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Atmospheric Chemistry and Transport Modelling: Introduction and current activities at CETEMPS
(also involving satellite data)
Gabriele Curci
CETEMPS – Dip. Fisica, Università degli Studi dell’[email protected]
28 Jan. 2010Università Tor Vergata, Roma
Dipartimento di Informatica, Sistemi e Produzione
OUR MULTI-DISCIPLINARY INTERACTIVE CLIMATE SYSTEM:A REMARKABLE “PlayStation” FOR SCIENTISTS!
[IPCC, 2007]
[IPCC, 2007]
Understanding of atmospheric
composition is key to understanding of
climate change …
[EEA, 2008]
… air quality,
and much more!
Year 2007
HOW TO MODEL ATMOSPHERIC COMPOSITION?Solve continuity equation for chemical mixing ratios Ci(x, t)
Fires Landbiosphere
Humanactivity
Lightning
Ocean Volcanoes
Transport
Eulerian form:
ii i i
CC P L
t
U
Lagrangian form:
ii i
dCP L
dt
U = wind vector
Pi = local source
of chemical i
Li = local sink
ChemistryAerosol microphysics
[adapted from D. J. Jacob, Harvard]
Deposition
EULERIAN MODELS PARTITION ATMOSPHERIC DOMAIN INTO GRIDBOXES
Solve continuity equation for individual gridboxes
• Detailed chemical/aerosol models can presently afford -106 gridboxes
• In global models, this implies a horizontal resolution of ~ 1o (~100 km) in horizontal and ~ 1 km in vertical
This discretizes the continuity equation in space
• Chemical Transport Models (CTMs) use external meteorological data as input• General Circulation Models (GCMs) compute their own meteorological fields
[D. J. Jacob, Harvard]
OPERATOR SPLITTING IN EULERIAN MODELSReduces dimensionality of problem
i i i
TRANSPORT LOCAL
C C dC
t t dt
… and integrate each process separately over discrete time steps:
( ) (Local)•(Transport) ( )i o i oC t t C t
• Split the continuity equation into contributions from transport and local terms:
Transport advection, convection:
Local chemistry, emission, deposition, aerosol processes:
(
ii
TRANSPORT
ii
LOCAL
dCC
dt
dCP
dt
U
) ( )iLC C
These operators can be split further:• split transport into 1-D advective and turbulent transport for x, y, z (usually necessary)• split local into chemistry, emissions, deposition (usually not necessary)
[D. J. Jacob, Harvard]
SPLITTING THE TRANSPORT OPERATORMust account for sub-grid turbulence
• Wind velocity UU has turbulent fluctuations over time step t:( ) '( )t t U U U
Time-averagedcomponent(resolved)
Fluctuating component(stochastic)
1( )i i i
xx
C C Cu K
t x x x
• Further split transport in x, y, and z to reduce dimensionality. In x direction:
( , , )u v wU
• Split transport into advection (mean wind) and turbulent components:
1ii i
CC C
t
U K air density
turbulent diffusion matrix
K
advection turbulence (1st-order closure)
advectionoperator
turbulentoperator
[D. J. Jacob, Harvard]
VERTICAL TURBULENT TRANSPORT (BUOYANCY)
Convective cloud(0.1-100 km)
Model grid scale
Modelverticallevels updraft
entrainment
downdraft
detrainment
Wet convection is subgrid scale in global models and must be treated as a vertical mass exchange separate from transport by grid-scale winds.
Need info on convective mass fluxes from the model meteorological driver.
• generally dominates over mean vertical advection• K-diffusion OK for dry convection in boundary layer (small eddies)• Deeper (wet) convection requires non-local convective parameterization
[D. J. Jacob, Harvard]
LOCAL (CHEMISTRY) OPERATOR:solves ODE system for n interacting species
1,i n
1( ) ( ) ( ,... )ii i n
dCP L C C
dt C C C
System is typically “stiff” (lifetimes range over many orders of magnitude)→ implicit solution method is necessary.
