Priscilla Le Mézo, Stelly Lefort, Roland Séférian, Olivier ... · Priscilla Le Mézo, Stelly...

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Priscilla Le Mézo, Stelly Lefort, Roland Séférian, Olivier Aumont, Olivier Maury, Raghu Murtugudde and Laurent Bopp Journal of Marine Systems, 2016

Transcript of Priscilla Le Mézo, Stelly Lefort, Roland Séférian, Olivier ... · Priscilla Le Mézo, Stelly...

Page 1: Priscilla Le Mézo, Stelly Lefort, Roland Séférian, Olivier ... · Priscilla Le Mézo, Stelly Lefort, Roland Séférian, Olivier Aumont, Olivier Maury, Raghu Murtuguddeand Laurent

Priscilla Le Mézo, Stelly Lefort, Roland Séférian, Olivier Aumont, Olivier

Maury, Raghu Murtugudde and Laurent Bopp

Journal of Marine Systems, 2016

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Motivation� North Pacific & North Atlantic Oceans ≈ 44 % marine fish catches in 2012

� Climate variability à ecosystem sustainability

Climate natural variability à Environmental conditions à ecosystems• NAO, AMO• ENSO, PDO

1/20

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Motivation� North Pacific & North Atlantic Oceans ≈ 44 % marine fish catches in 2012

� Climate variability à ecosystem sustainability

Climate natural variability à Environmental conditions à ecosystems• NAO, AMO• ENSO, PDO

Climate change

1/20

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Motivation� North Pacific & North Atlantic Oceans ≈ 44 % marine fish catches in 2012

� Climate variability à ecosystem sustainability

Climate natural variability à Environmental conditions à ecosystems• NAO, AMO• ENSO, PDO

Effects are difficult to characterize

Data limitation : • Length of observations (low freq)• Commercial species• Harvested size classes

1/20

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Motivation� North Pacific & North Atlantic Oceans ≈ 44 % marine fish catches in 2012

� Climate variability à ecosystem sustainability

Climate natural variability à Environmental conditions à ecosystems• NAO, AMO• ENSO, PDO

Effects are difficult to characterize

Data limitation : • Length of observations (low freq)• Commercial species• Harvested size classes

Alternative tools:Mechanistic models

1/20

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Experimental set up3D Physics :

IPSL-CM5-LR coupled model

Biogeochemistry :

Pelagic Interaction Scheme for Carbon and Ecosystems Studies

(PISCES)

Upper trophic levels :

Apex Predators ECOsystem Model (APECOSM)

Pre-industrial conditions - No external forcing (e.g., volcanoes, anthropogenic activities)

Offline forcing Offline forcing

2/20

300 years2°x2°

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Experimental set up3D Physics :

IPSL-CM5-LR coupled model

Biogeochemistry :

Pelagic Interaction Scheme for Carbon and Ecosystems Studies

(PISCES)

Upper trophic levels :

Apex Predators ECOsystem Model (APECOSM)

Pre-industrial conditions - No external forcing (e.g., volcanoes, anthropogenic activities)

Offline forcing Offline forcing

2/20

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The PISCES model

Phytoplankton :• Nanophytoplankton• Diatoms

Zooplankton : • Mesozooplankton• Macrozooplankton

Detritus : • Small organic particles• Large organic particles

Nutrients :NO3, NH4, PO4, Fe, Si

Low trophic levels : LTL = Nano + Diat + MesoZoo + MacroZoo + SmallOP+ LargeOP

Aumont and Bopp, 2006

Production at the base of the trophic chainOcean 3D dynamicsPARWater temperature

3/20

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Experimental set up3D Physics :

IPSL-CM5-LR coupled model

Biogeochemistry :

Pelagic Interaction Scheme for Carbon and Ecosystems Studies

(PISCES)

Upper trophic levels :

Apex Predators ECOsystem Model (APECOSM)

Pre-industrial conditions - No external forcing (e.g., volcanoes, anthropogenic activities)

Offline forcing Offline forcing

Biogeochemistry :

Pelagic Interaction Scheme for Carbon and Ecosystems Studies

(PISCES)

4/20

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The APECOSM modelOrganisms from 1mm to 2m in three pelagic communities

