zhanqing li dept of atmos. & oceanic science university of maryland
DESCRIPTION
Applications of Aerosol Remtoe Sensing Products in Climate Studies. Zhanqing Li Dept of Atmos. & Oceanic Science University of Maryland. 2000s: aerosol climatologies, aerosol-cloud interaction. 2003: GLAS. 2002: GLI. 1999: MODIS, MISR. - PowerPoint PPT PresentationTRANSCRIPT
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Zhanqing Li
Dept of Atmos. & Oceanic Science University of
Maryland
Applications of Aerosol Remtoe Sensing Products
in Climate Studies
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0 1 10 100 10001960
1970
1980
1990
2000
Publication per year on "Aerosol AND satellite"
Year
1967: Sekera, aerosol from satellites polarization meas. 1967: ATS III
1972: ATS III dust transport Carlson and Prospero,
1976: dust - Landsat, Fraser
1972: Landsat,
1980s: study of transport aerosol species - effect on climate; stratospheric, aerosol overland: Stowe, McCormicK
1999: MODIS, MISR
2002: GLI
1990s: analysis of POLDER, ATSR, methods for MODIS, MISR, radiation budget
2000s: aerosol climatologies, aerosol-cloud interaction
1996: POLDER
1991: ATSR
1984: Earth Radiation budget satellites
1975: GOES-VISSR
1981: AVHRR afternoon
1965: stratospheric aerosol profiles from Vostok 6 -
Rosenberg and Tereshkova
1979: SAGE
2003: GLAS
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The effects of aerosol pollution on clouds
• Since the 1960s, measurements have provided numerous and consistent pieces of evidence that an increase in pollution leads to increases in Cloud Condensation Nuclei (CCN) [a sub-set of atmospheric aerosols].
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Indirect Effect Haywood and Boucher Revs. Geophys. (accepted) 2000
1) Increased CCN - reduces reff
2) Drizzle suppression - increases LWC
3) Increased cloud height
4) Increased cloud lifetime
‘First’ indirect effect
‘Second’ indirect effect
eff
LWP
r2
3~
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Ice nuclei (IN) are a much smaller sub-set of atmospheric aerosols than CCN.
Their role in precipitation formation in certain clouds is critical.
Finding correlation between the concentrations of ice nuclei and ice crystals in clouds is difficult because of the low concentrations of IN and the numerous mechanisms by which ice crystals can form including ice multiplication mechanisms.
Ice in clouds
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How do clouds form?
Clouds form in regions of the atmosphere where water vapor is supersaturated. We focus on liquid water clouds.
Water vapor supersaturation is generated by cooling (primarily through expansion in updraft regions and radiative cooling)
Cloud droplets form from pre-existing particles found in the atmosphere (aerosols). This process is known as activation.
Aerosols that can become droplets are called cloud condensation nuclei (CCN).
CCN that activatesinto a cloud drop
Aerosol particlethat does not activate
Cloud
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Köhler curve
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0
where)3
(
asgiven valuecritical a has
11 Define
ationSupersatur
6.6Equation See
1
*2
1*
3
*
3
dr
ds
a
br
rr
b
r
a
e
eSS
r
b
r
a
e
e
s
hr
s
hr
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Activity SpectrumLet Nc be the number of particles per unit volume that are activated to become cloud droplets.
Data from cloud chamber measurements are often parameterized as
Nc = C (S-1)k
where C and k are parameters that depend on air mass type.
Rogers gives:
Maritime air: 30 < C < 300 cm-3; 0.3 < k < 1
Continental air: 300 < C < 3000 cm-3; 0.2 < k < 2
Thus, for the same saturation ratio, one would expect to find small numbers of CCN per unit volume in maritime air and large numbers per unit volume in continental air.
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How can humans affect clouds?
By changing CCN; cloud properties are a strong function of their concentration.
This phenomenon is known as aerosol indirect effect.
The aerosol indirect effect can lead to climatic cooling by:
• Increasing cloud reflectivity (albedo)
• Increasing cloud lifetime & coverage.
Clean Environment
CCN
Lower Albedo
Polluted Environment (few CCN)
(more CCN)
CCN
Higher Albedo
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Asian pollution plumes.
Is the indirect effect globally important?
Biomass burning in the Amazon.
Pollution is a global problem. CCN are emitted together with greenhouse gases.
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• Aerosol-cloud interactions take place at smaller spatial scales than global climate models can resolve, and must be parameterized.
