Using TWP-ICE Observations and CRM Simulations to Retrieve Cloud Microphysics Processes

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3 Νοε 2013 (πριν από 3 χρόνια και 5 μήνες)

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Using TWP
-
ICE Observations and CRM Simulations to Retrieve Cloud Microphysics Processes


Xiping Zeng
1,2
, Wei
-
Kuo Tao
2
, Shaocheng Xie
3
, Minghua Zhang
4
, and Steve Lang
2



1
GEST/UMBC, Greenbelt
2
NASA/GSFC, Greenbelt


3
LLNL, Livermore
4
Stony Brook University, New York



CONCLUSIONS & FUTURE WORK




Simulations

show

that

TWP
-
ICE

cloud

ensembles

are

sensitive

to

IN

and

ice

crystal

enhancement

factor
.

However,

they

are

not

so

sensitive

as

the

ARM
-
SGP

ones,

which

resembles

that

of

SCSMEX/NESA

and

KWAJEX

simulations

(Zeng

et

al
.

2008
a)
.




Present

and

other

sensitivity

experiments

reveal

that

the

modeled

cloud

ensembles

are

close

to

observations

when


N
i

is

high

in

the

Tropics
.

In

contrast,

they

are

close

to

observation

when


N
i

is

low

in

middle

latitudes
.




Suppose

that

the

ensemble

average

of

IN

concentration

in

the

Tropics

is

close

to

or

less

than

that

in

middle

latitudes
.

Thus,

the

ice

crystal

enhancement

factor

in

the

Tropics

is

about

10
3

times

that

in

middle

latitudes,

which

is

consistent

with

the

model

of

Blyth

and

Latham

(
1997
)

based

on

observations
.





Future

study

will

address

the

proper

representation

of

IN

and

ice

crystal

multiplication

in

CRM

simulations
.


Acknowledgements
:

This

work

was

supported

by

the

DOE

ARM

Program
.


ARM
-
SGP SIMULATIONS



Three

ARM
-
SGP

campaigns,

conducted

in

the

summer

of

1997
,

spring

of

2000

and

summer

of

2002
,

provided

large
-
scale

forcing

data

to

drive

the

model
.

Their

numerical

simulations

lasted

for

20
,

29

and

20

days,

respectively
.

Fig
.

4

displays

the

time
-
pressure

cross

sections

of

ice

water

content

from

observations

and

the

two

numerical

experiments
.

Fig
.

5

displays

the

modeling

biases

of

three

variables

against


N
i

at

-
10

C
.






Fig. 1

Schematic of a convective cloud with a cirrus anvil that modulates solar and
infrared radiation. Stars and circles denote ice crystals and water drops, respectively.
The Bergeron process exists in the mixed
-
phase region in the middle troposphere.

Fig
.

5

Modeling

biases

of

upward

infrared

flux

at

the

top

of

the

atmosphere

(thin

lines

in

the

lower

part),

precipitable

water

(thick

lines)

and

surface

precipitation

rate

(thin

lines

in

the

upper

part)

versus

the

IN

concentration

at

-
10
°
C

in

midlatitudes

(or

the

ARM
-
SGP

site)
.

Red

and

blue

lines

represent

the

results

in

spring

and

summer,

respectively
.


Fig
.

4

Time
-
pressure

cross

section

of

ice

water

content

for

the

ARM
-
SGP
-
2000

numerical

experiments

and

field

observations
.

The

upper,

middle

and

lower

panels

represent

the

retrievals

(observations)

and

the

experiments

with

low

and

high

IN

concentration,

respectively
.


INTRODUCTION



Ice

nuclei

(IN),

a

form

of

aerosol

particles,

can

change

clouds

via

the

Bergeron

process

and

thus

modulate

radiation

(see

Fig
.

1
)
.

Previous

Cloud
-
Resolving

Model

(CRM)

simulations

revealed

that

cloud

ensembles

and

radiation

are

sensitive

to

IN

concentration

as

well

as

the

ice

crystal

enhancement

factor

(Zeng

et

al
.

2008
a,

b)
.

In

this

study,

TWP
-
ICE

and

ARM
-
SGP

observations

are

used

to

analyze

the

difference

in

the

ice

crystal

enhancement

factor

between

the

Tropics

and

midlatitudes
.


NUMERICAL SIMULATIONS


The

number

concentration

of

active

natural

IN

changes

with

air

temperature

T

as

(Fletcher

1962
)


N
i
=
n
0
exp[

(
T
0
-
T
)]

where

n
0

is

typically

about

10
-
8

cm
-
3

with

variations

of

several

orders

of

magnitude
;



=
0
.
6

and

can

range

from

0
.
4

to

0
.
8
;

and

T
0
=
273
.
16

K
.

