Aerosols: What are we missing? What should we do in the future?

Peter J. Adams

Carnegie Mellon University

Chemistry-Climate Interactions Workshop

February 11, 2003

Overview

How good are models?

What observations are needed?

How to deal with subgrid variability?

Where do we stand in modeling the

indirect effect?

How good are models?

Uncertainty in Direct Forcing Estimates

0.00

0.25

0.50

0.75

1.00

1.25

1.50

0 0.1 0.2 0.3 0.4 0.5 0.6

Anthropogenic SO42- (Tg S)

-Fo

rcin

g (

W /

m2 )

Koch, 99Charlson, 91Kiehl, 00

Feichter, 97

Boucher, 95Kiehl, 93

Penner, 98

COSAM

(nmol SO4 / mol air)Barrie et al., Tellus 53B, 615-645, 2001

Model Capabilities

IPCC 2001 workshop compared 11 models against observations:• Sulfate: monthly average concentrations

generally within a factor of two• Other species are “inferior” • BC: factor of 10• Model-model discrepancies strong in free

and upper troposphere• Models differ in terms of transport distance

Insufficient for climate studies

Reasons for Model Uncertainties

Sparse data• Spatial: free troposphere / remote regions• Temporal: short-term field campaigns

Measurement difficulties• Black carbon

Comparisons often use inconsistent meteorological fields• GCM aerosol models for climate studies• But GCM met fields generally do not match time

period of observations• Especially problematic for short-term comparisons

(i.e. field campaigns)

What observations are needed?

Future Directions: Models / Observations

New data sets• Satellite instruments: MODIS, MISR, others• Lidar

Consistent meteorological fields• GCMs with nudging capabilities• GCM / CTM combinations (e.g. GISS GCM and

GEOS-CHEM)• “Correct” for meteorological differences

Detailed comparisons not “glamorous” but sorely needed

Need to move from minimal to systematic comparisons

Future Directions: Observations

Need more long-term data sets• Field campaigns provide process

understanding but are weak at providing aerosol climatologies

AERONET as a prototype Other ideas

• Lidar networks• Size and chemically resolved data• Regular aircraft sampling (John Ogren)• Coordination

AERONET

~180 sun photometers across globe From 1993- Standardized instruments and

processing Provides: spectral optical depth Infers size distribution for column Levels of data: raw, quality-assured,

climatological Available on web

AERONET

Holben et al., JGR 106, 12067-12097, 2001

How to deal with subgrid variability?

Subgrid Variability: Direct Effect

Calculated direct forcing with and without subgrid variability in clouds and RH

Limited area model (2 x 2 km)

Forcing

GCM: -1.92 W m-2

LAM: -3.09 W m-2

Haywood et al., GRL 24, 143-146, 1997.

Challenge: Subgrid Variability

Direct Effect• water uptake is nonlinear function of RH

Indirect Effect • subgrid spectrum of updraft velocities and

cooling rates

Microphysics • nucleation is often a subgrid phenomenon

Models and observations

Confronting Subgrid Variability

Frameworks• Brute force (probably not)• Probability distribution functions• Spatial homogenization (computational

mechanics)

Data availability• Observations: aircraft / satellites• Models: Large eddy simulations

Probability Distribution Functions

Cloud modeling• P(w, l, qt)

w: updraft velocity

l: liquid water potential temperature

qt: total specific water

Functional form of P assumed Parameters describing P become

prognostic variables

Larson et al., JAS 59, 3519-3539, 2002

Probability Distribution Functions

Applications• Diagnose aerosol variability from cloud

parameters• Integrate prognostic variability into scheme• Focused studies in single column models• Use in GCMs and CTMs

Where do we stand in modeling the indirect

effect?

