Parameterization of cloud Parameterization of cloud droplet formation and droplet formation and

autoconversion in large-scale autoconversion in large-scale modelsmodels

Wei-Chun Hsieh Wei-Chun Hsieh Advisor: Athanasios NenesAdvisor: Athanasios Nenes

10,Nov 200610,Nov 2006EAS Graduate Student SymposiumEAS Graduate Student Symposium

Photo source: CSTRIPE imagery

• How does aerosol affect climate?• Aerosol act as Cloud Condensation Nuclei (CCN).• Anthropogenic emissions increase their levels Decreases cloud droplet size more reflection of sunlight cloud precipitation decreases

Both effects are called “aerosol indirect climatic effect”. The “second” aerosol indirect effect affect cloud lifetime and the hydrological cycle. This is potentially a very important (but uncertain) component of climate change.

More CCNLess CCN

Evidence from Satellite: ship tracks

Rosenfeld, Kaufman, and Koren, ACPD, 2005

• In the region of shiptracks, droplet size is VERY small and clouds are thicker (i.e., they don’t drizzle as much).

• Outside of the shiptrack region, droplet are very big (enough to drizzle and form rain).

West coast (California) in the visible.

Effective radius (μm)

West coast (California)

Estimate of aerosol indirect effect subject to large uncertainty

And this is just for the “first” indirect effect! No estimate with any degree of certainty for“second” indirect effect.

Linking aerosols to cloud & rain formation

Autoconversion is the process which describe collision and coalescence of cloud drops in warm liquid clouds, initializing precipitation and is the dominate process of the drizzle formation in stratiform clouds.

larger/cloud droplets

aerosols

droplets

nucleation, activation

diffusional growth

collision-coalescence

drizzle formationrain drops

rain

Size Classification

Processes

Autoconversion schemes I

Lc

PK

L

•PK: Autoconversion rate•L: Liquid water content •Lc: critical liquid water content, represents when precipitation starts. •qc: cloud water mixing ratio•EMC refers to an average collection efficiency

H: Heaviside functionThreshold process

cloud droplet number concentration

Parameterization Formulation

Kessler (1969) )( cKK LLLHcP

Manton-Cotton (1977) cMC

wMC RRHLNEP 33

3/73/13/4

1 43

Cohard and Pinty (2000)

16

3202

)5.7105.0(7.3

4.010161107.2

c

ca

ccca

CP

q

DqP

mean volume drop diameter

standard deviation of the cloud-drop size distribution

L: Liquid water content

(MC)

(CP)

Autoconversion schemes II

Parameterization Formulation

Liu and Daum (2004) R4 c

w

RRHLNEP 443/73/14

44

3/4

14 43

Liu and Daum (2004) R6 c

w

RRHLNNLP 66

3/73/13/2

6626 4

3

•P: Autoconversion rate

•Two parameters 4 and 6 are related to cloud spectrum dispersion

12/122

4/12

4121

31

6/1

22

222

6 211514131

relative dispersion (defined as the standard deviation of cloud drop distribution divide by the mean drop size)

(R4)

(R6)

time/ height

Smax

air parcel

Activation parameterization• Fountoukis and Nenes

activation parameterization (2005), which predicts the equilibrium cloud droplet number concentration based on parcel maximum supersaturation(Smax).

• Köhler theory: for those CCN (Cloud Condensation Nuclei) with critical supersaturation(Sc) less than Smax can be activated to cloud droplets.

• compute the droplet size distribution at the point of Smax

Parcel supersaturation

S

New Framework• Our framework computes the evolution of the droplet size distribution as a function

of height in the cloud; P at each point in the cloud are calculated and then integrated over the whole depth to obtain total P.

• droplet ascend in an updraft and evolve within a Lagrangian parcel.• Growth beyond the point of smax in a cloud is represented by the diffusional growth

of the droplet size distribution as it ascends in the cloud.• At each point in the cloud, autoconversion is calculated using existing

parameterizations.

cloud base

cloud top

Droplet growth and development of collision- coalescence(New framework)

Droplet activationsmax

updraft

heig

ht

autoconversion

air parcel continually goes up

0.00E+00

5.00E-02

1.00E-01

1.50E-01

2.00E-01

2.50E-01

3.00E-01

3.50E-01

4.00E-01

4.50E-01

5.00E-01

1.00E-07 1.00E-06 1.00E-05 1.00E-04 1.00E-03 1.00E-02

LWMR (kg/kg)

S(%

)

Parcel modelParameterization

0.00E+00

2.00E-04

4.00E-04

6.00E-04

8.00E-04

1.00E-03

1.20E-03

1.40E-03

1.60E-03

1.80E-03

2.00E-03

0 50 100 150 200 250 300 350

Time (s)

LWM

R (k

g/kg

)

