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 2q q

TKE schemes

TKE-Scalar Variance Closure ModelDmitrii Mironov

• Transport (prognostic) equations for TKE and variances of scalars (<’2> and (<qt’2>) 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).

Treatment of Scalar Variances

TKE equation:

22

2

1

2

1wzz

wt

pwuwz

wg

z

vvw

z

uuw

t

ei2

2

1

Scalar-variance equation:

Convection/stable stratification = Potential Energy Kinetic Energy

No reason to prefer one form of energy over the other!

Comparison with One-Equation Models(Draft Horses of Geophysical Turbulence Modelling)

Scalar variance equation:

22

2

1

2

1wzz

wt

Production = Dissipation

Flux equation:

No counter-gradient term

2

gC

zeCw bg

EDMF schemes

EDMF at ECMWFConvective 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

EDMF at ECMWF:Stratocumulus

• sl, qt conserved variables

• M surface driven

• cloud top down diffusion

• cloud top entrainment

• cloud scheme: conversion (Beta distr.)

• stability criteria allowing strcu

lqtq

preVOCA: VOCALS at Oct 2006 – Low Cloud

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

Brian Mapes (~1995 GCSS meeting):Postulates that convection selects favourable environment.

Peter Bechtold (2008):Moist environments yield less entrainment.

Convective premoistening

Brown, Zhang 1997

RH during TOGA/COARE

Moist low levels (~800hPa) favour deep convection

PDF

RH (%)

Derbyshire et al 2004MetO CRM CNRM CRM

MetOffice SCMIFS SCM

Environment RH

RH (%)

mass flux mass flux

• small ε to get high cloud top

• large ε to get large RH sensitivity

Jarecka, Grabowski, Pawlowska, 2009

cloud fraction (grid box)

box

env

RH

RHenvironment

Entrained air is premoistened.

BOMEX LES runentrainment

regime

BOMEX LES cloud blobs

x

t

cloud blob time scalecloud

dt

cloud blob identification from LWP boundaries

WVP

x

y

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

shifte

d b

lob m

ean W

VP [

g/m

2]

/ cloudt

100g/m2

40g/m2

2890g/mWVP

prognostic total water variance equation

most moist environment favours shallow convection

decay time-scale outside BL

3 hours

DUALM convective preconditioningMartin Köhler & Olaf Stiller & Thijs Heus

2 2' ' '

2 ' 't tq qt t tq w q qw q

t z z

, ( )t upup env

qq q

z

LCLqt

10%

qt

10%

qt

10%

time

height

prog. 2

tq

decay2

tq

moist

CGILS results

Equilibrium state (80-100days)

cloud cover[%]

liquid water[g/m2]

water vapor[kg/m2]

sensible[W/m2]

latent[W/m2]

S12 ctr 100 79 40 13 19 19 21 10 72 68

p2k 100 79 51 16 24 24 16 6 86 84

S11 ctr 100 71 115 49 22 23 15 7 93 87

p2k 100 79 122 64 26 28 15 6 101 100

S6 ctr 16 17 26 25 36 35 9 8 108 108

p2k 17 22 30 35 42 43 10 9 113 116

EDMF-strcu EDMF-DUALM-shallowcu

EDMF-strcu (and Tiedtke shallow)

ql RH

Time [days]

S12ctl p2k

S6ctl p2k

S11ctl p2k

ql RH

Time [days]

ql RH

Time [days]

ql RH

Time [days]

ql RH

Time [days]

ql RH

Time [days]

EDMF-DUALM-shallowcu

ql RH

Time [days]

S12ctl p2k

S6ctl p2k

S11ctl p2k

ql RH

Time [days]

ql RH

Time [days]

ql RH

Time [days]

ql RH

Time [days]

qlRH

Time [days]

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

Extra Slides: CGILS talk

EDMF differences

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

ECMWF EDMF framework

Siebesma & Cuijpers, 1995

)()1( uu

e

e

u

u wawawaw

M

M-fluxenv. fluxsub-core flux

K-diffusion

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.

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.

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

Gaussian

skewed

skewed

Louise Nuijens: LES of cumulus, influence of wind speed

BOMEX LES preconditioning of convection?

LES by Thijs Heus:

no sheardx=dy=25m, dt=30sduration: 10h6.4km x 6.4km

WVP’ [g/m2]

PD

F

WVP

x

y

WVP’ [g/m2]

LWP [

g/m

2]

buoyancy

v dz

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

PDF Data

UKMO: PC2 prognostic variables

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 qt=qv+ql+qi, qi from microphys.)

Assumptions:

no T variability

mixed cloud: ice/liquid co-located (no PC2)

equilibrium vapor/liquid (not ice!)

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: ql, qi, cloud fraction cc

Microphysics:

Doms & Seiffert

Ice homogenious and heterogenious nucleation

Saturation adjustment on the sub-grid scale

2tq 22tq

2tq 2 q

q

Clouds and temperature/moisture variability

Tompkins, 2003

MOZAIC T, RH and e variability

PDF of 300km legs at 166-222 hPa. Gierens et al 1997.

K4.0T%2RH Pa07.0e

Estimate T variability if = const:

Estimate z displacement from T variability: =>

Estimate ΔRH from Δe: =>Estimate ΔRH from ΔT: =>

v

Temperature RH Vapor partial pressure

K4.00005.0 TKT

lvv qqT 1

km

K10

z

T m40z

PaCTe osat 5030 %13.0RH

%3RH

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

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

Pro

port

ion

all-c

lear

or

all-c

loud

y le

gs

Leg length [km]

Based on 4400km of flight data near ARM SGP at 1-3km height.

2q

GME and COSMO clouds

Stratiform sub-grid scale cloud:

RH based

Notes: • qsat is interpolated between qsat,liq and qsat,ice

between -5ºC and -25ºC

• ql and qi are 5% of qsat

Ideas: cloud physics at macro- and micro-scales

fc=diagnostic

4 moments: vq lq22qttq iq

Liquid cloud

qt

liqsatq ,

tq

PDF

liquid

Mixed cloud

qt

effsatq ,tq

PDF

liquid

ice

icesatq ,

Ice cloud

qt

effsatq ,tq

PDF

ice

icesatq ,

Sub-grid variability:

• assume Gaussian

• neglect variability

• take fixed ice fraction from

microphysics

tq

il

i

qq

q