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Acknowledgements: Laurent Bopp, Karen Kohfeld, Erik Buitenhuis, Olivier Aumont

Explore the impact of climate change on oceanic ecosystems, analyzing elevated CO2, warming, and nutrient supply effects. Understand the responses of marine ecosystems to these changes and learn from historical data and simulations. Delve into the predictions made by models and compare them with observed data, shedding light on the sensitivity of climate systems.

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Acknowledgements: Laurent Bopp, Karen Kohfeld, Erik Buitenhuis, Olivier Aumont

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  1. Climate sensitivity:what observations tell us about model predictionsCorinne Le QuéréMax-Planck-Institut für Biogeochemie, Jena, Germany Acknowledgements:Laurent Bopp, Karen Kohfeld, Erik Buitenhuis, Olivier Aumont

  2. no biology, no climate change today CO2 sink (PgC/y) time (J. Orr and OCMIP-2 participants)

  3. no biology, no climate change Prentice et al. 2001, based on data from Manning 2001, Langenfelds et al., 1999, and Sabine et al., 2002 Heimann and Maier-Reimer 1996 Le Quéré et al. 2003 CO2 sink (PgC/y) Gruber and Keeling 2001 Takahashi et al. 2002 Battle et al. 2000 time (J. Orr and OCMIP-2 participants)

  4. outline

  5. CO2 + H2O + CO2-3 2HCO-3 oceanic carbon cycle 90 CO2 biological activity chemical reactions 45 Si PO4 NO3 NH4 Fe DMS 34 Calcifiers Nano phytoplankton DMS producers N2 fixers Silicifiers CaCO3 DOM 33 11 11 physical transport

  6. how do marine ecosystems respond to: • elevated CO2 • warming • nutrient supply • stratification

  7. physical response to elevated CO2 Reduced CO2 solubility Reduced vertical circulation (Sarmiento et al., in prep.)

  8. CO2 + H2O + CO2-3 2HCO-3 oceanic carbon cycle 90 CO2 biological activity chemical reactions 45 Si PO4 NO3 NH4 Fe DMS 34 Calcifiers Nano phytoplankton DMS producers N2 fixers Silicifiers CaCO3 DOM 33 11 11 physical transport

  9. Si PO4 NO3 NH4 Fe Calcifiers Nano phytoplankton DMS producers N2 fixers diatoms CaCO3 DOM nutrient-based models (Najjar et al., 1992; Maier-Reimer 1993)

  10. NPZD Si PO4 NO3 NH4 Fe Calcifiers Nano phytoplankton DMS producers N2 fixers diatoms CaCO3 DOM (Fasham et al., 1993)

  11. ecosystem models Si PO4 NO3 NH4 Fe Calcifiers Nano phytoplankton DMS producers N2 fixers diatoms CaCO3 DOM (Aumont et al. 2003; Moore et al., 2001)

  12. Modelled changes in export production at 2xCO2 N • reduction in nutrient supply • increase of oligotrophic gyres • longer growing season equator latitude S +30 % -30 % (Bopp 2001)

  13. climate feedback CO2 sink (PgC/y) time (Prentice et al., 2001)

  14. CO2 sink (PgC/y) CO2 + climate time (Prentice et al., 2001)

  15. C L I M A T E R E S P O NS E O F OCE A N I C U PT A KE S a r mi e nto M ate a r a n d J o os e t a l. et a l. (1 9 9 8 ) H i r s t (1 9 9 9 ) ( 19 9 9 ) T i me S p a n 19 9 0 - 20 6 5 18 5 0 - 210 0 17 6 5 - 210 0 W a rm i n g E f f e ct -11 % -1 2 % -1 3 % C i rc u la t io n E ff ect - 2 2% -1 0 % - 3 % B iolo gi cal E ff ect + 2 4 % + 8 % + 6 % T O TA L - 9 % -1 4 % -1 0 % (slide from J. Sarmiento)

  16. 960 ppm 550 ppm CO2 (ppm) 280 ppm 200 ppm 5.8°C Temperature (oC) 0°C 1.4°C -8°C 100,000 yrs variations 400,000 years (data from Petit et al., 1999)

  17.  x2 over the ocean Model simulations of the last glacial maximum • Cooling of SST (CLIMAP 1981) • Circulation Changes (Simulation OPA model, O. Marti) • Increased Sea -Ice in Winter (Crosta et al. 1998) • Increased dust deposition on the ocean (Mahowald et al. 1999) (Bopp et al., 2003)

  18. total LGM impact on export production gC/m2/y • • decreased export production (-7 %) • decreased atmospheric CO2 (-30 ppm) (Bopp et al., 2003)

  19. Nano-phyto Diatomées Latitude total LGM impact on export production diatoms relative abundance iron LGM impact on export production Diatoms Nano-phyto  Diatoms • increased export production (+6 %) • but increase of oligotrophic gyres (Bopp et al., 2003) • shift from nano-phyto to diatoms

  20. Data Confidence high medium low Evaluation of Paleo-Data Paleo-Export Proxies: Ranking Criteria: • Opal (SiO2) • Calcium Carbonate (CaCO3) • Organic Carbon • Biomarker (C37 Alkenones) • 10Be • 231Pa • Excess Barium • Authigenic Uranium • Authigenic Cadmium • Benthic Foraminifera Fluxes • Age Models • Radiocarbon dating (AMS) • Oxygen Isotope Stratigraphy • Lithogenic Correlation • Flux measurement • Constant Flux Normalization (230Th) • Mass Accumulation Rates • Sediment Concentration • Proxy Agreement • How many? • Percentage agreement Ranked Classes: (Kohfeld et al., in prep.)

