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Core Theme 5 – WP 17
Overview on Future Scenarios
- Update on WP17 work (5 european modelling groups : IPSL, MPIM, Bern, Bergen, Hadley)
- Strong link with Euroceans (FP6-NoE) (WP3.3 Lead : Fortunat Joos) - Ocean part of C4MIP
(Coupled Carbon Cycle - Climate Model Intercomparison Project : an IGBP/AIMES project, Lead : Pierre Friedlingstein)
Laurent Bopp, IPSL/LSCE, Gif, France
On behalf of the WP17 members
Results from C4MIP (Friedlingstein et al. 2006)
W17 CARBON –CLIMATE COUPLING
- 11 Climate-Carbon Coupled Models(7 GCMs + 4 EMICs)
- Same emissions scenario from 1860 to 2100- 2 simulations each : Uncoupled + Coupled
Results from C4MIP
W17 CARBON –CLIMATE COUPLING
Feedback Analysis : g = – (L + O ) / (1 + L + O)
Climate sensitivity to CO2
Ocean and Land carbon sensitivity to atmospheric CO2
Ocean and Land carbon sensitivity to climate
ocean ocean
Part I. More constrains on the term [ Uptake / Atm. pCO2 ]
Use of anthropogenic DIC estimates
Use of other tracers (CFC…)
Mechanisms that explain the models divergence
Part II. More constrains on the term [ Uptake / Climate change ]
Mechanisms that explain the models divergence
Sensivity experiments to explore the mechanisms
Constrains from observations
Part I. More constrains on the term [ Uptake / Atm. pCO2 ]
Use of anthropogenic DIC estimates
Use of other tracers (CFC…)
Mechanisms that explain the models divergence
IPSL-CM2C
IPSL-CM4 MPIM UNIBE/NCAR HADLEY FRCGC
o (PgC/ppm) 1.6 1.1 1.1 0.9 0.8 1.2
Sabine et al. 2004
Anthropogenic DIC
Waugh et al. 2006
Anthropogenic DIC : Regional Scale
IPSL GLODAP (Sabine et al. 2004)
Atl
anti
cP
acif
ic
96.5 GtC106 +/- 17 GtC119 +/- 17 GtC
(Cadule et al.)
Matsumoto et al. 2004, Orr et al. 2003
CFC : Learn from OCMIP
Use other tracers observations to get higher constrain on modelled carbon uptake
CFC11 in the IPSL coupled model
CFC11 inventories (pmol/m2)
IPSL Model
GLODAP
Run offline with the same circulation fields than thecoupled run
Ocean Cant uptake ↔ Volume of light water
Mechanisms that explain the models divergence
y = 258.28x - 194.37
R2 = 0.8747
0
50
100
150
200
250
300
0 0.5 1 1.5 2
Beta (PgC/ppm)
Mix
ed
La
ye
r D
ep
th (
m)
IPSL-CM2C
IPSL-CM4
MPI
UNIBE/CSSM1
HADLEY
FRCGC
Mechanisms that explain the models divergence
Ocean Cant uptake (beta factor) ↔ Volume of light water
0
50
100
150
200
250
0.7 0.9 1.1 1.3 1.5 1.7
Beta (PgC/ppm)
Mix
ed
La
ye
r D
ep
th (
m)
IPSL-CM2C
IPSL-CM4
MPI
UNIBE/CSSM1
HADLEY
FRCGC
From Climatology of Mixed Layer Depth : o = 0.93-0.95
(Boyer de Montegut et al. 2004)
Mechanisms that explain the models divergence
Part II. More constrains on the term [ Uptake / Climate change ]
Mechanisms that explain the models divergence
Sensivity experiments to explore the mechanisms
Constrains from observations ?
IPSL-CM2C
IPSL-CM4 MPIM UNIBE/NCAR HADLEY FRCGC
o (PgC / °C) -30 -16 -22 -17 -24 -46
Mechanisms : -Increasing Sea Surface Temperature decreases CO2 solubility
- Decreased Mixing prevents the penetration of C ant.
- Decrease in Biological Production reduces the amount of carbon transported to depth.
…..
