Ecosystem composition and export production variability Corinne Le Qur, Erik Buitenhuis, Christine K

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Ecosystem composition and export production
variabilityCorinne Le Quéré, Erik Buitenhuis,
Christine KlaasMax-Planck-Institute for
Biogeochemistry, Germany and Olivier
AumontLaboratoire de Dynamique du Climat et des
Océans, France
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climate feedback
OCEAN
CO2 sink (PgC/y)
LAND
1850
today
2100
Prentice et al., 2001
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NPZD PISCES Dynamic Green Ocean Model (DGOM)
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SeaWiFS
Chla (mgChl/m3)
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SeaWiFS NPZD PISCES DGOM
Chla (mgChl/m3)
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Interannual standard deviation of Chla ()
Chla (mgChl/m3)
SeaWiFS
50
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Interannual standard deviation of Chla ()
Chla (mgChl/m3)
SeaWiFS NPZD PISCES DGOM
50
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Phytoplankton traits
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Abundance of PFT ()
DGOM
cocco
nano
diatoms
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Interannual standard deviation of chla per PFT ()
cocco
nano
diatoms
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Interannual standard deviation of chla per PFT ()
cocco
nanno
diatoms
5 PFTs
4 PFTs
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0.02
diatoms
0.1
coccolithophorids
1.5
nano phytoplankton
Plankton-specific chla in the North Atlantic
(40N-45N, mgChla/m3)
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Abundance of diatoms ()
Uitz, Claustre et al., from an HPLC pigment and
SeaWIFS Chla
DGOM
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Coccolithophorid bloom frequency
Analysis from C. Brown
DGOM
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Export Production (PgC/y)
MEAN (Standard deviation)
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sensitivity of plankton biomass to
nano
nano
grazing
growth
diatoms
diatoms
Meso-zoo
Meso-zoo
Cocco.
Cocco.
Micro-zoo
Micro-zoo
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Conclusions

• More complexity in marine ecosystems gives more
  variability in fluxes
• More in line with observations
• Mean spatial distribution in PFTs is critical to
  reproduce chl variability

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Phytoplankton traits
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Phytoplankton traits
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Zooplankton traits
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Zooplankton traits
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Impact of El Nino in on Chla (full line from
SeaWiFS) and export (dots from traps)
POC CaCO3 Si sediment trap data base of C.
Klaas
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Abundance of PFT ()
DGOM
PISCES
cocco
nanno
diatoms