WWOSC, Montreal Influence of wild land fires on atmospheric dynamics and air quality: A process study with the modelling system COSMO-MUSCAT Ralf Wolke, Detlef Hinneburg, Wolfram Schrder Leibniz Institute for Tropospheric Research Leipzig, Germany WWOSC, Montreal Outline Motivation Main features of COSMO-MUSCAT Description of different model setups Model study (focus on East + Central Europe) Impact of wild land fires on air pollution Feedback on atmospheric dynamics Sensitivity and robustness Summary and outlook Motivation WWOSC, Montreal It is estimated that wild land fires in European Russia emitted over 80 % of the anthropogenic CO emissions in 2010 (Konovalov et al., 2014). High impact of wild land fires on climate and air quality is not longer in any doubt. Several studies of effect in literature (e.g. Sofiev et al.,2009, 2012; Witham and Mannig, 2007; Saarikoski et al., 2007; Grell et al., 2011). Is there also an effect on weather forecast? To get feeling for observed model feedbacks in relation to other model variations Different setups are investigated for the corresponding period (1 April 15 April 2010) to analyze especially the influence on PM concentration fields and the feedback on meteorological dynamics. WWOSC, Montreal Chemistry-Transport Model MUSCAT ( MUltiScale Chemistry Aerosol Transport ) Transport and chemical transformation of gas phase pollutants and particles in the atmosphere Online coupling with COSMO Applied from regional to urban scale Mainly used in forecast mode (without data assimilation and nudging) Direct and semi-direct feedback is implemented. Parallel COSMO Parallel MUSCAT Coupler WWOSC, Montreal The Meteorological Model COSMO (DWD: Doms, Schttler, et al ; Baldauf et al. 2011) non-hydrostatic, compressible formulated with regard to a hydrostatic reference state staggered grid horizontal: uniform, orthogonal rotated Lambda-Phi grid hybrid vertical coordinate operational mode for weather forecast, regional scale boundary and initial data from GME highly parallel Usually: Operational setup (Version: 5.01) prognostic TKE, multi-layer surface model, WWOSC, Montreal Chemistry Transport Model System COSMO-MUSCAT Gas phase ( read in ): RACM (Stockwell et al., 1997) + MIM2 (Karl et al., 2006) Aerosol model: Mass-based approach (e.g., EMEP) or Modal approach M7 (Vignati et al, 2004): 4 internal-mixed and 3 external modes sulphate, sea salt, dust, EC, OC extended by nitrate and ammonium SIA by ISORROPIA (Nenes et al., 1998) SOA by SORGAM (Schell et al., 2001) Dust: sectional (5 bins) Dry and wet deposition, sedimentation Emissions: Anthropogenic (11 snaps, area + point, fires) Biogenic (Gnther et al., 1993) Seasalt (Guelle et al., 2001) 19/02/2016ACCENT/GLOREAM, Berlin Gas phase chemistry: Most applications: RACM-MIM2 (Stockwell et al, 1997; Karl et al, 2006) Cloud chemistry: INORG, CAPRAM3.0red (Schrdner et al, 2014) Reaction mechanism from input file: High flexibility. Difference to KPP (Sandu et al.): Data structures are generated. KPP generates FORTRAN code. WWOSC, Montreal Numerical Methods in MUSCAT Space discretization Staggered grid. Finite-volume techniques. Multiblock approach (different grid resolutions in the domain) Advection: Third-order upwind scheme Time-integration: IMEX scheme Explicit second-order Runge-Kutta for horizontal advection Second order BDF method for the rest: Jacobian is calculated explicitly, linear systems by Gauss-Seidel iterations or AMF Automatic step size control Multirate approach (Schlegel et al, 2012) Parallelization domain decomposition (blockwise) dynamical load-balancing by redistribution of blocks WWOSC, Montreal Coupling Scheme: Grid Structure Multiblock data structures: COSMO (left) and MUSCAT (right) WWOSC, Montreal Spatial grid transformation of meteorological arrays COSMOMUSCAT interpolation doubling averaging T, , q, uu scalar flux Finite volume approach saves mass conservation if this is fulfilled in the original meteorological grid !! WWOSC, Montreal Coupling Scheme (+ feedback to COSMO) Time interpolation of the meteorological fields: Linear interpolated in [t n,t n+1 ]: Temperature, Density, Time-averaged values on [t n,t n+1 ]: Projected wind field Separate time step size control for MUSCAT Coupling step in consistency with the numerical scheme Feedback WWOSC, Montreal Feedback of Modelled Aerosol and Gas Phase Concentrations on Meteorology The climatological fields in the COSMO radiation scheme are replaced by the modelled fields. Aerosol optical properties are calculated from aerosol mass and composition assuming external mixing (Meier et al., 2012). Hygroscopic growth behaviour that influences extinction coefficients is described for the individual compounds by empirical parameterizations. As a result of the evaluation of the AOD scheme in AQMEII-2 (Curci et al., 2014), our scheme is adjusted using an approach of Ayash et al. (2008). WWOSC, Montreal Time Schedule for Model Runs WWOSC, Montreal Model Evaluation Study Performed in the framework of AQMEII-2: Air Quality Model Evaluation International Initiative. Simulations are performed for the EU domain. The annual simulation for 2010 is included in the ENSEMBLE data base and involved in the joint analysis. Anthropogenic emissions (TNO), fire emissions (FMI) and CTM boundary conditions (MACC) provided by the AQMEII community. Simulations are performed in the forecast mode without nudging and DA. COSMO is forced by reanalyzed GME data provided by the DWD. Cyclic time schedule with one day spin-up of the COSMO model. WWOSC, Montreal Evaluation of COSMOMUSCAT by the Annual Run for 2010 Performed in several joint papers submitted to the SI of AQMEII-2 Main results: PM2.5 and PM10 are under predicted. Sulphate, ammonia and nitrate can be reproduced over a wide range. EC and SOA are underestimated. WWOSC, Montreal Outline Motivation Main features of COSMO-MUSCAT Description of different model setups Model study (focus on East + Central Europe) Impact of wild land fires on air pollution Feedback on atmospheric dynamics Sensitivity and robustness Summary and outlook WWOSC, Montreal Model Study: Impact and Feedback of Fire Emissions Here: Focus on East + Central Europe and on a 2-week period (115 April 2010) with wild land fires in Russia and the Ukraine. Nested subdomain with finer grid resolution is used. 7 different model setups are compared. Simulation results are compared with ground-based measurements, radiosonde and satellite data. Statistical analysis only for ground data. Fire Emissions: Emission fluxes are computed using the dataset of FMI (Sofiev et al., 2009). Daily fire emissions are provided as point sources with a prescribed vertical splitting. FMI fire data contain emissions for selected gas phase precursors (CO, NOx, NH3,SO2) as well as for primary emitted EC, POC, PM2.5 and PM WWOSC, Montreal Analysis of different model setups Model SetupFire Emissions Semi-direct Feedback on Dynamics Feedback on Photolysis Cycle Length FB-FEyes no48 h noFB-FEyesno 48 h noFB-noFEno 48 h FB-noFEyes 48 h FB-FE-92hyes no92 h oldFB-FEMeier (2012)yesno48 h FB-modFEyesmod. Profileno48 h