Representing and Representing and Constraining Cloud Droplet Constraining Cloud Droplet

FormationFormation

Athanasios NenesSchool of Earth & Atmospheric Sciences

School of Chemical & Biomolecular EngineeringGeorgia Institute of Technology

Aerosol Indirect Effects WorkshopVictoria, BC, November 13, 2007

The complexity of aerosol-cloud interactionsThe complexity of aerosol-cloud interactions

DynamicsDynamicsUpdraft VelocityUpdraft VelocityLarge Scale ThermodynamicsLarge Scale Thermodynamics

Particle characteristicsParticle characteristicsSizeSizeConcentrationConcentrationChemical CompositionChemical Composition

Cloud ProcessesCloud ProcessesCloud droplet formationCloud droplet formationDrizzle formationDrizzle formationRainwater formationRainwater formationChemistry inside cloud dropletsChemistry inside cloud droplets

All the links need to be incorporated in global modelsAll the links need to be incorporated in global modelsThe links need to be COMPUTATIONALLY feasible.The links need to be COMPUTATIONALLY feasible.

aerosolaerosol

activation

drop growth

collision/coalescencecollision/coalescence

Everything depends on everything across multiple scalesEverything depends on everything across multiple scales

DynamicsDynamicsUpdraft VelocityUpdraft VelocityLarge Scale ThermodynamicsLarge Scale Thermodynamics

Particle characteristicsParticle characteristicsSizeSizeConcentrationConcentrationChemical CompositionChemical Composition

Cloud ProcessesCloud ProcessesCloud droplet formationCloud droplet formationDrizzle formationDrizzle formationRainwater formationRainwater formationChemistry inside cloud dropletsChemistry inside cloud droplets

aerosolaerosol

activationactivation

drop growthdrop growth

collision/coalescencecollision/coalescence

We focus on the aerosol-CCN-droplet link

The complexity of aerosol-cloud interactionsThe complexity of aerosol-cloud interactionsEverything depends on everything across multiple scalesEverything depends on everything across multiple scales

The “simple” story of cloud droplet formationThe “simple” story of cloud droplet formation

Basic idea: Solve conservation laws for energy and water for an ascending Lagrangian parcel containing some aerosol.

aerosol

activation

drop growth

S

Smax

t

Steps are:

• Parcel cools as it rises

• Exceed the dew point at LCL

• Generate supersaturation

• Droplets start activating as S exceeds their Sc

• Condensation of water becomes intense.

• S reaches a maximum

• No more droplets form

The “simple” story of cloud droplet formation.The “simple” story of cloud droplet formation.

Basic idea: Solve conservation laws for energy and water for an ascending Lagrangian parcel containing some aerosol.

aerosol

activation

drop growth

S

Smax

t

Steps are:

• Parcel cools as it rises

• Exceed the dew point at LCL

• Generate supersaturation

• Droplets start activating as S exceeds their Sc

• Condensation of water becomes intense.

• S reaches a maximum

• No more droplets form

Theory known for many years. Too slow toimplement “completely” in large scale models

Input: P,T, vertical wind, particle characteristics.Output: Cloud properties (droplet number, size distribution).How: Solve an algebraic equation (instead of ODE’s).

“Mechanistic” Cloud Parameterizationsefficiently solve the drop formation problem

Characteristics:- 103-104 times faster than numerical parcel models.- some can treat very complex chemical composition.- have been evaluated using in-situ data with large success (e.g., Meskhidze et al., 2006; Fountoukis et al., 2007)

Examples:

Abdul-Razzak et al., (1998); Abdul-Razzak et al., (2000); Nenes and Seinfeld (2003), Fountoukis and Nenes (2005), Ming et al., (2007); Barahona and Nenes (2007)

Mechanistic Parameterizations Mechanistic Parameterizations Current state of the art in GCMsCurrent state of the art in GCMs

Physically-based prognostic Physically-based prognostic representations of the representations of the activation physics.activation physics.

Cloud droplet formation is Cloud droplet formation is parameterized by applying parameterized by applying conservation principles in an conservation principles in an ascending ascending adiabaticadiabatic air air parcel.parcel.

All parameterizations All parameterizations developed to date rely on developed to date rely on the assumption that the the assumption that the droplet formation is an droplet formation is an adiabatic processadiabatic process..

Aerosol particles in an closed adiabatic parcel

Cloud droplets

CCN Activation

Major “workhorse” for producing the aerosol-cloud Major “workhorse” for producing the aerosol-cloud datasets we need for parameterization evaluation and datasets we need for parameterization evaluation and development. development.

