LES modeling of precipitation in Boundary Layer Clouds and parameterisation for

General Circulation Model

Olivier GeoffroyOlivier Geoffroy

Jean-Louis Brenguier, Frédéric Burnet, Irina Sandu, Odile Thouron

CNRM/GMEI/MNPCA

AUTO

Nc=cste

- Formation of precipitation =

non linear process :

LWC

The problem of modeling precipitation formation in GCM

A parameterisation of the precipitation flux averaged over an ensemble of cells is more relevant for the GCM resolution scale

Problem

- no physically based parameterisations, numerical instability due to step function

Are such parameterisations, with tuned coefficients, still valid to study the AIE?

- Variables in GCM = mean values over a large area in GCM.

Underestimation of precipitation in GCM

Biais corrected by tuning coefficients against observations

)(3/73/1critCottonManton rvrvHLWCNAUTO c

Parameterisations in GCM = CRM bulk parameterisation.

Ex :

Super bulk parameterisation

Pawlowska & Brenguier, 2003 :At the scale of an ensemble of cloud cells : quasi stationnary state

Is it feasible to express the mean precipitation flux at cloud base Rbase as a function of macrophysical variables that characterise the cloud layer as a whole ?

Pawlowska & Brenguier (2003, ACE-2):

N

HRbase

3

act

g

N

HR

4

75.1)(N

LWPRbase

Comstock & al. (2004, EPIC) :

Van Zanten & al. (2005, DYCOMS-II) :

Which variables drive Rbase at the cloud system scale ?

Adiabatic model :LWP = ½CwH2 Rbase (kg m-2 s-1 or mm d-1)

H (m)or

LWP (kg m-2) N

(m-3)

In GCMs, H, LWP and N can be predicted at the scale of the cloud system

Objectives & Methodology

Methodology:

3D LES simulations of BLSC fields with various LWP, Nact and corresponding Rbase values

Objectives : - To establish the relationship between Rbase, LWP and Nact, and empirically determine the coefficients.

act

baseN

LWPaR

a = ? α = ? β = ?

Suppose power lawrelationship

Regression analysis

Outline

• Presentation of the LES microphysical schemeParticular focus on cloud droplet sedimentation parameterisation• Validation of the microphysical schemeSimulation of 2 cases of ACE-2 campaign and GCSS Boundary

layer working group intercomparaison exercise• Come back to the problematic :Results of the parameterisation of precipitation in BLSC

LES microphysical scheme- Modified version of the Khairoutdinov & Kogan (2000) LES bulk microphysical scheme (available in next version of MESONH).

Condensation& Evaporation : Langlois (1973)

Autoconversion : K&K (2000)

Accretion : K&K (2000)

Sedimentation of drizzle drops : K&K (2000)

Activation : Cohard and al (1998)

Evaporation : K&K (2000)

Air:

Aerosols :

C (m-3), k, µ, ß

(= constant parameters)

W (m s-1)

θ (K)

Na (m-3)

Cloud :

qc (kg/kg)

Nc (m-3)

Drizzle :

qr (kg/kg)

Nr (m-3)

Sedimentation of cloud droplets :

Stokes law + generalized gamma law

Air :

qv (kg/kg) θ (K)

microphysical Processes and variables

Specificities :

- 2 moments

- low precipitating clouds : local qc < 1,1 g kg-1

- coefficients tuned using an explicit microphysical model as data source -> using realistic distributions.

- valid only for CRM.

Parameterisation of cloud droplets sedimentation

Which distribution to select? With which parameter ?

