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

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Presentation on theme: "WP 4: Climate Change and Ocean Acidification 2nd Annual Meeting Paris, 14-16 May 2012 MACROES."— Presentation transcript:

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

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: Impact of CC and OA on marine ecosystems: end-to-end Retroactions in the coupled system - Top-down control from higher to lower trophic levels - Biophysical coupling through heat trapping and bio-induced turbulence 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 Climate Change impact on surface chlorophyll RCP8.5 RCP6.0 RCP4.5 RCP2.6 Historical T (°C) Chl de surface (mgChl/m3) Premiers Résultats avec CM5 CO 2, T et chlorophylle de surface

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

6 Biogeochemical Drivers Changes in Net Primary Productivity driven by climate change Net Primary Productivity as simulated by 8 CMIP5 models IPSL-CM5A-LRIPSL-CM5A-MRMIROC-ESM-CHEM MIROC-ESMHadGEM2-ESHadGEM2-CC MPI-ESM CanESM2 IPSL-CM5 IPSL-CM5 Biogéochimie Marine : Séférian et al. in press Comparaison des modèles IPCC – CMIP5 / Productivité marine :Kidston et al. in prep

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

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

11 Biogeochemical Drivers Changes in pH / Ocean Acidification RCP4.5 RCP8.5 Aragonite / Calcite undersaturation reached at the surface in polar oceans Implications on calcification / trophic food webs? [CO 3 2- ]

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 Stramma et al Observed increase of hypoxic waters in the Eq. Pacific

15 O2 ( mol/L) Changes in [O 2 ] (micromol/L) (5-model mean, SRES-A2) : 0 m Biogeochemical Drivers Changes in Oxygen / Desoxygenation Large decrease of O 2 in surface waters: solubility-driven Hatched regions: when >75% of the models agree on the sign of change (IPSL-CM4, UVIC, CSM1.4, CCSM3, BCM-C)

16 O2 ( mol/L) Changes in [O 2 ] (micromol/L) (5-model mean, SRES-A2) : 200 m Biogeochemical Drivers Changes in Oxygen / Desoxygenation Consistent at mid/high lat but models do not agree in the tropics ! Hatched regions: when >75% of the models agree on the sign of change

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 changeDiatoms relative change Microzooplankton relative changeMesozooplankton relative change 15% drop of total biomass in 2100 compared to preindustrial values Large disparity among plankton functional types: Phyto : -8%, Diatoms : -16%, Microzoo : -20%, Mesozoo : -20%. Latitude Time (1850 to 2100) LOWER TROPHIC

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

20 Etapes / Stratégie pour le WP4 End-to-End Etape 1 M12 : Simulations offline sur (RCP8.5) IPSL-CM ( PISCES APECOSM ) M18 : Analyse de limpact 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 ) Etape 3 M42: Simulations offline sur (biodiversité) IPSL-CM PISCES-APECOSM-DEB/Biodiv (?) M48: Analyse de ces simulations En cours

21 Climatic scenarios: Climatic scenarios: Governance scenarios: IPSL model 3.Fishing scenarios ? E2E model 2. Retroactions 1.Sensitivity (acidification ?) Towards coupled climate & end-to-end ecosystem modelling

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 ( ) 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? 10 members Ensemble mean Decadaly-smoothed control run 50 ans Séférian et al. in prep -Some decadal predictions with climate models in IPCC-AR5 (over , with initialization procedure) -Do models have some previsibility skills for marine productivity evolution? PP in North Atlantic simulated by IPSL-PISCES


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