In-situ airborne platformsIn-situ airborne platforms

CIRPAS Twin Otter

NOAA P3

Aerosol Aerosol size size

distributiodistributionn

Cloud Cloud updraft updraft VelocityVelocity

Aerosol Aerosol compositiocompositio

nn

ParameterizationParameterization

Predicted Predicted Cloud Cloud

droplet droplet numbernumber

Cloud Drop Parameterization Evaluation

CDNC “closure”

Aerosol Aerosol size size

distributiodistributionn

Cloud Cloud updraft updraft VelocityVelocity

Aerosol Aerosol compositiocompositio

nn

ParameterizationParameterization

Predicted Predicted Cloud Cloud

droplet droplet numbernumber

ComparComparee

Observed Observed Cloud Cloud

Droplet Droplet NumberNumber

Parameterization EvaluationCDNC “closure”

Input: P,T, vertical wind, particle characteristics.Output: Cloud properties.How: Solve an algebraic equation (instead of ODE’s).

01)()(2 0

2211max

max

part

part

S S

S

w dssfCdssfCaV

GS

Adiabatic Cloud Formation Parameterization:Nenes and Seinfeld, 2003 (and later work).

Features:- 103-104 times faster than numerical cloud model.- can treat very complex chemical composition.- FAST formulations for lognormal and sectional aerosol is available

We evaluate this with the in-situ data.

CDNC closure during ICARTT during ICARTT (Aug.2004)(Aug.2004)

Cumuliform and Stratiform clouds sampled

Investigate the effect of power plant plumes on clouds

Fountoukis et al., JGR (2007)

CDNC closure

2 3 4 5 6 7 8 9 2 3 4 5 6 7 8 9100 1000 10000

N a t the cloud base (cm )

2

3

4

5

6

789

2

3

4

5

6

789

100

1000

10000

Par

amet

eriz

ed N

(cm

)D

-3

D-3

H 4-1H 4-2H 4-3H 4-4

C 4C 6-1C 6-2

C 6-3

C 8-1

C 8-2

C 10-1C 10-2C 11-1C 11-2C 12-1C 12-2C 16-1C 16-2C 17-1C 17-2C 17-3

Meskhidze et al.,JGR (2005)

CRYSTAL-FACE (2002)CRYSTAL-FACE (2002)Cumulus cloudsCumulus clouds

Meskhidze et al.,JGR (2005)

100 1000Measured CDNC (cm )

100

1000

-3

1:1 line

CS1-1 CS1-2CS1-3CS1-4CS1-5CS1-6CS1-7CS2-1 CS2-2CS2-3CS2-4CS2-5CS3-1 CS3-2 CS3-3CS3-4CS3-5CS3-6CS4-1 CS4-2 CS4-3 CS4-4CS4-5CS4-6

CS5-1CS5-2CS5-3CS5-4CS5-5CS5-6CS6-1 CS6-2 CS6-3CS6-4CS6-5CS6-6CS6-7CS7-1 CS7-2 CS7-3CS7-4CS7-5CS7-6CS7-7

CS8-1 CS8-2 CS8-3 CS8-4 CS8-5CS8-6CS8-7 CS8-8

30% difference line

Pa

ram

ete

rize

d C

DN

C (

cm

)-3

CSTRIPE (2003)Coastal Stratocumulus

What we have learned from What we have learned from CDNC closure studiesCDNC closure studies

““Mechanistic” parameterizations do a good job Mechanistic” parameterizations do a good job of capturing droplet number for nearly of capturing droplet number for nearly adiabatic clouds and… when you know the adiabatic clouds and… when you know the input (they capture the physics).input (they capture the physics).

Gaussian PDF of updraft velocity is sufficient to Gaussian PDF of updraft velocity is sufficient to capture average CDNC.capture average CDNC.

In fact, the average updraft velocity does In fact, the average updraft velocity does equally wellequally well (and is much faster) in predicting (and is much faster) in predicting CDNC, compared to integrating over a PDF. CDNC, compared to integrating over a PDF.

CDNC closure studies also can be used to infer CDNC closure studies also can be used to infer a range of droplet growth kinetic parameters a range of droplet growth kinetic parameters (“water vapor mass uptake coefficient”).(“water vapor mass uptake coefficient”).

CIRPAS Twin Otter

Range of Range of aa inferred inferred from from in-situin-situ droplet droplet

closure studiesclosure studies

ICARTT (2004)ICARTT (2004)

Optimum Optimum closure closure

obtained for obtained for αα betweenbetween0.03 – 1.0 0.03 – 1.0

Same range Same range found in CSTRIPE, found in CSTRIPE,

CRYSTAL-FACE CRYSTAL-FACE and MASE studiesand MASE studies

We’ll get back to We’ll get back to this point later onthis point later on

-60

-40

-20

0

20

40

60

80

100

120

140

0.01 0.1 1

Accommodation Coefficient

Dro

ple

t N

um

be

r U

nc

ert

ain

ty (

%)

Parcel

Parameterization

Observed

Predicted

optimum

-60

-40

-20

0

20

40

60

80

100

120

140

0.01 0.1 1

Accommodation Coefficient

Dro

ple

t N

um

be

r U

nc

ert

ain

ty (

%)

Parcel

Parameterization

Observed

Predicted

optimum

Fountoukis et al., JGR, 2007

Issues of ParameterizationsIssues of Parameterizations Highly idealized description of clouds. Most Highly idealized description of clouds. Most

often they are adiabatic (few feedbacks)...often they are adiabatic (few feedbacks)...

