CALIPSO-inferred aerosol direct radiative e ects: bias ... · 3 \Climo": single climatological...
Transcript of CALIPSO-inferred aerosol direct radiative e ects: bias ... · 3 \Climo": single climatological...
CALIPSO-inferred aerosol direct radiative effects:CALIPSO-inferred aerosol direct radiative effects:bias estimates using ground-based Raman lidarsbias estimates using ground-based Raman lidars
Tyler Thorsen and Qiang FuTyler Thorsen and Qiang Fu
LANCE Rapid Response MODIS images: Aug 22, 2015
https://ntrs.nasa.gov/search.jsp?R=20160006605 2020-07-26T04:33:52+00:00Z
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
Aerosol direct effect (DRE)
• The change in radiative flux caused by the presence of aerosols (both natural and
anthropogenic)
• How aerosol affects the Earth’s radiation balance in the present climate• Evaulate how GCMs represent aerosol chemistry, transport and radiative properties
(e.g. Kinne JGR 2003)
• Estimation of aerosol radiative forcing (i.e. anthropogenic aerosols)
(Bellouin et al. Nature 2005, Kaufman GRL 2005, Su et al. JGR 2013)
CALIPSO aerosol DRE bias estimates (2/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
Aerosol direct effect (DRE)
• The change in radiative flux caused by the presence of aerosols (both natural and
anthropogenic)
• How aerosol affects the Earth’s radiation balance in the present climate• Evaulate how GCMs represent aerosol chemistry, transport and radiative properties
(e.g. Kinne JGR 2003)
• Estimation of aerosol radiative forcing (i.e. anthropogenic aerosols)
(Bellouin et al. Nature 2005, Kaufman GRL 2005, Su et al. JGR 2013)
CALIPSO aerosol DRE bias estimates (2/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
Satellite estimates of aerosol DRE
• Many estimates of the shortwave (SW) aerosol DRE have been made using passiveremote sensors (Yu et al. ACP 2006 and references therein)
• Longwave aerosol DRE is usually much smaller• Mostly MODIS-based
• The global-mean SW aerosol DRE at the TOA is about −5.0 Wm−2
• The presence of aerosols increases the amount of reflected SW by 5.0 Wm−2
CALIPSO aerosol DRE bias estimates (3/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
Satellite estimates of aerosol DRE
• Many estimates of the shortwave (SW) aerosol DRE have been made using passiveremote sensors (Yu et al. ACP 2006 and references therein)
• Longwave aerosol DRE is usually much smaller• Mostly MODIS-based
• The global-mean SW aerosol DRE at the TOA is about −5.0 Wm−2
• The presence of aerosols increases the amount of reflected SW by 5.0 Wm−2
CALIPSO aerosol DRE bias estimates (3/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
“Global” estimates of aerosol DRE from passive sensors“Global” estimates of aerosol DRE from passive sensors
Often limited to daytime cloud-free oceanOften limited to daytime cloud-free ocean
Over land?Over land?
Over cloud?Over cloud?
Contamination by undetected cloud / cloud edgesContamination by undetected cloud / cloud edges
No vertical informationNo vertical information
CALIPSO aerosol DRE bias estimates (4/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
“Global” estimates of aerosol DRE from passive sensors“Global” estimates of aerosol DRE from passive sensors
Often limited to daytime cloud-free oceanOften limited to daytime cloud-free ocean
Over land?Over land?
Over cloud?Over cloud?
Contamination by undetected cloud / cloud edgesContamination by undetected cloud / cloud edges
No vertical informationNo vertical information
CALIPSO aerosol DRE bias estimates (4/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
“Global” estimates of aerosol DRE from passive sensors“Global” estimates of aerosol DRE from passive sensors
Often limited to daytime cloud-free oceanOften limited to daytime cloud-free ocean
Over land?Over land?
Over cloud?Over cloud?
Contamination by undetected cloud / cloud edgesContamination by undetected cloud / cloud edges
No vertical informationNo vertical information
CALIPSO aerosol DRE bias estimates (4/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
“Global” estimates of aerosol DRE from passive sensors“Global” estimates of aerosol DRE from passive sensors
Often limited to daytime cloud-free oceanOften limited to daytime cloud-free ocean
Over land?Over land?
Over cloud?Over cloud?
