Operational EMEP Eulerian Acid Deposition Model

Transcription

1 EMEP /MSC-W Note /99 Date: July 1999 DET NORSKE METEOROLOGISKE INSTITUTT Norwegian Meteorological Institute Research Note no. 9 Operational EMEP Eulerian Acid Deposition Model Krzysztof Olendrzynski EMEP/MSC-W 1999 ISSN

2 CONTENTS Preface and Acknowledgements... 3 Introduction Recent updates to the model Extension of the model domain PARLAM-PS meteorology Modifications in the code New countries in the budget and source-receptor computations Measurement database for Annual scatter plots for Concentrations in air Concentrations in precipitation Wet depositions Comparison of computed and measured daily time series in Maps of computed concentration and deposition fields for Conclusions References

3 Preface This report was prepared for the twenty third session of the Steering Body of EMEP (Co-operative Programme for Monitoring and Evaluation of the Long Range Transmission of Air Pollutants in Europe). EMEP is one of the scientific programs of the UN Convention on Longrange Transboundary Air Pollution (LRTAP Convention). The main objective of this report is to present the status of the development of the EMEP Eulerian Acid Deposition model, which is run operationally for the first time this year. The report provides a detailed description of the model performance with regard to 1997 meteorological and measurement data. The application of the model with regard to source-receptor matrix computations and country budgets is described in Bartnicki (1999, EMEP/MSC-W Note 5/99). Acknowledgements The author would like to acknowledge his current and former colleagues from the EMEP modelling group at MSC-W who helped in preparation of this report through numerous discussions on the model, its implementation and its post-processing tools. In particular I would like to thank: Jan-Eiof Jonson, Erik Berge, Hugo Jakobsen, and Jerzy Bartnicki. I would also like to thank Egil Stoeren for his valuable contributions in various parts of this work, and Leonor Tarrason for reviewing the manuscript and inspiring comments. The work would not be completed without support from: the DNMI s meteorological section, CCC/NILU, and staff of the computer department at the Technical University in Trondheim, on which CRAY T3E computer, the model has been developed and run operationally

4 Introduction The main objective of Meteorological Synthesizing Centre - West (MSC-W) of the EMEP Programme is computation of atmospheric transboundary transport and deposition of air pollutants (sulfur, nitrogen compounds and tropospheric ozone) in Europe. The computations of annual concentration and deposition fields, as well as country-to-grid matrices for acidifying species, have been performed routinely for and 13 years respectively for sulfur and nitrogen compounds. Two-dimensional (-D) Lagrangian model (Eliassen and Saltbones, 1983; Barret and Berge, 1996) had been successfully used for routine computations up till However, one important limitation of the -D Lagrangian model is its simplified vertical structure, which makes it is very difficult to parameterize atmospheric transport of pollutants above the mixing height. In order to overcome this limitation, a 3-D Eulerian transport/deposition model has been developed at MSC-W since 1993 (Berge, 1993; Jakobsen et al., 1995; Jonson and Berge, 1995; Jakobsen et al., 1996; Jakobsen et al., 1997; Berge, 1997; Berge and Jakobsen, 1998, Jonson et al., 1998a, 1998b, Olendrzynski et al., 1998a, 1998b, 1999). This year - for the first time - the model was applied to operational computations. The results of routine calculations: country budgets and country-to-country pollution exchange, are presented in Bartnicki (1999). The current status of the model and its performance with respect to 1996 data was extensively described in EMEP 1997 Report (Bartnicki et al., 1998, Olendrzynski et al., 1998), and is available at: Therefore, only the recent updates to the model are presented here (Section 1) together with the analysis of the model performance for The model was run for 1997 meteorological and emission data. EMEP measurement database for used for model validation - is described in Section. In Section 3 annual scatter plots are presented and discussed. Examples of daily time series are given in Section, while computed maps of annual concentration and deposition fields are presented in Section 5 followed by conclusions and references. 1. Recent updates to the model This section documents significant changes introduced in the model during 1998 and early The most important development was a rather detailed analysis of pollutant mass conservation, which led to the modifications in the chemistry scheme and to the debugging of the model book keeping. The overall outcome was an updated code which produces annual or monthly results with mass conservation error below 1%. Also, the comparison of the model performance with respect to the EMEP Lagrangian model (Bartnicki and Tarrason, 1998), showed that the Eulerian model performs comparably or better in most tests. Some of the tests for 1996 data were repeated with the updated Eulerian model (not discussed here). They showed that the conclusions reached in 1998 are still valid. These developments made the EMEP Eulerian acid deposition model ready for operational use. The individual developments with respect to the model are discussed in detail below. The following modifications were made in the model compared to the 1998 version: - extension of the model domain to 17 x 133 grid - application of the PARLAM-PS meteorological model for producing meteorological data - modifications in the physical routines imposed by the new meteorological data; mass conservation analysis - introduction of new countries/parties to the CRLTAP Convention in the budget and source receptor computations - -

