Megacities impact on air quality and climate: model integration

and model ‘bridging’ in context of the MEGAPOLI project

Alexander A. Baklanov Danish Meteorological Institute,

DMI, Research Department, Lyngbyvej 100, Copenhagen, DK-2100, Denmarkalb@dmi.dk, phone: +45 39157441

In cooperation with MEGAPOLI, Enviro-HIRLAM, COST728 and COST ES0602 consortiums

ARPA-ARIANET Seminar, Milan, Italy, 29 May 2009

Overview

• MEGAPOLI overview• Integrated ACT-NWP modelling• Chemical weather forecasting: new concept• Aerosol feedbacks • Urbanization of models• Multi-scale effects from megacities • Enviro-HIRLAM online coupled system• First results from Paris study• Conclusions and further research

Megacities: Emissions, Impact on Air Quality and Climate, and Improved Tools for Mitigation Assessments (MEGAPOLI)EC 7FP project for: ENV.2007.1.1.2.1. Megacities and regional hot-spots air quality and climate

Project duration: Oct. 2008 – Sep. 2011 27 European research organisations from 11 countries are involved. Coordinator: A. Baklanov (DMI)Vice-coordinators: M. Lawrence (MPIC) and S. Pandis (FRTHUP)

(see: Nature, 455, 142-143 (2008), http://megapoli.info )

The main aim of the project is

(i) to assess impacts of growing megacities and large air-pollution “hot-spots” on air pollution and feedbacks between air quality, climate and climate change on different scales, and

(ii) to develop improved integrated tools for prediction of air pollution in cities.

• Urban (and Regional and Global and some Street) Scale Modelling

• Available and New Observations

• Tool Application and Evaluation

• Mitigation

• Regional (and Global and some Urban) Modelling

• Available Observations

• Implementation of Integrated Tools

• Global Modelling

• Satellite studies

Paris, London,

Rhine-Ruhr, Po Valley

Moscow, Istanbul, Mexico City, Beijing, Shanghai, Santiago, Delhi,

Mumbai, Bangkok, New York, Cairo, St.Petersburg, Tokyo

All megacities: cities with a population > 5 Million

1st Level

2nd Level

3rd Level

MEGAPOLI Partners:

UKUCamCentre for Atmospheric Science, University of Cambridge23

GermanyIfTInstitute of Tropospheric Research22

Czech RepublicCUNICharles University, Prague21

Switzerland (Int.)WMOWorld Meteorological Organization20

GermanyUSTUTTUniversity of Stuttgart19

UKUH-CAIRUniversity of Hertfordshire – Centre for Atmospheric and Instrum. Research 18

FinlandUHelUniversity of Helsinki17

GermanyUHamUniversity of Hamburg 16

UKMetOUK MetOffice15

The NetherlandsTNOTNO-Built Environment and Geosciences14

SwitzerlandPSIPaul Scherrer Institute13

NorwayNILUNorwegian Institute for Air Research12

NorwayNERSCNansen Environmental and Remote Sensing Center11

UKKCLKing's College London 10

ItalyICTPInternational Centre for Theoretical Physics9

ItalyJRCJoint Research Center, Ispra8

FinlandFMIFinnish Meteorological Institute7

FranceCNRSCentre National de Recherche Scientifique (incl. LISA, LAMP, LSCE, GAME, LGGE) 6

GreeceAUTHAristotle University Thessaloniki5

ItalyARIANETARIANET Consulting (SME)4

GermanyMPICMax Planck Institute for Chemistry3

GreeceFORTHFoundation for Research and Technology, Hellas, University of Patras2

DenmarkDMIDanish Meteorological Institute1

Countryshort nameBeneficiary nameNr

WP7: Integrated Tools and

Implementation

WP5: Regional and Global Atmospheric Composition

WP4: Megacity Air Quality

WP6: Regional and Global Climate Impacts

WP8: Mitigation, Policy Options and Impact Assessment

WP9: Dissemination and Coordination

WP2: Megacity features

WP3: Megacity Plume Case Study

WP1: Emissions

Work Packages (WPs) structure & integration

A. BaklanovS. PandisM. Lawrence

Dissemination and Coordination

9

R. FriedrichD. van den Hout

Mitigation, Policy Options and Impact Assessment

8

R. SokhiH. Schlünzen

Integrated Tools and Implementation

7

W. CollinsF. Giorgii

Regional and Global Climate Effects

6

J. KukkonenA. Stohl

Regional and Global Atmospheric Composition

5

N.MoussiopoulosMegacity Air Quality4

M. BeekmannU.Baltensperger

Megacity Plume Case Study

3

S. GrimmondI. Esau

Megacity Environments: Features, Processes and Effects

2

P. Builtjes H. Denier van der Gon

Emissions1

Lead Participant(s)

TitleWP No.