They require information not currently found They require information not currently found in most GCMs (cloud-base updraft velocity, in most GCMs (cloud-base updraft velocity, aerosol chemical composition, etc.).aerosol chemical composition, etc.).

Few processes are represented and are Few processes are represented and are largely decoupled from other processes or largely decoupled from other processes or interact at the “wrong scale” (e.g., dynamics, interact at the “wrong scale” (e.g., dynamics, entrainment and autoconversion/drizzle)entrainment and autoconversion/drizzle)

Very difficult to address… but not impossible.Very difficult to address… but not impossible.

Issues of ParameterizationsIssues of Parameterizations

Highly idealized description of clouds. Most Highly idealized description of clouds. Most often they are adiabatic (few feedbacks)...often they are adiabatic (few feedbacks)...

They require information not currently found They require information not currently found in most GCMs (cloud-base updraft velocity, in most GCMs (cloud-base updraft velocity, aerosol chemical composition, etc.).aerosol chemical composition, etc.).

Few processes are represented and are Few processes are represented and are largely decoupled from other processes or largely decoupled from other processes or interact at the “wrong scale” (e.g., dynamics, interact at the “wrong scale” (e.g., dynamics, entrainment and autoconversion/drizzle)entrainment and autoconversion/drizzle)

Very difficult to address… but not impossible. Very difficult to address… but not impossible.

Real Clouds are not Adiabatic Real Clouds are not Adiabatic

Peng, Y. et al. (2005). J. Geophys. Res., 110, D21213

Entrainment of air into cloudy parcels decreases cloud droplet number relative to adiabatic conditions

In-situ observations often show that the liquid water content measured is lower than expected by adiabaticity.

Neglecting entrainment may lead to an overestimation of in-cloud droplet number biasing indirect effect assessments

We need to include entrainment in the parameterizations

Barahona and Nenes (2007)Barahona and Nenes (2007)Droplet formation in entraining cloudsDroplet formation in entraining clouds

Cloud droplet formation is Cloud droplet formation is parameterized by integrating parameterized by integrating conservation principles in an conservation principles in an ascending ascending entrainingentraining air parcel. air parcel.

Equations are similar to Equations are similar to adiabatic activation – only that adiabatic activation – only that mixing of outside air is allowed .mixing of outside air is allowed .

““Outside” air with (RH, T’) is Outside” air with (RH, T’) is assumed to entrain at a rate of assumed to entrain at a rate of ee (kg air)(kg parcel) (kg air)(kg parcel)-1-1(m ascent)(m ascent)--

11

Cloud droplets

CCN Activation RH, T’

The formulation is the first of its kind and can treat all the chemical complexities of organics (which we will talk about in a bit). Formulations available for either lognormal or sectional aerosol.

Entraining Parameterization Entraining Parameterization vs. parcel modelvs. parcel model

V=0.1,1.0, and 5.0 ms-1. T-T’=0,1,2 °C. RH=60, 70, 80, 90 %. Background aerosol. 2000 simulations.

25% difference

Comparison with Comparison with detailed detailed numerical model.numerical model.

Parameterization Parameterization closely follows closely follows the parcel model the parcel model

Mean relative Mean relative error ~3%.error ~3%.

101044 times faster times faster than numerical than numerical parcel model. parcel model.

Barahona and Nenes, (2007)

Issues of ParameterizationsIssues of Parameterizations Highly idealized description of clouds. Most often they Highly idealized description of clouds. Most often they

are adiabatic (few feedbacks)...are adiabatic (few feedbacks)...

They require information not currently found in most They require information not currently found in most GCMs (cloud-base updraft velocity, aerosol chemical GCMs (cloud-base updraft velocity, aerosol chemical composition, etc.).composition, etc.).

Few processes are represented and are largely Few processes are represented and are largely decoupled from other processes or interact at the decoupled from other processes or interact at the “wrong scale” (e.g., dynamics, entrainment and “wrong scale” (e.g., dynamics, entrainment and autoconversion/drizzle)autoconversion/drizzle)

Very difficult to address… but not impossible. Very difficult to address… but not impossible.

Aerosol Problem: Vast Aerosol Problem: Vast ComplexityComplexity

An integrated “soup” An integrated “soup” of of Inorganics, organics (1000’s)Inorganics, organics (1000’s)Particles can have uniform Particles can have uniform composition with size.composition with size.… … or notor notCan vary vastly with space Can vary vastly with space and time (esp. near sources)and time (esp. near sources)

Predicting CCN concentrations is a convolution Predicting CCN concentrations is a convolution of size of size distribution and chemical composition distribution and chemical composition information. information.

CCN activity of particles is a strong function CCN activity of particles is a strong function (~d(~d-3/2-3/2) of aerosol dry size and (a weaker but ) of aerosol dry size and (a weaker but important) function of chemical composition (~ important) function of chemical composition (~ salt fractionsalt fraction-1-1).).