5 1.1 Extension of the model domain The model domain has been extended from [1:151] x [1:133] to [1:17] x [1:133] in the EMEP 5km grid. The description of the EMEP 5km grid system is given e.g. in the Appendix of EMEP/MSC-W Report 1/98 (see also Figure 1.1 below). The extension of the model domain in the x direction from [1:151] to [1:17] grid cells, enabled the inclusion of Cyprus and Armenia (Parties to the LRTAP Convention), as well as the entire Mediterranean Sea, entire Turkey, Armenia, Azerbaijan, the Caspian Sea, Syria, and parts of Iran and Irak. At the same time, the deposition calculation area increased from ([6:38] x [:36]) used by the EMEP Lagrangian model to ([1:] x [1:37]). This corresponds to grid cells: ([36:167] x [1:1]) in the 5km notation (Figure 1.1). The extension of the domain required extra input data for the parts not covered by the model so far. These data included - among others - anthropogenic emissions, natural marine emissions from eastern part of the Mediterranean Sea, landuse and surface roughness. The extended emission and meteorological data are discussed in other EMEP/ MSC-W Notes: Mylona (1999), and Tsyro and Støren (1999). The landuse data were processed - aggregated to the classes of the original RIVM formulation - by David Simpson based on the database developed at Stockholm Environment Institute (Simpson - private communication, 1999) Figure 1.1 Extended EMEP 5km grid deposition area. It corresponds to [1:] x [1:37] of the EMEP 15km grid. 1. PARLAM-PS meteorology In 1998 computations, the meteorological data for 1996 were derived from the output of the LAM5E meteorological model. This year, however, for the first time the PARLAM-PS model was applied to produce the meteorological data for use in the EMEP Eulerian model. PAR

6 LAM-PS is a dedicated version of the HIRLAM model - operational weather forecast system of the Norwegian Meteorological Service. In PARLAM-PS the σ-coordinates replaced HIR- LAM s hybrid η coordinates, and polar stereographic projection replaced rotated spherical horizontal grid. The detailed description of the PARLAM-PS model and its output is given by Tsyro and Støren (1999). Here, only the basics are given. The domain of the PARLAM-PS model covers the entire domain of the EMEP Eulerian model. The horizontal and vertical grids of both model are identical. The temporal resolution of the meteorological data increased from 6 hours in the LAM5E model to 3 hours for PARLAM- PS. For the purposes of the EMEP Eulerian model eight 3-D and four -D fields of data are needed. The 3-D fields are: - wind u-component [m/s] - wind v-component [m/s] - specific humidity [kg/kg] - potential temperature. [K] - cloud water [(kg water)/(kg air)] - cloud cover [%] - precipitation at each vertical level accumulated over 3-hours [mm] - vertical velocity [1/s] Compared to the LAM5E data, 3-D cloud cover data were added. Previously, the cloud cover was given for two layers: above and below the normalized sigma level.85 (~85hPa). The - D surface fields include: - air pressure [hpa] - temperature at m [K] - surface flux of sensible heat [W/m ] - surface stress [N/m ] Because of the increased temporal resolution, extension of the grid and adding 3-D cloud data, the size of the meteorological data files increased to the total of 1 9 MB required for one year of meteorological data. This figure is the product of: 365days times eight 3-hour intervals per day, times 7.5MB - the size of an individual binary file. The size of the meteorological data together with enormously abundant model output (annual, monthly and daily data from runs for individual countries) determines the disk space requirements for the Eulerian model. As in 1998, the model is run on parallel computer CRAY T3E at the Technical University in Trondheim (Norway). Depending on the load at particular time, the model is run on 8-to-16 processors out of the total number of ca. 88 processors available to users. A run for entire year takes (16 processors) about 11 hours of real time. The computation time is practically inversely proportional to the number of used processors. 1.3 Modifications in the code Several modifications in the code were made to account for the changes in the meteorological and other input data. The main temporal loop now is 3 hours (previously 6 hours). At the beginning of the 3-hour loop new set of meteorological data is read from input files. The basic time step is 1 minutes. It is reduced to s for chemistry calculations. By increasing the temporal resolution of the basic meteorological data, it became possible to more accurately simulate meteorological conditions in the model. Especially, the diurnal course of basic meteorological variables can be simulated more precisely. Every 1 minutes, there is a linear - 6 -

