recent advances in the use of chemical transport models in atmospheric chemistry studies greg...
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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