Contamination by undetected cloud / cloud edgesContamination by undetected cloud / cloud edges
No vertical informationNo vertical information
CALIPSO aerosol DRE bias estimates (4/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
CALIPSO(Cloud-Aerosol Lidar and Infrared Pathfinder Satellite)
• Vertically-resolved aerosol properties over allsurface types during both day and night
• Easier to separate cloud from aerosol in thesame profile
• Recent studies have made new estimates of theglobal-mean aerosol DRE using CALIPSO:
1 Oikawa et al. JGR 2013: −0.61 Wm−2
2 Matus et al. JCLIM 2015: −1.9 Wm−2
• Clear-sky ocean = −3.21 Wm−2 / −2.6 Wm−2
• Previous passive estimates: −5.0 Wm−2
Assess CALIPSO’s capabilities for deriving the aerosol DREto better understand this discrepancy
CALIPSO aerosol DRE bias estimates (5/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
CALIPSO(Cloud-Aerosol Lidar and Infrared Pathfinder Satellite)
• Vertically-resolved aerosol properties over allsurface types during both day and night
• Easier to separate cloud from aerosol in thesame profile
• Recent studies have made new estimates of theglobal-mean aerosol DRE using CALIPSO:
1 Oikawa et al. JGR 2013: −0.61 Wm−2
2 Matus et al. JCLIM 2015: −1.9 Wm−2
• Clear-sky ocean = −3.21 Wm−2 / −2.6 Wm−2
• Previous passive estimates: −5.0 Wm−2
Assess CALIPSO’s capabilities for deriving the aerosol DREto better understand this discrepancy
CALIPSO aerosol DRE bias estimates (5/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
CALIPSO(Cloud-Aerosol Lidar and Infrared Pathfinder Satellite)
• Vertically-resolved aerosol properties over allsurface types during both day and night
• Easier to separate cloud from aerosol in thesame profile
• Recent studies have made new estimates of theglobal-mean aerosol DRE using CALIPSO:
1 Oikawa et al. JGR 2013: −0.61 Wm−2
2 Matus et al. JCLIM 2015: −1.9 Wm−2
• Clear-sky ocean = −3.21 Wm−2 / −2.6 Wm−2
• Previous passive estimates: −5.0 Wm−2
Assess CALIPSO’s capabilities for deriving the aerosol DREto better understand this discrepancy
CALIPSO aerosol DRE bias estimates (5/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
CALIPSO(Cloud-Aerosol Lidar and Infrared Pathfinder Satellite)
• Vertically-resolved aerosol properties over allsurface types during both day and night
• Easier to separate cloud from aerosol in thesame profile
• Recent studies have made new estimates of theglobal-mean aerosol DRE using CALIPSO:
1 Oikawa et al. JGR 2013: −0.61 Wm−2
2 Matus et al. JCLIM 2015: −1.9 Wm−2
• Clear-sky ocean = −3.21 Wm−2 / −2.6 Wm−2
• Previous passive estimates: −5.0 Wm−2
Assess CALIPSO’s capabilities for deriving the aerosol DREto better understand this discrepancy
CALIPSO aerosol DRE bias estimates (5/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
CALIPSO(Cloud-Aerosol Lidar and Infrared Pathfinder Satellite)
• Vertically-resolved aerosol properties over allsurface types during both day and night
• Easier to separate cloud from aerosol in thesame profile
• Recent studies have made new estimates of theglobal-mean aerosol DRE using CALIPSO:
1 Oikawa et al. JGR 2013: −0.61 Wm−2
2 Matus et al. JCLIM 2015: −1.9 Wm−2
• Clear-sky ocean = −3.21 Wm−2 / −2.6 Wm−2
• Previous passive estimates: −5.0 Wm−2
Assess CALIPSO’s capabilities for deriving the aerosol DREto better understand this discrepancy
CALIPSO aerosol DRE bias estimates (5/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
Evaluating CALIPSO
1 Radiative flux → aerosol extinction →assumed lidar ratio (ratio of extinction-to-backscatter)
2 Is all radiatively-significant aerosoldetected? (Kacenelenbogen et al. 2014, Rogers et al. 2014,
Thorsen et al. 2015)
ARM Raman lidars (RL)
SGP
TWP Darwin
1 Direct extinction measurements(no critical assumptions)
2 Strong signals from aerosols (it’s closer)
CALIPSO aerosol DRE bias estimates (6/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
Evaluating CALIPSO
1 Radiative flux → aerosol extinction →assumed lidar ratio (ratio of extinction-to-backscatter)