PISCES LTL APECOSM HTL

Body-size constrains trophic interactions, active movements and metabolic rates

Maury et al., 2007

5/20

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The APECOSM modelOrganisms from 1mm to 2m in three pelagic communities

For an organism :

Maury et al., 2007Ingestion based on size ratio between prey and predator

Maury et al., 2007

Energy used in the same way by all organisms

6/20

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The APECOSM modelOrganisms from 1mm to 2m in three pelagic communities

For a community :

Biomass decreases with size

Epipelagic

Migratory

Mesopelagic

Defined by their vertical behavior

Modified from Maury and Poggiale, 2013

7/20

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The APECOSM modelOrganisms from 1mm to 2m in three pelagic communities

For a community :

Biomass decreases with size

Epipelagic

Migratory

Mesopelagic

Defined by their vertical behavior

Modified from Maury and Poggiale, 2013

8/20

0-200m

200-1000m

0-1000mDay/Night

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Model EvaluationLimited by :• Amount of data (HTL)• Idealized experiment

LTL HTL all 3 communities, all size classes

9/20

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Model EvaluationPossible evaluation of :• Chlorophyll (PISCES)• Meso- and macro-zooplankton (PISCES+APECOSM)

GlobColour surface ChlModeled surface ChlR = 0.4

RMSE = 0.75 µgChl/LUnderestimation due to model resolution

NATL R=0.6

NPAC R=0.49 10/20

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Model EvaluationPossible evaluation of :• Meso- and macro-zooplankton (PISCES+APECOSM)

MAREDAT

R=0.24

R=-0.15

11/20

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Model EvaluationPossible evaluation of :• Meso- and macro-zooplankton (PISCES+APECOSM)

MAREDAT

GlobalR=0.24

NATLR=0.37NPACR=0.41

GlobalR=-0.15

NATLR=0.32NPAC

R=-0.0612/20

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Natural variability : North Pacific

For each size class in the three communities

• Pondered average• Fast Fourier Transform• Test against an AR(1)

As size increases high frequency variability diminishes

13/20

Summary on five time periods

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Natural variability : North Pacific

variance

14/20

D = ( / ) / 7

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Natural variability : North Pacific

( - )

S = x100

variance

>66%<33% 33-66%

D = ( / ) / 7

14/20

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Natural variability : North Pacific

15/20

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Natural variability : North Pacific

15/20

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Natural variability : North Pacific

15/20

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Natural variability : North Pacific

Resonant rangeBottom-up and top-down

effects

Community differencesEpipelagic > migratory >

mesopelagic

Environmental conditionsDirect/indirect interactions

16/20

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Natural variability : North Pacific

Small organismsHigh freq

High correlation

Large organismsLow freq

Lagged correlation

Life span and generation time

Processed signal, filter variability

17/20

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Natural variability : North Pacific

Small & large organisms vs intermediate

organisms

• Bottom-up vs top-down on intermediate size classes

• Non linear biological response

• Extraction of weak signals• Induced shift in ecosystem

• Whole basins

18/20

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Conclusions

19/20

Size

High frequency variability

Lag of maximum correlation with climate modes

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Conclusions

19/20

Size

High frequency variability

Lag of maximum correlation with climate modes

Community

Larger size classes in resonant range

Mesopelagic Migratory Epipelagic

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Conclusions

19/20

Size

High frequency variability

Lag of maximum correlation with climate modes

Community

Larger size classes in resonant range

Mesopelagic Migratory Epipelagic

Oceanic region

Effect of climate variability : different climate modes

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Limitations/perspectives

20/20

• Idealized simulation :No direct analysis of specific eventsBiased representation of climate modes

• One way coupling between PISCES and APECOSM

• Large oceanic basins:Heterogeneous effects of climate modes

• Use of a different biogeochemical model

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Limitations/perspectives

20/20

• Idealized simulation :No direct analysis of specific eventsBiased representation of climate modes

• One way coupling between PISCES and APECOSM

• Large oceanic basins:Heterogeneous effects of climate modes

• Use of a different biogeochemical model

Thank you for your attention

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The APECOSM modelOrganisms from 1mm to 2m in three pelagic communities

Maury, 2010

8/20

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Climate variability

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Climate modes

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Evaluation table

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Correlation table