• Aerosol-cloud interactions are complex; many aspects are unknown or poorly understood.
• Climate models provide important but limited information about clouds and aerosols.
Why is the Indirect Effect Poorly Characterized?
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AerosolSize Distribution and Chemical Composition
Cloud Radiative Properties
Cloud Droplet Number and Size?? Well
Well
Defined
Defined
This problem has historically been reduced to finding the relationship between aerosol number concentration and cloud droplet number concentration. Empirical relationships are often used.
Quantification of the Indirect Effect
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Goal Goal Couple all aerosol-cloud-radiation interactions within a Couple all aerosol-cloud-radiation interactions within a framework of parameterizations appropriate for global framework of parameterizations appropriate for global models.models.
““Input” variables (from GCM)Input” variables (from GCM)• Cloud liquid water content.Cloud liquid water content.
• Aerosol size distribution and chemistry.Aerosol size distribution and chemistry.
• Wind fields.Wind fields.
• Static stability/turbulence.Static stability/turbulence.
““Output” variables (to GCM)Output” variables (to GCM)• Droplet number, distribution characteristicsDroplet number, distribution characteristics
• Cloud optical propertiesCloud optical properties
• Cloud coverage, subgrid statisticsCloud coverage, subgrid statistics
Aerosol-Cloud Interaction ModulesAerosol-Cloud Interaction Modules
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Simplest aerosol-cloud interaction module: correlationsD
ropl
et C
once
ntra
tion
Aerosol sulfate concentration
(Boucher & Lohmann, 1995)
Very large variability.
Why?
• Meteorology
• Cloud microphysics
• Chemical composition
• etc…
Pro: Very simple relationship to implement. Fast computation.
Con: Large predictive uncertainty, without chance of improving.
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Fig. 12
Predicted and Observed CCN
0
500
1000
1500
2000
0 500 1000 1500 2000
CN
CC
N
Ramanathan et al., 2000; composite scheme
Cantrell et al., 2001; INDOEX KCO
Cantrell et al., INDOEX Sagar Kanya
0.29 Na1.25 Sk
0.33 Na1.14 Sk
S : Super saturation =0.5%k = 0.76
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y = 0.0027x0.643
R2 = 0.87
0.010
0.100
1.000
10 100 1000 10000
CCN0.4 [cm-3]
AO
T5
00
Remote Marine
Remote Continental
Polluted Marine
Polluted Continental
Andreae, ACPD 2008
Maximum at AOD ~ 0.25Giant CCN shift max to greater AOD
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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(aq)
A(g)
A(aq)
A(g)
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).
The departure from pure water values can be very large!
Surface tension change isdifferent for each CCN.
C(mol l-1)
1e-4 1e-3 1e-2 1e-1
Sur
face
tens
ion
(dyn
e/cm
)
50
55
60
65
70
75
Droplet concentrationrange at activation
Surface tension data from cloud and fog water samples.
Pure water
Charlson et al., Science, 2001
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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 necessarily alter droplet thermodynamics; they affect the kinetics of droplet growth.
If present, such substances can strongly affect droplet number.
Film breaks
water molecule
water molecule
Slow Rapid
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... advectionevapactivationdrop QQQ
dt
dN
Uncertainties can be decreased by using first principles. Cloud droplet balance:
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 parameterize.
Physically-based aerosol-cloud interaction modules
0)()( dwwNwp
dt
d
Probability of updraft w Activated droplets for updraft w
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Mechanistic parameterizations: underlying ideas
Approach:• Assume an aerosol size distribution and chemical composition below cloud.
• Aerosols rise into cloud.
• Expansion generates cooling and supersaturation.
• Aerosols activate into droplets.
• Köhler theory links aerosols to CCN properties.
aerosol
activation
drop growth
S
Smax
t
Major challenge:Derive expression for the condensational growth of CCN; include within the supersaturation balance for the parcel, and solve for the maximum.
Solution:• Depends on the approach used in each parameterization. (e.g. Nenes and Seinfeld, JGR, 2003)
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0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.1 1 10
Updraft Velocity (m/s)
Act
ivat
ion
Fra
ctio
nNumerical Simulation (s.t. effects present)Parameterization (s.t. effects present)Parameterization (s.t. effects absent)
Nenes and Seinfeld, in pressNenes and Seinfeld, JGR, 2003
N & S (2003) evaluation: compare with numerical model
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Underprediction: common to many parameterizations
0.001
0.010
0.100
1.000
0.001 0.010 0.100 1.000
Activation ratio (Parcel Model)
Act
ivat
ion
rat
io (
Gh
an P
aram
etri
zati
on
)SM1
SM2
SM3
SM4
SM5
TM1
TM2
prist
ine
pollu
ted
Nenes and Seinfeld, JGR, 2003
Abdul-Razzak et al. parameterization “family”
= 1.0
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Satellite observation of aerosol
indirect effect in the Black Sea.