Mechanistic vs. Empirical Models

Sulfate Mass (g m-3)C

loud

Dro

plet

s (c

m-3)

Boucher & Lohmann, 1995

Particle Size

Num

ber

Mechanistic: number of cloud drops depends on number of particles large enough to activate

Empirical: number of cloud drops correlated with sulfate mass based on observations

Empirical Approach: Limitations

I: Martin et al. [1994]: -0.68 W/m2

II: Martin et al. with background CCN: -0.40 W/m2

III: Jones et al. [1994]: -0.80 W/m2

IV: Boucher and Lohmann [1995]: -1.78 W/m2

“It is argued that a less empirical and more physically based approach is required…”

Clo

ud D

ropl

ets

(cm

-3)Sulfate Mass (g m-3)

Kiehl et al., JGR 105, 1441-1457, 2000

Aerosol Microphysics Algorithms

Modal

Ni, Dpgi, i

Variable i makes a difference

Moment

Prognostic equations for Mi

0

ppipi dDDnDM

Aerosol Microphysics Algorithms

Modal

Ni, Dpgi, i

Variable i makes a difference

Moment

Prognostic equations for Mi

0

ppipi dDDnDM

Sectional

Mass(species, bin)

Moment-Sectional

Number(bin)

Mass(species, bin)

Two moments of the size distribution (mass and number) are tracked for each size bin.

The average size of particles in a given section is not constant with time

Two-moment method conserves both mass and number precisely

Prevents numerical diffusion present in single-moment methods

Excellent size resolution: 30 sections from .01 m to 10 m

Two-Moment Sectional Algorithm

mo 2mo … Mass

M1

N1

M2

N2

...

...

Tzivion et al., JAS 44, 3139 – 3149, 1987

Adams et al., JGR 10.1029/2001JD001010, 2002

CCN 0.2%

Microphysical Models: Uncertainties

Particulate Emissions• Most sulfate aerosols results from gas-phase SO2

emissions• Particulate sulfate: <5% of anthropogenic sulfur

emissions Nucleation of new aerosol particles

• Important uncertainties in mechanism and rate Both processes contribute significant numbers of

small particles• insignificant contribution to sulfate mass• important contribution to aerosol number

concentrations and size distributions Must quantify sensitivity to these uncertainties

Sensitivity Scenarios

Base Case• 1985 sulfur emissions• all emissions as gas-phase SO2

• nucleation based on critical concentration from binary (H2SO4-H2O) theory

Primary Emissions• 3% of sulfur emissions as sulfate

Enhanced Nucleation• critical H2SO4 concentration factor of 10 lower

Pre-industrial• no anthropogenic emissions (but no sea salt)

Sources

0.0E+00

2.0E-04

4.0E-04

6.0E-04

8.0E-04

1.0E-03

1.2E-03

1.4E-03

1.6E-03

1.8E-03

2.0E-03

#/cm

3 s

Primary Emissions

Nucleation

Base Case

Enhanced Nucleation

Primary Emissions

Pre-industrial

Sources

0.0E+00

2.0E-04

4.0E-04

6.0E-04

8.0E-04

1.0E-03

1.2E-03

1.4E-03

1.6E-03

1.8E-03

2.0E-03

#/cm

3 s

Primary Emissions

Nucleation

Base Case

Enhanced Nucleation

Primary Emissions

Pre-industrial

Aerosol Number

0

200

400

600

800

1000

1200

# cm

-3

Base Case

Enhanced Nucleatio

Primary Emission

s

Pre-industrial

Sources

0.0E+00

2.0E-04

4.0E-04

6.0E-04

8.0E-04

1.0E-03

1.2E-03

1.4E-03

1.6E-03

1.8E-03

2.0E-03

#/cm

3 s

Primary Emissions

Nucleation

Base Case

Enhanced Nucleation

Primary Emissions

Pre-industrial

Sinks

-2.0E-03

-1.8E-03

-1.6E-03

-1.4E-03

-1.2E-03

-1.0E-03

-8.0E-04

-6.0E-04

-4.0E-04

-2.0E-04

0.0E+00

#/cm

3 s

Wet deposition

Dry deposition

Coagulation

Aerosol Number

0

200

400

600

800

1000

1200

# cm

-3

Base Case

Enhanced Nucleatio

Primary Emission

s

Pre-industrial

Sources

0.0E+00

2.0E-04

4.0E-04

6.0E-04

8.0E-04

1.0E-03

1.2E-03

1.4E-03

1.6E-03

1.8E-03

2.0E-03

#/cm

3 s

Primary Emissions

Nucleation

Base Case

Enhanced Nucleation

Primary Emissions

Pre-industrial

Sinks

-2.0E-03

-1.8E-03

-1.6E-03

-1.4E-03

-1.2E-03

-1.0E-03

-8.0E-04

-6.0E-04

-4.0E-04

-2.0E-04

0.0E+00

#/cm

3 s

Wet deposition

Dry deposition

Coagulation

Aerosol Number

0

200

400

600

800

1000

1200

# cm

-3

Base Case

Enhanced Nucleatio

Primary Emission

s

Pre-industrial

CCN Concentration

0

20

40

60

80

100

120

# cm

-3

Vertical Profiles

-1000

-900

-800

-700

-600

-500

-400

-300

-200

-100

0

0 50 100 150 200CCN 0.2% Concentration (cm-3 STP)

