Definitional Issues
Tami C. Bond
University of Illinois at Urbana-Champaign

2 May 2005
IPCC Aerosol Meeting
Geneva, Switzerland

Or, in other words…
words…
whaaat? we don’teven know what it isand you want us toadd up emissions?

I mean, it israther uncertain
and all…

Relax.

Questions I think we are asking
 Climatic effects of carbonaceous particles

depend on composition.
 There are thousands of carbon compounds.
(obviously, we will not model each individually) What divisions between carbon types

mustwe draw to represent effects on climate?
 Can we measure those divisions inpractice?

What II’ll’ll cover here
1. carbon particles… hmm.
a. could you identify a black carbon particle…if you had nanovision?

b. Houston, we have a (measurement) problem
c. what can we do with all these names?

2. modeled climate forcing
a. overview: forcing calculations
b. the pieces: direct forcing
c. some comments: indirect forcing

3. summary: role of definitions
outlineIf you had microvision…
microvision…

1 µm

Nanovision (and a very small scalpel)

2.

3.

1.
4.
1.
2.

Micrograph of diesel soot: Stanmore, Brilhac, and Gilot, Carbon. 2001, 39 , 2247-2268.
Structure of spherule extracted from HRTEM image: Palotás et al., Energy and Fuels 1996, 10 ,254-259.

3, 4. Structure of spherule and layers inferred from electron microscopy and X-ray diffraction:
Heidenreich, Hess and Ban, J. Appl. Crystallography 1968, 1, 1-19

Structure
Structure has
has been
been known
known forfor many
many years.
years.
Heckman,Heckman,F.A.
F.A.Microstructure
Microstructureof
ofcarboncarbonblack.
black.
RubberChem.
Technol.
1964,37,1245-1298.
Rubber Chem. Technol. 1964, 37 , 1245-1298.

1a. nanovisionNanovision (and a very good chemistry set)
thousands of compounds
much carbon and hydrogensometimes oxygen &a little nitrogen

Schauer, J.J.; Kleeman, M.J.; Cass, G.R.;
Simoneit, B.R.T., Environ. Sci. Tech. 1999,33 , 1578-1587.

1a. nanovision

Information from combustion research-“soot” appears to form in a very narrow region (now you don’t see it,
now you do)
implies there is a sharp difference between “special” BC and othercaveat: most formation studies look only at simplified situations; isthis true for real combustion?

Siegmann, K.; Sattler, K.; Siegmann,H.C. J. Electron Spectrosc. Rel. Phenom.
2002, 126 , 191-202.

1a. nanovision

Van der Wal, R.L.; Jensen, K.A.; Choi, M.Y.
Comb. Flame. 1997, 109 , 399-414.

Measurement goals (I)We
We can’t
can’t measure
measure climate
climate forcing
forcing byby individual
individual constituents.
constituents.
 Aerosol concentrations are patchy

Individual measurements don’t represent the globe.
 Satellites can measure globally

but they can’t distinguish components, nor tell thedifference between anthropogenic and natural.

Image: Aerosol size from MODIS(red=fine PM), NASA

1b. measurements

Measurement goals (II)Thus,
Thus, we
we havehave toto model
model thethe forcing.
forcing. Measurements
Measurements can:can:
 Provide model inputs
 Corroborate model results

ForFor this
this purpose,purpose, thethe measurements
measurements must:must: Distinguish between important groups ofcarbon compounds
 Measure a conserved property

1b. measurements

measurement tools
 combustion analysis Æ total carbon

OKOK **

 light absorption Æ dark carbon

next
next

 thermal-optical analysis

next

next
Æ some carbon division (light/dark?)
 gas chromatography/mass spectrometry
Æ individual species
identifies
identifies 10-20%
10-20% ofof carbon
carbon Fourier Transform Infraredusefuluseful but
but expensive
expensive
Æ functional groups
real-timereal-time ability
ability isis newnew
 soluble fraction
 single-particle analysis
usefuluseful but
but expensiveexpensive* not immune to measurement artifacts, though
1b. measurements

measurements: thermal
Principle: Heat, measurecarbon released atdifferent temperatures
 Complication 1: Charring
during analysis
Complication 2: Oxidation
rates can vary in different
mixtures Different protocols (rates/magnitudes of heating) yield
different results

