1. PBL schemes for ICON: CGILS test Martin Köhler (DWD) Dmitrii Mironov, Matthias Raschendorfer, Ekaterina Machulskaya (DWD) Roel Neggers (KNMI)  Prognostic TKE (Raschendorfer, COSMO & GME)  Prognostic TKE,,, (Mironov, Machulskaya, experimental)  EDMF-dry/stratocu (Köhler, Beljaars, ECMWF)  EDMF-DUALM-shallow (Neggers, Köhler, Beljaars, experimental)  CGILS test

2. TKE schemes

3. TKE-Scalar Variance Closure Model Dmitrii Mironov Transport (prognostic) equations for TKE and variances of scalars ( and ( ) including third-order transport. Algebraic (diagnostic) formulations for scalar fluxes, Reynolds-stress components, and turbulence length scale (for speed). Statistical SGS cloud scheme, either Gaussian (e.g. Sommeria and Deardorff 1977), or with exponential tail to account for the effect of cumulus clouds (e.g. Bechtold et al. 1995). Optionally, prognostic equations for scalar skewness (mass-flux ideas recast in terms of ensemble-mean quantities ).

4. Treatment of Scalar Variances TKE equation: Scalar-variance equation: Convection/stable stratification = Potential Energy  Kinetic Energy No reason to prefer one form of energy over the other!

5. Comparison with One-Equation Models (Draft Horses of Geophysical Turbulence Modelling) Scalar variance equation: Production = Dissipation Flux equation: No counter-gradient term

6. EDMF schemes

7. EDMF at ECMWF Convective Boundary Layer dry EDMF theory & SCM Pier Siebesma & Joao Teixeira 2000, 2007 stratocumulus EDMF & unified implementation Martin Köhler 2005, 2010 stratocumulus inversion entrainment numerics Martin Köhler 2008 shallow cumulus DUALM EDMF Roel Neggers & Martin Köhler 2007-2010 ECMWF operational

8. EDMF at ECMWF: Stratocumulus s l, q t conserved variables M surface driven cloud top down diffusion cloud top entrainment cloud scheme: conversion (Beta distr.) stability criteria allowing strcu

9. preVOCA: VOCALS at Oct 2006 – Low Cloud

10. EDMF at ECMWF: Shallow Cumulus DUALM Neggers, Köhler, Beljaars 2009 Concepts: multiple updrafts mass-flux closure entrainment pre-moistening bimodal statistical cloud scheme cloud overlap

11. Brian Mapes (~1995 GCSS meeting): Postulates that convection selects favourable environment. Peter Bechtold (2008): Moist environments yield less entrainment. Convective premoistening

12. Brown, Zhang 1997 RH during TOGA/COARE Moist low levels (~800hPa) favour deep convection PDF RH (%)

13. Derbyshire et al 2004 MetO CRMCNRM CRM MetOffice SCMIFS SCM Environment RH RH (%) mass flux small ε to get high cloud top large ε to get large RH sensitivity

14. Jarecka, Grabowski, Pawlowska, 2009 cloud fraction (grid box) environment Entrained air is premoistened. BOMEX LES run entrainment regime

15. BOMEX LES cloud blobs x t cloud blob time scale cloud blob identification from LWP boundaries WVP x y

16. BOMEX LES cloud blobs blobs size 1000: (250m) 2 · 300s Time, lagged around blob center, normalized by blob time scale 166 blobs size 1000-10000 shifted blob mean WVP [g/m2] 100g/m 2 40g/m 2

17.  prognostic total water variance equation  most moist environment favours shallow convection  decay time-scale outside BL 3 hours DUALM convective preconditioning Martin Köhler & Olaf Stiller & Thijs Heus LCL qtqt 10% qtqt qtqt time height prog. decay moist

18. CGILS results

19. Equilibrium state (80-100days) cloud cover [%] liquid water [g/m 2 ] water vapor [kg/m 2 ] sensible [W/m 2 ] latent [W/m 2 ] S12ctr10079 401319 2110 72 68 p2k10079 51162416 6 86 84 S11ctr1007111549222315 7 93 87 p2k1007912264262815 6101100 S6ctr 1617 26253635 9 8108 p2k 1722 3035424310 9113116 EDMF-strcu EDMF-DUALM-shallowcu

20. EDMF-strcu (and Tiedtke shallow) qlql RH Time [days] S12 ctl p2k S6 ctl p2k S11 ctl p2k qlql RH Time [days] qlql RH Time [days] qlql RH Time [days] qlql RH Time [days] qlql RH Time [days]

