Single Column Model representation of RICO shallow cumulus convection
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Transcript of Single Column Model representation of RICO shallow cumulus convection
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 QuestionsAre 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
tif
dtd
dtd
dtd
lsphystot
0
vuqwheret ,,,)0( 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 aspectswith observations (both real and pseudo-obs (LES) ), i.e.
obsvs )(
Two Flavours of the game
T
timeLSphys
dtdtd
dtdT
0
)0()(
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.
T
LSphys
dtdtd
dtdT
0
)0()(
Model Type Participant Institute
CAM3/GB GCM (Climate) C-L Lappen CSU (US)
UKMO GCM (NWP/Climate) B. Devendish UK Metoffice (UK)
JMA GCM (NWP/Climate) H. Kitagawa JMA (Japan)
HIRLAM/RACMO LAM (NWP/Climate) W. De Rooy KNMI (Netherlands)
GFDL GCM (Climate) C. Golaz GFDL (US)
RACMO/TKE LAM (Climate S. De Roode KNMI (Netherlands)
COSMO NWP/regional/mesoscale J. Helmert DWD (Germany)
LMD GCM Climate) Levefbre LMD (France)
LaRC/UCLA LAM (Mesoscale) Anning Cheng NASA-LaRC (US)
ADHOC C-L Lappen CSU (US)
AROME LAM (Mesoscale) S. Malardel Meteo-France (France)
ECHAM GCM (Climate) R. Posselt ETH (Switzerland)
ARPEGE GCM (Climate) P. Marquet Meteo-France (France
ECMWF GCM (NWP) R. Neggers ECMWF (UK
Model PBL Scheme Convection CloudCAM3/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, diagns 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 basedLarson/Golaz (2005)
3rd order pdf basedLarson/Golaz (2005)
3rd order pdf basedLarson/Golaz (2005)
ADHOC Assumed pdfhigh order MF
Assumed pdfhigh order MF
Assumed pdfhigh 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 coverL=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/detrM_b , w_uExtended in bl
Cloud scheme: stat
progn
Precip
precip?
microphysics
precip
PBL:K-profileTKEHigher order
ac, q
, q
acEstimating: ac,qlac,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 detrainment3. Good estimate of the variance of qt 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
s
st qqtQ
Cloud cover
Bechtold and Cuijpers JAS 1995
Bechtold and Siebesma JAS 1999Wood (2002)
Statistical Cloud schemes
Convective and turbulent transport