Online integrated meteorology-chemistry

models for air quality forecasting

(EuMetChem experience)

A. Baklanov (1), H. Schluenzen (2), P. Suppan (3), M. Gauss (4), Ch. Seigneur (5), D. Brunner (6), S. Joffre (7), X. Kong (8)

(1) Danish Meteorological Institute, Copenhagen (alb@dmi.dk, +45 391 57460), (2) Meteorologisches Institut, Universitat Hamburg, (3) Karlsruhe Institute of Technology (KIT), Institute for

Meteorology and Climate Research, (4) Norwegian Meteorological Institute, (5) CEREA, École des Ponts ParisTech, (6) Empa, Swiss Federal Laboratories for Materials Science and

Technology, (7) Finnish Meteorological Institute, (8) University of Hertfordshire

and the COST ES1004 team: Aksoyoglu S., Baldasano J.M., Carmichael G., Douros J., Flemming J., Forkel R., Galmarini S.,Grell

G.,Hirtl M.,Kaas E., Kaasik M., Kallos G., Korsholm U., Lohmann U., Manders A., Maurizi A., Moussiopoulos N., Orba O., Rao S.T., Savage N., R. Sokhi, Sørensen B., Vignati E., Vogel B.,

Zakey A., Zhang Y.

4th International Workshop on Air Quality Forecasting Research (IWAQFR)

4th International Workshop on Air Quality Forecasting Research (IWAQFR)

Physical and Chemical Weather: Off-line coupling

• Numerical Weather Prediction (Meteorological) and Air Quality Modelling – two independent problems and research communities.

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

The most common simplified concept of CWF 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 (no feedbacks).

NWP:

CWF:

European operational CWF models

• Kukkonen et al, ACP, 2011: overview of 18 selected regional CWF models used in Europe: ALADIN-CAMx (Austria), CAMx-AMWFG (Greece), EURAD-RIU (Germany), Enviro-HIRLAM (Denmark and others), FARM (Italy), LOTOS-EUROS (The Netherlands), MATCH (Sweden), MM5-CAMx (Greece), MM5-CHIMERE (France and Portugal), MM5/WRF-CMAQ (Spain, UK), MOCAGE (France, Spain, Romania), NAME (United Kingdom), OPANA (Spain and others), RCG (Germany), SILAM (Finland, Estonia, Russia, Lithuania and Spain), SKIRON/Dust (Greece), THOR (Denmark), WRF-Chem (Spain and others)

• Only two of them (Enviro-HIRLAM and WRF-Chem) are online coupled

• Recently published Springer book (Baklanov et al., 2011) with description of main coupled ACT-MM models => 15 online =>

ISBN: 978-3-642-13979-6

• European CWF portal: http://www.chemicalweather.eu/Domains

Motivation:

Physical and Chemical Weather Forecast: New concept

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

• Meteorological data assimilation (in particular assimilation of radiances) depends on the chemical composition.

• Studies also show that air quality forecasts loose accuracy when CTM’s are run offline.

New generation of online integrated meteorology and chemistry modelling systems for predicting atmospheric composition, meteorology and climate change is really needed.

Online integrated or online access Atmospheric Chemistry-

Meteorology (ACM) models developed or applied in Europe

Page 1:

Model, Country, Web-site

Meteorology component

Gas phase chemistry and aerosol component

Feedback of pollutants to meteorology

Applications Scale

1 BOLCHEM, Italy http://bolchem.isac.cnr.it/

BOLAM SAPRC90 gas phase chemistry, AERO3 aerosol module

Under development CWF, climate, episodes

Continental to regional

2 COSMO-ART, Germany http://www.imk-tro.kit.edu/3509.php

COSMO Extended RADM2 gas phase chemistry, modal aerosol, soot, pollen, mineral dust, volcanic ash?

