Recent Advances in the Use of Chemical Transport Models in Atmospheric Chemistry Studies Greg Carmichael, University of

Iowa

The University of Iowa, USA

Characterization of Urban Signals

Science Support to Policy

UnderstandingUnderstandingUnderstanding

Field Experiments

Field Field ExperimentsExperiments

Long-termMonitoring

LongLong-- termtermMonitoringMonitoring

Satellites &Data Systems

Satellites &Satellites &Data Systems Data Systems

Regional and Global Simulations

Regional and Global Regional and Global SimulationsSimulations

PollutionPrediction

PollutionPollutionPredictionPrediction

PollutionDetection

PollutionPollutionDetectionDetection

Enhanced Enhanced Quality Quality of Lifeof Life

InformedInformedPolicyPolicy

DecisionsDecisions

ProcessProcessStudiesStudies UnderstandingUnderstandingUnderstanding

Field Experiments

Field Field ExperimentsExperiments

Long-termMonitoring

LongLong-- termtermMonitoringMonitoring

Satellites &Data Systems

Satellites &Satellites &Data Systems Data Systems

Regional and Global Simulations

Regional and Global Regional and Global SimulationsSimulations

PollutionPrediction

PollutionPollutionPredictionPrediction

PollutionDetection

PollutionPollutionDetectionDetection

Enhanced Enhanced Quality Quality of Lifeof Life

InformedInformedPolicyPolicy

DecisionsDecisions

ProcessProcessStudiesStudies

Models are an Integral Part of Atmospheric Chemistry Studies

• Flight planning• Provide 4-Dimensional context of the

observations• Facilitate the integration of the different

measurement platforms • Evaluate processes (e.g., role of biomass

burning, heterogeneous chemistry….)• Evaluate emission estimates (bottom-up

as well as top-down)• New comprehensive data sets to

test/evaluate models

TRACE-P/Ace-Asia EXECUTION

Emissions-Fossil fuel-Biomass burning-Biosphere, dust

Long-range transport fromEurope, N. America, Africa

ASIA PACIFIC

Satellite datain near-real time:MOPITTTOMSSEAWIFSAVHRRLIS

3D chemical model forecasts: - x - GEOS-CHyEM - CFORS - z

FLIGHTPLANNING

Boundary layerchemical/aerosolprocessing

ASIANOUTFLOW

Stratosphericintrusions

PACIFIC

Two aircrafts – DC8DC8 and P3P3

Chemical evolution during continental outflow, biomass burning, dust outbreaks, and urban urban plumesplumes

2222 flights out of Hong Kong, Okinawa and Tokyo

O3, CO, SOx, NOx, HOx, RH and J

100m to 12000m

110 E 120 E 130 E 140 E 150 E 160 E

Longitude

0 N

10 N

20 N

30 N

40 N

50 N

Lat

itu

de

DC-8 FlightsP-3B Flights

China

NASA GTE TRACE-P Mar’01-Apr’01

CFORS/STEM-2K1 Model Data Flow Chart

Large-scale Meteorological

Fields (JMA, NCEP, ECMWF

CFORS/RAMS

STEM-2K1On-Line TUV

wind velocities, temperature, pressure,water vapor content, cloud water content, rain water content and PV etc.

Dust, Sea Salt, Lightning NOx

BiogenicEmisisons

Emission Preprocessor

Biomass Emissions

Volcanic SO2 Emissions

Anthropogenic Area Emissions

Fuel/activity info

Large PointSources

Satellite Observations (fire counts, ozone columns, sea surface temperature, etc.)

