Integrated systems for weather and air quality

forecastingLeif Laursen,

Alexander Baklanov, Ulrik Korsholm, Alexander Mahura

Danish Meteorological Institute, DMI, Research Department, Lyngbyvej 100, Copenhagen, DK-2100, Denmark

In cooperation with COST728, HIRLAM and MEGAPOLI consortiums

Environmental Prediction into the Next Decade: Weather, Climate, Water and the Air We Breathe

Technical Conference preceding CAS XVIncheon, Republic of Korea, 16-17 November 2009

Keywords in integrated modelling:

• Chemical weather • Coupling/Integration/on-line/two-way feedbacks• Practical; fewer operational models• Prediction of consequences of climate change on

pollution levels• Models more consistent• Air pollutants interact with meteorology: aerosols,

trace gases affecting radiation balance and clouds

• Verification more difficult

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 ACTMs with 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

• To account for variability in trace gases and aerosols with time scales less than the off-line coupling interval on-line models with a 2-way coupling between radiatively active species and meteorology must be used.

• Aerosols affect the radiation balance through: direct interaction with incoming/outgoing radiation, changes in cloud top reflectance, changes in precipitation development (and thereby cloud lifetime).

• Clouds and radiation affect aerosols through: in-cloud / below-cloud scavenging, heterogeneous chemistry, local and regional thermally induced circulation cells, reaction rates depends on temperature, photolysis strongly modified by cloud cover.

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

• Improvement of NWP itself

• Direct effect - Decrease solar/thermal-infrared radiation and visibility: – Processes involved: radiation (scattering, absorption, refraction, etc.);– Key variables: refractive indices, extinction coefficient, single-scattering albedo,– asymmetry factor, aerosol optical depth, visual range; – Key species: - cooling: water, sulphate, nitrate, most OC;

- warming: BC, OC, Fe, Al, polycyclic/nitrated aromatic compounds;

• Semi-direct effect - Affect PBL meteorology and photochemistry:– Processes involved: PBL, surface layer, photolysis, meteorology-dependent processes; – Key variables: temperature, pressure, relative and water vapour specific humidity, wind speed and direction, clouds fraction,

stability, PBL height, photolysis rates, emission rates of meteorology-dependent primary species (dust, sea-salt, pollen and other biogenic);

• First indirect effect (so called the Twomey effect) – Affect clouds drop size, number, reflectivity, and optical depth via CCN or ice nuclei:

– Processes involved: aerodynamic activation / resuspension, clouds microphysics, hydrometeor dynamics;– Key variables: int./act. fractions, CCN size/compound, clouds drop size / number / liquid water content, cloud optical depth,

updraft velocity;

• Second indirect effect (also called as the lifetime or suppression effect) - Affect cloud liquid water content, lifetime and precipitation:

– Processes involved: clouds microphysics, washout, rainout, droplet sedimentation;– Key variables: scavenging efficiency, precipitation rate, sedimentation rate.

High-resolution on-line models with a detailed description of the PBL structure are necessary to simulate such effects.

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

Examples of aerosol-meteorology feedbacks

Chemical weather forecast: The new concept

Several model developments in Europe and international projects and collaboration points in this direction:

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

Only European short range models with aerosol indirect effects

WMO-COST728 GAW 177

Chemical weather forecast: The new concept

European COST Actions 728 (2005-2009): "Enhancing Meso-scale Meteorological Modelling Capabilities for

Air Pollution and Dispersion Applications" Coord. – Ranjeet S Sokhi , University of Hertfordshire

The main objective is to develop advanced conceptual and computational frameworks to enhance significantly European capabilities in mesoscale meteorological modelling for air pollution and dispersion applications.

• WG1: Meteorological parameterization/ applications (Maria Athanassiadou, UK MetOffice)

• WG2: Integrated systems of MetM and CTM: strategy, interfaces and module unification (Alexander Baklanov, DMI)

• WG3: Mesoscale models for air pollution and dispersion applications (Mihkail Sofiev, FMI)

• WG4: Development of evaluation tools and methodologies (Heinke Schluenzen, University of Hamburg)

• New Cost action ES0602, CWF

MEGAPOLI EU FP7 project

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

• Policy • 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

Megacities: Emissions, Impact on Air Quality and Climate, and Improved Tools for Mitigation Assessments

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 )

DMI-HIRLAM Modelling Domains DMI-HIRLAM Modelling Domains Multy-scale Modelling and M2UE nestingMulty-scale Modelling and M2UE nesting

Hor. Resol.:

T: 15 km

S: 5 km

U01: 1.4 km

I01: 1.4 km

M2UE resol.:

10-300 m

Urban Areas

M2UE

Enviro-HIRLAM results:Effect of Paris on regional thermal structure

Case with low winds, deep convective clouds, little precipitation Reference run without feedbacks (REF), Perturbed run with first (1IE) and second (2IE) indirect effects and urban heat fluxes (HEA) and roughness (DYN).•Domain covering 665 x 445 km around Paris, France,•Case study days: 2005-06-28 - 2005-07-03,•300 s time step, NWP-Chem chemistry (18 species),

Korsholm et al., 2009

MSG1 satellite image 2005-06-30, 12 UTC

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

665 km

445

km

T2m comparison at a measurement station downwind from Paris

Aerosol indirect effects

Korsholm et al., 2009

T2m comparison; average over all 31 stations

Diff

ere

nce

fro

m m

easu

rem

ent

s (C

º)

Daytime improvement

Korsholm et al., 2009

Findings

In this particular meteorological case: 2IE led to a general betterT2m comparison during Daytime; only small changes during night,1IE was small in comparison (larger for thin clouds),urban parameterization had negligible effect (strong large scale forcing).

Korsholm et al., 2009

Dominating process in this case:

Paris Aerosols Increased Cloud cover (2nd aerosol indirect effect)

Daytime cooling, night time heatingShortwave, long wave response

Additionally: local thermally induced circulations redistribute the aerosols and trace gases:

Pre

ssur

e (p

a)

concentration (μg m-3)

Vertical NO2 profile in point of max. increase (49.2N;2.7E) during daytime 2005-06-29 at 12 UTC for the REF simulation (red) and the simulation including the indirect effects (green)

Conclusions

• Indirect effects induce large changes in NO2

• Changes mediated through changes in dynamcis• Residual circulation induced by temperature

changes• Redistribution both vertically and horizontally• Also applies for night-time conditions• Chem vs dynamics• Fist indirect effect is much smaller than second

one • Large non-linear component

In this particular case (Korsholm et al., EMS, 2008):

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 chain feedbacks included on each time step (on-line coupling/integration)

Aerosol Dynamics Model

Transport & Chemistry Models

Atmospheric Dynamics /

Climate Model

Ocean and Ecosystem Models

Atmospheric Contamination Models

Climate / Meteorological Models

Inte

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ce / C

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r

Thank You !Thank You !