• Simplest method: backward Euler. Transform into system of n algebraic equations with n unknowns
( ) ( )( ( )) ( ( )) 1,i o i o
i o i o
C t t C tP t t L t t i n
t
C C
( )ot tC
Solve e.g., by Newton’s method. Backward Euler is stable, mass-conserving, flexible (can use other constraints such as steady-state, chemical family closure, etc… in lieu of Ct ) But it is expensive. Most 3-D models use higher-order implicit schemes such as the Gear method.
For each species
[D. J. Jacob, Harvard]
TROPOSHERIC OZONE-NOx-HOx-CO-HC CHEMISTRY
NO
NO2
O3
OH
HO2
RO2
CO
CH4
VOC
H2O2HNO3
STRATOSPHERE
TROPOSPHERE
O2 O3
Dry depositionNOx
family
HOx family
ATMOSPHERIC COMPOSITION MODELS @ CETEMPS
MM5MM5http://www.mmm.ucar.edu/mm5/
Regional Scale Meteorological Model
Chimere ChimereChimerehttp://euler.lmd.polytechnique.fr/chimere/
Regional Scale Chemistry Transport Model
GEOS-ChemGEOS-Chemhttp://www.as.harvard.edu:16080/chemistry/trop/geos/
Global Scale Chemistry Transport Model
WRF/ChemWRF/Chemhttp://ruc.fsl.noaa.gov/wrf/WG11/
Regional Scale Meteorological-Chemistry
model
ForeChem: Experimental “Chemical Weather” Forecast
http://pumpkin.aquila.infn.it/forechem/ CURRENT VERSION
• European Domain, 0.5°x0.5°• Forecast 2 days ahead
(D-1 D+2)• Maps of max and mean of
PM10, PM2.5, O3, NO2, CO, SO2
• Animations 72-h
UNDER DEVELOPMENT
• Italian nested domain, 10x10 km
• Graphics• Historical archive
• NRT comparison with observations
DATA FLOW IN A CHEMISTRY-TRANSPORT MODEL
METEO FIELDS
Global or regional model (e.g. ECMWF,
MM5, WRF)
METEO FIELDS
Global or regional model (e.g. ECMWF,
MM5, WRF)
EMISSIONS
Anthropogenic and Biogenic/Natural sources of gas and aerosols
EMISSIONS
Anthropogenic and Biogenic/Natural sources of gas and aerosols
LANDUSE INFOLANDUSE INFO
BOUNDARY CONDITIONS
From larger scale CTM
simulations
BOUNDARY CONDITIONS
From larger scale CTM
simulations
Model Coresimulates transport, chemical and
deposition processes and solves continuity equation for chemical species
METEOROLOGICAL FIELDS DRIVE ADVECTION, TURBULENT VERTICAL DIFFUSION, REACTION RATES, BIOGENIC EMISSIONS AND DEPOSITION
Vertical LIDAR profile over Milan
Vertical model particulate profile
Model particulate chemical composition
Freshly emitted
pollutants mix up to the
PBL top
[Stocchi et al., in prep.]
