Sensitivity of Aerosol Indirect Effects to Representation of

Autoconversion

Wei-Chun Hsieh with Peter J. Adams , and John H. Seinfeld

Advisor: A. Nenes

Earth and Atmospheric Science Sixth Annual Graduate Student Symposium

Motivation

• Estimate of Indirect effect is subject to the largest uncertainty for climatic forcing assessment (IPCC, 2007)

More CCN

Less CCN

Indirect effect

• “first” indirect effect: decrease cloud droplet size • “second” indirect effect: change precipitation and lifetime

• Uncertainty in estimate of indirect effect is related to cloud Uncertainty in estimate of indirect effect is related to cloud microphysical schemes, especially autoconversion parameterizationmicrophysical schemes, especially autoconversion parameterization (Lohmann and Feitcher, 2005) (Lohmann and Feitcher, 2005)

High albedo

Reflect more sunlight

Model• GISS GCM II-prime [Hansen et al., 1983]• An online aerosol simulation [Adams et al., 1999,

2001; Koch et al., 1999]• Cloud activation parameterization [Fountoukis and

Nenes, 2005]• 40 x 50 horizontal resolution and nine vertical layers

between the surface and the model top at 10 mb• 6 years of run for each pair of simulation

– Present day (PD) and Pre-industrial (PI) aerosols

• The GISS autoconversion scheme Sundqvist et al., 1989 SD

qc: Cloud water mixing ratioAutoconversion rate

Computing Autoconversion• Using microphysical parameterization

Simulations Dependence SourceREF LWMR Sundqvist et al. [1989 ]P6 N, LWC, droplet spectrum Liu and Daum [2004]KK N, LWMR Khairoutdinov and Kogan [2000]MC N, LWC Manton and Cotton [1977]

• Direct integration of Kinetic Collection Equation (KCE)

LWMR: Liquid Water Mixing Ratio; N: Cloud droplet number concentration; LWC: Liquid Water Content

x, x’: mass of two droplets, n(x): droplet size distribution, K(x,x’): collection kernel

Simulations Description

TURUsing Ayala kernel (Ayala et al., 2008), considering turbulent and gravitational effect for collection

GRVUsing Hall kernel (Hall, 1980), considering gravitational effect only

A: Autoconversion rate

• The annual mean, global distribution of the GCM's first-layer autoconversion rate for present day simulation. • Autoconversion is expressed in unit of 10-10 kg m-3 s-1.

How does the change of autoconversion affect indirect forcing estimate?

Autoconversion Globalmean value

Indirect forcingDefined as changes in net short wave flux (W m-2) at Top Of Atmosphere (TOA) between present day and pre-industrial simulation.

• Strong negative forcing in highly polluted areas

Changes in LWP

• Difference of LWP between present day and preindustrial day simulations

• Patterns of indirect forcing are in accord with patterns for changes in LWP

-90 -75 -60 -45 -30 -15 0 15 30 45 60 75 90-8

-6

-4

-2

0

2

Latitude [degree]

SW

forc

ing

[W m

-2]

-90 -75 -60 -45 -30 -15 0 15 30 45 60 75 90-50

0

50

100

150

200

250

Latitude [degree]

CD

NC

diff

. [cm

-3]

-90 -75 -60 -45 -30 -15 0 15 30 45 60 75 90-0.5

0

0.5

1

1.5

2

2.5

Latitude [degree]

Clo

ud

op

tica

l de

pth

diff

. REFP6KKMCTURGRV

-90 -75 -60 -45 -30 -15 0 15 30 45 60 75 90-1.5

-1

-0.5

0

0.5

Latitude [degree]

Effe

ctiv

e r

ad

ius

diff

. (

m)

Zonal averaged properties

Turbulent condition: dissipation rate = 34.71 cm2s-3, velocity fluctuation= 0.5 ms-1.

Sensitivity of collection kernel to forcing estimateTurbulent Collection Kernel

(a) Ayala kernel [Ayala et al., 2008a, b]

(b) Zhou kernel [Zhou et al., 2001]

- combining turbulent (Zhou et al., 2001) and gravitational kernel (Long, 1974)

Autoconversion

LWP (PD-PI)

Indirect forcing

ConclusionConclusion The predicted autoconversion rate may increase or decrease as The predicted autoconversion rate may increase or decrease as

compared to model's default parameterization, depends on compared to model's default parameterization, depends on autoconversion scheme used.autoconversion scheme used.

Considering water vapor feedback, we saw an increase in liquid water Considering water vapor feedback, we saw an increase in liquid water path due to the suppression of precipitation as a result of increasing path due to the suppression of precipitation as a result of increasing aerosol concentration.aerosol concentration.

The spatial distribution of indirect forcing strongly correlates with The spatial distribution of indirect forcing strongly correlates with simulated changes in LWP, the largest cooling is seen in highly polluted simulated changes in LWP, the largest cooling is seen in highly polluted areas.areas.

Effect of turbulent collection kernel on indirect forcing is smaller as Effect of turbulent collection kernel on indirect forcing is smaller as compared to uncertainty from applying different microphysical schemes.compared to uncertainty from applying different microphysical schemes.

The estimated indirect effect is very sensitive to the autoconversion The estimated indirect effect is very sensitive to the autoconversion scheme used, ranging from -2.05 Wmscheme used, ranging from -2.05 Wm--22 for KK and to -0.89 W m for KK and to -0.89 W m--22 for GRV for GRV simulation.simulation.

Acknowledgments• Dr. Lian-Ping Wang (KCE code)• Earth & Atmospheric Science,

Georgia Institute of Technology• Aerosol-Cloud-Climate Interaction

Research group• Friends & Family

Thank youThank you

Questions?Questions?