Pre

ssu

re (

mb

)

Modern Day: SO2

Modern Day: SO2/SO4

Preindustrial

Impact of Particulate Emissions

Continental / Marine

1

10

100

1000

0.1 1 10 100

Sulfate (g m-3)

CD

NC

(cm

-3)

ContinentalstratiformContinentalcumuliformMaritime

Implications

More aerosol models are including explicit microphysics to predict CCN concentration

Such models are sensitive to inputs that influence aerosol number• Nucleation / Primary particles

Physical insight into factors controlling CCN Needs

• Size-resolved emission inventories• Better understanding of nucleation

Aerosol number budgets (e.g. sea-salt)

Fitting ambient size distributions to prescribed functional form introduces biases which can be important for indirect effect.

Parameterizations: prescribed size distribution bias

This aerosol is“shifted” to larger sizes.

This will bias droplet number

Predictions.

source: Roberts et al., in press

Current parameterizations: other weaknesses

Lack of explicit treatment of mass transfer limitations in droplet growth; this has been shown to be important for polluted conditions (Nenes et al., 2001).

Empirical correlations are used in many. They are derived from numerical simulations and can introduce biases when used outside their region of applicability.

They lack important chemical effects that can influence cloud droplet formation. Such effects are the presence of :

• slightly soluble species in the aerosol (Shulman et al., 1996)

• water soluble gas-phase species (Kulmala et al., 1993)

• surface tension changes from surface-active species in the aerosol (Facchini et al., 1999).

• changes in water vapor accommodation coefficient from the presence of film-forming compounds (Feingold & Chuang, 2002).

-0.04

-0.02

0.00

0.02

0.04

0.06

0.08M

axim

um a

lbed

o ch

ange

, R

*

10 cm/s

30 cm/s

100 cm/s

300 cm/s

insoluble

organicno

organicwith

5 ppbHNO3

x 2conc.

0.1 m s-1

0.3 m s-1

1.0 m s-1

3.0 m s-1

marine aerosol

Coolingeffect

Warmingeffect

Chemical effects: assessment of their importance.

Calculate the maximum change in cloud properties when a chemical effect is present. Numerical cloud parcel model used for the calculations.

Chemical effects can be as effective in altering

cloud properties as doubling the aerosol concentrations!

New effect: Black Carbon heating

Black carbon exists in polluted aerosol; it absorbs visible sunlight and heats the surrounding air. This can leads to decreased cloud coverage, and climatic warming.

If black carbon is included in cloud droplets, the heat released can increase the droplet temperature enough to affect the droplet equilibrium. This is a new effect.

drop BC core

Absence of heating Presence of heating: droplet and gas phase get heated

Black Carbon heating: potential effect on drizzle

0

100

200

300

400

500

600

700

800

900

0 10 20 30 40 50

GCCN average size (m)

He

igh

t (m

)

0% BC, Pristine

10% BC, Pristine

20% BC, Pristine

BC can effectivelydecrease the probabilityfor drizzle formation.

A heating mechanismcan lead to climatic cooling!

This effect can be parameterized (not shown).

Is it important?We don’t know yet.

500 parcel average

Cloud base

Cloud top

Conclusions

Observations• Long-term• Standardized networks• Vertical profiling• Satellites

Conclusions

Observations• Long-term• Standardized networks• Vertical profiling• Satellites

Comparisons• Systematic and critical• Assimilated / nudged meteorologies• Correct for meteorology

Conclusions

Models• Explicit microphysics• Particle number budgets

Primary emissions Nucleation

Conclusions

Models• Explicit microphysics• Particle number budgets

Primary emissions Nucleation

• Activation: “chemical effects”• Other processes?

E.g. Could black carbon lead to cooling?

• Subgrid parameterizations