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

Maury, Raghu Murtugudde and Laurent Bopp

Journal of Marine Systems, 2016

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

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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

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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

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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

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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

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300 years2°x2°

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

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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

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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)

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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

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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

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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

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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

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0-200m

200-1000m

0-1000mDay/Night

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

LTL HTL all 3 communities, all size classes

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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

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

MAREDAT

R=0.24

R=-0.15

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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

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

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Summary on five time periods

Natural variability : North Pacific

variance

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D = ( / ) / 7

Natural variability : North Pacific

( - )

S = x100

variance

>66%<33% 33-66%

D = ( / ) / 7

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Natural variability : North Pacific

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Natural variability : North Pacific

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Natural variability : North Pacific

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Natural variability : North Pacific

Resonant rangeBottom-up and top-down

effects

Community differencesEpipelagic > migratory >

mesopelagic

Environmental conditionsDirect/indirect interactions

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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

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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

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Conclusions

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Size

High frequency variability

Lag of maximum correlation with climate modes

Conclusions

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Size

High frequency variability

Lag of maximum correlation with climate modes

Community

Larger size classes in resonant range

Mesopelagic Migratory Epipelagic

Conclusions

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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

Limitations/perspectives

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• 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

Limitations/perspectives

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• 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

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

Maury, 2010

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Climate variability

Climate modes

Evaluation table

Correlation table