2 Is all radiatively-significant aerosoldetected? (Kacenelenbogen et al. 2014, Rogers et al. 2014,
Thorsen et al. 2015)
ARM Raman lidars (RL)
SGP
TWP Darwin
1 Direct extinction measurements(no critical assumptions)
2 Strong signals from aerosols (it’s closer)
CALIPSO aerosol DRE bias estimates (6/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
Evaluating CALIPSO
1 Radiative flux → aerosol extinction →assumed lidar ratio (ratio of extinction-to-backscatter)
2 Is all radiatively-significant aerosoldetected? (Kacenelenbogen et al. 2014, Rogers et al. 2014,
Thorsen et al. 2015)
ARM Raman lidars (RL)
SGP
TWP Darwin
1 Direct extinction measurements(no critical assumptions)
2 Strong signals from aerosols (it’s closer)
CALIPSO aerosol DRE bias estimates (6/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
Methodology
• Collocate (±200 km, ±2 hr) CALIPSO aerosol products (VFM, ALay) and ARMRL-FEX product over a 5 year period at SGP, 4 year period at TWP
• Calculate aerosol DRE using the NASA Langley Fu-Liou radiative transfer model:
DRE ↑(TOA) = F ↑aerosol(TOA)− F ↑no aerosol(TOA)
DRE ↓(SFC ) = F ↓aerosol(SFC )− F ↓no aerosol(SFC )
• Modify RL retrievals to mimic CALIPSO to test effect of ¶ lidar ratio assumptionsand · detection sensitivity
CALIPSO aerosol DRE bias estimates (7/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
Methodology
• Collocate (±200 km, ±2 hr) CALIPSO aerosol products (VFM, ALay) and ARMRL-FEX product over a 5 year period at SGP, 4 year period at TWP
• Calculate aerosol DRE using the NASA Langley Fu-Liou radiative transfer model:
DRE ↑(TOA) = F ↑aerosol(TOA)− F ↑no aerosol(TOA)
DRE ↓(SFC ) = F ↓aerosol(SFC )− F ↓no aerosol(SFC )
• Modify RL retrievals to mimic CALIPSO to test effect of ¶ lidar ratio assumptionsand · detection sensitivity
CALIPSO aerosol DRE bias estimates (7/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
Effect of assumed lidar ratios
• Detect → cloud/aerosol → 6 aerosol subtypes → lidar ratio → extinction → flux
Perform a CALIPSO-like retrieval using the RL with 3 types of lidar ratio averages
1 “Profile”: single vertical-mean in each profile
2 “Overpass”: single temporal- and vertical-mean in each collocated overpass
3 “Climo”: single climatological value
Lidar ratio assumptions introduce minimal error in CALIPSO-inferred mean aerosol DRE
CALIPSO aerosol DRE bias estimates (8/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
Effect of assumed lidar ratios
• Detect → cloud/aerosol → 6 aerosol subtypes → lidar ratio → extinction → flux
Perform a CALIPSO-like retrieval using the RL with 3 types of lidar ratio averages
1 “Profile”: single vertical-mean in each profile(majority of CALIPSO profiles contain a single aerosol type)
2 “Overpass”: single temporal- and vertical-mean in each collocated overpass(majority of aerosol in an overpass is a single type)
3 “Climo”: single climatological value(50.08 sr at SGP and 40.26 sr at TWP)
Lidar ratio assumptions introduce minimal error in CALIPSO-inferred mean aerosol DRE
CALIPSO aerosol DRE bias estimates (8/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
Effect of assumed lidar ratios
• Detect → cloud/aerosol → 6 aerosol subtypes → lidar ratio → extinction → flux
Perform a CALIPSO-like retrieval using the RL with 3 types of lidar ratio averages
1 “Profile”: single vertical-mean in each profile
2 “Overpass”: single temporal- and vertical-mean in each collocated overpass
3 “Climo”: single climatological value
Direct Profile Overpass Climo
Aer
oso
l D
RE
[W
m-2
]
-14
-12
-10
-8
-6
-4
-2
0
2
4
6
8
10
12
6.55 6.65
(1.5%)∆=0.10
6.67
(1.9%)∆=0.12
6.75
(3.1%)∆=0.21
-8.62 -8.75
∆=-0.13(1.5%)
-8.78
∆=-0.16(1.9%)
-8.88
∆=-0.26(3.1%)
TWP
TOA SW up
Surface SW down
(a)
Direct Profile Overpass Climo-14
-12
-10
-8
-6
-4
-2
0
2
4
6
8
10
12
4.01 4.00
(-0.2%)∆=-0.01
4.02
(0.5%)∆=0.02
4.11
(2.7%)∆=0.11
-8.14 -8.13
∆=0.01(-0.2%)
-8.18
∆=-0.04(0.5%)
-8.39
∆=-0.24(3.0%)
SGP
TOA SW up
Surface SW down
(b)
Lidar ratio assumptions introduce minimal error in CALIPSO-inferred mean aerosol DRE
CALIPSO aerosol DRE bias estimates (8/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
