SEVENTH FRAMEWORK PROGRAMME THEME 7 Environment ...

SEVENTH FRAMEWORK PROGRAMME THEME 7 Environment Collaborative project (Large-scale Integrating Project)

Project no: 246 933 Project Acronym: EURO-BASIN Project title: European Basin-scale Analysis, Synthesis and Integration

Deliverable 6.4 Report on major controls on the ecosystem and biogeochemical cycling at the basin scale Contributors: Laurent Memery (CNRS/LEMAR), Christoph Stegert (CNRS/LEMAR), Momme Butenschon (PML), Icarus Allen (PML), Andrew Yool (NOCS), Tom Anderson (NOCS)

Due date of deliverable: Nov. 2013 Actual submission date: Feb. 2014 Organisation name of the lead contractor of this deliverable: CNRS Start date of project: 31.12.2010 Duration: 48 months Project Coordinator: Michael St John, DTU Aqua

PU PP RE CO

Project co-funded by the European Commission within the Seventh Framework Programme, Theme 6 Environment Dissemination Level Public X Restricted to other programme participants (including the Commission) Restricted to a group specified by the consortium (including the Commission) Confidential, only for members of the consortium (including the Commission)

Deliverable Deliverable 6.4 Report on major controls o nthe ecosystem and biogeochemical cycling at the basin scale is a contribution Task 6.1.3: Biological carbon budgets will be derived from the ensemble of ecosystem states (phytoplankton, zooplankton and micro nekton) to determine under what conditions carbon is either driven towards higher tropic levels (marine resources) or towards the deep waters (carbon pump, export flux, vertical migration). Responsible: CNRS, Brest Start month 1, end month 36

Executive Summary: Three different ecosystem models have been coupled to the same circulation model and the results spanning the 1980 – 2010 period are analysed. This analysis shows that the models are consistent and agree globally with observations for the well constrained fluxes, such as Net Primary Production. Moreover, although of different complexity, all of them are able to create their own internal dynamics to hydrological and climate variability forcing, mostly for the export fluxes. Nevertheless, for processes much less constrained, such as the regeneration loop or the fate of carbon in the mesopelagic layer, the models behave quite differently and tend to diverge. Although a thorough analysis of the impact of parameterization (parameter values, functions for metabolic rates or biotic interactions, etc...) goes beyond the purpose of this work and has not been done, it is believed that the main reason for these different behaviours lies in the structure of the models itself. Without generalizing, at least with the three models used, it seems that the level in complexity (microbial loop, uncoupled element cycles, particle dynamics) is associated with a system which is simultaneously less linear and more reactive in terms of its internal cycle, but more stable and consistent in terms of general behaviour. This preliminary work emphasizes the need for better knowledge and new observations on processes which impact the ocean Carbon budget, and for an understanding of the mathematical structure of the models used (feedbacks, stability, etc..). Part of WP6, the outputs of this work are used to force the upper trophic levels (towards resources) in the framework of studies planned in WP6 as well as in WP4 (habitats), WP7 (fish models), and end – to end models (WP8).

Relevance to the project & potential policy impact: Changes in distribution and trophic interactions resulting from shifts in the geographic range of ecosystem components have the potential to result in alterations of ecosystem resilience and productivity due to loss of critical habitat and changes in food web structure. These simulations provide model estimates of the a range of habitat indicators including temperature, oxygen, pH, food / prey availability in terms of primary production, phytoplankton biomass, and chlorophyll as a proxy, salinity, nutrients, hydrodynamic transport and mixed layer depth. The use of three different models is of fundamental importance, as it gives a first insight of the variability of the outputs caused by the design of the models in the framework of ensemble approach. The reasons for the dispersion of these outputs are addressed in this study. EURO-BASIN | D6.4 Report on major ecosystem & bgc cycling controls at basin-scale, Memery et al., 2014

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Access to Data and/or model code (where relevant): The data sets provided through these simulations available to partners of the consortium include monthly 3D fields of the main biogeochemical dynamics and physical states of the system, the major fluxes between the compartments of the modelled food-web and data informing the habitats of higher trophic levels (phytoplankton and zooplankton biomass, primary production, dissolved oxygen ocean temperature, pH). Access to the individual full simulations can be requested from: MEDUSA contact Andrew Yool; [email protected], PISCES Christophe Stegert; [email protected] and ERSEM Momme Butenschon; [email protected].

EURO-BASIN | D6.4 Report on major ecosystem & bgc cycling controls at basin-scale, Memery et al., 2014

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1. Introduction Interactions between climatic forcing, ocean circulation and changes in greenhouse gas concentrations all influence the dynamics of the thermohaline circulation of the North Atlantic, a factor that has being identified as a key influence on global climate (e.g. Sutton and Hodsen, 2005). Such changes have been linked to fluctuations in the population dynamics of zooplankton and exploited fish stocks in the open ocean as well as on associated shelves (e.g. Beaugrand et al., 2003, 2005). Critically for feedbacks to climate, the characteristics of trophic webs is critical in determining the fate of biogenic carbon, in particular its export below the euphotic zone, either by the sinking of particles or by the diel vertical movements of the organisms (Wilson et al., 2008). This is of fundamental importance for the climate system as the biological CO 2 pump in the ocean is one of the major sinks of atmospheric CO2. These feedback processes, linking bottom up and top down processes, cannot be understood and described without an effective understanding of the links between lower and higher trophic levels, as well as with the biogeochemical cycles. Numerical modelling provides a methodology whereby we can explore these interactions and help to elucidate the balance between biological and environmental controls on the biological carbon pump. 1.1 The Biological Carbon Pump The biological pump consist of a series of interacting processes through which CO2 is fixed as organic matter by photosynthesis and then transferred to the ocean interior, resulting in the temporary or permanent storage of carbon. The long-term storage of carbon by the biological pump is the primary concern regarding the role of the ocean in climate change. The efficiency of the biological pump is currently regarded as a basic measure of the ocean’s ability to store biologically fixed carbon. It is a significant component of the global carbon cycle, transferring approximately 10GT C yr-1 derived from planktonic photosynthesis into the oceans interior, mainly in the form of sinking particles with an organic component (Boyd and Trull, 2007). Two key mechanisms are involved, the sedimentation of particulate organic matter (POM) from surface waters towards the seabed and the export of dissolved organic matter (DOM) from the euphotic zone to deeper waters by mixing and down-welling of water parcels. In both cases, POM and DOM are subject to microbial mineralization, and most of the organic carbon is returned to dissolved inorganic carbon (DIC). Thus these processes remove organic carbon from the surface waters and convert it to DIC at depth, maintaining the surface-to deep-ocean gradient of DIC. POM is initially formed as autotrophic biomass and is then transformed through multiple trophic pathways at each level of the marine food web. There are many mechanisms of DOM production in the upper ocean, and they vary spatially and temporally. It is difficult to specify or predict the dominant sources of DOM in a given ecological scenario. The magnitude of the Phytoplankton release of DOM is highly variable, but at times is a substantial, fraction of primary production into seawater as DOM (10-50% of the gross production e.g. Karl et al 1998, Biddandna et al 1997). Other important mechanisms include; the release of DOM resulting from viral lysis, ‘Sloppy feeding’ by metazoan grazers releasing phytoplankton cytosol as DOM, and DOM production via the solubilisation POM by bacteria. A small fraction of POM escapes mineralization and reaches the sediment, where organic carbon can be buried and stored for thousands and even millions of years. However our knowledge of how this sinking flux is recycled (and therefore stored) within the oceans is poor with most models using a simple parameterisation of carbon recycling based on the observed decline in flux with depth EURO-BASIN | D6.4 Report on major ecosystem & bgc cycling controls at basin-scale, Memery et al., 2014

