be underestimated in summer asthe deep Chl-a maximum (DCM) is not accounted in the calcula-tion. However, Pabi et al. (2008) suggest that annual primaryproduction derived from remote sensing data would only be10% higher with the DCM production. In addition, the under-estimation can be considered as a second order uncertainty withrespect to that due to the Chl-asat retrieving and the choice ofthe photosynthetic parameters in the PPsat calculation at highlatitudes.
3. Results and discussion
3.1. General features
3.1.1. Physics
The physical–biological coupled model simulates well theincursion of the warmer and saltier North Atlantic Water (NAW)
into the Barents Sea (Figs. 2 and 3a). In the southern and midBarents Sea, surface temperatures (0–50 m) range between 5.5 1Cin the Norwegian Coastal Current (NCC) and coastal waters and ca.3 1C in the area of Bjørnøyrenna channel. Salinity is larger than 35,except in the less saline NCC and coastal waters, where salinityvalues between 34 and 35 are encountered. In the south-east,coastal waters are supplied with low salinity water inflowing fromthe White Sea. Farther North, the Arctic Water (AW, To0 1C andSo35) is separated from the NAW by a mixture resulting from theconfluence of the two water masses and mixing with sea ice meltwater. The salinity patterns calculated by the model agree with thegeneral description given by Loeng (1991). In the easternmost partof the domain, the brackish waters (So30) of the Kara Sea arewell simulated. In particular, the location of the simulated 30-isohaline compare well with that observed by Pavlov and Pfirman(1995). The brackish waters extend the farthest north on theeastern part of the Kara Sea, which reflects the mean north-eastward circulation in this sea (Pavlov and Pfirman, 1995). Thesimulated seasonal cycle of the sea ice cover is also realistic.Surface ice coverage reaches a maximum of 2.7�107 km2 inMarch. The melt season starts in early April and ends by aboutmid-September (e.g. Loeng, 1991).
We compared water temperatures simulated by the physical–biological model in the southern Barents Sea with coincidentmeasurements made along the Kola section (Fig. 4). In winter, thewater column is fully mixed down to at least 200 m because ofboth wind mixing and mixed layer deepening caused by surfacecooling. As the ocean surface starts warming by about May, thewater column begins to stratify and the turbulent couplingbetween the upper and lower parts of the water column weakens.As the mixed layer warms up, so does the deep ocean throughvertical heat exchange with the mixed layer, although at a lowerrate. The maximum temperature differences (up to 1.5 1C)between the model and the observations (up to 1.5 1C) are foundin July–August in the upper 50 m. The model underestimates
1 ms-1
30°E 60°E 90°E
30
30
30
30
30
60
60
60
60 60
60
60
6060
90
90
90 9090
120
120
120
120120
150
150
150 150150
150
150
150
210
210
210
240
240
240
240
270
270
270
270
300
300
300
330
330
330
330
0
0.5
1
1.5
2
2.5
3
30°E 60°E 90°E
<1cms-1
3-5 cms-1
1-3 cms-1
30°E 60°E 90°E65°N
70°N
75°N
80°N
Fig. 3. Simulated annual mean of (a) depth-averaged (0–50 m) currents, (b) depth-averaged (surface to bottom) tidal ellipses and (c) M2 tidal elevation amplitude in
metres (colour with black contours, contours spaced every 0.1 m between 0 and 1 m and spaced every 0.5 m from 1 to 3 m) and phase in degrees (white contours) overlaid.
V. Le Fouest et al. / Continental Shelf Research 31 (2011) 1869–18791872
Author's personal copy
surface temperatures, which is likely due to biases in the surfaceheat fluxes. By the end of the summer, the surface heat flux hasdecayed to a point where the mixed layer starts cooling; but it is stillwarmer than the deep ocean and so continues losing heat down-wards by turbulent mixing, hence the further warming of the deepocean simulated and observed until November. The deep ocean thenstops warming when it reaches the same temperature as the upperocean. Throughout the winter, the water column is thermally wellmixed. The model reproduces well the observed seasonal pattern ofthermal stratification, which is determinant for the timing of thespring bloom.
3.1.2. Biology
Simulated annual primary production in the Barents Sea is82 Tg C. It is lower than previously reported estimates based onin situ (136 Tg C yr�1, pluri-annual; Sakshaug, 2004), remotelysensed (ca. 120 Tg C yr�1 in 2000–2001; Pabi et al., 2008) orphysical–biological model data (118 Tg C yr�1 in 1998; Popovaet al., 2010). Integrated over March to August 2001, simulatedprimary production is in average 18% lower than its time coin-cident counterpart derived from remote sensing data (Fig. 5).However, the lateral mesh of the coupled model is coarse(25�25 km) compared to the resolution of the ocean colour dataused in this study (4.6�4.6 km) or the data from Pabi et al.(2008) (9�9 km) and Popova et al. (2010) (13.8�13.8 km). Assuch, any mesoscale activity that would promote in the coupledmodel higher primary production rates (by a factor 1.2–2; e.g.Levy et al., 1998; Hansen and Samuelsen, 2009) through nitrateinjection into the photic layer is precluded. If such a factor was
applied to the annual primary production rate simulated by thecoupled model (82 Tg C yr�1) the new rate would be higher and lie inthe reported range of values. Moreover, the proportion of annualprimary production channeled towards secondary production in thecoupled model (22%) is within the range given by Legendre andRivkin (2002) (20–30%) suggesting a realistic carbon flow.
