effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates ...
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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 , - PowerPoint PPT PresentationTRANSCRIPT
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 mer2LOCEAN-IPSL, Paris
3INRIA, Sophia Antipolis / Paris4LPO, CNRS/IFREMER/UBO, Plouzané
5School of Environmental Sciences, Liverpool
Session: mesoscale16 May 2013
45th Liège ColloquiumBelgium
Acclimation of phytoplankton
• To light conditions: photo-acclimationAdjustment of the pigment content -> Variability of the Chlorophyll:Carbon (Chl:C) ratio Importance to evaluate phytoplankton biomass from satellite data!
• To nutrient availability: variable stoichiometryDeviations 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 8.50
6.10 to 11.4
7.44 to 8.69
5.69 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)
Impact on production estimates?
• Central questions:
Introduction
How to represent photo-acclimation & variable stoichiometry of phytoplankton in
marine ecosystem model?
Part 2Model comparison at basin scale
(3D study)
Part 1Model comparison at local scale
(1D study)
Which consequences on production estimates?
Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates
Impact on production estimates?
Part 1Model 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
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 comparisonafter 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
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.
Withphoto-acclimation
(variable Chl:C)
Withoutphoto-acclimation
(constant Chl:C) No deep ChlMonth
Depth
Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates
Variable stoichiometry and production
• Lowest misfit with variable C:N ratio
Higher productionwith 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 studyEffects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates
Impact on production estimates?
Part 2Model comparison at basin scale
(3D study)
Basin scale configuration with mesoscaleFocusing 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
A basin-scale configuration with mesoscale
• 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
Part 2. Methods
Surface temperature
Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates
Mesoscale structures
Biogeochemical modelling
• Northern eutrophic gyre vs. Southern oligotrophic gyreAnnual averages of surface concentrations
Eutrophic area in the North
Oligotrophic area in the South
Part 2. Results
Mean [NO3](mmolN/m3)
High [phytoplankton]
Low [phytoplankton]
Mean [Phyto](mmolN/m3)
Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates
Variability of the C:N ratio at large scale
• Differences between the oligotrophic and productive areasAnnual averages of surface phytoplanktonic C:N ratio
Higher C:N ratioin oligotrophic area
-> Hovmöller diagram along the 70°W meridian
Mean C:N ratio(molC/molN)
9
8
7
6
5
Part 2. Results
Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates
Variability of the C:N ratio at large scale
• Differences between the oligotrophic and productive areasHovmöller 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)
J F M A M J J A S O N D
9
8
7
6
5
Part 2. Results
Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates
• Variability due to mesoscale processesSnapshot 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 theC:N ratio
Snapshot of theLog[NO3]
9
8
7
6
5
Part 2. Results
Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates
• Variability due to mesoscale processesSnapshot on the surface on April 16th
Variability induced by mesoscale processes
Variability of the C:N ratio at mesoscale
Snapshot of theC:N ratio
9
8
7
6
5
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
J F M A M J J A S O N D
Latitudinal evolution(time-averaged along the 70°W meridian)
South North
Impact of the C:N ratio on the production
• The flexibility of the C:N ratio decreases the production variabilityComparison with a Redfield model (constant C:N)Unbiased production
Temporal evolution(latitudinal average along the 70°W meridian)
With constant C:N ratioWith variable C:N ratio
J F M A M J J A S O N D
Unb
iase
d pr
oduc
tion
(ver
tical
ly in
tegr
ated
)
Part 2. Results
Temporal and spatial damping effect of the
flexible C:N ratioon 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
Impact on production estimates?
Conclusions & perspectives
Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates
• 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
Main resultsConclusions
Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates
• 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
Main resultsConclusions
Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates
• 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
PerspectivesConclusions
Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates
Effects of photo-acclimation and variable stoichiometry of phytoplankton
on production estimates
45th Liège ColloquiumBelgium
May 2013Thank you for your [email protected]
Sakina-Dorothée AYATA,Olivier BERNARD, Olivier AUMONT, Alessandro TAGLIABUE, Antoine SCIANDRA, Marina LEVY