))ln

)Ø/Øln((

2

1exp(

lnØ2

1)Ø( 2

g

n

g

cn

))Ø(exp(Ø)(

)Ø( 1

cnGeneralized gamma :

Lognormal :

Methodology.By comparing with ACE-2 measured droplet spectra (resolution = 100 m),find the idealized distribution which best represents the :

- diameter of the 2nd moment ,- diameter of the 5th moment ,

- effective diameter .e 52

21

0

21 )( MNkdnkF ccNc

52

0

51 )(

6MNkdnkF ccwqc

(H) : Stokes regime:2

1)( kv

measureddvmeasureddv

measureddv

Results for gamma law, α=3, υ=2

Color =

number of spectra in each

pixel in% of nb_max

100 %

50 %

0 %

d2

deffdeff

d5 measured

measuredgamma

d

dd)d(

p

pppE

only spectra at cloud top

E(d5) (%)E(d2) (%)

E(deff) (%)

measureddv

E(deff) (%)

- Generalized gamma law: best results for α=3, υ=2- Lognormal law, similar results with σg=1,2-1,3~ DYCOMS-II results (Van Zanten personnal

communication).

measureddvmeasureddv

measureddvmeasureddv

Results for lognormal law, σg=1.5

Color =

number of spectra in each

pixel in% of nb_max

100 %

50 %

0 %

d2

deffdeff

d5 measured

measuredlognormale

d

dd)d(

p

pppE

only spectra at cloud top

E(d5) (%)E(d2) (%)

E(deff) (%) E(deff) (%)

Lognormal law, with σg=1.5, overestimate

sedimentation flux of cloud droplets.

Scheme validation

GCSS intercomparison exercise Case coordinator : A. Ackermann (2005)

Case studied : DYCOMS-II RF02 experiment (Stevens et al., 2003)

• Domain : 6.4 km × 6.4 km × 1.5 km

horizontal resolution : 50 m,

vertical resolution : 5 m near the surface and the initial inversion at 795 m.

• fixed cloud droplet concentration : Nc = 55 cm-3

• 2 simulations :

- 1 without cloud droplet sedimentation.

- 1 with cloud droplet sedimentation : lognormale law with σg = 1.5

• 2 Microphysical schemes tested : - KK00 scheme,

- MESONH 2 moment scheme

= Berry and Reinhardt scheme (1974).

4 simulations : KK00, no sed / sed BR74, no sed / sed

Results, LWP, precipitation flux

Central half of the simulation ensemble

Ensemble range

Median value of the ensemble of

modelsKK00, sed

KK00, no sed

NO DATA

LWP (g m-2) = f(t)

Rsurface (mm d-1) = f(t)

Rbase (mm d-1) = f(t)

BR74, sed

BR74, no sed

6H3H

observation

~0.35 mm d-1

~1.29 mm d-1

6H3H

6H3H6H3H

6H3H

- KK00 : underestimation of precipitation flux by only a factor 2 at cloud base- BR74 : underestimation at cloud base by a factor 2, Rsurface = Rbase no evaporation

- LWP too low

- KK00 : underestimation of precipitation flux by a factor 10 at surface- BR74 : good agreement at surface

50 µm

KK00&

measurements 84 µmBR74

d d

cloud drizzle cloud drizzle

Results, What about microphysics ?

Averaged profils of Ndrizzle, dvdrizzle in each 30 m layer after 3 hours of simulation and averaged value of measured Ndrizzle, dmeandrizzle (resolution : 12 km) at cloud

base and at cloud top (Van Zanten personnal communication)

BR74

KK00

- KK00 scheme reproduce with good agreement microphysical variables at cloud top and cloud base- BR74 scheme : too few and too large drops.

CB

CT

hsurf (m) hsurf (m)Ndrizzle (l-1) dvdrizzle (µm)

CB

CTBR74

KK00

Simulation of 2 ACE-II cases

Simulation of 2 ACE-II cases

26 june, pristine case 9 July, polluted case

Macrophysical variables H (m) Nact (cm-3) H (m) Nact (cm-3)

measurements 202 51 167 256

Simulations KK00, BR74 190 48-49 170 193

Macrophysical variables for measurements (Pawlowska and Brenguier, 2003) and simulations after 2H20

• Domain : 10 km × 10 km,

resolution : horizontaly : 100 m, verticaly : 10 m in/above the cloud

• initialisation : corresponding profile of thermodynamical variables.

Objective : comparison of mean profiles of qr , Nr , dvr for 1 polluted and 1 marine case.

