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Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates from 1D and 3D marin

Session: mesoscale 16 May 2013. 45 th Liège Colloquium Belgium. 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 ,

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Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates from 1D and 3D marin

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  1. Session: mesoscale 16 May 2013 45th Liège Colloquium Belgium Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates from 1D and 3D marine ecosystem modelling Sakina-Dorothée AYATA1,2,3, Olivier BERNARD1,3, Olivier AUMONT4, Alessandro TAGLIABUE5, Antoine SCIANDRA1, Marina LEVY2 1LOV, UPMC/CNRS, Villefranche surmer 2LOCEAN-IPSL, Paris 3INRIA, Sophia Antipolis / Paris 4LPO, CNRS/IFREMER/UBO, Plouzané 5School of Environmental Sciences, Liverpool

  2. Acclimation of phytoplankton Introduction • 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 from Martiny et al. (2013) Redfield: 6.56 molC/molN 7.35 to 8.50 6.10 to 11.4 • Potential impact on production since high C:N ratio may lead to carbon overconsumption (Toggweiler, 1993) 7.44 to 8.69 5.69 to 6.00 Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates

  3. Impact on production estimates? Introduction • Central questions: How to represent photo-acclimation & variable stoichiometry of phytoplankton in marine ecosystem model? Which consequences on production estimates? Part 1 Model comparison at local scale (1D study) • Part 2 • Model comparison at basin scale • (3D study) 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 Part 1. Methods More details in Ayata et al (JMS, in press) • NPZD-type model • Constant or variable Chl:C and C:N ratios for the phytoplankton 5 phytoplankton growth formulations with increasing complexity (from constant to variables ratios) and inspired from Geider et al (1996, 1998) Rigorous comparison after parameter calibration at BATS using microgenetic algorithm LOBSTER model (Lévy et al. 2001; 2012b) Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates

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

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

  9. A basin-scale configuration with mesoscale Part 2. Methods • Double gyre configuration of a northern hemisphere basin • Size of the domain: 3.180 km x 2.120 km x 4 km • Resolution: 1/54° degraded to 1/9° (Lévy et al. 2010; 2012a) Surface velocity (m/s) on April 16th Surface temperature Mesoscale structures Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates

  10. Biogeochemical modelling Part 2. Results • Northern eutrophic gyre vs. Southern oligotrophic gyre Annual averages of surface concentrations Eutrophic area in the North High [phytoplankton] Mean [Phyto] (mmolN/m3) Mean [NO3] (mmolN/m3) Oligotrophicarea in the South Low [phytoplankton] Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates

  11. Variability of the C:N ratio at large scale Part 2. Results • Differences between the oligotrophic and productive areas Annual averages of surface phytoplanktonic C:N ratio 9 Mean C:N ratio (molC/molN) Higher C:N ratio in oligotrophic area 8 7 -> Hovmöller diagram along the 70°W meridian 6 5 Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates

  12. Variability of the C:N ratio at large scale Part 2. Results • Differences between the oligotrophic and productive areas Hovmöllerdiagram along the 70°W meridian of the surface phytoplanktonic C:N ratio 9 Higher C:N ratio under oligotrophic conditions 8 7 6 Variability seems also due to mesoscale… 5 J F M A M J J A S O N D Phytoplanktonic C:N ratio (molC/molN) Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates

  13. Variability of the C:N ratio at mesoscale Part 2. Results Variability induced by mesoscale processes • Variability due to mesoscale processes Snapshot on the surface on April 16th 9 Snapshot of the Log[NO3] Snapshot of the C:N ratio 8 7 6 5 Related to the variability of the [nutrient] at mesoscale Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates

  14. Variability of the C:N ratio at mesoscale Part 2. Results Variability induced by mesoscale processes • Variability due to mesoscale processes Snapshot on the surface on April 16th 9 Snapshot of the C:N ratio Log[NO3] C:N ratio 8 7 6 J F M A M J J A S O N D Temporal evolution of the C:N ratio and of the nitrate supply at 70°W25°N 5 Related to the variability of the [nutrient] at mesoscale Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates

  15. Impact of the C:N ratio on the production Part 2. Results • The flexibility of the C:N ratio decreases the production variability Comparison with a Redfield model (constant C:N) Unbiased production • Increase of +39% in the southern oligotrophic area • Decrease of -34% in the northern high-productive area With constant C:N ratio With variable C:N ratio Temporal and spatial damping effect of the flexible C:N ratio on production Unbiased production (vertically integrated) South North J F M A M J J A S O N D Temporal evolution (latitudinal average along the 70°W meridian) Latitudinal evolution (time-averaged along the 70°W meridian) 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. Main results Conclusions • Rigorous comparison of formulations under oligotrophic regime (1D) • Photo-acclimation is required to simulate the deep ChlMAX • Production is underestimated (limit of 1D modelling) • But higher production with variable stoichiometry Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates

  18. Main results Conclusions • Rigorous comparison of formulations under oligotrophic regime (1D) • Photo-acclimation is required to simulate the deep ChlMAX • 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 productionin 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 Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates

  19. Perspectives Conclusions • 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 Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates

  20. Thank you for your attention! sakina@obs-vlfr.fr 45th Liège Colloquium Belgium May 2013 Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates Sakina-Dorothée AYATA, Olivier BERNARD, Olivier AUMONT, Alessandro TAGLIABUE, Antoine SCIANDRA, Marina LEVY

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