LES modeling of precipitation in Boundary
Layer Clouds and parameterisation for
General Circulation Model
O. Geoffroy
J.L. Brenguier
CNRM/GMEI/MNPCA
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
Why studying Stratocumulus clouds ?

Radiative properties : ALB
strato
~10*ALB
sea

Large occurrence : ~ 20

30 % of the ocean’s surface.
Negative global radiative forcing
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
N
a
r
v
N
c
precipitations
The problem of modeling precipitation formation in GCM
Presently in GCM : parameterisation schemes of precipitation
directly transposed from CRM
bulk parameterization. Example :
)
(
3
/
7
3
/
1
crit
Cotton
Manton
rv
rv
H
LWC
N
AUTO
c
Problem

no
physically based parameterisations

Numerical instability due to step function
Are such parameterisations, with tuned
coefficients, still valid to study the AIE?
2
nd
solution
A parameterisation of the precipitation flux averaged over an ensemble of cells is more
relevant for the GCM resolution scale
Underestimation of precipitation
1
st
solution
This bias is corrected by using
tuning coefficients
In Manton

Cotton parameterisation : rv
crit
=10 µm
In GCM : rv
crit
reduced down to 5 µm.
Problem :
Inhomogeneity of microphysical variables.
Formation of precipitation = non linear process
local value have to be explicitely resolved
LES resolution: ~100m horizontally,
~10 m vertically
3D view of LWC = 0.1 g kg

1
isocontour, from the side and above.
LES domain
Corresponding cloud in
GCM grid point
~100m
in BL
~100km
Homogeneous
cloud
Cloud fraction F
<qc>, <Nc> (m

3
)
In GCM : variables are mean values
over 10 to 100 km scales
smoothing effect on local peak values
.
Super bulk parameterisation
At the scale of
an ensemble of cloud cells
:
quasi stationnary state
Is it feasible to express the
mean precipitation flux at cloud base <
F
prec
> as a function of
macrophysical variables
that characterise the cloud layer as a whole ? (
Pawlowska & Brenguier, 2003)
Pawlowska &
Brenguier
(2003, ACE

2):
N
H
F
prec
3
N
H
F
prec
4
75
.
1
)
(
N
LWP
F
prec
Comstock & al.
(2004, EPIC) :
Van Zanten & al.
(2005, DYCOMS

II) :
Which variables drive <F
prec
> at the cloud system scale ?
Adiabatic model :
LWP = ½C
w
H
2
<F
prec
>
(kg m

2
s

1
or mm d

1
)
H
(m)
or
<LWP>
(kg m

2
)
N
(m

3
)
In GCMs, H (or LWP) and N can be predicted at the scale of the cloud system

The
LWC sink term due to precipitation, averaged over numerous cloud cells, can then be
expressed as a function of these two variabless :
H
F
t
LWC
prec
prec
)
(
(kg m

3
s

1
)
Objectives & Methodology
Methodology:
3D LES simulations of BLSC fields with various
H (LWP) and N
values
Objectives :

use LES to establish the relationship between <
F
prec
>, LWP and N, and
empirically determine the coefficients.
H or <LWP>, N
<F
prec
>
a = ?
α
= ?
β
= ?
LES domain
GCM grid point
averaged LWP, N, and <F
prec
>
over the simulation domain
N
H
a
F
prec
10 km
LES microphysical scheme

Implementation in MESONH of a modified version of the
Khairoutdinov & Kogan (2000)
LES bulk
microphysical scheme
(available in MASDEV4_7 version).
Specificities :

2 moments

> predict N for studies of the aerosol impact


specifically designed for BLC =
low precipitating clouds

coefficients tuned using an explicit microphysical model as data source

> using
realistic distributions
.

LES scheme

>
valid only for CRM
.