Red: clouds with large drops.
White: clouds with small drops.
Observational evidence of indirect effect
Rosenfeld et al., Science
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Power plant
Lead smelter
Port
Oil refineries
Observational evidence of indirect effect
Rosenfeld et al., Science
Satellite observation of aerosol
indirect effect in the Black Sea.
Red: clouds with large drops.
White: clouds with small drops.
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DER-AOD relationship
Yuan et al. (2008, JGR)
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AIE efficiency distribution
Yuan et al. (2008)
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AIE efficiency determining factor
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Global Analysis Region Latitude
rangeLongitude range
Dominant Aerosol/Cloud
Types
Period AIE efficiency
Sample size
North Atlantic 10-20N 20-40 W Dust, Stratocumulus
June-August, 2002 Negative 99,978
South Atlantic 5-20S 5E-20W Smoke, Stratocumulus
June-August,2002 Negative 100,377
Southern Pacific
5-25S 75-105W Sea salt, sulfate and pollution,
Stratocumulus
August-October,2002
Negative 74,216
Indian Ocean 12-20N 60-70E Dust with pollution, Trade cumulus
June-August, 2002 Negative 94,023
India 13-24N 70-85E Mixture of sulfate, dust, sea salt and smoke, cumulus
June-August,2002 Neutral 53,888
Amazonia 8S-12N 44-76W Mainly smoke August-October,2002
Negative 672,421
Southeastern China
23-43N 100-120E Mixture, cumulus June-August,2002 Positive 179,533
Student-t test indicates except India the difference among different loading of aerosols are statistically significant at least at the 95% level
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h = 2.72 N
0
1000
2000
3000
4000
5000
6000
0 500 1000 1500 2000
War
m R
ain
Dep
th (
m)
Average Droplet Concentration (cm ) -3
More cloud drops deeper cloud for onset of rain
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Can the slowing of auto-conversion result in increasingprecipitation?
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Conceptual model:
Graphics by Robert Simmon, NASAHAIL
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Conceptual model:
Graphics by Robert Simmon, NASAHAIL
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Annual average lightning density [flashes km-2]Lightning prevail mostly over land, whereas rainfall is similar over land and ocean, indicates fundamental differences between continental and maritime rainfall.
Why is Continental - Maritime classification so fundamental?
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Why is Continental - Maritime classification so fundamental?
TRMM annual average rainfall amount [mm / day]
There is little relation between lightning and rainfall amount
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Global effects of pollution on precipitationGCM-- estimates 0 to - 4.5% change in global mean precipitation over the last
100 years due to the direct and indirect aerosol effects.
The differences among models over land range from -1.5% to -8.5%.
Global N. Hemisphere S. Hemisphere
Ocean Land
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A lot have been done concerning aerosol’s impact on rainfall
Rainfall Suppressed by Aerosols
Rosenfeld, 1999; Rosenfeld, 2000; Andreae, 2004;
etc
Khain, 2004 , 2005; Tao 2007, Fan, 2007; Van den Heever, 2007
etc
Rainfall Enhanced by Aerosols
Koren, 2005;Lin, 2006;Bell, 2007;
etc
Observational studies
Modeling studies
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But little has been done for rain frequency!
