Georgia Institute of Technology School of Earth and Atmospheric Sciences

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AEROSOL-CLOUD-CLIMATE INTERACTIONS Athanasios
Nenes Georgia Power Workshop April 14, 2003
Georgia Institute of Technology School of
Earth and Atmospheric Sciences 311 Ferst Drive,
N.W. Atlanta, GA 30332-0340 404-894-3893
www.eas.gatech.edu
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Aerosol indirect effect the elusive component of
climate
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Why is the indirect effect poorly understood?

• Indirect forcing uncertainties arise because
• Aerosol-cloud interactions take place at smaller
  spatial scales than climate models can resolve,
  and must be parameterized.
• Aerosol-cloud interactions are complex many
  aspects are unknown or poorly understood.
• Climate models provide limited information about
  clouds, and aerosols.
• Central problem of indirect effect
• Determine the relationship between aerosol and
  cloud radiative properties, using the limited
  information available by climate models.
• This problem has historically been reduced to
  finding the relationship between aerosol
  concentration and cloud droplet number
  concentration.
• Two approaches have been adopted.

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First approach empirical
Most studies utilize this approach. Large
predictive uncertainty, without chances of
improving.
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Source of uncertainty Droplet formation is
complex.
Cloud supersaturation is affected by changes in
the CCN population
t
Activation is a highly nonlinear process.
Empirical relations cannot capture
this accurately enough.
S
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Second approach from first principles.
Cloud droplet number balance in each grid box of
the model

• Activation is the direct aerosol-cloud
  microphysical link. Two types of information are
  necessary for its calculation
• Aerosol chemistry and size distribution (CCN)
• Representation of subgrid dynamics in
  cloud-forming regions.
• Embedding a numerical activation model is too
  slow must use a parameterization. Existing
  parameterizations are derived assuming idealized
  cloud dynamics, aerosol composition and size
  distribution.
• Are they good enough? (Hint No).

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Prescribed size distribution bias
Fitting ambient size distributions to prescribed
functional form introduces biases which can be
important for indirect effect.
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Unaccounted chemical effects on droplet
activation
Slightly soluble compounds (Shulman et al.,
1996) They add solute to the drop as it grows
this facilitates their ability to
activate. Examples organics (succinic acid),
CaSO4.
Soluble gases (Kulmala et al., 1993) They add
solute to the drop as it grows this facilitates
their ability to activate. Examples HNO3, HCl,
NH3.
A(g)
A(g)
A(g)
A(aq)
A(aq)
A(aq)
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Unaccounted chemical effects on droplet
activation
Surface-active soluble compounds (Facchini et
al., 1999) They decrease surface tension of
droplets this facilitates their ability to
activate. Examples organics (succinic acid,
humic substances).
Surface tension data from cloud and fog water
samples.
Pure water
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The departure from pure water values can be very
large! Surface tension change is different for
each CCN.
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Droplet concentration range at activation
Surface tension (dyne/cm)
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Charlson et al., Science, 2001
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1e-4
1e-3
1e-2
1e-1
-1
C(mol l
)
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Unaccounted chemical effects on droplet
activation
Film-forming compounds (e.g., Feingold Chuang,
2002) They can slow down droplet growth. Once
the film breaks, rapid growth is resumed
Examples hydrophobic organics. Such substances
do not alter droplet thermodynamics they affect
the kinetics of droplet growth. If present,
such substances can strongly affect droplet
number.
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Cloud droplet formation dynamics with different
chemical effects
Soluble CCN
vs.
CCN with films
movie
movie
log10(concentration)
log10(size)
log10(size)
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Chemical effects assessment of their importance.
2.20
Nenes et al., GRL, 2002
2.00
1.80
10 surface-active organics
1.60
10 film-forming organic
Nd /Nd, basecase
1.40
1.20
Basecase concentration ? 2
1.00
10 soluble organics (no surface tension change)
0.80
0.0
0.5
1.0
1.5
2.0
2.5
3.0
Updraft Velocity (m s-1)
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Chemical effects summary, implications for
parameterizations
Chemical effects are seen to be important for
many conditions, and even be more effective than
doubling aerosol concentrations. Chemical
effects can be synergistic. One effect can be
important for low updrafts (e.g. soluble gas
effects) and another at higher updrafts (e.g.
surface tension effects). This would lead to a
systematic increase in droplet number for almost
any cloud type. Lack of including them in
activation parameterizations can lead to
important uncertainties in indirect
forcing. What does all this mean for current
aerosol activation parameterizations? They are
not adequate. We need to develop a new
parameterization.
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New parameterization underlying ideas
Use sectional representation of aerosol chemistry
and size distribution.

