Modeling BC Sources and Sinks- research plan

Charles Q. Jia and Sunling GongUniversity of Toronto and Environment Canada

@ 1st annual NETCARE workshop

Outline Objective and approach GEM-MACH Canadian Aerosol Module (CAM) An example: Arctic BC seasonal

variation

Objective and Approach 1. To enhance the capacity of GEM-MACH in

forecasting BC in the Arctic • incorporating the science from NETCARE to better

represent BC processes in the CAM (e.g. BC aging)• using the measurement data from NETCARE to

validate the model2. To better understand the sources and sinks of BC in

the Arctic region• using the enhanced GEM-MACH • focusing on relative contributions of various

sources (e.g. natural vs. anthropogenic) and long-range transport

• GEM: Global Environmental Multi-scale Model– An operational numerical weather forecasting model– Developed by Meteorological Service of Canada (MSC)

[Cote et al., 1998; Yeh et al., 2002]. • MACH: Two air quality modules (ADOM and CAM)

– The Acid Deposition and Oxidants Model (ADOM) is an integrated gas-phase chemistry module [Venkatram et al., 1988].

– The Canadian Aerosol Module (CAM) simulates physical and chemical processes of size-resolved aerosol in the atmosphere [Gong et al., 2003]

GEM-MACH Global Environmental Multiscale

Modeling Air Quality and CHemistry

GEM-MACH Structure

SMOKERegional DataCanada & US

SMOKERegional DataCanada & US

Gas PhaseChemistry

Gas PhaseChemistry

CAMCanadian Aerosol Module

CAMCanadian Aerosol Module

GEM

Met

eoro

logy

Tra

nspo

rt

Emis

sion

Inte

rfac

eEm

issi

on In

terf

ace

Chem

istr

y In

terf

ace

Chem

istr

y In

terf

ace

Global Emissions

Global Emissions

Canadian Aerosol Module (CAM)

• Simulates physical and chemical processes of aerosols in the atmosphere [Gong et al., 2003]• Emissions, in-cloud and below-cloud scavenging, dry

deposition, coagulation, condensation, nucleation et al.

• Size-resolved: 12 particle size bins (0.01 to 41 μm in diameter)

• Multi-component: 5 species (BC, OC, sulphate, sea salt, soil dust)

An example: importance of depositional processes in

seasonal variation of the Arctic BC

(Huang L. et al., JGR, V115, D17207, 2010)

Seasonal Variation of the Arctic BCModel Simulation vs. Observation

(surface BC)

8

BC

(pp

tm)

Obs. (Sharma et al., 2006)

11-model predictions

[Shindell et al., 2008]

Barrow

ZeppelinAlert

Red- IMPROVE site at Barrow (1996-1998)

9

Enhanced In-cloud Scavenging Parameterization

)1( tcloudin

cloudin

et

F

Cdt

dC

Original (empirical)

[Giorgi and Chameides, 1986]

Enhanced

F – GEM cloud cover

P – GEM precipitation

air

P

z L

0

0

(cloud cover)QF

FQ L

0

0 0

(rate of precip. formation)Q

F F L

Effects of Enhanced Parameterization of In-cloud Scavenging

10

Correlation Coefficient, R

before after

Alert 0.49 0.91

Barrow -0.51 0.58

Zeppelin -0.13 0.77

Feb Apr Jun Aug Oct Dec

0

30

60

90

120

Observed Original In-cloud scavenging Enhanced In-cloud scavenging

BC

(ng/

m3)

(a) Alert (2001)

Feb Apr Jun Aug Oct Dec

0

10

20

30

40

50

60

BC

(ng/

m3)

(b) Barrow (2001)

Feb Apr Jun Aug Oct Dec

0

30

60

90

BC

(ng/

m3)

(c) Zeppelin (2001)

Observations at Zeppelin: Eleftheriadis et al., 2009

11

Enhanced below-cloud scavenging parameterization

)(,360010 3

tp

cloudbelow

cloudbelow

ufED

pE

Cdt

dC

Original

Valid when Re < 0.1

Enhanced

, (any Re)

Re – [Feng, 2007]

pa

at du

Re

a

papt

gdu

18

)( 2

12

Effects of Enhanced Parameterization of Below-cloud Scavenging

Correlation Coefficient, R

beforeafter

Alert 0.91 0.95

Barrow 0.58 0.50

Zeppelin 0.77 0.81

Feb Apr Jun Aug Oct Dec

0

30

60

90

120

Observed Original below-cloud scavenging Enhanced below-cloud scavenging

BC

(ng/

m3)

(a) Alert (2001)

Feb Apr Jun Aug Oct Dec

0

10

20

30

40

50

60

BC

(ng/

m3)

(b) Barrow (2001)

Feb Apr Jun Aug Oct Dec

0

30

60

90

BC

(ng/

m3)

(c) Zeppelin (2001)

Thanks!