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LES modeling of precipitation in Boundary Layer Clouds and parameterisation for General Circulation Model. CNRM/GMEI/MNPCA. Olivier Geoffroy. Jean-Louis Brenguier, Frédéric Burnet, Irina Sandu, Odile Thouron. Parameterisations in GCM = CRM bulk parameterisation. Ex :. AUTO.

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slide1
LES modeling of precipitation in Boundary Layer Clouds and parameterisation for General Circulation Model

CNRM/GMEI/MNPCA

Olivier Geoffroy

Jean-Louis Brenguier, Frédéric Burnet,

Irina Sandu, Odile Thouron

the problem of modeling precipitation formation in gcm

Parameterisations in GCM = CRM bulk parameterisation.

Ex :

AUTO

- Formation of precipitation

=

non linear process :

Nc=cste

LWC

Underestimation of precipitation in GCM

Biais correctedby tuning coefficientsagainst observations

  • Problem
  • nophysically based parameterisations, numerical instability due to step function
  • Are such parameterisations, with tuned coefficients, still valid to study the AIE?
The problem of modeling precipitation formation in GCM
  • Variables in GCM =mean valuesover a large area in GCM.

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

super bulk parameterisation

Which variables drive Rbase at the cloud system scale ?

H (m)

or

LWP (kg m-2)

N

(m-3)

Adiabatic model :

LWP = ½CwH2

Rbase(kg m-2 s-1 or mm d-1)

Pawlowska & Brenguier

(2003, ACE-2):

Comstock & al.

(2004, EPIC) :

Van Zanten & al.

(2005, DYCOMS-II) :

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 baseRbase as a function of macrophysical variables that characterise the cloud layer as a whole ?

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

objectives methodology
Objectives & Methodology

Objectives :

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

Methodology:

3D LES simulations of BLSC fields with variousLWP, Nactand corresponding Rbase values

Suppose power law

relationship

Regression analysis

a = ? α= ? β = ?

outline
Outline
  • Presentation of the LES microphysical scheme

Particular focus on cloud droplet sedimentation parameterisation

  • Validation of the microphysical scheme

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

microphysical Processes and variables

Evaporation : K&K (2000)

Air :

qv (kg/kg)

θ (K)

Autoconversion :

K&K (2000)

Cloud :

qc(kg/kg)

Nc (m-3)

Drizzle :

qr(kg/kg)

Nr(m-3)

Condensation

& Evaporation :

Langlois (1973)

Accretion :

K&K (2000)

Air:

Aerosols :

C (m-3), k, µ, ß

(= constant parameters)

W (m s-1)

θ (K)

Na(m-3)

Activation :

Cohard and al (1998)

Sedimentation of cloud droplets :

Stokes law + generalized gamma law

Sedimentation of drizzle drops : K&K (2000)

parameterisation of cloud droplets sedimentation

Which distribution to select? With which parameter ?

Generalized gamma :

Lognormal :

(H) : Stokes regime:

  • 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 .
Parameterisation of cloud droplets sedimentation
results for gamma law 3 2

E(d5) (%)

E(d2) (%)

Results for gamma law, α=3, υ=2

d2

d5

100 %

Color

=

number of spectra in each pixel in

% of nb_max

50 %

0 %

E(deff) (%)

E(deff) (%)

deff

only spectra

at cloud top

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).
results for lognormal law g 1 5

E(d5) (%)

E(d2) (%)

Results for lognormal law, σg=1.5

d2

d5

100 %

Color

=

number of spectra in each pixel in

% of nb_max

50 %

0 %

E(deff) (%)

E(deff) (%)

only spectra

at cloud top

deff

deff

Lognormal law, with σg=1.5,overestimate sedimentation flux of cloud droplets.

gcss intercomparison exercise case coordinator a ackermann 2005
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

6H

6H

6H

3H

3H

3H

Median value of the ensemble of models

observation

Results, LWP, precipitation flux

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

KK00, sed

BR74, sed

Central half of the simulation ensemble

KK00, no sed

  • LWP too low

Ensemble range

BR74, no sed

6H

3H

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

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

~0.35 mm d-1

6H

3H

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

  • 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

NO DATA

~1.29 mm d-1

results what about microphysics

KK00

&

measurements

drizzle

cloud

drizzle

cloud

BR74

50 µm

d

84 µm

d

Results, What about microphysics ?

