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Tony Del Genio - Mpimet Startseite · Song et al. (2013) Day Night. YOTC MJO 20-day hindcasts (2009...

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Tony Del Genio NASA/GISS What do we need to know about convection?
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Tony Del GenioNASA/GISS

What do we need to know about convection?

−20 −16 −12 −8 −4 0 4 8 12 16 20 −20

−16

−12

−8

−4

0

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20 2xCO2−1xCO2 A30 QFLUX NINT ANN

Low Cloud Cover Change (%)

SW

Clo

ud

Forc

ing

Ch

an

ge

(W/m

**2

)

−20 −16 −12 −8 −4 0 4 8 12 16 20 −20

−16

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0

4

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20 Conditional 2xCO2−1xCO2 A30 QFLUX NINT ANN

Low Cloud Cover Change (%)

SW

Clo

ud

Forc

ing

Ch

an

ge

(W/m

**2

)34% 5%

43% 18%

63% 3%

17% 18%

GISS GCM: 2.6-2.9°C climate sensitivity…but Δ low cloud cover ~ -2%

Why? Masking by high cloud increases

ARM SGP SCMsimulations:

Good place to look for convection errors is in

weak dynamical forcing conditions

(entrainment?)

Song et al. (2013)

DayNight

YOTC MJO 20-day hindcasts (2009 Event E)Hovmöller composite of all W-H phase 1 rain anomalies

TRMM TMI CMIP5

+ strongerentrainment,rain evaporation,downdrafts

+ Cold pool

SCM, AMIE-DYNAMO forcing, 3 hr relaxationConvective cloud top height vs. column water vapor

KAZR and SCM Forcing Data

40 44 48 52 56 60 64 68 72 COLUMN WATER VAPOR (mm)

0

2

4

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16 18

CL

OU

D T

OP

HE

IGH

T (

km

)

-1.6

-1.4

-1.2

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

log10(%)

KAZR

GAN AR5 W/Relaxation of Forcings Tot=763 (nint)

40 44 48 52 56 60 64 68 72 COLUMN WATER VAPOR (mm)

0

2

4

6

8

10

12

14

16

CL

OU

D T

OP

HE

IGH

T (

km

)

-1.6

-1.4

-1.2

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

log10(%)

GAN AR5 07 W/Relaxation of Forcings Tot=858 (nint)

40 44 48 52 56 60 64 68 72 COLUMN WATER VAPOR (mm)

0

2

4

6

8

10

12

14

16

CL

OU

D T

OP

HE

IGH

T (

km

)

-1.6

-1.4

-1.2

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

log10(%) GAN AR5 66 W/Relaxation of Forcings Tot=931 (nint)

40 44 48 52 56 60 64 68 72 COLUMN WATER VAPOR (mm)

0

2

4

6

8

10

12

14

16

CL

OU

D T

OP

HE

IGH

T (

km

)

-1.6

-1.4

-1.2

-1.0

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

log10(%)

CMIP5

+ strongerentrainment,etc.

+ cold pool

If radiative heating drives the MJO,

(a) Maybe it is a nice test of the GCM clouds we form from convection

(b) Since the relevant heating anomalies are largely due to organized convection, maybe we are getting the right answers in GCMs for the wrong reasonCloudSat-CALIPSO FLXHR-LIDAR MJO composite


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