21. EDMF-DUALM-shallowcu qlql RH Time [days] S12 ctl p2k S6 ctl p2k S11 ctl p2k qlql RH Time [days] qlql RH Time [days] qlql RH Time [days] qlql RH Time [days] qlql RH Time [days]

22. conclusions ICON model boundary layer: TKE and/or EDMF closures clouds: probably prognostic PDF, prognostic ice EDMF models at ECMWF have negative cloud climate feedback mostly more LWP

23. Extra Slides: CGILS talk

24. EDMF differences Cloud diagnostic: EDMF-strcu: Beta-distribution (bounded) CCstrcu=100% EDMF-DUALM: Gaussian distribution (open) CCstrcu=80%

25. ECMWF EDMF framework Siebesma & Cuijpers, 1995 M M-fluxenv. fluxsub-core flux K-diffusion

26. Single-Column Tests: Dry Convective PBL Mean potential temperature in shear-free convective PBL. Red – TKE scheme, blue – TKE-scalar variance scheme, black dashed – LES data.

27. Single-Column Tests: Nocturnal Stratocumuli Fractional cloud cover (left) and cloud water content (middle) in DYCOMS-II. Red – TKE scheme, blue – TKE-scalar variance scheme. Black solid curve in the right figure shows LES data.

28. Single-Column Tests: Shallow Cumuli Fractional cloud cover (upper row) and cloud water content (lower row) in BOMEX. Red – TKE scheme, blue – TKE-scalar variance scheme. Black solid curves in the middle figures show LES data. Gaussian skewed

29. Louise Nuijens: LES of cumulus, influence of wind speed

30. BOMEX LES preconditioning of convection? LES by Thijs Heus: no shear dx=dy=25m, dt=30s duration: 10h 6.4km x 6.4km WVP’ [g/m2] PDF WVP x y WVP’ [g/m2] LWP [g/m2] buoyancy

31. 31 Conclusion: PDFs are mostly approximated by uni or bi-modal distributions, describable by a few parameters More examples from Larson et al. JAS 01/02 Note significant error that can occur if PDF is unimodal PDFData

32. UKMO: PC2 prognostic variables

33. Ideas for ICON-NWP  Questions on complexity:  Skewness, PC2, temperature variability  Questions on framework (prognostic variables):  Tiedtke, PC2  Summeria/Deardorff, Tompkins  Possible compromise:  Concept (Gaussian q t =q v +q l +q i, q i from microphys.)  Assumptions:  no T variability  mixed cloud: ice/liquid co-located (no PC2)  equilibrium vapor/liquid (not ice!)

34. Ideas for ICON-NWP  Turbulence parameterization:  TKE (Raschendorfer)diagnostic,,  UTCS (Mironov)prognostic,,  EDMF (Köhler et al)prognostic  Convection parameterization:  Bechtold et al 2008 (evolved Tiedtke 89)  Tendencies: q l, q i, cloud fraction cc  Microphysics:  Doms & Seiffert  Ice homogenious and heterogenious nucleation  Saturation adjustment on the sub-grid scale

35. Clouds and temperature/moisture variability Tompkins, 2003

36. MOZAIC T, RH and e variability PDF of 300km legs at 166-222 hPa. Gierens et al 1997. Estimate T variability if = const: Estimate z displacement from T variability: => Estimate ΔRH from Δe: => Estimate ΔRH from ΔT: => Temperature RH Vapor partial pressure

37. Final Thoughts  Cloud variability is important down to <1km.  radiation  microphysics  Ice microphysics are equally important  Both macro and micro-scales involve long time-scales  We need at least  prognostic total water variance (or cloud fraction)  prognostic ice water

38. LES clouds  LES: mostly all-or-nothing (e.g. SAM, UCLA-LES, KNMI, UKMO)  GCM:  diagnostic (RH based, Slingo)  prognostic CC (Tiedtke)  prognostic (Tompkins) Tompkins, 2003 Proportion all-clear or all-cloudy legs Leg length [km] Based on 4400km of flight data near ARM SGP at 1-3km height.

39. GME and COSMO clouds Stratiform sub-grid scale cloud: RH based Notes: q sat is interpolated between q sat,liq and q sat,ice between -5ºC and -25ºC q l and q i are 5% of q sat

40. Ideas: cloud physics at macro- and micro-scales f c =diagnostic 4 moments: Liquid cloud liquid Mixed cloud liquid ice Ice cloud ice Sub-grid variability: assume Gaussian neglect variability take fixed ice fraction from microphysics