Cloud Microphysics and Climate

George A. Isaac, Ismail Gultepe and Faisal Boudala

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

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

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

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

Short wave cloud forcing: Model and satellite in Winter

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

Global distribution of anomaly in energy balance TOA in Winter

• Mostly negative NH

Winter Dge Dge+s∆E -∆E

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

Anomaly in cloudines in Winter

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: ismail.gultepe@ec.gc.ca

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

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

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)

Conversion rate versus cloud cover

LA

2

w

sL

TLlPqc)

cC/q(exp1cqdt/dqCR −

−−−==

Cs versus TWC (10 km versus 50 km)

RACE (Liquid case)

AIRS I (Mixed phase)

AIRS I (Liquid case)

RACE (Liquid case)

AIRS I (Liquid case)

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

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.

Korolev, Isaac, Cober and Strapp, 2001: Microstructure of mixed phase clouds. Part I: Observation Submitted to QJRMS

R = 4Smeas/π D2max

CFDE III

FIRE.ACE

AIRS I

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

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

TWC (g m-3) vs Temperature

TWC (g kg-1) vs Temperature

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?