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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.
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Single Column Model representationof 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 • Start with the observed mean state: 2. Let the initial state evolve until it reaches steady state: • 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 • Use the mean LS-forcing of the suppressed period: i.e. the composite case. 2. Use directly the the time-varying LS forcing for the whole suppressed period.
Submitted versions Each model asked to submit: • Operational resolution / prescribed resolution • Operational physics / Modified physics • Composite constant forcing / variable forcing
Profiles after 24 hrs Composite Case (High resolution) 80 levels ~ 100m resolution in cloud layer
sq, sq ac K-profile TKE Higher order PBL: Different Building Blocks • need increasingly more information from eachother • demands more coherence between the schemes ac microphysics Cloud scheme: Moist Convection stat progn ac,ql Estimating: ac,ql sq, sq precip entr/detr M_b , w_u Extended in bl precip? Precip on/off
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)
Cloud fraction LES 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: • Good estimate of cloud base mass flux : M~ac w* • Good estimate of entrainment and detrainment • Good estimate of the variance of qt and ql 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?
Statistical Cloud schemes Cloud cover Bechtold and Cuijpers JAS 1995 Bechtold and Siebesma JAS 1999 Wood (2002)