emerging modules for asms : chemistry – aerosols – clouds

Emerging Modules for ASMs: Emerging Modules for ASMs: Chemistry – Chemistry – Aerosols – CloudsAerosols – Clouds

J-P Blanchet, É. Girard, C. Jones, J-P Blanchet, É. Girard, C. Jones, P. Grenier, R. Munoz-Alpizar, T. Ayash, A. Stefanof, C. Stefanof, P. Du,P. Grenier, R. Munoz-Alpizar, T. Ayash, A. Stefanof, C. Stefanof, P. Du,

A.Tatarevic, P. Dehasse, Y. Melin, D. Simjanoski, C. Jouan, J. Dorais, G. DueymesA.Tatarevic, P. Dehasse, Y. Melin, D. Simjanoski, C. Jouan, J. Dorais, G. DueymesS-A. Demers-Giroux, J.-N. BlanchetS-A. Demers-Giroux, J.-N. Blanchet

Department of Earth and Atmospheric Sciences (SCTA)Department of Earth and Atmospheric Sciences (SCTA)Institute of Environmental Sciences (ISE)Institute of Environmental Sciences (ISE)

University of Quebec at Montreal (UQAM)University of Quebec at Montreal (UQAM)

and collaboration withand collaboration with

S. GongS. GongMSCMSC

Presented at the Presented at the International collaboration in Arctic System Modelling WorkshopInternational collaboration in Arctic System Modelling Workshop

July 16–17 July, University of Quebec at MontrealJuly 16–17 July, University of Quebec at Montreal

Funding AgenciesPartners

Aerosol Impact on the Weather and Climate SystemAerosol Impact on the Weather and Climate System

To be completed …

PermafrostSoil Moisture

Sea Ice

Sea Ice

Snow Cover

River Flow

Cold WinterTemperatures

ForestFires

Sea

Ice

Exp

ort

IndustrialAerosols

WarmSummer

DMSaerosol

L

Sea Ice

Smoke

Bio aerosolsClimateHydrologyAir Quality

AVHRR T 20 yr Summer Temperature Trend NASA/Goddard Space Flight Center

Scientific Visualization Studio, Larry Stock, Robert Gersten based on data analysis by Joey Comiso (NASA)

Sea ice-albedo feedback (+)

Snow-albedo feedback (+)

A rapidly declining perennial sea ice cover in the Arctic, Geophysical Research Letters, Vol. 29, No. 20, October 2002

http://svs.gsfc.nasa.gov/search/Keyword/Arctic.html

Mean Annual Trend °C / yr

AVHRR T 20 yr Winter Temperature Trend 1982-2002 NASA/Goddard Space Flight CenterScientific Visualization Studio, Larry Stock, Robert Gersten (2003)

Raatz, 1991

CGCM1/IS92a-Winter

2040-60 minus 1975-95

Mean Annual Trend °C / yr

Evidences of Evidences of aerosol alterations in the Arcticaerosol alterations in the Arctic

Bigg (1980) observed Bigg (1980) observed sulfuric acid coatingsulfuric acid coating on on nearly all other aerosol nearly all other aerosol particles during winterparticles during winter

Boris observed Boris observed reduced reduced ice nuclei activityice nuclei activity by 100 by 100 to 10000 fold in crystal to 10000 fold in crystal counts during counts during anthropogenic Arctic anthropogenic Arctic haze event.haze event.

Reaction on calcium fluorideRef.: Bigg, 1980

NARCM: Aerosol Size-Specie ResolvedNARCM: Aerosol Size-Specie Resolved

• Flexible model structure• Multi-components Simulations• Physically based size distribution• Numeric diffusion for particle growth• Computational intensive (large bin no.)

Gong, Barrie and Blanchet et al. 2003, JGR

Size

Mas

s

CanadianAerosol ModuleGong et al (1997, 2003)

D yn am icsP h ysics

S em i-lagran giantracer tran sp ort

C R C MC a na d ia n R e g io na l C lim a t M o d e l

A eroso l sou rcef u n ction

A eroso l p rocesses

C A MC a na d ia n A e r o so l M o d ule

N A R C MN o r the r n A e r o so l C lim a te M o d e l

• Saltation• Sea spray • Chemistry• Incloud oxydation• DMS• Volcano• Anthropogenic

• Nucleation• Condensation• Coagulation• Sedimentation• Wet deposition• dry deposition

65 full 4D tracers

12 size bins X 5 species

Model and Validation Structure

+

Canadian Regional Climate Model (GEM-Arctic)

