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Cloud Microphysics and Climate
George A. Isaac, Ismail Gultepe and Faisal Boudala
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Parameterization of effective sizes of ice crystals in climate models and the effect of small crystals: CCCMA
GCM simulations preliminary results
Faisal B. (1,2), George I. (1,2), and Norm M. (3)
1) Department of Oceanography, Dalhousie University, Halifax,
Nova Scotia, Canada
2) Cloud Physics Research Division, Meteorological Service of Canada, Toronto, Ontario, Canada
3) Canadian Centre for Climate Modeling and Analysis, Meteorological Service of Canada
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ObjectiveTo test the sensitivity of Dge in climate models and particularly the effect of small particles
4 simulations have been conducted
Dge(T) - without small particles
Dge+s(T) - with small particles
Dge+s(IWC,T) - with small particles
Preliminary results
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Comparisons of parameterized ice crystals mean effective sizes
• Tropical (M202)• Boudala et
al(2002)• L and R (1996)• CCCMA
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
15
30
45
60
75
90
105
120
135
IWC (g m-3)
De
(µ
m)
-65 o C-55o C -45 o C-35 -20 o C0o C
Bo udala e t. al (2002)
M (2002)
CCCMA
Lo hmann and Ro e c kne r (1996)
-6 5 -5 5 -4 5 -3 5 -2 5 -1 5 -51 0
2 0
3 0
4 0
5 0
6 0
7 0
Dg
e (µ
m)
Te m p e ratu re (o C)
Dg e
D
g e +sR
R =1 0 0 *( Dg e
-Dg e +s
) /Dg e
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Long wave cloud forcing: Model and satellite in Winter
( , ) ( ) ( , ) cross section4 ( ) ( )( , ) 1 exp( ) effeciency
( )
bs c abs
ibs
c
D A D Q Dn m DDA D
λ λπ λ
λλρ
= ↔
= − − ↔ Arnott et al.(1994)
3 0a t 1 0 ,1 2D m sig n ific a n t
m mµ
λ µ µ= →=
• Direct effects (increased clouds)
• Indirect effect
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Short wave cloud forcing: Model and satellite in Winter
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Anomaly (Dge - Dge+s) in IR flux at the top of the atmosphere. Summer (JJA), Fall (SON), winter (DJF) . The IR flux is considered positive in the
upward direction.
Maximum near the TropicsMoves southward in NH WinterThe anomaly is mostly positivePositive anomaly >Atm. Is optically thick
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Global distribution of anomaly in energy balance TOA in Winter
• Mostly negative NH
Winter Dge Dge+s∆E -∆E
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Summary of preliminary observations• Sensitivity to addition of small particles
– IR radiation• Less energy flux at the top of the atmosphere (more absorption)• More pronounced in the tropics
– Solar radiation• Increased cloud forcing ( more reflection)
– The net effect is spatially variable• Comparisons of Dge(T) , Dge(T,IWC), and CCCMA
– Model• CCCMA and Dge(T) gave similar results• No significant difference between Dge+s (T) and Dge+s(T,IWC)
– With observation• Hard to compare since CCCMA Dge is tuned, but generally including
small particles seems to improve the model results particularly in the Northern hemisphere mid-latitude, but not in the tropics and the tropics is better captured by Dge(T) simulation . This suggests that Dges in the tropics are larger.
• General comments and future works– Dge in the tropics seems to be larger– Tropics and Mid-latitude need to be combined some way
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Anomaly in cloudines in Winter
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Cloud cover versus RH and microphysical parameters (MP),
and statisticalsummary of MP
• I. Gultepe and G. Isaac• Meteorological Service of Canada, Cloud Physics Research Division,
Toronto, Ont. M3H5T4
• E-m: [email protected]
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Objectives
• Cloud cover parameterization as a function of characteristics (e.g. LWC, TWC, IWC, and Nd;i) of the condensed water (ice or liquid, or mixed phase)
• Statistical analysis (PDFs) of cloud microphysical parameters for modeling studies
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AIRS 1999-2000
Comparison of Xu and Randall (1996) fit and fit of present study for calculated cloud cover versus TWC . Cloud cover calculated from equation Cs = RHρ [1-exp(-αqc)], where α and ρ are derived coefficients. qc is the condensed water content
Cloud cover versus TWCCs0.005 TWC>0.005 g m-3
TWC=LWC+IWC
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FIRE.ACE 1998
Comparison of Xu and Randall (1996) fit and fit of present study for calculated cloud cover versus qc / (qs – qv)γ . Cloud cover is calculated from equation
Cs = RHρ [1-exp(-αqc)], where α and ρare derived coefficients.