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(Martin et al., 1987). This is despite there being a clear qualitative understanding of potential factors, which drive efficient export of organic matter. These factors include ballasting by lithogenic and biogenic mineral phases (Klaas and Archer, 2002), aggregation mediated by the release of extracellular expolymeric particles which bind material together (Passow et al., 1994), grazing by mesozooplankton which aggregates and packages material into rapidly sinking aggregates (Ebersbach and Trull, 2008) and pelagic ecosystem structure, particularly the size distribution of photosynthetic organisms. It is the view of some researchers that the production and fate of the large pool of recalcitrant DOM (RDOM) in the oceanic water column has not been adequately considered in the classical view of biological pump. Marine bacteria and archaea are responsible for the respiration of most of the carbon that sinks into the ocean’s depths. The microbial carbon pump has been proposed (e.g. Jiao et al 2011) as a conceptual framework to address the role of microbial generation of RDOM and relevant carbon storage, with the aim of improving our understanding of oceanic carbon cycling and global climate change. The biological carbon pump is responsible for a substantial storage of inorganic carbon dioxide in the ocean - without its existence atmospheric CO2 levels would be 30% (Siegenthaler and Sarmiento, 1993) or 200 ppm (Parekh et al., 2006) higher than in a preindustrial world. It represents an economically valuable service and as such has received substantial attention particularly since it is substantially larger than the annual accumulation of carbon dioxide in the atmosphere and hence small changes in its magnitude in response to climate drivers could result in an increased storage of carbon dioxide within the oceans. 1.2 Approaches to modelling the biological C pump The approaches to use to model the biological carbon pump reflect the debate between the classical ‘particulate’ model and inclusion of the microbial carbon pump (MCP). The North Atlantic is characterized by strong seasonality in mixed layer depths, resulting in replenishment of surface layer nutrients during the winter followed by a spring phytoplankton bloom co-incident with the onset of stratification. The spring accumulation of biomass is accompanied by a marked drawdown of inorganic carbon in the water column and pulses of particle flux to the seafloor. In the classical model, the decline of the CO2 is balanced by accumulation of biogenic carbon and particle export. In contrast the MCP approach assumes the accumulation and export of dissolved organic carbon also has a major role in the North Atlantic carbon balance. In BASIN we consider the lower trophic level biogeochemical models, MEDUSA, PISCES and ERSEM which between them cover the full range of modelling approaches of the Biological Carbon Pump. The three models used simulate phytoplankton, zooplankton, detritus and dissolved nutrients. Their complexity varies among them, either in their structure and trophic interactions (number of phytoplankton and zooplankton types), or in the processes they represent (intensity of the coupling between the nutrient and carbon cycles, remineralization, etc…). One of challenges is the get some hints to explain the different fluxes simulated by the models in terms of their specific food web structure and the regional hydrodynamics (intensity of the seasonality, impact of stratification etc…).


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MEDUSA (Model of Ecosystem Dynamics, nutrient Utilisation and Sequestration; Yool et al 2011) takes a classical approach to describing the biological carbon pump. It is a modestly complex ecosystem model, it includes two phytoplankton, two zooplankton and three nutrients, and is specifically designed for open ocean applications. MEDUSA uses an implicit remineralisation form, whereby POM is directly re-mineralised to bioavailable nutrients according to a prescribed rate.

PISCES (Pelagic Interaction Scheme for Carbon and Ecosystem Studies, Aumont and Bopp, 2006) considers two phytoplankton (with 4 co-limiting nutrients: N/P/Si/Fe) and two zooplankton, with an explicit semi labile DOM and two particle sizes. Using N as the main currency, as well as P, Si and Fe, it also simulates the C (DIC and alkalinity) and O cycles. PISCES considers semi-labile DOM and particles of two size classes (distinguished by settling velocity). This model provides multiple pathways and hence timescales for nutrient regeneration. PISCES can be viewed as taking a hybrid approach, it describes both the classical model and a semi-implicit microbial loop which represents both and POM, but bacteria are implicit in the DOM pool. DOM ERSEM (European Regional Seas Ecosystem Model; Baretta et al 1995, Blackford et al 2004) was developed as a generic lower-trophic level/biogeochemical cycling model. ERSEM is an intermediate/high complexity model originally designed for simulating shelf seas biogeochemistry and ecosystem function. ERSEM simultaneously describes pelagic and benthic ecosystems in terms of phytoplankton, bacteria, zooplankton, zoobenthos, and the biogeochemical cycling of C, N, P, Si. ERSEM has a fully explicit microbial loop model whereby phytoplankton explicitly produce DOM, bacteria remineralise both POM and DOM, and are allowed to compete with phytoplankton for nutrients (Polimene et al., 2006).


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The simulations have used the same circulation model (NEMO) with a ¼° resolution and 64 vertical levels. The atmospheric forcing are also identical (DFS5), and the period simulated spans three decades (1980 – 2010). A substantial part of the analysis is based on detailed model carbon budgets at four specific sites, each of which is a Long Time Series Stations and has been sampled over many years (several decades for BATS for instance : references !!). Each site is representative of different regions of the North Atlantic (fig. 1). BATS and ESTOC are located in the sub-tropical gyre, whereas Mike is more representative of a sub polar environment with a strong seasonal variability, such as PAP in the inter gyre zone. After an analysis of the results at these sites, the global C budget and the inter-annual variability of the main fluxes will be presented.

