AREP GAW

Introduction and Overview of Course AREP GAW

Air Quality Modeling Introduction AREP GAW Models Play a Critical Role in Linking Emissions to Aerosol and Trace Gas Distributions and Subsequent Effects

Modeled

Observed

Section 3 – Introduction and Overview of Course

3 AREP GAW Models are an Integral Part of Air Quality Studies

• Field experiment 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) • Emission control strategies testing • Air quality forecasting • Measurement site selection

Section 3 – Introduction and Overview of Course

4 AREP GAW

Section 3 – Introduction and Overview of Course

5 AREP GAW Air Quality Modeling: Improving Predictions of Air Quality (analysis and forecasting perspectives)

Met model Predicted Quantity: e.g., ozone AQ violation

Chemical, Aerosol, Removal modules CTM

How confident are we in the models & predictions?

Emissions Observations

Section 3 – Introduction and Overview of Course

6 AREP GAW Chemical Transport Model

• 3D atmospheric transport-chemistry model (STEM-III)

∂c 1 i = −u ⋅∇c + ∇ ⋅(ρK∇c ) + f (c) + E ∂ i ρ i i i t

where chemical reactions are modeled by nonlinear stiff terms = − fi (c) Pi (c) Di (c)ci • Use operator splitting to solve CTM

=Δt2//////Δ2Δ2ΔΔ t2Δ2Δ2 t t t t t M[Δt,] t + tTTTCTTT X Y Z Z Y X

Section 3 – Introduction and Overview of Course

7 AREP GAW EULERIAN MODELS PARTITION ATMOSPHERIC DOMAIN INTO GRIDBOXES

This discretizes the continuity equation in space

Solve continuity equation for individual gridboxes

• Detailed chemical/aerosol models can presently afford -106 gridboxes

• In global models, this implies a horizontal resolution of ~ 0.5-1o (~50 to 100 km) in horizontal and ~ 0.5-1 km in vertical • Chemical Transport Models (CTMs) use external meteorological data as input (or run on-line) • General Circulation ModelsSection 3(GCMs) – Introduction compute and Overview their of Course own meteorological fields From D. Jacob 8 AREP GAW

Section 3 – Introduction and Overview of Course

9 AREP GAW

Section 3 – Introduction and Overview of Course

10 AREP GAW

Section 3 – Introduction and Overview of Course

11 AREP SPECIFIC ISSUES FOR AEROSOL CONCENTRATIONS GAW • 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 coagulation size distributions condensation by volume nucleation

Section 3 – Introduction and Overview of Course From D. Jacob 12 AREP LAGRANGIAN APPROACH: TRACK TRANSPORT OF POINTS IN GAW MODEL DOMAIN (NO GRID) • Transport large number of points with trajectories from input meteorological data base (U) + random turbulent component (U’) over time steps ∆ t

• Points have mass but no volume

• Determine local concentrations as the number of points within a given volume position ∆ • Nonlinear chemistry requires Eulerian mapping at to+ t every time step (semi-Lagrangian) U’∆ t PROS over Eulerian models: • U∆ t no Courant number restrictions position • no numerical diffusion/dispersion

to • easily track air parcel histories • invertible with respect to time CONS: • need very large # points for statistics • inhomogeneous representation of domain • Section 3 – Introduction convection and Overview is ofpoorly Course represented From D. Jacob • nonlinear chemistry is problematic 13 AREP LAGRANGIAN RECEPTOR-ORIENTED MODELING GAW Run Lagrangian model backward from receptor location, with points released at receptor location only

• Efficient cost-effective quantification of source influence distribution on receptor (“footprint”)

• Enables inversion of source influences by the adjoint method (backward model is the adjoint of the Lagrangian forward model)

Section 3 – Introduction and Overview of Course From D. Jacob 14 AREP GAW Air Quality Prediction: A Challenge of Scales and Integration

Megacity Air quality Analysis Impacts

Regional Prediction

Global Assimilation

Satellite Products

Modified after Pierce NASA/Langley Section 3 – Introduction and Overview of Course

15 AREP GAW Integrated Science Studies: Impacts of Global Composition on Regional Air Quality Global-Regional-Urban nesting of CTMs

Effects of Boundary Conditions are significant and improve predictions (Tang et al., JGR 2007).

