Plankton ecosystem functioning and nitrogen fluxes in the most oligotrophic waters of the Beaufort Sea , Arctic Ocean : a modeling study

Introduction Conclusions References


Introduction
The Arctic Ocean (AO) undergoes profound changes of its physical and biotic environments due to climate change.Overall net primary production (PP) is shown to have increased in the last decades (B élanger et al., 2012;Arrigo et al., 2011) and is expected to follow this trend in the future (Slagstad et al., 2011).Nevertheless, the PP Figures response is not same everywhere in the AO with regions showing stable or even decreasing PP (Arrigo et al., 2011;Slagstad et al., 2011).The greater light exposure and stratification of the water column also results in earlier spring blooms (Kahru et al., 2011) and a growing contribution of small phytoplankton cells to the planktonic community in summer (Li et al., 2009) suggesting oligotrophy is expanding in some Arctic regions.Furthermore, the 40 % projected widening of the productive time period will probably allow heterotrophic organisms to optimize grazing on phytoplankton and hence alter the carbon quality and quantity exported to the benthic realm (Wassmann and Reigstad, 2011).In this context of accelerating Arctic warming, a better knowledge of the mechanistic processes and biogenic fluxes mediating PP is required, with a particular attention to the oligotrophic season when biogenic fluxes are complex and so far are poorly quantified.
In the AO, more than 80 % of the PP takes place in shelf seas (Sakshaug, 2004).The Beaufort Sea exhibits the lowest production rate (8 Tg C; Sakshaug, 2004) with respect to its surface area (ca.476 000 km 2 ), which makes it the most oligotrophic shelf sea in summer (Ardyna et al., 2012).After the bloom occurring in June, a deep chlorophyll (Chl) maximum (DCM) forms as a result of relatively low nitrate concentrations in the surface layer at the end of spring (Tremblay et al., 2008).Over the growth season, the DCM progressively lowers the nitracline down to 60 m depth, where light becomes the limiting factor (Martin et al., 2010).On the slope of the Mackenzie Shelf, where the most oligotrophic waters were found (Tremblay et al., 2012), picoplankton (Micromonas ecotype) and phytoplankton < 5 µm dominated respectively the surface and DCM autotrophic community (Balzano et al., 2012;Claustre and Ras, unpublished data) whose role is central in mediating carbon fluxes in summer (Li et al., 2009).
The ability of ecosystem models applied to the AO to simulate realistic summer plankton dynamics and production rates is generally poor (e.g.Le Fouest et al., 2011).
It is mostly due to a simplistic representation of key processes partly resulting from the lack of joint multiparametric measurements, especially nutrients turnover rates and light-related parameters.Such measurements were done in the Beaufort Sea during Figures the Malina project (http://malina.obs-vlfr.fr) in summer 2009 providing an opportunity to improve plankton ecosystem models.A physical-biological coupled model of the water column was set up based on the extensive use of physical and biogeochemical variables and rates measured during the Malina cruise.Steady state runs were analyzed to budget the system and to gain a better understanding of the plankton ecosystem functioning in the most oligotrophic shelf waters of the AO.The objectives of this study are, on one hand, to infer the functioning and nitrogen fluxes within the summer plankton ecosystem and, on the other hand, to assess the model sensitivity to key light-associated processes involved in nutrient recycling and phytoplankton growth.
2 Material and methods