Effect of assumed lidar ratios
• Detect → cloud/aerosol → 6 aerosol subtypes → lidar ratio → extinction → flux
Perform a CALIPSO-like retrieval using the RL with 3 types of lidar ratio averages
1 “Profile”: single vertical-mean in each profile
2 “Overpass”: single temporal- and vertical-mean in each collocated overpass
3 “Climo”: single climatological value
Direct Profile Overpass Climo
Aer
oso
l D
RE
[W
m-2
]
-14
-12
-10
-8
-6
-4
-2
0
2
4
6
8
10
12
6.55 6.65
(1.5%)∆=0.10
6.67
(1.9%)∆=0.12
6.75
(3.1%)∆=0.21
-8.62 -8.75
∆=-0.13(1.5%)
-8.78
∆=-0.16(1.9%)
-8.88
∆=-0.26(3.1%)
TWP
TOA SW up
Surface SW down
(a)
Direct Profile Overpass Climo-14
-12
-10
-8
-6
-4
-2
0
2
4
6
8
10
12
4.01 4.00
(-0.2%)∆=-0.01
4.02
(0.5%)∆=0.02
4.11
(2.7%)∆=0.11
-8.14 -8.13
∆=0.01(-0.2%)
-8.18
∆=-0.04(0.5%)
-8.39
∆=-0.24(3.0%)
SGP
TOA SW up
Surface SW down
(b)
Lidar ratio assumptions introduce minimal error in CALIPSO-inferred mean aerosol DRECALIPSO aerosol DRE bias estimates (8/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
Detection sensitivity
TWP(a)
Solid: allDashed: night
Dotted: day
RL-FEXCALIPSO
Aerosol occurrence (transparent profiles)0 0.2 0.4 0.6 0.8 1
Hei
ght [
km]
0
1
2
3
4
5
6
7
8
9
10SGP
(b)
0 0.2 0.4 0.6 0.8 1
Is this undetected aerosol radiatively-significant?
CALIPSO aerosol DRE bias estimates (9/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
Detection sensitivity
TWP(a)
Solid: allDashed: night
Dotted: day
RL-FEXCALIPSO
Aerosol occurrence (transparent profiles)0 0.2 0.4 0.6 0.8 1
Hei
ght [
km]
0
1
2
3
4
5
6
7
8
9
10SGP
(b)
0 0.2 0.4 0.6 0.8 1
Is this undetected aerosol radiatively-significant?
CALIPSO aerosol DRE bias estimates (9/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
Effect of detection sensitivity
• In each collocated overpass: force RL aerosoloccurrence profile to match CALIPSO’s byremoving aerosol
• Monte Carlo method: obtain multiplerealizations of what the missing aerosol might be
• “RL-RM”: RL degraded to CALIPSO’s sensitivity
RL RL-RM
Aer
osol
DR
E [
Wm
-2]
-10
-8
-6
-4
-2
0
2
4
6
8
10
6.87 4.96
(-28%)"=-1.91
-9.04 -6.53
"=2.51(-28%)
TWP
TOA SW up
Surface SW down
(a)
RL RL-RM
-10
-8
-6
-4
-2
0
2
4
6
8
10
4.12 2.16
(-47%)"=-1.95
-8.39 -4.37
"=4.02(-48%)
SGP
TOA SW up
Surface SW down
(b)
CALIPSO’s lack of sensitivity causes a significant reduction of 30–50% in the magnitudeof the aerosol DRE
CALIPSO aerosol DRE bias estimates (10/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
Effect of detection sensitivity
• In each collocated overpass: force RL aerosoloccurrence profile to match CALIPSO’s byremoving aerosol
• Monte Carlo method: obtain multiplerealizations of what the missing aerosol might be
• “RL-RM”: RL degraded to CALIPSO’s sensitivity
RL RL-RM
Aer
osol
DR
E [
Wm
-2]
-10
-8
-6
-4
-2
0
2
4
6
8
10
6.87 4.96
(-28%)"=-1.91
-9.04 -6.53
"=2.51(-28%)
TWP
TOA SW up
Surface SW down
(a)
RL RL-RM
-10
-8
-6
-4
-2
0
2
4
6
8
10
4.12 2.16
(-47%)"=-1.95
-8.39 -4.37
"=4.02(-48%)
SGP
TOA SW up
Surface SW down
(b)
CALIPSO’s lack of sensitivity causes a significant reduction of 30–50% in the magnitudeof the aerosol DRE
CALIPSO aerosol DRE bias estimates (10/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
Effect of detection sensitivity
• In each collocated overpass: force RL aerosoloccurrence profile to match CALIPSO’s byremoving aerosol
• Monte Carlo method: obtain multiplerealizations of what the missing aerosol might be
• “RL-RM”: RL degraded to CALIPSO’s sensitivity
RL RL-RM
Aer
osol
DR
E [
Wm
-2]
-10
-8
-6
-4
-2
0
2
4
6
8
10
6.87 4.96
(-28%)"=-1.91
-9.04 -6.53
"=2.51(-28%)
TWP
TOA SW up
Surface SW down
(a)
RL RL-RM
-10
-8
-6
-4
-2
0
2
4
6
8
10
4.12 2.16
(-47%)"=-1.95
-8.39 -4.37
"=4.02(-48%)
SGP
TOA SW up
Surface SW down
(b)
CALIPSO’s lack of sensitivity causes a significant reduction of 30–50% in the magnitudeof the aerosol DRE
CALIPSO aerosol DRE bias estimates (10/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
Conclusions
• Assumptions about the aerosol lidar ratio used by CALIPSO likely cause minimalerror in global estimates of the aerosol DRE.
