Atmospheric Chemistry and Transport Modelling: Introduction and current activities at CETEMPS

(also involving satellite data)

Gabriele Curci

CETEMPS – Dip. Fisica, Università degli Studi dell’Aquilagabriele.curci@aquila.infn.it

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]