Alaskan BB Impacts Northern Boundary

Assessment of continental inflow/outflow requires unified modeling/measurement strategy to accurately characterize coupling between the continental boundary layer, free troposphere, and long-

Section 3 – Introduction and Overview of Courserange transport.

16 AREP GAW An Air Quality Modelling System

Site data

Topography Land cover Weather forecast and local data

Model Observations results Site data processing

Emission inventories Chemical data Industries 3D meteo modelling Monitoring Vehicular data traffic Chemical 3D IC / BC Emissions modelling processing Models interface Model Area results sources

3D meteo with 3D chemical- on-line chemistry -transport modelling Analysis of concentrations Comparison with regulatory norms and objectives

Concentrations: 3D & ground

Post-processing & graphic Section 3 – Introduction and Overview of Course

17 AREP GAW Need to Estimate Emissions at Appropriate Scales

Section 3 – Introduction and Overview of Course

18 AREP GAW Detail: a matter of scale (3)

SO2 emissions in the vicinity of Shanghai, China

1º x 1º .5º x .5º

10’ x 10’ 30’’ x 30’’

Section 3 – Introduction and Overview of Course

19 AREP GAW Emissions processing for AQM

Inventories “reference raw Thematic data emission data” (point / line / area)

SPACE DISAGGREGATION

TIME MODULATION Modulation profiles Residential (hourly, daily, monthly)

Somma di FACTOR 2.5 Highways

2 NMVOC & PM

DAY 1.5 NOM_PROFIL 1 - Leggeri_Extraurbane SPECIATION & SIZE 2 - Leggeri_Extraurbane 3 - Leggeri_Extraurbane Deciduous 4 - Leggeri_Extraurbane 5 - Leggeri_Extraurbane 1 6 - Leggeri_Extraurbane 7 - Leggeri_Extraurbane

0.5 3 NonIndComb Formaldeide gg_0_150 Speciation & gg_150_300 Acetaldeide 0 2.5 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 gg_300_500 HOUR gg_500_1000 Aldeidi superiori dimensional 2 gg_1000_2000 P rodInd Chetoni 1.5 SmalBusi profiles DomActiv Etilene 1 Airtraf Colture Alcani (B.R.) VdA_auto_EA Latifoglie 0.5 VdA_comm_EA Alcani (A.R.) Conifere Decid 0 Auto Conif Aromatici (B.R.) 1 2 3 4 5 6 7 8 9 10 11 12 ThPowPlants TotElP owReq Aromatici (A.R.) mese Veicoli_VdA Olefine (B.R.)

Olefine (A.R.)

Isoprene

Monoterpeni

0% 20 % 4 0 % 60 % 80 % 1 0 0 % Model-ready input (hourly,Section gridded, 3 – Introduction and Overview of Course speciated emissions) 20 AREP GAW How do we build upon what is done and move beyond to improve air quality prediction?

 Informed by comparisons of predictions with observations.  Informed by process studies.  Informed by model inter-comparison studies.

