Modeling 1

Air Quality Forecasting Gregory R. Carmichael University of Iowa USA

Getting better ? ….

Chemical

Figure 4

Courtesy John Reilly, MIT

Current CTMs Do Have Appreciable Skills In Predicting A Wide Variety Of Parameters INTEX B – STEM Forecasts

Courtesy John Reilly, MIT

CO

Figure 2

Figure 3

Courtesy John Reilly, MIT

O3

Figure 5

Courtesy John Reilly, MIT

NOy

Figure 6

Courtesy John Reilly, MIT

Figure 8

Aerosols

Figure 9

Tracking Air Masses During INTEX-B

Figure 15

Experiments such as TRACE-P and ACE-Asia employ mobile “Super-Sites” and study pollution outflow from source regions 50 N

40 N

China

Latitude

30 N

20 N

10 N DC-8 Flights P-3B Flights 0N 110 E

Spring 2001 120 E

130 E

140 E

Longitude

150 E

160 E

Post-mission analysis has shown that the inventory seems good for most species, except for high CO and BC observations in the Yellow Sea Model under-prediction

(Carmichael et al., JGR, 2003)

Comparison of New CO Inventory with Trace-P

Million tons

180

New estimate

160

Others Biomass Burning Transport Domestic Fossil Fuels

140

Domestic Biofuels Industry

120

Power

100

80

60

40

20

0 1999

2001

Trace-P

Ensemble Forecasting of Air Quality OZONE

* Persistence

* Single Forward Model w/o assimilation * Ensemble forecast (8 models) w/o assimilation (further improvements with bias corrections based on obs) McKeen et al., JGR, 2005

Ensemble Forecast Evaluation During Major Field Experiments PM2.5 Remains a Challenge O3

NEAQS2004

PM2.5 TexAQS -2006

McKeen et al., JGR, 2005 & 2009

Regional-Scale Chemical Analysis for Air Quality Modeling: A Closer Integration Of Observations And Models Transport Meteorology

Optimal analysis state

Chemical kinetics

CTM

Data Assimilation

Observations

Aerosols

Emissions

Improved: • forecasts • science • field experiment design • models • emission estimates • S/R relationships

Assimilation of ICARTT Ozone Observations -- Assessing Information Content

Assimilation Produces An Optimal State Space the importance of measurements above the surface!

w/o assimilation

with assimilation

Ozone predictions Example July 20, 2004

with

w/o assimilation

Chai et al., JGR 2007

Information below 4 km most important

Region-mean profile

Rapid Updates of Emissions Are Needed

Scaling factors

MILAGRO • Experimental Setup • Model performance and improvements. • Regional Effect on Ozone Production Regimes. • Effect of aerosol loading in ozone formation. • Conclusions Photo Credit: Frank Flocke.

Operational forecast products Emissions inventories •Mexico City anthropogenic emissions (UNAM, 3km, 1998) •NARSTO (36km) and EDGAR (100km) for rest of domain. •BEIS 3 Biogenic emissions. Forward modeling •60km (100x64) •12km (110x95) •60km tracer model •WRF V2.1.2 offline meteorology •18Z GFS from NCEP. •Boundary Conditions •RAQMS Global model.

60km tracer

60km Chemistry

12km chemistry

Adjoint analysis.

The high-resolution simulation yield different results not only due to the improved resolution of emissions, but also due to the terrain/landuse and wind field.