Ndrizzle (l-1)

dvdrizzle (µm)

hsurf (m)

hsurf (m)

CT

CT

BR74

BR74

KK00

KK00

CB

CB

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)

  • - KK00 scheme reproduce with good agreement microphysical variables at cloud top and cloud base
  • BR74 scheme : too few and too large drops.
simulation of 2 ace ii cases1

In situ measurements :

Fast-FSSP 256 bins

OAP-200X : 14 bins

35 µm

3,5 µm

315 µm

20 µm

<0,25 µm

Simulations :

drizzle

cloud

drizzle

cloud

KK00

BR74

50 µm

d

84 µm

d

Simulation of 2 ACE-II cases

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

  • Domain : 10 km × 10 km,
  • resolution : horizontaly : 100 m, verticaly : 10 m in/above the cloud
  • initialisation : corresponding profile of thermodynamical variables.

Comparison of macrophysical variables

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

results 26 june pristine

KK00 / measurements

hbase

Results 26 june (pristine)

BR74 / measurements

hbase

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

results 9 july polluted

KK00 / measurements

Results 9 july (polluted)

BR74 / measurements

BR74 : values < 10-2 l-1

BR74 : values < 10-5 g kg-1

Pristine case : KK00 represents with good agreement precipitating variables

Polluted case : KK00 underestimate precipitation.

BR74 : underestimate precipitation by making too large drops but with very low concentration

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)

results super bulk parameterisation1
Results, super bulk parameterisation
  • 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

Rbase

(kg m-2 s-1)

(= 1,7 mm d-1)

summary
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
rf02 0 800 m

BR74, no sed

KK00, no sed

KK00, sed

BR74, sed

RF02 0–800 m
rf02 450 m

BR74, no sed

KK00, no sed

KK00, sed

BR74, sed

RF02 > 450 m
profils ace 2
Profils ACE-2

9july

26 june

results what about microphysics1

Observations

Simulations

Ndrizzle (l-1)

hsurf (m)

Results, What about microphysics ?

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

CT leg

CB leg

CT

BR74

KK00

CB

Øvdrizzle (µm)

hsurf (m)

CT leg

Øgc, Øgdrizzle (µm)

CB leg

CT

BR74

KK00

CB

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.

slide33

hsurf

hbase

hbase

sigma

dv

hsurface

param trisations bulk

cloud

rain

r=20 µm

Paramétrisations « bulk »

Modèle bulk

On prédit les moments de la distribution qui représentent des propriétés d’ensemble (bulk) de la distribution.

ex : M0=Ni , M3=qi

Modèle explicite ou bin

On prédit la distribution elle même.

~ 200 classes.

Modèle bulk moins de variables

parametrisations bulk valides dans les gcm
Parametrisations bulk valides dans les GCM?

collection

accrétion

autoconversion

    • 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 résoudreexplicitement les variables locales pour que paramétrisations bulk soient valides.
  • utiliser paramétrisations bulk dans les GCM (~ 50 km, ~ 10 min) peut être remis en question.
simulations
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.
  • Schémamicrophysique : schéma modifié du schéma Khairoutdinov-Kogan (2000)

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

Début des simulations avec schéma microphysique

sch ma k k modifi
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)
slide39
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 m lange en eau de pluie en fonction du temps
Profil moyen du rapport de mélange en eau de pluie en fonction du temps

NCCN =25 cm-3

NCCN =50 cm-3

NCCN =400 cm-3

NCCN =100 cm-3

calcul de h lwp n r
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 donn es dycoms ii ace 2
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 lwp n
R = f(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
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 gcm1

~100m

in BL

Homogeneous cloud

Cloud fraction F

<qc>, <Nc> (m-3)

In GCM : variables are mean values

smoothing effect on local peak values.

~100km

Corresponding cloud in

GCM grid point

Underestimation of precipitation

1st solution

coefficients tuned against observations

  • Problem
  • nophysically 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

Presently in GCM : parameterisation schemes of precipitation directly transposed from CRM bulk parameterization. Example :

The problem of modeling precipitation formation in GCM

Problem :

Inhomogeneity of microphysical variables.

Formation of precipitation = non linear process

local value have to be explicitely resolved

LES domain

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

why studying precipitation in blsc boundary layer stratocumulus clouds

Hydrological point of view :

  • Precipitation flux in BLSC ~mm d-1 against~mm h-1 in deep convection clouds
  • BLSCare considered as non precipitatingclouds

Aerosol impact on climate

Energetic point of view :

1mm d-1 ~ -30 W m-2

Significant impact on the energy balance of STBL and on their life cycle

Nc

rv

Na

precipitations

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