Fig. 6 shows Chl-a concentration and density simulated by thephysical–biological model between April and August 2001 along atransect starting nearshore in the southern Barents Sea andcrossing the marginal ice zone (MIZ) to the north of Franz-JosefLand (see Fig. 1 for transect position, in red). In the ice-free watersof the central Barents Sea, a simulated phytoplankton bloomdominated by the large size fraction is found in late May with atotal Chl-a concentration reaching up to ca. 13–14 mg m�3,comparable to that previously measured during spring bloomconditions in May by Reigstad et al. (2002) (ca. 10–14 mgChl-a m�3). The simulated bloom coincides with the decline of themean simulated nitrate concentration in the surface waters alongwith a mean simulated f-ratio (i.e. the proportion of nitrate-based‘‘new’’ primary production in the total primary production) of ca.0.75 (lower left panel in Fig. 7). New production rates simulatedat the surface in May lie between ca. 200 mg C m�3 d�1 and ca.500 mg C m�3 d�1, in agreement with previous measurements(ca. 150–400 mg C m�3 d�1; Matrai et al., 2007). Maximumdepth-integrated new production rates (ca. 5 g C m�2 d�1) simu-lated by the model are also in the range of values given in Loeng(1989) (0.27–5.26 g C m�2 d�1). Moreover, the model captures thetiming of the spring bloom throughout the Barents Sea as seen in thecomparison of the simulated primary production with its coincidentremotely sensed counterpart (Fig. 5).
Fig. 4. Simulated (white) versus measured (black) water temperature (in 1C) along the Kola section. Note that there were no measurements available for stations 8–10 from
February to May 2001. The latitudes of each group of stations are given in the text in Section 2.2.
V. Le Fouest et al. / Continental Shelf Research 31 (2011) 1869–1879 1873
Author's personal copy
After the simulated spring bloom, the upper water columnstratifies and the total Chl-a in the surface layer (o0.8 mg m�3,Fig. 6) decreases to concentrations close to measurements(o1 mg m�3, Sakshaug, 1991; Reigstad et al., 2002). The meansimulated concentration of nitrate falls down to 2–3 mmol m�3
and primary production is mostly ensured by the uptake ofremineralized ammonium (mean f-ratio of ca. 0.1–0.2) (lower leftpanel in Fig. 7). At depths of 30–50 m, the model simulates a DCMthat maintains until late July (Fig. 6). Simulated total Chl-a concen-trations in the DCM are ca. 2–3 mg m�3 lying in the lower range ofprevious measurements reported in the DCM in the southern (ca.2 mg Chl-a m�3, Reigstad et al., 2002) and northern Barents Sea(10–15 mg Chl-a m�3, Sakshaug, 1991). Assuming phytoplanktonduring the bloom phase experience light conditions equivalent tothe mean irradiance in the mixed layer (e.g. Levasseur et al., 1984;Prieur and Legendre, 1988), a mean C:Chl-a mass ratio of 55(20oC:Chl-ao100) is likely appropriate to simulate Chl-a con-centration in the mixed layer at the bloom inception. However,this may not necessarily be the case in the DCM, where simulatedChl-a is likely underestimated due to the lack of phytoplanktonphotoacclimation in the model. Nonetheless, this is expected tohave only a limited effect on phytoplankton growth and primaryproduction in the model (e.g. Sakshaug, 1991; MacIntyre et al.,2002). Simulated summer primary production rates may ratherbe dampened (see Fig. 5) by the use of a fixed C:N molar ratio,shown to increase with increasing irradiance or under nutrientlimitation (Laws and Bannister, 1980; Falkowski et al., 1985), andby the absence in the coupled model of nitrate pulses into thephotic zone mediated by mesoscale or sub-mesoscale processes.Farther north in the seasonally ice-covered waters of the BarentsSea, summer ice-edge blooms are commonly observed features(e.g. Reigstad et al., 2002; Perrette et al., 2011). The modelsimulates such a bloom with total Chl-a concentrations of ca.5–7 mg m�3 (Fig. 6) (ca. 4.5 mg m�3 in Reigstad et al., 2002) thatfollows the ice edge as sea ice melts and recedes northwards,exposing nutrient-rich waters to light.
With respect to zooplankton, the highest levels of biomass aresimulated in summer in the model (Fig. 6), as generally reportedin the Barents Sea (Slagstad and Tande, 1990; Rat’kova and
Wassmann, 2002). Mean simulated biomasses of mesozooplanktonin August (o1–18 mg C m�3, averaged between 0 and 100 m)and of microzooplankton in July (1–70 mg C m�3 between 0and 50 m, Fig. 6) are of the same order of magnitude as thoseobserved for the same period of the year (2.5–37 mg C m�3 and1–40 mg C m�3, respectively; Rat’kova and Wassmann, 2002;Dvoresky and Dvoresky, 2009b). The tendency to slightly under-estimate mesozooplankton with respect to microzooplankton inthe model can be mostly attributed to the oversimplified repre-sentation of the seasonal dynamics of mesozooplankton (assumedto be mostly copepods) that does not consider the complexity ofthe life-cycle of such metazoan organisms. The latter account forprocesses such as stage molting (i.e. discrete development ofindividuals), ontogenic and diurnal vertical migrations, overwin-tering of species specific quiescent stages (i.e. diapause, implyingsome lipid storing) and stage-dependent mortality (includingcannibalism for some species) known to shape the metazoanseasonal abundance and biomass (e.g. Runge et al., 2005). Allthose cannot be included in a mass-based model and this is aninherent default of the NPZD (Nutrient-Phytoplankton-Zooplank-ton-Detritus) models (e.g. Runge et al., 2005), where mesozoo-plankton mainly act as a closure term for phytoplanktonregulation. Nonetheless, NPZD models have been shown toadequately simulate the first order response of the lower trophiclevels to physical forcings (seasonal stratification and light avail-ability), particularly phytoplankton biomass regulation (Franks,2002). As the coupled model reproduces realistic zooplanktonseasonal variations and levels of biomass, we can assume arealistic regulation through predation of the simulated phyto-plankton biomass and primary production.
