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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
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
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
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
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
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°
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
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
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
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
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
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
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
Model EvaluationLimited by :• Amount of data (HTL)• Idealized experiment
LTL HTL all 3 communities, all size classes
9/20
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
11/20
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
13/20
Summary on five time periods
Natural variability : North Pacific
variance
14/20
D = ( / ) / 7
Natural variability : North Pacific
( - )
S = x100
variance
>66%<33% 33-66%
D = ( / ) / 7
14/20
Natural variability : North Pacific
15/20
Natural variability : North Pacific
15/20
Natural variability : North Pacific
15/20
Natural variability : North Pacific
Resonant rangeBottom-up and top-down
effects
Community differencesEpipelagic > migratory >
mesopelagic
Environmental conditionsDirect/indirect interactions
16/20
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
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
Conclusions
19/20
Size
High frequency variability
Lag of maximum correlation with climate modes
Conclusions
19/20
Size
High frequency variability
Lag of maximum correlation with climate modes
Community
Larger size classes in resonant range
Mesopelagic Migratory Epipelagic
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
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
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
The APECOSM modelOrganisms from 1mm to 2m in three pelagic communities
Maury, 2010
8/20
Climate variability
Climate modes
Evaluation table
Correlation table