ForecastsOr PostAnalysis

Tracers/MarkersTracers/Markers::SO2/Sulfate DMS

BC OC

Volcanic Megacities

CO fossil CO-Biomass

Ethane Ethene

Sea Salt Radon

Lightning NOx Dust 12 size bins

Ace-Asia & Trace-P Focused on Asian Outflow

March 2, 2001

March 7, 2001March 6, 2001

March 5, 2001

March 4, 2001March 3, 2001

March 10, 2001March 9, 2001March 8, 2001

110 115 120 125 130 135 1400

1

2

3

4

5

6

7

CO Scale(ppbv)300+250 to 300200 to 250150 to 200100 to 15050 to 100

110 115 120 125 130 135 1400

1

2

3

4

5

6

7

K(ug/m3)1+0.8 to 10.6 to 0.80.4 to 0.60.2 to 0.40 to 0.2

Fight Planning: Frontal outflow of biomass burning plumes E of Hong Kong

Observed CO –Sacshe et al.

Observed aerosol potassium - Weber et al.

Biomass burning CO forecast

Longitude

100 ppb

2 4 6 8 10T IM E (G M T )

0

200

400

600

800

1000

CO

(pp

bv)

0

2000

4000

6000

Alt

itu

de

(m)

ObservedModeledFlight Height

2 4 6 8 10T IM E (G M T )

4 0

6 0

8 0

1 0 0

O3 (

pp

bv)

0

2000

4000

6000

Alt

itu

de

(m)

ObservedModeledFlight Height

2 4 6 8 10T IM E (G M T )

0

0.5

1

1.5

2

2.5

NO

2 (pp

bv)

0

2000

4000

6000

Alt

itu

de

(m)

ObservedModeledFlight Height

2 4 6 8 10T IM E (G M T )

0

2

4

6

8

10

SO2 (

ppbv

)

0

2000

4000

6000

Alt

itu

de

(m)

ObservedModeledFlight Height

2 4 6 8 10T IM E (G M T )

0

1

2

3

4

5

SO4 (

ppbv

)

0

2000

4000

6000

Alt

itu

de

(m)

ObservedModeledFlight Height

P-3B

SO2

How Well Do Models Capture the Observed Features?

P-3B

0.00

0.20

0.40

0.60

0.80

1.00

1.20

Te

mp

era

ture

H2

O

Win

d S

pe

ed

O3

SO

4

J[O

1D

]

SO

2

PA

N

Eth

en

e

Pro

pa

ne

CO

J[N

O2

]

Eth

an

e

No

y

Eth

yn

e

RN

O3

Be

nze

ne

+ T

olu

en

e

OH

AO

E

HN

O3

NO

2

NO

Co

rrela

tio

n C

oeff

icie

nt

R(<1KM)

R(1-3KM)

R(>3 KM)

Predictability – as Measured by Correlation Coefficient

Met Parameters are Best

Uncertainty analysis has revealed wide differencesin our knowledge of the emissions of particular

species in particular parts of Asia …

0%

100%

200%

300%

400%

500%

600%

700%

800%

900%

China Japan Other EastAsia

SoutheastAsia

India OtherSouth Asia

Ships All Asia

(95%

Con

fiden

ce In

terva

l,

? )SO2

NOx

CO2

CO

CH4

VOC

BC

OC

NH3

CO and some other species are sometimes systematically underpredicted …

What does this tell us about the model –

Model deficiency?

Emissions problem?

Back Trajectories from High CO points.

--- CO > 700

--- CO > 600

--- CO > 500

--- CO > 450

--- CO > 400

Back Trajectories from High CO point(Zoom & CO > 500 ppbv)

--- CO > 700

--- CO > 600

--- CO > 500

06GMT, April 07, 2001

06GMT, April 11, 2001

Ace Asia – Focus on Aerosols

April 9 April 12

Asian Outflow: Complex Mixture of Dust, Bio, and

Fossil Components

Data: Sugimoto, et al.

Poster: A11A0060

Sataka et al.

Ron Brownobserved high AOD

in Japan Sea

14

12

10

8

6

4

2

0

El A

l &

Ca (

ug/m

3)

10510095908580

Jday (UTC)

400

300

200

100

0

DU

ST_C

FO

RS (

ug/m

3)

totElAl 'DUST-fine_CFORS' 'DUST-all_CFORS' totElCa

Takla makan

Gobi

Log10(Dust)

AOD

Observed Al and model dust

Lidar Ext.Of Ron Brown= 0.2/kmThis may be Miyakejima sulfate

Plate 2

BC

SO4

BC at surface BC C-130 #11 April 19th

What Do The Observations and Models Tell Us About Asian Emissions?