In the model, large phytoplankton dominate during the springbloom and in the DCM while the small size fraction outcompeteslarge phytoplankton in the surface layer in postbloom conditions(late July, Fig. 6) in accordance with observations made byLuchetta et al. (2000). However, in late August, winds blowingat ca. 8–13 m s�1 trigger sufficient mixing south of the BarentsSea Central Bank to deepen the simulated mixed layer by about10 m relative to conditions 10 days earlier. As a result of thismixed layer deepening, nitrate concentrations rise in the photic
Fig. 5. Simulated and remotely sensed primary production (Tg C) integrated monthly and vertically over Svalbard Bank, in the marginal ice zone, the central Barents Sea,
and the eastern Kola Peninsula (boxes on the right panel of Fig. 2 identify the sub-regions mentioned).
V. Le Fouest et al. / Continental Shelf Research 31 (2011) 1869–18791874
Author's personal copy
zone and promote growth and production of large phytoplanktonat the expense of the small size phytoplankton (Figs. 6 and 7).Sakshaug and Slagstad (1992) noticed that winds faster than8 m s�1 were required to trigger pulsed primary productionevents in summer in the Barents Sea.
3.2. Role of tides and wind mixing
The objective of this section is to examine the relativecontribution of tides and wind mixing to the summer primaryproduction of the Barents Sea. Summer is here defined as theperiod when phytoplankton is nutrient-limited in the model, i.e.from June to August. Three experiments were carried out asfollows: first, a standard run that included all the forcing asdescribed in the Material and methods section; second, a run in
which the eight model tidal constituents (Q1, O1, P1, K1, N2, M2,S2 and T2) were removed in summer; third, a run in which windsfaster than 8 m s�1 were set to 0 m s�1 in summer. The windthreshold was based on the study of Sakshaug and Slagstad(1992), which showed that summer wind velocities above thisthreshold were needed to promote primary production. In themodel, the number of days when winds were above 8 m s�1
ranged from 1 to 20 in the summer period considered.Fig. 8 shows the mean nitricline depth (upper left panel) and
time and depth-integrated primary production (lower left panel)simulated by the coupled model over the period June to August,and the differences between the run without tides (centralpanels) and the run without winds Z8 m s�1 (right panels) andthe standard run. In summer in open waters, the simulatednitricline results from the sharp vertical gradient of nitrate between
Fig. 6. Norway to Franz-Josef Land section of wind speed (m s�1) with the thresholds at 8 m s�1 (red dashed line), sea ice coverage (%), large and small phytoplankton
chlorophyll-a concentration (mg m�3), and meszooplankton and mesozooplankton biomass (mg C m�3) with sigma-t contours overlaid (kg m�3) from late April to late
August. Note the irregular scale for chlorophyll-a. Wind speed and sea ice coverage are nil over the land grid cells. (For interpretation of the references to colour in this
figure legend, the reader is referred to the web version of this article.)
V. Le Fouest et al. / Continental Shelf Research 31 (2011) 1869–1879 1875
Author's personal copy
the photic zone depleted in nitrate (ca. o1–3 mmol N m�3, seeFig. 7) due to phytoplankton uptake and the aphotic zone exhibitinghigher nitrate concentrations (up to 11–12 mmol N m�3). By con-trast, in sea ice-covered areas, nitrate concentrations in the photiczone are higher than in open waters (see Figs. 7 and 9). The smoother
vertical gradient of nitrate simulated by the model where sea ice ispresent results predominantly from the dilution of nitrate due to seaice melt and import of freshwater from the Kara Sea, and no so muchfrom phytoplankton uptake, which is severely thwarted by the verylow levels of underwater light (o6 mol photons m�2 d�1) associated
Fig. 7. Simulated nitrate concentration and f-ratio averaged monthly and between 0 and 50 m over Svalbard Bank, in the marginal ice zone, the central Barents Sea and the
eastern Kola Peninsula (boxes on the right panel of Fig. 2 identify the sub-regions mentioned).
Fig. 8. Mean nitricline depth (in m) and integrated primary production (in g C m�2) over June to August for the standard run (left panels) and difference between the run
without tides or winds above 8 m s�1 and the standard run (middle and right panels, respectively). Blueish (reddish) tones indicate either a shallower (deeper) nitricline or
less (more) primary production. Pointing up right to left (left to right) hatching indicates less (more) sea ice (variations in sea ice volume o5% are not shown). The black
and red lines correspond to the location of the 200 m isobath and of the isoline of 30% of sea ice cover (mean over June to August), respectively. Note the irregular scale for
the nitricline (upper panels). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
V. Le Fouest et al. / Continental Shelf Research 31 (2011) 1869–18791876
Author's personal copy
with the presence of sea ice (see Fig. 9). This explains why in themodel the nitricline can occur deeper in ice-covered and lowlyproductive areas than in open water areas (Fig. 8).
