aerosol direct forcing observational perspective stephen … · 2000-09-15 · aerosol direct...
TRANSCRIPT
AEROSOL DIRECT FORCING
OBSERVATIONAL PERSPECTIVE
STEPHEN E. SCHWARTZ
BROOKHAVEN NATIONAL LABORATORY
UPTON NY
WORKSHOP ON
MONITORING GLOBAL AEROSOL FORCING OF CLIMATE:EVALUATING REQUIREMENTS FOR SATELLITE
MONITORING, GROUND-BASED MONITORING, IN-SITUMEASUREMENTS AND GLOBAL MODELING
GFDL-NOAAPRINCETON NJ
SEPTEMBER 13-14, 2000
WORKSHOP OBJECTIVES(From Charge to Workshop)
Define what is needed to evaluate the anthropogenicaerosol climate forcing, its uncertainty, and itstemporal change on decadal time scales.
¥ Define a strategy for obtaining the time-dependentaerosol climate forcing from a combination ofsatellite monitoring, ground-based monitoring, in-situ measurements, and global modeling.
¥ Evaluate the contribution that the NPOESSprogram could make to determination of the aerosolclimate forcing.
REQUIREMENTS
What is needed in order to succeed?
Represent aerosol forcing in climate models withknown and reasonable uncertainty.
¥ Provide a secular representation of aerosol forcingover the industrial period.
¥ Quantitatively attribute aerosol forcing to aerosoltype (natural, anthropogenic; by source category).
¥ Provide the capability of representing aerosolforcing in climate models for a range of assumedfuture scenarios.
These requirements can be met only with evaluatedaerosol models.
( Models are the only means to describe aerosolforcing for past time and for future scenarios. )
MODEL EVALUATION REQUIREMENTS
4-D distribution of aerosol loading properties andproperties that may be compared with observation forhighly diverse conditions of aerosol properties.
¥ Model must be driven by observationally-derivedmeteorological fields.
¥ Model requires realistic source strengths ofaerosols and aerosol precursors.
4-D observational data set suitable for modelevaluation.
OBSERVATIONAL REQUIREMENTS
Provide the understanding necessary to representaerosol forcing in climate models.
Provide an observational basis for present aerosoldirect forcing.
Provide an observational data base to permitevaluation of models:
¥ Loading (mass concentration, column burden)
¥ Composition (as a function of size; homogeneity vs.heterogeneity)
¥ Physical properties (size distribution and itsRH-dependence, non-sphericity...)
¥ Optical properties (extinction coefficient, singlescattering albedo, phase function or asymmetryparameter...) including wavelength dependence
¥ Vertical and geographical distribution of all theabove
Not at all places and times, but at sufficient, andsufficiently diverse, locations and times as to providerequired confidence in models.
AEROSOL DIRECT SHORTWAVE FORCINGGlobal Average for Nonabsorbing Aerosol
Cloud Fraction
Surface Reflectance
Mean Upscatter FractionAerosol Optical Depth
Atmospheric Transmittance
Solar ConstantChange in Net TOA Flux
∆F F T A R= − − −12
1 102 2( )( )c βτ
Mass Extinction EfficiencyMass ConcentrationLight Extinction Coefficient
τ α σ= =∫ ∫Cdz dzep
Global Average for Absorbing Aerosol
∆F F T A RR
R= − − − −
−−
12
1 1 12
1
10
2 22( )( )
( )
( )c βτω ω
βω
Single Scattering Albedo
DIRECT AEROSOL FORCING AT TOP OF ATMOSPHERE
Dependence on Aerosol Optical Thickness
Comparison of Linear Formula and Radiation Transfer ModelParticle Radius r = 85 nm. Surface Reflectance R = 0.15
-30
-20
-10
0 24-hr Average, C
loud-Free
0.30.20.10.0
Aerosol Optical Thickness at 550 nm
-12
-8
-4
0 24-hr Average, 60%
Cloud C
over
-60
-40
-20
0
Inst
anta
neou
s, C
loud
-Fre
e
∆F F T R= − −02 21( ) βτ
Radiation Transfer Model
TOA AEROSOL FORCING, W m -2
Slope ≈ -170 W m -2 per AOT
Global-average AOT 0.03 corresponds to global-average forcing -1 W m-2.