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Methods : Numerical experiments

A set of three hindcast experiments for the years 1980 – 2010 was designed based on using the same geographical domain in the same spatial grid with a resolution of approximately ¼ of a degree, allowing for an adequate level of detail in important features of the ocean circulation pushing the limit of affordability in computational cost at basin scale for simulations of this extend with models of this complexity. Two of the models (ERSEM and PISCES) were run in a dedicated North-Atlantic domain illustrated in figure 1, while the simpler model, MEDUSA, was run in a global simulation and the corresponding sub-set of data for the North Atlantic was provided allowing for an analysis of the impacts of the boundary conditions in the simulation. All three biogeochemical models were coupled online to the NEMO ocean engine, in open-ocean configuration using a rigidlid formulation with z-coordinates and a two-equation turbulence closure using a TKE -scheme. Initial conditions for the physical variables and dissolved nutrients and gases were interpolated from climatology while all other biogeochemical tracers were initialised at low concentrations and allowed a year to spin-up the food-web dynamics. The boundaries for the domain of the pure Atlantic simulations where set relatively far from the region of interest to reduce the impact on the dynamics in the core of the domain, the North Atlantic proper and the NW European shelf, it extends from South of the equator into the Arctic Basin and resolves parts of the Mediterranean Sea and Baltic Sea as buffers for the inlets at Gibraltar and the Skagerrak. Boundary conditions were implemented using a "sponge" region of six points for the physical variables characterized by a nudging towards climatological data (same as the initial fields) of increasing intensity towards the outer limit while the biogeochemical tracers use no flux conditions across the boundaries. Atmospheric forcing data used is the DFS data set provided through the DRAKKAR project. Further details on the experimental design have been provided in Deliverable 6.1. The data sets provided through these simulations available to partners of the consortium include monthly 3D fields of the main biogeochemical dynamics and physical states of the system, the major fluxes between the compartments of the modelled food-web and in particular informing the habitats of higher trophic levels (phyto- and zooplankton biomass, primary production, dissolved oxygen ocean temperature, pH).


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Figure 1: Position of the four Time Series Station. Light blue : depth 1000m; Dark blue : depth 2000m

Results The purpose of this section is to document the fate of carbon in the North Atlantic Basin simulated by the three models and more specifically to make the link between hydrodynamic conditions, primary production and carbon export towards both the higher trophic levels and the deep ocean. 3.1 The physical environment Figures 2 shows the important physical parameters (heat flux, sea surface temperature and mixed layer depth) which characterise the 4 stations under investigation. In terms of the annual average, the surface heat fluxes are small at BATS (negative: net cooling) and ESTOC (positive: net warming), whereas they are larger and negative (cooling) at the northern stations (fig.2a). The seasonal cycles of heat flux show the expected behaviour, with a relatively small seasonal variability at ESTOC, a larger variability in the North. The increase during spring and early summer occurs earlier at BATS (fig 2b), than at the other stations. The SST ranges from an annual average of 6.2°C at MIKE, to 23°C at BATS (13°C at PAP and 20.5°C at ESTOC : fig. 2c), with a very strong inter-annual variability and seasonal cycle (maximum at BATS associated with a strong vertical stratification and minimum at ESTOC, with smaller atmospheric forcing variability) (fig. 2d). The resulting Mixed Layer Depth (MLD) shows a maximum in winter with an average around 120 m at ESTOC, 160m at BATS, 180m at PAP, and 300m at MIKE (fig. 2e). Re-stratification occurs between end of February at ESTOC and beginning of April at MIKE. Associated with the atmospheric forcing, the inter-annual variability of the winter MLD is large at every station (fig 2f).


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Figure 2a: annual surface heat fluxes between 1980 and 2010 at the 4 stations.

Figure 2b: average seasonal cycle (1980 – 2010) of the daily surface heat fluxes.


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Figure 2c: annual Sea Surface Temperature (SST) between 1908 and 2010

Figure 2d : daily average SST between 1980 and 2010


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Figure 2e: average seasonal cycle (1980 – 2010) of the daily Mixed Layer Depth (MLD)

Figure 2f: monthly average MLD between 1980 and 2010


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3.2 The annual cycle Figures 3 represent an average seasonal cycle in Net Primary Production (NPP), along with the relative evolution of the MLD (daily outputs for PISCES and ERSEM and monthly outputs for MEDUSA). They all show a clear seasonal signal, with a pronounced bloom at all stations. The bloom at the two southern stations (BATS, ESTOC) occurs when the MLD is deep, while the two other stations show a classical bloom triggered when the mixed layer shallows in the spring. This behaviour is representative of two different “regimes”: a) the sub-tropical regime, associated with a winter bloom, when the nutrient limitation is low because the MLD reaches the nitracline, and b) the sub polar regime, with a spring bloom, inhibited by light limitation during winter. MEDUSA and PISCES behave in a similar qualitative way, whereas ERSEM exhibits more dynamic variability between the sites. Compared with PISCES, the meridional gradient in NPP is larger for ERSEM, with a relatively smaller value at BATS and larger values at PAP and MIKE (see ERSEM/PISCES for NPP in table 1). Moreover, the bloom occurs earlier in the sub polar stations (the pickup in NPP takes place one month earlier, and there is clear, although smaller, bloom in the fall).

Figure 3a: mean seasonal variation of Net Primary Production at BATS (dot line : the MLD “shape”)


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Figure 3b: same as 3a for ESTOC

Figure 3c: same as 3a for PAP


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Figure 3d: same as 3a for OWSMIKE

Figure 4a: mean seasonal variation of export production at 150m at BATS EURO-BASIN | D6.4 Report on major ecosystem & bgc cycling controls at basin-scale, Memery et al., 2014

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Figure 4c: same as 4a for PAP EURO-BASIN | D6.4 Report on major ecosystem & bgc cycling controls at basin-scale, Memery et al., 2014

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Figure 4d: same as 4a for PAP Figure 4 shows the particle export flux below the euphotic layer, at 150m.The seasonal signal of NPP is unsurprisingly found in the export flux, with the variability smoothed out. This is a consequence of both export rates and recycling processes and is mostly striking for ERSEM. The more complex ERSEM model filters more efficiently high frequency variability, whereas for PISCES, the PPN variability is more directly transferred into the export flux. This smoothing implies the remineralisation processes may be more dominant in ERSEM in the near surface ocean than in PISCES. Focusing on the classical particulate carbon flux, our results show (see table 1) that PISCES exports more carbon than the other models (by a factor 2 compared to ERSEM and 50% to MEDUSA) although its NPP is generally close (ERSEM) or smaller (MEDUSA). The average e-ratio (export/NPP) of PISCES is almost the double than the e-ratio of the two other models (30% vs. 16%: table 1). Moreover, PISCES generates a higher variability of this ratio between sites, which tends to indicate that the particle dynamics are more active in this model. The export fluxes at 1000m and 2000m (for ERSEM and PISCES) are presented in table 1. There is a strong decrease for MEDUSA in the first 1000m (by a factor 20), whereas, on average, the decrease is much less pronounced with the other models (factor of about 4): the 1000m flux and the associated e-ratio are then much lower for MEDUSA (respectively 3.5 mgC/m2/day vs. 28.8 and 11.1 for PISCES and ERSEM, and 0.8% vs. 3.7% and 9.3%). Between 1000 and 2000m, the fluxes in PISCES and ERSEM decrease by a factor 2 (no data for MEDUSA), which gives finally an e-ratio at 2000m of 4.8% and 1.5% and a particle flux more than three times higher for PISCES than for ERSEM. Table 1 emphasizes the strong variability of the e-ratio among the different stations in PISCES compared to ERSEM: the northern stations (PAP and MIKE) tend to have much higher export efficiency in EURO-BASIN | D6.4 Report on major ecosystem & bgc cycling controls at basin-scale, Memery et al., 2014

