parameterization of cloud droplet formation and autoconversion in large-scale models
DESCRIPTION
Parameterization of cloud droplet formation and autoconversion in large-scale models. Wei-Chun Hsieh Advisor: Athanasios Nenes 10,Nov 2006 EAS Graduate Student Symposium. Photo source: CSTRIPE imagery. How does aerosol affect climate? Aerosol act as Cloud Condensation Nuclei (CCN). - PowerPoint PPT PresentationTRANSCRIPT
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?