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Input data for models of MSC-E

This presentation discusses the use of geophysical and meteorological data in the EMEP/MSC-E model to analyze pollutant properties, emissions, and the effects of meteorological variability on modeling results. The selection of a reference year and minimizing the effects of variability are also explored.

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Input data for models of MSC-E

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  1. Input data for models of MSC-E Ilia Ilyin EMEP/MSC-E 6th TFMM Meeting, Zagreb, April 2005 EMEP/MSC-E

  2. MSCE-HM&POP model Pollutant properties Geophysical data Emissions Meteoro-logical data MSCE-HM/POP Model Model output 6th TFMM Meeting, Zagreb, April 2005 EMEP/MSC-E

  3. Geophysical data - Land-use (18 categories) - Leaf Area Index - Organic content of soil - Chemical reactants - Topography - etc …. 6th TFMM Meeting, Zagreb, April 2005 EMEP/MSC-E

  4. Emission data Within EMEP 44 countries Available official data: - National totals: 11 - 35 countries - Spatial distribution: 18 countries (HMs), 10 (POPs) - Emission sector data: 23 countries - Seasonal variability: no official data - Uncertainty analysis: Denmark only Official data are incomplete Expert estimates are used extensively Natural emission, re-emission of HMs: the data are prepared by MSC-E 6th TFMM Meeting, Zagreb, April 2005 EMEP/MSC-E

  5. Meteorological data • Meteorological preprocessor • Meteorological variability • Reference meteorology 6th TFMM Meeting, Zagreb, April 2005 EMEP/MSC-E

  6. Meteorological data - For hemispheric model: SDA (System of Diagnosis of low Atmosphere) , Hydrometeorological Centre of Russia - For regional version of the model: MM5 system (Pennsylvania State University/NCAR Community model) 6th TFMM Meeting, Zagreb, April 2005 EMEP/MSC-E

  7. PSU/NCAR mesoscale model Meteorological pre-processor: MM5 (version 3) MM5 features: • Support various map projections, including stereographic one • Support various data sets, e.g. re-analysis of NCEP/DOE or ECMWF • Parameterizations selected by user • Works reasonably fast • Available nesting • World-wide spread and tested 6th TFMM Meeting, Zagreb, April 2005 EMEP/MSC-E

  8. MM5 adaptation for EMEP tasks • Vertical structure: Like in transport model • Horizontal structure: EMEP grid + surrounding 6 gridcells • 3D precipitations are introduced by MSC-E • NCEP/DOE Reanalysis data EMEP domain MM5 domain 6th TFMM Meeting, Zagreb, April 2005 EMEP/MSC-E

  9. Parameters derived form MM5 (1990 - 2002) Temporal resolution: 6 hours - Horizontal wind components - Surface pressure - Air temperature - Water vapour mixing ratio - Liquid water mixing ratio - Ice mixing ratio - Convective precipitation - Large-scale precipitation - Turbulent coefficient - Surface temperature - Monin-Obukhov length scale - Friction velocity - Snow cover height 6th TFMM Meeting, Zagreb, April 2005 EMEP/MSC-E

  10. Specific tasks (effect of meteo variability is important): - Long-term pollution trends in response to emission reduction - Future emission projections - Critical loads approach and risk assessment 6th TFMM Meeting, Zagreb, April 2005 EMEP/MSC-E

  11. Effects of meteorological variability on modelling results - Pb and Hg - 1990 – 2002 - Constant emission - Concentrations in air, in precip, total depositions Relative deviation: Yi,j - model output parameter 6th TFMM Meeting, Zagreb, April 2005 EMEP/MSC-E

  12. Relative deviations (air concentrations, Pb) Lead Map of relative deviation 6th TFMM Meeting, Zagreb, April 2005 EMEP/MSC-E

  13. Model output uncertainty caused by meteorological variability (Pb) MSRE - Mean-Square Relative Error Error bars: range (90% interval) MSRE = 10 - 45% for Pb 6th TFMM Meeting, Zagreb, April 2005

  14. Relative deviations (TGM air concentrations) TGM Map of relative deviation 6th TFMM Meeting, Zagreb, April 2005 EMEP/MSC-E

  15. Model output uncertainty caused by meteorological variability (Hg) Error bars: range (90% interval) MSRE = 3 - 10% (TGM) = 10 - 40% (Conc. in precip, total depositions) 6th TFMM Meeting, Zagreb, April 2005

  16. How can we minimize the effects of meteorological variability? • 2 possible solutions: • Long-term model runs with constant annual emission and further averaging • Meaningful selection of “reference” year Reference year: A year, which computed model output parameters most resemble those averaged over long period of time 6th TFMM Meeting, Zagreb, April 2005 EMEP/MSC-E

  17. Selection of the reference year Parameters analyzed (1990 - 2002): - Air concentrations - Concentrations in precipitation - Total depositions - Precipitation annual sums Key parameters (WGE request): - Lead total depositions - Mercury concentrations in precipitation 6th TFMM Meeting, Zagreb, April 2005 EMEP/MSC-E

  18. Di,j - annual depositions in (i,j) point Dmean, i,j - multi-annual mean deposition in (i,j) point - spatial mean Dmean - spatial mean D Statistical criteria of the selection: Normalized Mean Square Error Fractional Bias Fractional Standard Deviation Correlation Coefficient Liner regression coefficients 6th TFMM Meeting, Zagreb, April 2005 EMEP/MSC-E

  19. Normalized Mean-Square Error, (Pb, total depositions) The lowest NMSE in 1990 6th TFMM Meeting, Zagreb, April 2005 EMEP/MSC-E

  20. Difference between mean deposition field of Pb and deposition in reference year (1990) Relative deviation = (D1990 - Dmean)/Dmean x 100% Relative deviation: ±20% over 90% of area 6th TFMM Meeting, Zagreb, April 2005 EMEP/MSC-E

  21. Normalized Mean-Square Error, (Hg, concentrations in precipitation) 6th TFMM Meeting, Zagreb, April 2005 EMEP/MSC-E

  22. Conclusions • Emission • Official data on HMs and POPs emissions are incomplete. To fill the gaps in the data, expert estimates are used • Meteorology • Variability of concentrations and depositions of aerosol species ranges from 10 to 45 % due to meteorological variability • Variability of TGM concentrations ranges from 3 to 10%, and of concentrations in precipitation and depositions of Hg from 10 to 40% due to meteorological variability. • 1990 can be chosen as reference year basing on data for 1990 – 2002. Relative deviation of depositions in 1990 from multi-annual mean depositions <20% 6th TFMM Meeting, Zagreb, April 2005 EMEP/MSC-E

  23. Criteria to select the reference year Normalized Mean Square Error Pi,j - depositions in point (i,j) in a separate year Oi,j - depositions in point (i,j) averaged over 1990–2002 - spatially averaged P - spatially averaged O N - number of grid cells

  24. Criteria to select the reference year(2) Fractional Standard Deviation Fractional Bias σO,σP - standard deviations Correlation Coefficient Liner regression coefficients

  25. Natural emission and re-emission of Pb and Cd Snow/ice surfaces – zero emission

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