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PISCES, whereas the e-ratio in ERSEM is much more constant. The transfer efficiency at 2000m (export 2000m / export 150m) reaches values with are consistent with the data shown in Henson et al. (2012) (average of 14.5% for PISCES and 9.3% for ERSEM). Nevertheless, contrary to that study, based on satellite and in situ observations, this efficiency tends to increase from oligotrophic to more productive stations / regions : if the PIECES and ERSEM models tend to produce variability in the export dynamics, they do not seem to capture all the processes at play. The impact of the particle dynamics can be visualized by following the propagation and attenuation of the NPP variability in the vertical. This can be seen in figures 5, which illustrate the different fluxes at ESTOC in 1995 on a daily scale (the export fluxes have been scaled in order to better follow the propagation of the bloom signal). As previously mentioned, at least in the upper layers, ERSEM smoothes more efficiently the surface variability of the NPP. Moreover, the decrease with depth is larger. Finally, the time shift between the start of the spring phytoplankton bloom and the maximum of export flux increases with depth, with the distance from the surface, but this shift is more pronounced for ERSEM (around 45 days vs. 20 days for PISCES for 1000m). The settling velocities of large particles (50 m/day for PISCES, and 10 m/day for ERSEM) are in a ratio of 5, whether the time shift ratio is only half of this value. This apparent discrepancy results from the different particle dynamics in the water column.

Table 1: NPP, export fluxes and e-ratio at the four stations for the three models (associated figures in Appendix 1) BATS

ESTOC

PAP

MIKE

BATS/PAP

ESTOC/PAP

MIKE/PAP

PISCES

557,9

137,8

377,4

254,6

1,48

0,37

0,67

ERSEM

303,8

106,7

379,5

358,5

0,80

0,28

0,94

MEDUSA

480,1

153,9

629,0

350,4

0,76

0,24

0,56

0,93

ERSEM/PISCES

0,54

0,77

1,01

1,41

1,26

MEDUSA/PISCES

0,86

1,12

1,67

1,38

EXPORT

PISCES

132,80

42,30

102,40

101,00

1,30

0,41

0,99

150m

ERSEM

46,80

21,30

59,70

48,50

0,78

0,36

0,81

MEDUSA

69,90

18,40

106,70

70,60

0,66

0,17

0,66

0,48

ERSEM/PISCES

0,35

0,50

0,58

0,48

0,68

MEDUSA/PISCES

0,53

0,43

1,04

0,70

EXPORT

PISCES

29,90

6,50

29,80

48,80

1,00

0,22

1,64

1000m

ERSEM

10,30

3,20

15,30

15,40

0,67

0,21

1,01

MEDUSA

4,05

0,53

6,31

3,20

0,64

0,08

0,51

0,42

ERSEM/PISCES

0,34

0,49

0,51

0,32

0,12

MEDUSA/PISCES

0,14

0,08

0,21

0,07

EXPORT

PISCES

13,30

2,69

13,70

28,70

0,97

0,20

2,09

2000m

ERSEM

3,92

1,44

5,76

6,02

0,68

0,25

1,05

MEDUSA

0,00

0,00

0,00

0,00

0,00

0,00

0,00

0,17

ERSEM/PISCES

0,13

0,22

0,19

0,12

0,00

MEDUSA/PISCES

0,00

0,00

0,00

0,00

BATS

ESTOC

PAP

MIKE

NPP

Average


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eratio

PISCES

23,80%

30,70%

27,13%

39,67%

30,33%

150m

ERSEM

15,40%

19,96%

15,73%

13,53%

16,16%

MEDUSA

14,56%

11,96%

16,96%

20,15%

15,91%

1000m

PISCES

22,52%

15,37%

29,10%

48,32%

28,82%

vs

ERSEM

22,01%

15,02%

25,63%

31,75%

23,60%

150m

MEDUSA

5,79%

2,87%

5,91%

4,53%

4,78%

eratio

PISCES

5,36%

4,72%

7,90%

19,17%

9,28%

1000m

ERSEM

3,39%

3,00%

4,03%

4,30%

3,68%

MEDUSA

0,84%

0,34%

1,00%

0,91%

0,78%

2000m

PISCES

44,48%

41,38%

45,97%

58,81%

47,66%

vs

ERSEM

38,06%

45,00%

37,65%

39,09%

39,95%

1000m

MEDUSA

---

---

---

---

---

2000m

PISCES

10,02%

6,36%

13,38%

28,42%

14,54%

vs

ERSEM

8,38%

6,76%

9,65%

12,41%

9,30%

150m

MEDUSA

---

---

-----

---

---

eratio

PISCES

2,38%

1,95%

3,63%

11,27%

4,81%

2000m

ERSEM

1,29%

1,35%

1,52%

1,68%

1,46%

MEDUSA

---

---

---

---

---

Figure 5a: NPP (PPT) and export fluxes (150, 1000, 2000m) at ESTOC in 2005 for PISCES (export fluxes scaled)


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Figure 5b: same as 5A for ERSEM

Figure 6a: average profiles of export fluxes at BATS for the 3 models. EURO-BASIN | D6.4 Report on major ecosystem & bgc cycling controls at basin-scale, Memery et al., 2014

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Figure 6d: same as 6a for OWSMIKE The resulting average export profiles at the four stations are presented in figure 6 (note : there are only two points for MEDUSA : 150m and 1000m). Both ERSEM and PISCES display a maximum particle flux, around 100m for ERSEM and 150m for PISCES. The initial export flux in ERSEM is smaller than from PISCES by around 70% in general. ERSEM’s more efficient remineralisation, combined with a smaller settling particle velocity, results in much smaller fluxes that produced by PISCES below several hundred meters. The remineralisation of POC in ERSEM is an explicit result of bacterial activity, while in PISCES is a more constant rate, the difference in process descriptions and parameter choice determining these gaps. Compared to ERSEM, the particle flux reaching the bottom can then be three times higher for PISCES. MEDUSA stores much less carbon in the deep ocean, as already mentioned, although information below 1000m is missing.. A comparison between ESTOC (fig. 6b) and MIKE (fig. 6d) emphasizes the large variability between stations, with a much better preservation with depth at MIKE in the northern region, contrary to ESTOC, located in the sub-tropical area. This means that, even with a rather simple representation of the particle dynamics in the water column, the models are able to generate different patterns of the fate of carbon in the deep ocean.