1000 800

6000

600 4000

400

2000

200 0

0 16

18

200

20 22 T IM E (U T C )

(p p b v )

4000

40

2000

O

80

2000 0

0 18

20 22 T IM E (U T C )

24

18

20 22 T IM E (U T C )

24

26

10000

F lig h t A ltit u d e O b s e rv e d 6 0 k m P r e d ic tio n 1 2 k m P r e d ic tio n

80

8000

(p p b v )

4000

10

0 16

60

6000

y

6000

16

0

40

4000

20

2000

N O

20

8000 6000

100

8000

10000

F lig h t A ltitu d e O b s e rv e d 6 0 k m P r e d ic tio n 1 2 k m P r e d ic tio n

120

10000

F lig h t A lt it u d e O b s e rv e d 6 0 k m P r e d ic tio n 1 2 k m P r e d ic tio n

A ltitu d e (m )

P r o p a n e (p p b v )

30

26

3

160

24

A ltitu d e (m )

C O (p p b v )

DC-8 flight #5 on 03/11

8000

26 0

0 16

18

20 22 T IM E (U T C )

24

26

A ltitu d e (m )

Resolution effect on model predictions

10000

F lig h t A ltitu d e O b s e rv e d 6 0 k m P re d ic tio n 1 2 k m P re d ic tio n

A ltitu d e (m )

1200

Experiments Like INTEX-A/ICARTT, INTEX-B/MILAGRO and Ace-Asia/Trace-P Provide Observations of Megacity Interactions at Various Scales "

Legend Texas

Legend

NOy (ppt)

March 19,2006 #

"

""

Filled Contours

Coahuila

MTBE (ppb)

Texas

0 - 0.001

Coahuila

155 - 300

0.001 - 0.002

300 - 500

##Nuevo Leon

500 - 700

"" ""Nuevo Leon

"

700 - 900

0.002 - 0.004

"

0.004 - 0.008 0.008 - 0.016

900 - 1,200

0.016 - 0.02

1,200 - 1,600

Tamaulipas

Zacatecas

Durango

2,000 - 3,000

0.02 - 0.04

Tamaulipas

1,600 - 2,000

0.04 - 0.06 Zacatecas

0.06 - 0.1

3,000 - 4,000

0.1 - 0.15

4,000 - 5,000 Zacatecas Aguascalientes

##

San Luis Potosi

#

# #

Michoacan

7,000 - 11,000

#

Jalisco Guanajuato

NOy

Aguascalientes

Guanajuato

Jalisco

""

"

" Michoacan

#

"

""

Yucatan

"

Hidalgo

""" "

Guerrero

1 - 2.2

"

Queretaro

#

Mexico Tlaxcala Distrito FederalPuebla

0.4 - 1

Veracruz

Campeche

Puebla

0.3 - 0.4

MTBE

"

Hidalgo

#

""

San Luis Potosi

Yucatan

Veracruz Queretaro

0.15 - 0.3

5,000 - 7,000

Data sources: Flocke & Abel

"

Mexico Distrito Federal Guerrero

Guerrero

Morelos

"" Campeche

Tlaxcala

" Puebla

"

"

Tabasco

Oaxaca

"

""

" "

"

""

"

Mena et al., 2008

MC Influence: March 11, VOC limited conditions. 10000

10000

1000

D C 8 F lig h t 3 . M a r c h 1 1 A ltitu d e o b s e rv e d fo re c a s t p o s t - a n a ly s is

8000

100

D C 8 F lig h t 3 . M a r c h 1 1 A ltitu d e o b s e rv e d m o d e le d p o s t - a n a ly s is

8000

NOy

80

100

4000

6000

60

4000

40

2000

20

N O y(p p b v )

A ltitu d e ( m )

O 3 /N O y r a tio

A ltitu d e ( m )

6000

10

O3/NOy

2000

0

0

1 12

13

14 lo c a l tim e ( h )

15

16

0 12

13

14 lo c a l tim e ( h )

15

16

Adjoint sensitivity on point along DC-8 path Originated in Mexico City. When MC outflow influences region, VOC lim conditions can be encountered. Modeled O3/NOy

Impact of Aerosols on Mexico City Photochemistry – MILAGRO Period

J-NO2 % Difference (without aerosol – with)/with Mena et al., ACP, 2009

Source Attribution at Global (and All) Scales is Becoming More Important (We need better tools) Air Quality Standards - Trends

