1. DMI-ENVIRO-HIRLAM An On-Line Coupled Multi-Purpose Environment Model U. Korsholm*, A. Baklanov, A. Mahura, C. Petersen, K. Lindberg, A. Gross, A. Rasmussen, J.H. Sørensen, J. Chenevez The Danish Meteorological Institute, Copenhagen, Denmark * usn@dmi.dk, phone: +45 39157439 ACCENT/GLOREAM Workshop 2006

2. Purpose Plans and current status, main model features Preliminary results: aerosol–meteorology feedbacks Motivation Climate: direct, indirect, semi-direct effects, large scale dynamical feedback (Kim & Lee, 2006; Kim et al., 2006) Local: direct, indirect, semi-direct effects, local scale feedbacks Importance for short range weather forecasting ? (Perez et al. 2006)

3. Radiation budgets Temperature profiles Chemistry/ Aerosols Cloud Condensation Nuclei Precipitation Chemistry/ Aerosols Examples of feedbacks Cloud- radiation interaction Temperature profiles Chemistry/ Aerosols

4. Definitions off-line models comprise: Separate CTMs driven by meteorological input data from meteo- preprocessors, measurements or diagnostic models Separate CTMs driven by analysed or forecasted meteo-data from NWP archives or datasets Separate CTMs reading output-files from operational NWP models or specific MetMs with limited temporal resolution (e.g. 1, 3, 6 hours) on-line models comprise: On-line access models, when meteo-data is available at each time-step On-line integration of a CTM into a MetM; feedbacks are possible: on-line coupled modeling

5. 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 Possibility of feedbacks with meteorology All 3D meteorological variables are available at the right time (each time step); no restriction in variability of met. fields Does not need meteorological- pre/post-processors Off-line Possibility of independent parameterizations Low computational cost; more suitable for ensembles and operational activities Independence of meteorological model computations

6. Future plans Numerical Weather Prediction Model Why develop an on-line coupled model ? Climate; NWP; Research; Operational; Emergency 1. Model nesting for high resolutions 2. Improved representation of pbl. and sl. 3. ‘Urbanisation’ of the NWP model 4. Improvement of advection schemes 5. Implementation of chemical mechanisms 6. Implementation of aerosol dynamics 7. Realisation of feedback mechanisms 8. Assimilation of monitoring data

7. Emission Transport Dispersion Deposition Current status Observational database ECMWF Surface analysisUpper air analysis BoundariesOutput Initialisation Forecast DMI-ENVIRO-HIRLAM

8. Model Description 1 Model identificationT15S05 grid points (mlon)610496 grid points (mlat)568372 number of vertical levels40 horizontal resolution (deg)0.15°0.05° time step (dynamics)360s120s time step (physics)360s120s host modelECMWFT15

9. Model Description 2 Transport and dispersion Bott advection (Bott, 1989) + Easter update for tracers (Easter, 1993); Semi-Lagrangian for meteorology –Risk of mass-wind inconsistency No horizontal diffusion Vertical diffusion: CBR-scheme (Cuxart et al., 2000) –Coefficient defined by mixing length formulation in stable/unstable conditions Mass conservation test for ETEX release

10. ETEX 1, 48 hours after start of release Semi-Lagrangian Bott scheme Bott-Easter sheme Model Description 3

11. Model Description 4 Deposition Particle size dependent parameterizations for dry and wet deposition Resistance approach for dry deposition (Wesley, 1989; Zanetti, 1990) Terminal settling velocity in different regimes: –Stokes law –non-stationary turbulence regime –correction for small particles Dependent on land use classification Below-cloud scavenging (washout); precipitation rates (Baklanov & Sorensen, 2001) Scavenging by snow (Maryon et al., 1996) Different scavenging of particles and gases Next step Rainout into 3D clouds (based on on-line coupling): –convective precipitation –stratiform precipitation

12. Preliminary results 1: Deposition Accumulated dry deposition [kBq/m 2 ] Chernobyl accident; point source emissions (Devell et al., 1995, persson et al., 1986) Date: 19860501 18:00 UTC Accumulated wet deposition [kBq/m 2 ]

13. Preliminary results 2: Feedback For water clouds: r³ eff = kr³ v r³ eff =3L/(4  l kN) (Wyser et al. 1999) L : Cloud condensate content N: Number concentration of cloud droplets ΔN cont = 10 8.06 conc 0.48 ΔN marine = 10 2.24 conc 0.26 (Boucher & Lohmann, 1995) Emission rate: 7.95 gs -1 ; ETEX Diameter: 1 µm kN [m -3 ] Marine0.8110 8 Cont0.694х10 8 Urban fractions [%; dark green – dark red]

14. Preliminary results 2: Feedback Difference (ref - perturbation) in accumulated dry deposition [ng/m 2 ] Difference (ref - perturbation) in accumulated wet deposition [ng/m 2 ] Accumulated (reference) dry deposition [μg/m 2 ] +48 h Accumulated (reference) wet deposition [μg/m 2 ] +48 h

15. DMI is developing an on-line coupled environment model: DMI-ENVIRO- HIRLAM –emission module, inventories –transport, dispersion, dry and wet deposition –aerosol dynamics –gas-phase and heteorogeneos chemistry –data assimilation –cloud, radiation coupling To be used for: research, operational, emergency ? Main advantages of on-line coupled meso-scale NWP model and CTM –no restriction in variability of input fields –possibility of feedbacks Previously tested for mass conservation Deposition being tested on Chernobyl accident –looks promising, local hot-spots Investigation into cloud-aerosol coupling –model sensitivity, large changes in deposition Summary

16. Acknowledgements The HIRLAM development program at DMI Copenhagen Global Change Initiative (COGCI) References Baklanov A. & Sorensen, H., J., 2001, Physics and Chemistry of the Earth, vol. 26, No. 10, 787-799 Bott, A., 1989, Mon. Wea. Rev., 117, 1006-1015 Boucher, O. & Lohmann, U., 1995, Tellus 47, Ser. B, 281-300 Cuxart, J. et al., 2000, Q.J.R. Meteo. Soc., 126, 1-30 Devell et al., 1995, CSNI report, OECD/NEA, Paris Easter, C., Mon. Wea. Rev., vol. 121, 297-304 Kim, M., K. et al., J. Clim., 2006, in press Kim, M., K. & Lee, W., S., GRL, vol. 33, L16704, 2006 Maryon R., H. et al., 1996, Depart. Of Env., UK, Met. Office. DoE Report # DOE/RAS/96.011 Perez, C. et al., JGR, vol. 111, D16206, 2006 Persson et al., SMHI/RMK report No. 55, 1986 Wesley, M.,L., 1989, Atm. Env., vol. 23, No. 6, 1293-1304 Wyser et al., 1999, Contr. Atmos. Phys., vol. 72, No. 3, 205-218 Zanetti, P., 1990, Air Pollution Modelling – Theories, Computational Methods and Available Software. Southhampton: Computational Mechanics and New York: Van Nostrand Reinhold