Comparison of macrophysical variables

50 µmKK00

84 µmBR74

d d

cloud drizzle cloud drizzle

Simulations :

Fast-FSSP 256 bins In situ measurements : OAP-200X : 14 bins

35 µm 20 µm 315 µm3,5 µm <0,25 µm

Results 26 june (pristine)KK00 / measurements

BR74 / measurements

Vertical profile of qr (g kg-1) Vertical profile of Nr (g kg-1) Vertical profile of dvr (g kg-1)

Mean values in each 30 m layers

hbase

hbase

Results 9 july (polluted)KK00 / measurements

BR74 / measurements

Vertical profile of qr (g kg-1) Vertical profile of Nr (g kg-1)Mean values in each 30 m layers

Vertical profile of dvr (g kg-1)

BR74 : values < 10-5 g kg-1 BR74 : values < 10-2 l-1

Pristine case : KK00 represents with good agreement precipitating variablesPolluted case : KK00 underestimate precipitation.BR74 : underestimate precipitation by making too large drops but with very low concentration

Results, super bulk parameterisation

0

0,000005

0,00001

0,000015

0,00002

0 0,000005 0,00001 0,000015 0,00002

Results, super bulk parameterisation

Rbase (kg m-2 s-1)

• Initial profiles : profiles (or modified profiles) of ACE-2 (26 june), EUROCS, DYCOMS-RF02 differents values of LWP : 20 g m-2 < LWP < 130 g m-2

• different values of Nact : 40 cm-3 < Nact < 260 cm-3

• Domain : 10 km * 10 km.

• horizontal resolution : 100 m,

vertical resolution : 10 m near surface, in and above cloud

13,2

77,214109,1

act

baseN

LWPR

13,2

77,214109,1

actN

LWP

(= 1,7 mm d-1)

Summary

- Cloud droplet sedimentation :

Best fit with α = 3 , υ = 2 for generalized gamma law,

σg = 1,2 for lognormal law.

- Validation of the microphysical scheme :

GCSS intercomparison exercise

The KK00 scheme shows a good agreement with observations for microphysical variables

Underestimation of the precipitation flux with respect to observations.

LWP too low ?

Simulation of 2 ACE-2 case

Good agreement with observations for microphysical variables for KK00

Parameterisation of the precipitation flux for GCM :

Corroborates experimental results : Rbase is a function of LWP and Nact

KK00, sedKK00, no sed BR74, sedBR74, no sedRF02 0–800 m

KK00, sedKK00, no sed BR74, sedBR74, no sedRF02 > 450 m

Profils ACE-2

9july

26 june

Results, What about microphysics ? Observations

Variations of mean values of N and geometrical diameter for cloud and for drizzle, along 1 cloud top

leg,, 1 cloud base leg. Mean values over 12 km.(Van Zanten personnal communication).

Averaged profils of Ndrizzle, Øvdrizzle in each 30 m layer after 3 hours of

simulation.

BR74

KK00

Simulations

Nc (cm-3), Ndrizzle (l-1)

Øgc, Øgdrizzle (µm)

CT leg CB leg

CB

CT

CT leg CB leg

hsurf

(m)

hsurf

(m)

Ndrizzle (l-1)

Øvdrizzle (µm)

CB

CT

BR74 KK00

9 juillet

26 juin

dnvF cwqc

0

3 )()(6

dnvF cNc

0

)()(

hsurface

hbase

hbasehsurf

sigmameasured

measuredgamma

Ø

ØØ)Ø(

p

pppE

dv

y = 2E+14xR2 = 0,9429

0,00E+00

2,00E-06

4,00E-06

6,00E-06

8,00E-06

1,00E-05

1,20E-05

1,40E-05

1,60E-05

1,80E-05

2,00E-05

0,00E+00 2,00E-20 4,00E-20 6,00E-20 8,00E-20 1,00E-19 1,20E-19

Paramétrisations « bulk »

cloudrain

r=20 µm

On prédit les moments de la distribution qui représentent des propriétés d’ensemble (bulk) de la distribution.ex : M0=Ni , M3=qi

Modèle bulk moins de variables

Modèle explicite ou bin

On prédit la distribution elle même.~ 200 classes.

