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 sur mer
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. Model comparison at local scale
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
Without
photo-acclimation
(constant Chl:C)
No deep Chl
Lowest misfit with variable Chl:C ratio
Month
Depth
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
Variable C:N
(Quota)
Constant C:N (Redfield)
Can this be generalized for different regime?
Impact on production at basin-scale?
3D study
Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates

8. Model comparison at basin scale
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. Surface velocity (m/s)
A basin-scale configuration with mesoscale
Part 2. Methods
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
Surface temperature
Mesoscale structures
Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates

10. Eutrophic area in the North
Biogeochemical modelling
Part 2. Results
Northern eutrophic gyre vs. Southern oligotrophic gyre
Annual averages of surface concentrations
Eutrophic area in the North
High [phytoplankton]
Low [phytoplankton]
Mean [Phyto]
(mmolN/m3)
Mean [NO3]
(mmolN/m3)
Oligotrophic area in the South
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) 8
Higher C:N ratio
in oligotrophic area 7 6
-> Hovmöller diagram along the 70°W meridian 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
Hovmöller diagram along the 70°W meridian of the surface phytoplanktonic C:N ratio 9
Higher C:N ratio under oligotrophic conditions 8 7 6 5
Variability seems also due to mesoscale… 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 induced by mesoscale processes
Variability of the C:N ratio at mesoscale
Part 2. Results
Variability due to mesoscale processes
Snapshot on the surface on April 16th
Variability induced by mesoscale processes 9
Snapshot of the
C:N ratio
Snapshot of the
Log[NO3] 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 induced by mesoscale processes
Variability of the C:N ratio at mesoscale
Part 2. Results
Variability due to mesoscale processes
Snapshot on the surface on April 16th
Variability induced by mesoscale processes 9
Snapshot of the
C:N ratio
C:N ratio
Log[NO3] 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. Temporal and spatial damping effect of the flexible C:N ratio
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
Latitudinal evolution
(time-averaged along the 70°W meridian)
South
North
Unbiased production
(vertically integrated) J F M A S O N D
Temporal evolution
(latitudinal average along the 70°W meridian)
Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates

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

19. Perspectives From regional to global scale
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. Sakina-Dorothée AYATA,
Thank you for your attention!
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