INFLUENCE OF ABSORPTIONON AEROSOL OPTICAL PROPERTIES
2.5
2.0
1.5
1.0
0.5
0.0
α abs, m
2 g(
SO
42-)-1
12
10
8
6
4
2
0
α scat
, m2
g(S
O42-
)-1
12
10
8
6
4
2
0
α ext, m
2 g(
SO
42-)-1
1.00.80.60.40.20.0
Radius, µm
k 0 0.004 0.008 0.02 0.04
α πρ λabs = 4 k
f
FRACTIONAL INFLUENCE OF ABSORPTIONON AEROSOL OPTICAL PROPERTIES
AND NORMALIZED FORCING
1.0
0.8
0.6
0.4
0.2
0.0
β(k)
/β(k
=0)
0.960.68
0.480.24
0.03-0.12
Radius, µm
Upscatter Fraction, SZA = 45°
-1.0
-0.5
0.0
0.5
1.0
G(k
)/G
(k=
0)
0.050.040.030.020.010.00
Imaginary component of refractive index, k
Radius, µm
Normalized Forcing, Rs = 0.15
0.030.042 0.96 0.06
0.68 0.085
0.480.12
0.17-0.34
1.0
0.8
0.6
0.4
0.2
0.0
α scat
(k)/
α scat
(k=
0)
0.960.68
0.48 0.24 0.12 0.03 - 0.06Radius, µm
Mass scattering efficiency
1.0
0.8
0.6
0.4
0.2
0.0
ω
0.03
0.060.96
0.12 0.680.48
0.24
Radius, µm
Single scattering albedo
GLOBAL AVERAGE NORMALIZED FORCINGRadius dependence compared to typical aerosol volume distributions
5
4
3
2
1
0
dV/d
r Accummulation mode Continental Urban
-500
-400
-300
-200
-100
0
100
G, W
g(S
O42-
)-1
1.00.80.60.40.20.0
Radius, µm
k 0 0.004 0.008 0.02 0.03
R = 0.15
GLOBAL AVERAGE NORMALIZED FORCINGDependence on absorption and surface reflectance for typical aerosol distributions
k k
k k
Compare to single scattering albedo w at SGP 0.92 ± 0.06 (one s.d.; 10,000 2-houraverages of 1-minute data).
WATER UPTAKE ANDLIGHT SCATTERING COEFFICIENT
Dependence on Relative Humidity18
16
14
12
10
8
6
4
2
0
Mol
es W
ater
/Mol
es S
olut
e
9080706050403020
Relative Humidity, %
Experiment Theory
(NH4) 42SO
D. Imre, T. Onasch, BNL
10
100
1
NH4HSO4
NaNO3
(NH4)2SO4
NH4NO3
(NH4)3H(SO4)2
Na2SO4
NaHSO4
Relative Humidity, %Lognormal, Dg = 0.3 µm; σg = 1.5; Tang, JGR, 1996
MEASUREMENT STRATEGIES
ApproachLongTerm
vs.Campaign
Globalvs.
Local
Aggregatevs.
Detailed
Satellite remote sensing LT G A
Ground-based remote sensing LT L -> G A
Ground-based in-situ composition,physical, optical properties
C -> LT L D
Aircraft remote sensing C L A
Aircraft in-situ composition,physical, optical properties
C L D
Local measurements can be extended to quasi-global by networkof measurements.Campaign measurements can be extended to LongTerm.
