Direct effects of aerosols on climate:
What do we know?

Joyce Penner
IPCC Expert Meeting on Aerosols
2-4 May 2005

Outline• Sources and processes determining direct forcing
estimates• Intercomparison of model results for surface
concentrations, aerosol optical depth, aerosol
extinction profile• Inverse methods for improving aerosol sources• Uncertainty in direct aerosol forcing

About half of the total aerosol source forD<2 µm is anthropogenic:
Anthropogenic/Natural Source Strength

Total AnthropogenicSource Strength(Tg/yr)

Total Natural SourceStrength (Tg/yr)

Aerosol direct effects: Anthropogenic
Aerosols• Fossil fuel sulfate and associated ammonium• Organic carbon and black carbon from fossil fuels• Smoke from biomass burning• Mineral dust
• Fossil fuel nitrate and associated ammonium

The major anthropogenic source types are
sulfates, nitrates, organic and black carbon,
smoke, and dust
Aerosol Sources by Type
Anthropogenic Sulfate

Athropogenic Sulfates (as HSO4-)
Natural Sulfates (as HSO4-)

Fossil fuelOC and BC

Anthropogenic Organic Carbon
Natural Organic Carbon
Black Carbon
SmokeAnthropogenic Nitrates (as NO3-)
Natural Nitrates (as NO3-)
Anthropogenic Ammonium (as NH4+)

AnthropogenicDust

Natural Ammonium (as NH4+)
Smoke

Sea salt
Anthropogenic Dust
Natural Dust

There are major
uncertainties inemissions(Chapter 5, Penner
et al., 2001 IPCC)
In assessing models,the emissions
should account fordifferences between
the year of theemissions and yearof observations
The quality of thesimulation should
be judged in light of
uncertainties inemissions

Aerosol typeSulfates(as HSO4-)

AnthropogenicSource
strengthRange-1(Tg yr )104(59-182)

NaturalSource
strengthRange-1(Tg yr )67(32-142)

OrganicCarbon

20(10-30)

Black
Carbon7(4-11)Smoke

70(50-90)?

Nitrates -)(as NO3

14(10-20)

4(2-8)

Ammonium(as NH4+)

19(11-34)

12(6-26)

88(30-165)

200(100-300)

Sea salt
Dust
r<1 um200(100-300)

14(8-40)

Uncertainties in direct aerosol forcing areassociated with every step in the modelingprocess

Observed and predicted black carbon

Average absolute error between models andobservations of aerosol species at selectedsurface locations (%).
Model

SulfateGISS
GSFC
HadleyCCM/Grantour
ECHAM
Stochem
ULAQ
Mozart
ECHAM/Grantour
TM3PNNL

6353
32
48
98
63
43
46
4196
49

Average of allmodels

57

Black
CarbonOrganic
CarbonDust

Sea Salt

127
219
220
111
276

121
134

178
92

40
30

85
285

157

68

84
211
230

100

95

88

135

122

33

133

220

179

154

16
129

46

Zhang, Penner et al., 2004, submitted

Total optical depth

Aerosol optical depth is determined by the sum of all
aerosol types

Sea salt (D<2 m)

Dust (D<2 m)

Aerosol optical depth at 865 nm in May 1997

Comparison of AERONET and AVHRR satellite
optical depthDifference between model-derived opticaldepth and that for each satellite-retrieved
optical depth
4-Month Average

Nakajima

Michchenko

Stowe

CCM1/GRANTOURULAQMPI/Dalhousie
GISS
ECHAM/GRANTOURGOCART

-0.04
-0.02
-0.06
-0.05
-0.06
0.01

-0.06
-0.04
-0.08
-0.07
-0.08
-0.01

-0.01
0.00
-0.03
-0.03
-0.03
0.04

Model Mean

-0.04

-0.06

-0.01

Penner et al., 2002

Are current models doing better?
(AEROCOM, Kinne et al., 2005)

all0.12

SS

0.08

DU

0.04

POM

SU

S*NC

LSUL
SP
CTAE

BCM
I
EH
NF
OT
O
GIM
GM
GO
GGI
R
NM

0LO

annual global aot

0.16

Difference between model-derived aerosol
forcing and that for each satellite-retrieved
forcing (W/m2)
4-Month Average

Nakajima

Michchenko

Stowe

CCM1/GRANTOUR
ULAQ

-2.34
0.86

-3.76
-2.00

-0.84
0.47

MPI/Dalhousie

-2.84

-4.64

-1.40

GISS

-2.82

-4.32

-1.35

ECHAM/GRANTOUR

-3.20

-4.64

-2.04

GOCART

1.04

-0.75

2.19

Model Mean

-1.80-3.32-0.47

Difference between satellite optical depth
and modeled optical depthComparison of satellite and modeled
optical depth may indicate that forcing
estimates from models are too lowWhat about the vertical distribution ofaerosols?
• Previously only limited analysis has been
available from a few vertical profiles• Zhang, Penner et al. [2004] compared SAGE
II extinction profiles with profiles determinedfrom the 2001 IPCC models

Zhang,Penneret al.
(2004)

(a) GISS(b) GSFC/GOCART(c) LLNL/U. Michigan(d) MPI/Dalhousie U.

(e) ULAQ(f) ECHAM/GRANTOUR(g) SAGE IIZonal average 1.02 µm extinction
for six different models and the SAGE
II background aerosol extinction(in units of 10-4 km-1)(Zhang, Penner et al., 2004)

(a) GISS(b) GSFC/GOCART(c) LLNL/U. Michigan(d) MPI/Dalhousie U.

