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Météo-France activities Philippe Arbogast, Marie Boisserie (CNRM-GAME, Toulouse)

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. French landscape (LMD and Météo-France).

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Météo-France activities Philippe Arbogast, Marie Boisserie (CNRM-GAME, Toulouse)

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

  3. Outline • Validation of GCM parameterizations • The use of Single Column Model (SCM) • Large Eddy Simulations (LES) to validate turbulence • Where are the sources of NAO predictability ? using nudging • 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) • 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, ... Global budget of T (K/day) Zonal tendency of Qv (g/kg/day)

  6. Image aladin Validation of GCM parameterization schemes (turbulence and convective schemes) on Western Africa; comparison of LAM and CRM simulations D. Pollack, J.F. Gueremy and I. Beau 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

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

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

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

  10. GCM LES SCM/1D 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

  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 • Validation of GCM parameterizations • The use of Single Column Model (SCM) • Large Eddy Simulations (LES) to validate turbulence • Where are the sources of NAO predictability ? using nudging • 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) subgrid explicit

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

  16. Motivation • DEMETER2 DJF hindcasts (1958-2001): 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. ERA40 Grid point nudging dX/dt = D(X) + P(X) – l(X-Xref) 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

  19. Control experiment Nudging of the tropical troposphere 1971-2000 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. Nudging of the extratropical stratosphere

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

  21. Assessment of spectra / effective horizontal resolution checking Log k

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

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

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

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

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