LIDAR data by ISAC-RM
Advection of Saharan dust
Mixing and photochemical
formation
MM5 METEOROLOGICAL MODEL: NESTED DOMAINS TO INCREASE RESOLUTION
UrbanDryland CropsIrrigated CropsGrass & ShrubsForestsWaterWetlandTundra/BareIce
USGS12 km
4 km
BOLOGNA/urbanS. PIETRO
CAPOFIUME/rural
TEM
PERA
TURE
(°C)
WIN
D S
PEED
(m/s
)MM5 simulation vs. DEXTER observations
(June 2007)
T is underpredicted at night
Wind Speed is overestimated
AT URBAN SITES MODEL UNDERESTIMATES TEMPERATURE AND OVERESTIMATES WIND
MM5 “TUNABLE” LANDUSE PARAMETERS
Name Description Urban Dry Crops Wet Crops Grass Forest
ALBD Albedo 18 17 18 20 13
SLMO Soil Moisture 10 30 30 15 35
SFEM Surface Emissivity 88 92 92 91 94
SFZ0 Roughness length 50 15 16 12 50
THERIN Thermal Inertia 3 4 4 3 4
SCFX ? 0.52 0.60 0.60 0.60 0.52
SFHC Heat Capacity 18.9e5 25e5 25e5 20e5 30e5
SUMMER
The highlighted parameters are very different between urban to dry crops. Since dry crops category corresponds to mostly urbanized areas
we try to modify these parameters toward urban-like values
WIND SPEED IS SENSITIVE TO CHANGES TO SURFACE ROUGHNESS, WHILE TEMPERATURE IS INSESITIVE TO ALL PARAMETERS
TEMPERATURE (°C) WIND SPEED (m/s)
1-25 April 200515 km resolution
SYNOP data
Daily cycle
Accurate landuse info e.g. from satellite observations may be very important to improve meteorological simulation!
MODEL OF EMISSIONS OF GASES AND AEROSOLS FROM NATURE(MEGAN, GUENTHER ET AL., ACP 2006)
Base Emission Factor [mg/m2/h]
MODIS Leaf Area Index [m2/m2]
MM5 Shortwave Radiation [W/m2]
MM5 2-m Temperature [K]
MEGAN Isoprene Emission Rate [µg/m2/h]
Temporal resolution 1 hSpatial resolution 0.5°x0.5°
STATIC
MONTHLY HOURLY
HOURLY
Can be increased up to 1 km
Satellite info: LANDUSE, VEGETATION DENSITY, SOIL MOISTURE
SATELLITE OBSERVATIONS MAY CONSTRAIN NOx AND VOC EMISSIONSTHE CASE OF BIOGENIC ISOPRENE EMISSIONS FROM HCHO COLUMN
CITYFOREST
VOCVOC
NOxNOx
VOCVOC
VOCVOC
HCHOHCHO
NOxNOx
HCHOHCHO
HCHOHCHO
WIND
WIND
THIS HCHO IS WELL CORRELATED TO ITS
PARENT VOC!HCHO = FormaldehydeVOC = Volatile Organic Compound
TOP-DOWN CONSTRAINT OF EMISSIONS FROM SATELLITES RELIES ON BAYESIAN APPROACH
ISOPEK
Maximum a posteriori (MAP) solution for scalar EISOP:
(Forward model)
A posteriori solution:
aa EKgEE ˆ
222
2
/ a
a
Km
Kg
with gain:
A posteriori uncertainty:
mKa /)/(
11ˆ1
222
r = 0.81
K is fitted from theEISOP:HCHO
scatter plot calculated with CTM
EISOP [1012 molec cm-2 s-1]Ω
[1
016 m
olec
cm
-2]
Ω (HCHO column) : EISOPRENE
[Curci et al., in prep.]
MAP SOLUTION REDUCES THE A-PRIORI ERROR BECAUSE IT ADDS “PIECES OF INFORMATION” FROM OBSERVATIONS
A = g · K = averaging kernelds = tr(A) = degrees of freedom of signal or pieces of info
Monthly mean map of “pieces of information” in OMI HCHO observations
(Jul 2005)
[Curci et al., in prep.]
MAP SOLUTION REDUCES THE A-PRIORI ERROR BECAUSE IT ADDS “PIECES OF INFORMATION” FROM OBSERVATIONS
-15%
[Curci et al., in prep.]
MODEL HCHO BIAS QUANTITATIVELY TRANSLATED INTO CORRECTION TO UNDERLYING ISOPRENE EMISSIONS
OMI corrects model bias over
Balkans and Spain
[Curci et al., in prep.]
