Aerosols Chemistry Clouds and Radiation

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Frontiers in Climate and Earth System Modeling: Advancing the Science

May 20, 2013

Geophysical Fluid Dynamics Laboratory

Speakers: Yi Ming and Chris Golaz

Moderator: Leo Donner

Using Models and Observations to

Understand

Climate Processes: Aerosols,

Chemistry, Clouds and Radiation

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• 

Key to reducing model biases and uncertainties; Affirmed by the 2012 National Research Council

(NRC) Report  on Advancing Climate Modeling and the

2010 NOAA Next-generation Strategic Plan (NGSP).

• 

Relevant to NOAA’s climate adaptation andmitigation goal;

•  Striving for a healthy balance between resolution and

complexity;

• 

New opportunities created by “marrying” more

advanced physics and chemistry with finer spatial

resolutions.

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Overarching theme: Understanding the complexroles of short-lived species and clouds in

influencing climate and air quality.

1. Atmospheric Composition

•  High resolution modeling of aerosol emissions and transport

[Paul Ginoux];

•  Influence of inter-continental transport and stratospheric

intrusion on the western U.S. air quality [Meiyun Lin]

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2. Radiation and climate forcing•  Parameterization of water vapor continuum [David Paynter];

•  Validation of modeled surface radiative flux [Stuart

Freidenreich];

• 

Surface radiative flux trends (global dimming) [Geeta Persad];•  Active participation in the Atmospheric Chemistry and Climate

Model Intercomparsion Project (ACCMIP) [Vaishali Naik and

Larry Horowitz]; 

3. Climate response•  Aerosol effects on South Asian monsoon [Massimo Bollasina];

•  Non-local aerosol effects on the Atlantic Meridional Overturning

Circulation (AMOC) [Dan Schwarzkopf];

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4. Cloud Processes•  Large-scale clouds and aerosol-cloud interactions [Chris

Golaz];

•  Deep cumulus and satellite/process-level observations [Leo

Donner];

•  Shallow cumulus and climate sensitivity [Ming Zhao].

Outline of this talk

1. Yi Ming: overview, and the first three research fields(atmospheric composition, radiation and climate response);

2. Chris Golaz: cloud processes, and future research directions.

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0E=H=?=A"F >G:A IFG@?: "=> F"=> G:?

 Accidents US I-60 near

Tulsa (Oct 19, 2012)

Credit: P. Ginoux

Dust Optical Depth and emission from

agriculture (Oct 18, 2012)

Nudged 50-km AM3 with land use dust sources (Ginoux et al., 2012)

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Sunphotometer @ CRT 

Emission (mg/m2/day) by land use type

   A   O   D   (   5   5   0

  n  m

   )

date

ObsModel

0

20

40

60

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.AB"AE:I9?B;< ;=MG?=<? E= N?:A?B= 5O.O :GBP"<? EQE=? 

Southern California (May 23)

GFDL AM3

1 2 0  

Lidar (CalNex)

O 3 S  

   M  o

   d  e   l   8  -   h  r   S  u  r   f  a  c  e

   O   3

   (  p  p   b   ) 

Observed 8-hr Surface O3 (ppb)

 Air qualitystandard

Stratospheric O3

(~50%)

   A   l   t   i   t  u   d  e   (   k  m

   a .  s .   l .

   ) 

Flight Track 

O3 [ppb]

Lin et al. (2012) Credit: M. Lin

Apr. 12-16

Total O3 (100%)

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Credit: G. Persad

Global Energy

Balance Archive

(GEBA) stations

used in Norris

and Wild (2009)

The GFDL AM3/CM3 model has the best representation of the

dimming trends among all CMIP5 model (Allen et al., 2012).

Obs.

 AM2.1

 AM3

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.GBP"<? :9EBAN"R? B">;"HR? MGS K..,L AB?=> K%L

   C   l  e  a

  r  -  s   k  y  s  u  r   f  a  c  e

  s  o

   l  a  r  r  a

   d   i  a   t   i  o  n

   (   W   /  m   2   ) Surface:

-12 W/m2 

AM3

Ext. 

Int. Mixing More Less Aerosol

+More absorption

More dimming

Less absorption

Less dimming 

AM2.1 AM3

A  b  s  or  p t  i   on (  W

 /  m2  )  

-10

-6

-2

2

6

10

 Absorption:8 W/m2

TOA

Surface

Cooling

Atmosphere

Warming

Similartrends

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*=A9BEIEC?=;< "?BE:EF: "=> .EGA9 *:;"= @E=:EE=

Linear trends ofaverage JJAS rainfall

over central-northern

Indian (mm day-1)GG

All forcing

CRUAERO

Credit: M. Bollasina

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Climatology GG

All forcingAERO

ALL_FAERO

WMGGO3Climatology

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*?BE:EF ;@I"<A E= @E=:EE= E=:?A

CRU

Obs.

