1 nasa ames research center natural pollution events and their role in ice cloud formation michal...

1NASA Ames Research Center

Natural pollution events and their role in ice cloud formationMichal Segal-Rosenheimer, Patrick Hamill, S. Ramachandran

ISSCP at 30 : What Do We Know and What Do We Still Need to Know?

OR: What can we learn from such events on the interaction between aerosol and

ice clouds?


Introduction


Potential effects: We know that increased IN concentration can cause mixed-phase

clouds glaciation [e.g.; Seifert et al., 2011, JGR; Choi et al., 2010, PNAS], formation of

new ice clouds [Sassen and Khvorostyanov 2008, Environ.Res.Let.] or a change of existing

ice clouds microphysical properties (COT and Deff) [e.g. Storelvmo et al., 2013, JGR]

Volcanic Dust Biomass burning



Dust, Ash , Soot are efficient IN, and have a wide range of Temperature and supersaturation “activation” values [Hoose and Möhler,2012]

What do we know?

Ground-based Lidar observations showed mixed-phase clouds glaciation [e.g. Seifert et al., 2011], and ice formation [e.g. Sassen and Khvorostyanov 2008]

Global models (ECHAM; Hendricks et al., 2011 and CAM5; Liu et al., 2012]show that heterogeneous ice nucleation leads to fewer but larger crystals

In-situ in-cloud sampling that measure IN amount under ambient conditions [e.g. DeMott et al., 2003] and residual chemical composition are used toderive model parameterizations for ice heterogeneous nucleation schemes.

These IN are responsible for ice formation via Heterogeneous nucleation


What do we still need to know?


While models can resolve concentrations, and ice properties in 3-D on a global scale; they only have sparse in-situ observations at specific locations to compare to [e.g. Hoose et al., 2010; Liu et al., 2012].Various parameterization schemes predict different amounts of ice concentrations and relative effects of heterogeneous versus homogeneous nucleation [Liu et al., 2012] and show that results are very sensitive to parameterization assumptions [Hendricks et al., 2011] and aerosol characterizationWe still need better constraints on heterogeneous

nucleationdescribed in models


How to bridge the gap?



Objectives and approach


To test whether heavy-aerosol events can serve as an “outdoor laboratory” that can improve our understanding on the connection between specific aerosol types and ice clouds incidence and their microphysical properties

1. To use heavy aerosol events – better spatial and temporal constrain to research domain.2. To use spectral analysis, together with microphysical and state parameters from IR sounders to link aerosol-ice3. Support observations with trajectory and dispersion/ chemical transformation analysis to constrain observed trends in ice cloud properties and amount that can be compared with specific model simulations.



How does Heavy-Aerosol Events Affect Ice Cloud

Formation?

The Eyjafjallajokull 2010 Eruption

case study



Constraining the case study domain – using Forward trajectory & literature [e.g. Petersen, 2010, Weather; Seifert et al., 2011, JGR]



Case study domain cloud related property distributions from AIRS – Eruption period

200 500 8000

0.01

0.02

Clo

ud

To

p P

an

om

ally

200 500 8000

0.01

0.02

200 500 8000

0.01

0.02

200 500 8000

0.01

0.02

200 250 3000

0.01

0.02

0.03

Clo

ud

To

p T

an

om

ally

200 250 3000

0.01

0.02

0.03

200 250 3000

0.01

0.02

0.03

200 250 3000

0.01

0.02

0.03

0 40 80 1200

0.005

0.01

O

LR

an

om

ally

0 40 80 1200

0.005

0.01

0 40 80 1200

0.005

0.01

0 40 80 1200

0.005

0.01

0 0.5 10

0.01

0.02

Clo

ud

fra

ctio

nC

F a

no

mal

ly

2010-04-1-140 0.5 1

0

0.01

0.02

2010-04-14-200 0.5 1

0

0.01

0.02

2010-04-21-280 0.5 1

0

0.01

0.02

2010-04-29-05-05



(a1) BTD pair values representative of ash from April 17th (granule 133, 13:20UT), and the corresponding (a2) MODIS-Aqua true color image granule (downloaded from EOSDIS Rapid Response website), which shows the ash plumes. Other panels (b-e) show zoomed-in areas for various pixels in (a), and their BT spectra (marked with different symbols) on the right of each row that further demonstrate the variety of spectral slopes for ice. Panels (f) show classification maps Symbols are the same as upper panels, and correspond to same BT spectra as above (for example, the diamond symbol was classified as ice containing ash).BTD relationships and thresholds are based on literature values (e.g. Francis et al., 2012;Clarisse et al. 2010, Gangale et al., 2010)And on local clear region thresholds.