In the experiment without tides, summer-integrated primaryproduction is severely dampened on Svalbard Bank and east ofthe Kola Peninsula (Fig. 8). Locally, primary production rates peakat 40–45 g C m�2 that is 60% less than in the standard run(70 g C m�2). These lower rates generally coincide with a deepernitricline that can exceed 20 m locally (Fig. 8). On Svalbard Bank,the circulation associated with relatively strong currents alongthe bank slopes (Fig. 3a,b) is less intense, and so too is thetopographic current shear that is responsible for sub-pycnoclinenitrate inputs in the model. The NAW area of the Barents Sea isthe most responsive to the suppression of winds faster than8 m s�1 (see the top right panel in Fig. 6). Primary productionrates that are 15–20 g C m�2 in the standard run decrease by upto 5 g C m�2 when wind speeds are set to 0 m s�1 in the forcing(Fig. 8). By removing the wind mixing, the supply of nitrate fromdepth into the photic zone decreases. This summer-integratedproduction loss (5 g C m�2) is comparable to the maximum dailyprimary production rate simulated during the peak of the springbloom (5 g C m�2 d�1). In contrast, the AW region is less affectedin this experiment (Fig. 8) because the strong water columnstratification due to melt water precludes effective wind mixinganyway. In the run without strong wind events, lower productionrates in the central Barents Sea occur in concurrence with anitricline that is, on average for the period, 5 m deeper than in thestandard run (Fig. 8), (up to 10 m deeper in August, when windsare stronger (see the top right panel in Fig. 6)). By contrast, in thesouthern part of the MIZ and west of the Barents Sea openinglower primary production rates are associated with a shallowernitricline when winds are set to 0 m s�1. Both sub-regions in thestandard run undergo bloom-like conditions and the depthof the productive layer is partly driven by wind mixing thatdistributes phytoplankton deeper in the water column. Withoutthis mixing, the simulated productive layer becomes shallowerand primary production decreases (Fig. 8). When sea ice ispresent, phytoplankton growth is, on average through the sum-mer, mostly light-limited at the sea surface (Fig. 9). Phytoplank-ton growth and production in the model are then affected bychanges in sea ice extent (through changes in underwater lightlevels) and thickness (through changes in both stratification andunderwater light levels). Therefore, simulated primary productiondecreases (increases) when the volume of sea ice over a grid cell ishigher (lower) in the runs without tides or without strong winds(Fig. 8).
The response of primary production to tides and wind mixingis regionally variable over the Barents Sea. June to Augustintegrated primary production was estimated for Svalbard Bank(box 1 in Fig. 2), the MIZ (box 2), the central Barents Sea (box 3)and the eastern Kola Peninsula (box 4) to synthesize the differ-ential effect of tides and winds Z8 m s�1. The MIZ is by far themost productive region in the model with 11.8 Tg C producedover the summer. Regenerated production (6.8 Tg C) prevailsagainst new production (5 Tg C) with a f-ratio of 0.43, the highestof the four study regions (Fig. 7). In balance, removing tides orstrong winds has only a limited effect on MIZ total production(�1.6% and �2.2%, respectively). On Svalbard Bank and in theeastern Kola Peninsula, primary production is 3.9 Tg C and2.3 Tg C, respectively, and is mostly ammonium-based (f-ratio of0.37 and 0.21, respectively; see also Fig. 7). These two regionsare the most affected by tides in the model with the strongesttidal currents and the highest M2 tidal elevation amplitudes(Figs. 3b,c and 8). The effect of tides on total primary production(�19% and –23.6%, respectively) is much greater than the effectof winds (�2.9% and –4.2%, respectively) and new production ismore severely affected (�40%) than regenerated production(�14%). The removal of tidal forcing promotes a regime moresustained by regenerated production with a f-ratio that decreasesby 20–26% with respect to the standard run. In the central BarentsSea, primary production is 4.6 Tg C, of which only 10% is newproduction. This is the region of the model where the contributionof nitrate-based production is the lowest. In contrast with theSvalbard Bank and eastern Kola Peninsula regions, the influence oftides in the central Barents Sea is negligible (þ0.04%) comparedto that of wind mixing (�9.6%). Removing winds faster than8 m s�1 dampens new production and the f-ratio by 40% and 35%,respectively, whereas ammonium-based production decreases byonly 6.2%. Using a 1-D model applied to the subarctic western NorthPacific, Fujii and Yamanaka (2008) estimated primary production tobe lower by 6.5% without summertime storms. In the3-D coupled model described here, summer primary production(22.6 Tg C integrated over the 4 study regions) decreases by 4.1%(�0.93 Tg C) without winds faster than 8 m s�1 and by 6.8%(�1.55 Tg C) without tides. To put this in context, the contribu-tion of strong wind events or tides to summer primary production(0.93–1.55 Tg C) is equivalent to the spring bloom integrated overthe Svalbard area.
4. Conclusion
Tides and winds are two important forcing componentsresponsible for pulsed primary production under postbloomconditions in coastal waters (Le Fouest et al., 2005; Sharples,2008). The objective of this study was to assess the effect of tidesand winds with speeds faster than 8 m s�1 on summer primaryproduction in the Barents Sea. In our model, tides are responsiblefor ca. 20% of the summer primary production on Svalbard Bankand in the eastern Kola Peninsula, respectively. By contrast, morethan 9% of the primary production is due to strong winds(Z8 m s�1) in the central Barents Sea. When integrating primaryproduction for all the Barents Sea regions, tides and strong windevents account respectively for 6.8% and 4.1% of the summerprimary production.