We ran back-trajectories from each 5 minute leg of merge data set, and kept track of each time a trajectory passed

over a grid cell of the city and below 2 km.Classification of trajectory by the

Source of Megacity. Age as determined by

trajectory is also shown

Before

Big difference !!!

We catch more number of fresh airmass from Shanghai and Seoul.

Beijing

y = 0.0079x - 1R2 = 0.4348

y = 0.0074x - 1R2 = 0.9076

0

0.2

0.4

0.6

0.8

1

1.2

1.4

75 125 175 225 275 325

CO Concentration

BC

Co

ncen

trati

on

Comparing Modeled and Measured Ratios: We extracted all points associated with a specified city and calculated measured and modeled ratios.

0.0148Emission

0.77070.0076Model

0.026180.0186ObsQingdao

0.0159Emission

0.32580.0072Model

0.06351-0.016ObsPusan

0.0193Emission

0.94120.0205Model

0.87930.0226ObsTokyo

0.014Emission

0.64120.0084Model

0.82660.0102ObsTianjian

0.0083Emission

0.87720.0092Model

0.95560.0107ObsShanghai

R-squareRatio

0.0148Emission

0.77070.0076Model

0.026180.0186ObsQingdao

0.0159Emission

0.32580.0072Model

0.06351-0.016ObsPusan

0.0193Emission

0.94120.0205Model

0.87930.0226ObsTokyo

0.014Emission

0.64120.0084Model

0.82660.0102ObsTianjian

0.0083Emission

0.87720.0092Model

0.95560.0107ObsShanghai

R-squareRatioBC/CO

This analysis suggests that there emissions may be related to an underestimation of a specific sector.

The Importance of Fossil, Biofuels and Open Burning Varies by Region -- Richness of Emissions Data Base

Can be Exploited

Using Measurements and Model – We Estimate Contributions of Fossil, Biofuel and Open Burning Sources

The Informatics Problem: Chemical Mass Balance

Source Information Air Mass Markers

Model Runs w/wo Source Sectors

How is Photochemistry Impacted By These Aerosols? 1) Photolysis

Impacts

Surface reflection

Ice cloud

Water cloud

EP/TOMS Total Ozone (Dobson)

DustBlack CarbonOrganic CarbonSulfateOther PM2.5 and Other PM10

Sea Salt

absorption by gas-phase species O3, SO2 and NO2

Inputs from STEM 3-D field

STEM TOP15km

O3 (Dobson) below STEM top height

TUV TOP80km

Overtop O3 =

Output:30 kinds of J-valuesfor SAPRC99mechanism

Framework for Analyzing Chemistry/Aerosol Interactions: Model (STEM+TUV) + Laboratory Studies + Field Experiment

Heterogeneous rxns on dust for NOx, O3, SO2, HNO3

Cloud Top Temperature (°C)

Flight Altitude (m)

A example: TRACE-P flights on March 27

DC-8 #15

P-3 #17

2 3 2 4 2 5 2 6 2 7 2 8 2 9 3 0 3 1 3 2 3 3TIM E (G M T)

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

J[N

O2]

(1/

s)

0

4000

8000

12000

Alti

tude

(m

)

O bserved

N O R M AL

N O AO D

C LEAR SKY

Flight A ltitude

0 1 2 3 4 5 6 7 8 9TIM E (G M T)

0

0.005

0.01

0.015

J[N

O2]

(1/

s)

0

1000

2000

3000

4000

5000

Alti

tude

(m

)

O bserved

N O R M AL

N O AO D

C LEAR SKY

Flight A ltitude

P-3 flight #17: volcanic plume observation DC-8 flight #15: frontal study

DC-8 J[NO2]

P-3 J[NO2]

