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$Page 1: CALIPSO-inferred aerosol direct radiative e ects: bias ... · 3 \Climo": single climatological value Direct Profile Overpass Climo Aerosol DRE [Wm-2]-14-12-10-8-6-4-2 0 2 4 6 8 10$

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)

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)

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

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

“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(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

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)

Evaluating CALIPSO

SGP

TWP Darwin

Evaluating CALIPSO

SGP

TWP Darwin

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

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

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

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)

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)

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)

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?

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?

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

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)

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)

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?

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

PDF

[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

• Recent studies have made new estimates of the global-mean aerosol DRE usingCALIPSO:

• Not just due to reduced DRE over cloud/landclear-sky ocean = −3.21 Wm−2 / −2.6 Wm−2

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