Although these estimates are likely to depend on the quality ofboth the forcing and the model, they constitute a baseline forfurther studies. To draw a more accurate picture of the role ofwinds and tides in controlling primary production in the wholeArctic Ocean, simulations with inter-annually varying forcingwould need to be conducted. This point is particularly relevantin a context of recent Arctic warming, which translates into more
Fig. 9. Surface concentration of nitrate averaged over June–August 2001. The hatching
overlaid indicates the area where surface phytoplankton are light-limited (mean over
June–August 2001). The black and red lines correspond to the location of the 200 m
isobath and of the isoline of 30% of sea ice cover (mean over June to August),
respectively. (For interpretation of the references to colour in this figure legend, the
reader is referred to the web version of this article.)
V. Le Fouest et al. / Continental Shelf Research 31 (2011) 1869–1879 1877
Author's personal copy
open waters potentially subject to mixing. For instance, theCanadian Arctic Archipelago (CAA) that has relatively large tidesand is quite shallow (ca. 50 m) would be a good candidate formixing and upward nutrient transport intensification as thesummer sea ice disappears (e.g. Hannah et al., 2009). In addition,future climate predictions, which do not include tidal mixing orany parameterization of it, may underestimate primary produc-tion from key regions such as CAA as they become ice free. Withrespect to winds, cyclones in the Arctic region tend to be longerlived and more numerous in summer (Zhang et al., 2004a) andthis may also promote more nutrients mixed upwards and, as aconsequence, primary production. Furthermore, future wind pro-jections for the Barents Sea are still uncertain but model studiessuggest slightly more numerous and stronger Arctic storms insummer (Orsolini and Sorteberg, 2009). Such events, in synergywith a prolonged exposure to light, could promote primaryproduction pulses that would oppose the production loss result-ing from stronger upper ocean stratification. Simulations fordifferent climate change scenarii might help to assess this balanceand calculate future trends in atmospheric CO2 drawdown in awarming Arctic.
Acknowledgements
This work was partly funded by the Natural EnvironmentResearch Council (NERC)’s Core Research Programme ‘‘Oceans2025’’ and by VLF NERC New Investigator research grant(NE/G000611/1). VLF also acknowledges support from the EuropeanSpace Agency and the Centre national d’etudes spatiales (CNES) aspart of the MALINA project, funded by the Centre national de larecherche scientifique (CYBER/LEFE and PICS programmes), theAgence nationale de la recherche, and the CNES. The Globcolourproject is gratefully acknowledged. MB is supported by theCanada Excellence Research Chair in ‘‘Remote sensing of Canada’snew Arctic frontier’’. M. L. Longhi (SAMS) is acknowledged for theremote sensing data treatment. The authors thank B. Zakardjianand two anonymous referees for their constructive commentsthat greatly improved the manuscript.
References
Arctic Climate Impact Assessment (ACIA), 2005. Arctic Climate Impact Assessment.Cambridge University Press, Cambridge 1042 pp.
Arrigo, K.R., Sullivan, C.W., 1994. A high resolution bio-optical model of microalgalgrowth: tests using sea-ice algal community time-series data. Limnology andOceanography 39, 609–631. doi:10.4319/lo.1994.39.3.0609.
Arrigo, K.R., Worthen, D., Schnell, A., Lizotte, M.P., 1998. Primary production inSouthern Ocean waters. Journal of Geophysical Research 103, 15587–15600.doi:10.1029/98JC00930.
Barron, C.N., Kara, A.B., Martin, P.J., Rhodes, R.C., Smedstad, L.F., 2006. Formulation,implementation and examination of vertical coordinate choices in the GlobalNavy Coastal Ocean Model (NCOM). Ocean Modeling 11, 347–375.
Dittmar, T., Kattner, G., 2003. The biogeochemistry of the river and shelfecosystem of the Arctic Ocean: a review. Marine Chemistry 83, 103–120.doi:10.1016/S0304-4203(03)00105-1.
Egbert, G.D., Erofeeva, S.Y., 2002. Efficient inverse modeling of barotropic oceantides. Journal of Atmospheric and Oceanic Technology 19, 183–204.doi:10.1175/1520-0426(2002)019o0183:EIMOBO42.0.CO;2.
Falkowski, P.G., Dubinsky, Z., Wyman, K., 1985. Growth–irradiance relationships inphytoplankton. Limnology and Oceanography 30, 311–321. doi:10.4319/lo.1985.30.2.0311.
Franks, P.J.S., 2002. NPZ models of plankton dynamics: their construction, couplingto physics, and application. Journal of Oceanography 58, 379–387.
Fujii, M., Yamanaka, Y., 2008. Effects of storms on primary productivity and air–sea CO2 exchange in the subarctic western North Pacific: a modeling study.Biogeosciences 5, 1189–1197.
Garver, S., Siegel, D., 1997. Inherent optical property inversion of ocean colorspectra and its biogeochemical interpretation 1. Time series from the SargassoSea. Journal of Geophysical Research 102, 18607–18625. doi:10.1029/96JC03243.
GRDC, 2003. River Discharge Data 1954–1999. Global Runoff Data Centre, Koblenz,56068, Germany.