European population distribution & megacities in focus

Paris: extensive field campaign

Rhine-Ruhr area

Po Valley area

London: modelling and measurements

Moscow

Istanbul

St-Petersburg

Managers for megacities:• Paris (Beekmann, CNRS)• London (Sokhi/Grimmond, UH/KCL)• Ruhr (Friedrich/Lawrence, US/MPIC)• Po Valley (Finardi, ARIANET)• Istanbul (Incecik, ITU) • Moscow (Baklanov, DMI)

0

5

10

15

20

25

30

35

Toky

o

Mex

ico

City

New

Yor

k

São

Pau

lo

Mum

bai

Kol

kata

Sha

ngha

i

Bue

nos

Aire

s

Del

hi

Los

Ang

eles

Osa

ka-K

obe

Jaka

rta

Beijin

g

Rio

de

Jane

iro

Cai

ro

Dha

ka

Mos

cow

Kar

achi

Megacities

Pop

ulat

ion

(milli

on)

0

10000

20000

30000

40000

50000

Sur

face

are

a (s

q.km

), Po

pula

tion

dens

ity (k

m-2

)

Population (Million) Surface area (sq. km) Population density (per sq. km)

Megacity Characteristics, Pollution & Emission

(Butler et al., Atmos. Env., 42 (2008) 703–719)

Megacity pollution index (MPI)

Tokyo (-0.3)Mexico City (0.5)

New York (-0.2)São Paulo (-0.3)

Mumbai (0.4)Kolkata (0.6)

Shanghai (0.9)Buenos Aires (0.0)

Delhi (0.9)Los Angeles (-0.2)

Osaka-Kobe (-0.4)Jakarta (1.2)

Beijing (2.0)Rio de Janeiro (0.1)

Cairo (1.9)Dhaka (2.4)

Moscow (1.1)Karachi (1.8)

-1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5

Fair air quality Poor air quality

(Gurjar et al., Atmos. Env., 42 (2008) 1593–1606)

New TNO Emission for MEGAPOLI: •Complete Pan-Europeaninventory at ~ 6x6 km for 2005 completed

•Next focus: nesting localinventories for 5 megacities at the highest resolution: - London: Detailed inventoryavailable at 1x1 km for 2004, upgrade to 2005 underway- Paris, Po Valley, Ruhr Region, Istanbul: underway

•Improved Global emission inventory for 2005 underway

•Future emission and mitigation scenario underway

TNO: Denier van der Gon et al.

Connections between megacities, air quality & climate: main feedbacks, ecosystem, health & weather impact pathways, & mitigation routes

• Our hypothesis is that megacities around the world have an impact on air quality not only locally, but also regionally and globally and can influence the climate.

• Some of the links shown have already been considered by previous studies and are reasonably well-understood.

• However, a complete quantitative picture of these interactions is clearly missing.

• Understanding and quantifying these missing links will be the focus of MEGAPOLI.

Integrated Atmospheric System Model Structure

One-way: 1. NWP meteo-fields as a driver for ACTM (off-line);

2. ACTM chemical composition fields as a driver for R/GCM (or for NWP)

Two-way: 1. Driver + partly feedback NWP (data exchange via an interface with a limited time period: offline or online access coupling, with or without second iteration with corrected fields);

2. Full feedbacks chains included on each time step (on-line coupling)

Aerosol Dynamics Model

Transport & Chemistry Models

Atmospheric Dynamics /

Climate Model

Ocean and Ecosystem Models

Atmospheric Contamination Models

Climate / Meteorological Models

Inte

rface

/ C

oupl

er

Definitions of integrated/coupled models

Definitions of offDefinitions of off--line models:line models:• separate CTMs driven by meteorological input data from meteo-preprocessors, measurements or diagnostic models,• separate CTMs driven by analysed or forecasted meteodata from NWP archives or datasets,• separate CTMs reading output-files from operational NWP models or specific MetMs with a limited periods of time (e.g. 1, 3, 6 hours).

Definitions of onDefinitions of on--line models: line models: • on-line access models, when meteodata are available at each time-step (it could be via a model interface as well), • on-line integration of CTM into MetM, when CTM is called on each time-step inside MetM and feedback chains are available. We will use this definition as on-line coupled modelling.