Direct aerosol effect on radiation, indirect effects

Episodes, Climate mode

Continental to regional

3 COSMO- MUSCAT, Germany http://projects.tropos.de/cosmo_muscat/

COSMO RACM gas phase chemistry, 2 modal aerosol models, mineral dust module

Direct aerosol effect on radiation for mineral dust

Episodes Continental to regional

4 Enviro-HIRLAM, Denmark and HIRLAM countries, http://hirlam.org/chemical

HIRLAM (*) NWP gas phase chemistry, modal and sectional aerosol modules, liquid phase chemistry

Direct and indirect aerosol effects

Episodes, chemical weather forecast

Hemispheric to regional and urban

5 IFS-MOZART (MACC/ ECMWF), C-IFS, http://www.gmes-atmosphere.eu

IFS MOZART gas phase chemistry with updates to JPL-06, MACC aerosol scheme

Direct aerosol effect, indirect aerosol effect

Forecasts, Reanalysis, Episodes

Global

6 MCCM, Germany, http://www.imk-ifu.kit.edu/829.php

MM5 RADM, RACM or RACM-MIM2 with modal aerosol module

Direct aerosol effect

Episodes, climate-chemistry

Continental to regional and urban

(*) New version of the model based on the HARMONIE meteorological core is under development.

7 MC2-AQ, Canada (used in Poland), http://collaboration.cmc.ec.gc.ca/science/rpn/modcom/eole/Models.html

MC2 ADOM gas phase chemistry none, but possible Episodes Regional to urban

8 MEMO/MARS, Greece, MEMO RACM gas phase chemistry, 3- modal aerosol submodel, SOA based on SORGAM

Direct aerosol effect Episodes, long term

Urban to regional

9 Meso-NH, France, http://mesonh.aero.obs-mip.fr/mesonh/

Meso-NH RACM, ReLACS or CACMgas phase chemistry, modal aerosol module

Direct aerosol effect Episodes Continental to regional to local

10 MetUM (Met Office Unified Model), UK,

MetUM 2 tropospheric chemistry schemes, 1 stratospheric chemistry scheme. 2 alternative aerosol schemes.

Direct & indirect aerosol effects, radiative impacts of N2O, O3, CH4

Episodes, CWF, climate-chem. studies

Regional to Global

11 M-SYS (online version), Germany, www.mi.uni-

hamburg/m-sys

METRAS RADM Gas phase chemistry, sectional aerosol module

none, but possible Episodes Regional to urban to local

12 NMMB/BSC-CTM (BSC-CNS), Spain, http://www.bsc.es/earth-sciences/mineral-dust/nmmbbsc-dust-forecast

NMMB BSC-mineral dust scheme CBM-IV and CBM05 chemical mechanisms

Direct aerosol effect on radiation for mineral dust

Episodes Forecast Reanalysis

Urban to Global

13 RAMS/ICLAMS, USA/Greece, http://forecast.uoa.gr/ICLAMS/index.php

RAMS Online photolysis rates. Coupled SAPRC99 gas phase, modal aerosol, ISORROPIA equilibrium and SOA, cloud chemistry.

Direct and indirect aerosol effect

Episodes, CWF, meteo-chemistry interactions

Continental to urban

Online integrated or online access Atmospheric Chemistry-

Meteorology (ACM) models developed or applied in Europe

Page 2:

14 RegCM-Chem4, Italy, Shalaby et al., 2012

RegCM4 Updated GEOS-CHEM RACM, CBMZ, uni-modal aerosol, sectional mineral dust, sulfur chemistry

Direct aerosol effect Climate-chemistry

Continental to regional

15 REMO, Germany Langmann, 2000, Langmann et al., 2008

REMO RADM gas phase, Walcec&Taylor liguid phase, M7 (Vignati et al., 2004)

none, but possible Episodes (particularly volcanic ash)

Continental to regional

16 WRF/Chem, US (used in Germany, UK, Spain, etc.), Grell et al., 2005; Fast et al., 2006; Zhang 2008; Zhang et al., 2010a, b, 2011a, 2012c, d, e, f; Chuang et al., 2011)

WRF RADM, RACM, RACM-MIM with modal aerosol module or CBM-Z/CB05 with sectional aerosol modules MOSIAC and MADRID, liquid phase chemistry

Direct aerosol effect, indirect aerosol effect

Episodes, CWF, climate-chemistry

Continental to regional , urban, cloud-resolving and LES

17 WRF-CMAQ Coupled System, USA (used in UK) , http://www.cmaq-model.org/cmaqwiki

WRF Gas-phase mechanisms: CB05 with updated toluene chemistry, SAPRC07TB; Modal aerosols based on the AERO6 CMAQ module

Direct aerosol effects on radiation and photolysis

Episodes to annual

Urban to Hemispheric

Online integrated or online access Atmospheric Chemistry-

Meteorology (ACM) models developed or applied in Europe

Page 3:

Main meteorology and chemistry/aerosols interacting

processes and effects in online coupled MetM-ACT

I) METEOROLOGY’S IMPACTS ON CHEMISTRY:

Temperature

Modulates chemical reaction and photolysis rates

Modulates biogenic emissions (isoprene, terpenes, Dimethyl Sulfide, etc.)