  21. Stage 5ad-today change in export production Unpublished map not available Stage 5a-d “Today” dust LGM-Stage 5ad CO2 Unpublished map not available LGM LGM-today time (Kohfeld et al., in prep.)

  22. change in export production / OPA-PISCES model (Bopp et al. 2003) (gC m-2 yr-1) Data-base (Kohfeld et al., in prep.)

  23. change in export production / OPA-PISCES model (Bopp et al. 2003) (gC m-2 yr-1) Data-base (Kohfeld et al., in prep.)

  24. CO2 drawdown with this model 30 ppm SST + SSS (+ sea ice + circ.) = –15 ppm Dust increase –15 ppm Maximum effect with this model 41 ppm (Bopp et al., 2003)

  25. CO2reduction due to dust at the LGM Reference 15 ppm Bopp et al. in press 8 ppm Archer et al. 2000 40 ppm Watson et al., 2000 dust CO2 time reasonable agreement considering the phasing of dust/CO2 changes (Watson et al., 2000)

  26. interannual CO2 variability atmosphere Mean: 3.2 PgC/y CO2 variability (Pg C/y) time 20 years Le Quéré et al., in press

  27. atmosphere CO2 variability (Pg C/y) ocean (Le Quéré et al., 2003)

  28. equatorial Pacific MIT model (McKinley 2002) CO2 variability (Pg C/y) OPA model (Le Quéré et al., 2000) Hamburg model (Winguth et al., 1994) • During El Nino events: CO2 • • warming • decreased upwelling • • decreased export production (Bousquet et al., 2000; data from Feely et al., 1999)

  29. northern sub-tropics MIT model OPA model CO2 variability (mol/m2/y) observations (Peylin et al., in prep)

  30. North Atlantic (Gruber et al., 2002)

  31. Standard deviation of interannual signal Chla (SeaWiFS) SST (Reynolds and Smith 1994) SSH (TOPEX/Poseidon) MLD (indirect estimate using SSH)

  32. Standard deviation of export production variability 1997-2002 (mol C/m2/yr) 1.5 SeaWiFS chla, PP from Behrenfeld and Falkowski (1997), ef-ratio from Laws et al. (2000) 1.0 0.5 0.0

  33. Si PO4 NO3 NH4 Fe Calcifiers Nano phytoplankton DMS producers N2 fixers diatoms CaCO3 DOM nutrient-based models (HAMOCC3) (Maier-Reimer 1993)

  34. HAMOCC3 Standard deviation of export production variability (mol C/m2/yr) 1.5 SeaWiFS chla, PP from Behrenfeld and Falkowski (1997), ef-ratio from Laws et al. (2000) 1.0 0.5 0.0

  35. NPZD Si PO4 NO3 NH4 Fe Calcifiers Nano phytoplankton DMS producers N2 fixers diatoms CaCO3 DOM (Aumont et al.,2002 based on Maier-Reimer and Six 1996)

  36. HAMOCC3 NPZD Standard deviation of export production variability (mol C/m2/yr) 1.5 SeaWiFS chla, PP from Behrenfeld and Falkowski (1997), ef-ratio from Laws et al. (2000) 1.0 0.5 0.0

  37. PISCES model based on plankton functional types Si PO4 NO3 NH4 Fe Calcifiers Nano phytoplankton DMS producers N2 fixers diatoms CaCO3 DOM (Aumont et al., 2003; Bopp et al., 2003)

  38. HAMOCC3 PISCES NPZD Standard deviation of export production variability (mol C/m2/yr) 1.5 SeaWiFS chla, PP from Behrenfeld and Falkowski (1997), ef-ratio from Laws et al. (2000) 1.0 0.5 0.0

  39. Si PO4 NO3 NH4 Fe coccolith. Nano phytoplankton DMS producers N2 fixers diatoms CaCO3 DOM Dynamic Green Ocean Model (Buitenhuis et al., 2003)

  40. HAMOCC3 PISCES 4 NPZD DGOM Standard deviation of export production variability (mol C/m2/yr) 1.5 SeaWiFS chla, PP from Behrenfeld and Falkowski (1997), ef-ratio from Laws et al. (2000) 1.0 0.5 0.0 (Le Quéré et al., in prep.)

  41. diatoms Growth rate coccolithophorids P nano-phytoplankton

  42. (Chavez et al 2003)

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