Advantages of On-line & Off-line modeling

On-line coupling• Only one grid; • No interpolation in space• No time interpolation• Physical parameterizations are the

same; No inconsistencies• Harmonised advection schemes for

all variables (meteo and chemical)• Possibility to consider aerosol forcing

mechanisms• All 3D met. variables are available at

the right time (each time step); No restriction in variability of met. fields

• Possibility of 2-way feedbacks: from meteorology to emission and chemical composition

• Does not need meteo- pre/post-processors

Off-line• Possibility of independent

parameterizations;• Low computational cost (if NWP

data are already available and no need to run meteorological model);

• More suitable for ensembles and operational activities;

• Easier to use for the inverse modelling and adjoint problem;

• Independence of atmospheric pollution model runs on meteorological model computations;

• More flexible grid construction and generation for ACT models,

• Suitable for emission scenarios analysis and air quality management.

Chemical weather forecast: common concept

• Chemical weather forecasting (CWF) - is a new quickly developing and growing area of atmospheric modelling.

• Possible due to quick growing supercomputer capability and operationally available NWP data as a driver for atmospheric chemical transport models (ACTMs).

• The most common simplified concept includes only operational air quality forecast for the main pollutants significant for health effects and uses numerical ACTMswith operational NWP data as a driver.

• Such a way is very limited due to the off-line way of coupling the ACTMs with NWP models (which are running completely independently and NWP does not get any benefits from the ACTM) and not considering the feedback mechanisms.

Chemical weather forecast: new concept

• Many experimental studies and research simulations show that atmospheric processes (meteorological weather, including the precipitation, thunderstorms, radiation budget, cloud processes and PBL structure) depend on concentrations of chemical components (especially aerosols) in the atmosphere.

• Therefore ACTMs have to be run together at the same time steps using online coupling and considering two-way interaction between the meteorological processes, from one side, and chemical transformation and aerosol dynamics, from other side.

• New concept and methodology considering the chemical weather as two-way interacted meteorological weather and chemical composition of the atmosphere.

• CWF should include not only health-effecting pollutants (air quality components) but also GHGs and aerosols effecting climate, meteorological processes, etc.

• Strategy of new generation online integrated meteorology and ACTmodelling systems for predicting atmospheric composition, meteorology and climate change (as a part of and a step to EarthModelling Systems).

COST-728: MESOSCALE METEOROLOGICAL MODELLING CAPABILITIES FOR AIR POLLUTION AND DISPERSION APPLICATIONS

Tasks/Sub-groups:1. Off-line models and interfaces2. On-line coupled modelling systems and feedbacks3. Model down-scaling/ nesting and data assimilation4. Models unification and harmonization

Working Group 2: Integrated systems of MetM and CTM/ADM: strategy, interfaces and module unification (http://cost728.org)The overall aim of WG2 is to identify the requirements for the unification of MetM and CTM/ADM modules and to propose recommendations for a European strategy for integrated mesoscale modelling capability.

1. COST-728 / WMO, 2007: “Overview of existing integrated (off-line and on-line) mesoscale systems in Europe” published by WMO, Geneva, 122p.2. COST-728 / NetFAM workshop on “Integrated systems of meso-meteorological and chemical transport models”, Copenhagen, Denmark, 21-23 May 2007. Springer (in press). Web-site: http://netfam.fmi.fi/Integ07/

NWP Communities Involved: - HIRLAM, COSMO, ALADIN/AROME, UM communities- MM5/WRF/RAMS users/developers

WG2 outcome => COST Action ES0602: Chemical Weather Forecasting WG2 outcome => COST Action ES0602: Chemical Weather Forecasting (2008(2008--12)12)1. NetFAM school and workshop “Integrated Modelling of Meteorological and Chemical Transport Processes / Impact of Chemical Weather on Numerical Weather Prediction and Climate Modelling”in Zelenogorsk, 7-15 July 2008, on http://netfam.fmi.fi

Model name On-line coupled chemistry Time step for coupling

Feedback

BOLCHEM Ozone as prognostic chemically active tracer

None

ENVIRO-HIRLAM Gas phase, aerosol and heterogeneous chemistry

Each HIRLAM time step

Yes

WRF-Chem RADM+Carbon Bond, Madronich+Fast-J photolysis, modal+sectional aerosol

Each model time step Yes

COSMO LM-ART Gas phase chem (58 variables), aerosol physics (102 variables), pollen grains

each LM time step Yes (*

COSMO LM-MUSCAT (** Several gas phase mechanisms, aerosol physics

Each time step or time step multiple

None

MCCM RADM and RACM, photolysis (Madronich), modal aerosol

Each model time step (Yes) (***

MESSy: ECHAM5 Gases and aerosols Yes MESSy: ECHAM5-COSMO LM (planned)

Gases and aerosols Yes

MC2-AQ Gas phase: 47 species, 98 chemical reactions and 16 photolysis reactions

each model time step None

GEM/LAM-AQ Gas phase, aerosol and heterogeneous chemistry

Set up by user – in most cases every time step

None

Operational ECMWF model (IFS) ECMWF GEMS modelling

Prog. stratos passive O3 tracer GEMS chemistry

Each model time ste Each model time step

Yes

GME Progn. stratos passive O3 tracer Each model time step OPANA=MEMO+CBMIV Each model time step *) Direct effects only; **) On-line access model; ***) Only via photolysis

Characteristics of On-line coupled MetM - CTMs

On-line integrated NWP-ACT models in Europe

(WMO-COST728, 2008, see: www.cost728.org)

• At the current stage most of the online coupled models do not consider feedback mechanisms or include only direct effects of aerosols on meteorological processes (like COSMO LM-ART and MCCM).

