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Constraining the Effects of Composition and Mixing State on CCN Activity

Athanasios NenesSchool of Earth & Atmospheric SciencesSchool of Chemical & Biomolecular EngineeringGeorgia Institute of TechnologyIAMA ConferenceDavis, CA, December 6, 2007Acknowledgments: NASA, NSF, NOAA, ONRNenes Group, Seinfeld/Flagan Group, CIRPAS


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Aerosol Problem: Vast Complexity CCN Activity

An integrated “soup” of

  • Inorganics, organics (1000’s)

  • Particles can have uniform composition with size.

  • … or not

  • Can vary vastly with space and time (esp. near sources)

Predicting CCN concentrations is a convolution of size

distribution and chemical composition information.

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


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Aerosol Description: Complexity range CCN Activity

The … headache of organic species

  • They can act as surfactants and facilitate cloud formation.

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

  • Oily films can form and delay cloud growth kinetics

  • Some effects are not additive.

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

The treatment of the aerosol-CCN link

is not trivial at all.


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CCN: Looking at what’s important CCN Activity

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.


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Medina et al., CCN ActivityJGR, 2007

Do we understand the aerosol-CCN link?

Test of theory: “CCN Closure Study”

CCN MEASUREMENTS

CCN PREDICTIONS

  • How is it done?

  • Measure aerosol size distribution and composition.

  • Introduce this information Köhler theory and predict CCN concentrations.

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

CCN closure studies going on since the 70’s.

Advances in instrumentation have really overcome limitations


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Measuring CCN: a key source of data CCN Activity

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

Continuous Flow Streamwise

Thermal Gradient Chamber

Inlet: Aerosol

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

  • Wall saturated with H2O.

  • H2O diffuses more quickly than heatand 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 wall

wet wall

Outlet: [Droplets] = [CCN]

Roberts and Nenes (2005), Patent pending


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An example of a CCN closure CCN Activity

AIRMAP Thompson Farm site

  • Located in Durham, New Hampshire

  • Measurements done during ICARTT 2004

  • Air quality measurements are performed on air sampled from the top of a 40 foot tower.

Two DMT CCN counters

(Roberts and Nenes, AST, 2005;

Lance et al., AST, 2006)

TSI SMPS, for size distribution

Aerodyne AMS, for chemical

composition

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


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7/31/04 - 8/11/04 CCN Activity

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.

Medina et al., JGR (2007)


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NOAA P3 (GoMACCs) CCN Activity

Larger-scale CCN variability (ageing)

How important is external mixing to “overall” CCN prediction

Can we understand CCN in rapidly changing environments?

T0 site (MILAGRO)

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


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September 21: Houston Urban Plume Ageing CCN Activity

Wind,

Flight

progression


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September 21 Flight CCN Activity

Plume transects

Freshly emitted aerosol “builds up”

Supersaturation (%)

CCN low and ~ const

near source !


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September 21 Flight CCN Activity

~ 100 km

Aerosol is aging & diluting

Supersaturation (%)

CCN increase while

plume ages


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September 21 Flight CCN Activity

~ 100 km

Supersaturation (%)

CCN tracks the CN variability

when plume ages


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Some “take home” points CCN Activity

  • CCN theory is adequate. Closure errors are from lack of information (size-resolved composition, mixing state).

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

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

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

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


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sample CCN Activity

Particle Counter

sheath

TEC 1

neutralizer

DMA

humidifier

Activated CCN

Fraction CN

TEC 2

=

TEC 3

OPC

Particle Size

CCN

CN

Get more out of CCN instrumentation: Size-resolved measurements

Measure CCN activity of aerosol with known diameter

Results: “activation curves”


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What the Activation Curves tell us CCN Activity

Fraction of non-hygroscopic

externally mixed particles

“characteristic” critical supersaturation:

a strong function of “average” aerosol composition


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Ammonium Sulfate Lab Experiment CCN Activity

Hypothetical “monodisperse”

single-component aerosol

Ambient Data

(Mexico City)

“characteristic” critical

supersaturation

What the Activation Curves tell us

Slope has a wealth of information as well.