Aerosol Description: Complexity Aerosol Description: Complexity rangerange

The … headache of organic speciesThe … headache of organic species

They can act as surfactants and facilitate cloud They can act as surfactants and facilitate cloud formation.formation.

They can affect hygroscopicity (add solute) and They can affect hygroscopicity (add solute) and facilitate cloud formation.facilitate cloud formation.

Oily films can form and delay cloud growth kineticsOily films can form and delay cloud growth kinetics

Some effects are not additive.Some effects are not additive.

Very difficult to explicitly quantify in any kind of Very difficult to explicitly quantify in any kind of model.model.

The treatment of the aerosol-CCN linkis not trivial at all.

How well do we understand the aerosol-CCN link?

What is the level of aerosol complexity required to “get things right”?

How much “inherent” indirect effect uncertainty is associated with different treatments of complexity?

Use in-situ data to study the aerosol-CCN link:

- Creative use of CCN measurements to “constrain” what the most complex aspects of aerosol (mixing state and organics) are.

- Quantify the uncertainty in CCN and droplet growth kinetics associated with assumptions & simplifications.

CCN: Looking at what’s important

How is it done?How is it done?• Measure aerosol size Measure aerosol size distribution and distribution and composition. composition.

• Introduce this information Introduce this information KKöhler theory and predict öhler theory and predict CCN concentrations.CCN concentrations.

• Compare with measured Compare with measured CCN over a supersaturation CCN over a supersaturation range and assess closure.range and assess closure.

+50%

-50%

Supersaturation (%)

0

1000

2000

3000

4000

5000

6000

0 1000 2000 3000 4000 5000 6000

Measured CCN (cm-3)

Cal

cula

ted

CC

N(c

m-3

)

0.60

0.50

0.37

0.30

0.20

+50%

-50%

Supersaturation (%)

0

1000

2000

3000

4000

5000

6000

0 1000 2000 3000 4000 5000 6000

Measured CCN (cm-3)

Cal

cula

ted

CC

N(c

m-3

)

0.60

0.50

0.37

0.30

Medina et al., JGR, 2007

Do we understand the aerosol-CCN Do we understand the aerosol-CCN link? link?

Test of theory: “CCN Closure Study”

CCN MEASUREMENTS CCN PREDICTIONS

CCN closure studies going on since the 70’s.Advances in instrumentation have really overcome limitations

Measuring CCN: a key source of Measuring CCN: a key source of datadata

Goal: Generate supersaturation, expose CCN to it and count how many droplets form.

Metallic cylinder with walls wet. Apply T gradient, and flow air.

• Wall saturated with H2O.

• H2O diffuses more quickly than heat and arrives at centerline first.

• The flow is supersaturated with water vapor at the centerline.

• Flowing aerosol at center would activate some into droplets.

Count the concentration and size of droplets that form with a 1 s resolution.

wet w

allw

et

wall

Outlet: [Droplets] = [CCN]

Inlet: Aerosol

Roberts and Nenes (2005), Patent pending

Continuous Flow Streamwise Thermal Gradient Chamber

Development of Streamwise TG Development of Streamwise TG ChamberChamber

scale

= 1

m

1st versionApril 2002

2nd versionJanuary 2003

DMTJuly 2004

mini versionAugust 2006

Roberts and Nenes, AS&T (2005); Lance et al., AS&T (2006)

Two DMT CCN counters Two DMT CCN counters (Roberts and Nenes, AST, 2005;(Roberts and Nenes, AST, 2005;

Lance et al., AST, 2006)Lance et al., AST, 2006)

TSI SMPS, for size distributionTSI SMPS, for size distribution

Aerodyne AMS, for chemical Aerodyne AMS, for chemical compositioncomposition

2 weeks of aerosol and CCN data (0.2 - 0.6 % supersaturation)2 weeks of aerosol and CCN data (0.2 - 0.6 % supersaturation)

AIRMAP Thompson Farm siteAIRMAP Thompson Farm siteLocated in Durham, New HampshireLocated in Durham, New HampshireMeasurements done during ICARTT 2004Measurements done during ICARTT 2004Air quality measurements are performed Air quality measurements are performed on air sampled from the top of a 40 foot on air sampled from the top of a 40 foot tower.tower.

An example of a CCN closureAn example of a CCN closure

CCN Measurements: “Traditional” Closure

20% overprediction (average).

Assuming uniform composition with size rougly doubles the CCN prediction error.

Introducing compreshensive composition into CCN calculation often gives very good CCN closure. 100

1000

10000

100 1000 10000

Measured CCN

Cal

cula

ted

CC

N

0.2

0.3

0.37

0.5

0.6

Average Error = 19%

Supersaturation (%)

7/31/04 - 8/11/04 Medina et al., JGR (2007)

NOAA P3 (GoMACCs)

Larger-scale CCN variability Larger-scale CCN variability (ageing)(ageing)How important is external mixing to “overall” CCN How important is external mixing to “overall” CCN

prediction prediction

Can we understand CCN in rapidly changing Can we understand CCN in rapidly changing environments?environments?