Hamre, J., 1994. Biodiversity and exploitation of the main fish stocks in theNorwegian-Barents Sea ecosystem. Biodiversity and Conservation 3, 473–492.doi:10.1007/BF00115154.
Hannah, C., Dupont, G., Dunphy, M., F., 2009. Polynyas and tidal currents in theCanadian Arctic Archipelago. Arctic 62, 83–95.
Hansen, C., Samuelsen, A., 2009. Influence of horizontal model grid resolution onthe simulated primary production in an embedded primary production modelin the Norwegian Sea. Journal of Marine Systems 75, 236–244. doi:10.1016/j.jmarsys.2008.10.004.
Hegseth, E.N., 1997. Phytoplankton of the Barents Sea—the end of a growthseason. Polar Biology 17, 235–241. doi:10.1007/s003000050127.
Holt, J., James, I.D., 2001. An s-coordinate density evolving model of the northwestEuropean continental shelf 1, Model description and density structure. Journalof Geophysical Research 106, 14,014–14,034. doi:10.1029/2000JC000304.
Hunke, E.C., Lipscomb, W.H., 2004. CICE: the Los Alamos Sea Ice Model, Doc-umentation and Software User’s Manual, LA-CC-98-16, v.3.1. Los AlamosNational Laboratory, Los Alamos, New Mexico.
Hunt Jr., G.L., Megrey, B.A., 2005. Comparison of the biophysical and trophiccharacteristics of the Bering and Barents Seas. ICES Journal of Marine Science62, 1245–1255. doi:10.1016/j.icesjms.2005.04.008.
Kristiansen, S., Fabrot, T., Wheeler, P.A., 1994. Nitrogen cycling in the Barents Sea-Seasonal dynamics of new and regenerated production in the Marginal IceZone. Limnology and Oceanography 39, 1630–1642.
Laws, E.A., Bannister, T.T., 1980. Nutrient- and light-limited growth of Thalassiosirafluviatilis in continuous culture, with implications for phytoplankton growth inthe ocean. Limnology and Oceanography 25, 457–473. doi:10.1016/0198-0254(80)96046-X.
Lee, Z.P., Carder, K.L., Arnone, R.A., 2002. Deriving inherent optical properties fromwater color: a multiband quasi-analytical algorithm for optically deep waters.Applied Optics 41, 5755–5772.
Lee, Z.P., Du, K.P., Arnone, R., 2005a. A model for the diffuse attenuation coefficientof downwelling irradiance. Journal of Geophysical Research 110, C02016.doi:10.1029/2004JC002275.
Lee, Z.P., Darecki, M., Carder, K.L., Davis, C.O., Stramski, D., Rhea, W.J., 2005b.Diffuse attenuation coefficient of downwelling irradiance: an evaluation ofremote sensing methods. Journal of Geophysical Research 110, C02017.doi:10.1029/2004JC002573.
Le Fouest, V., Zakardjian, B., Saucier, F.J., Starr, M., 2005. Seasonal versus synopticvariability in planktonic production in a high-latitude marginal sea: the Gulf ofSt. Lawrence (Canada). Journal of Geophysical Research 110, C09012.doi:10.1029/2004JC002423.
Le Fouest, V., Zakardjian, B., Saucier, F.J., C- izmeli, S.A., 2006. Application ofSeaWIFS- and AVHRR-derived data for mesoscale and regional validation ofa 3-D high-resolution physical–biological model of the Gulf of St. Lawrence(Canada). Journal of Marine Systems 60, 30–50. doi:10.1016/j.jmarsys.2005.11.008.
Legendre, L., Rivkin, R.B., 2002. Fluxes of carbon in the upper oceans: regulation byfood-web control nodes. Marine Ecology Progress Series 242, 95–109.
Levasseur, M., Therriault, J.-C., Legendre, L., 1984. Hierarchical control of phyto-plankton succession by physical factors. Marine Ecology Progress Series 19,211–222.
Levy, M., Memery, L., Madec, G., 1998. The onset of a bloom after deep winterconvection in the northwestern Mediterranean sea: mesoscale process studywith a primitive equation model. Journal of Marine Systems 16, 7–21.doi:10.1016/S0924-7963(97)00097-3.
Loeng, H., 1989. Ecological features of the Barents Sea. In: Rey, L., Aleander, V.(Eds.), Proceedings of the Sixth Conference of the Comite Arctic International,Leiden, pp. 327–365.
Loeng, H., 1991. Features of the physical oceanographic conditions of the BarentsSea. Polar Research 10, 5–18.
Luchetta, A., Lipizer, M., Socal, G., 2000. Temporal evolution of primary productionin the central Barents. Journal of Marine Systems 27, 177–193. doi:10.1016/S0924-7963(00)00066-X.
MacIntyre, H.L., Kana, T.M., Anning, T., Geider, R.J., 2002. Photoacclimation ofphotosynthesis irradiance response curves and photosynthetic pigments inmicroalgae and cyanobacteria. Journal of Phycology 38, 17–38. doi:10.1046/j.1529-8817.2002.00094.x.
Maritorena, S., Siegel, D.A., Peterson, A.R., 2002. Optimization of a semianalyticalocean color model for global-scale applications. Applied Optics 41, 2705–2714.doi:10.1364/AO.41.002705.
Maritorena, S., Siegel, D.A., 2005. Consistent merging of satellite ocean color datasets using a bio-optical model. Remote Sensing of Environment 94, 429–440.doi:10.1016/j.rse.2004.08.014.