Pollutant A

Concentration

NATIONAL Local

Regional

International Natural Local Regional/Domestic Distant

Background

Distance/Time From Sources

Source Attribution is an Important and Challenging Problem Source/Receptor Relationships

Pollution Distributions

How Important Are Distant Sources To Local Air Quality? Model estimates for surface O3 pollution Annual mean surface O3 change from 20% Perturbation in NOx+CO+NMVOC regional anthrop. emissions

Source region:

NA NAEU EUEA EASA SAsum sum3 of 3 foreign regions sum3 Similar impact from 3 foreign regions

ppb

ppb

1.4 1.4 1.2 1.2 1 1 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0 0 region: NA Receptor NA NA 0.45 Import sensitivity:

EU

EEA Asia

EU EAREGION SA 0.75 0.65 RECEPTOR

SSA Asia 0.45

EU Task Force on Hemispheric Transport of Pollutants. Interim Assessment Report 2008 http://www.unece.org/env/eb/Air.Pollution%20Studies.No.16.Hemispheric%20Transport.pdf

RECEPTOR REGION

NASA ARCTAS MISSION 2008 Tagged CO Along Flight Paths

S/R relations Arctic

Photosynthetic control of atmospheric carbonyl sulfide during the growing season Campbell, J.E.,1, Carmichael, G.R.1, Chai, T.1, Mena-Carrasco, M.1, Tang, Y.1, Blake, D.R.2, Blake, N.J.2, Vay, S.A.3, Collatz, G.J.4, Baker, I.5, Berry, J.A.6 Montzka, S.A.7, Sweeney, C.8, Schnoor, J.L.1, Stanier, C.O1 1-University of Iowa, 2-University of California, Irvine, 3-NASA Langley Research Center, 4-NASA-Goddard Space Flight Center, 5-Colorado State University, 6-Carnegie Institution of Washington, 7-NOAA Earth System Research Laboratory,8- University of Colorado.

A)

B)

12 Observa tion

Observa tion 10

Model GPP

Model

Model NPP

8 6

) m e(k d ltiu A

Climate models incorporate photosynthesisclimate feedbacks, yet we lack robust tools for large-scale assessments of these processes. DC8 airborne observations of COS and CO2 concentrations from NASA INTEX A experiment were analyzed during the growing season over North America with a 3-D atmospheric transport model. The persistent vertical drawdown of atmospheric COS was successfully modeled using the quantitative relationship between COS and photosynthesis that has been measured in plant chamber experiments. These results provide quantitative evidence that COS gradients in the continental growing season may have broad use as a measurement-based photosynthesis tracer.

4 2 0 365

370 CO2 (ppm)

375

380

405

430

455

480

COS (ppt)

COS is best modeled using photosynthesis uptake model (GPP), while CO2 reflects both uptake and respiration (NPP). The use of COS along with CO2 measurements allows the estimation of the individual fluxes due to photosynthesis and respiration, which can not be done using CO2 measurements by themselves. Data obtained from the DC 8 INTEX-A flights.

PACDEX Hiaper Air Craft Flight Path

Flight Tracks – Back Trajectories Animation

Flight 4: Japan to Alaska D . C . B. A .

D C A B

Flight 4: Japan to Alaska

A .

B .

D .

C .

D A

C B

Flight 4: Japan to Alaska

A .

B .

D .

C .

D A

C B w/o China

PACDEX Flight 3 May 2nd 2007 STEM Aerosol Japan to Japan At low altitudes you have higher concentration of marine air (Sea Salt aerosol) At high altitudes the aerosol contribution is mainly from dust At mid-altitudes the aerosol contribution is mainly from continental pollution/dust 2.0

10

2.0

1.5

50

20

20

1.5

15

0

15

1.0

3

5

10

3

3

Altitlude (x10 m)

3

3

10

Total Dust (ug/m )

0.5

Sulfate (ug/m )

0.0

3

-20

0.5

Total BC (ug/m )

0

-10

Total OC (ug/m )

Sea Salt (ug/m )