Observations
The large multiparametric dataset of physical, chemical and biological measurements collected during the Malina cruise (18 July-24 August, 2009) in the Beaufort Sea was used (i) to initiate and constrain the model runs, (ii) to set parameters and transfer functions and (iii) to compare with the model outputs.We provide here a summary of the data used along with their respective reference in the Malina special issue, where the detailed methodology for each measurement can be found.Temperature, salinity and fluorescence were measured using a Conductivity-Temperature-Depth (CTD) sensor.Temperature and salinity data were used to compute potential density, which were in turn used to compute Brunt-V äis äl ä frequencies (N).The latter were calculated in a leap-frog fashion, with the potential density from the previous and following depths (i.e.N at 5 m is computed with the data at 4 m and 6 m) (Gratton and Prieur, unpublished data).Surface and vertical profiles of downwelling photosynthetic available radiation (PAR) were respectively measured by an on-deck sensor and a Compact-Optical Profiling System (C-OPS) profiler (Hooker et al., 2012).With respect to photosynthesis  (2012).Ammonium concentrations (NH 4 ) were determined on board by fluorometer according to Holmes et al. (1999).Nitrate concentrations (NO 3 ) were quantified at laboratory using an automatic colorimetric procedure (Raimbault et al., 1990).Rates of primary production, NH 4 and NO 3 uptake, and NH 4 regeneration and nitrification were measured using a dual 13 C/ 15 N isotopic technique (Raimbault et al., 1999) applied during 24 h in-situ incubation.Size-fractionated Chl concentrations measured during the Malina cruise following the methodology described in Ardyna et al. (2011) were used (B élanger, unpublished data).Particulate organic carbon (POC) measurements (Doxaran et al., 2012) were used to compute POC : Chl ratios.Bacterial biomasses were derived from the product of the measured cell counts with the measured mean carbon content per cell (15.2 fg; Ortega-Retuerta et al., 2012a).Production rates estimated in pmol Leu l −1 h −1 were converted into carbon equivalent using a conversion factor of 1.5 kg C (mol Leu) −1 (Kirchman et al., 2009).Copepods biomasses were obtained from underwater video profiler data converted into carbon unit (Forest et al., 2012) and then into nitrogen using a molar C : N ratio of 8.1 (Forest et al., 2010).

The coupled physical-biological model
Based by fitting a cosine function to E 0 on-deck measurements (14-15 August) at the same station.Both physical forcing fields are shown in Fig. 2. The plankton ecosystem model (Fig. 3) fully detailed in the appendix is of moderate complexity and includes 10 compartments chosen according to the ecosystem structure observed during the cruise and measurements available.Phytoplankton is size-fractionated into large (> 5 µm) and small (< 5 µm) phytoplankton (LP and SP, respectively).The two zooplankton compartments represent large (LZ, mainly copepods) and small (SZ, protozooplankton) organisms.Bacteria are explicitly represented following the model of Fasham et al. (1990).Available nutrients for phytoplankton growth are nitrate (NO 3 ) and ammonium (NH 4 ).Detrital (i.e.produced by the ecosystem model compartments) particulate and dissolved organic nitrogen (PON and DONl, respectively) close the nitrogen cycle.The standing stock of potentially photosensitive DON (DONp) is photochemically transformed into NH 4 within the first 10 m of the water column.LP and SP growth depends on light, NO 3 and NH 4 availability according to the Liebig's law of minimum.LZ graze on LP and SZ, whereas SZ graze on SP and bacteria.Fecal pellets and LP basal mortality fuel the detrital PON pool.The detrital DONl pool is made of unassimilated nitrogen resulting from SZ grazing, SP and SZ basal mortality and detrital PON fragmentation.Bacterial release, LZ excretion and unassimilated nitrogen resulting from SZ grazing are the sources of NH 4 in the model.NH 4 is converted into NO 3 through the nitrification process.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).
Profiles of initial conditions were defined as the linear interpolation (1 m as in the model grid) of vertical distributions from bottle casts collected at station 345 (sampled depths are shown in Fig. 5).For NO 3 and NH 4 , we used surface to 90 m deep (the maximum sampling depth at this station) concentrations averaged from 2 casts from 14 August.Below 90 m and to the end of the numerical vertical domain, we averaged concentrations (0.02 < CV < 0.04) from stations of the entire sampling grid for which nutrients  3 Results and discussion

