Diversas técnicas de regionalización del clima para el estudio del cambio climático en Europa

1. Diversas técnicas de regionalización del clima para el estudio del cambio climático en Europa Different methodologies for the study of climate change in Europe at regional scale E. SanchezGomez, S. Somot, M. Déqué, J. Najac, J. Beauvier, P. Quintana-Seguí

2. OUTLINE • Introduction : the need of high resolution data for climate studies over European-Mediterranean region • Dynamical downscaling with ARPEGE-Climat • Application as a high resolution atmospheric forcing on NEMOMED8 • 2. Dynamical downscaling with limited area models (LAMs). • Study of the Mediterranean water and heat budgets with an ensemble of LAMs • 3. Hybrid (statistical+dynamical) Methodology to study the impact of climate change on winds in France

3. To study the Mediterranean climate we need high spatial resolution data Mediterranean orography Wind over the Gulf of Lyon Etesian IPCC, 350km Bora Mistral High spatial resolution is required 150 km (ERA40) 50 km (RCM) Arpege Climat (strectched grid) Ruti et al. 2007, JMS

4. Spectral nudging Large spatial scales (> 125 km) driven by ERA40 Small spatial scales are free Vorticity (6h), Divergence (24h), Temperature (24h), Mean Sea Level Pressure (48h) are relaxed (Humidity free) ARPERA run: 1961-2000 Nudging term Large scale ERA40 ARPEGE Nudging coef. Nudging term 1. Dynamical downscaling of ERA40 with ARPEGE-Climat ARPEGE-Climate • Global Spectral AGCM • Zoom facility available • Mediterranean resolution: 50 km • Can be spectrally driven by ERA40 ARPERA

5. Mer Adriatique Seuil d’Otrante Golfe du Lion Détroit de Sicile Détroit de Gibraltar Mer Egée Mer Ionienne Buffer zone Bassin Levantin Mai 1991 Mai 1994 1. Dynamical downscaling of ERA40 with ARPEGE-Climat Using ARPERAto perform an « hindcast » for the Mediterranean Sea • NEMO-MED8 driven by ARPERA • Simulation hindcast 1960-2001 NEMOMED8 (Madec, 2008) - New version of OPAMED8 with some changes in the physics (partial step, free surface etc) • - 1/8° horizontal resolution, 43 vertical levels Density at 1550 m (Med Est)‏ montly volume of dense water (m3) Good results: Correct simulation of the EMT (1992-1993)

6. LS91 1. Dynamical downscaling of ERA40 with ARPEGE-Climat Deep water formation in the Gulf of Lyon Heat Flux (W/ m2) 1986-1987 Daily maximum mixed layer depth (m) ARPERAERA Dec Jan Feb Mar Apr Quantile-Quantile plot MED-ERA MED-ARPERA ARPERA heat flux Dec Jan Feb Mar Apr ERA heat flux Herrmann and Somot, GRL, 2008

7. 2. Dynamical downscaling with LAMs RCMs: FP6 ENSEMBLES project Domaine spatial RCM High resolution database to study the impact of global warming on Europe and its uncertainties 12 LAMs Spatial resolution : 25 km Two numerical experiments: ERA40 et GCM Some pararel studies by using LAMs models • Internal variability of LAMs (Laprise et al., 2008, Luchas-Picher et al., 2008a,2008b, SanchezGomez et al. 2008) associated to large-scale circulation. • Ability of LAMs to simulate the water and heat budgets on the Mediterranean Sea.

8. 2. Dynamical downscaling with LAMs Internal variability of LAMs LAMs may provide different solutions within the domain, even forced by the same LBCs. This variability is often called the internal variability of RCMs and can be determined by the spread among the members in an ensemble of simulations driven by identical LBCs with the same LAM. Spread among 10 members normalized by the transient variabillity for Z500, for ALADIN-Climat (50km) The spatial structure de RIV depends on the large scale situation (weather regimes) Composites of RIV according to North Atlantic Weather Regimes in winter Blocking At. Ridge NAO+ NAO+

