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WP 4: Climate Change and Ocean Acidification

2nd Annual Meeting. MACROES. Paris, 14-16 May 2012. WP 4: Climate Change and Ocean Acidification. WP4: Climate Change and Ocean Acidification. WP4 main objectives: --- Use the MACROES modelling framework to study the effects of anthropogenic

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WP 4: Climate Change and Ocean Acidification

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  1. 2nd Annual Meeting MACROES Paris, 14-16 May 2012 WP 4: Climate Change and Ocean Acidification

  2. WP4: Climate Change and Ocean Acidification WP4 main objectives: --- Use the MACROES modelling framework to study the effects of anthropogenic emissions (greenhouse gases, aerosols) through climate change and ocean acidification on the marine ecosystems (incl. fish ressources) --- A particular emphasis will be given to the identification and characterization of the feedbacks between the different (natural) systems considered here (climate, biogeochemical cycles, marine ecosystems) WP4 structure: --- 4.1 Impact of CC and OA on marine ecosystems: end-to-end --- 4.2 Retroactions in the coupled system - Top-down control from higher to lower trophic levels - Biophysical coupling through heat trapping and bio-induced turbulence --- 4.3 Impact of CC and OA on marine ecosystems: biodiversity

  3. WP4: Climate Change and Ocean Acidification Les « drivers » : productivité marine, acidification, dé-oxygénation Les premières simulations avec IPSL-CM / PISCES-APECOSM A venir cette année…

  4. Premiers Résultats avec CM5 850 RCP8.5 RCP6.0 RCP4.5 RCP2.6 Historical 650 450 Climate Change impact on surface chlorophyll 250 6.0 DT (°C) 3.0 CO2, DT et chlorophylle de surface 0.0 0.19 0.17 Chl de surface (mgChl/m3) 0.15

  5. Biogeochemical Drivers • Changes in Net Primary Productivity driven by climate change

  6. Biogeochemical Drivers • Changes in Net Primary Productivity driven by climate change IPSL-CM5 Net Primary Productivity as simulated by 8 CMIP5 models MPI-ESM IPSL-CM5A-LR IPSL-CM5A-MR MIROC-ESM-CHEM CanESM2 MIROC-ESM HadGEM2-CC HadGEM2-ES IPSL-CM5 Biogéochimie Marine : Séférian et al. in press Comparaison des modèles IPCC – CMIP5 / Productivité marine :Kidston et al. in prep

  7. Biogeochemical Drivers • Changes in Net Primary Productivity driven by climate change Relative Change in NPP from 2005 to 2100 (RCP85 scenario) IPSL-CM5A-LR IPSL-CM5A-MR MPIM-ESM MIROC-ESM MIROC-ESM-CHEM CanESM2 HadGEM2-ES HadGEM2-CC  A global decrease of NPP by -5 to -18% in 2100

  8. Biogeochemical Drivers • Changes in Net Primary Productivity driven by climate change Relative Change in NPP from 2005 to 2100 (RCP85 scenario, model-mean, %) Hatched regions: when >75% of the models agree on the sign of change • Large regional contrasts: -50% in N. Atl, -20% in the tropics, increase in the SO

  9. Biogeochemical Drivers • Changes in pH / Ocean Acidification

  10. Biogeochemical Drivers • Changes in pH / Ocean Acidification IPSL-CM5A-LR, IPSL-CM5A-MR, HadGEM2-ES, HadGEM2-CC, MPIM-ESM, MIROC-ESM, MIROC-ESM-CHEM, CanESM RCP4.5 RCP8.5 • Consistent decrease in pH from several CMIP5 models RCP45: -0.3 RCP85: from -0.4 to -0.8 in 2300 ! Orr et al. in prep

  11. Biogeochemical Drivers • Changes in pH / Ocean Acidification [CO32-] RCP4.5 RCP8.5 Aragonite / Calcite undersaturation reached at the surface in polar oceans  Implications on calcification / trophic food webs?

