# General Circulation Model

Mechanics

Feb 22, 2014 (7 years and 4 months ago)

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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 ?

-

strato

~10*ALB
sea

-

Large occurrence : ~ 20
-
30 % of the ocean’s surface.

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 ?

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

.

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 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,

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
2,00E-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 (kg m-2 / m-3)
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