7 interpolation of wind, humidity and temperature data within each 3-hour interval. Cloud cover is now given at all twenty vertical layers. It is a an arithmetic mean of 15-minute intervals over the 3-hour period. The 3-D cloud cover data are used directly when computing in-cloud SO oxidation and in-cloud scavenging. The maximum cloud cover above the given layer (including the surface layer) is used in computing NO dissociation and in computing the wet part of grid cells in the dry deposition scheme. Precipitation is taken as a 3-hour cumulative value. It was found in the original PARLAM-PS data that there are common occasions when there is net evaporation for a given vertical layer. Physically, it means that rain water formed in cloud layers evaporates below the clouds before reaching the surface. Precipitation reaching the ground is thus the difference between the rain water and evaporation. The latter process is not taken into account in the present parameterization of cloud scavenging. Instead, wet scavenging is parameterized in terms of the actual precipitation reaching the surface. The code was modified accordingly to neglect the evaporation process. A detailed parameterization of cloud process in PARLAM-PS provides a good basis for a review of the cloud scavenging processes in the EMEP Eulerian model. The introduction of the evaporation into the pollutant scavenging parameterization, is intended to begin next fall. Horizontal velocities (u and v) are now defined for the staggered grid. The first value at the left boundary is given at the node x = 1+1/. Similarly the first the value in the y direction is given at y=1+1/. Previously, in the model version with LAME5e meteorology, the horizontal velocities were given at the centers of grid cells. They were interpolated linearly to grid cell boundaries when computing horizontal advection. Vertical velocities - on the other hand - are now given at the grid centers. The values at the half-grids between vertical layers are recovered precisely through interpolation and by setting to zero velocities at the surface and at the top of the atmosphere. A reverse calculation is done in PARLAM-PS, when vertical velocities are stored for output. Previously with LAM5e, the situation was just the opposite. Equilibrium chemical reactions leading to the production of ammonium sulfate and ammonium nitrate were taken outside of the iteration within the TWOSTEP method. This modification in the numerical scheme for solving the chemistry, helped to maintain the mass balance especially for reduced nitrogen. It should be noted that, in general, the TWOSTEP conserves mass only if a sufficient number of iterations is performed. In the current implementation only single iteration is used with a time step of s. This was found sufficient for practical applications. Pollutant mass conservation is an important and desired feature of an operational air pollution model. It is especially important in case of budget computations and transboundary exchange. For the annual runs with all emissions accounted for, the total mass of pollutants is conserved in the model domain within 1% range for total sulfur, total oxidized nitrogen and reduced nitrogen. Similar accuracy is achieved in runs for individual countries. 1. New countries in the budget and source-receptor computations. Due to the extension of the computational domain described in paragraph 1.1, several new countries - Parties to the LRTAP Convention - could be included in the budget calculations

