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

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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

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Outline

1. Validation of GCM parameterizations2. The use of Single Column Model (SCM)3. Large Eddy Simulations (LES) to validate turbulence4. Where are the sources of NAO predictability ? using nudging5. 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

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Diagnoses by horizontal domains (DDH)

Zonal tendency of Qv (g/kg/day) Global budget of T (K/day)

Produce diagnostic files during the forecastProduce diagnostic files during the forecast

Horizontal domains (global, zonal bands, limited domains, isolated pts)Horizontal domains (global, zonal bands, limited domains, isolated pts)

Allow the calculation of budgets : air mass, water mass, enthalpy, kinetic Allow the calculation of budgets : air mass, water mass, enthalpy, kinetic energy, kinetic momentum, entropy, ...energy, kinetic momentum, entropy, ...

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Validation of GCM parameterization schemes (turbulence and convective schemes) on Western Africa; comparison of LAM and CRM simulations

Image aladinExplicit 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

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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 km1.5 km

CALIPSO Lidar simulator12.5 km12.5 km

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Validation of GCM parameterization schemes (using  Model to Sat. approach)

CloudSat Radar simulator

Altit

ude

(km

)

Reflectivity (dBz)Al

titud

e (k

m)

Reflectivity (dBz)

Lack of overshooting in the model…..

Also verification against Meteosat 8 data (IR,WV)

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Outline

1. Validation of GCM parameterizations2. The use of Single Column Model (SCM)3. Large Eddy Simulations (LES) to validate turbulence4. Where are the sources of NAO predictability ? using nudging5. Computation of effective horizontal resolution of a model using spectra

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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

GCMSCM/1D LES

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SCMLES

'θw' L 'θw' L

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

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Outline

1. Validation of GCM parameterizations2. The use of Single Column Model (SCM)3. Large Eddy Simulations (LES) to validate turbulence4. Where are the sources of NAO predictability ? using nudging5. Computation of effective horizontal resolution of a model using spectra

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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)

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

explicit

subgrid

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Outline

1. Validation of GCM parameterizations2. The use of Single Column Model (SCM)3. Large Eddy Simulations (LES) to validate turbulence4. Where are the sources of NAO predictability ? using nudging5. Computation of effective horizontal resolution of a model using spectra

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Motivation

DEMETER2 DJF hindcasts (1958-2001): Poorly predictability of the North Atlantic Oscillation index (e.g. Palmer et al. 2004)

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• 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

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X/t = D(X) + P(X) – (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

ERA40

Grid point nudging

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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.

Control experiment Nudging of the tropical troposphere

Nudging of the extratropical stratosphere

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Outline

1. Validation of GCM parameterizations2. The use of Single Column Model (SCM)3. Large Eddy Simulations (LES) to validate turbulence4. Where are the sources of NAO predictability ? using nudging5. Computation of effective horizontal resolution of a model using spectra

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Log k

Assessment of spectra / effective horizontal resolution checking

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Spectrum vs forecast range to address the spin-up (~3 hours)

wavenumber

Kin

etic

ene

rgy

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

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

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Summary

1. Importance of zonally averaged diagnoses2. 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 spectra6. 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 ….