1.0

0.0 -0.5

-50 -30

-1.0

-100

-1.5

-40 122.0

122.1

122.2

122.3 122.4 Jullian Day

122.5

5 -0.5

-1.0 122.6

0

-5

0 122.7

PACDEX Flight 3 May 2nd 2007 Measured CCN comparison to STEM aerosol data 2000

2.0

A

B

2.0

1.5

50 0

1.5

15

5

3

3

3

3

Sea Salt (ug/m )

10

-0.5 -4000

5

-6000 122.0

-1.5 122.1

122.2

122.3

122.4 Jullian Day

122.5

-0.5

-1.0 122.6

Total Dust (ug/m )

10

0.0

-1.0

-100

0.5

3

0.0

Total BC (ug/m )

0.5

Sulfate (ug/m )

CCN Concentration (#/cm )

1.0

3

-50

-2000

20

15

1.0

Total OC (ug/m )

0

20

0

-5

0 122.7

PACDEX Flight 3 May 2nd 2007 Measured CCN comparison to STEM aerosol data 2000

C

Cloud 2.0

D

2.0

1.5

50 0

1.5

15

5

3

3

3

3

Sea Salt (ug/m )

10

-0.5 -4000

5

-6000 122.0

-1.5 122.1

122.2

122.3

122.4 Jullian Day

122.5

-0.5

-1.0 122.6

Total Dust (ug/m )

10

0.0

-1.0

-100

0.5

3

0.0

Total BC (ug/m )

0.5

Sulfate (ug/m )

CCN Concentration (#/cm )

1.0

3

-50

-2000

20

15

1.0

Total OC (ug/m )

0

20

0

-5

0 122.7

PACDEX Flight 3 May 2nd 2007

CCN aerosol from continental pollution/dust sources

Measured CCN comparison to STEM aerosol data

E

2000

2.0

2.0

1.5

50 0

1.5

15

5

3

3

3

3

Sea Salt (ug/m )

10

-0.5 -4000

5

-6000 122.0

-1.5 122.1

122.2

122.3

122.4 Jullian Day

122.5

-0.5

-1.0 122.6

Total Dust (ug/m )

10

0.0

-1.0

-100

0.5

3

0.0

Total BC (ug/m )

0.5

Sulfate (ug/m )

CCN Concentration (#/cm )

1.0

3

-50

-2000

20

15

1.0

Total OC (ug/m )

0

20

0

-5

0 122.7

Our Analysis Approach

Prediction/ Analysis State

Data Assimilation

Documenting improvement (ICART) 0.14

probability

0.12

Forecast

0.1

NEI2001- Frost LPS NEI2001FrostLPS*

0.08 0.06 0.04 0.02 0 -100 -80 -60 -40 -20

0

20

40

60

80 100

% O3 bias

Left: Quantile-quantile plot of modeled ozone with observed ozone for DC-8 platform, data points collected at altitude less than 4000m, STEM-2K3, Forecast: NEI 1999, Post Analysis: NEI2001-Frost LPS*. MOZART-NCAR boundary conditions Right: Probability distribution of % ozone bias for Forecast (NEI 1999) and post analysis runs (NEI2001-FrostLPS and NEI2001FrostLPS*) for DC-8 measurements under 4000m. Mena et al., JGR, 2007

NAS 2008 www.nap.edu/catalog.php?record_id=12540

What’s wrong with these pictures?

Soil Moisture Networks

PM2.5

O3

Common Measurement Needs Threads

X important gaps may exist;

so inadequate that no

Observations Priorities Stemming from Common Threads MOST NEEDED:

 Height of the planetary boundary layer  Soil moisture and temperature profiles  High resolution vertical profiles of humidity  Measurements of air quality and atmospheric composition above the surface layer NEEDED:  Direct and diffuse radiation  Vertical profiles of wind  Sub-surface temperature profiles (e.g., under pavement)  Icing near the surface  Vertical profiles of temperature  Surface turbulence parameters

61