While rain amount and frequency change in harmony in general, the
impact of aerosol on initiation of rain is likely to be more significant than rain amount, as the latter is dictated more by dynamics and abundance of
available water
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Datasets Used
• Daily ARM SGP data 2003-2008 (~20000 data samples)
• Most complete and highest quality measurements of aerosol, cloud, atmospheric state
• Key variables used:– Aerosol CN number concentration on the ground
– Tipping bucket rain gauge
– LWP from microwave radiometer
– Cloud bottom and top heights from cloud radar & lidar
– NOAA/NCAR Reanalysis
– MODIS cloud particle size
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Rainfall Frequency for clouds with different liquid water path at SGP
(All-Season Data)
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
0-1000 1000-2000
2000-3000
3000-4000
4000-5000
5000-6000
CN Number Concentration (1/cm^3)
Ra
infa
ll F
req
ue
nc
y f
or
Clo
ud
s
wit
h H
igh
an
d M
od
era
te L
WP
0.00%
0.40%
0.80%
1.20%
1.60%
2.00%
Ra
infa
ll F
req
ue
nc
y f
or
Clo
ud
s
wit
h L
ow
LW
P
LWP:>0.8mm
LWP:0.4-0.8mm
LWP:0.0-0.4mm R2 = 0.7803
R2 = 0.9088
R2 = 0.037
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5
10
15
20
25
0-1000 1000-2000
2000-3000
3000-4000
4000-5000
5000-6000
CN Number Concentration (per cm^3)
Su
rfa
ce
te
mp
era
ture
(D
eg
ree
Ce
lsiu
s)
LWP:>0.8mm
LWP:0.4-0.8mm
LWP:0.0-0.4mm
970
972
974
976
978
980
982
0-1000 1000-2000
2000-3000
3000-4000
4000-5000
5000-6000
CN Number Concentration (/cm^3)
Su
rfa
ce
Pre
ss
ure
(h
p)
LWP:>0.8mm
LWP:0.4-0.8
LWP:0.0-0.4
0
10
20
30
40
50
60
0-1000 1000-2000
2000-3000
3000-4000
4000-5000
5000-6000
CN Number Concentration (/cm^3)
Wat
er V
apo
r (c
m)
>0.8
0.4-0.8
0.0-0.4
3
3.5
4
4.5
5
5.5
6
6.5
0-1000 1000-2000
2000-3000
3000-4000
4000-5000
5000-6000
CN Number Concentration (per cm^3)
Su
rfa
ce
Win
d S
pe
ed
(m
/s)
LWP:>0.8mm
LWP:0.4-0.8mm
LWP:0.0-0.4mm
T P
WV Wind
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Cloud Thickness for clouds with different cloud base heights
0
500
1000
1500
2000
2500
3000
3500
0-1000 1000-2000
2000-3000
3000-4000
4000-5000
5000-6000
CN number concentration (1/cm^3)
Clo
ud
Th
ickn
ess
(m
)
CBH:<1km
CBH:1km-2km
CBH:2km-4km R2 = 0.1055
R2 = 0.9718
R2 = 0.9159 y = 638.06x + 1258.6
R2 = 0.9169
y = 97.05x + 2321.2
R2 = 0.5543
y = 56.399x + 3819.7
R2 = 0.1658
0
1000
2000
3000
4000
5000
6000
0-1000 1000-2000
2000-3000
3000-4000
4000-5000
5000-6000
CN Number Concentration (/cm^3)
Clo
ud
To
p H
eig
ht
(m)
CBH:<1km
CBH:1km-2km
CBH:2km-4km
Linear (CBH:<1km)
Linear (CBH:1km-2km)
Linear (CBH:2km-4km)
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Cloud Base Heights
y = -10.06x + 2969.9
R2 = 0.2024
y = 12.978x + 1404.7
R2 = 0.586
y = 7.9095x + 330.05
R2 = 0.5579
0
500
1000
1500
2000
2500
3000
3500
0-1000 1000-2000
2000-3000
3000-4000
4000-5000
5000-6000
CN Number Concentration (/cm^3)
Clo
ud
Ba
se H
eig
ht
(m)
CBH:<1km
CBH:1km-2km
CBH:2km-4km
Linear (CBH:2km-4km)
Linear (CBH:1km-2km)
Linear (CBH:<1km)
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Cloud Top Heights(for clouds of cloud base <1km)
All Seasons Summer Only
y = 237x + 4160.4
R2 = 0.8169
y = 32.296x + 964.41
R2 = 0.7245
0
1000
2000
3000
4000
5000
6000
7000
0-1000 1000-2000
2000-3000
3000-4000
4000-5000
5000-6000
CN Number Concentration (/cm^3)
Clo
ud
To
p H
eig
ht
(m)
CTH:>2km
CTH:<2km
Linear (CTH:>2km)
Linear (CTH:<2km)
y = 907.49x + 3297.4
R2 = 0.82
y = 22.14x + 1159.4
R2 = 0.2692
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
0-1000 1000-2000
2000-3000
3000-4000
4000-5000
5000-6000
CN Number Concentration (/cm^3)
Clo
ud
To
p H
eig
ht
(m)
CTH:>2km
CTH:<2km
Linear (CTH:>2km)
Linear (CTH:<2km)
For clouds with CBH<1km, clouds are classified into two categories with cloud top heights greater (blue) and less than (red) 2km.
