Single Column Model representation of RICO shallow cumulus convection

1. Single Column Model representationof RICO shallow cumulus convection A.Pier Siebesma and Louise Nuijens, KNMI, De Bilt The Netherlands Many thanks to: All the participants

2. 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.

3. 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.

4. 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.

5. Initial State = mean observed state Remark: need to reconcile initial theta-profile

8. 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

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

10. Convective and turbulent transport

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

12. Initial “mean” state

14. 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)

15. Cloud fraction Liquid water

16. ARM

18. Profiles after 72 hrs

21. gradient in cloud layer to characterizethe state

23. Are you all still with me?

24. Time Series

25. v v CAM, UKMO : too low GFDL,UKMO: too high v v v v v

26. ECHAM, Arpege: very noisy (probably on average ok)

27. ADHOC CAM Directly related to mean q,q near surface

28. On/off switching convection scheme (ECHAM,UKMO)? GFDL, LMD: unrealistically high

29. w’qt’ ~ M ( qt,u – qt) Possible cause for intermittant behaviour: Constant with height w’qt’_scm Increasing with height q q z

30. Most models don’t go to deep conv.

31. GFDL

32. ECHAM, UKMO, LMD : too low

33. RACMO-TKE ADHOC Arpege

34. RACMO-surface wind too low CAM: ??? Surface wind + qt ok???

35. ECHAM: very noisy

37. JMA too cold near surface ECHAM: very noisy

39. Intermittant: Arpege, LMD, RACMO/TKE, GFDL

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

42. Most models do not have a very active Diffusion scheme in cloud layer!! Moist pbl-schemes are not overtaken the convection schemes.

44. ECMWF LES

46. Evaporation of Rain in PBL Relative precipitation ratio: For LES in Cu : R ~ 0. (hardly rain evap)

47. R

48. 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.

49. 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!!)

50. 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: • 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.