Modifications : Cohard and Pinty
(1998) activation scheme and add of droplet sedimentation process.
Condensation
& Evaporation
:
Langlois (1973)
Autoconversion
:
K&K (2000)
Accretion
:K&K (2000)
Sedimentation of
drizzle
: K&K (2000)
Activation
:
Cohard et al
(1998)
Evaporation
: K&K (2000)
Aerosol :
N
CCN
(m

3
)
(Constant parameter)
+
Vertical velocity :
W
N
act
(m

3
)
Cloud
:
q
cloud
(kg/kg)
N
cloud
(m

3
)
Drizzle
:
q
drizzle
(kg/kg)
N
drizzle
(m

3
)
Sedimentation
of cloud droplets
Stokes law + gamma
Vapour:
q
vapour
(kg/kg)
Microphysical processes & microphysical variables
.
79
,
1
47
,
2
1350
)
(
c
c
auto
r
N
q
t
q
15
,
1
)
(
67
)
(
r
c
accr
r
q
q
t
q
1
,
0
007
,
0
vr
N
r
V
r
2
,
0
012
,
0
vr
q
r
V
r
2
1
0
2
1
)
(
M
N
k
d
n
k
F
c
c
N
c
d
n
v
F
c
w
q
c
0
3
)
(
)
(
6
d
n
v
F
c
N
c
0
)
(
)
(
5
2
0
5
1
)
(
6
M
N
k
d
n
k
F
c
c
w
q
c
(H) : Stokes regime:
2
1
)
(
k
v
Parameterisation of cloud droplets sedimentation
Calculation of the cloud droplet sedimentation process requires an
idealized droplet size distribution
.
Objective :
Which distribution to select? With which parameter ?
)
)
ln
)
Ø
/
Ø
ln(
(
2
1
exp(
ln
Ø
2
1
)
Ø
(
2
g
n
g
c
n
)
)
Ø
(
exp(
Ø
)
(
)
Ø
(
1
c
n
Generalized gamma law :
Lognormal law :
Methodology
.
By comparing with ACE

2 measured spectra (resolution = 100 m),
find the idealized distribution which best represents the :

diameter of the 2
nd
moment
,

diameter of the 5
th
moment
,

effective diameter
.
e
5
2
The cloud sedimentation flux depends on the
2
nd
and 5
th
moments
Radiatives flux in LW depends on the
effective radius
.
Results for gamma law,
α
=3,
υ
=2
Number of spectra in
% of max_pts
100 %
50 %
0 %
Ø
2
σ
Ø
e
Ø
e
Ø
5
measured
gamma
5
5
Ø
Ø
measured
gamma
2
2
Ø
Ø
measured
gamma

Generalized gamma law : best results for
α
=3,
υ
=2

Lognormal law, similar results with
σ
g
=
1.2
~ DYCOMS

II results (M.C. Van Zanten personnal
communication).
measured
2
Ø
measured
5
Ø
measured
measured
e
Ø
measured
e
Ø
measured
e
gamma
e
Ø
Ø
measured
e
gamma
e
Ø
Ø
only spectra
at cloud top
Results for lognormal law,
σ
g
=
1.5
% of max_pts
100 %
50 %
0 %
Ø
2
σ
Ø
e
Ø
e
Ø
5
measured
gamma
5
5
Ø
Ø
measured
gamma
2
2
Ø
Ø
measured
gamma
Lognormal law, with
σ
g
=
1.5,
overestimate sedimentation flux of cloud droplets
.
measured
2
Ø
measured
5
Ø
measured
measured
e
Ø
measured
e
Ø
measured
e
gamma
e
Ø
Ø
measured
e
gamma
e
Ø
Ø
only spectra at cloud top
GCSS intercomparison exercise
Case coordinator : A. Ackermann (2005)
Case studied : 2
nd
research flight (RF02) of DYCOMS

II 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 LW radiative fluxes,
•
fixed surface fluxes,
•
fixed cloud droplet concentration : Nc = 55 cm

3
•
2 simulations :

1 without cloud droplet sedimentation
.

