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Use of model intercomparisons to evaluate the Unified EMEP model

Use of model intercomparisons to evaluate the Unified EMEP model. Hilde Fagerli EMEP, MSC-W, ACCENT meeting, Paris, june 2004. Background. EMEP model state-of-the-art? Fit for pupose? TFMM model review workshop ’Long’ timeseries ICP forest data ’Local’ evaluations; UK, NL,..

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Use of model intercomparisons to evaluate the Unified EMEP model

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  1. Use of model intercomparisons to evaluate the Unified EMEP model Hilde Fagerli EMEP, MSC-W, ACCENT meeting, Paris, june 2004

  2. Background • EMEP model state-of-the-art? • Fit for pupose? • TFMM model review workshop • ’Long’ timeseries • ICP forest data • ’Local’ evaluations; UK, NL,.. • model intercomparisons

  3. Evaluation of the EMEP model • Evaluation through model intercomparisons • EMEP modelintercomparison • CITY DELTA • EURO DELTA

  4. Review of the Unified EMEP Model Intercomparison of regional models over Europe – a flavour of results Maarten van Loon (change of affiliation per April 1st: EMEP MSC/W) Michiel Roemer Peter Builtjes + all participating modelling groups

  5. Set-up of the intercomparison • Two base years (1999 and 2001). Original intention: also 1997 • 162 output locations spread over Europe, EMEP+AIRBASE (rural background, European coverage ) • Compounds: • Hourly, gases: O3, NO2, NO, CO • Daily, gases: SO2 • Daily, aerosols: SO4, (T)NO3, (T)NH4, PM10 • Daily, in precipitation: SO4, NO3, NH4, mm • Goal • Establish the performance of the Unified EMEP model amidst state-of-the-art air quality models. This is done by evaluating the model results for a number of statistical indices. • Find aspects that may be improved in the model(s) • Participating models: EMEP + 6 others models of very different structure and complexity.

  6. Map of observational sites used

  7. Participating Models • CHIMERE (LMD) • DEHM (NERI) • LOTOS (TNO-MEP) • MATCH (SMHI) • MODELS3 (Innogy) only for 1999 • REM3 (FU Berlin) • Unified EMEP model

  8. Results Ozone, correlation coefficients hourly data Blue bars indicate range from the other models

  9. Results for O3, 2001 Example of the basic summary output of the analysis.

  10. Results O3 (I) Hourly values

  11. Results O3 (II) Daily maximum Spread of the models (except REM3 and LOTOS) on the low side.

  12. Results, PM10 (I) Right: mean Lower left: correlation NB: emission input for the two years is not done in the same way for the models.

  13. Results, PM10 (II) Right: fraction model/obs Lower left: correlation EMEP

  14. Results, correlation SO4

  15. Results, bias SO4

  16. Results, aerosol nitrate (NO3)  correlation Bias  (outlier removed) Correlation: good, but overestimation

  17. NOx: modelled versus observed General underestimation of NOx by the models

  18. Conclusions; EMEP model comparison Conclusions: • Behaviour of the EMEP model in line with other state-of-the art air quality models. • Consistent performance between the two years considered • Results for Ozone are good • Results for PM10 show an underestimation for all models. • Well known problem • Solution to this problem is “less well known”. Combination of underestimation of emissions and model resolution. • EMEP model is performing in a consistent way (I.e. for all compounds considered the model showed good performance, no outliers) • Off topic: ensemble modelling promising!

