aerosols: what are we missing? what should we do in the future?
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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? - PowerPoint PPT PresentationTRANSCRIPT
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