Maritorena, S., d’Andon, O.H.F., Mangin, A., Siegel, D.A., 2010. Merged satelliteocean color data products using a bio-optical model: characteristics, benefits
V. Le Fouest et al. / Continental Shelf Research 31 (2011) 1869–18791878
Author's personal copy
and issues. Remote Sensing of Environment 114, 1791–1804. doi:10.1016/j.rse.2010.04.002.
Matrai, P., Vernet, M., Wassmann, P., 2007. Relating temporal and spatial patternsof DMSP in the Barents Sea to phytoplankton biomass and productivity.Journal of Marine Systems 67, 83–101. doi:10.1016/j.jmarsys.2006.10.001.
Matsuoka, A., Huot, Y., Shimida, K., Saitoh, S.-I., Babin, M., 2007. Bio-opticalcharacteristics in the Western Arctic Ocean: implications for ocean coloralgorithms. Canadian Journal of Remote Sensing 33, 503–518.
Mei, Z.-P., Saucier, F., Le Fouest, V., Zakardjian, B., Senneville, S., Xie, X., Starr, M.,2010. Effects of colored dissolved organic matter (CDOM) and temperature onprimary and secondary productions of Gulf of St. Lawrence: insights from 3-Dcoupled physics-NPZD model. Continental Shelf Research 30, 2027–2042,10.1016/j.csr.2010.10.003.
Mellor, G.L., Yamada, T., 1974. Hierarchy of turbulence closure models forplanetary boundary-layers. Journal of Atmospheric Science 31, 1791–1806.doi:10.1175/1520-0469(1974)031o1791:AHOTCM42.0.CO;2.
Morel, A., 1978. Available, usable and stored radiant energy in relation to marinephotosynthesis. Deep-Sea Research 25, 673–688. doi:10.1016/0146-6291(78)90623-9.
National Oceanographic Data CentreWorld Ocean Atlas 2005, 2006. Documenta-tion accompanying WOA05 DVD (ASCII text and portable document format).Prepared by the Ocean Climate Laboratory, National Oceanographic DataCenter, Silver Springs, MD 20910, 12 pp.
Orsolini, Y.J., Sorteberg, A., 2009. Projected changes in Eurasian and arctic summercyclones under global warming in the Bergen climate model. AtmosphericOceanic Science Letters 2, 62–67.
Pabi, S., van Dijken, G.L., Arrigo, K.R., 2008. Primary production in the Arctic Ocean,1998–2006. Journal of Geophysical Research 113, C08005. doi:10.1029/2007JC004578.
Pavlov, V.K., Pfirman, L., 1995. Hydrographic structure and variability of the KaraSea: implications for pollutant distribution. Deep-Sea Research II 6,1369–1390. doi:10.1016/0967-0645(95)00046-1.
Perrette, M., Yool, A., Quartly, G.D., Popova, E.E., 2011. Near-ubiquity of ice-edgeblooms in the Arctic. Biogeosciences 8, 515–524. doi:10.5194/bg-8-515-2011.
Peterson, B.J., Holmes, R.M., McClelland, J.W., Vorosmarty, C.J., Lammers, R.B.,Shiklomanov, A.I., Rahmstorf, S., 2002. Increasing river discharge to the ArcticOcean. Science 298, 2171–2173. doi:10.1126/science.1077445.
Popova, E.E., Yool, A., Coward, A.C., Aksenov, Y.K., Alderson, S.G., de Cuevas, B.A.,Anderson, T.R., 2010. Control of primary production in the Arctic by nutrientsand light: insights from a high resolution ocean general circulation model.Biogeosciences 7, 3569–3591. doi:10.5194/bg-7-3569-2010.
Postlethwaite, C.F., Morales Maqueda, M.A., Le Fouest, V., Tattersall, G.R., Holt, J.,Wilmott, A.J., 2011. The effect of tides on dense water formation in Arctic shelfseas. Ocean Science 7, 203–217. doi:10.5194/os-7-203-2011.
Prieur, L., Legendre, L., 1988. Oceanographic criteria for new phytoplanktonproduction. In: Rothschild, B.J. (Ed.), Toward a Theory on Biological–PhysicalInteractions in the World Ocean, Kluwer, Dordrecht, pp. 71–112.
Rat’kova, T.N., Wassmann, P., 2002. Seasonal variation and spatial distribution ofphyto- and protozooplankton in the central Barents Sea. Journal of MarineSystems 38, 47–75. doi:10.1016/S0924-7963(02)00169-0.
Reigstad, M., Wassmann, P., Wexels Riser, C., Øygarden, S., Rey, F., 2002. Variationsin hydrography, nutrients and chlorophyll a in the marginal ice-zone and thecentral Barents Sea. Journal of Marine Systems 38, 9–29. doi:10.1016/S0924-7963(02)00167-7.
Rey, F., 1991. Photosynthesis-irradiance relationships in natural phytoplanktonpopulations of the Barents Sea. Polar Research 10, 105–116.
Ricchiazzi, P., Yang, S.R., Gautier, C., Sowle, D., 1998. SBDART: a research andteaching software tool for plane-parallel radiative transfer in the Earth’satmosphere. Bulletin of the American Meteorological Society 79, 2101–2114.
Runge, J., Franks, P.J.S., Gentleman, W.C., Megrey, B.A., Rose, K.A., Werner, F.E.,Zakardjian, B., 2005. Diagnosis and prediction of variability in secondaryproduction and fish recruitment processes: developments in physical–biological modeling. In: Robinson, A.R., Brinks, K.H. (Eds.), The Sea, GlobalCoastal Ocean: Multi-Scale Interdisciplinary Processes, vol. 13. HarvardUniversity Press, Cambridge, pp. 413–473.
Sakshaug, E., 1991. Food webs and primary production in the Barents Sea. PolarBiology 4, 1–8.
Sakshaug, E., Slagstad, D., 1992. Sea ice and wind: effects on primary productivityin the Barents Sea. Atmosphere-Ocean 30, 579–591.
Sakshaug, E., 2004. Primary and secondary production in the Arctic Seas. In: Stein, R.,MacDonald, R.W. (Eds.), The Organic Carbon Cycle in the Arctic Ocean, Springer,Berlin, pp. 57–81.
Sharples, J., 2008. Potential impacts of the spring-neap tidal cycle on shelf seaprimary production. Journal of Plankton Research 30, 183–197. doi:10.1093/plankt/fbm088.
Shiklomanov, A., 2010. River runoff. /http://www.arctic.noaa.gov/reportcard/rivers.htmlS.
Sibert, V., Zakardjian, B., Gosselin, M., Starr, M., Senneville, S., LeClainche, Y., 2011.3D bio-physical model of the sympagic and planktonic productions in theHudson Bay System. Journal of Marine Systems 88, 401–422. doi:10.1016/j.jmarsys.2011.03.014.
Slagstad, D., Tande, K.S., 1990. Growth and production dynamics of Calanusglacialis in an arctic pelagic food web. Marine Ecological Progress Series 63,189–199.
Stroeve, J.C., Serreze, M.C., Fetterer, F., Arbetter, T., Meier, W., Maslanik, J., Knowles, K.,2005. Tracking the Arctic’s shrinking ice cover: another extreme Septemberminimum in 2004. Geophysical Research Letters 32, L04501. doi:10.1029/2004GL021810.
Uppala, S.M., Kallberg, P.W., Simmons, A.J., Andrae, U., Da Costa Bechtold, V.,Fiorino, M., Gibson, J.K., Haseler, J., Hernandez, A., Kelly, G.A., Li, X., Onogi, K.,Saarinen, S., Sokka, N., Allan, R.P., Andersson, E., Arpe, K., Balmaseda, M.A.,Beljaars, A.C.M., Van De Berg, L., Bidlot, J., Bormann, N., Caires, S., Chevallier, F.,Dethof, A., Dragosavac, M., Fisher, M., Fuentes, M., Hagemann, S., Holm, E.,Hoskins, B.J., Isaksen, L., Janssen, P.A.E.M., Jenne, R., McNally, A.P., Mahfouf, J.-F.,Morcrette, J.-J., Rayner, N.A., Saunders, R.W., Simon, P., Sterl, A., Trenberth, K.E.,Untch, A., Vasiljevic, D., Viterbo, P., Woollen, J., 2005. The ERA-40 re-analysis.Quarterly Journal of the Royal Meteorological Society 131, 2961–3012.
Wadhams, P., Davis, N.R., 2000. Further evidence of ice thinning in the ArcticOcean. Geophysical Research Letters 27, 3973–3975. doi:10.1029/2000GL011802.
Walsh, J.J., Dieterle, D.A., Maslowski, W., Grebmeier, J.M., Whitledge, T.E., Flint, M.,Sukhanova, I.N., Bates, N., Cota, G.F., Stockwell, D., Moran, S.B., Hansell, D.,McRoy, C.P., 2005. A numerical model of seasonal primary production withinthe Chukchi/Beaufort seas. Deep-Sea Research II 52, 3541–3576. doi:10.1016/j.dsr2.2005.09.009.
Wassmann, P., Slagstad, D., Wexels Riser, C., Reigstad, M., 2006. Modelling theecosystem dynamics of the Barents Sea including the marginal ice zone. II.Carbon flux and interannual variability. Journal of Marine Systems 59, 1–24.doi:10.1016/j.jmarsys.2005.05.006.
Wassmann, P., Slagstad, D., Ellingsen, I., 2010. Primary production and climaticvariability in the European sector of the Arctic Ocean prior to 2007:preliminary results. Polar Biology 33, 1641–1650. doi:10.1007/s00300-010-0839-3.
Woert, M.L.W., Zou, C.-Z., Meier, W.N., Hovey, P.D., Preller, R.H., Posey, P.G., 2004.Forecast verification of the Polar Ice Prediction System (PIPS) sea ice concen-tration fields. Journal of Atmospheric and Oceanic Technology 21, 944–957.doi:10.1175/1520-0426(2004)021o0944:FVOTPI42.0.CO;2.
Zhang, Y.C., Rossow, W.B., Lacis, A.A., Oinas, V., Mishchenko, M.I., 2004b. Calcula-tion of radiative fluxes from the surface to top of atmosphere based on ISCCPand other global data sets: refinements of the radiative transfer model and theinput data. Journal of Geophysical Research. 109, D19105. doi:10.1029/2003JD004457.
Zhang, J., Spitz, Y.H., Steele, M., Ashjian, C., Campbell, R., Berline, L., Matrai, P.,2010. Modeling the impact of declining se ice on the Arctic marine planktonicecosystem. Journal of Geophysical Research 115, C10015. doi:10.1029/2009JC005387.
V. Le Fouest et al. / Continental Shelf Research 31 (2011) 1869–1879 1879