Plankton ecosystem functioning and nitrogen fluxes
The coupled model was run in steady state mode so that the diffused state variables reached a near equilibrium state (Fig. 4) ("standard" run).Concentrations at the surface, in the DCM and integrated over the whole numerical domain tended towards near equilibrium (upper panels).This was not the case for surface NO 3 and LP.Very low NO 3 concentrations (ca.0.003 mmol m −3 ) were quickly taken up by severely nutrient-limited LP (lim LP N = 0.01).Nutrient limitation combined with increasing LZ grazing pressure on LP explained the decrease of surface LP towards concentrations near 0 mmol N m −3 .
As concentrations were very low, this pattern had no influence on the stability of the model.The model outputs were then compared with the time coincident multiparametric measurements (10:00 a.m.local time for all variables, except for downwelling PAR measured at 11:00 a.m.local time) (Figs. 5 and 6).The profiles of measured NO 3 , NH 4 , size-fractionated Chl, PON, LZ and bacterial biomass used for the comparison were same as those used to initiate the model state variables.This approach permits to assess the model ability to reproduce the observed concentrations and rates.significant but low and likely imprecise (i.e.within the 50 % of the detection limit 0.0006-0.0008mmol N m −3 d −1 ).
With respect to phytoplankton, production rates and Chl are highly constrained by variations of the nutrients and light.The shape of the vertical light field was well reproduced by the coupled model as were the simulated PAR values at the surface and within the DCM (Fig. 6a).While the range of measured C : N ratios at study station 345 (6.744 at 3 m and 6.362 at 60 m) was analogous to the 6.625 Redfield ratio, the observed POC : Chl ratios showed a ca.5-fold decrease from the surface (ca.312 g g −1 ) to the DCM (ca.57 g g −1 ).Assuming phytoplankton carbon can represent 20 % of POC in oligotrophic waters with a high regenerative capability (e.g.Claustre et al., 1999), the observed C : Chl range would reach ca.62 g g −1 at the surface and ca.11 g g −1 within the DCM.These values compare with those given by Sherr et al. (2003) and Booth and Horner (1997) for a phytoplankton assemblage dominated by < 5 µm sized cells observed in the central oligotrophic AO in summer (13-70 g g −1 , ca. 30 g g −1 on average).Furthermore, these studies report abundant picophytoplankton ecotype Micromonas as observed during the Malina cruise (Balzano et al., 2012).DuRand et al. (2002) measured Micromonas sp.cellular carbon and Chl content and estimated the mean C : Chl ratio to be ca.30 g g −1 .To that respect, it can be assumed that the simulated C : Chl ratios for SP (10-45 g g −1 ) lied within the observed range (11-63 g g −1 , Fig. 6b).The vertical variations of the measured light saturation parameter (E k ) (ca. 0.8 mg m −3 formed at 87 % by SP in agreement the observations (Fig. 5c).At the surface, the simulated SP Chl was twice (ca.0.2 mg m −3 ) that measured (ca.0.1 mg m −3 ) but values remained low.With respect to PP, the rates and shape of the profile showed comparable values and pattern in both the model and measurements (Fig. 6c).Higher Introduction

Conclusions References
Tables Figures

Nutrients recycling
As for PP, the profiles of simulated and observed NH 4 uptake and regeneration showed similar shapes and values (Fig. 6e, f).NH 4 uptake in both measurements and the model was due to phytoplankton and bacteria.While their respective contribution is difficult to assess in-situ, phytoplankton and bacteria in the model respectively consumed 75 % and 25 % of the NH 4 pool at the surface and 60 % and 40 % within the DCM.In the data, total DCM PP (ca.measurements at the surface (ca.0.6 mg C m −3 d −1 ) but was one order of magnitude higher in the DCM (ca.0.9 mg C m −3 d −1 ) showing that the contribution of bacteria was likely overestimated (Fig. 6d).At this station, bacteria were found to be strictly N-limited at the surface but both N-and C-limited within the DCM (Ortega-Retuerta et al., 2012b).Carbon limitation, which was not accounted in the bacterial growth model due to the large uncertainty in assessing the fraction of the measured DOC pool that can be taken up for growth, certainly explains the discrepancy.Nevertheless, the model overestimation of bacterial biomass (0.06 mmol N m −3 ) only had a limited impact on the DCM dynamics and simulated total PON concentration (i.e sum of phytoplankton, SZ, bacteria and detrital PON) (Fig. 5d).A model run (not shown) initiated with the interpolated profile of measured bacterial biomass and with the steady state solutions of the "standard run" for the other 9 state variables showed that the simulated NH 4 regeneration (ca.0.010 mmol N m −3 d −1 ) would still fairly approximate the measured value (ca.