Photosynthetically active radiation: modulates biogenic emissions (isoprene, monoterpenes)

Affects the volatility of chemical species

Rules aerosol dynamics (coagulation, condensation, nucleation)

Temperature vertical gradients

Determines vertical diffusion intensity

Temperature & humidity Affect aerosol thermodynamics (e.g., gas/particle partitioning, secondary aerosol formation)

Water vapour Modulates OH radicals

Liquid water Determines wet scavenging and atmospheric composition

Precipitation Influences atmospheric composition

Soil moisture Modulates dust emissions

Influences dry deposition (biosphere and soil)

Lightning Influences NOx emissions

Radiation Modulates chemical reaction and photolysis rates

Wind speed Rules dust emissions and sea-salt emissions

II) CHEMICAL SPECIES’ IMPACTS ON METEOROLOGY:

Aerosols

Modulate radiation transfers (SW scattering/absorption, LW absorption)

Affect boundary layer meteorology (Temperature, humidity, wind speed, PBL height, stability)

Act as cloud condensation nuclei

Aerosols size distribution

Influences cloud droplet or crystal number and hence cloud optical depth and hence radiation

Modulates cloud morphology (e.g., reflectance)

Aerosols properties Influences precipitation (initiation, intensity)

Aerosols hygroscopicity: Affects haze formation and atmospheric humidity

Soot deposited on ice Changes albedo

Radiatively-active gases Modulate radiation transfers

III) ALTERED METEOROLOGY IMPACTS ON METEOROLOGY:

Clouds Modulate boundary layer outflow/inflow

Water vapour Modulates radiation

Temperature gradient Influence cloud formation

Rules turbulence intensity

IV) ALTERED CHEMISTRY IMPACTS ON CHEMISTRY

Biogenic emissions Affects concentrations of biogenically limited species

Polymerisation of organic aerosols

Yields long chain secondary organic aerosol (SOA) with lower volatility.

Chains/loops of interactions required online

integration of MetM & ACT

• Aerosol → radiation → photolysis → chemistry

• Temperature gradients → turbulence → surface

concentrations, boundary layer outflow/inflow

• Aerosol → cloud optical depth through influence of

droplet number on mean droplet size → initiation of

precipitation

• Aerosol absorption of sunlight → cloud liquid water

→ cloud optical depth

EuMetChem Meteorology-Chemistry Interactions Expert

Poll Survey: Summary results for all model categories

Score1 Quite well

Fairly well Poor Not inc.

Don't know Score2

NWP 2.3 5.4 11.2 25.4 30.0 28.0 1.4

CWF 3.1 11.0 31.4 32.7 6.1 18.9 2.1

Climate 2.4 1.8 14.9 29.6 1.8 52.0 1.1

All models 2.6 6.1 19.2 29.2 12.6 32.9 1.5

Next Step: Based on the rank of score1, top six interactions (out of 24

interactions) of each model category has been selected and reviewed how

well they are represented in the models. Full lists are available in excel file.

Average score1 (rank of the importance of the interactions) and score2

(rank of the representation in the model) for all the 24 interactions

Results show that in general most of the meteorology and chemistry

interactions are more important for CWF models than NWP and climate

models and those interactions are represented better in CWF models than

NWP and climate models based on the expert poll opinion.

Ranked top six interactions for CWF models

Rank

Meteorology and Chemistry interactions

Score1

Rate the representation in models (%)

Score2

Quite

well

Fairly

well Poor

Not

incl.

Don't

know

1

Changes in precipitation

(frequency/intensity) affect atmospheric

composition 3.9 30 60 0 0 10 3.0

2

Changes in wind speed affect dust emissions

and seasalt emissions 3.8 10 40 40 0 10 2.4

3

Changes in temperature affect biogenic

emissions (isoprene, terpenes, Dimethyl

Sulfide, etc.) 3.7 0 81.8 9.1 0 9.1 2.6

4

Changes in temperature vertical gradients

affect vertical diffusion 3.7 10 70 10 0 10 2.7

5

Changes in radiation affect chemical

reaction rates and photolysis 3.6 27.3 54.5 9.1 0 9.1 2.9

6

Changes in temperature affect chemical

reaction rates and photolysis 3.6 36.4 54.5 0 0 9.1 3.1

Interactions of physical, chemical and biological

weather calling for an integrated approach (Collaboration with COST ES0602 and ES0603)

Definitions:

• Physical weather is defined as the short-term atmospheric state and variation, characterized by physical atmospheric variables, such as solar radiation, temperature, humidity, pressure and wind speed and direction.

