The task aimed to develop a parameterisation (module) that can simulate the sub-grid spatial distributions of mean annual concentrations and deposition rates of air pollutants (specifically ammonia, nitrogen dioxide and nitrogen deposition) within the grid cell of a chemical transport model (e.g. the EMEP MSC-W model) using high spatial resolution emission and land cover data. This included a simple parameterisation of short-range dispersion to estimate the spatial distribution (at a resolution of 1 × 1 km2) of the concentrations of ammonia (NH3) and nitrogen dioxide (NO2) within the 50 × 50 km2 (approx.) grid squares of the EMEP model. Pollutant dispersion from emission sources was parameterised using a simple scenario of a single 1 × 1 km2 source with a constant emission of 1 tonne km-2 yr-1 in the centre of a square domain (of dimensions 101 × 101 km2). Figure 2.7 shows the overall methodology for producing the sub-grid concentration predictions.
The resulting ‘sub-grid distributions’ provide an estimate of the spatial variability of the concentrations at a 1 km resolution, which were then used to ‘redistribute’ the 50 × 50 km2 concentration predictions of the EMEP (MSC-W) model. This was done by interpolating the concentration predictions of the EMEP model and the mean concentrations of the sub-grid distribution within each 50 × 50 km2 km grid square across the whole domain. The ‘grid predictions’ were then calculated by multiplying the sub-grid distributions by the interpolated EMEP predictions and then dividing by the interpolated sub-grid distribution The sub-grid model predictions of NO2 and NH3
concentrations are shown in Figure 2.8.
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Figure 2.7: Schematic showing the process of producing the sub-grid concentration predictions from short-range dispersion model simulations and high spatial resolution emission data.
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Figure 2.8: Sub-grid model predictions (top row) of mean annual concentrations of NO2 and NH3 for the two domains (Central Scotland and The Netherlands). EMEP model predictions at a resolution of 50x50km2 are shown for comparison (bottom row).
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A comparison of the modelled (sub-grid) concentrations with measured values shows that the modelled values compare reasonably with the measured values.
Separate sub-grid parameterisations were also developed for three of the four components of nitrogen deposition (wet oxidised, wet reduced and dry reduced). It was not possible to develop a simple parameterisation for dry deposition of oxidised nitrogen due to the contributions from multiple compounds, each affected by different transport and transformation processes. Dry deposition of reduced nitrogen is mainly the dry deposition of NH3 and therefore the sub-grid distribution of NH3 can be used for the sub-grid distribution of the dry deposition of reduced nitrogen. The proxy used to estimate the sub-grid variability of wet deposition was the product of high spatial resolution annual precipitation data and the atmospheric concentrations of nitrate and ammonium (from the EMEP model), for deposition of oxidised and reduced nitrogen, respectively. Sub-grid total nitrogen deposition is calculated as the sum of the total wet deposition and the dry deposition of reduced nitrogen simulated by the sub-grid model plus the EMEP model estimate of dry deposition of oxidised nitrogen. Overall both the EMEP and the sub-grid model underestimate wet deposition of oxidised and reduced nitrogen by an average of 50%. There is very little difference between the predictions of the models for the Netherlands as a result of low spatial variability of the precipitation.
With respect to the development of a sub-grid module, these overall conclusions could be drawn at the end of the project:
• The sub-grid model for nitrogen deposition uses the high resolution NH3
concentration data to simulate the spatial distribution of dry deposition of reduced nitrogen. The spatial distributions of wet deposition of both reduced and oxidises nitrogen are based on the spatial distributions of high resolution precipitation maps.
It has not been possible to develop a sub-grid model for the dry deposition of oxidised nitrogen
• Both sub-models have been applied to two contrasting areas (Central Scotland and the Netherlands) and model performance of both the EMEP model and the sub-grid model has been assessed using monitoring data of atmospheric concentrations and wet deposition for both study areas
• The sub-grid model for atmospheric concentrations represents a substantial improvement on the predictions of the EMEP model reducing both model error and increasing the spatial correlation with the measured concentrations
• The performance of the sub-grid model for wet deposition, however, is similar to that of the EMEP model and provides only a small improvement on the deposition predictions.