DON photoammonification into NH 4
In surface waters, NH 4 can be produced from the photochemical degradation of photosensitive DON mediated by the ultra-violet (UV) radiation (i.e.photoammonification; see Bushaw et al., 1996).This photochemical process was set up in the model in a simple fashion using an empirical formulation (Eq.A24 in the Appendix) linking the decrease with depth of a mean photoammonification rate within the upper 10 m (Xie et al., 2012).This approach based on measurements had been chosen at the expense of a more complex bio-optical spectral model involving accurate daily UV data, which were not available for station 345.
A simulation without the photoammonification process ("no photoammonification" run) was run in order to assess the contribution of this photochemical process to PP and its role in the plankton ecosystem functioning.During the time window simulated by the model, the the measured 10% UV irradiance depths at 325-340 nm (ca.7.8-10.3m), wavelengths at which most photoammonification occurred (Xie et al., 2012), were the highest encountered during the whole Malina sampling period (Para et al., 2012) A closer match with surface observations was achieved in the run accounting for the photochemical process (Figs. 7 and 8).Within the upper 10 m of the numerical domain, the simulated PON biomass was 40 % higher (53 %, 42 % and 23 % higher for bacteria, SZ and SP, respectively) than in the "no photoammonification" run (Fig. 7d).By stimulating SP and bacterial growth and subsequent SZ grazing the model (Fig. 8e, f).The NH 4 photo-produced met 25 % of the simulated nitrogen demand by SP.This contribution is in line with previous estimations for the Orincco river plume (50 %; Morell and Corredor, 2001) that drains high loads of terrigenous organic matter.In terms of production, photoammonification translated into a 3.2-fold increase of the autotrophic and bacterial production (Fig. 8c, d).It is consistent with the 2.9fold increase reported in the bioassay study of V äh ätalo et al. (2011).For the whole water column, it represented a 30 % increase in the simulated PP (37.8 mg C m ) and bacterial production (37.5 mg C m The simulated photoammonification rate represented 6.5 % of bacterial production and was close to 2-5 % contribution given by V äh ätalo et al. ( 2011).In the model, the photoammonification process is an important driver of the regenerative capability of the system supported by the microbial food web.

C : Chl ratios
In the model, the competition for resources between SP and LP was driven primarily by differences in nutrient uptake, light use and C : Chl ratios.Simulated C : Chl ratios varied according to PAR and nitrogen limitation (see Eqs. A10 and A11 in the Appendix) and constrained the light-based growth rate, which was limiting in the vicinity of DCM.LP was characterized by C : Chl ratios between 35 and 65 g g −1 while SP showed lower values in the 15-45 g g −1 range.Generally, biogeochemical models applied to the AO typically distinguish diatom phytoplankton from non-diatom phytoplankton.The C : Chl ratio used for diatoms generally lies between 33 and 50 g g −1 (Slagstad et al., 2011;Walsh et al., 2011;Zhang et al., 2011;Le Fouest et al., 2011;Popova et al., 2010), which overlaps the range simulated by the model (35 and 65 g g −1 ).However, the C : Chl ratio used for non-diatom phytoplankton varies amongst the different models.Generally invariant in space and time, it can be the same (e.g.Zhang et al., 2011;Le Fouest et al., 2010) or more than twice the value used for diatoms (83-100 g g −1 , e.g.Slagstad et al., 2011;Walsh et al., 2011).Figures

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Full These values for non-diatom phytoplankton are much higher than those simulated for SP in the model (15 and 45 g g −1 , Fig. 6b).
To infer the sensitivity of the model to C : Chl ratios, a simulation with a time-and depth-invariant C : Chl ratio respectively of 50 and 100 g g −1 for LP and SP was run ("constant C : Chl" run) and compared to the observations (Figs. 9 and 10).The simulated and measured Chl concentrations at the DCM were close (0.7-0.75 mg m −3 ) but, contrary to the observation, LP dominated the DCM at the expense of SP (Fig. 9c).Simulated PP rates in the "standard" and "constant C : Chl" runs were comparable (0.9-0.95 mg C m −3 d −1 ) but mostly new production in the "constant C : Chl" run (f -ratio of 0.63 and 0.23 in the "constant C : Chl" and "standard" runs, respectively) (Fig. 10c, e).
In terms of nitrogenous biomass, LP dominated the DCM contributing to 62 % of total PON (as compared to only 18 % in the "standard" run).This increase translated into more LZ biomass (Fig. 9f) and a higher NH 4 concentration in the DCM resulting from more NH 4 release by LZ (Fig. 9b).By contrast, the activity of the microbial food web dropped within the DCM, as illustrated by the 70 % decrease of NH 4 regeneration (Fig. 10f) mediated by both SZ and bacteria in the model.SZ represented only 7 % of total PON in the "constant C : Chl" run, which was a much lower contribution than the 37 % simulated in the "standard" run.Similarly, the bacterial biomass and production both decreased by 50 % (Figs.9e and 10d).C : Chl ratios involved in the simulation of the light-based phytoplankton growth rate are important drivers of the large versus small phytoplankton competition within the system.

