single column model representation of rico shallow cumulus convection
DESCRIPTION
Single Column Model representation of RICO shallow cumulus convection. A.Pier Siebesma and Louise Nuijens, KNMI, De Bilt The Netherlands. Many thanks to: All the participants. Main Question. - PowerPoint PPT PresentationTRANSCRIPT
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Single Column Model representation
of RICO shallow cumulus convection
A.Pier Siebesma and Louise Nuijens,KNMI, De Bilt
The Netherlands
Many thanks to: All the participants
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Main Question
• Are the single column model versions of GCM’s, ‘LAM’s and mesoscale models capable of representing realistic mean state when subjected to the best guess of the applied large scale forcings.
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The game to be played
tifdt
d
dt
d
dt
d
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 aspects
with observations (both real and pseudo-obs (LES) ), i.e.
obsvs )(
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Two Flavours of the game
timeLSLS dt
d
dt
d
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.
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Initial State = mean observed state
Remark: need to reconcile initial theta-profile
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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)
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
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Model PBL Scheme Convection Cloud
CAM3/GB TKE (bretherton/grenier) MF (Hack) Prog l,
UKMOK-profile/expl entr. /moist(?)
MF (Gregory-Rowntree)Mb=0.03w*
Stat/RH_cr (Smith)
JMAK-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
GFDLK-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/UCLA3rd order pdf basedLarson/Golaz (2005)
3rd order pdf basedLarson/Golaz (2005)
3rd order pdf basedLarson/Golaz (2005)
ADHOCAssumed pdfhigh order MF
Assumed pdfhigh order MF
Assumed pdfhigh order MF
AROME TKE-moist MF (pbl/cu-updraft) Stat. diagnostic
ECHAMTKE-moist Tiedtke(89) Entr/detr
(Nordeng)Stat Tompkins 2002)
ARPEGETKE-moist
MFStat ,cloud coverL=prognostic
ECMWF K-profile (moist) MF (pbl/cu-updraft) Stat. diagnostic
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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
ac, q
, q
acEstimating: ac,qlac,ql
on/off
• need increasingly more information from eachother
• demands more coherence between the schemes
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s
st qqtQ
Cloud cover
Bechtold and Cuijpers JAS 1995
Bechtold and Siebesma JAS 1999Wood (2002)
Statistical Cloud schemes
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Convective and turbulent transport
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Profiles after 24 hrs
Composite Case (High resolution)
80 levels ~ 100m resolution in cloud layer
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Initial “mean” state
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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)
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Cloud fraction Liquid water
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ARM
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Profiles after 72 hrs
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gradient in cloud layer to characterizethe state
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Are you all still with me?
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Time Series
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vv
vv
vv
v
CAM, UKMO : too low
GFDL,UKMO: too high
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ECHAM, Arpege: very noisy
(probably on average ok)
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ADHOC
CAM
Directly related to mean q, near surface
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On/off switching convection scheme
(ECHAM,UKMO)?
GFDL, LMD: unrealistically high
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Possible cause for intermittant behaviour:
w’qt’_scm
Mdz
dM
w’qt’ ~ M ( qt,u – qt)
Constant with height
Increasing with height
qz
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Most models don’t go to deep conv.
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GFDL
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ECHAM, UKMO, LMD : too low
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RACMO-TKE
ADHOC
Arpege
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RACMO-surface wind too low
CAM: ??? Surface wind + qt ok???
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ECHAM: very noisy
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JMA too cold near surface
ECHAM: very noisy
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Intermittant: Arpege, LMD, RACMO/TKE, GFDL
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Cause of noise might reside in TKE-scheme: Arpege, ECHAM,
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Most models do not have a very active
Diffusion scheme in cloud layer!!
Moist pbl-schemes are not overtaken the convection schemes.
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LES
ECMWF
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Evaporation of Rain in PBL
Relative precipitation ratio:
500
500
P
PPR srf
For LES in Cu : R ~ 0. (hardly rain evap)
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R
500
500
P
PPR srf
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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.
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Other remarks
• Noise in time-series related to TKE-scheme.
• on/off switching of convection related to mass flux profile.
• There seems to be no agreement on the precipitation evaporation efficiency.
• Most models don’t seem to trigger deep convection.
• Cloud cover, and liquid water profile 1st order problem, microphysics is a 2nd order problem (but might affect the mean state considerably!!)
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Some models behave remarkably well
• ECMWF, HIRLAM, AROME
• These models worked actively on shallow cumulus (but did not tune their parameterization on the present case)
• 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 qt and l in the cloud layer in order to have a good estimate of cloud cover and liquid water.
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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)
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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?
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Thank you