• Only two meso-scale on-line integrated modelling systems (WRF-Chem and Enviro-HIRLAM) consider feedbacks with indirect effects of aerosols.

Aerosol feedbacks to be considered• Direct effect - Decrease solar/thermal-IR radiation and visibility

– Processes needed: radiation (scattering, absorption, refraction, etc.)– Key variables: refractive indices, ext. coeff., SSA, asymmetry factor, AOD, visual range – Key species: cooling: water, sulfate, nitrate, most OC

warming: BC, OC, Fe, Al, polycyclic/nitrated aromatic compounds• Semi-direct effect - Affect PBL meteorology and photochemistry

– Processes needed: PBL/LS, photolysis, met-dependent processes – Key variables: T, P, RH, Qv, WSP, WDR, Cld Frac, stability, PBL height, photolysis rates,

emission rates of met-dependent primary species (dust, sea-salt, biogenic)• First indirect effect – Affect cld drop size, number, reflectivity, and optical depth via CCN

– Processes needed: aero. activation/resuspension, cld. microphysics, hydrometeor dynamics– Key variables: int./act. frac, CCN size/comp., cld drop size/number/LWC, COD, updraft vel.

• Second indirect effect - Affect cloud LWC, lifetime, and precipitation– Processes needed: in-/below-cloud scavenging, droplet sedimentation– Key variables: scavenging efficiency, precip. rate, sedimentation rate

• All aerosol effects – Processes needed: aero. thermodynamics/dynamics, aq. chem., precursor emi., water uptake– Key variables: aerosol mass, number, size, comp., hygroscopicity, mixing state

⇒ High-resolution on-line models with a detailed description of the PBL structure arenecessary to simulate such effects

⇒ Online integrated models are necessary to simulate correctly the effects involved 2nd feedbacks

DMI multi-scale MetM and ACT modelling system

1. PSC aerosols2. Tropospheric

aerosols

Approaches:Normal distribution,Bin approach

Physics:1. Condensation2. Coagulation3. Evaporation4. Emission5. Nucleation6. Deposition

Aerosol Module1. Gas Phase2. Aqueous phase3. Chemical equil.4. Climate Modeling

Approaches:RACM, CBIV, ISORROPIA

Chemical Solvers

Lagrangiantransport, 3-Dregional scale

UTLS Trans. Models

Eulerian trans-port 0..15lat-lon grid,3-D regional scale

ECMWF

DMI-HIRLAM

Eulerian trans-port 0.2-0.05lat-lon, 25-40 vert. layer, 3-D regional scale

StochasticLagrangiantransport,3-D regional scale

On-Line Chemical Aerosol Trans.

ENVIRO-HIRLAM

Off-Line Chemical Aerosol Trans.

CAC

Emergency Pre-parednes & Risk Assess-

ment. DERMA

Nuclear, veterinary and chemical.

Regional (European) to city scale air pollution: smog and ozone.

Regional (European) scaleair pollution: smog and ozone, pollen.

Met. Models

Micro-Scale Obstacle Re-solved CFD-type Model

M2UE (TSU)

Tropo. Trans. Models

© Luft-group

Urban Air Pollution models

Population Exposure models

Populations/Groups Indoor concentrations

Outdoor concentrations

Time activity

Micro-environments E x p o s u r e

Urban heat flux parametrisation

Soil and

sublayer models for urban areas

Urban roughness classification &

parameterisation

Usage of satellite information on

surface

Meso- / City - scale NWP models

Mixing height and eddy diffus ivity es timation

Down-scaled models or ABL

parameteris ations

Estimation of additional advanced

meteorological parameters for UAP

Grid adaptation and interpol ation,

assimilatio n of NWP data

Interface to Urban Air Pollution models

Meteorological models for urban areas

Module of feedback

mechamisms:

- Direct gas & aerosol forcing

- Cloud condensa-tion nuclei model

- Other semidirect & indirect effects

FUMAPEX UAQIFS:

