Observational constraints on aerosol indirect effects and controlling

processes

Robert Wood, Atmospheric Sciences,

University of Washington

Motivation• Aerosol impacts on clouds are not simply explained by

Twomey’s arguments– Changes in macrophysical cloud properties produce radiative

impacts of same order as those from Twomey (e.g. Lohmann and Feichter 2005, Isaksen et al. 2009)

• Conceptually, it is useful to divide AIEs into two types: – primary or quasi-instantaneous effects (e.g. Twomey effect,

dispersion effect);– effects that require an understanding of the system feedbacks

on timescales comparable to or longer than the cloud element lifetime.

Theoretical expression for AIE

• Response of cloud optical thickness t to change in some aerosol characteristic property A

primary feedback

(secondary)

(Mostly) regulating feedbacks in stratocumulus

Twomey’s hypothesis • Increases in the number of aerosol particles will

lead to increases in the concentration Nd of cloud droplets

• For a given LWC, greater Nd implies smaller droplets (since droplet radius r {LWC/Nd}1/3)

• Greater Nd total surface area will increase (t Nd r2h, so t Nd

1/3h5/3) and clouds reflect more solar radiation

• d(lnt)/d(lnNd) = 1/3

Albrecht’s hypothesis (1989)• A greater concentration of smaller drops (Twomey)

suppresses precipitation because the coalescence efficiency of cloud droplets increases strongly with droplet size.

• Reduced precipitation leads to increased cloud thickness, liquid water content, coverage more reflective clouds

from Wood (2005)

cloud

base

driz

zle ra

te [m

m d

-1]

Nd

[cm-3]

Twomey

Albrecht

Model estimates of the two major aerosol indirect effects (AIEs)

• Pincus and Baker (1994) – 1st and 2nd AIEs comparable

• GCMs (Lohmann and Feichter 2005) 1st AIE: -0.5 to -1.9 W m-2

2nd AIE: -0.3 to -1.4 W m-2

Large scale models problematic, so need observational and process

model constraints

Shiptrack surprises!

• Liquid water content in shiptracks is typically reduced compared with surrounding cloud

• Clear refutation of Albrecht’s hypothesis

courtesy Jim Coakley, see Coakley and Walsh (2002)

3.7 m

Precipitation suppression in ship tracks

Christensen and Stephens (submitted JGR)

• First observed in MAST (Ferek et al. 2000,

JAS)

CloudSat brings new perspective:

• Marked suppression of drizzle in most

shiptracks in closed cellular Sc.

• Enhancement of precipitation in some

open cell cases

• Poor data below 800 m

• Reliable statistics?

CloudSat

LES results

Cloud droplet concentration [cm-3]

LWP [g m-2]

P0

[mm d-1]

we

[cm s-1]

• Impact of aerosols simulated by varying Nd

• Increased Nd Reduced precipitation increased TKE increased entrainment we

• Changes in we can sometimes result in cloud thinning (reduced LWP)

• Also noted by Jiang et al. (2002)

Ackerman et al. (2004)

Precipitation reduces TKE

short

timescales

long

timescales

short

timescales

Different processes, different timescales

RIE

- ratio of secondary to primary AIEs

short term thinning

Transient response of an equilibrated mixed layer PBL model to Nd increases

• Ratio of 2nd to 1st AIE RIE (right) is a strong function of

cloud base height• More elevated cloud base heights zcb lead to Albrecht effects which partly cancel

those due to Twomey effect• Elevated zcb associated with

dry FT and less surface drizzle, consistent with LES results, but with a far less

sophisticated model hope for the representation in

climate models Wood (J. Atmos. Sci., 2007),

also seen in LES (Chen et al. 2011)

2nd AIE cancels 1st

2nd AIE = 1st

2nd AIE = 0

Sedimentation of cloud droplets

Bretherton, Blossey and Uchida, GRL, 2007

Cloud droplet sedimentation removes water from the (10 m thick) entrainment interface, lowers LWC there, reduces

evaporative cooling, and suppresses entrainment, resulting in thicker clouds

Since increased Nd

reduces sedimentation pollution can lead to thinner clouds

Entrainment enhancement raises inversion/cloud top height....in some cases

Christensen and Stephens, J. Geophys. Res. (2011)

.....also Taylor and Ackerman, QJRMS, 1999 (MAST Sanko Peace case study)

Marked deepening appears to occur only in previously collapsed MBLs

surrounding cloud remains

radiatively active

little or radiatively inactive surrounding cloud

Christensen and Stephens, J. Geophys. Res. (2011)

