Download WP4.2 - NCAS

ENSEMBLES
RT4-RT5 meeting
10-11 February ‘05, Paris
RT4, WP4.2: Mechanisms of regional-scale
climate change and the impact of climate
change on natural climate variability
Participants: CERFACS, CNRM, IfM, ICTP, INGV,
MPIMET, NERSC, UREADMM
Leader: INGV
Objective: to determine the impact of climate change on
climate variability, and to investigate the
mechanisms that govern regional patterns of
climate change, including ocean heat uptake
INGV
Scope:
advance understanding of the mechanisms that govern modes of natural climate
variability and regional characteristics of climate change.
In order to quantify and predict changes in climate regimes as a result of an
external forcing (e.g., GHG), it is necessary to understand the processes that
determine the natural, internal, variability of the system, and then to assess how
these may be modified by the effects of the external forcings.
The analysis will be performed on both existing climate simulations and on
simulations performed with the ENSEMBLES models. Results with the different
models will be compared and evaluated by comparison with analyses and
observational data.
Coordinated sensitivity experiments will be conducted to identify causal
mechanisms and to explore the role of coupling between different components of
the Earth System.
Synergies with RT5 (WP5.2) and EU FP6 DYNAMITE will be exploited.
INGV
60-Month Scientific Plan
Description of work:
Task 4.2a: analysis of the mechanisms involved in modes of
natural climate variability
[CERFACS, CNRM, ICTP, IfM, INGV, NERSC, UREADMM]
Task 4.2b: assessment of the sensitivity of natural (internal)
modes of climate variability to changes in the
external forcings
[CERFACS, CNRM, ICTP, IfM, INGV, MPIMET, NERSC, UREADMM]
Task 4.2c: regional climate change, the mechanisms of
ocean heat uptake and sea level change
[CNRM, NERSC, UREADMM]
INGV
60-Month Scientific Plan
Deliverables:
D4.2a:
characterization of the modes of natural climate variability and
analysis of the physical mechanisms underlying these modes and their
interaction
papers addressing: tropical and extra-tropical modes of variability in
ENSEMBLES models
D4.2b:
improved understanding of the relationship between the mean climate
and climate variability
papers addressing:
reliability and significance of regime statistics;
impacts on the modes of natural variability
induced by changes in the mean climate produced
by GHG forcing;
INGV
impacts on natural climate variability induced by
the 11-year solar cycle
60-Month Scientific Plan
Deliverables:
D4.2c:
improved understanding of the processes that influence regional
patterns of climate variability and change
papers addressing:
regional and large-scale changes in surface
climate;
physical processes determining the
characteristics of regional climate change;
geographical patterns of sea-level rise.
INGV
60-Month Scientific Plan
Milestones:
M4.2.1: development of methodologies to explore climate variability, tested
initially on existing simulations (Month 18)
M4.2.2: design and commence of a set of coordinated time-slice experiments designed to
explore the sensitivity of climate, and its modes of variability, to specific forcings
(e.g., GHG) and model formulation (e.g., resolution, components …) (Month 18)
M4.2.3: preliminary analysis of principal modes of climate variability in the ENSEMBLES
control integrations (Month 30)
M4.2.4: assessment of the model characteristics that determine the amplitude and
periodicity of ENSO by exploiting the modularity of the ENSEMBLES models
enabled by the PRISM infrastructure (Month 36)
M4.2.5: preliminary assessment of impacts of GHG forcing on principal modes of climate
variability in the ENSEMBLES climate change scenarios (Month 48)
M4.2.6: assessment of the impact of climate change on climate variability and of the
mechanisms that govern regional patterns of climate change, including ocean heat
uptake (Month 60)
INGV
Detailed Implementation Plan – first 18 months
Objective and scope:
Study the mechanisms to assess the regional features of climate change, including
changes that may result from a modification of the patterns of natural variability.
In collaboration with RT5, research will be carried out to advance understanding
of the mechanisms that govern modes of natural climate variability.
The characteristics of global and regional modes will be analysed in climate models,
and the relationships between modes of large-scale, low frequency variability and
variability on shorter time and space scales will be investigated.
Results from the different models will be compared, and will be evaluated by
comparison with analyses of observational data.
In order to better understand the ocean’s response to anthropogenic forcing,
research will also be conducted to investigate the processes that govern the ocean
uptake of heat.
INGV
Detailed Implementation Plan – first 18 months
Deliverables:
D4.2.1: Characterisation of modes of large scale, low frequency climate
variability in existing climate model control simulations (Month 18)
D4.2.2: Assessment of climate variability in existing simulations to provide
benchmark against which the new ENSEMBLES multi-model system
can be judged (RT5) (Month 18)
Milestones:
M4.2.1: Development of methodologies to explore climate variability and
predictability, as well as climate feedbacks, tested initially on
existing simulations (Month 18)
M4.2.2: Commence a set of co-ordinated time-slice experiments designed
to explore the sensitivity of climate and its modes of variability to
specific forcings (e.g., GHG) and model formulation (e.g., resolution,
components ...) (Month 18)
INGV
CERFACS contribution to WP4.2 – 18 Month Plan
L. Terray, C. Cassou, C. Caminade (2 P-months)
 study the low-frequency variability of the meridional overturning
circulation (MOC) in coupled integrations performed within EU
PREDICATE. The analysis focuses on the potential interaction
between the tropical Atlantic variability (TAV), MOC and modes of
the North Atlantic/European sector
 explore the influence of ocean basins (especially the Indian Ocean)
on low-frequency extra-tropical atmospheric variability using the
ARPEGE and ARPEGE/OPA Climate GCM
Results will be provided for D4.2.1, D4.2.2 and M4.2.1
o Preliminary steps: two 20-year AGCM simulations forced with climatological
Indian Ocean SST for the [1950-1976] and [1977-2001]
periods (ERSST2) (with climatological 1950-2001 SST
elsewhere)
INGV
CERFACS contribution to WP4.2
Influence of the Indian Ocean (IO) on extra-tropical LFV
IO SST index [30S-20N; 45E-110E]
with the 1976 shift
20-year SST-forced
AGCM exp. with IO
Clim SST [1950-1976]
and [1977-2001]
MSLP diff.
IA - IB
INGV
DJF
HN
JJA
HS
CNRM contribution to WP4.2 – 18 Month Plan
D. Salas, H. Douville
(3 P-months)
 explore the influence of soil moisture and/or snow mass
on natural climate variability.
 explore the influence of soil moisture and/or snow
feedbacks on climate sensitivity
 use existing coupled simulations (CNRM ESM) to identify
key coupled processes shaping the natural variability in
the Arctic, focusing on the sea-ice feedbacks on the
regional climate
Results will be provided for D4.2.1, D4.2.2 and M4.2.1
INGV
CNRM contribution to WP4.2
Influence of soil moisture on climate variability
Impacts of the relaxation towards GSWP-1 on the JJAS Z500
stationary eddy anomalies simulated by the ARPEGE AGCM
Observed
anomalies
Free
Soil
moisture
Relaxed
Soil
moisture
INGV
Douville & Chauvin (2000), Climate Dyn.,16,719-736; Douville H. (2OO2), J.Climate,15,701-720
CNRM contribution to WP4.2
Influence of soil moisture on climate variability
Preliminary steps:
• produce a 10-yr global monthly mean land surface
climatology using the 3-hourly atmospheric
forcing provided by GSWP-2
• run ensembles of global atmospheric simulations
(prescribed observed SSTs from 1986 to 1995)
with GSWP-2 vs interactive land surface
boundary conditions (role of initial conditions is
explored in WP4.4)
INGV
CNRM contribution to WP4.2
Influence of soil moisture on climate sensitivity
Impacts of soil
moisture feedbacks
on JJAS surface
air temperature
anomalies simulated
with ARPEGE AGCM
in pairs of time-slice
experiments for
1950-1999 and
2050-2099
respectively
INGV
Interactive
soil
moisture
No soil
moisture
feedbacks
CNRM contribution to WP4.2
Influence of soil moisture on climate sensitivity
Preliminary steps:
• Method 1: run time-slice experiments with
future SSTs and radiative forcing, but with
present-day soil moisture and/or snow mass
boundary conditions
• OR Method 2 : rerun a transient coupled
scenario with climatological present-day soil
moisture and/or snow mass boundary
conditions.
(Could it be a coordinated experiment ?)
INGV
CNRM contribution to WP4.2
focus on sea ice feedbacks)
•
Available data: from RT2A. CNRM’s IPCC simulations, others
welcome ! (region of interest: the Arctic)
•
Preliminary results from the simulations & observations:
- 20th century simulations+current observations: decreasing
amount of multiyear sea ice
- 21st century simulations: negative trend confirmed
- Sea ice becomes seasonal after 2080 in the « warmest
scenario » (A2), after 2100 for B1
•
Questions (focused on sea ice-atm feedbacks): variability of sea ice
in transient climate change simulations + stabilizations: correlation
with atmospheric patterns (T2M, SLP, surface inc. SW); surface
ocean thermal preconditioning; role of ice compaction due to
redistribution vs thermo
•
Suggested experiments: for selected years (large ice anomalies),
take surface ice+SST boundary conditions and run forced AGCM
experiments
ICTP contribution to WP4.2 – 18 Month Plan
F. Molteni
(6 P-months)
 produce large ensembles of multi-decadal currentclimate simulations performed with an intermediatecomplexity ESM (SPEEDY-MICOM)
 assess the statistical significance of trends and
interdecadal variations in ENSO, teleconnections
and flow-regimes.
Results will be provided for D4.2.1 and M4.2.1
• Preliminary steps: production of an ensemble of 10-member 50-yr
simulations performed with SPEEDY_8lev coupled with MICOM2.9 in
the Indian Ocean and SPEEDY_8lev forced with HadISST elsewhere
INGV
ICTP contribution to WP4.2
Relationship between ENSO and Indian Ocean Dipole in AO-GCM ensembles
10-member 50-yr ensemble :
SPEEDY_8lev + MICOM2.9
in Indian Ocean,
SPEEDY_8lev + HadISST
elsewhere
Regres.
JJA precip
vs. Nino3.4
Regres.
JJA precip
vs. IOD
ICTP contribution to WP4.2
Decadal-scale interactions between the Indo-Pacific
ocean and NH extratropical variability
Regression of HadISST onto
11-yr-mean NAO index
Nino3.4 index in
SPEEDY_8lev + MICOM 2.9 in the
Indo-Pacific ocean (60N-30S)
Green : direct coupling (no correction)
Black : SST-anomaly coupling
IfM contribution to WP4.2 - 18 Month Plan
N. Keenlyside, M. Latif
(6 P-months)
 investigate the mechanisms of climate variability
from seasonal to centennial timescales.
 estimate the space-time structure of the climate
variability, assessing the role of tropics-extratropics
teleconnections and interactions of different basins.
Special emphasis is given as to whether global modes
exist, in which all ocean basins are involved.
Results will be provided for D4.2.1, D4.2.2 and M4.2.1
Preliminary steps:
• Analysis of the causes of North Pacific and North Atlantic variability
and its interaction with the tropical oceans. The analysis is performed
using an existing 2000-year coupled simulation and partially coupled
INGV runs.
IfM contribution to WP4.2
interaction between North Atlantic and North Pacific
low-frequency variability and tropical oceans
ratio of SST
standard deviation
between
Partially coupled
and
Fully coupled
runs
INGV
Climatol. SST
Strengthened
variability
Climatol.
SST
V. Semenov (IfM)
INGV contribution to WP4.2 - 18 Month Plan
S. Gualdi, A. Navarra, A. Cherchi, A. Bellucci (6 P-months)
 analyse the interactions between interannual and decadal
variability in the Indo-Pacific region in present-day climate
simulations performed with a coupled model
 perform sensitivity experiments to investigate the modulation of
the interannual variability induced by the low-frequency modes of
variability in the Indo-Pacific.
Results will be provided for D4.2.1, D4.2.2, M4.2.1 and M4.2.2
Preliminary steps:
• Analysis of the impacts of the air-sea feedbacks on the simulation of
the Indian Summer Monsoon. Comparison of Amip-type and fully
coupled simulations.
• Analysis of the impacts of the atmospheric resolution on the
simulation of the ENSO variability with a coupled GCM.
INGV
INGV contribution to WP4.2
Impacts of interactive SSTs on the simulation of the Indian Summer Monsoon
Composites of JJA SST anomalies (deg C) (strong – week monsoon years)
obs &
re-analysis
Amip-type run
coupled run
INGV
INGV contribution to WP4.2
Impacts of atmospheric resolution on the ENSO variability simulation
Lagged Regression of Heat Content on NINO-3 SSTA
Monthly means
NINO3 leads
T30
T106
#
Analysis
LAG 0
LAG 3m
LAG 6m
#
ODA, Masina et al, 2004
MPIMETcontribution to WP4.2 - 18 Month Plan
M. Giorgetta, H. Schmidt
(0 P-months)
 explore the effects of the 11-year solar cycle on the
atmosphere using simulations performed with the
HAMMONIA GCM coupled with chemistry and resolving
the atmosphere from the lower thermosphere (~250Km)
to the surface.
Results will be provided for D4.2.1
Preliminary steps:
• Interpretation of existing time slice experiments for solar maximum
and minimum conditions, focusing on the effects on the stratosphere
• Develop a version of HAMMONIA, with higher vertical resolution,
able to simulated the QBO. This model will allow to investigate the
interaction between QBO and solar cycle.
INGV
INGV
~ 80 km
~ 30 km
Ion Drag
Molecular Processes
Gravity Wave Drag
Chemical heating
IR Cooling (non-LTE)
MOZART3 Gas Phase Chemistry
Turbulent Diffusion
Clouds & Convection
Surface Fluxes
IR Cooling
~ 250 km
Solar Heating (SRB&C, Ly-a, EUV)
Solar Heating (near UV, vis. & near IR)
ECHAM
MAECHAM
HAMMONIA
MPIMET contribution to WP4.2
HAMMONIA – Hamburg Model of the Neutral and Ionized Atmosphere
(Schmidt et al., J. Climate, submitted, 2004)
MPIMET contribution to WP4.2
Solar cycle effect on wintertime zonal wind (solar max-solar min) – Northern hemisphere
NCEP analyses,
(Kodera and Kuroda, JGR, 2002)
INGV
HAMMONIA (10-year mean)
NERSC contribution to WP4.2 - 18 Month Plan
H. Drange, Y. Gao, I. Bethke (2 P-months)
 investigate the processes responsible for the ocean heatuptake, with special emphasis on the convective-type of
sinking at high-latitudes, subduction at mid-latitudes and
mixing at low-latitudes, and the subsequent propagation
and mixing of the absorbed heat.
Results will be provided for D42.1, D4.2.2 and M4.2.1
Preliminary steps:
• Analysis of the ocean heat-uptake in an existing 300-year currentclimate simulation performed with the Bergen Climate Model (BCM)
INGV
NERSC contribution to WP4.2
INGV
NERSC contribution to WP4.2
INGV
NERSC contribution to WP4.2
INGV
NERSC contribution to WP4.2
INGV
CGAM contribution to WP4.2 - 18 Month Plan
J. Slingo, E. Guilyardi, R. Sutton, B. Dong, J. Gregory, A. Turner (4 P-months)
 design and set up of coordinated time-slice experiments
presentation)
(see Rowan’s
 explore the factors that influence land-sea temperature contrast by
analysing existing climate change integrations
 develop methodologies for identifying processes in coupled models
that influence El Nino behaviour, such as coupling strength
Results will be provided for D4.2.1, D4.2.2 and M4.2.1
Preliminary steps:
•
INGV
analysis of the impact of model bias on ENSO variability and its
teleconnections with the monsoon.
UREADMM contribution to WP4.2
Impact of flux correction
INGV
Turner et al. 2004: QJRMS, in press
UREADMM contribution to WP4.2
Impact on Nino3 Power Spectrum
INGV
UREADMM contribution to WP4.2
Stronger ENSO variability
associated with stronger
stochastic forcing?
Westerly wind events (WWE)
above the indicated threshold
for longer than 5 days.
WWEs averaged over 150° 180°E, 1.25°N-1.25°S, using 40
years daily data.
INGV
UREADMM contribution to WP4.2
….. and coupling is important for the ENSO-Monsoon teleconnection
INGV
UREADMM contribution to WP4.2
What determines the land/sea contrast in warming?
We have little understanding of what really determines the land/sea
temperature contrast, and this is a critical issue for understanding the
regional patterns of climate change
Multi model ensemble annual mean temperature change for
2071-2100 relative to 1961-1990 under SRES A2 scenario
INGV
Source: IPCC
R.Sutton
summary
All of the partner groups have already started their work
CERFACS, CNRM, ICTP, IfM,
INGV, MPIMET, NERSC, UREADMM
D4.2.1: Characterisation
of modes of large scale,
low frequency climate
variability in existing
climate model control
simulations (Month 18)
INGV
CERFACS, CNRM, IfM,
INGV,, NERSC, UREADMM
D4.2.2: Assessment
of climate variability
in existing simulations
to provide benchmark
against which the new
ENSEMBLES multimodel system can be
judged (RT5) (Month 18)
Evaluation dataset for WP4.2
INGV
Dataset
Short description
Period covered
Use
NCEP/NCAR
Atmospheric
reanalysis
1948-present
Characterisation of modes
of climate variability
ERA-40
Atmospheric
reanalysis
1957-present
Ditto
CRU (Climate Research
Unit)
Precipitation
and
surface temperature
over land
1901-1995
Regional
variability
climate
CMAP
Precipitation
1979-present
Relationship
between
modes of variability and the
hydrological cycle
HadISST
SST
1930-2002
Longer term indices of SST
variability
NOAA AVHRR
Outgoing longwave
radiation
1974-present
Independent information on
convective
anomalies
particularly in the tropics.
EU ENACT
Ocean analyses
1958-2000
Description of the ocean
behaviour associated with
modes of climate variability
SODA
Ocean analyses
1950-1995
Ditto
TOGA-TAO
In
situ
buoy
measurements
in
tropical Pacific
1983-2004
Evolution of El Nino events
in the ocean.
changes
and
in
surface