MONTHLY AVERAGE AEROSOL JUNE 1997
Optical Thickness at 865 nm
Ångström Exponent
Polder/Adeos CNES/NASDA LOA/LSCE
INSTANTANEOUS AEROSOLOptical Thickness at 865 nm Ångström Exponent
April 16, 1997
March 25, 1997
Polder/Adeos CNES/NASDA LOA/LSCE
AEROSOL OPTICAL DEPTH FROM AVHRR AND TOMS
King et al. (BAMS, 1999
“SULFATE” PLUME EXTENDING EAST OF NORTH AMERICA180˚ BACKSCATTER RETURN
LITE (Lidar In-Space Technology Experiment)
Superior WI
Steubenville OH
Virginia Beach VA
1200 km Eof Cape
Canaveral
AEROSOL OPTICAL DEPTHAND ÅNGSTRÖM EXPONENT
Determined by SunphotometryNorth Central Oklahoma
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Aer
osol
Opt
ical
Dep
th a
t 500
nm
1993 1994 1995 1996 1997 1998 1999
0.0
0.1
0.
2
0
.3
0.4
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Ång
strö
m e
xpon
ent
1993 1994 1995 1996 1997 1998 1999
0.0
0.5
1.
0
1
.5
2.0
J. Michalsky, PNNL
AERONET (AErosol RObotic NETwork)
Holben et al., NASA GSFC, http://aeronet.gsfc.nasa.gov:8080/
AEROSOL SIZE DISTRIBUTION FROM AERONETSPECTRAL OPTICAL DEPTH AND SKY RADIANCE
King et al. (BAMS, 1999) after Remer et al. (1996)
RAMAN LIDAR AEROSOL AND WATER VAPORNorth Central Oklahoma, October 6, 1999
DOE ARM Program; D. Turner, PNNLhttp://yard.arm.gov/~turner/raman_lidar_quicklooks.html
MULTIPLE-WAVELENGTH LIDAR - RAMAN-LIDAR
Institut für Troposphärenforschung, Leipzig
DEPENDENCE OF AEROSOL COMPOSITION ON LOCATION
EPA, National Air Quality and Emissions Trends Report, 1997, 1998
REGIONAL AND DIURNAL DIFFERENCESIN AEROSOL LOADING AND SIZE
Composite of 4-hr samples over 9 days,September-October, 1996
Hughes, Cass, Prather et al. (1999)
REGIONAL DIFFERENCES IN AEROSOLLOADING, COMPOSITION, AND SIZE
Composite of 4-hr samples on several days,September-October, 1996
Hughes, Cass, Prather et al. (1999)
TIME DEPENDENT PARTICLE SIZE DISTRIBUTIONDifferential Mobility Analyzer, Low Relative HumidityRural Germany, May, 1999. Time Resolution 10 min
0 2 4 6 8 10 12 14 16 18 20 22 243
10
100
600
17
3
10
100
600
16
Par
ticle
dia
met
er in
nm 3
10
100
600
15
Time of Day
200.0 321.7 517.5 832.4 1339 2154 3464 5572 8963 1.44E4
dN/dlog D , cm-3p
May1999
W. Birmili (IFT, Leipzig)
TIME-DEPENDENT PARTICLE COMPOSITIONAtlanta GA, Summer 1999
0
0.2
0.4
0.6
0.8
NO3- SO4
2-+ 2
18:00 20:00 22:00 24:00 26:00 28:00
Day : Time, EST
Mol
ar C
once
ntra
tion,
µm
ol m
-3
+NH4
0
10
20
30
40
50
60M
ass
Con
cent
ratio
n, µ
g m
-3Total Mass
SO42-
a
c
0
1
2
3
4
NO3-
Mas
s C
once
ntra
tion,
µg
m-3
b
0
3.6
7.2
10.8
14.4
Concentration, µ
g m-3
+N
H4
Y.-N. Lee, BNL; R. Weber, GaTech
• Rapid (sub-daily) variation of aerosol components.• Lack of correlation of several aerosol components.• Varying extent of neutralization of acid by ammonia.• Correlation of sulfate concentration with total aerosol mass.
