Single Column Model representation of RICO shallow cumulus convection A.Pier Siebesma and Louise Nuijens, KNMI, De Bilt The Netherlands And all the participants.

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Presentation transcript:

Single Column Model representation of RICO shallow cumulus convection A.Pier Siebesma and Louise Nuijens, KNMI, De Bilt The Netherlands And all the participants to the case Many thanks to: All the participants

Main Questions Are the single column model versions of GCM’s, ‘LAM’s and mesoscale models capable of: representing realistic mean thermodynamic state when subjected to the best guess of the applied large scale forcings. Reproducing realistic precipitation characteristics

The game to be played 1.Start with the observed mean state: 2. Let the initial state evolve until it reaches steady state: 3.Evaluate the steady state with observations in all its aspects with observations (both real and pseudo-obs (LES) ), i.e.

Two Flavours of the game 1.Use the mean LS-forcing of the suppressed period: 2. Use directly the the time-varying LS forcing for the whole suppressed period. i.e. the composite case.

ModelTypeParticipantInstitute CAM3/GBGCM (Climate)C-L LappenCSU (US) UKMOGCM (NWP/Climate)B. DevendishUK Metoffice (UK) JMAGCM (NWP/Climate)H. KitagawaJMA (Japan) HIRLAM/RACMOLAM (NWP/Climate)W. De RooyKNMI (Netherlands) GFDLGCM (Climate)C. GolazGFDL (US) RACMO/TKELAM (ClimateS. De RoodeKNMI (Netherlands) COSMONWP/regional/mesoscaleJ. HelmertDWD (Germany) LMDGCM Climate)LevefbreLMD (France) LaRC/UCLALAM (Mesoscale)Anning ChengNASA-LaRC (US) ADHOCC-L LappenCSU (US) AROMELAM (Mesoscale)S. MalardelMeteo-France (France) ECHAMGCM (Climate)R. PosseltETH (Switzerland) ARPEGEGCM (Climate)P. MarquetMeteo-France (France ECMWFGCM (NWP)R. NeggersECMWF (UK

ModelPBL SchemeConvectionCloud CAM3/GB TKE (bretherton/grenier) MF (Hack)Prog l, UKMO K-profile/expl entr. /moist(?) MF (Gregory-Rowntree) Mb=0.03w* Stat/RH_cr (Smith) JMA K-profile/expl entr/moist. MF (Arakawa-Schubert)Stat/RH_cr (Smith) HIRLAM/ RACMO TKE/moist MF(Tiedtke89) New entr/detr, M=a w* closure Stat, diagn  s from K and MF GFDL K-profile/expl entr/moist(?) MF (Rasch) l,c prognostic RACMO/TKE TKE moist MF (Tiedtke(89)l,c,prognostic LMD Ri-number MF (Emanuel)Stat LaRC/UCLA 3rd order pdf based Larson/Golaz (2005) 3rd order pdf based Larson/Golaz (2005) 3rd order pdf based Larson/Golaz (2005) ADHOC Assumed pdf high order MF Assumed pdf high order MF Assumed pdf high order MF AROME TKE-moist MF (pbl/cu-updraft)Stat. diagnostic ECHAM TKE-moist Tiedtke(89) Entr/detr (Nordeng) Stat Tompkins 2002) ARPEGE TKE-moist MF Stat,cloud cover L=prognostic ECMWF K-profile (moist) MF (pbl/cu-updraft)Stat. diagnostic

Submitted versions Each model asked to submit: Operational resolution / prescribed resolution Operational physics / Modified physics Composite constant forcing / variable forcing

Initial State (identical to LES case)

Profiles after 24 hrs Composite Case (High resolution) 80 levels ~ 100m resolution in cloud layer

Different Building Blocks Moist Convection entr/detr M_b, w_u Extended in bl Cloud scheme: stat progn Precip precip? microphysics precip PBL: K-profile TKE Higher order acac  ,  q acac Estimating : a c,ql a c,ql on/off need increasingly more information from eachother demands more coherence between the schemes

At least in general much better than with the previous Shallow cumulus case based on ARM (profiles after ~10 hours Lenderink et al. QJRMS 128 (2002)

LES Cloud fraction In general too high

Time series Composite Case (High resolution) 80 levels ~ 100m resolution in cloud layer

Some models behave remarkably well These models worked actively on shallow cumulus It seems that there are 3 crucial ingredients: 1.Good estimate of cloud base mass flux : M~ac w* 2.Good estimate of entrainment and detrainment 3.Good estimate of the variance of q t and  l in the cloud layer in order to have a good estimate of cloud cover and liquid water.

Conclusions Mean state (slightly) better than for the ARM case Most models are unaccaptable noisy (mainly due to switching between different modes/schemes. Probably due to unwanted interactions between the various schemes No agreement on precipitation evaporation Performance amazingly poor for such a simple case for which we know what it takes to have realistic and stable response. Difficult to draw conclusions on the microphysics in view of the intermittant behaviour of the turbulent and convective fluxes.

We should clear up the obvious deficiencies Check LS Forcings: should we ask for it as required output? u,v –profiles : RACMO-TKE, ECMWF, UCLA-LaRC, ECHAM Ask for timeseries for u,v,q,T near surface to check surface fluxes and cloud base height off-line.

Required observational data Liquid water path (or even better profiles) cloud cover profiles (should be possible).precipitation evaporation efficiency. Cloud base mass flux. Incloud properties., entrainment, detrainment mass flux (Hermann??) Variance of qt and theta (for cloud scheme purposes)

Further Points: Proceed with the long run?? Get the the RICO-sondes into the ECMWF/NCEP analysis in order to get better forcings? Should we do 3d-GCM RICO?

Thank you

Cloud cover Bechtold and Cuijpers JAS 1995 Bechtold and Siebesma JAS 1999 Wood (2002) Statistical Cloud schemes

Convective and turbulent transport