Look at CCN data from GoMACCs (Houston August, Look at CCN data from GoMACCs (Houston August, 2006) and MILAGRO (Mexico City, March 2006).2006) and MILAGRO (Mexico City, March 2006).

T0 site (MILAGRO)

31.5

31.0

30.5

30.0

29.5

29.0

-96.0 -95.5 -95.0 -94.5 -94.0

20x103

15

10

5

Total A

erosol Concnentration

Wind, Flight

progression

September 21: Houston Urban Plume September 21: Houston Urban Plume AgeingAgeing

10

100

1000

10000

100000

0 1 2 3 4 5

hours after takeoff

Co

nce

ntr

atio

n (

cm-3

)

0.10

0.15

0.20

Su

per

satu

rati

on

(%

)

Measured CCN

Total CN

predicted CCN

Su

per

satu

rati

on

(%

)

CCN low and ~ const near source !

Freshly emitted aerosol “builds up” Plume transects

September 21 FlightSeptember 21 Flight

10

100

1000

10000

100000

0 1 2 3 4 5

hours after takeoff

Co

nce

ntr

atio

n (

cm-3

)

0.10

0.15

0.20

Su

per

satu

rati

on

(%

)

Measured CCN

Total CN

predicted CCN

Su

per

satu

rati

on

(%

)

CCN increase while plume ages

Aerosol is aging & diluting

~ 100 kmSeptember 21 FlightSeptember 21 Flight

10

100

1000

10000

100000

0 1 2 3 4 5

hours after takeoff

Co

nce

ntr

atio

n (

cm-3

)

0.10

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Su

per

satu

rati

on

(%

)

Measured CCN

Total CN

predicted CCN

Su

per

satu

rati

on

(%

)

CCN tracks the CN variability when plume ages

~ 100 kmSeptember 21 FlightSeptember 21 Flight

Some “take home” pointsSome “take home” pointsCCN theory is adequate. Closure errors are from lack of CCN theory is adequate. Closure errors are from lack of information (size-resolved composition, mixing state).information (size-resolved composition, mixing state).

Aerosol variability close to source regions often does not Aerosol variability close to source regions often does not correlate with CCN variability. CCN levels are often correlate with CCN variability. CCN levels are often controlled by “background” (or “aged”) concentrations.controlled by “background” (or “aged”) concentrations.

As plumes age, CCN increase and covary with total CN. As plumes age, CCN increase and covary with total CN. This happens on a typical GCM grid size.This happens on a typical GCM grid size.

… … so external mixing considerations may be required only so external mixing considerations may be required only for GCM grid cells with large point sources of CCN (like for GCM grid cells with large point sources of CCN (like megacities). Encouraging for large-scale models.megacities). Encouraging for large-scale models.

Potential problemPotential problem: Megacities are increasing in number : Megacities are increasing in number (primarily in Asia), so the importance of external mixing (primarily in Asia), so the importance of external mixing (i.e. # of GCM cells) may be important in the future.(i.e. # of GCM cells) may be important in the future.

CCN

Particle Size CN

TEC 1

TEC 3

TEC 2

Particle Counter

OPC

hu

mid

ifie

r

sheath

sample

neutralizer

DM

A

Activated CCNFraction CN

=

Results: “activation curves”

Measure CCN activity of aerosol with known diameter

Get more out of CCN instrumentation: Get more out of CCN instrumentation: Size-resolved measurementsSize-resolved measurements

00.10.20.30.40.50.60.70.80.9

11.11.2

0 0.2 0.4 0.6 0.8 1

Instrument Supersaturation (%)

Act

ivat

ed F

ract

ion

(C

CN

/CN

)

What the Activation Curves tell usWhat the Activation Curves tell us

00.10.20.30.40.50.60.70.80.9

11.11.2

0 0.2 0.4 0.6 0.8 1

Instrument Supersaturation (%)

Act

ivat

ed F

ract

ion

(CC

N/C

N)

Fraction of non-hygroscopicexternally mixed particles

“characteristic” critical supersaturation: a strong function of “average” aerosol composition

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Supersaturation [%]

No

rma

lize

d A

cti

va

ted

F

rac

tio

n (

CC

N/C

N)* Ammonium Sulfate Lab Experiment

Ambient Data(Mexico City)

Hypothetical “monodisperse”single-component aerosol

“characteristic” critical supersaturation

Ambient Data Is Much Broader Because Chemistry varies alot

What the Activation Curves tell usWhat the Activation Curves tell us

Slope has a wealth of information as well.

First Approach; slope is from chemistry alone

00.10.20.30.40.50.60.70.80.9

11.11.2

0 0.2 0.4 0.6 0.8 1

Instrument Supersaturation (%)

Ac

tiv

ate

d F

rac

tio

n (

CC

N/C

N)

The slope of the activation curve directly translates to the width of the chemical distribution

Sigmoidal fitSoluble Volume Fraction SVF

Köhler Theory

C

SS

E

CN

CCN*

*

1

),( pc dSVFfS

Pro

bab

ility

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.01

0.05 0.

10.

15 0.2

0.25 0.