3.3 The Carbon budget at the “time-series stations” The models differ in structure in terms of the complexity of their foodwebs and the biogeochemical processes they take into account. Although all three models respond in a similar way to the environmental forcing (light, MLD, nutrient) of the different North Atlantic regions (in terms of intensity of NPP and export), the response of the biological carbon pump is a function of the underlying structure. Figures 7, 8 and 9 show the average budgets of carbon at the four stations considered for the three models integrated over the productive layer, e.g. 150m (boxes in grey do not exist in the considered model). Because of the differences in the food web design, the fluxes have been aggregated in large pools (dissolved inorganic carbon, phytoplankton, zooplankton, detritus, dissolved organic carbon and bacteria). In addition to the number of classes of EURO-BASIN | D6.4 Report on major ecosystem & bgc cycling controls at basin-scale, Memery et al., 2014

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phytoplankton and zooplankton and the decoupling between the different elements (N, P, C), the major differences are found in the regeneration or microbial loop. It is not considered explicitly in MEDUSA (no DOC, no bacteria), while only DOC is considered in PISCES. In contrast, ERSEM simulates both DOC production by phytoplankton and bacteria explicitly. It is then not surprising that the major contrasts between the model are found in the fluxes associated with regeneration. Although the numbers differ, the NPP produced by the three models have in fact the same order of magnitude (table 1). However, the more detailed the microbial loop is considered, the more intense the associated fluxes are. The biogenic DIC source (respiration and bacterial activity) is proportionally higher compared to the C uptake by photosynthesis as the models increase in complexity from MEDUSA to PISCES to ERSEM. In ERSEM a large part of phytoplankton biomass is recycled towards DOC and hence bacteria BAC. This results in substantial C fluxes through the microbial loop with approximately 75% respired to DOC and the remaining 25% re-enters the particulate pathway through the grazing of bacteria. In the other two models the main phytoplankton sink is zooplankton grazing, supplying C to the POC pools (MEDUSA) and both the POC and DOC pools (PISCES) : although less intense, the regeneration loop in PISCES is mostly supplied by detritus (and not phytoplankton as it is the case for ERSEM). The explanation comes partly from the decoupling between the N and C cycles in ERSEM, which drives the production and accumulation of DOC under nutrient stressed conditions. In contrast, the production of detritus (normalized to NPP) is very high in PISCES: this result must be linked to the relative importance of diatoms in this model, which also could partly explain high export fluxes at 150m (fig. 11). All models emphasize the rather low biological activity at ESTOC, a highly stratified region, and higher fluxes at PAP, where the seasonal cycle is strongly intensified. All models show surprisingly high fluxes at BATS (most notably PISCES), which are unexpected, as BATS is in the low productive sub-tropical gyre. We believe this anomaly is caused by the poor representation of the dynamics and water masses in this area. In fact, with a ¼° resolution, it is well known that the Gulf Stream cannot be simulated correctly, and the stratification, as well as the tracer distributions (such as nitrate), are biased.


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Figure 7a: average C budget at BATS for PISCES integrated on the productive zone (0 – 150m)



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Figure 7d: same as 7a for OWSMIKE EURO-BASIN | D6.4 Report on major ecosystem & bgc cycling controls at basin-scale, Memery et al., 2014

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Figure 8a: average C budget at BATS for ERSEM (0 – 150m). Note the flux of C from DIC to Phytoplankton is Gross Production, not for NPP as for the other models.

Figure 8b: same as 8a for ESTOC EURO-BASIN | D6.4 Report on major ecosystem & bgc cycling controls at basin-scale, Memery et al., 2014

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26

Figure 9a: average C budget at BATS for MEDUSA (0 – 150m)

Figure 9b: same as 9a for ESTOC EURO-BASIN | D6.4 Report on major ecosystem & bgc cycling controls at basin-scale, Memery et al., 2014

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The major fluxes displayed in the previous diagrams show that, for each model, when NPP increases, the other fluxes increase as well, which is perfectly expected. Nevertheless, considering the different degrees of complexity of the three models, one can wonder whether this behaviour is linear for all the models, or if non linearities or internal feedbacks, coming from either the parametrization of the fluxes, or more possibly from the food web structure, can amplify or damp the NPP input signal. In order to check that point, table 2 shows, for each model, the different fluxes normalized to NPP (column ‘norm’). If the models are linear and scaled by NPP, the normalized fluxes should not be modified when NPP is modified, e.g. these fluxes should not depend on the station : the four first columns should be identical. Or, if we normalized again these normalized fluxes to a specific station (here PAP), the ratios should always be equal to 1 (the three columns on the left) : the distance from 1 “measures” the intensity of the non linear feedbacks of the different models. The red (blue) squares display ratios larger (smaller) than 1, e.gt. amplification (damping). In bold a gap between 0.8 and 0.9, or 1.1 and 1.2 (>+/- 10%) and in red (blue), larger (smaller) than1.2 (0.8) : +/- 20%. These tables shows that ERSEM is much more non linear than the other two models. Moreover, there is no clear tendency when stations are compared, but with ERSEM : the MIKE station ratios seems to be globally anticorrelated to the ratios of the other stations. This type of pattern cannot be observed for the two other stations. Nevertheless, in terms of export production, PISCES and ERSEM have a similar behavior, and opposite to MEDUSA, except for MIKE, where PISCES shows a very strong positive amplification for the export fluxes at every depth compared to PAP. This “anomaly” is also clearly seen on figure 6d, which emphasizes the significant preservation of carbon particle in the water column in MIKE for PISCES. Table 2

DIC-PHY PHY-ZOO PHY-DET PHY-DOC ZOO-DIC ZOO-DET ZOO-DOC DET-DOC DOC-BAC BAC-ZOO REMIN*

norm PAP 1,000 0,858 0,051 0,050 0,276 0,319 0,184 0,263 NaN NaN 0,410

norm ESTOC 1,000 0,940 0,051 0,050 0,270 0,328 0,180 0,343 NaN NaN 0,458

Norm BATS 1,000 0,871 0,027 0,050 0,303 0,378 0,202 0,251 NaN NaN 0,405

Norm MIKE 1,000 0,706 0,171 0,050 0,225 0,313 0,150 0,169 NaN NaN 0,368

ESTOC PAP 1,000 1,096 0,995 1,000 0,978 1,027 0,978 1,306 NaN NaN 1,117

BATS PAP 1,000 1,015 0,520 1,000 1,096 1,186 1,096 0,956 NaN NaN 0,988

MIKE PAP 1,000 0,823 3,316 1,000 0,813 0,980 0,813 0,644 NaN NaN 0,898

Exp150 Exp1000 Exp2000

0,271 0,079 0,036

0,307 0,047 0,020

0,238 0,054 0,024

0,397 0,192 0,113

1,131 0,597 0,475

0,877 0,679 0,749

1,462 2,427 2,124

PISCES


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DIC-PHY PHY-ZOO PHY-DET PHY-DOC ZOO-DIC ZOO-DET ZOO-DOC DET-DOC DOC-BAC BAC-ZOO REMIN*