May June

• 

Aerosols tend to

increase rainfall

in May and June,

whilesuppressing it in

JAS;

• 

An earlier

monsoon onset;

• 

Consistent withobservations.

JAS (July-Aug-Sep)

AERO

Linear trends of precipitation [mm day-1 50 yr -1]

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*?BE:EF ;@I"<A E= @E=:EE= E=:?A K%L

warm &moist air

warm &moist air

May

June

Aerosols

Higher

SLP

LowerSLP

Surface

wind

Precipitation

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Sensitivity and forcing are key

climate properties. At

equilibrium: 

!" " #  $  

$  "#$%&'()$* forcing: radiative"#$%&'()$* forcing: radiative

perturbation (GHGs, aerosols,clouds, land-use,!)

#  sensitivity: temperaturesensitivity: temperature

response per unit of forcing.

!  Clouds impact both.

Forster et al. (2013); Kiehl (2007).

Anti-correlation between sensitivity

and forcing among models that

reproduce observed warming.

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Zhao (2013 , J. Climate, submitted)

Red: tropical average

Blue: global average

Credit: M. Zhao

" 

Increased climate sensitivityin HiRAM (and AM3)

compared to AM2.

"  Sensitivity highly correlated

with cloud feedback (change

in cloud radiative effect).

Cloud feedback

!  impacted by details of

convective parameterization,

!  linked to convective

precipitation efficiency.

   S  e  n

  s   i   t   i  v   i   t  y

Cloud feedback parameter AM2

0 c48 HiRAM

1-4 perturbed cumulus mixing

5-8 perturbed cumulus microphysics

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0FEG>: "=> PEB<;=CY ;=>;B?<A ?Z?<A

ModelsCM3: official GFDL CMIP5

model.

CM3w,c: configurations

with alternate but plausible

parameter choices.

NOAA NCDC 0.59 ºC

NASA GISS 0.53 ºC

HadCRUT3 0.56 ºC

CM3w 0.57 ºC

CM3 0.22 ºC

CM3c -0.01 ºC

Net warming

Golaz et al. (2013, GRL)Credit: C. Golaz

Observations

Models

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0FEG>:Y 9"R? N? @">? IBECB?::V

Evaluation of CFMIP1 and CFMIP2 modelsKlein et al. (2013, JGR)

g2: AM2(GAMDT, 2004,

J. Climate)

G3: AM3(Donner et al.,

2011, J. Climate)

AM3/CM3 results are being widely analyzed (53 citations to date).GFDL CFMIP credit: L. Donner, C. Seman, L. Horowitz, B. Hurlin

Shortwave relevant cloud properties Longwave relevant cloud properties

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•  Long-standing stratocumulus biases

are reduced in AM3-CLUBB.

•  Overall performance slightly lags AM3.

Credit: H. Guo

CLUBB* (NOAA/NSF Climate Process Team)Short-wave cloud forcing error [W m-2]

   S   t  r  a   t  o  c  u  m  u   l  u  s   b   i  a  s  e  s

*Cloud Layers Unified by Bi-Normals

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7?N <FEG> "=> AGBWGF?=<? I"B"@?A?B;Q"HE=

!  Full liquid water path response to aerosols could

potentially decrease magnitude of indirect effect.

Droplet concentration (cm-3)

CLUBB

Guo et al. (GRL, 2011)

   L   i  q  u   i   d  w  a   t  e  r  p  a   t   h

    (  g  m  -

   2   )

Credit: H. Guo

CLUBB* (NOAA/NSF Climate Process Team)Indirect effect for different stratocumulus cases

Droplet concentration (cm-3)   L   i  q  u   i   d  w  a   t  e  r  p  a   t   h

    (  g  m  -

   2   )

 Ackerman et al. (Nature, 2004)

Large eddy simulations

*Cloud Layers Unified by Bi-Normals

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Improved understanding and new modeling capabilities:•  Aerosol-ice cloud interactions (e.g., black carbon as ice nuclei);

•  Double-moment aerosol/cloud microphysics;

•  Aerosols (e.g., black carbon and dust) on snow;

• 

 Aerosol-vegetation-biogeochemistry coupling (e.g., dust andwild fires);

•  Chemistry-climate interactions (e.g., methane lifetime);

•  Improvement of radiative transfer parameterization (e.g., water

continuum);

•  More unified and physically sound cloud and convective

parameterizations (e.g., CLUBB coupled with double-moment

cloud microphysics).

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,?F?R"=<? AE A9? 7-**\: 7].8 CE"F:

• 

Our process-oriented research generates1)  mechanistic understanding of existing

model biases and uncertainties,

2)  new modeling capabilities that enhance the

realism of regional climate and Earth

System simulation. 

• 

Both aspects are crucial for developing the

next-generation GFDL Earth System Model, anessential tool for advancing NOAA’s climate

adaptation and mitigation goal.