  19. CITY-DELTA A European model-intercomparison study in support to the CAFE programme organised by JRC-IES (coordinator), IIASA, EMEP, EUROTRAC / TNO-MEP C. Cuvelier and P. Thunis

  20. CityDelta contributes to CAFE Objectives: To explore changes in air-quality in cities (CITY) due to changes in emission (DELTA) as predicted by atmospheric models with different scales, i.e: • Identify differences between regional and urban model answers (scale delta) • How are these differences depending on emissions (emission delta) • How these deltas vary due to physical characteristics across cities (city delta) • What is the range of variability in model answers? (model delta) Goal: Implementation of urban signal into the RAINS model, development of functional relationship between the regional and urban responses

  21. Cities: Comparisons are conducted for a number of European cities with distinct differences in climatic conditions, geographical settings, and emissiondensities. London Paris Milan Marseille Copenhagen Berlin Katowice Prague

  22. Input Data: Monitoring data: For the 8 cities delivered by the city-authorities: O3, NO2, PM2.5, PM10 Meteorological data: Provided to CityDelta by Meteo-France, or by the modelling group themselves for reference year 1999. Emission inventories: • High-resolution (1 km to 5 km) city-emission inventories • Low-resolution (50 km) EMEP-TNO emission inventory Boundary conditions: Provided by EMEP, or by the modelling group themselves

  23. Emission Scenarios 0 --- 1999 1 --- 2010 CLE: Current LEgislation 2 --- 2010 NOx MFR: Maximum Feasible Reduction 3 --- 2010 NOx (CLE+MFR)/2 4 --- 2010 VOC MFR 5 --- 2010 NOX and VOC MFR 6 --- 2010 PMcoarse MFR 7 --- 2010 PM2.5 MFR

  24. Output requested: • Hourly values for O3 (and NO2) for 6 months (Summer) • Daily values for PM2.5 and PM10 for 12 months CALGRID Univ. Brescia (Italy) CAMX Ag. Mobilita Ambiente (Italy) CHIMERE INERIS-IPSL (France) EMEP EMEP (Norway) EPISODE NILU (Norway) EUROS RIVM (Netherlands) LOTOS TNO (Netherlands) MOCAGE Meteo-France (France) MUSCAT IFT (Germany) MUSE AUT (Greece) OFIS AUT (Greece) REM FU Berlin (Germany) SMOG Univ. Prague (Czech R.) STEM CESI (Italy) THOR NERI (Denmark) TRANSCHIM CORIA (France) 16 modelling groups with 40 different model configurations

  25. Validation with Obs. Monitoring data 2D Avg. Fields Scenario Deltas The JRC tool Emissions

  26. ENSEMBLE:O3 mean daytimein City Centre O3 exceedance days over 60 ppb Fine S models Coarse S models

  27. Conclusions Emission inventories are crucial. Local inventories are not always compatible with regional emissions Milan shows the largest differences between LS and FS models. Further scenarios were required for PM deltas to be analysed (CityDelta Phase 2). Final CityDelta workshop: 14-15/10/2004, JRC-IES, Ispra-Italy

  28. EuroDelta An inter-comparison of regional-scale model-responses to emission-reduction scenarios Organised by: JRC-IES, IIASA, EMEP, TNO-MEP, CONCAWE Coordinated by: JRC-IES (C. Cuvelier, P. Thunis)

  29. Objectives: The purpose of the regional model inter-comparison is to: • Evaluate the performance of regional-scale atmospheric dispersion models against observations and quantify their performance • Gain insight into the ability of regional-scale models to reproduce chemical nonlinearities in response to emission changes. The s/r relationships are an important input to IAM for policy applications. The results from this study will allow to: • Establish the performance of the UNIFIED EMEP model with respect to other state-of-the-art regional-scale models • Determine the range of modelled responses to emission changes at regional scale.

  30. Input data Monitoring data:All EMEP stations with O3, NO2, SO2, HNO3, NH3, SULF, NITR, AMMO, PM10, WD_SULF, WD_NITR, WD_AMMO, PREC Meteorological year:1999 Emission inventory:Updated EMEP

  31. Modelled pollutants

  32. Participation in EuroDelta

  33. EuroDelta Graphical Tool

  34. Final remarks • model intercomparisons give insight into • uncertainties in models • process understanding • what models can be used to • where development is needed • EMEP model in hemispheric scale • model intercomparison; vertical data

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