Transcript Laurent Bopp, Overview on future scenarios

Core Theme 5 – WP 17
Overview on Future Scenarios
Laurent Bopp, IPSL/LSCE, Gif, France
On behalf of the WP17 members
- 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)
W17 CARBON –CLIMATE COUPLING
Results from C4MIP (Friedlingstein et al. 2006)
- 11 Climate-Carbon Coupled Models
(7 GCMs + 4 EMICs)
- Same emissions scenario from 1860 to 2100
- 2 simulations each : Uncoupled + Coupled
W17 CARBON –CLIMATE COUPLING
Results from C4MIP
Feedback Analysis : g = – a (gL + gO ) / (1 + bL + bO)
a Climate sensitivity to CO2
b Ocean and Land carbon sensitivity to atmospheric CO2
g Ocean and Land carbon sensitivity to climate
b ocean
g ocean
Part I.
More constrains on the bo 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 go 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 bo term
[ Uptake / Atm. pCO2 ]
Use of anthropogenic DIC estimates
Use of other tracers (CFC…)
Mechanisms that explain the models divergence
bo (PgC/ppm)
IPSLCM2C
IPSL-CM4
MPIM
UNIBE/NCAR
HADLEY
FRCGC
1.6
1.1
1.1
0.9
0.8
1.2
Anthropogenic DIC
Waugh et al. 2006
Sabine et al. 2004
Anthropogenic DIC : Regional Scale
IPSL
GLODAP (Sabine et al. 2004)
96.5 GtC
(Cadule et al.)
106 +/- 17 GtC
119 +/- 17 GtC
CFC : Learn from OCMIP
Use other tracers observations to get higher constrain on modelled carbon uptake
Matsumoto et al. 2004, Orr et al. 2003
CFC11 in the IPSL coupled model
Run offline with the same
circulation fields than the
coupled run
CFC11 inventories (pmol/m2)
IPSL Model
GLODAP
Mechanisms that explain the models divergence
Ocean Cant uptake ↔ Volume of light water
Mechanisms that explain the models divergence
Mixed Layer Depth (m)
Ocean Cant uptake (beta factor) ↔ Volume of light water
300
250
y = 258.28x - 194.37
R2 = 0.8747
200
IPSL-CM2C
IPSL-CM4
MPI
UNIBE/CSSM1
HADLEY
FRCGC
150
100
50
0
0
0.5
1
Beta (PgC/ppm)
1.5
2
Mixed Layer Depth (m)
Mechanisms that explain the models divergence
250
200
IPSL-CM2C
IPSL-CM4
MPI
UNIBE/CSSM1
HADLEY
FRCGC
150
100
50
0
0.7
0.9
1.1
1.3
1.5
1.7
Beta (PgC/ppm)
From Climatology of Mixed Layer Depth :
(Boyer de Montegut et al. 2004)
bo = 0.93-0.95
Part II. More constrains on the go term
[ Uptake / Climate change ]
Mechanisms that explain the models divergence
Sensivity experiments to explore the mechanisms
Constrains from observations ?
go (PgC / °C)
IPSLCM2C
IPSL-CM4
MPIM
UNIBE/NCAR
HADLEY
FRCGC
-30
-16
-22
-17
-24
-46
DSST (°C)
Mechanisms that explain the models divergence
Mechanisms :
-Increasing Sea Surface Temperature
decreases CO2 solubility
- Decreased Mixing prevents
the penetration of C ant.
DMXL (m)
- Decrease in Biological Production
reduces the amount of carbon
transported to depth.
…..
D THC (Sv)
D O.M export (PgC/y)
3
IPSL-CM2C
IPSL-CM4
0
-1
-2
-3
-4
-5
-6
HADLEY
MPIIPSL-CM2C
IPSL-CM2C
IPSL-CM4
FRCGC
UNIBE/CSSM1
-10
-20
IPSL-CM4
MPI
HADLEY
FRCGC UNIBE/CSSM1
HADLEY
FRCGC
MPI
FRCGC
-30
-40
-7
-30
-8
-40
-9
-50
-50
MPI IPSL-CM2C
UNIBE/CSSM1
IPSL-CM4
0
Mixed Layer Depth (m)
THC (Sv)
0
-0.2
2
-0.4
1.5 -0.6
-0.8
1
-1
-1.2
0.5
-1.4
0 -1.6
-50-1.8
-2
Export (PgC/yr)
SST (°C)
2.5
-20
-10
Gamma (PgC/°C)
-40
-30
-20
-10
0
Gamma (PgC/°C)
-10
-50 -60
0
-40
-50
-30
-20
-40 (PgC/°C)
-30
Gamma
-10
0
-20
-10
Gamma (PgC/°C)
No clear global relationship : Need to break down the responses at regional levels.
Sensitivity experiment : Role of THC
3 simulations with the same Coupled GCM (1 Control and 2 scenarios)
CTL
THC (Sv)
GW1
GW2
CTL : Control – No Climate Change
GW1 : 1xCO2 > 4xCO2 – No additional ice melting in the North
GW2 : 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)
CTL
THC (Sv)
GW1
GW2
Swingedouw et al. subm.
Cumulative
Carbon
Uptake (GtC)
CTL > GW1 = GW2
THC-related SST and SSS effects counter-balance the dynamical effect
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
Matear et al. 2000
(1995-1968)
- Interannual / decadal variability of atmospheric APO signal.
… relation bewteen g and the modelled O2 outgassing ?
… observed trends in APO ?
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, Kim
South Pacific : Shaffer
South Indian : Bindoff and McDougall
North Atlantic : Garcia
Southern 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. 2005
90s-80s
North Pacific
D17.2 CARBON –CLIMATE COUPLING
New input from CARBOOCEAN
Atmospheric CO2 Difference (ppm)
. 5 new coupled climate-carbon coupled GCMs
. some groups have already completed their runs
MPI : + 83 ppm
IPSL : +32 ppm
D17.2 CARBON –CLIMATE COUPLING
New input from CARBOOCEAN : Oceanic Focus
b ocean
MPI
IPSL
g ocean
- Comparison to observations when possible ?
- Comparison of models to determine major mechanisms
Oceanic Focus
b ocean
g ocean
- Comparison of models to determine major mechanisms of
climate impact
- Comparison to observations (validation, benchmarking, …)