Download WP4.1: Feedbacks and climate surprises (IPSL, HC, CNRM

WP4.1:
Feedbacks and climate surprises
(IPSL, HC, LGGE, CNRM, UCL, NERSC)
WP4.1 has two main objectives
• (a) to quantify the role of different
feedbacks in the Earth system on the
climate predictions uncertainty,
• (b) to investigate the risk of abrupt
climate changes associated to THC.
Tasks
• Task 4.1.a: Analysis and evaluation of the physical
processes involved in the water vapour and cloud
feedbacks in the Tropics
• Task 4.1.b: Quantification of the climate-carbon cycle
feedback, with a specific focus on terrestrial carbon cycle
sensitivity to climate change
• Task 4.1.c: Explore the effects of non-linear feedbacks in
the atmosphere-land-ocean-cryosphere system and the
risks of abrupt climate change/climate surprises
Partners involved
I.
Feedbacks
i.
ii.
II.
CFMIP (HC and IPSL)
C4MIP (HC and IPSL)
Climate surprises
i.
ii.
iii.
iv.
v.
CNRM
IPSL
LGGE
NERSC
UCL
Deliverables
• D4.1.1: Characterisation of the water vapour and cloud
feedbacks in response to anthropogenic forcing.
(Month 18)
Hadley Centre and IPSL
• D4.1.2: Analysis of the results from the first phase of the
Coupled Climate Carbon Cycle Intercomparison project
(C4MIP).
(Month 18)
Hadley Centre and IPSL
Milestones and expected result
• M4.1.1: Development of methodologies to explore
climate feedbacks, tested initially on existing simulations,
for use with the ENSEMBLES multi-model system
(Month 12)
Hadley Centre and IPSL
• M4.1.2: Assessment of feedbacks in existing simulations
to provide benchmark against which the new
ENSEMBLES multi-model system can be judged
(Month 18)
Hadley Centre and IPSL
Analysis of cloud feedbacks: IPSL work (1)
The range of response in the CMIP runs is still very large
Analysis of cloud feedbacks: IPSL work (2)
Cloud radiative properties are
mainly driven by:
- large-scale circulation
- local thermodynamic structure
(e.g. Pierrehumbert 1995, Miller 1997,
Larson et al. 1999, Lindzen et al. 2001)
Analysis of cloud properties as a function of circulation regimes
allows to separate the cloud variations du to circulation changes
from the variations du to thermodynamic or other changes.
We will characterise, for the ENSEMLES models, as a function of
circulation regimes:
● mean cloud properties in current climate
● cloud properties sensitivity in recent climate (inter-annual
variability)
● cloud properties sensitivity with anthropogenic forcings
Analysis of cloud feedbacks: IPSL work (3)
Goal: to relate the cloud sensitivity at inter-annual time scale to climate
change response
CO2: + 1% / year
X : Clouds, CRF …
XCO2 -XCTL
« current climate »
Relationship between X and Temperature :
XCTL(t)XCTL
Analysis of cloud feedbacks: Hadley work (C. Senior)
CFMIP Climate Sensitivity dT2CO2
(2.4-6.1K)
CFMIP CRF response LWSWNet
(Wm-2K-1)
CFMIP Clear response LWSWNet
(Wm-2K-1)
• The range in CFMIP slab model
responses has not reduced since
the TAR.
• Cloud feedback is still the
biggest uncertainty, but clear-sky
feedbacks still make a significant
contribution.
• The magnitude of the positive
SW cloud feedback is the biggest
feedback uncertainty. Further
analysis shows this is mainly
driven by changes in lower level
cloud
Analysis of feedbacks in a multi-model ensemble
Possible future Hadley Centre Work (C. Senior)
• We will continue to analyse the CFMIP models focussing
initially on;
– Developing cleaner feedback separation methods (e.g. approximate
partial radiative perturbation (PRP) method equivalent to Wetherald
& Manabe 1988)
– Use of a range of diagnostic techniques aimed at demonstrating a
relationship of cloud response to climate change with cloud
response to present day variability, e.g. cloud clustering. We aim to
identify the cloud types primarily responsible for the different cloud
feedback between models.
Climate-carbon feedback
C4MIP
• Existing simulations from IPSL and HC
• New simulations with common protocol
• Feedback analysis
– Climate sensitivity
– Carbon cycle sensitivity to climate
– Carbon cycle sensitivity to CO2
– Gain of the C-C feedback
Climate-carbon feedback
C4MIP
Hadley
IPSL
Methodology: feedback analysis (Friedlingstein et al., 2003)
g = a ( g L + g O ) / ( 1 + bL + bO )
Model
a
K/ppm
bL
GtC/ppm
bO
GtC/ppm
gL
GtC/K
gO
GtC/K
Gain
g
Hadley
0.0076
1.54
0.89
-213.1
-22.1
0.40
IPSL
0.0064
1.61
1.63
-98.0
-30.1
0.15
Hadley Centre: Strong, positive carbon cycle feedbacks
•
•
•
•
•
Our coupled Climate-Carbon cycle model
simulates strong, positive feedbacks over 21st
Century
When climate-carbon cycle feedbacks are
included (red line) we see much higher rates of
CO2 increase and climate change.
Extra C comes from terrestrial biosphere
– increased soil respiration (T) greater than
increased growth (CO2)
Soil respiration is a key uncertainty in the size of
the carbon feedback
Perform off-line simulations with different soil
carbon model
– “RothC” – 4-pool soil carbon model
– Forced with climate data from coupled
model simulations
– Compare with original (single pool) soil
carbon model also run off-line
Modelled change in soil C (kg C m-2) between
1860 and 2100
Assumes constant land use.
HadCM3 climate model with land C feedbacks included
IS92a “Business as usual scenario”
HadCM3LC for soil
RothC for soil
Jones et al. GCB (2004)
Changes in global soil carbon amount (GtC)
predicted by RothC and HADCM3LC
RothC
HadCM3
Climate surprises
•
•
•
•
•
CNRM
IPSL
LGGE
NERSC
UCL
The role of salinity in climate response to GHG forcing
Eric Guilyardi, Pascale Braconnot, Didier Swingedouw (LSCE/IPSL)
Paul Williams (CGAM)
Questions:
1.
2.
How can salinity modify the ocean response to GHG forcing ?
Why does the water cycle in coupled GCMs display such a variety of
reponses (i.e. THC in IPCC AR3) ?
Method:
•
•
•
Understand present-day salinity structure in CGCMs (mechanisms,
feedback loops, time and space scales)
Identify processes likely to be perturbed by GHGF
Analyse scenario runs to assess relative importance of previously
identified processes.
THC index
Impact of a globally modified fresh water flux
in the IPSL coupled GCM
R0
EPR0
CTL
EP0
100 years
Swingedouw et al., in preparation
CNRM: Feedbacks and climate surprises
• Region of interest: the Arctic, data: IPCC
• From the simulations (CNRM):
- Few « ice surprises »… except a large positive sea ice
anomaly for ~15 years in the preindustrial experiment
- Increasing river discharge into the Arctic basin during the
21st century
• Questions:
- Triggering / Mechanism of the large ice anomaly ? Relation
with THC ? Atmospheric circulation ?
- Role of increasing Arctic river discharge on surface ocean
and sea ice in transient climate change experiments ?
• Experiments:
- Try to reproduce a large sea ice anomaly after identifying
the underlying mechanism (validation of suggested mech.)
- Sensitivity experiments involving modified river runoff (to
be defined)