Such information is not available withconventional filter sampling.
DIFFERENTIAL HUMIDITY RESPONSE WITHIN AEROSOL
First analyzer, low RH; Second analyzer variable RHHopi Point, Grand Canyon, Arizona, March 1, 1990, 14:15
Zhang, McMurry et al. (1993)
PARTICLE-TO-PARTICLECOMPOSITION DIFFERENCES
Time-of flight mass spectra of individual particles,South Coast, CA, September-October, 1996
Sea Salt
Organic Carbon
NH4NO3 + OC
NH4NO3EC +
Hughes, Cass, Prather et al. (1999)
TRANSMISSION ELECTRON MICROGRAPHOF INDIVIDUAL PARTICLE
Soot inclusion in single particle consisting mainly of (NH4)2SO4
Unpolluted air near Tasmania.
Buseck and Pósfai (1999)
AEROSOL VERTICAL DISTRIBUTIONAerosol Lidar Backscatter on Cloud-Free Days
North Central Oklahoma
5
4
3
2
1
0
Hei
ght (
agl),
km
Mixed Layer
August 22, 1996
Relative Backscatter
CumulativeRelative
Backscatter
5
4
3
2
1
0
Hei
ght (
agl),
km
Mixed Layer
July 5, 1996
5
4
3
2
1
0
Hei
ght (
agl),
km
1.61.41.21.00.80.60.40.20.0
Mixed Layer
September 9, 1996
5
4
3
2
1
0
Hei
ght (
agl),
km
Mixed Layer
October 14, 1996
Bergin, Schwartz, Halthore, Ogren & Hlavka (2000)
VERTICAL DISTRIBUTION OF AEROSOL PROPERTIES
North Central Oklahoma
J. Ogren, NOAA CMDL
STATISTICS OF VERTICAL DISTRIBUTIONS OF AEROSOL PROPERTIES
North Central Oklahoma
J. Ogren, NOAA CMDL
MODELING EVOLUTION OF AEROSOL LOADING AND PROPERTIESMoment-based representation of aerosol microphysics
Day Log10 N Log10 µ3 Mean Radius
1
3
7
10
Wright, McGraw, Benkovitz & Schwartz (2000)
MODEL EVALUATIONComparison of measured and modeled sulfate concentrationsModel is driven by observation-derived meteorological data
1 2 3 4 5 6 7
8 9 10 11 12 13 14
15 16 17 18 19 20 21
22 23 24 25 26 27 28
29 30 31 32 33 34 35
36 37 38 39 40 41 42
43
[SO
42
- ], p
pb
453015Date Oct/Nov 1986
model
obs'd
14
5
10
0
Benkovitz, Schwartz et al. (1994)
MEASUREMENT STRATEGIES
ApproachLongTerm
vs.Campaign
Globalvs.
Local
Aggregatevs.
Detailed
Satellite remote sensing LT G A
Ground-based remote sensing LT L -> G A
Ground-based in-situ composition,physical, optical properties
C -> LT L D
Aircraft remote sensing C L A
Aircraft in-situ composition,physical, optical properties
C L D
Local measurements can be extended to quasi-global by networkof measurements.Campaign measurements can be extended to LongTerm.
Looking tothe Future...
DOE-SC-XXXX
Tropospheric
Aerosol
ProgramRG99060050.3
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Preliminary Program Plan
June 1999
U. S. Department of EnergyOffice of ScienceOffice of Biological and Environmental ResearchEnvironmental Sciences Division
Our quest for progress must be sustainable. We haveno right to destroy the earth.
Ecological damage, including global warming, mustbe curbed.
All low-lying countries must be saved: When theUnited Nations meets to usher in yet another century,will the Maldives and other low-lying island nationsstill be represented here?
MAUMOON ABDUL GAYOOM, President of Maldives
United Nations Millennium Summit, September, 2000