Ambient Data Broader From Chemical Variability (mixing state)


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Probability CCN Activity

SVF

First Approach; slope is from chemistry alone

Soluble Volume Fraction

Sigmoidal fit

Köhler Theory

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

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

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


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“Chemical Closure” CCN Activityinferred vs measured soluble fraction

Look at CCN data from MILAGRO (Mexico City, March 2006).

Compare against “bulk” composition measurements

The average distribution of soluble fraction agrees very

well with measurements. Our measurements give what’s

“important” for CCN mixing state.


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Diurnal Variability in CCN mixing state CCN Activity(Mexico City)


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Diurnal Variability in CCN mixing state CCN Activity(Mexico City)


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CCN: Looking at what’s important CCN Activity

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

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

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

    How important is this kind of uncertainty?

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

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


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CCN predictions: “take home” points CCN Activity

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

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

  • Even if size distribution were perfectly simulated, more simplistic treatments of aerosol composition imply even larger uncertainties in indirect effect. Size is not the only thing that matters in CCN calculations.

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


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Organics & CCN: The Challenges CCN Activity

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

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

  • Desired information:

    • Average molar properties (molar volume, solubility)

    • Droplet growth kinetics

    • Chemical heterogeneity (mixing state) of aerosol

    • Surface tension depression

Once more, size-resolved CCN measurements can provide a key source of data


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Go back to the Activation Curves CCN Activity

“characteristic” critical supersaturation:

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


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Inferring Molar Volume from CCN activity: CCN Activity

Köhler Theory Analysis (KTA)

  • 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:

(NH4)2SO4

(NH4)2SO4

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


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

Constants

Molar Volume

This is what you need to know about organics for CCN activity

Measured surface tension and CCN data

From IC/WSOC measurement

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


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KTA: Major findings on soluble organics organic properties

Many “aged” soluble organics from a wide variety of sources have a 200-250 g mol-1 . For example:

  • Aged Mexico City aerosol from MILAGRO.

  • 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 Twin Otter during the MASE, GoMACCs field campaigns

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

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


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180 organic properties

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

160

Size distribution

of activated droplets

measured in the CCN

instrument

140

120

Droplet Concentration (cm-3)

100

Model-derived size distribution,

30% (NH4)2SO4,  = 0.06

80

60

40

20

0

0

1

2

3

4

5

6

m)

Droplet Diameter at exit of instrument (

Growth kinetics from CCN measurements

  • Size 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, .

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


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Pure (NH organic properties4)2SO4

In-Cloud Aerosol

Sub-Cloud Aerosol

Do growth kinetics of CCN vary?

Measurements of droplet size in the CCN instrument

Marine Stratocumulus (MASE)

All CCN grow alike

Urban (Mexico City, MILAGRO)

CCN don’t grow alike.

Model; particles that grow “like”

(NH4)2SO4

Model; slowly growing particles

Observations

Critical supersaturation (%)

~ 250 g mol-1, no surfactants

Strong surfactants present


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Mexico City droplet growth kinetics organic properties

Mid - day:

Internally mixed, but many CCN do NOT grow like (NH4)2SO4

Slow growing,

kinetically delayed

droplets

bring down

the average

Early morning aerosol:

Externally mixed, but CCN grow like (NH4)2SO4


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Overall Summary organic properties

  • CCN theory is adequate for describing cloud droplet formation.

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

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

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


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THANK YOU! organic properties


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Pure (NH organic properties4)2SO4

In-Cloud Aerosol

Sub-Cloud Aerosol

Does growth kinetics change?

Measurements of droplet size in the CCN instrument

Marine Stratocumulus (MASE)

All CCN grow alike

Urban (Mexico City, MIRAGE)

CCN don’t grow alike.

How important is this  range?

Compare Indirect Forcing that arises from

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

  • Preindustrial-current day aerosol changes

Model; high uptake

Coefficient

Model; low uptake

Coefficient

Observations

Critical supersaturation (%)

~ 250 g mol-1, no surfactants

Strong surfactants present


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Global Modeling Framework Used organic properties

  • General Circulation Model

  • NASA GISS II’ GCM

  • 4’5’ horizontal resolution

  • 9 vertical layers (27-959 mbar)

  • Aerosol 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).