  12. Biogeochemical Drivers • Changes in pH / Ocean Acidification RCP4.5 RCP8.5 Increase in C/N ratios of organic matter (Riebesell et al. 2008)  Implications on food “quality” ? (Tagliabue et al. 2011)

  13. Biogeochemical Drivers • Changes in Oxygen / Desoxygenation

  14. Biogeochemical Drivers • Changes in Oxygen / Desoxygenation • Observed increase • of hypoxic waters • in the Eq. Pacific Stramma et al. 2008

  15. Biogeochemical Drivers • Changes in Oxygen / Desoxygenation Changes in [O2] (micromol/L) (5-model mean, SRES-A2) : 0 m Hatched regions: when >75% of the models agree on the sign of change DO2 (mmol/L) (IPSL-CM4, UVIC, CSM1.4, CCSM3, BCM-C)  Large decrease of O2 in surface waters: solubility-driven

  16. Biogeochemical Drivers • Changes in Oxygen / Desoxygenation Changes in [O2] (micromol/L) (5-model mean, SRES-A2) : 200 m Hatched regions: when >75% of the models agree on the sign of change DO2 (mmol/L)  Consistent at mid/high lat but models do not agree in the tropics !

  17. Towards coupled climate & end-to-end ecosystem modelling Towards Online Coupling: PISCES-APECOSM

  18. Towards coupled climate & end-to-end ecosystem modelling PISCES-APECOSM : : Preliminary RCP85 results (see talk by S. Dueri for more details) Nanophytoplankton relative change Diatoms relative change Latitude Time (1850 to 2100) Microzooplankton relative change Mesozooplankton relative change LOWER TROPHIC Large disparity among plankton functional types: Phyto : -8%, Diatoms : -16%, Microzoo : -20%, Mesozoo : -20%. 15% drop of total biomass in 2100 compared to preindustrial values

  19. Towards coupled climate & end-to-end ecosystem modelling PISCES-APECOSM : : Preliminary RCP85 results Total biomass relative change Epipelagic biomass relative change Latitude Time (1850 to 2100) Migratory biomass relative change Mesopelagic relative change UPPER TROPHIC Large disparity among communities: Epipelagic : -22%, Migratory : -8%, Mesopelagic : -30% 23% drop of total biomass in 2100 compared to preindustrial values

  20. Etapes / Stratégie pour le WP4 End-to-End Etape 1 M12 : Simulations “offline” sur 1860-2100 (RCP8.5) IPSL-CM  ( PISCES  APECOSM ) M18 : Analyse de l’impact du CC (et OA) sur les écosystèmes Etape 2 M24: Mise en place de PISCES-APECOSM dans IPSL-CM (biomixing) M24 : Importance du top-down control dans un contexte de CC IPSL-CM  ( PISCES  APECOSM ) En cours Etape 3 M42: Simulations “offline” sur 2000-2100 (biodiversité) IPSL-CM  PISCES-APECOSM-DEB/Biodiv (?) M48: Analyse de ces simulations

  21. Towards coupled climate & end-to-end ecosystem modelling Climatic scenarios: IPSL model • Sensitivity • (acidification ?) E2E model Governance scenarios: 2. Retroactions 3.Fishing scenarios ?

  22. Some issues: spatial resolution, internal variability, model spread Model Spread? : use of CMIP5 models ? Spatial resolution? : towards higher resolution (global) / regional configurations ? Internal variability? Climate simulations: difficult to use for the next decade or so (2010-2030) as internal variability tends to dominate on these time-scales ?

  23. Some issues: spatial resolution, internal variability, model spread Model Spread? Spatial resolution? Internal variability? PP in North Atlantic simulated by IPSL-PISCES • Some decadal predictions • with climate models in IPCC-AR5 Decadaly-smoothed control run (over 2000-2030, with initialization procedure) • Do models have some previsibility • skills for marine productivity • evolution? 10 members Ensemble mean 50 ans Séférian et al. in prep

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