9. 2. Dynamical downscaling with LAMs Ability of LAMs to simulate the Mediterranean water budget From Mariotti et al. 2002 Mediterranean Sea water content: dM/dt = G + BLK + R –D GW = net water transport from Gibraltar BLK = water input from Black Sea R = river discharge D  E – P (on annual mean basis) atmo D P E sea R GW M BLK Long term mean : dM/dt  0E – (P + R +BLK)  GW Water Budget Estimates WB: 520 – 950 mm/yr (observations) WB: 391 – 524 mm/yr (NCEP, ERA) GW: 630 –1135 mm/yr Castellari et al. 1994, Gilman and Garret 1994, Bethoux and Gentili 1999, Boukthir and Barnier 2000, Mariotti et al. 2002, Bryden and Kinder (1991), Bryden (1994) et Garret (1996), Tsimplis et Bryden (2000), Candela (2001), Baschek et al. (2001), Garcia de La Fuente et al. (2007 )

10. LIW AW WMDW EMDW 2. Dynamical downscaling with LAMs Motivation • The water budgetinfluence the water mass characteristics(density, salinity),and hence the Mediterranean thermohaline circulation. • Changes in the water budget can impact the Atlantic thermohaline circulation through the Mediterranean Outflow Water transport at Gibraltar. • Changes in the water budget may influence the amount of moisture thatflows into Europe, northeast Africa and the Middle East. • Validation of LAMs. High resolution atmospheric forcing for a ocean model of the Mediterranean Sea. MOW

11. 2. Dynamical downscaling with LAMs Area-averaged Water budget on the Mediterranean Sea WB1 -> Combination GPCP + OAFlux WB2 -> HOAPS km3/yr Net mass transport estimates of G : (1577 – 2838) km3/yr The LAMs and WB2 provide water budgest estimates consistent with G estimates. SanchezGomez et al., in preparation

12. 2. Dynamical downscaling with LAMs Water budget changes over the EBRO river catchment Precipitation Evaporation Quintana-Segui et al. 2008

13. 2. Dynamical downscaling with LAMs Ability of LAMs to simulate the Mediterranean Heat budget dHC/dt= GH + HF GH:Net heat transport at Gibraltar HF:SW+LW+LH+SH Long terme: dHC/dT=0 SW+LW+LH+SH  GH Short wave absorbed by water (SW) Net surface emission, Long wave (LW) Latent Heat (LH) Sensible Heat (SH) GH Mediterranean Sea Heat budget estimates GH: (-3 ,– 8.5) W/m2 (mooring based) HB : +6 W/m2 (S. Josey, observations) HB : –5 W/m2 (E. Tragou, observations) HB : -3. 9 W/m2 (S. Somot, coupled model)

14. 2. Dynamical downscaling with LAMs ERA40 forced runs : GCM forced runs : ECHAM5 BCM HadCM3 ECHAM5 ARPEGE HadCM3 ECHAM5 ECHAM5 HadCM3 CGCM3 Heat gain Heat loss Heat budget estimates GH: (-3 ,– 8.5) W/m2 (mooring based) HB : +6 W/m2 (S. Josey, observations) HB : –5 W/m2 (E. Tragou, observations) HB : -3. 9 W/m2 (S. Somot, coupled model)

15. 2. Dynamical downscaling with LAMs: Climatic Change 17%, +208mm/yr +21% 94mm/yr 20 years mean +1.94std 90% -33% -148mm/yr -1.94std -7%, -94mm/yr Ensemble mean: +4.5%, +57mm/yr Ensemble mean: -6%, -27mm/yr +61% 60mm/yr +22%, +22 mm/yr -85% -82mm/yr -42%, -40 mm/yr Ensemble mean: -10%, -9 mm/yr Ensemble mean: -11%, -11mm/yr

16. 2. Dynamical downscaling with LAMs: Climatic Change +30%, +376 mm/yr Ensemble : +13%, +164mm/yrmean -4%, -47 mm/yr More statistically significant changes in hydrological variables from 2050… +3%, +15 mm/yr Ensemble mean: -16%, -73 mm/yr -35%,-160 mm/yr

17. 2. Dynamical downscaling with LAMs: Climatic Change +445 mm/yr, +55 % +28 mm/yr, +3% Ensemble mean: + 236 mm/yr, +29% Mean (1979-2000) = 793 mm/yr SanchezGomez and Somot , in preparation Increase of +55% of fresh water deficit in the Mediterranean Sea (excluiding the runoff terms), due to precipitation reduction and warming-enchanced evaporation. Progressive drying of the Mediterranean region (Mariotti et al. 2008)

18. 3. Statistical + dynamical downscaling : 10m wind in France Statistical downscaling scheme (SDS): Large-scale circulation (prédicteurs) Daily Wind 850hPa UV850 Statistical Model Local state Daily wind at 10m UV10 • Advantages: • Low computational cost. • We can obtain a big number of future climate projections from different statistical models, predictors etc. • Links between LSC circulation and the local climate offers attractive physical interpretation.

