constraining the aerosol indirect effect
DESCRIPTION
Constraining the aerosol indirect effect. Jón Egill Kristjánsson (Univ. Oslo) Many thanks to: Alf Kirkevåg ( met.no ), Corinna Hoose (Univ. Oslo), Leo Donner (GFDL). Problem Definition. - PowerPoint PPT PresentationTRANSCRIPT
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Constraining the aerosol indirect effect
Jón Egill Kristjánsson (Univ. Oslo)
Many thanks to: Alf Kirkevåg (met.no), Corinna Hoose (Univ. Oslo), Leo Donner (GFDL)
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Problem Definition
• Most GCMs give an aerosol indirect effect which is too high compared to results from residual calculations – Why?
• Many models have built in constraints on parameter values that keep the indirect effect within reasonable bounds – Is this justifiable?
• What can be done?
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Aerosol Indirect Effect
• Definition: Change in Cloud Radiative Forcing due to Anthropogenic Aerosols
• Model estimates of AIE are sensitive to:• The Aerosol Scheme, in particular the Treatment of
Natural Aerosols• Parameterizations of Aerosol-Cloud Interactions• The Atmospheric State in the host model, in
particular the Cloud Properties
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Models tend to overestimate the aerosol indirect effect
Anderson et al. (2003: Science)
DIRECT +INDIRECT FORCING
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Models tend to overestimate the aerosol indirect effect?
Lohmann and Feichter (2005: ACP) IPCC (2007)
DIRECT +INDIRECT FORCING
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Menon et al. (2002: J.Atmos.Sci.)
Sensitivity to background aerosols
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Cloud Susceptibility
Hobbs (1993: Academic Press)
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Whole sky forcing
-2
-1.5
-1
-0.5
0
0.5
1
1.5
Commonmethodology;
no 2nd indirecteffect
Individual Na toNd
Common effecton precipitation
efficiency
Individualtreatment ofprecipitation
efficiency
Individualprediction of
aerosol
Add aerosolheating
Forc
ing
(W/m
2)
CAM-OsloLMDCCSR/FRCGC
Penner et al. (2006: ACP)
Indirect forcing in 3 GCMs: Model estimates differ mainly due to different
parameterizations and different emissions
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Cloud Fraction vs. AOD: GCM and MODISMyhre et al. (2007: ACP)
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Myhre et al. (2007: ACP)
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Constraining the indirect effect with observations
ORIGINAL ADJUSTED
Quaas et al. (2006: ACP)
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Constraining the indirect effect with observations
Quaas et al. (2006: ACP)
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Constraining CDNC reduces the indirect effect
• ECHAM does not allow CDNC < 40 cm-3
• How realistic is this constraint?• What is the implication of it?
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McFarquhar et al. (2007: JGR)
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10 30 50 70 90 110 130 1500
0.2
0.4
0.6
0.8single layer water phase
#/cc
10 30 50 70 90 110 130 1500
0.1
0.2
0.3
0.4single layer mixed-phase
#/cc
10 30 50 70 90 110 130 1500
0.2
0.4
0.6
0.8multi layer water phase
#/cc
10 30 50 70 90 110 130 1500
0.2
0.4
0.6
0.8multi layer mixed-phase
#/cc
mean 13.46,STD 14.46 mean 46.00,STD 30.17
mean 22.87,STD 18.97 mean 18.34,STD 13.69
McFarquhar and Zhang, U. Illinois
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CDNCmin = 40 cm-3
-0.53 W m-2
CDNCmin = 10 cm-3
-1.30 W m-2
CDNCmin = 1 cm-3
-1.49 W m-2
no cut-off on CDNC
-1.50 W m-2
Tests with CAM3-Oslo (1-year runs)
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Anthropogenic Ice Nuclei
Exp. LIQ-UID
CON-TROL
KAO-LINITE
LESS DUST
COAT-ING
∆LWP (g m-2)
+ 0.87 - 0.07 - 0.32 + 0.68 + 0.64
∆IWP (g m-2)
- 0.04 + 0.20 + 0.36 + 0.52 - 0.46
∆Reff
(μm)- 0.44 - 0.33 - 0.32 - 0.41 - 0.43
INDIR (W m-2)
- 0.49 - 0.07 - 0.10 - 0.18 - 0.27
Storelvmo et al. (2008: J.Atm.Sci., in press)
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Summary and Conclusions• Most GCMs struggle to keep the aerosol indirect forcing low enough
to yield realistic climate simulations (~ -1 W m-2)• The AIE is very sensitive to background (pre-industrial) aerosols• Comparisons to observations indicate too large sensitivity of cloud
parameterizations to aerosol burdens
PROBLEM
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Summary and Conclusions• Most GCMs struggle to keep the aerosol indirect forcing low enough to yield
realistic climate simulations (~ -1 W m-2)• The AIE is very sensitive to background (pre-industrial) aerosols• Comparisons to observations indicate too large sensitivity of cloud
parameterizations to aerosol burdens
• Constraining with satellite data yields significantly suppressed indirect effect• Constraining with prescribed bounds on CDNC reduces AIE, but violates
observations in remote regions• Indications that AIE of mixed-phase clouds may reduce the overall indirect