All 3D meteorological

& surface fields are

available at each time step

Different types of integrated urban air quality integrated models

1. Off-line integrated urbanised UAQIFS in FUMAPEX (Baklanov et al., ACP, 2006, 2008)

2. On-line integrated new generation system with feedbacks: urbanised EnviroHIRLAM(Chenevez etal, MA 2004, Baklanov et al., ASR 2008, KorsholmPhD 2009)

3. Simplified urban models for emergency preparedness: e.g. ARGOS (Hoe et al., 2007; Baklanov et al., JER 2008)

Enviro-HIRLAMIntegrated (on-line coupled) modeling system structure

for predicting the atmospheric composition

Radiative & optic properties models

General Circulation & Climate models

Cloud condensation nuclei (CCN) model

Aerosol dynamics models

Ecosystemmodels

Ocean dynamics

model

Atmospheric chemistry and transport models

Emission databases, models and scenarios

Inverse methods and adjoint models

WP7

Integrated Assessment Model

WP5

NWP / Climate Model

Enviro-HIRLAM

On-line integrated new generation system with feedbacks: urbanised EnviroHIRLAM(Baklanov et al., ASR 2008, Korsholm et al., HN 2009)

Main steps of Enviro-HIRLAM realisation:

(i) model nesting for high resolutions, (ii) improved resolving boundary and surface

layers characteristics and structure, (iii) ‘urbanisation’ of the NWP model, (iv) improvement of advection schemes, (v) implementation of chemical mechanisms, (vi) implementation of aerosol dynamics, (vii) realisation of feedback mechanisms, (viii) assimilation of monitoring data.

Enviro-HIRLAM 10-years development history

• 1999: Started at DMI as an unfunded initiative• Used previous experience of Novosibirsk sci. school and SMHI (A. Ekman PhD)• 2001: Online passive pollutant transport and deposition in HIRLAM-Tracer

(Chenevez, Baklanov, Sørensen)• 2003: Aerosol model tested first as 0D module in offline CAC (Gross, Baklanov)• 2004: Test of different formulations for advection of tracers incl. cloud water

(K.Lindberg)• 2005: Urbanisation of the model (funded by FP5 FUMAPEX) (Baklanov, Mahura,

Peterson) • 2005: COGCI grant for PhD study of aerosol feedbacks in Enviro-HIRLAM

(Korsholm)• 2006: Test of CISL scheme in Enviro-HIRLAM (Lauritzen, Lindberg)• 2007: First version of Enviro-HIRLAM for pollen studies (Mahura, Korsholm,

Baklanov, Rasmussen)• 2008: New economical chemical solver NWP-Chem (Gross)• 2008: First version of Enviro-HIRLAM with indirect aerosol feedbacks

(U.Korsholm PhD)• 2008: Testing new advection schemes in Enviro-HIRLAM (UC: E. Kaas,

A.Christensen, B.Sørensen, J.R.Nielsen)• 2008: Decision to build HIRLAM Chemical Brunch (HCB) with Enviro-HIRLAM

as baseline system, Enviro-HIRLAM becomes an international project• 2009: Integrated version of Enviro-HIRLAM based on reference version 7.2 and

HCB start

Enviro-HIRLAM research team:

Currently 4 institutions are working:• Danish Meteorological Institute (A.Baklanov, U. Korsholm, A. Gross, A. Mahura,

B.H. Sass, K.P. Nielsen, etc),• University of Copenhagen (E. Kaas, etc), • Tomsk State University (R. Nuterman, etc.), • Russian State Hydro-Meteorological University (S. Smyshlyaev, etc.)• HIRLAM-A program of the HIRLAM consortium (HIRLAM Chemical brunch).

Teams recently joining the development team: • University of Tartu, Estonia (R. Room, etc.),• Belgium Royal Meteorological Institute, • Vilnius University, Lithuania,• Odessa State Environmental University, Ukraine.

There is an initial working group (under COST728 and HIRLAM-A) for HIRLAM-ACTM integration work and a sub-program for the Enviro-HIRLAM/HARMONIEdevelopment cooperation.

Any HIRLAM and other teams are also welcome to join the team!

DMIDMI--HIRLAM HIRLAM ModellingModelling Domains Domains MultyMulty--scalescale ModellingModelling and M2UE and M2UE nestingnesting

Hor. Resol.:

T: 15 km

S: 5 km

U01: 1.4 km

I01: 1.4 km

M2UE resol.:

10-300 m

Urban Areas

M2UE

City-scale and micro-scale nested modelling in urban areas

Release site

Obstacle-resolved modelling for near-source area

Statistical description of building characteristics

Micro-scale Model for Urban Environment (M2UE) Downscaling

Release position sensitivity study: Results for Copenhagen area

Near surface velocity field and isosurfaces of concentration: 10 m difference of release position (left and right)

Wind directionWind direction

Baklanov and Nuterman, ASR, 2009

Up-scaling of modelling

• MEGAPOLI: megacity effects on largerscales pollution and climate

• Street => City => Region => Global• Different approaches:

- 2-way nesting- unregular grid concentration over megacities- Parameterisations to larger scale- Inner BC, assimilation, ..

