Wie beeinussen Aerosole Stratokumuluswolken? Hendrik ... ·...

Aerosol-Effekte Konzept Daten/Methodik Ergebnisse/Diskussion

Aerosole - Stratokumuluswolken 07.11.2014 | climate.rub.de/team/hendrik-andersen/ 1

Wie beeinflussen AerosoleStratokumuluswolken?

Hendrik Andersen, Jan CermakRuhr-Universität Bochum

AK Klima 2014


Überblick

1 Effekte von Aerosolen in der Atmosphäre

2 Konzept

3 Daten und Methodik

4 Ergebnisse und Diskussion



Direkte und indirekte Effekte

Verändert nach Forster et al. (2007)



Abhängigkeiten indirekter Effekte

1 Relevantes Wolkenregime identifizierenAbgrenzung des Untersuchungsgebietes

2 Relevante meteorologische Parameter identifizieren3 Aerosolkontext



1. Relevantes Wolkenregime identifizieren

Terra MODIS Komposit, 28. März 2013, NASA LANCE-MODIS



1. Relevantes Wolkenregime identifizieren

Verändert nach Russell et al. (2013)



1. Abgrenzung des Untersuchungsgebiets

20°S

10°S

0°

0° 10°E25°S

20°S

15°S

10°S

0° 5°E 10°E 15°E5°W 0° 5°E 10°E 15°E0.16

0.24

0.32

0.40

0.48

0.56

0.64

0.72

0.80

StandardDeviation

CLF

MeanCLF

Homogene Zeitreihe → Dominanz von Stratokumulus



2. Relevante meteorologische Parameter identifizieren

(Klein and Hartmann, 1993)

...und relative Feuchte bei 950 hPa (RH)



3. Aerosolkontext

(Bréon et al., 2002)



10 Jahre (2003–2012) täglichAerosol- und Wolkendaten: Terra MODIS L3 C5.1,1◦x1◦ AuflösungMeteorologie: ERA-Interim (12:00 UTC), interpoliert auf1◦x1◦ Gitter

10 15 20 25 30Lower tropospheric stability

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Num

ber

of o

bser

vatio

ns

1e4

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4Aerosol index

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

Num

ber

of o

bser

vatio

ns

1e4



Ergebnisse und Diskussion

0.02 0.05 0.1 0.2 0.5 1 1.5 2Aerosol index

10

12

14

16

18

20

22

24

CDR

[µm

]

CDR: stable environmentCDR: unstable environment

50

60

70

80

90

100

Rel

ativ

e hu

mid

ity a

t 950

hP

a [%

]RH: stable environmentRH: unstable environment



20 40 60 80 100

-0.4

-0.2

0.0

0.2

0.4

R: l

og(A

I), l

og(C

DR

) -

(ran

dom

)

a): All data

20 40 60 80 100Meteorological factor (random data)

-0.4

-0.2

0.0

0.2

0.4 b): Low AI

20 40 60 80 100

-0.4

-0.2

0.0

0.2

0.4 c): High AI

0.01

0.05

0.10

1.00

P v

alue

Faktoren zur Interpretation der Punktewolke:1 Absolute Lage auf der Y-Achse2 Ausrichtung3 Horizontale Verschiebung auf der X-Achse (2. & 3. Panel)



15 20 25 30

-0.4

-0.2

0.0

0.2

0.4R

: log

(AI)

, log

(CD

R) a) all data

15 20 25 30Lower-tropospheric stability [K]

-0.4

-0.2

0.0

0.2

0.4 b) low AI

15 20 25 30

-0.4

-0.2

0.0

0.2

0.4 c) high AI

0.01

0.05

0.10

1.00

P v

alue

20 40 60 80 100

-0.4

-0.2

0.0

0.2

0.4

R: l

og(A

I), l

og(C

DR

) d) all data

20 40 60 80 100Relative humidity at 950 hPa [%]

-0.4

-0.2

0.0

0.2

0.4 e) low AI

20 40 60 80 100

-0.4

-0.2

0.0

0.2

0.4 f) high AI

0.01

0.05

0.10

1.00

P v

alue



Was heißt das fürs Prozessverständnis?



Backup slides

(Matsui et al., 2004)



Backup slides

0.100.05 0.07 0.10 0.20 0.30 0.40Aerosol index

13

14

15

16

Clou

d dr

ople

t siz

e [µm

]

links (Bréon et al., 2002)



Backup slides

(Costantino and Bréon, 2013)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month0.0

0.5

1.0

1.5

2.0

Aero

sol I

ndex

20°S

15°S

10°S

0° 5°E 10°E0° 5°E 10°E 0.00

0.04

0.08

0.12

0.16

0.20

0.24

0.28

0.32

0.36

0.40

Aero

sol i

ndex



Backup slides

20°S

15°S

10°S

0° 5°E 10°E0° 5°E 10°E0.5

0.0

0.5

R: l

og(A

I), lo

g(CD

R)



Backup slides

975 950 925 900 875 850 825 800 775 750 700

Pressure levels [hPa]0.0

0.2

0.4

0.6

0.8

1.0Cl

oud

fract

ion



Backup slides

Data normally distributed?

Logarithmic transformation

Data stationary?

Detrend data

Mean > standard dev.

CTT > 273 K

Mean study area statistics?

Latitudinal weighting

Preprocessing complete

yes

yes

no

no

no

yes

= conditional = unconditional



Backup slides

log(CDR) CDNC CLF log(LWP) log(COT)

Cloud parameters

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5R

: log

(AOD

), cl

oud

para

met

ers

Entire datasetWeather type 1Weather type 2Weather type 3



Backup slides

log(CDR) CDNC CLF log(LWP) log(COT)

Cloud parameters

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5R

: log

(AOD

), cl

oud

para

met

ers

Entire datasetAug, Sep dataOther months



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Chen, Y.-C., Christensen, M. W., Stephens, G. L., and Seinfeld, J. H. (2014). Satellite-based estimate of globalaerosol-cloud radiative forcing by marine warm clouds. Nature Geoscience, 7(9):643–646.

Coakley, J. and Walsh, C. (2001). Limits to the Aerosol Indirect Radiative Effect Derived from Observations of ShipTracks. Journal of the Atmospheric Sciences, 59(1997):668–680.

Costantino, L. and Bréon, F.-M. (2013). Aerosol indirect effect on warm clouds over South-East Atlantic, fromco-located MODIS and CALIPSO observations. Atmospheric Chemistry and Physics, 13(1):69–88.

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Wie beeinussen Aerosole Stratokumuluswolken? Hendrik ... ·...

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