Concluding remarks
The developed to gain a better understanding of the plankton ecosystem functioning in these stratified, clear and very oligotrophic offshore waters.The coupled model was forced by a stationary field of vertical turbulent diffusion and by a diurnal cycle of surface PAR based on measurements at station 345.Simulations at steady state were produced and the outputs compared to an extensive dataset of space and time coincident and multiparametric data sampled at the same station.
The 10-compartment ecosystem model approximated the observed nitrogen fluxes and biomass levels.It suggested that NH 4 photo-produced from DONp was a necessary nitrogen source to achieve the observed levels of autotrophic and heterotrophic biomass and production.The photo-chemical process fueled SP regenerated PP directly through the NH 4 uptake by SP and indirectly by stimulating the heterotrophic protists activity.Increased SP growth stimulated grazing and the subsequent release of NH 4 and DONl by SZ.NH 4 was used up by both SP and bacteria while the latter also beneficiated from DONl for growth.Increased bacterial growth led to an increased bacterial release of NH 4 .Photoammonification occurring within the upper 10 m of the water column contributed to ca. one-third of the simulated depth-integrated primary and bacterial daily production rates.The model also suggested that C : Chl ratios (83-100 g g −1 ) typically used for the non-diatom phytoplankton compartment in plankton ecosystem models applied to the AO were not appropriate to reproduce the plankton ecosystem structure of the oligotrophic Beaufort Sea.Applying such ratios in the model led to a DCM dominated by large phytoplankton ensuring mostly new PP, whereas observations reported an autotrophic community dominated by small phytoplankton growing essentially on regenerated nitrogen.Relatively low C : Chl ratios (ca.15-45 g g −1 ) for small phytoplankton were required to simulate the observed herbivorous versus microbial food web competition and realistic nitrogen fluxes within the DCM.
The accelerated sea ice shrinking and thinning might promote in the AO deep changes in autotrophic and heterotrophic biomass levels, production rates and carbon export (Wassmann and Reigstad, 2011;Boyce et al., 2010;Li et al., 2009, Arrigo et al., 2008).Enhanced stratification and nutrient limitation already suggest the increasing Introduction

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Full role of the microbial food web in the plankton ecosystem (Li et al., 2009;Tremblay et al., 2009).In this context and in view of the current modeling effort in assessing the oceanic (e.g.Le Fouest et al., 2010;Popova et al., 2010) and continental (e.g.Tank et al., 2011) drivers for AO primary production, more attention should be paid in the future to the mechanistic processes involved in food webs and functional groups competition, nutrient recycling and primary production in poorly productive Arctic waters as they are expected to expand rapidly (Wassmann and Reigstad, 2011).In particular, the still debated real contribution of the summer DCM in the annual primary production budget should be clarified (e.g.Ardyna et al., 2012;Popova et al., 2010).Such a better knowledge is required for robust model projections of AO primary production and carbon fluxes in response to the accelerated warming.Introduction

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Full  are the half-saturation constants for NH 4 and NO 3 uptake, respectively.NH 4 is set to be the preferred inorganic nitrogen source (Dorch, 1990) with a higher affinity for SP (Tremblay et al., 2000).This is expressed in the model by half-saturation constants for NH 4 uptake (K LP,SP NH 4 ) significantly lower than for NO 3 that, when used with the substitutable model, allow for an inhibitory effect of NH 4 on NO 3 uptake as often observed (Dorch, 1990) ) computed as follows: where C Chl is the carbon to Chl ratio (g g −1 ) and α LP,SP the initial slope (mg C (mg Chl) −1 (Ein m −2 d −1 ) −1 ) of the photosynthesis-irradiance curve.Photoacclimation translates the adaptative response through varying Chl : C ratios in response to light and nutrient availability (e.g.Cloern et al., 1995;Geider et al., 1997;Mac-Intyre et al., 2002).data (DuRand et al., 2002;Sherr et al., 2003) as follows: where K

LP,SP E
is the half saturation parameter driving the curvature of the Chl : C versus light relationship.E z (Ein m −2 d −1 ) is the downwelling PAR propagating according to the Beer-Lambert's law: where the diffuse attenuation of PAR with depth (z) is due to the simulated Chl (kchl) (m −1 ; Morel, 1988), water molecules (kw) (0.04 m −1 ; Morel, 1988) and nonchlorophyllous matter (knonchl).knonchl is set to 0.05 m −1 from 0 to 5 m depth to account for the release of optically active matter by melting sea ice observed during Malina (Doxaran et al., 2012) and to 0 below.kchl is calculated according to Morel et al. (1988)