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Global Modeling Framework Used organic properties

Emissions

Current day, preindustrial

  • Cloud droplet number calculation

  • Nenes 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.

Autoconversion

Khairoutdinov & Kogan (2000), Rotstayn (1997)

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.


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Sensitivity of indirect forcing to the organic properties

water vapor uptake coefficient

Current-Preindust.

a=1.0 - a=0.03

Wm-2

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


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Sensitivity of indirect forcing to the organic properties

value of cloud-base updraft velocity

Current-Preindust.

Doubling of updraft

Wm-2

Uncertainty in indirect forcing doubling updraft velocity is a bit smaller than indirect forcing itself.

Spatial patterns are much different.


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Current Day Simulation (annual average) organic properties

Conditions for aerosol-cloud linking

Cloud base updraft (m s-1): 0.25 (marine), 0.5 (continental)

Water vapor uptake coefficient: 0.06

Cloud smax (%)

Cloud droplets (cm-3)

Nenes et al., in preparation


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Increasing D organic propertiesp

CCN Activation Curves

100nm

  • Activation curves can vary significantly throughout the day

  • Here, transition from very externally mixed at morning to well mixed by afternoon)

GMT

Activation curves change significantly with dry particle size


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Assuming organic properties

an average organic density of 1.4 g cm-3

(Turpin and Lim, 2001)

a

CCN Measurements: T0-T1 Contrasts

Average Organic

Average Organic

Average Organic

Average Organic

Molecular Weight

Molecular Weight

Site

Site

Integration Period

Integration Period

Molar

Molar

Volume

Volume

a

a

(g mol

(g mol

)

)

-

-

1

1

(cm

(cm

mol

mol

)

)

3

3

-

-

1

1

T0

T0

12 hr day

12 hr day

404.16

404.16

±

±

290.70

290.70

288.68

288.68

±

±

207.65

207.65

T1

T1

24 hr

24 hr

129.06

129.06

±

±

61.90

61.90

89.10

89.10

±

±

61.51

61.51

T1

T1

12 hr night

12 hr night

145.40

145.40

±

±

41.53

41.53

103.86

103.86

±

±

29.67

29.67

T1

T1

12 hr day

12 hr day

121.36

121.36

±

±

16.28

16.28

86.69

86.69

±

±

11.63

11.63

T0: organic properties is highly variable (~2 larger than T1)

T1: organic properties are quite constant regardless of day – this

suggests T1 expresses large scale (“background”) properties.

T1: Nightime filters have larger MW than Daytime. Photochemical

ageing increases the hygroscopicity


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Fast CCN Measurement Technique: Scanning Mobility CCN Analysis (SMCA)

Operation:

- Aerosol flows through DMA.

- DMA voltage is scanned.

- Classified aerosol is detected by CCN instrument and total particle counter.

Information we can get:

- Size-resolved CCN & aerosol concentrations.

- Size-resolved droplet growth kinetics.

- Complete spectrum can be obtained in a matter of minutes (vs. hours before).

Nenes and Medina, AS&T, in review


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Example of SMCA Analysis (SMCA)

Nenes and Medina, AS&T, in review


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Example of SMCA Analysis (SMCA)

Droplet Growth Kinetics

CCN activation curves

Nenes and Medina, AS&T, in review


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Example of SMCA Analysis (SMCA)

CCN “closure” during ICARTT

CCN counter

measuring aerosol

Integrated SMCA

distibutions

Nenes and Medina, AS&T, in review


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Extract water-soluble component of aerosol Analysis (SMCA)

Characterize

Measure CCN properties, WSOC & Inorganic concentration and surface tension depression

Separate into Hydrophilic & Hydrophobic Fraction

(XAD-8 Solid Phase Extraction)

Hydrophilic Fraction

Hydrophobic Fraction

Characterize

Characterize

Desalt

Desalt

Characterize

Characterize

Separating the effects of hydrophobics/philics


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Fresh Biomass burning sample: Analysis (SMCA)

Köhler Theory Analysis

The “original” aerosol sample from prescribed fire burning (Sullivan et al., 2006)

250 g mol-1 (assuming s = 1.4 g cm-3)