30.

35 0.4

0.45 0.

50.

55 0.6

0.65 0.

70.

75

Soluble FractionSVF

What we get: PDF of composition as a function of particle size every few minutes.

This is a complete characterization of CCN ‘mixing state’.

““Chemical Closure”Chemical Closure”inferred vs measured soluble fractioninferred vs measured soluble fraction

The average distribution of soluble fraction agrees very well with measurements. Our measurements give what’s

“important” for CCN mixing state.

Look at CCN data from MILAGRO (Mexico City, March 2006).Look at CCN data from MILAGRO (Mexico City, March 2006).Compare against “bulk” composition measurementsCompare against “bulk” composition measurements

Diurnal Variability in CCN mixing state(Mexico City)

Diurnal Variability in CCN mixing state(Mexico City)

Some “take home” pointsSome “take home” points

Local signatures of aerosol sources on CCN mixing Local signatures of aerosol sources on CCN mixing state largely disappear when the sun comes up state largely disappear when the sun comes up (Photochemistry? Boundary layer mixing? New (Photochemistry? Boundary layer mixing? New particle formation and growth? Cloud processing?).particle formation and growth? Cloud processing?).

… … so external mixing considerations may be so external mixing considerations may be required only for GCM grid cells with large point required only for GCM grid cells with large point sources of CCN (like megacities). Encouraging for sources of CCN (like megacities). Encouraging for large-scale models, which really need simple but large-scale models, which really need simple but effective ways to predict CCN.effective ways to predict CCN.

Potential problemPotential problem: Megacities are increasing in : Megacities are increasing in number (primarily in Asia), so the importance of number (primarily in Asia), so the importance of external mixing (i.e. # of GCM cells) may be external mixing (i.e. # of GCM cells) may be important in the future.important in the future.

The problem of CCN prediction in global models is The problem of CCN prediction in global models is not “hopeless”. Good news.not “hopeless”. Good news.

Size distribution plus assuming a uniform mixture of Size distribution plus assuming a uniform mixture of sulfate + insoluble captures most of the CCN sulfate + insoluble captures most of the CCN variability (on average, to within 20-25% but often variability (on average, to within 20-25% but often larger than that).larger than that).

Scatter and error is because we do not consider Scatter and error is because we do not consider mixing state and impact of organics on CCN activity.mixing state and impact of organics on CCN activity.

How important is this kind of uncertainty? How important is this kind of uncertainty?

The term “good closure” is often used, but how The term “good closure” is often used, but how “good” is it really?“good” is it really?

Due to time limitations… let’s get to the point.Due to time limitations… let’s get to the point.

CCN: Looking at what’s important

CCN predictions: “take home” CCN predictions: “take home” pointspoints

Resolving size distribution and a uniform mixture of Resolving size distribution and a uniform mixture of sulfate + insoluble can translate to 50% uncertainty in sulfate + insoluble can translate to 50% uncertainty in indirect forcing.indirect forcing.

The effect on precipitation can be equally large (or The effect on precipitation can be equally large (or even larger because of nonlinearities).even larger because of nonlinearities).

Even if size distribution were perfectly simulated, more Even if size distribution were perfectly simulated, more simplistic treatments of aerosol compositionsimplistic treatments of aerosol composition imply even imply even larger uncertaintieslarger uncertainties in indirect effect. Size in indirect effect. Size is is notnot the only thing that matters in CCN calculations. the only thing that matters in CCN calculations.

Scatter and error is because we do not consider mixing Scatter and error is because we do not consider mixing state and impact of organics on CCN activity.state and impact of organics on CCN activity.

Organics & CCN: The Organics & CCN: The ChallengesChallenges

Since CCN theory works well, we can use it Since CCN theory works well, we can use it to infer the impact of organics on droplet to infer the impact of organics on droplet formation.formation.

We want “key” properties that can easily be We want “key” properties that can easily be considered in current parameterizationsconsidered in current parameterizations

Desired information:Desired information:– Average molar properties (molar volume, Average molar properties (molar volume,

solubility)solubility)– Droplet growth kineticsDroplet growth kinetics– Chemical heterogeneity (mixing state) of aerosolChemical heterogeneity (mixing state) of aerosol– Surface tension depressionSurface tension depression

Once more, size-resolved CCN Once more, size-resolved CCN measurements measurements

can provide a key source of datacan provide a key source of data

Go back to the Activation CurvesGo back to the Activation Curves

00.10.20.30.40.50.60.70.80.9

11.11.2

0 0.2 0.4 0.6 0.8 1

Instrument Supersaturation (%)

Ac

tiv

ate

d F

rac

tio

n (

CC

N/C

N)

“characteristic” critical supersaturation: a strong function of “average” (most probable) aerosol composition. We can use this to infer composition of organics

Inferring Molar Volume from CCN activity: Köhler Theory Analysis (KTA)

5257.15523.1 dsc

Plot characteristic supersaturation as a function of dry particle size.

Fit the measurements to a power law expression.