• Chemical weather is defined as the short-term (less than two weeks) state and variation of the atmospheric chemical composition.

• By analogy, the short-term state and variation of concentrations of bioaerosols, such as allergenic pollen and fungal spores, can be defined as the biological weather.

=> Towards Integrated Services

Klein, Kukkonen, Dahl, Bossioli, Baklanov, Vik, Agnew, Karatzas, Sofiev, AMBIO, 2012

Development: Started by DMI team >10 years ago

1st European online coupled model with

feedbacks (WMO, 2007)

HIRLAM Chemical Branch HIRLAM-B

joined: universities from several countries

collaboration with WRF-Chem and ECMWF

Main steps of realisation:

(i) model nesting for high resolutions,

(ii) improved resolving PBL and SL

structure,

(iii) ‘urbanisation’ of the NWP model,

(iv) improvement of advection schemes,

(v) emission inventories and models,

(vi) implementation of chemical

mechanisms,

(vii) implementation of aerosol dynamics,

(viii) realisation of feedback mechanisms,

(ix) assimilation of monitoring data.

New products and Applications =>

Enviro-HIRLAM

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

ETEX: On-line/Off-line Comparison

False peak due to

off-line coupling

Korsholm, Baklanov, Gross, Sørensen, AE, 2009

Changes in: temperature – up to 2-3 (mon. av. 0,5) deg C, wind speed –

up to 2-4 m/s, urban boundary layer height – up to 200 m; local thermally

induced circulations redistribute the aerosols and trace gases

Enviro-HIRLAM: Indirect Aerosols Feedbacks

665 km

445

km

Paris Case Study:

28 Jun – 3 Jul 2005

Difference (reference - perturbation)

Temp 2 m, deg C

Wind 10 m, m/s

(Korsholm et al., 2010)

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)

Dominating process in this case:

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

Daytime cooling, night time heatingShortwave, long wave response

Pre

ssur

e (p

a)

concentration (μg m-3)

Pre

ssur

e (p

a)

concentration (μg m-3)

WRF/Chem: Aerosol Effects on Meteorology & Chemical Composition

Differences in ozone (left) and PM10 (right) concentrations in July 2006 between two WRF/CHEM simulations.

Simulation BASE does not consider interactions between aerosols and meteorology. Simulation RFBC considers

both direct and indirect effects (RFBC).

Forkel et al. (Atmos. Environ. 2012).

a b c

West to East cross-section of rain mixing ratio (colour palette in g kg-1) and ice mixing ratio (red line contours in g kg-1) at the time of highest cloud

top over Haifa. a) 9 UTC 29 January 2003 assuming 5% hygroscopic dust. b) 10 UTC 29 January 2003 assuming 20% hygroscopic dust. c) 9 UTC

29 January 2003 assuming 5% hygroscopic dust and INx10.

Solomos et al. (ACP, 2011)

RAMS/ICLAMS: Effects of pollution on precipitation

• Suggested concept - chemical weather as two-way interacted meteorological weather and chemical composition of the atmosphere => COST Action ES1004 EuMetChem.

• On-line integration of MesoMetMs and ACTMs enables: utilisation of all meteorological 3D fields in ACTMs at each time step;

consideration of feedbacks of air pollution on meteo-processes & climate forcing.

• New generation of integrated models - not only for CWF, but also for climate change modelling, NWP (e.g., in urban areas, severe events, etc.), air quality and mitigations, long-term assessment of chemical composition, etc.

• Main advantages of on-line coupling: only one grid for MMM and ACTM, no interpolation in space and time;

physical parameterizations are the same, no inconsistencies;

all 3D meteorological variables are available at each time step;

no restriction in variability of meteorological fields;

possibility to consider two-way feedback mechanisms;

does not need meteo- pre/post-processors.

