Transcript climate change

MACROES
2nd Annual Meeting
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
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
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…
Premiers Résultats avec CM5
850
650
Climate
Change
impact
on surface
chlorophyll
450
250
6.0
3.0
CO2, DT
et chlorophylle de surface
RCP8.5
RCP6.0
RCP4.5
RCP2.6
Historical
DT (°C)
0.0
0.19
0.17
0.15
Chl de surface
(mgChl/m3)
Biogeochemical Drivers
•
Changes in Net Primary Productivity driven by climate change
Biogeochemical Drivers
•
Changes in Net Primary Productivity driven by climate change
Net Primary Productivity as simulated by 8 CMIP5 models
MPI-ESM
MIROC-ESM
IPSL-CM5
IPSL-CM5A-LR
IPSL-CM5A-MR
MIROC-ESM-CHEM
HadGEM2-CC
CanESM2
HadGEM2-ES
IPSL-CM5 Biogéochimie Marine : Séférian et al. in press
Comparaison des modèles IPCC – CMIP5 / Productivité marine :Kidston et al. in prep
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
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
Biogeochemical Drivers
•
Changes in pH / Ocean Acidification
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
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?
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)
Biogeochemical Drivers
•
Changes in Oxygen / Desoxygenation
Biogeochemical Drivers
•
Changes in Oxygen / Desoxygenation
Observed increase
of hypoxic waters
in the Eq. Pacific
Stramma et al. 2008
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
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 !
Towards coupled climate & end-to-end ecosystem modelling
Towards Online Coupling: PISCES-APECOSM
Towards coupled climate & end-to-end ecosystem modelling
PISCES-APECOSM::Preliminary RCP85 results
Diatoms relative change
Latitude
Nanophytoplankton relative change
(see talk by S. Dueri for more details)
Time
(1850 to 2100)
Microzooplankton relative change
Mesozooplankton relative change
LOWER
TROPHIC
15% drop of total biomass in 2100 Large disparity among plankton functional
compared to preindustrial values types:
Phyto : -8%, Diatoms : -16%,
Microzoo : -20%, Mesozoo : -20%.
Towards coupled climate & end-to-end ecosystem modelling
PISCES-APECOSM::Preliminary RCP85 results
Epipelagic biomass relative change
Latitude
Total biomass relative change
Time
(1850 to 2100)
Migratory biomass relative change
Mesopelagic relative change
UPPER
TROPHIC
23% drop of total biomass in 2100
compared to preindustrial values
Large disparity among communities:
Epipelagic : -22%, Migratory : -8%,
Mesopelagic : -30%
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
En cours
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 2000-2100 (biodiversité)
IPSL-CM  PISCES-APECOSM-DEB/Biodiv (?)
M48: Analyse de ces simulations
Towards coupled climate & end-to-end ecosystem modelling
Climatic scenarios:
IPSL model
E2E model
Governance scenarios:
1. Sensitivity
(acidification ?)
2. Retroactions
3.Fishing scenarios ?
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
?
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
Séférian et al. in prep
50 ans