1b. measurements

Interlab comparison of same filtermeasurements: optical
Principle: Collect particles onfilter; monitor transmittance Complication 1: Amplification
by filter (x2-4)
 Complication 2: Absorption
per mass changes withparticle form
5000
 Complication 3: Non-carbon
4000
absorbers (dust?)
 Drawback: Doesn’t
3000
measure negligibly2000
absorbing particles
 Different protocols (filters,1000wavelengths) and locations
0yield different results25

Absorption enhancement
4.5

shell diameter (nm)41b. measurements

3.5
3
2.5
2
1.5

50
100
core diameter (nm)250All those names!
graphitic carbon

e graphite”
kliskolotsolma“substance that
troscopy”
cepsnamaRfo“resultelemental carbon

By the way,
there’s little enoughinformation that we use
any of these to informmodels.

“substancewe talk about for air“result ofq
thermal measuremen uality”
t””sledmoet
anomicarb sed in clnt ”
ek???
cmaelrubuse
acenmta“subs of opticallt“resuorganic carbon“everything else”

1c. names???

Modeling procedure
Particle
shapeTransport

Aerosol
opticalproperties

Directaerosol
forcing

Particle
size
ChemicalComposition

Emission
rates

Atmospheric
Concentration
Chemical
reactionsRemovalrates

Lifetime

2a. modeling overview

Cloudproperties

Interactionwith water

Indirectaerosolforcing

Aerosol lifetime estimates Few ways to measure; must be modeled Depends on meteorology (esp. rainfall) Estimates are about ~1 week Model corroborated by comparison withmeasurements
 Hard to make that comparison unlessquantity measured is invariant!

2b. modeling details: direct forcing

Model comparisons: black carbon
BC forcing (W/m2)
00.2 0.4 0.6 0.8

Ratio NDRF/SFE

Normalized DRF (W/g)
11.201000

2000 0246

Haywood & Ram. 1998
H&R, diff. radius
Cooke 1999
Myhre 1998
Penner, 1998
Jacobson
Koch 2001
Chung & Seinfeld 2002
Wang 2004

coef. var: 73% (unfair)accounting for emission& lifetime differences
coef. var: 32%

2b. modeling details: direct forcing

accounting for opticalproperty differences
coef. var: 17%

Variation in model results
ModeledModeledintegrated
integratedforcing
forcingby
byBC
BCduring
duringits
itslifetime:
lifetime:
860
860MJ/gram
MJ/gramemitted
emitted(350-2000)(350-2000)
Sources
Sourcesof
ofsquared
squareduncertainty
uncertainty
Optical properties-fresh
Optical properties-coating
Physical location - clouds
Physical location - other
Atmospheric lifetime

2b. modeling details: direct forcing

Model comparisons: organic carbon
Normalized DRF (W/g)

OC forcing (negative, W/m2)
00.1

0.2

0.3

0.4

0200

Normalized DRF(no abs, no hygroscopic)
400

050

100

Cooke 1999
Myhre 2001
Penner, 1998
Jacobson
Jacobson, nonabs
Koch 2001
Chung & Seinfeld 2002
C&S, hygroscopic

coef. var: 83% (unfair)accounting for emission& lifetime differences
coef. var: 96%2b. modeling details: direct forcing

accounting for someproperty differences
coef. var: 10%

Indirect forcing: some questions
 Which aerosols dominate number

concentration in critical regions?
 Primary? (BC, dust, some organic) Precursors of nucleation?

 Which aerosols/precursors affect cloud

droplet number and size?

 Solubility, or other better metric?

2c. modeling comments: indirect forcing

How defined classes of carbon
affect forcing estimates

Uncertainties
Uncertainties remain
remain in:in: Emission estimates(what’s the conserved quantity?) Model corroboration(what’s the lifetime?)
 Species representation(what are the properties?)

3. summary