Relate fitted coefficients to aerosol properties (e.g. molecular weight, solubility) by using Köhler theory:

23 d2

32

12

1

1

27

256

d

M

MRT

MS

i iii

i

w

w

w

wc

Padró Padró et al.et al., ACPD; Asa-Awuku , ACPD; Asa-Awuku et al.et al., ACPD, ACPD

(NH4)2SO4(NH4)2SO4

organiciii

i

i

w

w

organicorganic

organic

organic

MRTM

M

2332

127256

If we know the inorganic composition, we can “infer” the

organic properties

iii

iii m

m

From IC/WSOC From IC/WSOC measurementmeasurement

ConstantsConstants

Measured surface Measured surface tension and CCN tension and CCN

datadata

Method shown to work well for laboratory-generated aerosol (Padró et al., ACPD) and SOA generated from ozonolysis of biogenic VOC (Asa-Awuku et al., ACPD), even marine organic matter!

Molar VolumeThis is what you

need to know about organics for

CCN activity

KTA: Major findings on soluble organicsMany “aged” soluble organics from a wide variety of Many “aged” soluble organics from a wide variety of

sources have a 200-250 g molsources have a 200-250 g mol-1 -1 . For example:. For example:

Aged Mexico City aerosol from MILAGRO.Aged Mexico City aerosol from MILAGRO.

Secondary Organic Aerosol from Secondary Organic Aerosol from a-pinene and monoterpene oxidation (Engelhart et al., ACPD).Ozonolysis of Alkenes (Asa-Awuku et al., ACPD)

Oleic Acid oxidation (Shilling et al., 2007)

In-situ cloudwater samples collected aboard the CIRPAS In-situ cloudwater samples collected aboard the CIRPAS Twin Otter during the MASE, GoMACCs field campaignsTwin Otter during the MASE, GoMACCs field campaigns

… … and the list continues (e.g., hygroscopicity data from and the list continues (e.g., hygroscopicity data from the work of Petters and Kreidenweis).the work of Petters and Kreidenweis).

What varies mostly is not the “thermodynamic” properties What varies mostly is not the “thermodynamic” properties of the complex organic “soup” but the fraction of of the complex organic “soup” but the fraction of soluble material… soluble material… Complexity sometimes simplifies Complexity sometimes simplifies things for us.things for us.

Growth kinetics from CCN Growth kinetics from CCN measurementsmeasurementsSize of activated droplets measured in the instrument.

The impact of composition on growth kinetics can be inferred:

• compare against the growth of (NH4)2SO4 (thus giving a sense for the relative growth rates), and,

• use a model of the CCN instrument (Nenes et al., 2001), to parameterize measured growth kinetics in terms of the water vapor uptake coefficient, .

0

20

40

60

80

100

120

140

160

180

0 1 2 3 4 5 6

Droplet Diameter at exit of instrument (m)

Dro

ple

t C

on

ce

ntr

ati

on

(c

m-3)

Model-derived size distribution 100% (NH4)2SO4, = 1.0

Model-derived size distribution,30% (NH4)2SO4, = 0.06

Size distributionof activated droplets measured in the CCN

instrument

Roberts and Nenes (2005); Lance et al. (2006)

Do growth kinetics of CCN vary? Do growth kinetics of CCN vary? Measurements of droplet size in the CCN instrument

Pure (NH4)2SO4

In-Cloud AerosolSub-Cloud Aerosol

1

2

3

4

5

6

7

8

9

10

0.2 0.4 0.6 0.8 1.0

Supersaturation (%)

Dro

plet

siz

e Observations

Model; slowly growing particles

Model; particles that grow “like”(NH4)2SO4

Critical supersaturation (%)

Marine Stratocumulus (MASE)All CCN grow alike

Urban (Mexico City, MILAGRO)CCN don’t grow alike.

~ 250 g mol-1, no surfactants Strong surfactants present

Mexico City droplet growth kineticsMexico City droplet growth kinetics

Slow growing, kinetically delayeddroplets bring downthe average

Early morning aerosol:Externally mixed, but

CCN grow like (NH4)2SO4

Mid - day:Internally mixed, but many CCN do

NOT grow like (NH4)2SO4

Does Does growth kineticsgrowth kinetics change? change? Measurements of droplet size in the CCN instrument

Pure (NH4)2SO4

In-Cloud AerosolSub-Cloud Aerosol

1

2

3

4

5

6

7

8

9

10

0.2 0.4 0.6 0.8 1.0

Supersaturation (%)

Dro

plet

siz

e Observations

Model; low uptake Coefficient

Model; high uptake Coefficient

Critical supersaturation (%)

Marine Stratocumulus (MASE)All CCN grow alike

Urban (Mexico City, MIRAGE)CCN don’t grow alike.

~ 250 g mol-1, no surfactants Strong surfactants present

How important is this How important is this range? range?

Compare Indirect Forcing that arises fromCompare Indirect Forcing that arises from

Changes in droplet growth kinetics (uptake Changes in droplet growth kinetics (uptake coefficient range: 0.03-1.0).coefficient range: 0.03-1.0).