Ice clouds Cloud Top Temperature bins

220 230 240 250 260 2700

20

40

60

80

100

(% ic

e)

Cloud Top Temperature [K]

Ice and Ash cloudy pixels (n=3347)

Ice only (n =274352)

Cloud top temperatures for the inherent AIRS FOV (15x15km) are taken from the Dual Regression (DR) algorithm package [Smith et al., 2012]



Cloud top Temperature distributions for each group

200 210 220 230 240 250 260 2700

5

10

15

Cloud top Temperature [K]

% Ic

e/Ic

e+

As

h c

lou

ds

re

lati

ve

to

to

tal c

lou

ds

Ice only (n =274352)

Ice and Ash cloudy pixels (n=3347)



Cloud phase fraction over the whole domain – April 10-20 2010SEVIRI (PATMOS-x retrieval L2B cloud product)

Day of Year

No

rmal

ized

fre

qu

ency

[-]

101 102 103 104 105 106 107 108 109 110 1110

0.2

0.4

0.6

0.8

1

1.2

1.4watersupercooledice

Ice/

sup

erco

ole

d (

frac

tio

n)

0.8

1

1.2

1.4

1.6

1.8

2

Major eruption period



2-D distribution functions of Cloud Top Temp versus Cloud Height

0 5 10 15

210

220

230

240

250

260

270

280

Cloud Height [km]

2010101

Clo

ud

To

p T

emp

[k]

0

0.02

0.04

0.06

0.08

0.1

0 5 10 15

210

220

230

240

250

260

270

280

Cloud Height [km]

2010102

Clo

ud

To

p T

emp

[k]

0

0.02

0.04

0.06

0.08

0.1

0 5 10 15

210

220

230

240

250

260

270

280

Cloud Height [km]

2010103

Clo

ud

To

p T

emp

[k]

0

0.02

0.04

0.06

0.08

0.1

0 5 10 15

210

220

230

240

250

260

270

280

2010104

Cloud Height [km]

Clo

ud

To

p T

emp

[k]

0

0.02

0.04

0.06

0.08

0.1

0 5 10 15

210

220

230

240

250

260

270

280

2010105

Cloud Height [km]

Clo

ud

To

p T

emp

[k]

0

0.02

0.04

0.06

0.08

0.1

0 5 10 15

210

220

230

240

250

260

270

280

2010106

Cloud Height [km]

Clo

ud

To

p T

emp

[k]

0

0.02

0.04

0.06

0.08

0.1

0 5 10 15

210

220

230

240

250

260

270

280

2010107

Cloud Height [km]

Clo

ud

To

p T

emp

[k]

0

0.02

0.04

0.06

0.08

0.1

0 5 10 15

210

220

230

240

250

260

270

280

2010108

Cloud Height [km]

Clo

ud

To

p T

emp

[k]

0

0.02

0.04

0.06

0.08

0.1

0 5 10 15

210

220

230

240

250

260

270

280

2010109

Cloud Height [km]

Clo

ud

To

p T

emp

[k]

0

0.02

0.04

0.06

0.08

0.1

0 5 10 15

210

220

230

240

250

260

270

280

2010110

Cloud Height [km]

Clo

ud

To

p T

emp

[k]

0

0.02

0.04

0.06

0.08

0.1

0 5 10 15

210

220

230

240

250

260

270

280

2010111

Cloud Height [km]

Clo

ud

To

p T

emp

[k]

0

0.02

0.04

0.06

0.08

0.1



2-D distribution functions of Cloud Top Temp versus Cloud Height

0 5 10 15

210

220

230

240

250

260

270

280

Cloud Height [km]

2010111

C

lou

d T

op

Tem

p [

k]0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0.05

0 5 10 15

210

220

230

240

250

260

270

280

Cloud Height [km]

2010101

Clo

ud

To

p T

emp

[k]

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0.05



a

b

c

a

b

c

Eruption Period

200 210 220 230 240 250 260 270 2800

2

4all ice

(%)

200 210 220 230 240 250 260 270 2800

0.5

1ice < 6km

(%)

200 210 220 230 240 250 260 270 2800

20

40ice > 6km

(%)


2010-04-11 (pre-eruption)

2010-04-20 (post-eruption)



Eruption Period

200 210 220 230 240 250 260 270 2800

2

4all ice

(%)

200 210 220 230 240 250 260 270 2800

0.5

1ice < 6km

(%)

200 210 220 230 240 250 260 270 2800

20

40ice > 6km

(%)






Baseline Period

a

b

c

200 210 220 230 240 250 260 270 2800

2

4all ice

(%)

200 210 220 230 240 250 260 270 2800

0.5

1ice < 6km

(%)

200 210 220 230 240 250 260 270 2800

20

40ice > 6km

(%)


2012-04-10

2012-04-20



Eruption Period

225 230 235 240 245 250 255 260 265 2700

50

100all ice

(% i

ce)

225 230 235 240 245 250 255 260 265 2700

50

100ice < 6km

(% i

ce)

225 230 235 240 245 250 255 260 265 2700

50

100ice > 6km

(% i

ce)






Baseline Period

a

b

c

225 230 235 240 245 250 255 260 265 2700

50

100all ice

(% i

ce)

225 230 235 240 245 250 255 260 265 2700

50

100ice < 6km

(% i

ce)

225 230 235 240 245 250 255 260 265 2700

50

100ice > 6km

(% i

ce)


