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

• 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.

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 )(

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.

Initial State = mean observed state

Remark: need to reconcile initial theta-profile

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

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

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

s

st qqtQ

Cloud cover

Bechtold and Cuijpers JAS 1995

Bechtold and Siebesma JAS 1999Wood (2002)

Statistical Cloud schemes

Convective and turbulent transport

Profiles after 24 hrs

Composite Case (High resolution)

80 levels ~ 100m resolution in cloud layer

Initial “mean” state

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 Liquid water

ARM

Profiles after 72 hrs

gradient in cloud layer to characterizethe state

Are you all still with me?

Time Series

vv

vv

vv

v

CAM, UKMO : too low

GFDL,UKMO: too high

ECHAM, Arpege: very noisy

(probably on average ok)

ADHOC

CAM

Directly related to mean q, near surface

On/off switching convection scheme

(ECHAM,UKMO)?

GFDL, LMD: unrealistically high

Possible cause for intermittant behaviour:

w’qt’_scm

Mdz

dM

w’qt’ ~ M ( qt,u – qt)

Constant with height

Increasing with height

qz

Most models don’t go to deep conv.

GFDL

ECHAM, UKMO, LMD : too low

RACMO-TKE

ADHOC

Arpege

RACMO-surface wind too low

CAM: ??? Surface wind + qt ok???

ECHAM: very noisy

JMA too cold near surface

ECHAM: very noisy

Intermittant: Arpege, LMD, RACMO/TKE, GFDL

Cause of noise might reside in TKE-scheme: Arpege, ECHAM,

Most models do not have a very active

Diffusion scheme in cloud layer!!

Moist pbl-schemes are not overtaken the convection schemes.

LES

ECMWF

Evaporation of Rain in PBL

Relative precipitation ratio:

500

500

P

PPR srf

For LES in Cu : R ~ 0. (hardly rain evap)

R

500

500

P

PPR srf

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.

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!!)

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.

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