Urban Parameterisations for Enviro-HIRLAM

DMI urban parameterisation:

• Displacement height,• Effective roughness and flux

aggregation, • Effects of stratification on the

roughness, • Different roughness for

momentum, heat, and moisture; • Calculation of anthropogenic and

storage urban heat fluxes; • Prognostic MH parameterisations

for UBL; • Parameterisation of wind and

eddy profiles in canopy layer. Baklanov et al., ACP 2006, 2008

1. Regional to global scales: Anthropogenic Heat Flux & Roughness – AHF+R (Baklanov et al., 2008)

2. Meso & city-scale: BEP - Building Effects Parameterization (Martilli et al., 2002)

3. Research for city-scale: SM2-U - Soil Model for Submeso Scale Urban Version (Dupont et al., 2006ab)

4. Obstacle-resolved approach (downscaled M2UE model, Nuterman et al., 2008)

PBL height from different versions of DMI-HIRLAM

urbanised 1.4 km operational 15 kmThe effects of urban aerosols on the urban boundary layer height, h, could be of the same order of magnitude as the effects of the urban heat island (∆h is about 100-200

m for stable boundary layer).

Copenhagen Copenhagen

Baklanov et al., JER, 2008

urbanised U01, 1.4 km resolution operational S05, 5 km resolution

Cs-137 air concentration for different DMI-HIRLAM dataA local-scale plume from the 137Cs hypothetical atmospheric release in Hillerød at 00 UTC, 19 June 2005

as calculated with RIMPUFF using DMI-HIRLAM and visualised in ARGOS for the Copenhagen Metropolitan Area (DEMA and DMI study).

Sensitivity of ARGOS dispersion simulations to urbanized DMI-HIRLAM NWP data

Baklanov et al., JER, 2008

Enviro-HIRLAM model description 1, Overview

•HIRLAM: High Resolution Limited Area Model: SRNWP model (HIRLAM Sci. Doc., Unden et al., 2002 )•Gas-phase chemistry: NWP-Chem(HIRLAM newsletter, No. 54, June 2008)•Aerosol representations, thermodynamic equilibrium, nucleation, coagulation and condensation (Gross and Baklanov, 2002)

•Advection: Bott (Bott, 1989) and CISL (Kaas, 2008) schemes•Vertical diffusion: native CBR-TKE-1 scheme (Cuxart et al., 2000)•Convection and condensation: STRACO (Sass, 2002)•Aerosol and gas deposition specie dependent (Wesely, 1989;

Binkowski, 1999; Seinfeld and Pandis, 1998; Baklanov and Sørensen, 2001)•Emissions from inventories (GEMS-, MEGAPOLI-TNO, etc) (TNO-report, 2007-A-R0233/B)

Model description 2, Chemistry and aerosolsThe NWP-Chem mechanism: Lumped tropospheric mechanism based on newest

chemical knowledge• Covers most important chemical processes responsible for air pollution and

aerosol formation in meso-scale models• Advected species: NO, NO2, SO2, CO, HC, HCHO, O3, HO2, HNO3, H2O2,

H2, H2SO4, OP, HO, OD, RO2, ROOH, (DMS, isoprene, monoterpene) 3 other chemical mechanisms available in EnviroHIRLAM: RACM, RADM, CBMZ

Aerosol module in Enviro-HIRLAM comprises a thermodynamic equilibriummodel (NWP-Chem-Liquid) and an aerosol dynamics modelNWP-Aero: modal aerosol dynamics model, 3 lognormal modes, characterizedby number concentration, geometric mean diameter and geometric meanstandard deviation; includes nucleation, coagulation and condensation (Whitbyand McMurry, 1997; Gross and Baklanov, 2004) 2 other aerosol models also available in EnviroHIRLAM:

- sectional MOSAIC (Zaveri et al., 2007) and - modal MADE (Ackermann et al., 1998) with SORGAM (Schell et al., 2001)

Model description 3, Aerosol feedbacks

The first aerosol indirect effect

r³eff =3L/(4πρwatkN)(Wyser et al. 1999)

L : Cloud condensate contentN: Number concentration of cloud

dropletsρwat : water density

ΔNcont = 108.06 conc0.48

ΔNsea = 102.24 conc0.26

(Boucher & Lohmann, 1995)

4 10^80.69Cont.