2012-04-10

2012-04-20



2-D distribution functions of Reff versus ice COT

Ice COT



2-D distribution functions of Reff versus ice COT

0 2 4 6 8

20

40

60

80

100

120

140

160

ice COT

2010111 low ice (< 6km)

Re

ff [ m

]

0

0.005

0.01

0.015

0.02

0.025

0.03

0 2 4 6 8

20

40

60

80

100

120

140

160

ice COT

2010111 high ice (> 6km)

Re

ff [ m

]

0

0.005

0.01

0.015

0.02

0.025

0.03

0 2 4 6 8

20

40

60

80

100

120

140

160

ice COT

2010101 low ice (< 6km)

Re

ff [ m

]

0

0.005

0.01

0.015

0.02

0.025

0.03

0 2 4 6 8

20

40

60

80

100

120

140

160

ice COT

2010101 high ice (> 6km)

Ref

f [ m

]

0

0.005

0.01

0.015

0.02

0.025

0.03



Eruption Period

0 20 40 60 80 100 120 140 1600

10

20

30all ice

(%)

0 20 40 60 80 100 120 140 1600

10

20

30ice < 6km

(%)

0 20 40 60 80 100 120 140 1600

10

20

30ice > 6km

(%)

Reff

[m]



a

b

c



Baseline Period

0 20 40 60 80 100 120 140 1600

10

20

30all ice

(%)

0 20 40 60 80 100 120 140 1600

10

20

30ice < 6km

(%)

0 20 40 60 80 100 120 140 1600

10

20

30ice > 6km

(%)

Reff

[m]

2012-04-10

2012-04-20

a

b

c



April 20, 2010, 4 days back-trajectory

Ice cloud heights (4-6km)

210 215 220 225 230 235 240 245 2500

10

20

30

40

50

60

70

80

90

100

(%)


2010-04-20 Ash effected ice region

2010-04-20 Clean ice region

-140 -120 -100 -80 -60 -40 -20 0 20 40 6040

45

50

55

60

65

70

75

80

85

90

LongitudeoE

Lat

itu

de

oN

Region A - back-trajectory (5 km)

Region A - back-trajectory (6 km)Region B - back-trajectory (5 km)

Region B - back-trajectory (6 km)

A

B

A

B

0 20 40 60 80 100 120 140 1600

5

10

15

20

25

30

(%)

Reff

[m]





210 215 220 225 230 235 240 245 2500

10

20

30

40

50

60

70

80

90

100

(%)




Ice cloud heights (6-10km)

April 20, 2010, 4 days back-trajectory

-140 -120 -100 -80 -60 -40 -20 0 20 40 6020

30

40

50

60

70

80

90

LongitudeoE

Lat

itu

de

oN

Region A - back-trajectory (7 km)

Region A - back-trajectory (9 km)Region B - back-trajectory (7 km)

Region B - back-trajectory (9 km)

A

B

AB

0 20 40 60 80 100 120 140 1600

5

10

15

20

25

30

(%)

Reff

[m]





Specific case studies, when compared with simulations can aid in the decision of whether parameterization will be sufficient for the whole chemical group (e.g. dust) or whether it needs to be fine-tuned in the models for each region

Case study results cover large regional areas, which are more proper to compare withRegional/global model simulations

Int. Summary (work in progress…)It seem that heavy aerosol events can serve as a test-bed to identify cloud microphysics

changes and processes via regional investigation such as the one demonstrated

We have observed cloud formation in temperatures higher than the ones observed forCleaner regions and baseline periods and between clear/polluted regions

We see an increase in ice cloud amount (both relative to super-cooled phase and high clouds) relative to baseline periods

We have observed Reff values increasing in mid-level clouds when comparing pre-eruption to post-eruption and baseline periods



Interact with global modeling groups (GEOS-Chem/CAM5) to investigate the specificCase studies and improve model parameterization (Pending proposal)

Next steps (work in progress…)Create 2-D or higher dimensional PDF of cloud microphysical properties for several

Pre-selected case studies designed to look at Ash, dust and Biomass-burning events.

Combine aerosol fields vertical distributions using back-trajectory analysis and globalAerosol models (RAQMS/GOCART) to link properties to specific aerosol types

Combine extracted cloud microphysical properties with aerosol properties derivedEither from satellite products for Ash-Dust (IR hyperspectral sounders or CALIPSO)And for biomass-burning (MODIS or CALIPSO)

Perform ice microphysical box modeling along back trajectories from cloud topsTo better assess the conditions needed to form clean/polluted ice in case study regions



© Michal Segal

Acknowledgements:Andrew Heidinger and Mike Foster – Geostationary cloud products

NASA Postdoctoral Program (NPP/ORAU)Weizmann Institute of Science, Israel

1 nasa ames research center natural pollution events and their role in ice cloud formation michal...

Documents

ice formation

ice properties

heterogeneous ice nucleation

ice clouds incidence

ice cloud properties

heterogeneous nucleation

heavyaerosol events

heavy aerosol events