norm PAP 1,000 0,262 0,042 0,697 0,190 0,117 0,117 0,080 0,680 0,181 0,572

norm ESTOC 1,000 0,064 0,035 0,902 0,125 0,071 0,071 0,078 1,245 0,197 1,027

Norm BATS 1,000 0,189 0,035 0,775 0,148 0,081 0,081 0,058 0,762 0,140 0,632

Norm MIKE 1,000 0,353 ,0,36 0,610 0,236 0,154 0,154 0,095 0,565 0,220 0,484

ESTOC PAP 1,000 0,244 0,830 1,294 0,657 0,601 0,601 0,984 1,830 1,083 1,795

BATS PAP 1,000 0,724 0,841 1,113 0,782 0,690 0,690 0,727 1,121 0,772 1,105

MIKE PAP 1,000 1,349 0,875 0,876 1,244 1,310 1,310 1,192 0,831 1,213 0,847

Exp150 Exp1000 Exp2000

0,157 0,040 0,015

0,200 0,030 0,014

0,154 0,034 0,013

0,135 0,043 0,017

1,269 0,744 0,701

0,979 0,841 0,868

0,860 1,065 1,286

MEDUSA DIC-PHY PHY-ZOO PHY-DET PHY-DOC ZOO-DIC ZOO-DET ZOO-DOC DET-DOC DOC-BAC BAC-ZOO REMIN*

norm PAP 1,000 0,654 0,210 NaN 0,158 0,279 NaN NaN NaN NaN 0,167

norm ESTOC 1,000 0,445 0,312 NaN 0,186 0,146 NaN NaN NaN NaN 0,280

Norm BATS 1,000 0,659 0,209 NaN 0,256 0,297 NaN NaN NaN NaN 0,204

Norm MIKE 1,000 0,649 0,215 NaN 0,116 0,236 NaN NaN NaN NaN 0,165

ESTOC PAP 1,000 0,681 1,487 NaN 1,181 0,523 NaN NaN NaN NaN 1,680

BATS PAP 1,000 1,008 0,999 NaN 1,618 1,062 NaN NaN NaN NaN 1,225

MIKE PAP 1,000 0,992 1,024 NaN 0,735 0,843 NaN NaN NaN NaN 0,988

Exp150 Exp1000

0,170 0,010

0,120 0,003

0,146 0,008

0,202 0,009

0,705 0,342

0,858 0,840

1,188 0,910

ERSEM


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Figure 10a: average horizontal distributions of NPP and export fluxes for PISCES


31

Figure 10b: same as 10a for ERSEM


32

Figure 10c: same as 10a for MEDUSA


33

Figure 11a: regional correlation between different main fluxes for PISCES


34

Figure 11b: same as 10a for ERSEM


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3.4 The Carbon budget at basin scale Figure 10 shows the horizontal mapping of the NPP and export fluxes for the three models at the basin scale. All three reproduce the expected low productive sub-tropical gyre and a sub polar gyre with higher NPP. Although the extent is small, due to the rather coarse resolution, the West Africa upwelling is clearly associated with the highest values in NPP. In agreement with the PP mapping produces in Henson et al. (2012), the three models tend to show an area of maximum of production around 40°N. Nevertheless, the models behave very differently in the inter gyre region, where the Gulf Stream separates from the coast and generates the North Atlantic Current. As previously stated, the model spatial resolution is relatively high in the context of the simulation of biogeochemical cycles. The resolution of the model is nevertheless not high enough to represent correctly the very energetic region of the Gulf Stream, and the impact of the small scales on the circulation and water mass distributions. Interestingly, the models all respond in a very different way to this strong flaw. MEDUSA generates a quasi - zonal tongue of higher productivity crossing the whole basin. It is located along a region where the MLD is known to be too deep in this type of models.. PISCES shows a recirculation region with very high NPP (region where BATS is located) which tends to emphasize a too large nutrient supply. In contrast, ERSEM does not seem to reflect too strongly this flaw in the circulation, with maximum production obtained along the coast and the Gulf Stream, where vertical movements are high and bring nutrient to the surface. One possible explanation concerning the differences between PISCES and ERSEM can be found in the DOM pool : as a matter of fact, this pool is efficiently supplied in ERSEM, which, through bacterial activity, is transformed into inorganic compounds, e.g. nutrients. In PISCES, the role of this DOM pool is relatively smaller, and the regeneration loop is much shorter. Therefore, in ERSEM, the time lag between primary production and remineralization can facilitate N transport outside the recirculation region, whereas in PISCES local accumulation and trapping of inorganic nutrient can take place. Moreover, the two models with fixed C:N ratios (PISCES, MEDUSA) will respond much more strongly to changes nutrient supply than the variable C:N model (ERSEM) which is able to buffer the changes and have a larger proportion of attenuation response. This leads us to speculate that the variable C:N ratio may be a contributory factor in the ERSEM response in this region. As already observed from the analysis of the 1D stations, the export and e-ratio distributions show a much stronger and more efficient vertical transfer of C for PISCES, whereas MEDUSA is not very efficient (see the changes in the scaling of the fields for ERSEM and MEDUSA in figures 10). In terms of spatial distribution, the vertical particle carbon export is strongly correlated to the NPP distribution, but the e-ratio distribution is de-correlated. The later point is important because it implies that variations of the biological response are controlling e-ratio. Both PISCES and MEDUSA tend to emphasize a relative more efficient transfer in the sub polar gyre, more specifically in the western part (Labrador Sea), whereas the ERSEM results do not show any specific tendency. Two processes could explain this different behaviour: PISCES and MEDUSA simulate larger proportions of diatoms (fig. 11 - mostly in the sub polar gyre) than ERSEM, which, on the other hand, produces a strong microbial loop: this microbial loop could act to decouple NPP from export production. More description of the spatial correlation between the different fluxes is proposed, based on figures 11 – the grid points with depth smaller than 150m have been excluded. All the models show very low proportion of diatoms in the sub-tropical gyre, in agreement with data. This proportion differs strongly between the models in the sub polar gyre and in the NW Africa upwelling, where ERSEM hardly reaches 30% in the NE coast of USA, when PISCES reaches more than 60% in a large EURO-BASIN | D6.4 Report on major ecosystem & bgc cycling controls at basin-scale, Memery et al., 2014