WWOSC, Montreal Analysis of different model setups Model SetupFire Emissions Semi-direct Feedback on Dynamics Feedback on Photolysis Cycle Length FB-FEyes no48 h noFB-FEyesno 48 h noFB-noFEno 48 h FB-noFEyes 48 h FB-FE-92hyes no92 h oldFB-FEMeier (2012)yesno48 h FB-modFEyesmod. Profileno48 h WWOSC, Montreal COSMO-MUSCAT Setups COSMO GridMUSCAT Grid Different grid resolutions: 14 km 7 km, 30 layers Uniform grid resolution: 14 km, 50 layers Forced by an COSMO-MUSCAT run on European domain (28 km grid resolution) ! WWOSC, Montreal Impact of Wild Land Fires on Air Quality: PM2.5 (max. 50g/m3) 13 April 2010, 10:00 WWOSC, Montreal Impact of Wild Land Fires on Air Quality: Satellite Image MODIS: 13 April 2010, 10:10 MODIS: 13/04/2010, 10:10 WWOSC, Montreal Impact of Wild Land Fires on Air Quality: PM2.5 (max. 50g/m3) Evolution of the plume: April 2010 WWOSC, Montreal Impact of Wild Land Fires on Air Quality: PM2.5 (max. 50g/m3) Evolution of the plume: April 2010 WWOSC, Montreal PM2.5 [g/m3] 13/04/, 12:0014/04/, 12:00 Diff = Fire -- noFire WWOSC, Montreal Ammonium Nitrate [g/m3] 13/04/, 12:00 mean with fires without fires WWOSC, Montreal AOD [ ] without fires with fires mean 13/04/, 12:00 WWOSC, Montreal CO [g/m3] without fires with fires O3 [g/m3] mean WWOSC, Montreal Impact of Wild Land Fires on Atmospheric Dynamics Analysis was performed for 32 measurement stations. PM2.5 [g/m3] WWOSC, Montreal measurements noFB-FE noFB-noFE noFB-FE = no feedback + fire emissions noFB-noFE = no feedback + no fire emissions BETN Impact of Wild Land Fires on PM2.5 Concentration WWOSC, Montreal measurements noFB-FE noFB-noFE noFB-FE = no feedback + fire emissions noFB-noFE = no feedback + no fire emissions S0066A Impact of Wild Land Fires on PM2.5 Concentration 50 WWOSC, Montreal Analysis of different model setups Model SetupFire Emissions Semi-direct Feedback on Dynamics Feedback on Photolysis Cycle Length FB-FEyes no48 h noFB-FEyesno 48 h noFB-noFEno 48 h FB-noFEyes 48 h FB-FE-92hyes no92 h oldFB-FEMeier (2012)yesno48 h FB-modFEyesmod. Profileno48 h WWOSC, Montreal EC [g/m3] AOD555 Difference of Temperature: FB -- noFB Impact of Wild Land Fires on Atmospheric Dynamics 13/04/2010, 10:0014/04/2010, 10:00 WWOSC, Montreal EC [g/m3] AOD555 Difference: FB -- noFB Impact of Wild Land Fires on Atmospheric Dynamics time-averaged Temp [K] Snet [W/m2] AOD555 WWOSC, Montreal measurements noFB-FE FB-FE noFB-FE = no feedback + fire emissions noFB-noFE = no feedback + no fire emissions BETN Impact of Wild Land Fires on Atmospheric Dynamics WWOSC, Montreal measurements noFB-FE FB-FE noFB-noFE FB-FE = feedback + fire emissions noFB-FE = no feedback + fire emissions noFB-noFE = no feedback + no fire emissions BETN Impact of Wild Land Fires on Atmospheric Dynamics WWOSC, Montreal Impact of Wild Land Fires on Atmospheric Dynamics 9 selected AERONET stations WWOSC, Montreal Summary and outlook Influence of wild land fires on atmospheric dynamics and air quality was investigated. A set of 7 different setups was used. Simulations of COSMO-MUSCAT are compared with measurements. The model can capture the range and variability of PM2.5. AOD is underestimated in several cases. One key finding is the relatively high responsively concerning changes in the model configuration. The impact of the fires on the dynamics needs more investigations considering also the indirect effect. Acknowledgement This work was supported by the AQMEII consortium, TNO, NIC Jlich and the ZIH Dresden. Furthermore, we thank the DWD for their cooperation. WWOSC, Montreal Thank You!