Transcript Effects of photo-acclimation and variable stoichiometry of

45th Liège Colloquium
Belgium
Session: mesoscale
16 May 2013
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
Introduction
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
from Martiny et al. (2013)
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
Potential impact on production
since high C:N ratio may lead to
carbon overconsumption
(Toggweiler, 1993)
Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates
Introduction
Impact on production estimates?
• 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
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
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
Part 1.
Results
Photo-acclimation and deep chlorophyll max.
• Lowest misfit with variable Chl:C ratio
Month
Without
photo-acclimation
(constant Chl:C)
Depth
No deep Chl
Obs.
Without photo-acclimation:
no deep Chl max in summer
With
photo-acclimation
(variable Chl:C)
 Photo-acclimation should
be taken into account
Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates
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
Bloom
Constant C:N
(Redfield)
Variable C:N
(Quota)
Because oligotrophy induces
higher C:N ratio, which
increases production
 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
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)
Description of the variability of the C:N ratio
at basin-scale and at mesoscale
Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates
Part 2.
Methods
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
Surface temperature
Mesoscale structures
Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates
Part 2.
Results
Biogeochemical modelling
• Northern eutrophic gyre vs. Southern oligotrophic gyre
Annual averages of surface concentrations
Eutrophic area in the North
High [phytoplankton]
Mean [NO3]
Mean [Phyto]
(mmolN/m3)
(mmolN/m3)
Oligotrophic area in the South
Low [phytoplankton]
Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates
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
Mean C:N ratio
9
(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
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
8
7
Higher C:N ratio
under oligotrophic
conditions
6
J
F M A M J
J A S O N D
Phytoplanktonic C:N ratio
(molC/molN)
5
Variability seems also
due to mesoscale…
Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates
Variability of the C:N ratio at mesoscale
• Variability due to mesoscale processes
Snapshot on the surface on April 16th
Snapshot of the
C:N ratio
9
Part 2.
Results
Variability induced by
mesoscale processes
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
Part 2.
Results
Variability of the C:N ratio at mesoscale
• Variability due to mesoscale processes
Variability induced by
mesoscale processes
Snapshot on the surface on April 16th
Snapshot of the
C:N ratio
9
8
Log[NO3]
C:N ratio
7
6
5
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
Related to the variability of the [nutrient] at mesoscale
Effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates
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
Temporal and spatial
damping effect of the
flexible C:N ratio
on production
(vertically integrated)
Unbiased production
With constant C:N ratio
With variable C:N ratio
• Increase of +39% in the southern
oligotrophic area
• Decrease of -34% in the northern
high-productive area
J
F M A M J J A S O N D
Temporal evolution
(latitudinal average along the 70°W meridian)
South
North
Latitudinal evolution
(time-averaged along the 70°W meridian)
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
Conclusions
Main results
• 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
Conclusions
Main results
• 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
Conclusions
Perspectives
• 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
Thank you for your attention!
45th Liège Colloquium
Belgium
May 2013
[email protected]
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