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Cloud Top Heights
y = 327.17x + 1168.6
R2 = 0.9727
y = 222.86x + 1848.1
R2 = 0.9344
y = 12.256x + 4290.7
R2 = 0.0428
0
1000
2000
3000
4000
5000
0-1000 1000-2000
2000-3000
3000-4000
4000-5000
5000-6000
CN Number Concentration (/cm^3)
Clo
ud
To
p H
eig
ht
(m)
CBH:<1km
CBH:1km-2km
CBH:2km-4km
Linear (CBH:<1km)
Linear (CBH:1km-2km)
Linear (CBH:2km-4km)
y = 638.06x + 1258.6
R2 = 0.9169
y = 97.05x + 2321.2
R2 = 0.5543
y = 56.399x + 3819.7
R2 = 0.1658
0
1000
2000
3000
4000
5000
6000
0-1000 1000-2000
2000-3000
3000-4000
4000-5000
5000-6000
CN Number Concentration (/cm^3)
Clo
ud
To
p H
eig
ht
(m)
CBH:<1km
CBH:1km-2km
CBH:2km-4km
Linear (CBH:<1km)
Linear (CBH:1km-2km)
Linear (CBH:2km-4km)
All Seasons Summer Only
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Competition of two opposite effects
Responses ofRainfall frequency to increasing CN
Meteorological effects
Invigoration EffectsIncrease rainfall
Suppress rainfall
Not always increase
Depend criticallyon cloudbase !!
Microphysical Effect
Depend criticallyon available water
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The WMO/IUGGINTERNATIONAL AEROSOL PRECIPITATION SCIENCE ASSESSMENT GROUP
(IAPSAG)
Aerosol Pollution Impact on Precipitation:A Scientific Review
Zev Levin, ChairmanWilliam Cotton, Vice Chairman
Approved by the WMO - May. 2007
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Recommendations• Implement a series of international projects targeted toward
unraveling the complex interactions among aerosols, clouds, and precipitation.
• WMO/IUGG should take the lead in such projects together with other UN and International Organizations.
• Some of these could be sponsored and financially supported by the countries involved. For example:
– Study the effects of an evolving industrial economy such as China on precipitation.
– Study the effects of biomass burning and dust in some of the African regions.
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1) Better characterization of aerosols
– Emission inventories• Size, number concentrations
– Chemical processes, physical properties and instrumentation
• Accurate knowledge of the chemical processes leading from gas pollution to CCN
• The ability of different types of particles (e.g. mineral dust, biomass smoke, biogenic, carbonaceous) to act as CCN, GCCN, and IN as a function of aerosol size, origin, and air mass history.
The WMO/IUGG can play a key coordination role in encouraging that the following recommendations are implemented.
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• Develop new and innovative instruments and measurements to determine CCN, GCCN and IN concentrations as a function of particle size, composition and supersaturation.
• Emphasis should be placed on understanding the different modes of ice nucleation.
• Global coordination of observational networks is needed for more complete coverage of global aerosols (ground-based
remote sensing methods e.g. AERONET) .
• More accurate assessment is needed from satellites of the aerosol distribution, concentration and properties.
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2) The effects on clouds and precipitation:
– Design experiments to better understand the role of ice in precipitation development;
– Multi-year measurements from space of precipitation patterns along with retrievals of cloud nucleating aerosols are needed to assess both regional and global impacts of aerosol pollution on precipitation.
– Improved satellite measurements of Liquid Water path (LWP) and Ice Water Path (IWP), which define the potential for precipitation – with pollution modulating how much will reach the ground.
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• Models should be used to provide a quantitative answer as to the relative effects of aerosols versus environmental parameters (temperature, Relative humidity, wind shear, land-surface properties, etc.) on precipitation.
• New methods are needed to estimate precipitation amounts with high enough accuracy to be able to resolve changes due to pollution.
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– The high variability in precipitation amounts from GCMs stresses the need to improve representation of aerosol and cloud processes to be able to answer with some confidence the question on the effects of pollution on precipitation.
– Detailed knowledge of ice formation in clouds is still lacking, requiring more laboratory, modeling, and field studies.
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Prof. John Seinfeld, Caltech
Prof. Athanasios Nenes, George Tech
Prof. W. Cotton, CSU
Prof. D. Rosenfeld, Hebrew University
Prof. V. Ramanathan, UCSD
Prof. Z. Li, UMD
Contributors