effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates ...

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?



Part 1Model comparison at local scale

(1D study)

BATS (Bermuda Atlantic Time-Series Study site)Oligotrophic regime

Chlorophyll concentration (source: NASA)


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)


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


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


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)


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


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)


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


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


• 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


• 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


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


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


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



Conclusions & perspectives


• 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


• 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


• 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

45th Liège ColloquiumBelgium

May 2013Thank you for your [email protected]

Sakina-Dorothée AYATA,Olivier BERNARD, Olivier AUMONT, Alessandro TAGLIABUE, Antoine SCIANDRA, Marina LEVY

effects of photo-acclimation and variable stoichiometry of phytoplankton on production estimates ...

Documents

production estimates4

production estimates3impact

primary production

nquotaconstant c

nitrogen c

high c

higher c

c ratio importance