• CALIPSO is unable to detect all radiatively-significant aerosol, resulting in anunderestimate in the magnitude of the aerosol DRE by 30–50%.
• Therefore, global estimates of the aerosol DRE inferred from CALIPSO observationsare likely too weak.
• CALIPSO-based: −0.61 Wm−2 to −1.9 Wm−2(Oikawa et al. JGR 2013, Matus et al. JCLIM 2015)
• Passive-based: −5 Wm−2(Yu et al. ACP 2006)
• What is the aerosol DRE?
CALIPSO aerosol DRE bias estimates (11/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
Conclusions
• Assumptions about the aerosol lidar ratio used by CALIPSO likely cause minimalerror in global estimates of the aerosol DRE.
• CALIPSO is unable to detect all radiatively-significant aerosol, resulting in anunderestimate in the magnitude of the aerosol DRE by 30–50%.
• Therefore, global estimates of the aerosol DRE inferred from CALIPSO observationsare likely too weak.
• CALIPSO-based: −0.61 Wm−2 to −1.9 Wm−2(Oikawa et al. JGR 2013, Matus et al. JCLIM 2015)
• Passive-based: −5 Wm−2(Yu et al. ACP 2006)
• What is the aerosol DRE?
CALIPSO aerosol DRE bias estimates (11/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
Conclusions
• Assumptions about the aerosol lidar ratio used by CALIPSO likely cause minimalerror in global estimates of the aerosol DRE.
• CALIPSO is unable to detect all radiatively-significant aerosol, resulting in anunderestimate in the magnitude of the aerosol DRE by 30–50%.
• Therefore, global estimates of the aerosol DRE inferred from CALIPSO observationsare likely too weak.
• CALIPSO-based: −0.61 Wm−2 to −1.9 Wm−2(Oikawa et al. JGR 2013, Matus et al. JCLIM 2015)
• Passive-based: −5 Wm−2(Yu et al. ACP 2006)
• What is the aerosol DRE?
CALIPSO aerosol DRE bias estimates (11/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
CALIPSO aerosol DRE bias estimates (12/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
Undetected aerosol extinction
• “RL-RM” = CALIPSO-like
• What goes undetected is consistentwith random noise considerations
• CALIPSO’s SNR is too low to detect allaerosol during both day and night.
0
1
2
3
4
5 (a)TWPDayNight
Solid: RLDashed: RL-RM
[km
]
0
1
2
3
4
5 (b)SGPDayNight
Solid: RLDashed: RL-RM
Extinction coefficient [1/km]10-3 10-2 10-1 100
0
2
4
6
8
10 (c)UndetectedDayNight Dotted-dashed: TWP
Dotted: SGP
CALIPSO aerosol DRE bias estimates (13/11)
Introduction Evaluating CALIPSO Lidar ratio Sensitivity Conclusions
• Recent studies have made new estimates of the global-mean aerosol DRE usingCALIPSO:
1 Oikawa et al. JGR 2013: −0.61 Wm−2
2 Matus et al. JCLIM 2015: −1.9 Wm−2
• Previous passive estimates: −5.0 Wm−2
• Not just due to reduced DRE over cloud/landclear-sky ocean = −3.21 Wm−2 / −2.6 Wm−2
CALIPSO aerosol DRE bias estimates (14/11)