• Feedback mechanisms - important (supported by simulation results) in CWF modelling and quantifying direct and indirect effects of aerosols (and probably GHGs).

• Indirect aerosol feedbacks: sensitivity of meteorology and chemistry: full chain is needed, strong non-linearity, e.g. indirect effects induce large changes in precipitation and chemistry.

• Evaluation of online models with feedbacks is needed: joint study within AQMEII Phase2

Conclusions

Future needs and recommendations

• Although we may continue to develop and run modeling systems of Earth system components separately, a scientific perspective would argue for an eventual migration to integrated modeling systems that allow two-way interaction of physical and chemical components of chemical weather forecasting systems.

• While this may be the obvious approach for air quality forecasting, more research and discussion may be needed for NWP.

• AQ and NWP communities should work more closely together.

• National weather centers are advised to include chemistry/aerosol interactions into NWP systems extending their forecasts to the chemical weather as well.

• Centers that are responsible for AQ forecasting should seriously consider online modeling as a necessary part of their suite of forecasts. Additional advantages will arise from cross evaluations for both disciplines. Chemical species will allow identification of short comings in currently used forecast models as well as lead to better use of meteorological data assimilation.

• Other outcomes from such collaboration and the online coupling may include benefits for:

(i) meteorological weather forecasting (e.g., in urban areas, severe weather events, fog, and visibility, UV-radiation and solar energy, etc.),

(ii) chemical weather/air quality and bio-meteorology forecasting,

(iii) seasonal and decadal air quality/climate prediction,

(iv) global and regional projections of the climate/Earth system.

(Grell and Baklanov, 2011)

Relevant publications: • Baklanov, A., 2010: Chemical weather forecasting: a new concept of integrated modelling, Advances in Science

and Research, 4, 23-27.

• Grell, G. and A. Baklanov, 2011: Integrated Modeling for Forecasting Weather and Air Quality: A Call for Fully Coupled Approaches, Atmospheric Environment, 8, 2895–2932, doi:10.1016/j.atmosenv.2011.01.017.

• Baklanov, A., A, Mahura and Sokhi, R. (Eds.) 2011: "Integrated Systems of Meso-Meteorological and Chemical Transport Models, Springer Book, ISBN:978-3-642-13979-6, p. 242.

• Kukkonen, J., Olsson, T., Schultz, D. M., Baklanov, A., Klein, T., Miranda, A. I., Monteiro, A., Hirtl, M., Tarvainen, V., Boy, M., Peuch, V.-H., Poupkou, A., Kioutsioukis, I., Finardi, S., Sofiev, M., Sokhi, R., Lehtinen, K. E. J., Karatzas, K., San José, R., Astitha, M., Kallos, G., Schaap, M., Reimer, E., Jakobs, H., and Eben, K, 2012: A review of operational, regional-scale, chemical weather forecasting models in Europe, Atmos. Chem. Phys., 12, 1-87, doi:10.5194/acp-12-1-2012, 2012.

• Klein, T., J. Kukkonen, Å. Dahl, E. Bossioli, A. Baklanov, A.F. Vik, P. Agnew, K.D. Karatzas, M. Sofiev, 2012: Interactions of physical, chemical and biological weather calling for an integrated assessment, forecasting and communication of air quality, Ambio , 05/2012, DOI: 10.1007/s13280-012-0288-z

• Zhang, Y., M. Bocquet, V. Mallet , Ch. Seigneur, A. Baklanov, 2012: Real-Time Air Quality Forecasting: part I: History, techniques, and current status; part II: State of the Science, Current Research Needs, and Future Prospects. Atmospheric Environment, 82 p., http://dx.doi.org/10.1016/j.atmosenv.2012.02.041 and ...06.31

• Alapaty, K., R. Mathur, J. Pleim, Ch. Hogrefe, S. T. Rao, V. Ramaswamy, S. Galmarini, M. Schaap, R. Vautard, P. Makar, A. Baklanov, G. Kallos, B. Vogel, R. Sokhi, 2012: New Directions: Understanding Interactions of Air Quality and Climate Change at Regional Scales. Atmospheric Environment. 49: 419–421

• Forkel, R., J. Werhahn, A.B. Hansen, S. McKeen, S. Peckham, G. Grell, P. Suppan. 2012: Effect of aerosol-radiation feedback on regional air quality - A case study with WRF/Chem. Atmospheric Environment 53, 202-211.

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