Mechanisms that explain the models divergenceSST (°C)
MXL (m)
THC (Sv)
O.M export (PgC/y)
0
0.5
1
1.5
2
2.5
3
-50 -40 -30 -20 -10 0
Gamma (PgC/°C)
SS
T (
°C)
IPSL-CM2C
IPSL-CM4
MPI
UNIBE/CSSM1
HADLEY
FRCGC
-2-1.8-1.6-1.4-1.2
-1
-0.8-0.6-0.4-0.2
0
-50 -40 -30 -20 -10 0
Gamma (PgC/°C)
Exp
ort
(P
gC
/yr) IPSL-CM2C
IPSL-CM4
MPI
UNIBE/CSSM1
FRCGC
-10
-9
-8
-7
-6
-5
-4
-3
-2
-1
0
-50 -40 -30 -20 -10 0
Gamma (PgC/°C)
TH
C (
Sv)
IPSL-CM2C
IPSL-CM4
MPI
HADLEY
FRCGC
No clear global relationship : Need to break down the responses at regional levels.
-60
-50
-40
-30
-20
-10
0
-50 -40 -30 -20 -10
Gamma (PgC/°C)
Mix
ed L
ayer
Dep
th (
m)
IPSL-CM2C
IPSL-CM4
MPI
UNIBE/CSSM1
HADLEY
FRCGC
3 simulations with the same Coupled GCM (1 Control and 2 scenarios)
THC (Sv)CTL
GW1
GW2
CTL : Control – No Climate ChangeGW1 : 1xCO2 > 4xCO2 – No additional ice melting in the NorthGW2 : 1xCO2 > 4xCO2 – Additional ice melting in the North
Swingedouw et al. subm.
Sensitivity experiment : Role of THC
3 simulations with the same Coupled GCM (1 Control and 2 scenarios)
THC (Sv)CTL
GW1
GW2
CumulativeCarbon Uptake (GtC)
THC-related SST and SSS effects counter-balance the dynamical effect
CTL > GW1 = GW2
Swingedouw et al. subm.
Sensitivity experiment : Role of THC
Constrains from observations on gamma ?
- Interannual / decadal variability of Carbon Fluxes / DIC inventories
: (difficult because model / real years do not match): difficult because of the additional C_ant signal
- Interannual / decadal variability of oceanic O2
- Interannual / decadal variability of atmospheric APO signal.… relation bewteen and the modelled O2 outgassing ?… observed trends in APO ?
Matear et al. 2000(1995-1968)
Motivation : Climate Impact on Carbon Fluxes
How can we test the models ?
Using Oceanic Carbon measurements : difficult because climate effect only a second order impact. First order is the anthopogenic signal
Using Oceanic Oxygen measurements :
impacted by same processes that Carbon : Dyn., Bio. , Thermo. …but no atmospheric signal in the ocean / no chemistry
the O2 oceanic data-base is expanding : several publications have reported changes in O2 concentrations over the last decades and for different basins
North Pacific : Emerson, Ono, Wanatabe, KimSouth Pacific : ShafferSouth Indian : Bindoff and McDougallNorth Atlantic : GarciaSouthern Ocean : Matear…….
Modelling Results
Models compare reasonably well with observed changes
Matear et al. 2000(1995-1968)
Plattner et al. 2002(90s – 70s)
North Pacific
Modelling Results
Models compare reasonably well with observed changes
Deutsch et al. 200590s-80s
North Pacific
New input from CARBOOCEAN
D17.2 CARBON –CLIMATE COUPLING
. 5 new coupled climate-carbon coupled GCMs
. some groups have already completed their runs
Atm
osph
eric
CO
2 D
iffer
ence
(pp
m)
IPSL : +32 ppm
MPI : + 83 ppm
New input from CARBOOCEAN : Oceanic Focus
D17.2 CARBON –CLIMATE COUPLING
ocean ocean
MPIIPSL
- Comparison to observations when possible ?- Comparison of models to determine major mechanisms
Oceanic Focus
ocean ocean
- Comparison of models to determine major mechanisms ofclimate impact
- Comparison to observations (validation, benchmarking, …)