les modeling of precipitation in boundary layer clouds and parameterisation for general circulation...
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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