A2 Zooplankton
Mathematical formulations and parameters related to large zooplankton (LZ) dynamics were chosen to reflect copepods as they dominate in abundance at the study station (Forest et al., 2012).Grazing (d −1 ) is described by an Ivlev function: LZ graze upon LP and protozooplankton (SZ) with a prey-specific grazing rate assumed to be proportional to the relative biomass of the prey (Campbell et al., 2009) defined for LP as follows: Losses in LZ biomass are due to NH 4 release, fecal pellets production (non-assimilated nitrogen ingested) and mortality.Mortality is assumed to be mainly due to predation (Eiane et al., 2002) and is described by a density-dependant quadratic function.The latter implicitly represents cannibalism as well as predation by appendicularians observed during the Malina cruise (Forest et al., 2012) and limits the occurrence of oscillations generated in such non-linear systems (Edwards and Bees, 2001).The constant of mortality is set to 0.2 (mmol N m −3 ) −1 to simulate realistic mortality rates (e.g.Ohman et al., 2004).SZ grazing upon SP and bacteria (BACT) is formulated by a sigmoid "Holling-type-III" function: The function provides a threshold-like limit for low SP biomass that enhances the biological system stability (e.g.Steele and Henderson, 1992).In polar waters, there is 14771 Introduction

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Full evidence that protozooplankton exert a control on small phytoplankton biomass only beyond a threshold (Lancelot et al., 1997).As for LZ, SZ graze upon both SP and BACT with a prey-specific grazing rate (d −1 ) assumed to be proportional to the relative biomass of the prey defined for SP as follows: According to the study of Riegman et al. (1993), we set the fraction of food ingested by SZ and being converted into biomass to 30 %. Lehrter et al. (1999) report that > 30 % of the total nitrogen release by SZ could be in the dissolved organic form.In the model, assuming that 40 % is released as labile DON (DONl), the remaining 30 % are lost as NH 4 .Remaining SZ loss terms are grazing by LZ and mortality.Similarly to LZ, mortality is expressed by a density-dependant quadratic function to represent grazing amongst SZ.

A3 Bacteria
Bacteria are explicitly simulated following the model of Fasham et al. (1990).DONl is the preferred substrate for bacterial uptake (d −1 ) (Kirchman et al., 1989) represented by a Michaelis-Menten model: where Ubact max is the maximum uptake rate, K Similarly, the uptake of NH 4 is represented as follows: This formulation ensures that the uptake of NH 4 will be 0.6 times the uptake of DONl, as required by the balanced growth model (e.g.Fasham et al., 1990).Bacterial losses are in the NH 4 form and represent 5 % of the bacterial biomass.

A4 Detritus
The pool of detrital particulate organic nitrogen (PON) is fueled by LZ fecal pellets production and by LZ and LP mortality.NH 4 photo-production rates comparable to those measured in late summer.Below 10 m, the rate is set to 0.

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Full Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | parameters, initial slopes (α) and light saturation parameters (E k ) were taken from Huot Discussion Paper | Discussion Paper | Discussion Paper | et al.
on the Malina cruise dataset, a mass-based (mmol N m −3 ) plankton ecosystem model was coupled to a vertically-resolved one-dimension (1-D) physical model to compute biogeochemical concentrations and fluxes at the slope and ice-edge station 345 sampled on 14-16 August, 2009 (Fig. 1).This station was chosen with regard to the very oligotrophic conditions observed and the extensive multiparametric dataset available.The coupled model extends vertically to 200 m deep with constant 1 m layers.It is constrained by a stationary field of vertical diffusion coefficient (K z , m 2 d −1 ) and a diurnal cycle of surface PAR (E 0 , Ein m −2 d −1 ).K z was computed from a mean Brunt-V äis äl ä (N) profile derived from measurements collected in 14-16 August and turbulent kinetic energy turbulent dissipation rates (ε = 5 × 10 −8 to 5 × 10 −7 m 2 s −3 ) using the Osborn (1980) formulation (K z = 0.25 ε N 2 ).A diurnal cycle of E 0 was obtained Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | , light, Chl and primary production Simulated NO 3 concentrations matched their measured counterparts with concentrations being very low at the surface (ca.0.003 mmol N m −3 ) and increasing with depth towards ca.12-14 mmol N m −3 (Fig. 5a).With respect to NH 4 , the measured subsurface peak (60 m) was also simulated by the model (ca.70 m) (Fig. 5b) although the simulated concentration (ca.0.11 mmol N m −3 ) was ca.3.5-fold higher than in measurements (ca.0.03 mmol N m −3 ).Note, however, that measured NH 4 exhibited much lower concentrations than generally reported in ancillary shelf seas as the Chukchi Sea (> 1 mmol N m −3 , Nishino et al., 2005).The simulated NH 4 nitrification rates within the DCM (ca.0.0015 mmol N m −3 d −1 ) compared with those measured, the latter being Discussion Paper | Discussion Paper | Discussion Paper |