8 These include: Armenia, Cyprus, Malta and Turkey. For each of these countries as well as for all countries and seas added to the computational domain, it was necessary to determine the fraction of the grid cell area belonging to the respective country or sea (Caspian or Mediterranean). This was done based on the geographical coordinates of coastlines and the country borders. In the following sections, results from model validation against 1997 data are presented. But first comes the basic information about the measurement database for Measurement database for 1997 The Eulerian model has been run with meteorological and emission data refer for Computed concentrations and depositions have been compared with available measurements in the model domain. Altogether, 87 EMEP stations have been used for the model validation (89 stations in 1996). These stations reported daily values of air concentrations and concentrations in precipitation. The number of days in 1997 for which measurements were performed varied form one station to another as well as the number of measured species at each station. The list of the stations used for the Eulerian model validation is given in Table.1. Geographical positions of these stations in the model domain are shown in Figure.1. The density of the EMEP measurement network is quite high in Central and Western Europe (Germany, France, UK, Switzerland, Poland, Czech Republic, Slovakia) and Scandinavia (Finland, Norway, Sweden and Denmark). The spatial coverage is less satisfactory in Southern Europe (Italy, Greece, Spain, the Balkans), where in general the quality of measurements is lower than in Central and Northern Europe. Figure.1 Geographical locations of the EMEP stations in the Eulerian model domain. The stations marked by black circles have been used for the model validation

9 Table.1. List of EMEP stations used for the model validation. Station Position EMEP grid Altitude No Code Name Lat. N Lon. E i-5 j-5 (m) 1 AT Illmitz AT St. Koloman AT5 Vorhegg CH1 Jungfraujoch CH Payerne CH3 Tönikon CH Chaumont CH5 Rigi CS1 Svratouch CS3 Kosetice DE1 Westerland DE Langenbrügge DE3 Schauinsland DE Deuselbach DE5 Brotjacklriegel DE7 Neuglobsow DE8 Schmücke DE9 Zingst DK3 Tange DK5 Keldsnor DK8 Anholt EE9 Lahemaa EE11 Vilsandy FI Ähtari FI9 Utö FI17 Virolahti II FI Oulanka FI37 Ähtari II FR3 La Crouzille FR5 La Hague FR8 Donon FR9 Revin FR1 Morvan FR11 Bonnevaux FR1 Iraty GB Eskdalemuir GB Stoke Ferry GB6 Lough Navar GB7 Barcombe Mills GB13 Yarner Wood GB1 High Muffles GB15 Strath Vaich D GB16 Glen Dye GR1 Aliartos HU K-puszta

10 Table.1. List of EMEP stations used for the model validation. Station Position EMEP grid Altitude No Code Name Lat. N Lon. E i-5 j-5 (m) 6 IE1 Valentia Obs IE Turlough Hill IE3 The Burren IS Irafoss IT1 Montelibretti IT Ispra LT15 Preila LV1 Rucava LV16 Zoseni NL9 Kollumerwaard NL1 Vreedepeel NO1 Birkenes NO8 Skreådalen NO15 Tustervatn NO39 Kårvatn NO1 Osen NO Spitzbergen, Z NO55 Karasjok PL Jarczew PL3 Sniezka PL Leba PL5 Diabla Gora PT1 Braganca PT3 V. d. Castelo PT Monte Velho RU1 Janiskoski RU13 Pinega RU16 Shepeljovo SE Rörvik SE5 Bredkälen SE8 Hoburg SE11 Vavihill SE1 Aspvreten SE13 Esrange SI8 Iskrba SK Chopok SK Stará Lesná SK5 Liesek SK6 Starina TR1 Cubuk YU5 Kamenicki vis YU8 Zabljak

