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Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates from 1D and 3D marine ecosystem modelling Sakina-Dorothée.

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Presentation on theme: "Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates from 1D and 3D marine ecosystem modelling Sakina-Dorothée."— Presentation transcript:

1 Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates from 1D and 3D marine ecosystem modelling Sakina-Dorothée AYATA 1,2,3, Olivier BERNARD 1,3, Olivier AUMONT 4, Alessandro TAGLIABUE 5, Antoine SCIANDRA 1, Marina LEVY 2 1 LOV, UPMC/CNRS, Villefranche sur mer 2 LOCEAN-IPSL, Paris 3 INRIA, Sophia Antipolis / Paris 4 LPO, CNRS/IFREMER/UBO, Plouzané 5 School of Environmental Sciences, Liverpool Session: mesoscale 16 May th Liège Colloquium Belgium

2 Acclimation of phytoplankton To light conditions: photo-acclimation Adjustment of the pigment content -> Variability of the Chlorophyll:Carbon (Chl:C) ratio Importance to evaluate phytoplankton biomass from satellite data! To nutrient availability: variable stoichiometry Deviations from the classical Redfield Carbon:Nitrogen (C:N) ratio have been observed in situ Introduction Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates 7.35 to to to to 6.00 Redfield: 6.56 molC/molN from Martiny et al. (2013) Potential impact on production since high C:N ratio may lead to carbon overconsumption (Toggweiler, 1993)

3 Impact on production estimates? Central questions: Introduction How to represent photo-acclimation & variable stoichiometry of phytoplankton in marine ecosystem model? Part 2 Model comparison at basin scale (3D study) Part 1 Model comparison at local scale (1D study) Which consequences on production estimates? Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates

4 Impact on production estimates? Part 1 Model comparison at local scale (1D study) BATS (Bermuda Atlantic Time-Series Study site) Oligotrophic regime Chlorophyll concentration (source: NASA) Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates

5 A simple biogeochemical model NPZD-type model Constant or variable Chl:C and C:N ratios for the phytoplankton Part 1. Methods LOBSTER model (Lévy et al. 2001; 2012b) Rigorous comparison after parameter calibration at BATS using microgenetic algorithm 5 phytoplankton growth formulations with increasing complexity (from constant to variables ratios) and inspired from Geider et al (1996, 1998) More details in Ayata et al (JMS, in press) Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates

6 Photo-acclimation and deep chlorophyll max. Lowest misfit with variable Chl:C ratio Without photo-acclimation: no deep Chl max in summer Photo-acclimation should be taken into account Part 1. Results Obs. With photo-acclimation (variable Chl:C) Without photo-acclimation (constant Chl:C) No deep Chl Month Depth Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates

7 Variable stoichiometry and production Lowest misfit with variable C:N ratio Higher production with variable C:N ratio Because oligotrophy induces higher C:N ratio, which increases production Can this be generalized for different regime? Impact on production at basin-scale? -Simulated primary production is always lower than observation (due to 1D modelling?) Part 1. Results Bloom Variable C:N (Quota) Constant C:N (Redfield) 3D study Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates

8 Impact on production estimates? Part 2 Model comparison at basin scale (3D study) Basin scale configuration with mesoscale Focusing on the comparison of 2 formulations: Constant C:N (Redfield) with photo-acclimation Variable C:N (quota) with photo-acclimation Chlorophyll concentration (source: NASA) Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates Description of the variability of the C:N ratio at basin-scale and at mesoscale

9 A basin-scale configuration with mesoscale Double gyre configuration of a northern hemisphere basin – Size of the domain: km x km x 4 km – Resolution: 1/54° degraded to 1/9° (Lévy et al. 2010; 2012a) Surface velocity (m/s) on April 16th Part 2. Methods Surface temperature Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates Mesoscale structures

10 Biogeochemical modelling Northern eutrophic gyre vs. Southern oligotrophic gyre Annual averages of surface concentrations Eutrophic area in the North Oligotrophic area in the South Part 2. Results Mean [NO3] (mmolN/m 3 ) High [phytoplankton] Low [phytoplankton] Mean [Phyto] (mmolN/m 3 ) Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates

11 Variability of the C:N ratio at large scale Differences between the oligotrophic and productive areas Annual averages of surface phytoplanktonic C:N ratio Higher C:N ratio in oligotrophic area -> Hovmöller diagram along the 70°W meridian Mean C:N ratio (molC/molN) Part 2. Results Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates

12 Variability of the C:N ratio at large scale Differences between the oligotrophic and productive areas Hovmöller diagram along the 70°W meridian of the surface phytoplanktonic C:N ratio Variability seems also due to mesoscale… Higher C:N ratio under oligotrophic conditions Phytoplanktonic C:N ratio (molC/molN) JFMAMJJASOND Part 2. Results Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates

13 Variability due to mesoscale processes Snapshot on the surface on April 16th Related to the variability of the [nutrient] at mesoscale Variability induced by mesoscale processes Variability of the C:N ratio at mesoscale Snapshot of the C:N ratio Snapshot of the Log[NO3] Part 2. Results Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates

14 Variability due to mesoscale processes Snapshot on the surface on April 16th Variability induced by mesoscale processes Variability of the C:N ratio at mesoscale Snapshot of the C:N ratio Part 2. Results Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates Related to the variability of the [nutrient] at mesoscale C:N ratio Log[NO3] Temporal evolution of the C:N ratio and of the nitrate supply at 70°W25°N JFMAMJJASOND

15 Latitudinal evolution (time-averaged along the 70°W meridian) SouthNorth Impact of the C:N ratio on the production The flexibility of the C:N ratio decreases the production variability Comparison with a Redfield model (constant C:N) Unbiased production Temporal evolution (latitudinal average along the 70°W meridian) With constant C:N ratio With variable C:N ratio JFMAMJJASOND Unbiased production (vertically integrated) Part 2. Results Temporal and spatial damping effect of the flexible C:N ratio on production Increase of +39% in the southern oligotrophic area Decrease of -34% in the northern high-productive area Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates

16 Impact on production estimates? Conclusions & perspectives Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates

17 Rigorous comparison of formulations under oligotrophic regime (1D) – Photo-acclimation is required to simulate the deep Chl MAX – Production is underestimated (limit of 1D modelling) – But higher production with variable stoichiometry Main results Conclusions Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates

18 Rigorous comparison of formulations under oligotrophic regime (1D) – Photo-acclimation is required to simulate the deep Chl MAX – Production is underestimated (limit of 1D modelling) – But higher production with variable stoichiometry Constant vs. variable C:N ratio at basin scale (3D) – Variability of the C:N ratio at basin scale and mesoscale Related to the nitrogen supply: higher C:N ratio under oligotrophy – Consequences on the production in agreement with the 1D study When production is low, a variable C:N ratio increases production (+39%) When production is high, a variable C:N ratio decreases production (-34%) Damping effect of the variable C:N ratio on production Main results Conclusions Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates

19 From regional to global scale – Because of its damping effect on production, taking into account the plasticity of the phytoplanktonic C:N ratio may impact the primary production estimates at global scale Taking into account phytoplankton functional types (PFT) – The phytoplanktonic communities are complex – Which consequence if a variable C:N ratio is simulated for the different PFT? – Impact on higher trophic level? Next step => fully model the C:N ratios for each ecosystem component Perspectives Conclusions Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates

20 45 th Liège Colloquium Belgium May 2013 Thank you for your attention! Sakina-Dorothée AYATA, Olivier BERNARD, Olivier AUMONT, Alessandro TAGLIABUE, Antoine SCIANDRA, Marina LEVY


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