Canadian Aerosol Module (CAM)+

Chemistry (GEM-AQ) +

Mixed Phase Cloud Resolving (GEM-CRM)

EarthCARE Instrument Simulator (Radar+Lidar+Rad+MLS)

+

Leve

l 2: V

alid

atio

n -

Clo

sure

A-Train: CloudSat-CALIPSO-MLS

Le

vel 1

Standard ProductsAlgorithms

PEARL / SHEBA / IPY

Laboratory

Instrumented Flights

New dedicated satellite instrument : TICFIRE

A-Train : CloudSat – CALIPSO – AQUA – AURA – AIRSA-Train : CloudSat – CALIPSO – AQUA – AURA – AIRS

Ref.: CALIPSO Web site

CALIPSO

CloudSat

PEARLab (CANDAC) at Eureka on PEARLab (CANDAC) at Eureka on Ellesmere Island in the Canadian ArcticEllesmere Island in the Canadian Arctic

(80 deg N, 86 deg W, 610 meters)(80 deg N, 86 deg W, 610 meters)

Methodology:

Compare Model Simulations to ground site measurements from Eureka, Alert, Spitsbergen, and Barrowand satellite data…

Cloud RadarLidar HSRL

µwave RadiometerAt sea level

Also at 610m

A-Train and PEARL ObservationsA-Train and PEARL Observations

PEARL

Observed Simulated

Monthly Mean Aerosol – Observed vs Simulated

Amount

Occurrence

Statistics for TIC – AerosolsJanuary & July 2007

Ref.: Grenier, Blanchet and Munoz-Alpizar (JGR, 2009)

Spain

Ala

ska

Kab

ul

Spain

Ala

ska

Kab

ul

Spain

Ala

ska

Kab

ul

TIC-2B

TIC-2A

TIC-1

Is there a 4th General Circulation Cell during extreme cold pole conditions?

A dynamics-aerosol-clouds-radiationInteraction on planetary scale

Lows drift NBring aerosol

Form TIC

Precipitate

Rad-CoolingAv. Pot. Energy

Summary Aerosol links air chemistry to climate via alteration

of cloud, precipitation, radiation and circulation in important ways.

Spreading over 5 orders of magnitudes, aerosol physics need adequate size and species resolution for treatment (6D).

Including explicit aerosol may now be optional, but will become essential for proper climate simulations.

Due to time and spatial scales, arctic aerosols are an important research topic.

Further at MOCA-2009

M 13.9 Stefanof et al (Monday 17h00)

M 13.10 Grenier et al (Monday 17h15)

M 13.12 Munoz et al (Monday 17h45)

J 03.17 Blanchet et al (Tuesday 9h30)

J 02.5 Blanchet et al (Wednesday 14h30)

M 12.1 Bertram et al (Thursday 10h30)

M 12.5 Girard et al (poster M557 Thursday)

J 02.2 Dorais et al (Poster J283 Wednesday)

J 02.1 Simjanovski et al (Poster J282 Wednesday)

Cold- Dry anomaly

Fast growing Ice

Aerosol lifting

Aerosol index

Process #1 – Adiabatic CoolingDynamics

Time Scale : ~ 6 – 24 hours

DT ≈ -10 to -20°C

Process #2 – Direct IR CoolingEmission from Ice Clouds

TIC-2B TIC-1

Time Scale : ~ 1 – 5 days

Heating Rate

0

2

4

6

8

10

12

14

16

18

20

-5.0 -4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0

00:00-03:00

03:00-06:00

06:00-09:00

09:00-12:00 TIC-2B

DT ≈ -3 to -8°C

TIC 1

0

2

4

6

8

10

12

14

16

18

20

-5.0 -4.0 -3.0 -2.0 -1.0 0.0 1.0 2.0 3.0

12:00-15:00

15:00-18:00

18:00-21:00

21:00-24:00 TIC-1

DT ≈ 0 to +2°C

Process #3 – Indirect IR CoolingEmission due to Lost Water Vapour

Time Scale : ~ 1 – 2 weeks

DT ≈ -5 to -10°C

PCP-Water ~ 1 mm Model Bias + 0.3 mm

DRY Anomaly

Net Effect of all 3 Processes

Total Cooling ≈ -30 to -40°C

TIC-2B TIC-1

Process #1: Dynamics

Process #2: Direct IR

Process #3: Indirect IRDry radiation

Dry adiabatic