LES modeling of open-

closed cell boundary

Berner et al. (2011, ACP), and Bretherton et al. (2010, JAMES)

Wang and Feingold (2009), Wang et al. (2010)

Nd

=10 cm-3Nd

=60 cm-3

secondary circulation above MBL

inversion rises at same

rate in both regions

(Mostly) regulating feedbacks in stratocumulus

Response pathways

Wood

Albrecht

Ackerman

Wood

Arnason and Greenfield (1972), Kogan and Martin (1995), Lee et al.(2009)

Prospects• Turbulence, entrainment and drizzle absolutely

central to how STBL clouds respond to aerosol injections– Well-defined and testable hypotheses exist for how the

STBL should respond– Process models becoming capable of handling all relevant

aerosol-cloud interactions• New in-situ and spaceborne measurements offer

tremendous possibilities to go beyond previous studies (e.g. airborne doppler radar and lidar turbulence/drizzle)

A proposal

• A limited area perturbation experiment to critically test hypotheses related to aerosol indirect effects

• Cost $30M

Mixed layer model• LW/SW radiation and bulk surface flux (LHF/SHF)

parameterizations

• Entrainment closure (Turton and Nicholls 1986)

• Precipitation: For standard runs use formulation derived from shipborne radar observations in SE Pacific stratocumulus (Comstock et al. 2004).

– cloud base precipitation PCB h3.5/Nd1.75

– treatment of evaporation below cloud to give surface precipitation

Effects of drizzle vs effects of sedimentation of cloud droplets GCSS DYCOMS-2 RF02 drizzling Sc case study

Ackerman et al. (MWR, 2009)

With

driz

zle,

with

out s

edim

enta

tion

With

driz

zle

and

sedi

men

tatio

nEffect of drizzle Effect of sedimentation

.....in this case, sedimentation dominates over drizzle impact

on cloud LWP

Indirect effect ratio RIE

1st AIE 2nd AIE

Define RIE

= 2ndAIE / 1st AIE

Relative strength of the Albrecht effect compared with Twomey

For adiabatic cloud layers, t Nd

1/3 LWP5/6

Suite of simulations

• Surface divergence [10-6 s-1]: {2, 3, 4}• Sea Surface Temp. [K]: {288, 292, 296}• Moisture above MBL [g kg-1]: {1, 3, 6}

• 700 hPa potential temperature set to 312 K• No advective terms• MLM is run to equilibrium twice:

– (control) Nd=Nd,control

– (perturb) Nd=1.05Nd,control

• RIE is calculated – examine dependence of RIE upon forcings and parameterizations

see Wood (2007), J. Atmos. Sci., 64, 2657-2669.

Base case, Nd,control=100 cm-3

• For most forcing conditions 2nd AIE > 1st AIE

• RIE scales with surface precipitation in the control

• Little dependence of scaling upon forcing conditions

Base case, Nd,control=200 cm-3

• Lower values of RIE because surface precip. is lower

• Same RIE scaling with surface precip

Different drizzle parameterizationsBASE (Comstock)

VanZanten et al. (2005)

Fixed entrainment

• Only surface moisture/energy budget important (Albrecht effect)

• Entrainment important in determining the nature of the feedback response

Non-equilibrium response

• Timescale for 2nd AIE is long – due to long zi adjustment timescale

• On short timescales RIE can be negative (noted in Ackerman, 2004)

• Important to understand timescales of aerosol evolution

Timescales

But what is the timescale tN for evolution of Nd?

tN=Nd {dNd/dt}-1

– Coalescence scavenging (removal of CCN by coalescence of cloud/drizzle drops): tN (cloudbase precip rate)-1 ≈ 0.5-1 days

• Coalescence scavenging timescale is comparable to entrainment drying timescale aerosol perturbation is largely removed before MBL reaches equilibrium

Short timescale cloud response• Cloud base height determined by a balance between surface

precipitation moistening (P) and entrainment drying (E)

• Derive expression for cloud thickness change dh/dt ≈ - dzCB/dt using moisture and energy budgets for MBL

is relative importance of entrainment drying compared with

surface precipitation moistening

What determines ?• Ackerman showed RHFT important

• But cloudbase height dominates over wider range of phase space

Annual mean LCL 400-600 m over much of the subtropical and tropical oceans

cancellation of aerosol indirect effects?