CTM MAY THEN BE USED TO EVALUATE THE IMPACT OF EMISSIONS ON AIR POLLUTANT LEVELS
[Curci et al., 2009]
Large episodic contribution from BVOC emissions to ozone throughout the Mediterranean basin
Up to 100 µg/m3 in one extreme case in Spain!
Observations from EMEP and AirBase databases
!
SATELLITE DATA MAY BE INTEGRATED IN CTMs ALSO IN “DATA ASSIMILATION” PROCESS
OMI/NO2 Column 30 August 2007
Fire region:OMI NO2 column is assimilated as a new source of
NOx during 28-30 August 2007
Data assimilation (DA) technique allows correction of
model concentrations of observed species and those
related to it.
Application of DA of OMI/NO2 to simulation of North African fires.
Influence of fires at the end of August 2007 was detected in ozone data at Monte Cimone
(MTC: 44N, 11E, 2165 m s.l.m)
MTC
NO2 plume from fires
[Grassi et al., in prep.]
NO2 COLUMN ASSIMILATION STRONGLY AFFECTS NO2 AND OZONE FIELDSN
O2
O3
28 Aug 2007 30 Aug 2007
Difference between simulation with and witout NO2 DA over North Africa
NO2 x5!
O3 +20%
[Grassi et al., in prep.]
OZONE CONCENTRATION AT MONTE CIMONE ARE BETTER SIMULATED WITH OMI/NO2 DATA ASSIMILATION
No DA
With DAObs O3
[Grassi et al., in prep.]
BOUNDARY CONDITIONS ARE ALSO AN IMPORTANT INPUT TO REGIONAL CTM
Domain of the regional model (e.g. Chimere)
Longitude
Alti
tude
NORTH
EASTWEST
SOUTH
BCs are implemented as concentrations
specified at domain edges and
trasported inside by winds
TOP
Hourly BC of dust in CHIMERE (regional) from GEOS-Chem (global)
BCs ARE TYPICALLY STATIC (MONTHLY MEANS) IN CONTINENTAL SCALE REGIONAL CTMs
SAHARAN DUST EVENT JULY 27-29, 2005MODIS AOT 550 nm
24/07 25/07 26/07
27/07 28/07 29/07
Sequence of RGB images composite with Brightness Temperatures Differences using InfraRed SEVIRI channels (IR 8.7, IR10.8, IR 12.0) : DUST appears Magenta
Thanks to W. Di Nicolantonio e A.Cacciari (CGS)
SAHARAN DUST EVENT JULY 27-29, 2005SEVIRI/MSG
Comparison of CHIMERE PM10 with measurements at EMEP ground sites
EMEP average
Chimere w/ std BCsChimere w/ Daily BCs Several dust
events are captured with updated BCs
BIAS decrease by
40%-4.4 -2.5
µg/m3
DRASTIC IMPROVEMENT OF PARTICULATE MATTER SIMULATION WITH REFINED BCs CORRECTED THROUGH COMPARISON WITH OBSERVED AOT
A-PRIORI INFORMATION FROM MODEL IS USED TO RETRIEVE GROUND CONCENTRATIONS OF FINE PARTICULATE MATTER
[Di Nicolantonio et al., 2009]
CHARACTERIZATION OF AEROSOL PHYSICAL AND CHEMICAL PROPERTIES IS THE HOT TOPIC IN ATMOSPHERIC CHEMISTRY
[Rosenfeld et al., Science 2008]
less rain first …
… more rain and clouds later
CL
EA
NP
OL
LU
TE
D
THE NEW WRF/CHEM MODEL SIMULATES AEROSOL-CLOUDS FEEDBACK AT UNPRECEDENT HIGH RESOLUTION
W/out aerosol
W/ aerosol
The model is under development also at CETEMPS.
In a first sensitivity simulation we tested model sensitivity to European anthropogenic aerosol emissions.
Total precipitation increases by only 2%
Aerosols delay onset of precipitation that is recovered later.
