Météo-France activities Philippe Arbogast, Marie Boisserie (CNRM-GAME, Toulouse) With contributions by I. Beau, H. Douville, F. Bouyssel, CH. Lac, D. Ricard, Y. Seity, R. Honnert, L. Descamps 7-9 July 2010

2 French landscape (LMD and Météo-France) 1.Same dynamical core but different physical packages (AROME/ALARO, ARPEGE NWP/ARPEGE-CLIMAT) 2.2 climate models (ARPEGE-CLIMAT and LMDZ); on going activity on physical parameterization 3.Same physical package but different dynamical cores (AROME vs MESO- NH) 4.ARPEGE based on stretched grid  test-bed for convective parameterization schemes 5.Verification team and NWP team are independent

3 Outline 1.Validation of GCM parameterizations 2.The use of Single Column Model (SCM) 3.Large Eddy Simulations (LES) to validate turbulence 4.Where are the sources of NAO predictability ? using nudging 5.Computation of effective horizontal resolution of a model using spectra

4 Objective verification against analyses (ECMWF…) and observations (RS, surface data…) Useful but not sufficient to validate model formulation including parameterizations

5 Diagnoses by horizontal domains (DDH) Zonal tendency of Qv (g/kg/day) Global budget of T (K/day)  Produce diagnostic files during the forecast  Horizontal domains (global, zonal bands, limited domains, isolated pts)  Allow the calculation of budgets : air mass, water mass, enthalpy, kinetic energy, kinetic momentum, entropy,...

6 Validation of GCM parameterization schemes (turbulence and convective schemes) on Western Africa; comparison of LAM and CRM simulations Image aladin Explicit simulations of convection / Parameterized simulations: (Méso-NH model) / (Aladin-Climat model) of observed case studies ALADIN-Climat simulations performed on the same domain, with the same initial and lateral conditions as Méso-NH. at: 10, 50, 125 and 300 km resolution and for 31 and 91 levels D. Pollack, J.F. Gueremy and I. Beau

7 Validation of GCM parameterization schemes (using Model to Sat. approach) M. D’Errico, I. Beau, D. Bouniol, F. Bouyssel EUCLIPSE FP-7 project CloudSatRadar simulator 1.5 km CALIPSOLidar simulator 12.5 km

8 Validation of GCM parameterization schemes (using Model to Sat. approach) CloudSatRadar simulator Altitude (km) Reflectivity (dBz) Altitude (km) Reflectivity (dBz) Lack of overshooting in the model ….. Also verification against Meteosat 8 data (IR,WV)

9 Outline 1.Validation of GCM parameterizations 2.The use of Single Column Model (SCM) 3.Large Eddy Simulations (LES) to validate turbulence 4.Where are the sources of NAO predictability ? using nudging 5.Computation of effective horizontal resolution of a model using spectra

10 LES/SCM (single column model) setting for parameterization validation (J. Pergaud, S. Malardel, V. Masson) Validation of a Mass flux scheme for unified parameterization of dry and cloudy convective updraft GCM SCM/1D

11 SCM LES ARM Case : part of the Eurocs project (1997) Brown et al.,2002 Diurnal cycle of shallow cumulus convection over land. Intercomparison Study Lenderink et al.,2002

12 Outline 1.Validation of GCM parameterizations 2.The use of Single Column Model (SCM) 3.Large Eddy Simulations (LES) to validate turbulence 4.Where are the sources of NAO predictability ? using nudging 5.Computation of effective horizontal resolution of a model using spectra

13 LES to develop and validate turbulence scheme (TKE) (R. Honnert PhD) What happens at intermediate horizontal scales ? E(explicit)>E(subgrib) E(explicit)<E(subgrib)

14 LES to develop and validate turbulence scheme (TKE) (R. Honnert PhD) explicit subgrid

15 Outline 1.Validation of GCM parameterizations 2.The use of Single Column Model (SCM) 3.Large Eddy Simulations (LES) to validate turbulence 4.Where are the sources of NAO predictability ? using nudging 5.Computation of effective horizontal resolution of a model using spectra

16 Motivation DEMETER2 DJF hindcasts ( ): Poorly predictability of the North Atlantic Oscillation index (e.g. Palmer et al. 2004)

17 Arpège-Climat atmospheric spectral GCM in its low-top configuration (T63L31) => only 4 levels above 100 hPa (model top at 10 hPa) Prescribed observed SST and radiative forcings (GHG, sulfate and volcanic aerosols) Ensembles of 5-member integrations from 1970 to 2000 (including a 1-yr spin-up): CT: Control (no nudging, observed SST) NS: Stratospheric nudging north of 25°N NCS: Tropospheric nudging between 25°S-25°N Model and simulations

18  X/  t = D(X) + P(X) – (X-X ref ) Nudging is applied: at each time step (every 30 min) towards linearly interpolated 6-hourly data to U/V and T using a 5-hour and 12-hour e-folding time respectively in a 3D domain with a smooth transition between the nudged and free atmosphere ERA40 Grid point nudging

timeseries of DJF NAO principal components. Ensemble mean anomalies (thick red lines) are compared to ERA40 (in black) and spread is also shown (+/- 1 standard deviation in dashed red lines and minimum and maximum anomalies in solid red lines). R is the ensemble mean anomaly correlation coefficient with ERA40. Control experimentNudging of the tropical troposphere Nudging of the extratropical stratosphere

20 Outline 1.Validation of GCM parameterizations 2.The use of Single Column Model (SCM) 3.Large Eddy Simulations (LES) to validate turbulence 4.Where are the sources of NAO predictability ? using nudging 5.Computation of effective horizontal resolution of a model using spectra

21 Log k Assessment of spectra / effective horizontal resolution checking

22 Spectrum vs forecast range to address the spin-up (~3 hours) wavenumber Kinetic energy

23 Meso-nh (2.5 km): effective resolution is 4-6DX

24 Arome (2.5 km): effective resolution is 8-9DX

25 Summary 1.Importance of zonally averaged diagnoses 2.Comparison against global climatologies 3.Systematic comparison of different parameterization packages 4.LES/SCM/CRM to tune, to choose the best formulation, to address the need of some schemes (convection or turbulence) 5.Effective resolution using spectra 6.Nudging within GCM together with process studies (to improve the understanding of the physics of teleconnections…) 7.Split forecast uncertainty in terms of initial condition error and model error : Marie’s talk ….