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region, MEDUSA being at an intermediate level around 40%. The ability of the models to reproduce the phytoplankton community structure in terms of the total chlorophyll-a to component chlorophyll-a fraction relationship for, i.e. diatoms compared against derived relationships published in literature, namely Hirata et al. 2011 and Brewin et al. (2011) was assessed in D6.3. This suggests the ERSEM may systematically underestimate the diatom fraction while PISCES and MEDUSA overestimate it. Nevertheless, there is no direct evidence between this ratio and NPP: as a matter of fact, in cold waters, annual NPP is rather low, although the diatom proportion is higher. Whereas in MEDUSA the correlation between NPP and export is rather straightforward and almost linear, it is not the case for the other two models, where the relationship breaks down in the northern part of the basin: the export production can reach much higher values for relatively low NPP. A striking result comes from ERSEM, with a wide range of export flux in the sub polar gyre for a given NPP (fig. 11b). Another major difference between the two models emphasizes the strong variability in the PPN - % diatoms relationship along with a much smaller variability in the NPP – export flux for PISCES : this tendency is opposite for ERSEM. Moreover, the correlation between export at 150m and export at 1000m, only driven by particle dynamics, is rather simple for PISCES and MEDUSA, whereas ERSEM can generate relatively large export at 1000m in the lower range of export at 150m. The link between NPP and export flux at depth has been debated since both fluxes have been measured or estimated at sea (Hansen et al., 2012; Antia et al., 2001) : a precise quantification of this link / correlation may be extremely useful to estimate deep export carbon fluxes from sea surface observations, such as satellite images. Besides MEDUSA which does not display large eratio at 1000m, ERSEM and PISCES emphasize a low eratio in highly productive regions, such as the NW Africa upwelling, and rather high eratio in the northern regions, with low to intermediate annual NPP. This result shows that a productive regime is not the most efficient in storing carbon in the deep ocean : the efficiency could certainly be more consecutive of strong seasonality in NPP and export flux, associated with pulsed inputs. Finally, figures 12 present the budget of the first 150m for the whole basin and table 3 the major fluxes for the three models. Although the ERSEM NPP is globally smaller than the NPP of the other models (table4), the main characteristic of this model concerns the strength of its regeneration / bacterial pathway (PHY  DOC  DIC) through bacterial respiration and DOC utilization. The two other models show a local biogenic source of DIC at much lower levels (between 3 and 6 times respectively for PISCES and MEDUSA), with nevertheless a large impact of this source for PISCES. Table 3 shows also that the supplying of “dead” carbon (DOC and DET) by phytoplankton is much lower for PISCES than for MEDUSA (PHY  DET/DOC) : in PISCES, most of the phytoplankton is grazed, whereas detritus production by phytoplankton in MEDUSA is much higher. Table 4 shows the Net Primary Production (net DIC  PHY) and the export flux at different depths integrated for the whole basin (from 10°N to 70°N). The average NPP for the three models is equal to 3.87 GtC/yr (+/- 0.69) or 298 mgC/m2/d; which is in the range of previous estimates either using satellite observations or numerical simulations. As the export flux at 150m, the NPP decreases with the complexity of the microbial loop of the model considered. As already discussed, the carbon is more efficiently accumulated in the deep ocean with PISCES, MEDUSA giving the lowest eratio of the three models. These different carbon remineralization kinetics in the deep ocean should have substantial consequences on the carbon, oxygen and nutrients cycles, as well as their ocean distributions on long periods (century and more).


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Table 3 : Major Carbon fluxes in the three models for the whole basin (10°N – 70°N) in GtC/yr. GtC/y

PISCES

ERSEM

MEDUSA

GPP PHY --> DET/DOC PHY/BAC --> ZOO ZOO --> DET/DOC DET/DOC --> DIC

3,89 0,43 3,37 2,12 2,89

9,35 7,51 3,63* 1,94 8,7

4,55 0,96 2,91 0,91 1,37

GPP = NPP for PISCES and MEDUSA. For ERSEM, NPP = 3.17 GtC/yr (table 3). (*) : for ERSEM, the grazing pressure on bacteria is considered in this flux (3.63 = 1.85 from PHY and 1.78 from BAC). This pathway (BAC  ZOO) allows an additional potential direct connection between the microbial loop (PHY  DOC  BAC  Regeneration) and the more classical export loop (PHY  ZOO  Export). Table 4 : NPP, export fluxes and eratio for the three models at basin scale (fluxes en GtC/yr) GtC/y

PISCES ERSEM*

MEDUSA ERSEM/PISCES MEDUSA/PISCES

NPP Export 150m eratio 150m Export 1000m eratio 1000m Export 2000m eratio 2000m * GPP =

3,89 3,17 0,971 0,386 24,96% 12,18%

4,55 0,637 14,00%

0,81 0,40 0,49

1,17 0,66 0,56

0,233 5,99%

0,069 1,77%

0,033 0,73%

0,30 0,30

0,14 0,12

0,095 2,44% 9,35

0,024 0,76%

Xxx Xxx

0,25 0,31

Xxx Xxx

Table 4bis : same fluxes as table 3 in mcg/m2/d (area of the basin ~ 35.53 1012 m2) mgC/m2/d NPP Export 150m Export 1000m Export 2000m

PISCES 300,0

ERSEM 244,4

MEDUSA 350,9

74,9

29,8

49,1

17,97

5,32

2,54

7,33

1,85

xxx


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3.5 The inter-annual variability of the biological carbon pump. Figures 2d and 2f emphasize the strong inter-annual variability of the SST and MLD driven by atmospheric forcing. The major climate variability of the basin is constrained by the variability of the North Atlantic Oscillation, associated with a bipolar pattern of high pressure in the subtropics (Azores) and low on Iceland. High index reinforce the westerlies at mid latitude, which makes western Europe warmer and wetter, and decreases (increases) temperature of the subpolar (sub tropical) region (Hurrell and Deser, 2010). Figure 13 presents the annual variability of NPP between 1980 and 2010 at the four studied stations for the three models. The models show the same time patterns. A simple statistical analysis shows that there is no significant correlation with the NAO index, although this correlation is marginally significant at ESTOC. This station is characterized by a very strong inter-annual variability. In contrast, MIKE is rather steady during the same period. The annual NPP and spring bloom are controlled by the maximum depth of the winter MLD. This deepening supplies nutrients to the ocean surface, which as a first approximation controls the bloom and the export. At MIKE, in the Norwegian Sea, the stratification is low, tracer distributions are vertically homogenised and the supply of nutrient does not depend on this maximum depth, although its inter-annual variability is strong (fig. 2f). In the sub-tropical region, the stratification is strong, and the nutrient vertical gradients are pronounced, with very nutrient poor waters above the nutricline. Depending on the intensity of the atmospheric forcing during winter, the ML can reach the nutricline, in which case, nutrients are brought upwards into the productive layer. When this forcing is not strong enough, this process does not take place and the NPP during the following year remains at a very low level. Figure 14a shows the daily evolution of the MLD and of the depth of the nitracline, which is at around 200m most of the time. During winter, it decreases while the MLD increases. Years associated with large picks in figure 13b (see 1994, 2005 1992) coincide with years when the nitracline depth reaches the MLD, whereas years of low annual productivity (1986, 1995, 1998, etc..) are associated with deep nitracline and shallow MLD. It can also be shown that year 1989 at BATS is the only year, in the simulations, where the nitracline remains always deeper than the ML. Figure 14b describes the inter annual variability of the annual NPP in terms of the maximum mixed layer depth (monthly average) : for ESTOC, the increase of NPP with the winter MLD is clear, whereas NPP at MIKE remains rather constant, and does not depend thus on the MLD.


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Figure 12a: average C budget for PISCES integrated on the productive zone (0 – 150m) for the whole basin


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Figure 12b: same as 12a for ERSEM. Note the flux of C from DIC to Phytoplankton is Gross Production, not for NPP as for the other models.