Modèle bulk

Parametrisations bulk valides dans les GCM?

•Processus microphysiques (~10 m, ~1 s) dépendent non linéairement des variables locales (qc, qr, Nc, Nr …).•Distribution temporelle et spatiale des variables non uniforme.

le modèle doit résoudre explicitement les variables locales pour que paramétrisations bulk soient valides.

utiliser paramétrisations bulk dans les GCM (~ 50 km, ~ 10 min) peut être remis en question.

collection

autoconversion accrétion

79,147,21350)(

ccautor Nqt

q 15,1)(67)(: rcaccrr qqt

qex

simulations

• On veut plusieurs champs avec différentes valeurs de <LWP>, <N>,, <R>.

• 7 simulation MESONH avec différentes valeurs de Na = 25, 50, 75, 100, 200, 400, 800 cm-

3.

• Fichier initial : champ de donnée à 12H de la simulation de cycle diurne d’Irina et al. sans schéma de précipitation.

• 24H de simulation pour chaque simulation -> LWP varie (cycle diurne du nuage).

• Domaine : 2,5 km * 2,5 km * 1220 m

• Resolution horizontale : 50 mailles,

verticale : 122 niveaux.

• Pas de temps : 1 s.

• Schéma microphysique : schéma modifié du schéma Khairoutdinov-Kogan (2000)

Début des simulations avec schéma microphysique

Fig. Profil moyen du rapport de mélange en eau nuageuse qc en fonction du temps

Schéma K&K modifié • K&K : schéma microphysique bulk pour les stratocumulus. Les coefficients ont été

ajustés avec un modèle de microphysique explicite (bin).

Intérêt : – Nact, Nc en variables pronostiques (on veut différentes valeurs de N).

– schéma développé spécialement pour les stratocumulus (particularité : pluie très faible)

7 simulations de 24 H.

1 sortie toutes les heures.

7*24 = 168 champs avec des valeurs différentes de H, <LWP>, N, <R>

Profil moyen du rapport de mélange en eau de pluie en fonction du temps

NCCN =25 cm-3

NCCN =400 cm-3NCCN =100 cm-3

NCCN =50 cm-3

Calcul de H, LWP, N, R

• mailles nuageuses : mailles ou qc > 0,025 g kg-1 cumulus sous le nuage sont rejetés.• Calcul de H

– Définition de la base?• Calcul de LWP• Calcul de N

– qc > 0,9 qadiab

– 0,4H < h <0,6 H– Nr < 0,1 cm-3

• Calcul de R– R = < qr * (Vqr-w) >, R = < qr * Vqr >– Sur fraction nuageuse, à la base.

Comparaison avec les données DYCOMS-II, ACE-2

• ACE-2– Mesures in-situ

-> vitesse des ascendances w pas prise en compte dans le calcul du flux.– Flux calculé sur la fraction nuageuse (dans le nuage)

• DYCOMS-II – Mesures radar

-> mesure du moment 6 de la distribution-> vitesse de chute réel. (vitesse ascendances w + vitesse

terminal des gouttes Vqr)– Flux calculé au niveau de la base du nuage.