forcing• Enhanced evaporation may lead to a positive indirect effect in trade wind
cumuli (Feingold)• Competition effect reduces AIE (Ghan)
PROBLEM
POSSIBLE SOLUTIONS
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Summary and Conclusions• Most GCMs struggle to keep the aerosol indirect forcing low enough to yield
realistic climate simulations (~ -1 W m-2)• The AIE is very sensitive to background (pre-industrial) aerosols• Comparisons to observations indicate too large sensitivity of cloud
parameterizations to aerosol burdens
• Constraining with satellite data yields significantly suppressed indirect effect• Constraining with prescribed bounds on CDNC reduces AIE, but violates
observations in remote regions• Indications that AIE of mixed-phase clouds may reduce the overall indirect
forcing• Enhanced evaporation may lead to a positive indirect effect in trade wind
cumuli (Feingold)• Competition effect reduces AIE (Ghan)
PROBLEM
POSSIBLE SOLUTIONS
MODIS image
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Reports of low CDNC measurements
• Bower et al. (2006: Atm. Res.): In-situ ship measurement from remote area SH ocean: 8 cm-3
• Yum and Hudson (2004: JGR): Southern Hemisphere Oceans: 20-40 cm-3
• Bennartz (2007: JGR): MODIS-based retrievals: Average values of 41±17 cm-3 in PBL clouds in South Pacific and South Indian Oceans
• McFarquhar et al. (2007: JGR): Arctic measurements in mixed-phase clouds (M-PACE) of between 23±10 cm-3 and 72±34 cm-3
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-1.50 W m-2
1+2. indirect radiative forcing
standard CDNC treatment (no lower cut-off)
-0.91 µm
Change in effective radius as seen from satellite
Change in cloud liquid water path
5.87 g m-2
Present day:LWP = 133.1 g m-2
Reff = 12.93 µmCDNCint = 3.95e6 cm-2
Pre-industrial:LWP = 127.2 g m-2
Reff = 13.85 µmCDNCint = 2.57e6 cm-2
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-1.49 W m-2
1+2. indirect radiative forcing
CDNCmin = 1 cm-3
-0.91 µm
Change in effective radius as seen from satellite
Change in cloud liquid water path
5.81 g m-2
Present day:LWP = 133.6 g m-2
Reff = 12.95 µmCDNCint = 3.95e6 cm-2
Pre-industrial:LWP = 127.8 g m-2
Reff = 13.87 µmCDNCint = 2.57e6 cm-2
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-1.30 W m-2
1+2. indirect radiative forcing
-0.79 µm
Change in effective radius as seen from satellite
Change in cloud liquid water path
3.91 g m-2
CDNCmin = 10 cm-3
Present day:LWP = 136.3 g m-2
Reff = 12.64 µmCDNCint = 3.95e6 cm-2
Pre-industrial:LWP = 132.4 g m-2
Reff = 13.44 µmCDNCint = 2.57e6 cm-2
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-0.53 W m-2
1+2. indirect radiative forcing
-0.44 µm
Change in effective radius as seen from satellite
Change in cloud liquid water path
1.33 g m-2
CDNCmin = 40 cm-3
Present day:LWP = 149.4 g m-2
Reff = 10.99 µmCDNCint = 3.95e6 cm-2
Pre-industrial:LWP = 148.1 g m-2
Reff = 11.43 µmCDNCint = 2.57e6 cm-2
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0 10 20 30 40 50 600
10
20
30
40single layer water phase
#/cc
0 50 100 150 2000
100
200
300
400single layer mixed-phase
#/cc
0 20 40 60 800
10
20
30
40
50multi layer water phase
#/cc
0 20 40 60 800
50
100
150multi layer mixed-phase
#/cc
mean 13.46,STD 14.46 mean 46.00,STD 30.17
mean 22.87,STD 18.97 mean 18.34,STD 13.69
McFarquhar and Zhang, U. Illinois
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10 30 50 70 90 110 130 1500
0.2
0.4
0.6
0.8single layer water phase
#/cc
10 30 50 70 90 110 130 1500
0.2
0.4
0.6
0.8single layer mixed-phase
#/cc
10 30 50 70 90 110 130 1500
0.2
0.4
0.6
0.8multi layer water phase
#/cc
10 30 50 70 90 110 130 1500
0.2
0.4
0.6
0.8multi layer mixed-phase
#/cc
mean 20.62,STD 14.97 mean 51.64,STD 29.18
mean 38.42,STD 29.18 mean 23.69,STD 13.66
LWC>0.05g/m3
McFarquhar and Zhang, U. Illinois
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0 10 20 30 40 50 600
2
4
6
8
10
12single layer water phase
#/cc
0 50 100 150 2000
50
100
150
200
250
300single layer mixed-phase
#/cc
0 20 40 60 800
2
4
6
8multi layer water phase
#/cc
0 20 40 60 800
20
40
60
80multi layer mixed-phase
#/cc
mean 20.62,STD 14.97 mean 51.64,STD 29.18
mean 38.42,STD 29.18 mean 23.69,STD 13.66
LWC>0.05g/m3
McFarquhar and Zhang, U. Illinois
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C. Hoose (2008: Ph.D. thesis)
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Warm and cold clouds
Warm clouds clouds with T > 0oC
mixed-phase clouds
(~ -35oC < T < 0oC) Cold clouds
ice clouds (cirrus) (T < ~ -35oC)
**
●● ●●● ●
* ●
●●
*●●
*
*
**
** ** * *
●
*●
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Aerosol Indirect Forcing in CAM3-Oslo
Diagnostic CDNC Prognostic CDNC
Seland et al. (2008: Tellus A)
Indirect forcing reduced by 35%, largely due to competition effect!
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Lohmann & Feichter (2005: ACP)
Model Estimates of the Aerosol Indirect Effect
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Cloud Feedback
• Sensitivity to the treatment of clouds and cloud-radiative processes
Stephens (2005: J. Climate)