Parcel modelParameterization

Evaluation of parameterization:Comparison between parcel model and parameterization

supersaturation (S) LWMR

Liquid Water Mixing Ratio

Evaluation of new framework• Comparison of autoconversion rates calculated from

Parcel model Parameterization In-situ field measurements data

• In-situ liquid water content, droplet number concentration, droplet spectrum

• NOT measured precipitation

Mission CRYSTAL-FACE CSTRIPE

Full name

The Cirrus Regional Study of Tropical Anvils and Cirrus Layers – Florida Area Cirrus Experiment

Coastal STRatocumulus Imposed Perturbation Experiment

Cloud type Cumulus Marine stratoculumus Time period July, 2002 July, 2003

Location Florida off the coast of Monterey, California

Autoconversion rates from parcel model, parameterization and in-situ data

• Autoconversion rates increase with increase of LWMR• R6 predicts lower autoconversion rates • Difference between autoconversion schemes can be up to 2 order of magnitude

1.00E-11

1.00E-10

1.00E-09

1.00E-08

1.00E-07

1.00E-06

1.00E-05

0.00E+00 2.00E-04 4.00E-04 6.00E-04 8.00E-04 1.00E-03

LWMR (kg kg-1)

Aut

ocon

vers

ion

rate

s (k

g m

-3 s

-1) Parcel_R4

Para_R4obs_R4Parcel_MCPara_MCobs_MCParcel_CPPara_CPobs_CPParcel_R6Para_R6obs_R6

• The predicted autoconversion rates by parcel model and paramterization agree well with observed ones.

• This good agreement indicates that the predicted cloud droplet number concentration by parcel model and parameterization is close to observed values, since the MC scheme depends on droplet number and liquid water content only.

Comparison of autoconversion rates calculated from parcel model, parameterization, in-situ data

MC autoconversion rates

0.00E+00

5.00E-09

1.00E-08

1.50E-08

2.00E-08

2.50E-08

3.00E-08

3.50E-08

0.00E+00

5.00E-09 1.00E-08 1.50E-08 2.00E-08 2.50E-08 3.00E-08 3.50E-08

observation

parc

el m

odel

, par

amet

eriz

atio

n

ParcelPara1:1

CP autoconversion rates

0.00E+00

5.00E-10

1.00E-09

1.50E-09

2.00E-09

2.50E-09

3.00E-09

0.00E+00 5.00E-10 1.00E-09 1.50E-09 2.00E-09 2.50E-09 3.00E-09

observation

parc

el m

odel

, par

amet

eriz

atio

n

ParcelPara1:1

• Observation is from CSTRIPE (marine stratocumulus)

R4 autoconversion rates

0.00E+00

5.00E-09

1.00E-08

1.50E-08

2.00E-08

2.50E-08

3.00E-08

3.50E-08

0.00E+00

5.00E-09 1.00E-08 1.50E-08 2.00E-08 2.50E-08 3.00E-08 3.50E-08

observation

parc

el m

odel

, par

amet

eriz

atio

n

ParcelPara1:1

R6 autoconversion rates

0.00E+00

5.00E-09

1.00E-08

1.50E-08

2.00E-08

2.50E-08

3.00E-08

0.00E+00 5.00E-09 1.00E-08 1.50E-08 2.00E-08 2.50E-08 3.00E-08observation

parc

el m

odel

, par

amet

eriz

atio

n

ParcelPara1:1

. Underestimation of autoconversion rates by parcel model, parameterization

. This underestimation mainly due to underestimation of droplet spectrum by parcel model and parameterization

spectrum width (m)

0.00E+00

2.00E-06

4.00E-06

6.00E-06

8.00E-06

1.00E-05

1.20E-05

1.40E-05

0.00E+00 2.00E-06 4.00E-06 6.00E-06 8.00E-06 1.00E-05 1.20E-05 1.40E-05observation

parc

el m

odel

, par

amet

eriz

atio

n

ParcelPara1:1

Summary A parameterization framework that links cloud

activation with collision-coalescence and drizzle formation is developed for usage in global models.

The calculated autoconversion rates from parcel model and parameterization agree well with those calculated from in-situ observations of droplet size distribution in cumulus and stratocumulus clouds.

The developed parameterization framework reasonably represents the evolution of cloud droplets in updraft regions and is capable for different cloud types.

Future plans Implementation of autoconversion

parameterization into GISS GCM GCM runs of precipitation patterns based

on implemented parameterization Evaluate the GCM precipitation with

satellite retrieved precipitation data from TRMM (Tropical Rainfall Measuring Mission)

Simulations of aerosol indirect forcing Evaluate the GCM cloud microphysics

properties with satellite data

AcknowledgmentsAcknowledgmentsDOE, Department of Energy

Athanasios Nenes

Nicholas Meshkhidze

Rafaella Sotiropoulou

Christos Fountoukis

Akua Asa-Awuku

Luz Padro

Jeessy Medina

Donifan Barahona

Thank you

Questions?