11 3. Annual scatter plots for Concentrations in air We begin the model validation by comparing measured and computed annual concentrations in air and in precipitation. For the comparison of air concentrations, only those stations from Table.1 were selected which reported measurements for at least 7 days in 1997, i.e. 75% of days. The number of stations that fulfil the requirement varied between 5 for nitric acid and nitrate, and 6 for sulfate. The same requirement had been applied in previous years in case of EMEP Lagrangian acid deposition model. A new element this year are the quality classes updated recently by CCC/NILU. The results of the comparison are presented in the form of scatter plots. Scatter plots illustrate the differences between calculated and measured annual concentrations and depositions for all selected stations available for a given compound. Five stations (DE3, FR1, PL3, SK and NO) have been excluded from the comparison due to either their location on mountain tops, or due to influence of local emission sources (Hjellbrekke, 1998). For annual average concentrations of sulfur dioxide in air, the scatter plot is shown in Figure 3.1. Calculated, mean concentration over 56 stations (.88 µg(s)m -3 ) is 91% higher than the mean concentration from the observations (1.51 µg(s)m -3 ). The difference is much larger for sulfur dioxide than for all other compounds both in air and in precipitation. In general, model overestimates high concentrations above 1.5 µg(s)m -3, and underestimates those below.6 µg(s)m -3. The factor of two agreement between computed and measured annual concentrations lies between external dotted lines in Figures: When the station is located in the factor of two zone in the scatter plots, annual average computed value for this station must be no more than two times higher or no less than two times lower than the measured value. Logarithmic scale is applied in order to better display stations with low concentration or deposition values. In case of linear plots, those stations typically are grouped together in the lower left corner of the plot, thus making station identification difficult. SO in air [ug S/m3] in PL GB HU CS1 SK5 CS3 SK IT1 DE8 DE7 SI8 DE FR9 DE PL5 SK NL1 AT 6.. FR8 DE5 IT. GB13 FR1 GB SE11 IE DK3 IE3. EULERIAN GB6 CH5 SE13 SE1 LV1 LV16 CH3RU16 CH CH FI17 TR1 DE9 GB7 FI DK5 LT15 GB16 PL OBSERVED mean = 1.51 EULERIAN mean =.88 correlation =.7 no. of stations = 56 SE DK8. NO1 FI9 DE1. SE8 FR SE5 IE OBSERVED Figure 3.1 Computed versus measured annual average sulfur dioxide concentrations in air in Units: µg(s)m

12 Figure 3.1 and all other scatter plots (Figures ) include EMEP stations of quality classes ranging between A (best quality; within +/- 1%) to D (lowest quality; worse than 3%). The quality classes for all station/component combinations have been determined by EMEP Chemical Coordinating Centre (CCC) at the Norwegian Institute for Air Research (NILU). CCC arrived at the quality ranking through inter-laboratory comparisons and analysis of various measurement and analytical methods. The tables with quality classes are included in the Appendix of EMEP/MSC-W Report 1/99. Correlation coefficient shown in Figure 3.1, between measured and computed annual concentrations, is.7. If only those stations with quality class A are considered (not shown here), the correlation coefficient increases to.81. This increase occurs also for other components. However, in general, no major improvement of the annual model performance (scatter and bias) is noted when only class A stations are taken into account. This is partly because there are relatively few stations ranked B-D for components other than SO. The performance of the EMEP Eulerian model for SO concentrations is not satisfactory. The model generally overestimates concentrations in source areas (Central and Western Europe) and underestimates concentrations in remote locations (Scandinavia). There are two possible reasons for this. On one hand, EMEP stations have been designed to monitor air pollution levels in relatively remote and clean areas. Measurements at such point locations are not usually representative for pollution levels in larger areas, to which model results should be compared - due to its grid cell area of ca. -5 km. Similar situation was in the case of EMEP Lagrangian acid deposition model, which - in addition - had grid cell area ca. nine times larger than the EMEP Eulerian model. SO in air [ug S/m3] in PL.. 1. GB IT1 DE8 GB7 SK NL1 DE7 DE SI8 IT FR9 GB16 FR8 DE GB13 PL5 PL SE11 DE9 CS1. SK5 HU CS3 SK6 IE3. AT 1. EULERIAN SE DK3 GB DK8 DK5 CH CH IE DE5 CH3 RU16 GB6 FR5 CH5 FR1 FI17 LV1 SE8 DE1 LT15 FI9 SE1 LV16 IE1 NO1 SE13 TR1 FI OBSERVED mean = 1.51 EULERIAN mean = 1.5 correlation =.78 no. of stations = SE OBSERVED Figure 3. Computed versus measured annual average sulfur dioxide concentrations in air in Test with fixed vd for SO. Units: µg(s)m