10^80.81Marine

N [m^-3]k

Polluted airmass has more aerosols => hence more cloud droplets

Korsholm et al., HN, 2008

Model description 4, Aerosol feedbacks

The second aerosolindirect effect

Rasch - Kristjansson condensation scheme in STRACOAuto-conversion: F(ql, ρair/ ρwat)N1/3 H(r - rc)r : droplet volume radius; r = r³eff * ρair

rc : critical value below which no auto-conversion takes place; 5 μm

ρair : air densityql : in-cloud liquid water mixing ratio

Korsholm et al., HN, 2008

Applications of Enviro-HIRLAM for:

(i) chemical weather forecasting(ii) air quality and chemical composition longer-term

assessment (iii) weather forecast (e.g., in urban areas, severe

weather events, etc.), (iv) pollen forecasting, (v) climate change modelling (Enviro-HIRHAM), (vi) volcano eruptions, nuclear explosion consequences(vii) Other emergency preparedness

Top: concentration as function of time at F15 and DK02 for different coupling intervals: 30, 60, 120, 240, 360 minutes. Bottom: concentration after 36 hours with the same coupling intervals

ON-LINE/OFF-LINE COMPARISON: ETEX-1

False peak due to off-line coupling

Korsholm et al., AE, 2008

Domain covering 665 x 445 km around Paris, France,Case study days: 2005-06-28 - 2005-07-03,Emission data from TNO-GEMS (year 2003, now modified for 2005), 300 s time step, NWP-Chem chemistry (18 species), DMI urbanisation scheme, CAC-aerosol mechanism: homogeneous nucleation, condensation, coagulation,Aerosols consists of H2O, HSO4-, SO4--, two log-normal modes: nuclei, accumulation,Accumulation mode aerosols used as CCN’s (Boucher & Lohmann, 1995), Case with low winds, convective clouds, little precipitation,Reference run without feedbacks, Perturbed runs with urban and aerosol indirect effects.

ENVIRO-HIRLAM study for Paris: Urban effects vs Aerosol 1st & 2nd indirect

feedbacksKorsholm et al., 2009

MSG1 satellite image 2005-06-30, 12 UTC

Horizontal resolution: 0.05º x 0.05ºVertical resolution: 40 levelsModel top: 10 hPa

665 km

445

km

Results 1: Aerosol effects on MeteorologyDay-time (2005-06-29 +036; 12 UTC) difference (reference - perturbation) of

T2m, C (left) and lowest level wind, ms-1 (right) .

Surface temperature changes are up to 4º C wind changes up to 3-6 m/s

Korsholm et al., 2009

Results 2: PBL height

Reference - Perturbation (in 100 m)

00 UTC

12 UTC

Changes in PBL height quite large (up to 900 m !)

Korsholm et al., 2009

Results 3: NO2 concentrations

Reference – Perturbation

Day-time (2005-06-29 +036; 12 UTC) and night-time (2005-06-29 +048; 00 UTC)reference - perturbation NO2 concentration (μg m-3)

Pre

ssur

e (p

a)

concentration (μg m-3)

Vertical NO2 profile in point of maximum increase (49.2N;2.7E) during daytime 2005-06-29 +036; 12 UTC for the reference simulation (red)

and the simulation including the indirect effects (green)

Korsholm et al., 2009

Paris Runs – Aerosol vs. Urban Effects - 1

URBANAEROSOL

COMBINED1IE & 2IE – 1st & 2nd indirect effectsHEA – anthropogenic heat fluxDYN – roughnessOBS – observationsREF – reference run / non-modified

Korsholm et al., 2009

Effect of urban heat and roughnessReference NO2

Effect of 1st and 2nd aerosol indirect effect Combined effect of urban areas and indirect effects

NO2 concentrations in μg m-3 at 2005-07-01 00 UTC; diff plots as reference-perturbation

Korsholm et al., 2009

Scientific questions still to be addressed(formulated on COST-NetFAM workshop in Copenhagen, May 2007)

• Hypothesis• Feedback mechanisms are important in accurate modeling of

NWP/MM-ACT and quantifying direct and indirect effects of aerosols.

=> the answer is ‘Yes, they can be very important’

• Key questions (still waiting for answers)• What are the effects of climate/meteorology on the abundance and properties

(chemical, microphysical, and radiative) of aerosols on urban/regional scales?• What are the effects of aerosols on urban/regional climate/meteorology and

their relative importance (e.g., anthropogenic vs. natural)?• How important the two-way/chain feedbacks among meteorology, climate, and

air quality are in the estimated effects?• What is the relative importance of aerosol direct and indirect effects in the

estimates on different time and space scales?• What are the key uncertainties associated with model predictions of those

effects?• How can simulated feedbacks be verified with available datasets?

