1. INGV 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

2. 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.

3. 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]

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

5. INGV 60-Month Scientific Plan 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:

6. 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)

7. 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.

8. 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)

9.  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) INGV oPreliminary 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)

10. 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 DJF HN JJA HS IO SST index [30S-20N; 45E-110E] with the 1976 shift CERFACS contribution to WP4.2 INGV

11. 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

12. Influence of soil moisture on climate variability Observed anomalies Free Soil moisture Relaxed Soil moisture Douville & Chauvin (2000), Climate Dyn.,16,719-736; Douville H. (2OO2), J.Climate,15,701-720 CNRM contribution to WP4.2 INGV Impacts of the relaxation towards GSWP-1 on the JJAS Z500 stationary eddy anomalies simulated by the ARPEGE AGCM

13. INGV 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 CNRM contribution to WP4.2

14. Interactive soil moisture No soil moisture feedbacks 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 Influence of soil moisture on climate sensitivity CNRM contribution to WP4.2

15. 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 Influence of soil moisture on climate sensitivity CNRM contribution to WP4.2

16. 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 CNRM contribution to WP4.2

17. INGV 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

18. 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

19. Decadal-scale interactions between the Indo-Pacific ocean 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

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

21. V. Semenov (IfM) INGV IfM contribution to WP4.2 ratio of SST standard deviation between Partially coupled and Fully coupled runs Climatol. SST interaction between North Atlantic and North Pacific low-frequency variability and tropical oceans Climatol. SST Strengthened variability

22. INGV 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.

23. INGV 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 INGV contribution to WP4.2

24.  Lagged Regression of Heat Content on NINO-3 SSTA Monthly means NINO3 leads T30 T106Analysis # LAG 0 LAG 3m LAG 6m INGV contribution to WP4.2 ODA, Masina et al, 2004 # Impacts of atmospheric resolution on the ENSO variability simulation

25. INGV 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.

26. INGV HAMMONIA – Hamburg Model of the Neutral and Ionized Atmosphere ECHAM MAECHAM HAMMONIA ~ 250 km ~ 80 km ~ 30 km Solar Heating (near UV, vis. & near IR) Molecular Processes Solar Heating (SRB&C, Ly-a, EUV) IR Cooling IR Cooling (non-LTE) Chemical heating Gravity Wave Drag Ion Drag Turbulent Diffusion Clouds & Convection Surface Fluxes MOZART3 Gas Phase Chemistry (Schmidt et al., J. Climate, submitted, 2004) MPIMET contribution to WP4.2

27. Solar cycle effect on wintertime zonal wind (solar max-solar min) – Northern hemisphere NCEP analyses, (Kodera and Kuroda, JGR, 2002) HAMMONIA (10-year mean) INGV MPIMET contribution to WP4.2

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

29. INGV NERSC contribution to WP4.2

30. INGV NERSC contribution to WP4.2

31. INGV NERSC contribution to WP4.2

32. INGV NERSC contribution to WP4.2

33. 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 (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 Results will be provided for D4.2.1, D4.2.2 and M4.2.1 Preliminary steps: analysis of the impact of model bias on ENSO variability and its teleconnections with the monsoon.

34. INGV Impact of flux correction Turner et al. 2004: QJRMS, in press UREADMM contribution to WP4.2

35. Impact on Nino3 Power Spectrum INGV UREADMM contribution to WP4.2

36. 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

37. ….. and coupling is important for the ENSO-Monsoon teleconnection INGV UREADMM contribution to WP4.2

38. INGV 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 scenario Source: IPCC R.Sutton UREADMM contribution to WP4.2 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

39. INGV summary 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) D4.2.1: Characterisation of modes of large scale, low frequency climate variability in existing climate 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

40. INGV DatasetShort descriptionPeriod coveredUse NCEP/NCARAtmospheric reanalysis 1948-presentCharacterisation of modes of climate variability ERA-40Atmospheric reanalysis 1957-presentDitto CRU (Climate Research Unit) Precipitation and surface temperature over land 1901-1995Regional changes and variability in surface climate CMAPPrecipitation1979-presentRelationship between modes of variability and the hydrological cycle HadISSTSST1930-2002Longer term indices of SST variability NOAA AVHRROutgoing longwave radiation 1974-presentIndependent information on convective anomalies particularly in the tropics. EU ENACTOcean analyses1958-2000Description of the ocean behaviour associated with modes of climate variability SODAOcean analyses1950-1995Ditto TOGA-TAOIn situ buoy measurements in tropical Pacific 1983-2004Evolution of El Nino events in the ocean. Evaluation dataset for WP4.2