R = f(H3/N)flux total à la base

0,00E+00

2,00E-06

4,00E-06

6,00E-06

8,00E-06

1,00E-05

1,20E-05

0,00E+00 5,00E-01 1,00E+00 1,50E+00 2,00E+00 2,50E+00 3,00E+00 3,50E+00

H(3%)^3/N

flux total à la base

0,00E+00

2,00E-06

4,00E-06

6,00E-06

8,00E-06

1,00E-05

1,20E-05

0,00E+00 2,00E-01 4,00E-01 6,00E-01 8,00E-01 1,00E+00

H(10%)^3/N

flux total à la base

0,00E+00

5,00E-06

1,00E-05

1,50E-05

2,00E-05

2,50E-05

0,00E+00 2,00E-01 4,00E-01 6,00E-01 8,00E-01 1,00E+00 1,20E+00

H(10%)^3/N

simul

ACE-2

DYCOMS-II

flux Vqr sur mailles nuageuses

0,00E+00

5,00E-06

1,00E-05

1,50E-05

2,00E-05

2,50E-05

0,00E+00 2,00E-01 4,00E-01 6,00E-01 8,00E-01 1,00E+00 1,20E+00

H(10%)^3/N

simul

ACE-2

DYCOMS-II

R = f(LWP/N)flux total à la base

y = 4E+14x2,2651

R2 = 0,9649

0,00E+00

2,00E-06

4,00E-06

6,00E-06

8,00E-06

1,00E-05

1,20E-05

0,00E+00 5,00E-10 1,00E-09 1,50E-09 2,00E-09 2,50E-09 3,00E-09

LWP/N

R = f(LWP/N), Na=50

0,00E+00

1,00E-06

2,00E-06

3,00E-06

4,00E-06

5,00E-06

6,00E-06

7,00E-06

8,00E-06

9,00E-06

0,00E+00

2,00E-10

4,00E-10

6,00E-10

8,00E-10

1,00E-09

1,20E-09

1,40E-09

1,60E-09

1,80E-09

LWP/N

Série1

flux total à la base

0,00E+00

2,00E-06

4,00E-06

6,00E-06

8,00E-06

1,00E-05

1,20E-05

0,00E+00 5,00E-10 1,00E-09 1,50E-09 2,00E-09 2,50E-09 3,00E-09

LWP/N

100

200

25

400

50

75

800

R = f(LWP/N), Na=200

0,00E+00

5,00E-07

1,00E-06

1,50E-06

2,00E-06

2,50E-06

3,00E-06

0,00E+00 2,00E-10 4,00E-10 6,00E-10 8,00E-10 1,00E-09 1,20E-09

LWP/N

R = f(LWP/N), Na=25

0,00E+00

2,00E-06

4,00E-06

6,00E-06

8,00E-06

1,00E-05

1,20E-05

0,00E+00 5,00E-10 1,00E-09 1,50E-09 2,00E-09 2,50E-09 3,00E-09

LWP/N

Observation d’un hystérésis :Déclenchement de la pluie avec un temps de retard.-> Il faut prendre en compte la tendance des variables d’état?

Conclusion• On retrouve bien les résultats expérimentaux :

dépendance de R en fonction des variables H ou LWP, N

• Hystérésis de + en + prononcé lorsque NCCN augmente (lorsque R augmente).

=> rajouter une variable pronostique supplémentaire (qr) ? utiliser la tendance de LWP : dLWP/dt ?

• Expliquer cette dépendance en isolant une seule cellule et en regardant comment varient qc, qr…

The problem of modeling precipitation formation in GCMPresently in GCM : parameterisation schemes of precipitation directly transposed from CRM bulk parameterization. Example : )(3/73/1

critCottonManton rvrvHLWCNAUTO c

Problem- no physically based parameterisations

Are such parameterisations, with tuned coefficients, still valid to study the AIE?

2nd solution

A parameterisation of the precipitation flux averaged over an ensemble of cells is more relevant for the GCM resolution scale

Underestimation of precipitation

1st solution

coefficients tuned against observations

Problem : Inhomogeneity of microphysical variables.

Formation of precipitation = non linear process local value have to be explicitely resolved

3D view of LWC = 0.1 g kg-1 isocontour, from the side and above.

LES domain Corresponding cloud inGCM grid point

~100min BL

~100kmHomogeneous

cloudCloud fraction F<qc>, <Nc> (m-3)

In GCM : variables are mean values smoothing effect on local peak values.

Why studying precipitation in BLSC (Boundary Layer Stratocumulus Clouds ) ?

Parameterization of drizzle formation and precipitation in BLSC is a key step in numerical modeling of the aerosol impact on climate

Hydrological point of view :Precipitation flux in BLSC ~mm d-1 against ~mm h-1 in deep convection clouds BLSC are considered as non precipitating clouds

Energetic point of view :1mm d-1 ~ -30 W m-2 Significant impact on the energy balance of STBL and on their life cycle

Aerosol impact on climate

Na

rvNc

precipitations