Cloud cover=f(qc,qs,qv)
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Conversion rate versus cloud cover
LA
2
w
sL
TLlPqc)
cC/q(exp1cqdt/dqCR −
−−−==
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Cs versus TWC (10 km versus 50 km)
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RACE (Liquid case)
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AIRS I (Mixed phase)
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AIRS I (Liquid case)
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RACE (Liquid case)
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AIRS I (Liquid case)
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CONCLUSIONS
• Cloud cover parameterizations are found comparable with these of earlier works with some differences in water and ice phase cases.
• PDFs are good way to validate model derived microphysical products and to test cloud development for various cloud types
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6224.50.05As, Ac5162.20.01Ci
72690.10Ns6217250.15Cu14512300.11St, Sc
Cloud Type
4181.50.007-50OC<T<-40OC71520.011-40OC<T<-30OC4202.50.021-30OC<T<-20OC62350.048-20OC<T<-10OC2221110.10-10OC<T< 0OC9516310.160OC<T<+10OC
Neff
(cm-3)Deff
( m)(km-1)TWC(gm-3)
Temperature(OC)
Korolev, Isaac, Mazin and Barker (2001): Microphysical properties of continental clouds from in-situ measurements. QJRMS, 127, 2117-2151.
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Korolev, Isaac, Cober and Strapp, 2001: Microstructure of mixed phase clouds. Part I: Observation Submitted to QJRMS
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R = 4Smeas/π D2max
CFDE III
FIRE.ACE
AIRS I
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101 102 103 10410-6
10-4
10-2
100
102
104
106
108
Diameter (microns)
Num
ber C
once
ntra
tion
(m-3
mic
rons
-1)
Points = 462Points = 462Points = 370Points = 318Points = 222Points = 179Points = 103Points = 155
CFDElll + AIRSNTWC Variation of the
Average Number Concentration for Liquid Phase
0.005 <= NTWC < 0.050
0.050 <= NTWC < 0.100
0.100 <= NTWC < 0.150
0.150 <= NTWC < 0.200
0.200 <= NTWC < 0.250
0.250 <= NTWC < 0.300
0.300 <= NTWC < 0.350
0.350 <= NTWC < 0.900
Figure LNTWCN36
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101 102 103 104
10-4
10-2
100
102
104
106
Diameter (microns)
Num
ber C
once
ntra
tion
(m-3
mic
rons
-1)
Points = 317Points = 492Points = 288Points = 129Points = 52Points = 47
CFDElll + AIRSNTWC Variation of the
Average Number Concentration for Glaciated Phase
0.005 <= NTWC <= 0.050
0.050 <= NTWC <= 0.100
0.100 <= NTWC <= 0.150
0.150 <= NTWC <= 0.200
0.200 <= NTWC <= 0.250
0.250 <= NTWC <= 0.900
Figure GNTWCN36
This is to remove
Data
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TWC (g m-3) vs Temperature
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TWC (g kg-1) vs Temperature
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Conclusions•Climate simulations are very sensitive to cloud microphysics.•A greater understanding of cloud processes is necessary. Modeling our current knowledge does not “fix” problems. (e.g. mixed phase, small ice particles, particle shape, precipitation formation, etc).•What do modelers want? (e.g. g/m-3 versus g/kg-1?)•Need to test sensitivity of GCMs to cloud microphysics. What model(s) to use? •How to we evaluate improvement in model performance?