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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, [email protected], 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 <[email protected]>