19. 3. Statistical + dynamical downscaling : 10m wind in France Statistical downscaling scheme (SDS): Large-scale circulation (prédicteurs) Daily Wind 850hPa UV850 Statistical Model Local state Daily wind at 10m UV10 • Disadvantages: • Stationarity hypothesis : links between the LSC and local variables are conserved in the future climate (Frias et al. 2008, Najac et al. 2009) • We can reconstruct distributions only where we have station data. • In general SDS underestimates the observed trends (Boe et al. 2006, Najac et al. 2008)

20. 3. Statistical + dynamical downscaling : 10m wind in France UV850 (daily means) (ERA40 reanalyses – 1974-2002) Type1 Type6 Weather types Weather types are divided into wind classes, taking into account a criteria for inter and intra group variance Najac el al. 2008 One day is ramdonly chosen inside each wind class Mesoscale simulations for selected days { U10m, i / i  wind class} Winter and summer separately

21. Impact of climate change Climate Models UV850 (Daily mean) Frequency of occurrence of the wind classes Distributions reconstructed by weighting each simulation by the corresponding wind class frequency Distributions of wind at 10 m 3. Statistical + dynamical downscaling : 10m wind in France UV850 (daily means) (ERA40 reanalyses – 1974-2002) Weather types Weather types are divided into wind classes, taking into account a criteria for inter and intra group variance Najac el al. 2008 One day is ramdonly chosen inside each wind class Mesoscale simulations for selected days { U10m, i / i  wind class}

22. Relief (m) 0 10 100 500 1000 2000 3000 3. Statistical + dynamical downscaling : 10m wind in France • Méso-NH: • non-hydrostatic mesoscale atmospheric model • - Developped in the Laboratoire d'Aérologie (UPS/CNRS) and the CNRM (CNRS/Météo-France) • - 3 embedded domains: • D1: 36km, 4300 x 4300 km2, 72s • D2: 9 km , 1300 x 1300 km2, 18s • D3: 3 km , 480 x 290 km2, 6s • - 40 vertical levels • - Simulations of 24 hours (+ initialisation of 6h) • - Boundary and initial conditions (6h): Réanalyses ERA40 • - Simulation of 200 days Validation : the grid point the closest to the station point

23. 3. Statistical + dynamical downscaling : 10m wind in France Origin of errors (%) • Errors due to simulation are large in regions of low relief. • Errors due to simulation are large in the high orography regions, Pyrenees, Massif Central. % % 100 90 80 70 60 50 50 60 70 80 90 100 Simulation Sampling

24. 3. Statistical + dynamical downscaling : 10m wind in France Multi-model mean changes for the Wind mean speed at 10 m and anomaly vectors 2046-2065 (Ref 1971-2000) UV10 DS Hyb UV10 DS Stat UV10 Simu Winter Summer

25. 3. Conclusions • We need high resolution datasets (models, observations) to study and interpret Mediterranean climate (over land and sea). • The dynamical dowscaling of ERA40 with ARPEGE-Climat as a high spatial resolution atmospheric forcing to NEMOMED8 show improvements on the simulation of circulation in the Mediterranean Sea. • The Mediterranean Sea water budget estimates obtained from an ensemble of LAMs simulations are correct and coherent with net mass transport at Gibraltar. • Future climate projections simulated by LAMs show an increase of freshwater deficit in the Mediterranean Sea (+55%). The low resolution CMIP3 models predicts +24% (Mariotti et al. 2008). • The statistical+dynamical dowscaling scheme show results consistent with SDS methodology for changes in winds at 10 m in France. Though it has a high computational costs, this method provides an homogenous information that can be used for policymakers.