2 2.4 2.8 3.2 3.6 4TIME (GMT)

5 2

5 6

6 0

6 4

6 8O

3 (p

pb

v)

0

400

800

1200

1600

2000

Alt

itu

de

(m)

ObservedNORMALNOAODCLEARSKYFlight Altitude

Figure 13. Observed and simulated ozone concentrations under the three conditions along with the flight paths of P3 #17 from 2 to 4 GMT

Aerosol Effects Dominate Ozone Formation Under This Case

How is Photochemistry Impacted By These Aerosols? 1) Photolysis Impacts

0 2 4 6 8T IM E (G M T )

0

1000

2000

3000

4000C

oars

e D

ust

(u

g/st

d m

3 )

0

2000

4000

6000

8000

Fli

ght

Alt

itud

e (m

)

O b serv ed C o a rse P a rtic leS im u la ted C o a rse D u stF lig h t A ltitu d e

O b serv ed a n d S im u la ted D u st in C -1 3 0 F lig h t # 6 (04 /1 1 /2 0 0 1 )

0 2 4 6 8T IM E (G M T )

0

0.0002

0.0004

0.0006

0.0008

AO

E @

550

nm

(/m

)

0

2000

4000

6000

8000F

ligh

t A

ltit

ud

e (m

)

O b serv ed A O ES im u la ted A O E w ith D u stS im u la ted A O E w ith o u t D u stF lig h t A ltitu d e

O b served an d S im u la ted A O E in C -130 F ligh t #6 (04 /11 /2001 )

How is Photochemistry Impacted By These Aerosols? 2) Heterogeneous Rxns:Direct Ozone Loss?

Dust

BC

Sulfate

Lessons Learned

• Asian outflow is complex, characterizing it requires 4-dimensional measurements.

• Impacts on chemistry and radiation appear to be important.

• Closer integration of emissions, models and measurements is necessary to better quantify our understanding of Asian aerosols.

Through a NSF ITR Grant we are developing data assimilation tools – we have a 3-d version ready for application

We are Developing General Software Tools to Facilitate the Close Integration of Measurements and Models

The framework will provide tools for: 1) construction of the adjoint model; 2) handling large datasets; 3) checkpointing support; 4) optimization; 5) analysis

of results; 6) remote access to data and computational resources.

We Have Now a Full 4d-VAR Version of STEM and are beginning to use it For Ace-Asia/Trace-P Analysis

Recent Papers• Daescu, D., and G. Carmichael, An adjoint sensitivity

method for the adaptive location of the observations in air quality modeling, Journal of the Atmospheric Sciences, Vol. 60, No. 2, 434-450 (2003)

• Damian, V., A. Sandu, M. Damian, F. Potra, and G. Carmichael, The Kinetic Preprosesor KPP – a Software Environment for Solving Chemical Kinetics, Computers & Chemical Engineering, 26, 1567-1579 (2002)

• Sandu, A., D. Daescu, and G. Carmichael, Direct and Adjoint Sensitivity Analysis of Chemical Kinetic Systems with KPP: Theory and Software Tools, Atmospheric Environment, in review, 2003

Thoughts on the Summer 2004 Experiments

UI/CGRER Focus: Improving Forecasting and Analysis through Closer Integration of

Observations and ModelsFlight Planning

Air Quality Quick-look

Post-Mission

Test:

•Our ability to forecast 4-dimensional distributions of ozone and PM

•The utility of forecasts of ozone, fine particles in flight planning and quick-look analysis

•The utility of why-cast products (e.g., O3-production, VOC vs NOx limited regions, influence functions, hydrocarbon reactivity….) in flight planning and analysis and air quality forecasting

•Our ability to assimilate surface chemical observations into the forecasts; the impact of assimilation on the forecasts (for a sub-region; e.g., the NE)

•Targeted measurements that explore the concept of aircraft as “mobile super-sites”

Forecasting Analysis

U. Iowa/Kyushu/Argonne/GFDL

With support from NSF, NASA (ACMAP,GTE), NOAA, DOE