Sensitive to size of drizzle drops

• Reducing mean radius of drizzle drops leads to more evaporation, and different ratio of surface moistening and entrainment drying

• Representation of evaporation critical

rdriz

=49 m

rdriz

=65 m

SEP stratocumulus in GCMs

Poor representation of the vertical structure of stratocumulus-topped boundary layers

– surface moisture budget is completely out to lunchBretherton et al. 2004, BAMS

Analogies in trade cumulus clouds

aerosol concentration [cm-3]

CF

LWP

Xue and Feingold (2006)• LES model of trade Cu

• Both cloud fraction (CF) and LWP decrease with increasing CCN conc.

• Effect attributed to more rapid evaporation of smaller cloud droplets (higher N) during entrainment events resulting in more rapid cloud dissipation

Conclusions• Relative strength of 2nd AIE strongly dependent upon

balance between precipitation suppression moistening and entrainment drying

• RIE reduced by ~50% by changing drizzle parameterization need to understand climatology of precipitation and its dependency on LWP and Nd

• Over timescales comparable with aerosol lifetime in the MBL, 1st and 2nd AIEs may cancel – implications for sensitivity of low clouds to aerosols

• Unlikely that current global models can capture the essential physics (evaporation/entrainment)

Shiptracks only seen in

shallow boundary

layers

Durkee et al. (2000)

Durkee et al.

(2000)

Track width as a

function of age

CloudSat observes drizzle

SE Pacific

The End

Low clouds in climate models

- change in low cloud amount for

2CO2

from Stephens (2005)

GFDL

CCM

model number

Re-examination of Klein and Hartmann data

Williams et al. (2006)

Change in LTS (K)

Low cloud amount in an ensemble of 2xCO2-control GCM simulations is poorly estimated using LTS’ (for

which a general increase is predicted)

Much better agreement with change in saturated stability

(≈EIS’)

Precipitation parameterizations

BASE: Comstock et al. (2004): PCBh3.5/Nd1.75

Van Zanten et al. (2005): PCBh3/Nd

Range typical in MBL clouds

Weak temperature gradient

Minimalist approach

No entrainment drying/warming

• Entrainment only allowed to influence zi

• leads to stronger LWP feedback

Doubling and halving entrainment efficiency

Enhanced entrainment counteracts (by drying) the increased LWP caused by reduced precip. efficiency…

….but data fall on same curve

2

/2

Cloud feedbacks remain the largest uncertainty in the

prediction of future climate change

from Cess et al. 1989, 1996

Sensitivity of cloud optical depth t to increasing Nd in the MLM

1st AIE 2nd AIE constant LWP feedback on LWP

For the MLM, t Nd

1/3 LWP5/6

Indirect effect ratio RIE

1st AIE 2nd AIE

Define RIE

= 2ndAIE / 1st AIE

Relative strength of the Albrecht effect compared with Twomey

• Relationship between LTS and EIS is not unique

• For a given value of LTS, EIS decreases with surface (or 700 hPa) temperature

(2) Aerosols and low clouds

Cloud droplet concentrationWood et al. (2006)

Chiquicamata, Chile

Primary effect (Twomey)• Seminal papers in 1974, 1977 hypothesizing an

important potential brightening of clouds subject to increased aerosol concentration

• Importance of fixed cloud macrophysical properties (LWP) – difficult to test in practice

Aerosol-cloud microphysical observations

Obse

rved

clo

ud d

ropl

et c

once

ntra

tion

[cm

-3]

Predicted droplet concentrationfrom aerosol spectrum [cm-3]

From Twomey and Warner, J. Atmos. Sci., 24, 704-706, 1967

Aerosol concentration [cm-3]

Clou

d dr

ople

t con

cent

ratio

n [c

m-3

]

From Martin et al., J. Atmos. Sci., 51, 1823-1842, 1994

….first measurements (1967)

….recent measurements (1994)

+ wind from land wind from sea

Aerosol loading and

cloud dropletradius….

….35 years on

from Breon et al. 2002, Science, 295,

834-838 6 8 10 12 14Cloud droplet radius [micron]

0.0 0.1 0.2 0.3 0.4 Aerosol index

Remote sensing estimates

from Feingold et al. 2003, GRL

IE = dlnre/dlnt

a dlnt/dlnN

a = (dlnt/dlnN

d)(dlnN

d/dlnN

a)

(dlnNd

/dlnNa) 0.5 to 0.7

Expect IE =1/3(0.5 to 0.7) 0.17 to 0.23

IE (Observations)

0.02-0.16 (Feingold et al.)

0.085 (Breon, ocean)

0.04 (Breon, land)

0.06 (Nakajima et al., ocean)