1 with cloud droplet sedimentation
: lognormale law with
σ
g
= 1.5
Microphysical schemes tested :

K&K scheme
,

C2R2 scheme
(= Berry and Reinhardt scheme (1974)).
4 simulations.
K&K, sed ON / sed OFF
C2R2, sed ON / sed OFF
Results, LWP, precipitation flux
Central half of the
simulation ensemble
Ensemble range
Median value of the
ensemble of models
K&K, sed : ON
K&K, sed : OFF
NO DATA
LWP (g m

2
) = f(t)
Precipitation flux at surface (mm d

1
) = f(t)
Precipitation flux at cloud base (mm d

1
) = f(t)
C2R2, sed ON
C2R2, sed OFF
6H
6H
3H
3H
3H
3H
6H
6H
3H
6H
observations

LWP a little too low

Underestimation of precipitation flux
~0.35 mm d

1
~1.24 mm d

1
Results,discussion
Strong variability of N and F
prec
:
Black : F
prec
> 5 mm d

1
Light grey : F
prec
< 1 mm d

1
N
c
(cm

3
)
Variation of N
c
along 1
cloud top leg
Resolution : 1 km
(Van Zanten et al, 20004)
measures
Nc < 55 cm

3
in heavily precipitating areas.
Results,
What about microphysics ?
Observations
Variations of
N
,
geometrical diameter
for cloud and for
drizzle, along 1 cloud top leg, 1 cloud base leg.
(Van Zanten personnal communication).
Averaged profils on precipitating grid points after 2 hours of
simulation : N
drizzle
, q
drizzle
,
Ø
v
drizzle
,
Ø
v
cloud
C2R2
K&K
<top height>
< base height>
N
drizzle
(l

1
)
q
drizzle
(g kg

1
)
Ø
v
drizzle
(µm)
Ø
v
cloud
(µm)
Simulations

Underestimation of precipitation flux at the base for K&K scheme and C2R2 scheme.
N
c
is too large in simulation? LWP is too low?

K&K scheme reproduce with good agreement microphysical variables. C2R2 scheme : large and few drops.
N
c
(cm

3
),
N
drizzle
(l

1
)
Ø
g
c
ø
,
Ø
g
drizzle
(µm)
Cloud
Top
leg
Cloud
base
leg
K&K
C2R2
Results, super bulk parameterization
y = 4E+14x
2,2651
R
2
= 0,9649
0,00E+00
2,00E06
4,00E06
6,00E06
8,00E06
1,00E05
1,20E05
0,00E+00
5,00E10
1,00E09
1,50E09
2,00E09
2,50E09
3,00E09
LWP/N (kg m2 / m3)
3
,
2
14
)
(
10
4
N
LWP
F
prec
<F
prec
> : averaged
precipitation flux
at cloud base
(kg m

2
s

1
)
•
7 simulations
with different values of
N : N
a
= 25, 50, 75, 100, 200, 400, 800 cm

3

>
different values of N
•
Simulations of diurnal cycles

>
variations of LWP
•
Domain : 2,5 km * 2,5 km * 1220 m
•
horizontal resolution : 50 m,
vertical resolution : 10 m.
<F
prec
> = (LWP/N)
Conclusion & Perspectives

Cloud droplet sedimentation
:
Best fit with
α
= 3 ,
υ
= 2 for generalized gamma law,
σ
g
= 1,2 for lognormal law.

Validation of the microphysical scheme :
GCSS intercomparison exercise
The K&K scheme shows a good agreement with observations for microphysical variables
Underestimation of the precipitation flux with respect to observations.
Nc too large ?

> Simulations with N
c
prognostic
Simulation of 2 ACE

2 case

> Simulations of a clear and a polluted case of the ACE

2 experiment and
comparison with observations

Parameterisation of the precipitation flux for GCM :
corroborates experimental results : <F
prec
> is a function of LWP and N

> 3D simulations over a larger domain in order to improve statistics

> 1D water budget simulations for explaining the dependence
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