• Each section can
• have its own chemical composition
• i-th section characterized by (i-1, i)
  boundaries
• piecewise linear profiles between boundaries
• Multiple populations with their own distributions
  can co-exist and compete for water vapor.
• Modified Köhler theory for computing CCN
  properties.

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New parameterization underlying ideas

• Properties calculated from energy, mass balances.
  Adiabatic parcel framework used
• Lagrangian framework of reference
• Parcel properties are uniform
• Constant updraft velocity
• Parcel pressure is equal to ambient

t
Smax
S
Derive expression for the condensational growth
of CCN include within the supersaturation
balance for the parcel, and solve for the
maximum. Challenge to derive an expression of
the condensation rate at Smax. Solution
Population splitting.
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New parameterization Population Splitting
Population 1 Size very different from critical
diameter
Population 2 Size close to critical diameter
recently activated
d(CCN)/dS
Supersaturation
Smax
Spart
Each population has an analytical expression of
the condensation rate.
Spart can be analytically derived or obtained
from simulations.
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New parameterization Formulation
Input P,T, updraft velocity (cooling rate), RH,
aerosol characteristics. Output Droplet number,
Smax How Solve the algebraic equation for Smax
(numerically)
Water vapor condensation from kinetically
limited CCN
Water vapor condensation from CCN that
instantaneously activate
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Performance of new parameterization (200 test
cases)
Nenes and Seinfeld, in press
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Performance of existing parameterization (Ghan et
al., 2000)
Nenes and Seinfeld, in press
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New parameterization marine aerosol with
surfactants
0.70
Numerical Simulation (s.t. effects present)
Parameterization (s.t. effects present)
0.60
Parameterization (s.t. effects absent)
0.50
0.40
Activation Fraction
0.30
0.20
0.10
Nenes and Seinfeld, in press
0.00
0.1
1
10
Updraft Velocity (m/s)
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New parameterization assessment.

• A powerful activation parameterization has been
  developed for aerosol of
• arbitrary (sectional) size distribution,
• multiple populations of aerosol (e.g.
  sulfateseasalt) present,
• complex chemical and size-dependant composition
  (surfactants,
• slightly soluble substances present).
• Furthermore, it
• is fast (103-104 times faster than full
  numerical parcel model).
• uses minimal amount of empirical information.
• exhibits increased robustness and accuracy.
• To be included in the future
• incorporate other activation effects (e.g.,
  films). Experiments will be
• done to provide an appropriate data set.
• parcel-scale entrainment/mixing for diabatic
  activation.
• collision-coalescence to parameterize
  aerosol-cloud lifetime effects.

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New parameterization implementation in global
model
PDF of updrafts in cloud-forming regions
Number of activated droplets for updraft w (new
parameterization)
Currently being implemented into the GISS/TOMAS
model (Adams and Seinfeld, JGR, 2002).
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GENERAL SUMMARY
The indirect effect of atmospheric aerosols is
one of the most important and challenging aspects
of climate prediction science. A large source of
uncertainty can be due to incomplete
consideration of chemical composition. These
chemical effects are included in a new
parameterization of aerosol-cloud
interactions. Parameterizations have been
developed, and included within a comprehensive
climate model (GISS/TOMAS).
Acknowledgments NASA, ONR