42



43

Figure 13a: NPP (0 – 150m) at BATS (1980 – 2010)

Figure 13b: same as 13a at ESTOC


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Figure 13c: same as 13a at PAP

Figure 13d: same as 13a at MIKE


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Figure 14a : daily variation of the MLD (black) and nitracline (red) at ESTOC (PISCES)

Figure 14b : NPP vs. Maximum winter MLD at ESTOC and MIKE (PISCES)


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4 Conclusions Although the three models differ in the representation of the food web, and of the coupling between the nutrient and carbon cycles, they produce results of NPP and export out of the productive layer which agree within a ratio 2 : this dispersion is not higher than dispersion obtained with observations. Moreover, they answer qualitatively the same way to the variable hydrodynamical forcing defining the biogeochemical provinces in the North Atlantic basin. Nevertheless, the divergence between the models can be much higher regionally and the associated local fluxes, such as NPP (see appendix 1 NPP at BATS and PAP for instance), are strongly model dependant. Moreover, the fate of carbon in the deep ocean is drastically different between the simulations. We remind that, if flaws in the ocean dynamics simulated by a model can possibly bring strong biases in the biogeochemical fields, in this study, the three models have used the same circulation (same model, same atmospheric forcing, and exactly same configuration for ERSEM and PISCES) : the gaps between the simulations result only from differences in the ecosystem models. All modelling efforts are limited by the lack of quantitative information on the magnitudes of the carbon fluxes between the major compartments (DIC, Phytoplankton, zooplankton, POC, DOC and bacteria). The best constrained fluxes (in a relative sense) are net primary production from C14 uptake measurements, zooplankton grazing rates and export flux from sediment traps. This has led to a focus on the particulate pathways when constructing numerical models. Models such as MEDUSA assume that the net effect of the microbial loop pathways on the DIC fluxes and export is negligible and there does not resolve them. In contrast PISCES also includes a DOC pool, thereby invoking a semi implicit microbial loop (bacteria are not explicitly resolved), thus allowing a significant proportion of the NPP (~30%) to be respired back to DIC via the microbial loop. Finally ERSEM explicitly resolves the microbial loop, whereby phytoplankton explicitly produces DOM and bacteria re-mineralise both POM and DOM. The biological C budget in ERSEM is based on gross production rather than net production. Nutrient stressed lysis results in much of the GPP entering the DOC pool directly and as a consequence up to 60% of the photosynthetically fixed carbon is respired back to DOC by bacteria. At time the bacterial respiration can exceed the NPP as suggested by del Giorgio et al, 1997. In all three models, the quantity of C entering the zooplankton pool is at the first order similar, the difference being that in ERSEM ~60% enters via zooplankton grazing, while the remainder comes via the grazing of bacteria. The big question is how realistic are any of these model fluxes. For example Karl et al (1997) argues (based on observations at HOTS) that the Gross production is under-estimated by 30-50% because the DOC production is not routinely measured. Similarly del Giorgio et al (1997) show that bacterial respiration exceeds net primary production in low productivity systems. In contrast, more recent work suggest that bacterial respiration may have been significantly overestimated and only contributes 30% of community respiration (Aranguren-Gassis et al, 2012). Carbon budgets based on observations from the JGOFS North Atlantic Bloom Experiment and Bermuda Atlantic Time Series could not be closed using the elements of the classical model (Ducklow et al, 1997). A number of explanations were offered including (i) trap estimates are in error and systematically biased; (ii) spatial variability aliases the observations making budgeting impossible without recourse EURO-BASIN | D6.4 Report on major ecosystem & bgc cycling controls at basin-scale, Memery et al., 2014

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to coupled three-dimensional models; and/or (iii) the classical model must be abandoned and replaced by a concept in which the accumulation and export of dissolved organic carbon assumes a major role in the North Atlantic carbon balance. They concluded that at Bermuda, where the most complete data set exists, the weight of the evidence favours the first and third possibilities. The fate of carbon in the deep ocean is not well constrained, and the mesopelagic layer or “twilight zone” remains basically a domain still to be discovered. The particle fate in the deep ocean depends on three main processes, which are only considered in a very simple way in the three models. Although particles are usually classified by size, the particles found in the deep ocean cannot simply be characterized by this parameter, often associated with a single vertical settling velocity. Although some observations tend to show a bimodal distribution of particles (slow ~1-10 m day-1 and fast ~several 100 m day-1 : Alonso-Gonzalez et al., 2010), this view is too simplistic. As a matter of fact, the density and the shape of the fecal pellets, marine and aggregates, as well as their “vulnerabillity” to heterotrophic activity (bacteria, micro zooplankton), are variable, and are not related only to their size (Turner, 1992; de La Rocha and Passow, 2007; McDonnell and Buesseler, 2010). This statement has strong impacts on the particle residence time in the water column. The importance of marine snow in export fluxes has been more and more emphasized in the last two decades: the fate of these aggregates is strongly controlled by phytoplankton blooms, DOM and TEP coagulation, as well as heterotrophic activity, and it might induce a strong scavenging effect on the water column (Passow et al., 2001 ; Simon et al., 2002 ; de La Rocha and Passow, 2007). Moreover, the role of the “hard” part (calcite, opal – opposed to the soft part, particle organic carbon) in increasing the settling velocity and/or the preservation of organic matter is still under debate (ballast effect : Klass and Archer, 2002; Dunne et al., 2007; de La Rocha et al., 2008 ; Thomalla et al., 2008 ; Buesseler and Boyd, 2009 ; Balch et al., 2010). The particle size spectrum and composition is also strongly modified by biotic interactions, e.g. bacterial activity, zooplankton grazing and fecal pellet production. Bacterial activity is one of the major driver of mineralization of POC in the water column (Aristegui et al., 2009 ; Anderson and Tang, 2010), and is also responsible of breaking down large particles in smaller ones with formation of DOC (Kriest and Oschlies, 2008). Nevertheless, very few is known about the bacterial efficiency at depths (Burd et al., 2010), which is nevertheless known to be pressure dependant (Tamburini et al., 2009). The zooplankton grazing pressure can either increase or decrease the vertical flux attenuation, either by repackaging small detritus and cells in larger particle or by breaking down large particles into smaller ones. (Stemmann et al., 2004 ; Wilson et al., 2008). One of the major impact of zooplankton on export flux is linked to its migratory behavior : by ingesting phytoplankton and organic matter at the surface during nights, and respiring and producing fecal pellets at depths (between 200m and 800m) during days, zooplankton is able to modify and increase to vertical POC flux (Hays et al., 2001 ; Hernandez-Leon et al., 2001 ; Steinberg et al., 2002). Even in a very simplified manner, all of the models presented can currently be argued as representing key aspects of the biological C pump required to quantify the capture and storage of carbon. This is perhaps more a function of inadequate process knowledge and observational data, which prevents a definitive evaluation of the models. A concerted effort is required to better observe and quantify all aspects of the biological C pump. EURO-BASIN | D6.4 Report on major ecosystem & bgc cycling controls at basin-scale, Memery et al., 2014

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Appendix 1 Supplementary figures illustrating the data in Table 1.


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