Strong surfactant characteristics

  • Hydrophilic Fraction

    ~ 200 g mol-1 (assuming s = 1.4 g cm-3) (note: “magic number”)

    No strong surfactants present

  • Hydrophobic Fraction

    500-700 g mol-1 (assuming s = 1.4 g cm-3)

    Strong surfactants

    Consistent with HULIS (e.g., Dinar and Rudich, 2007; Wex et al., 2007)

  • Hydrophilic/hydrophobic molar ratio 3:1

    (Asa-Awuku et al., ACPD)


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Highly Heterogeneous Environment Analysis (SMCA)September 15, 2006 flight

Data collected aboard the CIRPAS Twin Otter


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September 15 Flight (CIRPAS Twin Otter data) Analysis (SMCA)

Tremendous variations in size distribution and composition


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September 15 Flight (CIRPAS Twin Otter data) Analysis (SMCA)

Using size-resolved sulfate fraction gives “typical” closure


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September 15 Flight (CIRPAS Twin Otter data) Analysis (SMCA)

Large organic

fraction

assumed insoluble

Using size-resolved sulfate fraction gives “typical” closure


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Measurements of water-soluble OC Analysis (SMCA)properties from Mexico City

Goals

  • Characterize CCN properties of ambient water soluble organics (WSOC) in Mexico City

  • Average molecular weight and surfactant characteristics, and growth kinetics.

  • Assess the impact of ageing on the WSOC properties.

Measurement Sites

Instituto Mexicano de Petróleo (T0), Univ.Tecnológica de Tecámac (T1)

Approach

Measure CCN activity and growth kinetics as a function of dry particle size (i.e., previously classified with a DMA).

Collect Particles (quartz filter samples)

Extract in water

Surface tension measurements

Measure inorganic composition and total carbon in WSOC

Use theory to infer the average molecular weights and the existence of films


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Supersaturation (fractional) Analysis (SMCA)

Particle Dry Diameter (nm)

CCN Measurements during MILAGRO

~ 400 g mol-1

Very hydrophobic at downtown (T0)

~ 250 g mol-1

Much more hydrophilic

Outside MC (T1)

Key points:

“parameterization-type”

knowledge

  • MW decreases by half over one day of transport.

  • Direct consequence of chemical ageing processes.

  • MW doesn’t really change after one day (even if meteorology does!).


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“Comprehensive Approach” – Analysis (SMCA)Accounting for DMA Size Distribution

p’(dp)

probability

p(Sc )

dp

p”(SVF)

probability

SVF


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Comparing the Different Approaches Analysis (SMCA)

Comprehensive

Approach

Sigmoidal fit

  • Comprehensive approach is computationally painful

  • Sigmoidal fit does a good job


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“Non CCN-active” Particles Analysis (SMCA)

(Data from Mexico City)


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Linking CCN data to Chemical Information Analysis (SMCA)

Use Köhler Theory to link CCN activity to amount of soluble material

Other properties

SVF is exactly what we need to know

Volume fraction

Scaled soluble Volume Fraction

(aggregate chemical property)

1.3 NaCl

0.6 (NH4)2SO4 (assuming a van’t hoff factor of 2.5 [Rose et al, 2007])

0.8 NH4NO3

0.2 low molecular weight organics (with MOrg = 125 amu, n =1, r =1.4 g cm-3)


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Quantifying CCN Prediction Uncertainty Analysis (SMCA)

Let’s use the data from the ICARTT dataset

The prediction error decreases as supersaturation increases.

This dataset corresponds to aged continental aerosol.

“Max-uncertainty” – uniform composition with size

“average uncertainty” – size-resolved composition determined.

“Max” ~ 2 x “Average” error.

Error range: 10 - 60%

Sotiropoulou et al (2005); Sotiropoulou et al (2007)

The degree of CCN closure in the ICARTT dataset is representative of the many studies found in the literature.


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IF Uncertainty from simplified CCN calculation Analysis (SMCA)

Indirect Forcing (W m-2)

CCN Prediction

Global average: 28%

Global average:

0

10

20

30

40

50%

Larger CCN prediction uncertainty

is found in regions where

in-cloud smax is low

Larger uncertainty is predicted downwind of industrialized and biomass burning regions

Sotiropoulou et al., in preparation


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