Major Upcoming MEGAPOLI Research:

• Two field campaigns in Paris (July 2009 and January/February 2010)

• Further emissions database and future scenario development• Continued model development from urban to global scale,

analysis and interactions• Bringing it together with integrated modelling and mitigations

scenarios • Modelling to quantify feedbacks among megacity air quality,

local and regional climate, and global climate change• Assessing different mitigation options to reduce health impacts

of megacity emissions

Relevant publications:• Baklanov A., 2008: Integrated Meteorological and Atmospheric Chemical Transport Modeling: Perspectives and Strategy for

HIRLAM/HARMONIE. HIRLAM Newsletter, 53.• Korsholm U.S., A. Baklanov, A. Gross, A. Mahura, B.H. Sass, E. Kaas, 2008: Online coupled chemical weather forecasting based on

HIRLAM – overview and prospective of Enviro-HIRLAM. HIRLAM Newsletter, 54.• Baklanov A., U. Korsholm, A. Mahura, C. Petersen, A. Gross, 2008: ENVIRO-HIRLAM: on-line coupled modelling of urban meteorology

and air pollution. Advances in Science and Research, 2, 41-46• Korsholm U. (2009) Integrated modeling of aerosol indirect effects - develoment and application of a chemical weather model.

PhD thesis University of Copenhagen, Niels Bohr Institute and DMI, Research department.• Korsholm U, Mahura A, Baklanov A, Gross A, Petersen C, Beekmann M (2009) Aerosol-meteorology feedbacks on short time-scale in a

convective case. Atmospheric Environment (submitted) • Baklanov, A., O. Hänninen, L. H. Slørdal, J. Kukkonen, J. H. Sørensen, N. Bjergene, B. Fay, S. Finardi, S. C. Hoe, M. Jantunen, A.

Karppinen, A. Rasmussen, A. Skouloudis, R. S. Sokhi, V. Ødegaard, 2006: Integrated systems for forecasting urban meteorology, air pollution and population exposure, Atmos. Chem. Phys., 7, 855–874.

• Baklanov, A., P. Mestayer, A. Clappier, S. Zilitinkevich, S. Joffre, A. Mahura, N.W. Nielsen, 2008: Towards improving the simulation of meteorological fields in urban areas through updated/advanced surface fluxes description. Atmospheric Chemistry and Physics, 8, 523-543.

• Baklanov, A. and R. Nuterman, 2009: Multi-scale atmospheric environment modelling for urban areas. Advances in Science and Research, 3, 53-57.

• Chenevez, J., Baklanov, A. and Sorensen, J. H., 2004. Pollutant transport scheme s integrated in a numerical weather prediction model: model description and verification results. Meteorological Applications, 11, 265-275.

• Wyser K., L. Rontu, H. Savijärvi, 1999: Introducing the Effective Radius into a Fast Radiation Scheme of a Mesoscale Model. Contr.Atmos. Phys., 72(3): 205-218.

• Li, J., J.G.D. Wong, J.S. Dobbie, P. Chylek, 2001: Parameterisation of the optical properties of Sulfate Aerosols. J. of Atm. Sci., 58: 193-209.

• Seinfeld, J.H., S.N. Pandis, 1998: Atmospheric chemistry and physics. From air pollution to climate change. A Wiley-IntersciencePublication. New-York.

• Baklanov A., Sørensen, H., J., 2001: Deposition parameterisation in ACT models, Physics and Chemistry of the Earth, vol. 26, No. 10, 787-799

• Baklanov, A. and U. Korsholm: 2007: On-line integrated meteorological and chemical transport modelling: advantages and prospective. In: ITM 2007: 29th NATO/SPS International Technical Meeting on Air Pollution Modelling and its Application, 24 – 28.09.2007, University of Aveiro, Portugal, 21-34.

• Baklanov, A., B. Fay, J. Kaminski, R. Sokhi, 2007: Overview of Existing Integrated (off-line and on-line) Mesoscale Meteorological and Chemical Transport Modelling Systems in Europe, WMO GAW Report No. 177, Joint Report of COST Action 728 and GURME, 107 pp. Available from: http://www.cost728.org

• Baklanov, A., A. Mahura, R. Sokhi (eds.), 2008: Integrated systems of meso-meteorological and chemical transport models, Materials of the COST-728/NetFAM workshop, DMI, Copenhagen, 21-23 May 2007, 183 pp. Available from: http://www.cost728.org

Thank You !Thank You !

MEGAPOLI web-site: megapoli.info

Contact e-mail: Alexander Baklanov <alb@dmi.dk>