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Progress towards the milestones and deliverables, use of resources and deviations from DoW
D5.1 Assessment of Current GCMs and CTMS to reproduce recent trends by comparing models and observations
D5.2 Report describing the range of future evolutions of global, hemispheric and European ozone, ozone precursors using a range of anthropogenic and natural emissions D5.3 Report describing the contribution of Regions and processes on key-environmental variables under future conditions.
D5.4 Boundary conditions for regional conditions
MS23 Evaluation of AR5 and other simulations with climate and other global models MS24 Future simulations with improved biogenic and soil emissions
D5.1, D5.2, D5.3 and D5.4 have been delivered, with some delays earlier in the project related to a change in computing platforms. Milestones MS23; MS24 have been reached.
MS 33: Inventory of relevant local scale models
The inventory of different relevant local scale models used in Europe has been completed.
D7.1 Maps of current air pollution metrics (APMs) across Europe, from the EMEP model and five other CTMs
D7.2 Improved EMEP model with climate-change and canopy-chemistry capabilities.
D7.3 Report on effects of in-canopy BVOC and NO emissions on in-canopy O3 and POD estimates.
D7.4 Report on effects of changes in global climate, chemistry, emissions and landcover changes on APMs.
D7.5 Source-receptor matrices of APMs for current and future conditions.
MS28 Implementation and initial testing of coupled model system.
MS29 Initial ensemble runs for current conditions.
MS30 Incorporation of sub-grid methodology from WP4 into EMEP model (M30) MS31 Future scenario data-sets ready
MS32 “Final” model-system ready. Commencement of source-receptor calculations.
D7.1, D7.2, D7.3, D7.4 and D7.5 have been delivered, with some delays arising from efforts towards the ESX model development. Milestones MS28, MS29, MS30, MS31 have been reached.
MS 34: Report on local scale models inventory
This literature study has been completed and submitted as D8.1 (combined with D8.2) MS 35: update of NitroScape to reflect ÉCLAIRE needs
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- The achievement of this MS has been seriously delayed and although major updates to NitroScape have been made they did not come t ogether as a useable final product within the timescale of the project. Some contingency plans were introduced (e.g. use of alternative high resolution model data in WP17) as an alternative strategy in order to deliver the needed results for the project..
MS 36: Concentration/Deposition maps
These concentration/deposition maps have been finalised and delivered to WP17 for further processing. The work (and resulting maps) are described in D8.2 and D8.3/D17.2.
MS 37: Description of local scale interactions between air quality and climate change (finalised and submitted as D8.1.)
MS 38: Sub-grid module available for implementation in EMEP model Finalised and submitted as D8.4.
D8.1: Synthesis report on the different local scale models dealing with atmosphere-biosphere exchange
D8.2: Report on local scale interactions between air quality and climate change (Month 30)
D8.3: Concentration and deposition maps (Month 16)
D8.4: Sub-Grid module for inclusion in the EMEP model (Month 30)
Deliverables D8.1, D8.2, D8.3 and D8.4 have been finalized and submitted to the ÉCLAIRE deliverables database.
The main deviation from the ÉCLAIRE Description of Work was related to the NitroScape work in WP8. This work didn’t result in a useable product, and since results were needed for WP17, a contingency plan of using concentration/deposition maps from existing models and previous projects. Eventually, this worked out fine and all activities in WP17 could be completed using these alternative datasets.
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Publications, posters and other dissemination activities (2014-2015)
Bergström, R.; Hallquist, M.; Simpson, D.; Wildt, J. & Mentel, T. F., Biotic stress: a significant contributor to organic aerosol in Europe? Atmospheric Chemistry and Physics, 2014, 14, 13643-13660
Begstrröm, R., Cabonaceous Aerosol in Europe, Out of the woods and into the blue? PhD Thesis, Univ. Gothenburg, Sweden, Sept. 2015
Engardt, M., Simpson, D. and Granat, L., Historical and projected (1900 to 2050) deposition of sulphur and nitrogen in Europe, in prep.