13 Another reason for relatively poor performance with respect to SO air concentrations, is the unsatisfactory performance of the dry deposition scheme applied in the Eulerian model. Figure 3. presents analogous results for a test run in which the SO dry deposition velocity at 1m was fixed at.8 m/s for all meteorological conditions and all surface types. The computed mean of SO concentrations decreased to 1.5 µg(s)m -3. Now, both the underestimations and overestimations seen in Figure 3.1 have been significantly reduced. The results of this model version for other than SO components (not shown here), are only slightly affected. This tests shows that most probably the computed by the model sum of the surface and laminar resistances is - on the average - overestimated in the source regions and underestimated in remote areas. That results in the opposite estimation for the respective dry deposition velocities for SO. Dry deposition parameterization for SO has to be, therefore, re-evaluated. It is intended to start with this task is in the coming months. The scatter plot for annual average concentrations of sulfate in air is shown in Figure 3.3. Calculated mean concentration over 6 stations (.73 µg(s)m -3 ) is 1% lower than the mean concentration from the observations (.83 µg(s)m -3 ). Model underestimates low concentrations below.6 µg(s)m -3, especially very low values measured at Norwegian and Swedish stations. However, for sulfate in air, 8% of the stations can be found in the factor of two area. Correlation between measured and computed concentrations (.87) is better in this case than for sulfur dioxide. The scatter plot for annual average concentrations of nitrogen dioxide in air is shown in Figure 3.. Calculated mean concentration over 7 stations (.6 µg(n)m -3 ) is almost identical (1% difference) to the mean concentration from the observations (. µg(n)m -3 ). The agreement between model results and measurements is best for NO among all components. For nitrogen dioxide, 87% of the stations can be found in the factor of two area. Correlation between measured and computed concentrations (.8) is also good in this case. SO-- in air [ug s/m3] in PL HU.. CS1 CS3 SK5 SK6 EULERIAN IE3 GB15 NO8 FI DE8 DE5 DE9 DE7 DE DE FR5 FR1GB13 DE1 FR11 SE11DK8 CH DK3 CH5GB16 GB LV1 SE IESE8 RU16 GB6 FI9 NO1 SE1 FR8 LV16 FI17 IE1 GB SK IT1 NL1 SI8 GB7 FR9 NL9 PL5 DK5 PL IT OBSERVED mean =.83 EULERIAN mean =.73 correlation =.87 no. of stations = NO1 NO55 SE13 SE5IS NO NO OBSERVED.6 Figure 3.3 Computed versus measured annual average sulfate concentrations in air in Units: µg(s)m

14 NO in air [ug N/m3] in NL PL DE IT.. SE1 PL5 HU SECS3 DE IT1 DE8 SE11 DK8 DE5 CH SK5 DE7 DE9 CH5 SK6 PL DE1 FI17 SK CH3 CH.. SE8 FI9 EE9 EE11 LT15 1. RU16 LV1 1. EULERIAN.8.6. NO8 NO1 LV16 IE1 NO1 YU5 YU OBSERVED mean =. EULERIAN mean =.6 correlation =.8 no. of stations = 7 SE5 NO39 FI TR1.. NO15.1 NO OBSERVED.1.8 Figure 3. Computed versus measured annual average nitrogen dioxide concentrations in air in Units: µg(n)m -3. The scatter plot for annual average concentrations of nitric acid plus nitrate in air is shown in Figure 3.5. Calculated mean concentration over 5 stations (.6 µg(n)m -3 ) is in good agreement (only 13% higher) with than the mean concentration from the observations (.55 µg(n)m -3 ). In general, model reproduces measured concentrations well, but there is underestimation of the concentrations in the range below. µg(n)m -3. For nitric acid plus nitrate, 88% of the stations can be found in the factor of two area. Correlation between measured and computed concentrations is.69. The scatter plot for annual average concentrations of ammonia plus ammonium nitrate in air is shown in Figure 3.6. Calculated mean concentration over 6 stations considered (1.5 µg(n)m -3 ) is 8% lower than the mean concentration from the observations (1.36 µg(n)m -3. For ammonia plus ammonium nitrate, 85% of the stations can be found in the factor of two area. Correlation between measured and computed concentrations is Concentrations in precipitation In case of concentrations in precipitation a different temporal coverage requirement is applied. For a station to be included in the comparison, there must be at least 5% of common days with measured concentrations between the model and the station. The same requirement has been used so far, when analyzing the results of the EMEP Lagrangian model. Figure 3.7 shows the differences between measured and computed total annual precipitation. It should be noted here that the computed value refers to the grid cell mean while the measured one is a point measurement