[Tuccella et al., in prep.]
FURTHER READING
• Di Nicolantonio, W., A. Cacciari, A. Petritoli, C. Carnevale, E. Pisoni, M. L. Volta, P. Stocchi, G. Curci, E. Bolzacchini, L. Ferrero, C. Ananasso, C. Tomasi (2009), MODIS and OMI satellite observations supporting air quality monitoring, Radiation Protection Dosimetry, doi: 10.1093/rpd/ncp231
• Hodzic, A., Jimenez, J. L., Madronich, S., Aiken, A. C., Bessagnet, B., Curci, G., Fast, J., Lamarque, J.-F., Onasch, T. B., Roux, G., Schauer, J. J., Stone, E. A., and Ulbrich, I. M. (2009), Modeling organic aerosols during MILAGRO: importance of biogenic secondary organic aerosols, Atmos. Chem. Phys., 9, 6949-6981
• Curci, G., Beekmann, M., Vautard, R., Smiatek, G., Steinbrecher, R., Theloke, J., Friedrich, R. (2009), Modelling study of the impact of isoprene and terpene biogenic emissions on European ozone levels, Atmospheric Environment, 43, 1444-1455, doi:10.1016/j.atmosenv.2008.02.070
• Steinbrecher, R., Smiatek, G., Koble, R., Seufert, G., Theloke, J., Hauff, K., Ciccioli, P., Vautard, R., Curci, G. (2009), Intra- and inter-annual variability of VOC emissions from natural and semi-natural vegetation in Europe and neighbouring countries, Atmospheric Environment, 43, 1380–1391, doi:10.1016/j.atmosenv.2008.09.072
• Bessagnet, B., L. Menut, G. Curci, A. Hodzic, B. Guillaume, C. Liousse, S. Moukhtar, B. Pun, C. Seigneur and M. Schulz (2008), Regional modeling of carbonaceous aerosols over Europe - Focus on Secondary Organic Aerosols, Journal of of Atmospheric Chemistry, 61, 175-202.
• Curci, G., G. Visconti, D. J. Jacob and M. J. Evans (2004), Tropospheric fate of Tunguska generated nitrogen oxides, Geophys. Res. Let., 31, L06123, doi:10.1029/2003GL019184
THANKS FOR YOUR ATTENTION!
EXTRAS
If you haven’t had enough!
SPECIFIC ISSUES FOR AEROSOL CONCENTRATIONS
• A given aerosol particle is characterized by its size, shape, phases, and chemical composition – large number of variables!
• Measures of aerosol concentrations must be given in some integral form, by summing over all particles present in a given air volume that have a certain property
• If evolution of the size distribution is not resolved, continuity equation for aerosol species can be applied in same way as for gases
• Simulating the evolution of the aerosol size distribution requires inclusion of nucleation/growth/coagulation terms in Pi and Li, and size characterization either through size bins or moments.
Typical aerosol size distributionsby volume
nucleation
condensationcoagulation
[D. J. Jacob, Harvard]
1. Total annual emissions (e.g. EMEP, European Monitoring and Evaluation of
Pollution) of:
CO, NH3, SO2, NOx, VOC, PM
2. Speciation of VOC [Passant, 2002]
3. Corrispondence of emitted and modelled species
CO CONOx NOx
…PM 20% PM fine, 80% PM coarse
VOCi ???
VOC
VOC1
VOC2
VOC350
…
350 VOC: too many!
1. Chemical degradation of many is unknown
2. Computational limits
AGGREGATION
EMISSION INVENTORY FOR ANTHROPOGENIC EMISSIONS
CTM MAY THEN BE USED TO EVALUATE THE IMPACT OF EMISSIONS ON AIR POLLUTANT LEVELS
Increase of ozone max due to biogenic VOC emissions
Average daily ozone maximum only with anthropic
emissions(summer 2000)
[Curci et al., Atmo Env 2009]