Section 3 – Introduction and Overview of Course

21 AREP GAW Model Resolution, Transport and Removal also Contribute to Differences

Emissions Monthly mean concentrations Temporal variation in concentrations

BC

Section 3 – Introduction and Overview of Course

22 AREP GAW Removal Processes Remain Poorly Characterized in Models Impact of Wet Removal on Predicted BC Progress limited by lack of understanding and observations

Section 3 – Introduction and Overview of Course

23 AREP Intensive field experiments provide GAWopportunities for comprehensive evaluations Current CTMs Do Have Appreciable Skills In C130 Predicting A Wide Variety Of DC8 Parameters INTEX B – STEM Forecasts

DC8 C130 Section 3 – Introduction and Overview of Course

24 AREP GAWMICS-AsiaMICS-Asia < Model InterComparison Study in Asia >

MainEvaluation goal of model performance to make an international common understanding and improve for air pollution modeling in East Asia

Nine different regional models Observations: •EANET (47 sites) (gas, aerosol, deposition) •Ozonesondes •Trace-P Obs. •Special obs. (aerosols) •Met obs (sondes and surface) •(daily & monthly analysis)

• Special Section of Atmospheric MICS-III Will look also Environment (8 papers)Section 3 – Introduction and Overview of Courseat megacities 25 AREP The ensemble mean near surface monthly mean total sulfur deposition GAW amounts (as sulfate) for the different seasons. March 2001 July 2001

20 100 300 500 1000 0103 ) 2

- 10000 m

g m (

1000 n o i t i s

o 100 p e d

t

e 10 w

- 2 4 O

S 1

Nitrate quantities typically underestimated. (mg m-2) Section 3 – Introduction and Overview of Course

26 AREP GAW Emissions are the Largest Single Source of Uncertainty

Uncertainties:Section 3SO2 – Introduction

27

e d

AREP GAW

Experiments suchu as TRACE-P and ACE-Asia employ mobile “Super-

Sites” and study pollution outflow from source regions

t i

50 N t

40 N a

30 N China L 20 N

10 N

DC-8 Flights P-3B Flights

0 N Spring 2001 110 E 120 E 130 E 140 E 150 E 160 E L o n g i t u d e

Section 3 – Introduction and Overview of Course

28 AREP GAWPost-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

Section 3 – Introduction and Overview of Course (Carmichael et al., JGR, 2003) 29 AREP GAW Comparison of New CO Inventory with Trace-P

180 s

n New Others o t

n estimate

o Biomass Burning i

l 160 l i Transport M Domestic Fossil Fuels 140 Domestic Biofuels Industry 120 Power

100

80

60

40

20

0 1999 2001 Trace-P Section 3 – Introduction and Overview of Course

30 AREP GAW Our Analysis Approach

Prediction/ Analysis State

Data Assimilation Section 3 – Introduction and Overview of Course

31 AREP GAW Documenting improvement (ICART)

0.14

0.12 Forecast

0.1 NEI2001- Frost y t i

l LPS i 0.08 b

a NEI2001- b o

r 0.06 FrostLPS* p 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 NEI2001- FrostLPS*) for DC-8 measurements under 4000m. Section 3 – Introduction and Overview of Course Mena et al., JGR, 2007 32 AREP Integrated Analysis Framework for Linking Meteorology, Air GAW Quality and Human Exposure

Section 3 – Introduction and Overview of Course Baklanov et al., ACP, 2007 33 AREP GAW Summary of Course – Introduction to Air Quality Modeling

Section 3 – Introduction and Overview of Course

34 AREP GAWAir quality monitoring and ensemble forecasting framework.

Feedbacks (setting standards Region and assess achievements)

Prediction by policy CTM Forecasts Information on observations Super sites and model calculations + monit. st. (many)

Section 3 – Introduction and Overview of Course

Modified from Y. Zhang , Guangzhou Meeting 2007 35 AREPMany Meteorological Services Already Supply Operational Chemical GAW Weather Products (e.g., FMI)

Fire Assimilation Satellite observations System

Phenological Phenological Final AQ products observations models SILAM AQ model Aerobiological EVALUATION: observations NRT model-measurement comparison Physiography, Assimilation forest mapping

HIRLAM NWP model Aerobiological Online AQ observations monitoring UN-ECE CLRTAP/EMEP emission database Meteorological Real-time data Section data: 3 – Introduction ECMWF and Overview of Course needs! 36 AREP GAW

Section 3 – Introduction and Overview of Course

37