Thus,

the

conversion

rate

of

cloud

ice

to

snow

due

to

vapor

deposition

in

the

Bergeron

process

is

directionally

proportional

to


3
q
i
-
m
I
50

-
1

N
i

where

q
i

is

the

mixing

ratio

of

cloud

ice,

m
I
50

the

mass

of

a

50


m

size

crystal,



the

ice

crystal

enhancement

factor

(Zeng

et

al
.

2008
a)
.

Obviously,


n
0

works

as

one

factor

in

the

conversion

rate
.


Three
-
dimensional

CRM

simulations,

driven

with

TWP
-
ICE

and

ARM
-
SGP

forcing,

are

carried

out

to

show

the

sensitivity

of

cloud

ensembles

to


n
0

in

the

Tropics

and

midlatitudes,

using

the

same

model

setup

as

the

previous

ones

(Tao

et

al
.

2003
,

Zeng

et

al
.

2007
,

2008
a)
.

Due

to

lack

of

sufficient

observations

of



and

N
i
,

the

modeled

cloud

ensembles

are

compared

with

observations

to

diagnose


n
0
.

If

the

cloud

ensembles

with

a

particular


n
0

agree

with

observations,

the

value

can

be

treated

as

an

in
-
situ


n
0
.



TWP
-
ICE SIMULATIONS



Two

TWP
-
ICE

simulations

were

conducted

over

the

mainland

subdomain
.
They

started

at

1500

UTC

19

January

2006

and

lasted

for

5

days
.

They

used

(

,


n
0
)=(
0
.
4
,

1
.
2
x
10
-
9

cm
-
3
)

and

(
0
.
6
,

1
.
2
x
10
-
6

cm
-
3
),

which

are

denoted

as

low

and

high

IN

concentrations,

respectively
.

Fig
.

2

displays

the

modeled

surface

precipitation

rate

and

precipitable

water

in

contrast

to

observations
.

Fig
.

3

displays

the

time
-
pressure

cross

sections

of

modeled

ice

water

content
.







REFERENCES

1
.

Blyth,

A
.

M
.

and

J
.

Latham,

1997
:

A

multi
-
thermal

model

of

cumulus

glaciation

via

the

Hallett
-
Mossop

process
.

Quart
.

J
.

Roy
.

Meteor
.

Soc
.
,

123
,

1185
-
1198
.

2
.

Fletcher,

N
.

H
.
,

1962
:

The

physics

of

Rain

Clouds
.

The

Cambridge

University

Press,

386

pp
.

3
.

Tao,

W
.
-
K
.
,

J
.

Simpson,

D
.

Baker,

S
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Braun,

M
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D
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Chou,

B
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Ferrier,

D
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Johnson,

A
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Khain,

S
.

Lang,

B
.

Lynn,

C
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-
L
.

Shie,

D
.

Starr,

C
.
-
H
.

Sui,

Y
.

Wang

and

P
.

Wetzel,

2003
:

Microphysics,

radiation

and

surface

processes

in

the

Goddard

Cumulus

Ensemble

(GCE)

model
.

Meteor
.

Atmos
.

Phys
.
,

82
,

97
-
137
.

4
.

Zeng,

X
.
,

W
.
-
K
.

Tao,

M
.

Zhang,

C
.

Peters
-
Lidard,

S
.

Lang,

J
.

Simpson,

S
.

Kumar,

S
.

Xie,

J
.

L
.

Eastman,

C
.
-
L
.

Shie

and

J
.

V
.

Geiger,

2007
:

Evaluating

clouds

in

long
-
term

cloud
-
resolving

model

simulations

with

observational

data
.

J
.

Atmos
.

Sci
.
,

64
,

4153
-
4177
.


5
.

Zeng,

X
.
,

W
.
-
K
.

Tao,

S
.

Lang,

A
.

Y
.

Hou,

M
.

Zhang

and

J
.

Simpson,

2008
a
:

On

the

sensitivity

of

atmospheric

ensembles

to

cloud

microphyics

in

long
-
term

cloud
-
resolving

model

simulations
.

J
.

Meteor
.

Soc
.

Japan
.

(in

press)
.

6
.

Zeng,

X
.
,

W
.
-
K
.

Tao,

M
.

Zhang,

A
.

Y
.

Hou,

S
.

Xie,

S
.

Lang,

X
.

Li,

D
.

Starr,

X
.

Li,

and

J
.

Simpson,

2008
b
:

The

indirect

effect

of

ice

nuclei

on

atmospheric

radiation
.

Submitted

to

J
.

Atmos
.

Sci
.



Fig
.

2

The

modeled

surface

precipitation

rate

versus

time

(left)

and

precipitable

water

versus

time

(right)
.

Black,

green

and

red

lines

represent

the

results

from

observations,

the

numerical

simulations

with

the

low

and

high

IN

concentrations,

respectively
.

Fig
.

3

Time
-
pressure

cross

section

of

ice

water

content

for

the

TWP
-
ICE

numerical

experiments
.

The

left

and

right

panels

represent

the

experiments

with

low

and

high

IN

concentration,

respectively
.