Leip, A.; Billen, G.; Garnier, J.; Grizzetti, B.; Lassaletta, L.; Reis, S.; Simpson, D.; Sutton, M. A.; de Vries, W.; Weiss, F. & Westhoek, H. Impacts of European livestock production:
nitrogen, sulphur, phosphorus and greenhouse gas emissions, land-use, water eutrophication and biodiversity, Environ. Res. Lett., 2015, 10, 115004
Messina P., Lathière J., Sindelarova K., Vuichard N., Granier C., Ghattas J., Cozic A, Hauglustaine D., “Investigating BVOC emission estimate discrepancies using the ORCHIDEE new emissions scheme and MEGAN model”, in prep.
Messina P., Lathière J., Sindelarova K., Hauglustaine D., Vuichard N., Viovy N., Szopa S., Cozic A, Ghattas J., “Biogenic volatile organic compound emissions in ORCHIDEE:
future evolution and impact on atmospheric composition”, talk at ÉCLAIRE Open Science Conference: Integrating Impacts of Air Pollution and Climate Change on Ecosystems, 1 -2 October -2014, Budapest, Hungary. -2014.
Messina P., Lathière J., Hauglustaine D., Sindelarova K., Vuichard N., Viovy N., Szopa S., Ghattas J., Cozic A, Zannoni N., Gros V., “Impact of future biogenic volatile organic compound emissions on atmospheric composition”, poster at 13th Quadrennial iCACGP Symposium/13th IGAC Science Conference, 22-26 September 2014 Natal, Brazil. 2014.
Sindelarova K., Granier C., Messina P., Lathière J., Guenther A., “Modeling sensitivity of biogenic VOC emissions to environmental factors”, poster at The third Chemistry-Climate Model Initiative (CCMI) Workshop, 20-22 May 2014, Lancaster, United Kingdom. 2014.
Messina P., Lathière J., Sindelarova K., Hauglustaine D., Vuichard N., Viovy N., Szopa S., Ghattas J., Cozic A, “Analysing and comparing the inter-annual variability of biogenic volatile organic compound emissions with ORCHIDEE and MEGAN: Impact of leaf area index”, poster at Biogenic Hydrocarbons & the Atmosphere - Interactions in a Changing World, Spain, June 29 - July 4 2014, Girona. 2014.
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Pleijel, H.; Danielsson, H.; Simpson, D. & Mills, G. Have ozone effects on carbon sequestration been overestimated? A new biomass response function for wheat, Biogeosciences, 2014, 11, 4521-4528
Schaap, M.; Cuvelier, C.; Hendriks, C.; Bessagnet, B.; Baldasano, J.; Colette, A.; Thunis, P.; Karam, D.; Fagerli, H.; Graff, A.; Kranenburg, R.; Nyiri, A.; Pay, M.; Rouil, L.; Schulz, M.; Simpspn, D.; Stern, R.; Terrenoire, E. & Wind, P. Performance of European chemistry transport models as function of horizontal resolution Atmospheric Environment , 2015, 112, 90 - 105
Simpson, D.; Christensen, J.; Engardt, M.; Geels, C.; Nyiri, A.; Soares, J.; Sofiev, M.;
Wind, P. & Langner, J., Impacts of climate and emission changes on nitrogen deposition in Europe: a multi-model study, Atmos. Chem. Physics, 2014, 14, 6995-7017
Simpson, D.; Arneth, A.; Mills, G.; Solberg, S. & Uddling, J. Ozone - the persistent menace; interactions with the N cycle and climate change Current Op. Environ. Sust., 2014, 9-10, 9-19
Werner, M., Kryza, M., Geels, C., Ellermann, T., and Ambelas Skjøth, C.: Spatial, temporal and vertical distribution of ammonia concentrations over europe – comparing a static and dynamic approach with wrf-chem, Atmos. Chem. Phys. Discuss., 15, 22935-22973, 10.5194/acpd-15-22935-2015, 2015.
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