1- 6
Ein m −2 d −1 within the DCM and at the surface, respectively) were reasonably captured by the model (ca.2-16 Ein m −2 d −1 for SP within the DCM and at the surface, respectively).The C : Chl ratio and E k are key parameters in the computation of Chl and primary production (PP) in the model.The model produced a DCM at ca. 65 m deep with a Chl concentration of ca.
Discussion Paper | Discussion Paper | Discussion Paper | PP values at the surface (ca.0.9 mg C m −3 d −1 ) decreased within the upper 40 m and then increased at the level of the DCM (ca.0.6 mg C m −3 d −1 and 0.9 mg C m −3 d −1 in the observations and the model, respectively) located at ca. 65 m deep.This 0.3 mg C m −3 d −1 discrepancy at the level of the DCM was due to higher NO 3 uptake in the model (ca.0.0025 mmol m −3 d −1 ) than in measurements (ca.0.001 mmol m −3 d −1 ) 0.6 mg C m −3 d −1 ) would represent ca.0.0075 mmol N m −3 d −1 using a Redfieldian ratio.Subtracting the measured regenerated PP (0.0072 mmol N m −3 d −1 ) from the measured NH 4 uptake (0.0115 mmol N m −3 d −1 ) would approximate the bacterial NH 4 uptake rate to 0.0043 mmol N m −3 d −1 .Assuming no mixotrophy, bacteria and phytoplankton would respectively be responsible for 37 % and 63 % of the total NH 4 uptake measured at the station DCM, which was very similar to what was simulated by the coupled model.With respect to NH 4 regeneration mostly driven in the model by SZ and bacteria, SZ and bacteria respectively contributed to 65 % and 35 % both at the surface and within the DCM.The simulated bacterial biomass was close to values measured in the upper 40 m (ca.0.07-0.08mg C m −3 in average) and below the DCM (ca.0.02-0.03mg C m −3 in average) but not within the DCM, where it was twice the observations (ca.0.06 mmol N m −3 measured versus ca.0.12 mmol N m −3 in the model) (Fig. 5e).Similarly, the simulated bacterial production matched that estimated from Introduction Discussion Paper | Discussion Paper | Discussion Paper | 0.014 mmol N m −3 d −1 ).Because of its grazing activity, LZ play an important role in shaping the biomass of SZ and hence its function in nitrogen remineralization.The simulated LZ biomass showed a maximum (ca.0.095 mmol N m −3 ) within the DCM at 60 m, as in the observations (ca.0.1 mmol N m −3 ) (Fig.5f).In the upper 40 m, simulated values were, however, one order of magnitude higher (ca.0.05 mmol N m −3 ) than in those measured (ca.0.005 mmol N m −3 ).As no LZ diurnal migrations were set in the model, the LZ biomass varied only as a function of the biomass of prey, namely SZ at the surface.Note, however, that LZ grazing (ca.0.0012 mmol N m −3 d −1 ) was not the primary loss term of SZ biomass.It was SZ basal mortality (ca.0.002 mmol N m −3 d −1 ) and hence the higher LZ biomass did not strongly constrain SZ in surface waters.Fecal pellets in sediment traps accounted for < 10 % (< 1.2 mg C m −2 d −1 ) of the total flux of particulate organic matter (i.e. 12 mg C m −2 d −1 ) above (45 m) and below (90 m) the DCM (J. C. Miquel, unpublished).Using a C : N molar ratio of 8.3, the simulated PON flux was in the same range, respectively 1 mg C m −2 d −1 and 3.6 mg C m −2 d −1 at 45 m and 90 m depth.Discussion Paper | Discussion Paper | Discussion Paper | 3.2 Model sensitivity to key light-related processes . A value of ca.0.0066 mmol m −2 d −1 of NH 4 photo-produced from DONp was simulated by the model within the upper 10 m, which compared well with the mean value estimated from measurements in August in the same area (0.008 mmol N m −2 d −1 ; Xie et al., 2012).In the model, photoammonification contributed to 13 % of the total NH 4 produced within the upper 10 m.It was the second highest source of NH 4 after the release by SZ (ca.79 %).
, photoammonification contributed indirectly to 67 % of total NH 4 production and 70 % of total NH 4 uptake in 14763 Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | biological conditions encountered in the Beaufort Sea during the August 2009 Malina cruise ([Chl] = 0.7 mg m −3 and PP = 0.6 mg C m −3 d −1 in the DCM at the slope and ice-edge study station 345) strikingly contrasted with those reported in summer in similar environments in the Chukchi, Barents and Western Beaufort seas ((Chl) = 2-11 mg m −3 and PP = 10-300 mg C m −3 d −1 ; Zhang et al., 2011; Matrai et al., 2007; Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | . The equation used to compute the light-based growth rate is: µ Values of α LP,SP were measured during the Malina cruise at 0-3 m and 65 m deep.In average for the study station 345, values of α LP,SP showed a decrease from the surface (2.22 mg C (mg Chl) .A linear function relating α LP,SP to depth is set from the surface to 65 m to account for this decrease: α LP,SP = 0.0826315z + 1.9721055 (A9) A constant value of 5.55 mg C (mg Chl) −1 (Ein m −2 d −1 ) −1 is set below 65 m based on reported measurements.Varying Chl : C ratios are computed using a modified version of the empirical relationship of Cloern et al. (1995) successfully applied to Hudson Bay in the Arctic (Sibert et al., 2011).The ratios can vary up to 4-to 6-fold based on the general photoacclimation rule given by MacIntyre et al. (2002) and on Arctic nano-and picophytoplankton Discussion Paper | Discussion Paper | Discussion Paper |