(e) ULAQ(f) ECHAM/GRANTOUR(g) SAGE II

Percentage difference in the zonal average
0.525-µm/1.02-µm extinction ratio between
each model and the SAGE II data.
The SAGE II 0.525-µm/1.02-µm extinction
ratio is shown in panel g.

What do measured vertical profiles oforganic aerosols tell us?
• Several field campaigns have identified significant
amounts of organic aerosols in the uppertroposphere:– TARFOX (Novakov et al., 1997)– Sonex– Ace Asia

• These large amounts cannot be explained by models:
Implication is that there is a large secondary sourceof organic aerosols that is not represented in themodels– Acid-catalyzed formation process??

Conclusions (direct forcing)• Reduced uncertainty in aerosol direct forcing will
require improvements in global models:– Aerosol sources– Processes determining aerosol lifetime and burden including
formation of secondary organic aerosols• The biggest uncertainties relate to determining:– BC, POM, and dust
– Dust appears to be overestimated in the upper troposphere– Organic aerosols are underestimated in the uppertroposphere

Can we improve our understanding of biomass
aerosol sources? Biomass aerosol emission aregenerally considered uncertain to a factor of 2•

Several new bottom-up estimates of emissions from satellite
estimates of burned area:– Ito, A., and J. E. Penner (JGR, 2004)– Hoelzemann et al. (JGR, 2004)– van der Werf et al. (Global Change Biol., 2003)•

Several new efforts based on inverse modeling for COemissions using satellite observations:– Arellano et al. [GRL, 2004]– Petron et al. [GRL, 2004]

Use of TOMs AI is more direct than the use of CO ininverse studies (not as many competing sources):•Use a Bayesian inverse technique designed to match satelliteretrieved aerosol index (AI) [Zhang, Penner, and Torres,submitted, 2004]•A priori estimates based on 2 cases:– Bottom-up estimate of BC emissions developed from the GBA 2000
data set for burned areas [Ito and Penner, 2004a]– Inferred BC from scaling the results of an inverse study of COemissions by Arellano et al. [2004] (spatial distribution within a regionis the same as that in Ito and Penner [2004a] and temporal variation
uses the TOMs AI)•Uncertainty in a priori estimates based on 2 cases:– Assumed 50% everywhere– Estimated from the difference between the bottom up and top-down
estimates (I.e. Ito and Penner vs Arellano et al.)

9 Regions considered in the analysis:

Uncertainties in apriori emissions
determined from difference of 2 cases:
Inversion
based onCO

Bottom-up
estimate

Difference

N. Africa

0.63

0.45

41%

S. Africa

0.35

0.49

28%

S. America

0.65

0.07

828%

Indonesia

0.08

0.01

685%

S. Asia

0.35

0.04

783%

Australia

0.27

0.08

243%

Boreal Sib.

0.04

0.20

-79%

Bor. N. Am.

0.01

0.03-79%

Rest

1.23

0.25

394%

Total

3.62

1.62

124%

RegionAll cases lead to a better agreement with the data:
Starting with a priori from the CO inverse study
leads to the closest agreement with data

Total emissions 70% larger with the CO inverse study
than the bottom up case and they are a factor of 3 larger
than the bottom up estimates

The largest increases occur in the earlypart of the yearTotal BC emissions are similar to those inIPCC
BC Source
categorySource(Tg/yr)

Reference

3.43

Ito and Penner,(2005)

Biofuel

2.97

Ito and Penner,(2005)

Open biomassburning

4.72This study

Total

11.12

This study

Fossil fuelConclusions (biomass aerosols)•Using a combination of inverse models and different
observations can lead to better estimates of sources. A priori
estimates are more important than estimates of uncertainty inthe a priori in determining inverse model results.

•For open biomass burning, better bottom up methods need to be
developed: MODIS appears to be giving good estimates ofburned area, but need better estimates of available fuel (Ito andPenner, 2005b).

•Assuming model procedures for determining aerosol
concentrations are correct, significant underestimates can becorrected using inverse methods

Research priorities:-Combine bottom up and top down methods to narrow
uncertainties in open biomass emissions-Improve model procedures for predicting aerosol massconcentrations

Forcing estimated in IPCC, 2001:

How can we assign unbiased estimates ofuncertainty?
•Estimates of uncertainty may be obtained from estimates of forcing
together with uncertainty in factors involved in determining forcing. Sofor direct forcing:∆F = Fd [Ta2 (1− fc )][2(1− Rs )2 β fb Mα sf (RH ) − 4RsMα sf (RH )((1− ω 0 ) / ω 0 )]••••••••••••

Where:
Fd=net downward solar flux
Ta=atmospheric transmissivity above the main aerosol layer
fc= global cloud fraction
Rs= global average surface albedo
β = up scatter fraction for isotropic incoming radiation
fb = hygroscopic growth factor for up scatter fraction
M = global mean column burden for anthropogenic aerosol constituent,(gm-2)
αs = aerosol mass scattering efficiency, (m2g-1)
f(RH) = hygroscopic growth factor for total particle scattering
ω0 = single scattering albedo at ambient RH (assumed to be 80% forthis analysis).
Parameters depend on Na, size distribution, chemical composition

Assuming the standard uncertainty
analysis:• The uncertainty is determined by the variation
forcing associated with each parameter:

Bottom-up estimates of uncertainty(Chapter 5, IPCC, 2001)• Fossil fuel direct:– -0.6 Wm-2

range: -0.1 Wm-2 to -1 Wm-2

• Biomass smoke direct:– -0.3 Wm-2

range: -0.1 Wm-2 to -0.5 Wm-2

• Results do not account for biases in estimatesNOTE:• Estimates of uncertainty are only as good as
our theoretical and observational
understanding (I.e. the models and data used
to estimate ranges)