Preindustrial-current day aerosol changesPreindustrial-current day aerosol changes

General Circulation ModelGeneral Circulation Model• NASA GISS II’ GCM• 4’5’ horizontal resolution• 9 vertical layers (27-959 mbar)

Aerosol MicrophysicsAerosol Microphysics

• The TwO-Moment Aerosol Sectional (TOMAS) microphysics model (Adams and Seinfeld, JGR, 2002) is applied in the simulations.

• Model includes 30 size bins from 10 nm to 10 m.

• For each size bin, model tracks: Aerosol number, Sulfate mass, Sea-salt mass

• Bulk microphysics version is also available (for coupled feedback runs).

Global Modeling Framework Global Modeling Framework UsedUsed

In-cloud updraft velocity (for N&S only)In-cloud updraft velocity (for N&S only)• Prescribed (marine: 0.25-0.5 ms-1;continental: 0.5-1

ms-1). • Diagnosed from large-scale TKE resolved in the GCM.

Global Modeling Framework Global Modeling Framework UsedUsed

Cloud droplet number calculationCloud droplet number calculationNenes and Seinfeld (2003); Fountoukis and Nenes

(2005) cloud droplet formation parameterizations. Sectional and lognormal aerosol formulations. Can treat very complex internal/external aerosol, and

effects of organic films on droplet growth kinetics.

EmissionsEmissionsCurrent day, preindustrial

AutoconversionAutoconversionKhairoutdinov & Kogan (2000), Rotstayn (1997)

Sensitivity of indirect forcing to Sensitivity of indirect forcing to thethewater vapor uptake coefficientwater vapor uptake coefficient

=1.0 - =1.0 - =0.03=0.03 Current-Preindust.Current-Preindust.

Forcing from range in growth kinetics (-1.12 W m-2) is as large as Present-Preindustrial change (-1.02 W m-

2) !

Spatial patterns are very different.Nenes et al., in preparation

Wm-2

Overall SummaryOverall Summary

CCN theory is adequate for describing cloud droplet CCN theory is adequate for describing cloud droplet formation.formation.

Simplified treatments of aerosol composition get Simplified treatments of aerosol composition get “most” of the CCN number right, but the associated “most” of the CCN number right, but the associated uncertainty in CCN and indirect forcing can still be uncertainty in CCN and indirect forcing can still be large.large.

Size-resolved measurements of CCN activity can be Size-resolved measurements of CCN activity can be used to infer the effects of organics in a simple way. It used to infer the effects of organics in a simple way. It seems that simple descriptions are possible and as a seems that simple descriptions are possible and as a community we need to systematically delve into this.community we need to systematically delve into this.

The impact of organics on droplet growth kinetics is a The impact of organics on droplet growth kinetics is a important issue that remains unconstrained to date. important issue that remains unconstrained to date. This is a “chemical effect” that has not been This is a “chemical effect” that has not been appreciated enough...appreciated enough...

Overall SummaryOverall SummaryDroplet formation parameterizations are at the point where Droplet formation parameterizations are at the point where they can explicitly consider all the chemical “complexities” they can explicitly consider all the chemical “complexities” of CCN calculations and droplet growth kinetics.of CCN calculations and droplet growth kinetics.

Observations should provide the “constraints” of organic Observations should provide the “constraints” of organic properties – classified with respect to age and source.properties – classified with respect to age and source.

They can even begin incorporating effects of dynamics They can even begin incorporating effects of dynamics (entrainment, variable updraft).(entrainment, variable updraft).

People developing aerosol-cloud parameterizations need to People developing aerosol-cloud parameterizations need to work very hard at linking them at the cloud-scale, starting work very hard at linking them at the cloud-scale, starting off from idealized “conceptual” feedback models.off from idealized “conceptual” feedback models.

A lot of work to do… but it’s exciting and challenging (not A lot of work to do… but it’s exciting and challenging (not impossible).impossible).

Acknowledgments: Nenes Research Group

Akua Asa-Awuku(PhD,ChBE)

ChristosFountoukis(PhD,ChBE)

Dr. RafaellaSotiropoulou(Researcher)

LuzPadró

(PhD,ChBE)

Wei-ChunHsieh

(PhD, EAS)

JeessyMedina

(PhD,ChBE)

DonifanBarahona

(PhD,ChBE)

Sara Lance

(PhD, EAS)

Richard Moore

(PhD,ChBE)

ACKNOWLEDGMENTSACKNOWLEDGMENTS

For more information and PDF reprints, go to For more information and PDF reprints, go to http://nenes.eas.gatech.eduhttp://nenes.eas.gatech.edu

Nicholas Meskhidze, NC StateGreg Huey group, Georgia TechRodney Weber group, Georgia TechAdams group, Carnegie Mellon UniversityWilliam Conant, University of ArizonaSeinfeld/Flagan group, CaltechGreg Roberts, Scripps Institute of Oceanography

NASA, NOAA, NSF, ONR, Blanchard-Milliken Young Faculty Fellowship

THANK YOU!