(
, phytoplankton loss terms include basal mortality and sinking for LP.LP sinking rates vary in the model from 0 to 0.1 m d −1 (e.g.Smith et al., 1991) depending on nutrients availability (Bienfang et al., 1983): sedlp = sed lp 1 − lim LP N Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

BACT NH 4 ,
DONl (mmol N m −3 ) the half-saturation constant for uptake and S the total nitrogenous substrate (mmol N m −3 ) defined as: S = (NH 4 , 0.6DONl) (Discussion Paper | Discussion Paper | Discussion Paper | The sedimentation loss term (d −1 ) is expressed as a quadratic function allowing for increasing implicit aggregation of particles with increasing PON concentrations: sedpon = sed ponPON (A23) where sed pon is the sedimentation constant (m d −1 (mmol N m −3 ) −1 ).The second loss term is the bacteria-mediated PON fragmentation into DONl (Grossart and Ploug, 2001).The DONl pool results from detrital PON fragmentation, SP and SZ mortality and SZ release.It is explicitly remineralized into NH 4 by bacteria.Based on measurements made in the Beaufort Sea in summer and during the Malina cruise (Xie et al., 2012), we incorporated the photochemical production of NH 4 from DONp (i.e.photoammonification) (mmol N m −3 d −1 ) within the first 10 m of the water column: mean constant rate for the June-August period was estimated to ca. 0.00016 d −1 .For mid-August, when the model is run, a value of 0.00004 d −1 is chosen to produce Introduction Discussion Paper | Discussion Paper | Discussion Paper |

N
nitrif and K light nitrif the half-saturation constants for NH 4 (mmol N m −3 ) and light (Ein m −2 d −1 ) use, respectively.The latter is defined as a fraction of surface PAR (E 0 ) as follows: Discussion Paper | Discussion Paper | Discussion Paper | The publication of this article is financed by CNRS-INSU.Discussion Paper | Discussion Paper | Discussion Paper | Le Fouest, V., Postlethwaite, C., Morales Maqueda, M. A., B élanger, S., and Babin, M.: On the role of tides and strong wind events in promoting summer primary production in the Barents Sea, Cont.Shelf Res., 31, 1869-1879, doi:10.1016/j.csr.2011.08.013, 2011.Lancelot, C., Becquevort, S., Menon, P., Mathot, S., and Dandois, J.-M.: Ecological modelling of the planktonic microbial food-web, in: Belgian Research Program on the Antarctic, Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper |

scheme derived from the Control Volume Approach, see Roach, 1972) with Choleski's double scanning method (also called Thomas algorithm in Roach, 1972). The coupled model was run with an hourly time step. The time evolution of each
of the 10 state variables (C) is computed with the general partial differential equation as follows: where t is time, z is the vertical coordinate and K z is the vertical eddy diffusion coeffi- The nutrient-based growth rate is computed as follows: