INGV RT4, WP4.2: Mechanisms of regional-scale climate change and the impact of climate change on natural climate variability Participants: CERFACS, CNRM,

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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

ENSEMBLES RT4-RT5 meeting 10-11 February ‘05, Paris

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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.

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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]

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60-Month Scientific PlanDeliverables:

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;

impacts on natural climate variability induced by the 11-year solar cycle

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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.

Deliverables:

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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)

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Detailed Implementation Plan – first 18 months

Objective and scope:

Study the mechanisms to assess the regional features of climate change, includingchanges that may result from a modification of the patterns of natural variability.

In collaboration with RT5, research will be carried out to advance understandingof 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 andvariability on shorter time and space scales will be investigated.

Results from the different models will be compared, and will be evaluated bycomparison 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 oceanuptake of heat.

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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)

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

CERFACS contribution to WP4.2 – 18 Month Plan L. Terray, C. Cassou, C. Caminade (2 P-months)

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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)

20-year SST-forced AGCM exp. with IO Clim SST [1950-1976] and [1977-2001]

MSLP diff. IA - IB

Influence of the Indian Ocean (IO) on extra-tropical LFV

DJFHN

JJAHS

IO SST index [30S-20N; 45E-110E]with the 1976 shift

CERFACS contribution to WP4.2

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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


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Influence of soil moisture on climate variability

Observedanomalies

FreeSoil

moisture

RelaxedSoil

moisture

Douville & Chauvin (2000), Climate Dyn.,16,719-736; Douville H. (2OO2), J.Climate,15,701-720

CNRM contribution to WP4.2

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Impacts of the relaxation towards GSWP-1 on the JJAS Z500stationary eddy anomalies simulated by the ARPEGE AGCM

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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)

Influence of soil moisture on climate variability


Interactive

soil moisture

No soil moisture feedback

s

Impacts of soilmoisture

feedbackson JJAS surfaceair temperature

anomalies simulated

with ARPEGE AGCM in pairs of

time-sliceexperiments for

1950-1999 and 2050-2099 respectively

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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 ?)

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Influence of soil moisture on climate sensitivity


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


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ICTP contribution to WP4.2 – 18 Month Plan F. Molteni (6 P-months)

produce large ensembles of multi-decadal current- climate simulations performed with an intermediate- complexity 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

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

ICTP contribution to WP4.2

Regres.JJA precip vs. IOD

Decadal-scale interactions between the Indo-Pacificocean and NH extratropical variability

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

Regression of HadISST onto 11-yr-mean NAO index

ICTP contribution to WP4.2

IfM contribution to WP4.2 - 18 Month Plan N. Keenlyside, M. Latif (6 P-months)

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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.


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 runs.

V. Semenov (IfM) INGV

IfM contribution to WP4.2

ratio of SSTstandard deviation

between Partially coupled

and Fully coupled

runs

Climatol. SST

interaction between North Atlantic and North Pacificlow-frequency variability and tropical oceans

Climatol. SST

Strengthenedvariability

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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.

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Composites of JJA SST anomalies (deg C) (strong – week monsoon years)

Impacts of interactive SSTs on the simulation of the Indian Summer Monsoon

obs &re-analysis

Amip-type run

coupled run

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Lagged Regression of Heat Content on NINO-3 SSTA

Monthly meansNINO3 leads

T30 T106 Analysis#

LAG 0

LAG 3m

LAG 6m

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ODA, Masina et al, 2004#

Impacts of atmospheric resolution on the ENSO variability simulation

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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 D4.2.1Preliminary 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.

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HAMMONIA – Hamburg Model of the Neutral and Ionized AtmosphereEC

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(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)

HAMMONIA (10-year mean)

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MPIMET contribution to WP4.2

NERSC contribution to WP4.2 - 18 Month Plan H. Drange, Y. Gao, I. Bethke (2 P-months)

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investigate the processes responsible for the ocean heat-uptake, 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 current-

climate simulation performed with the Bergen Climate Model (BCM)

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NERSC contribution to WP4.2

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CGAM contribution to WP4.2 - 18 Month PlanJ. Slingo, E. Guilyardi, R. Sutton, B. Dong, J. Gregory, A. Turner (4 P-

months)

design and set up of coordinated time-slice experiments (see Rowan’s presentation)

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


Preliminary steps: • analysis of the impact of model bias on ENSO variability and its

teleconnections with the monsoon.

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Impact of flux correction

Turner et al. 2004: QJRMS, in press

UREADMM contribution to WP4.2

Impact on Nino3 Power Spectrum

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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.

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….. and coupling is important for the ENSO-Monsoon teleconnection

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What determines the land/sea contrast in warming?

Multi model ensemble annual mean temperature change for 2071-2100 relative to 1961-1990 under SRES A2 scenarioSource: IPCC R.Sutto

n


We have little understanding of what really determines the land/seatemperature contrast, and this is a critical issue for understanding theregional patterns of climate change

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summary

D4.2.2: Assessmentof climate variabilityin existing simulationsto provide benchmarkagainst which the newENSEMBLES multi-model system can bejudged (RT5) (Month 18)

D4.2.1: Characterisationof modes of large scale,low frequency climate variability in existingclimate model control simulations (Month 18)

All of the partner groups have already started their work

CERFACS, CNRM, ICTP, IfM, INGV, MPIMET, NERSC, UREADMM

CERFACS, CNRM, IfM, INGV,, NERSC, UREADMM

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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 changes and variability in surface 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.

Evaluation dataset for WP4.2