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HIRLAM-A Work Plan 2007 Draft 2007-01-25 Integrated systems for weather and air quality forecasting Leif 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 XV Incheon, 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 / belowcloud 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 Examples of aerosol-meteorology feedbacks 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 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 BOLCHEM Ozone as prognostic chemically active tracer Only European short range models with aerosol indirect effects ENVIRO-HIRLAM Gas phase, aerosol and heterogeneous chemistry Each HIRLAM time Yes step WRF-Chem RADM+Carbon Bond, Madronich+Fast-J photolysis, modal+sectional aerosol Each model time step Yes WMO-COST728 GAW 177 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: ECHAM5COSMO 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 Operational ECMWF model Prog. stratos passive O3 tracer (IFS) ECMWF GEMS modelling GEMS chemistry GME OPANA=MEMO+CBMIV *) Time step for coupling Feedback None None Each model time ste Each model time step Yes Progn. stratos passive O3 tracer Each model time step Each model time step Direct effects only; **) On-line access model; ***) Only via photolysis 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 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 ) • 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. st 1 Level 2 rd nd Level 3 Level 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 • • • • 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 DMI-HIRLAM Modelling Domains Multy-scale Modelling and M2UE nesting Urban Areas M2UE Hor. Resol.: T: 15 km S: 5 km U01: 1.4 km I01: 1.4 km M2UE resol.: 10-300 m Enviro-HIRLAM results: Korsholm et al., 2009 445 km Effect of Paris on regional thermal structure 665 km Horizontal resolution: 0.05º x 0.05º Vertical resolution: 40 levels Model top: 10 hPa MSG1 satellite image 2005-06-30, 12 UTC 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), T2m comparison at a measurement station downwind from Paris Aerosol indirect effects Korsholm et al., 2009 Difference from measurements (Cº) T2m comparison; average over all 31 stations Daytime improvement Korsholm et al., 2009 Findings In this particular meteorological case: 2IE led to a general better T2m 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). Dominating process in this case: Paris Aerosols Increased Cloud cover (2nd aerosol indirect effect) Shortwave, long wave response Daytime cooling, night time heating 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) Pressure (pa) Additionally: local thermally induced circulations redistribute the aerosols and trace gases: concentration (μg m-3) Korsholm et al., 2009 Conclusions In this particular case (Korsholm et al., EMS, 2008): • 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 Integrated Atmospheric System Model Structure Aerosol Dynamics Model Transport & Chemistry Models Climate / Meteorological Models Interface / Coupler Atmospheric Contamination Models Atmospheric Dynamics / Climate Model Ocean and Ecosystem Models 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) Thank You !