15 HNO3 and NO3- in air [ug N/m3] in GB FI9 SI8 GB PL DK5 HU DK8 DK3 SE11 SE PL CH PL5 LT LV1.. NO8 FI17 SE1 LV16. EULERIAN NO1 NO OBSERVED mean =.55 EULERIAN mean =.6 correlation =.69 no. of stations = FI SE5 NO OBSERVED Figure 3.5 Computed versus measured annual average nitric acid plus nitrate concentrations in air in Units: µg (N)m -3. NH3 and NH+ in air [ug N/m3] in PL. DK3 CH. PL5 SI8 SE11 GB GB1 DK5. DK8 PL SE LV LV16 LT SE1.6 FI17 EULERIAN.. NO1 FI9 NO1 NO8.. OBSERVED mean = 1.36 EULERIAN mean = 1.5 correlation =.8 no. of stations = 6 FI.1 SE5 NO NO NO OBSERVED. Figure 3.6 Computed versus measured annual average ammonia plus ammonium concentration in air in Units: µg(n)m

16 In case of precipitation no individual point is a good measure for an area equivalent to the grid cell area cell (ca. to 5km ). It should be noted that the computed mean (57 mm) is significantly lower (9%) than the observation mean (88 mm). As it is discussed below, it has consequences for wet deposition. The scatter plot for annual mean sulfate concentration in precipitation is shown in Figure 3.8. Calculated, mean concentration over 5 stations (.7 mg(s)l -1 ) is 3% higher than the mean concentration from the observations (.51 mg(s)l -1 ), which means significant overestimation. In this case, still 87% of the stations can be found in the factor of two area. Correlation (.6) between measured and computed concentrations in precipitation of sulfate is not so good as in the case of air concentrations (.87). The scatter plot for annual mean concentration of nitrate in precipitation is shown in Figure 3.9. There is little difference (3%) between calculated mean over 5 stations (.36 mg(n)l -1 yr - ) and the observation mean (.35 mg(n)l -1 yr - ). In general, model slightly underestimates measured mean concentrations in the range below.1 mg(n)l -1 yr -1. For concentration in precipitation of nitrate, 9% of the stations can be found in the factor of two area. Correlation between measured and computed depositions (.59) is, however, worse than in the case of sulfate. The scatter plot for annual mean concentration of ammonium in precipitation is shown in Figure 3.1. Calculated mean concentration in precipitation (.8 mg(n)l -1 yr -1 ) over 5 stations, is 1% higher than the mean over observations (. mg(n)l -1 yr -1 ). In general, model reproduces measured concentrations well except for high values above.5 mg(n)l -1 yr -1 which tend to be overestimated. For nitrate concentration in precipitation, 7% of the stations can be found in the factor of two area. Correlation between measured and computed depositions is.55. Although, agreement between mean computed and measured values, as well as correlation are good, the scatter of the stations within a factor of two area is larger than for air concentrations. The same remarks applies to concentrations of sulfate and nitrate in precipitation. Accumulated precipitation in YU8 SK SK5 NO15 IE NO39 NO GB15 GB13 LV16 FR1 IE3 GB GB6 NO1 IS NO55 EE9 FI9 DK5 RU1DE7 FI TR1 RU13 SE1 EE11 FI17 LT15 AT SE5 NL9 HU DE9 RU16 PL5 DK3 SE FI DE1 DK8 DE PL PL NO1 SE11 SK6 DE GB1 YU5 CH AT5 AT CH5 DE5 FR9 FR5 DE8 CS1 CH CH3 IT LV1 FR8 FR OBSERVED mean = 88.1 EULERIAN mean = correlation =.53 no. of stations = 67 CS3 IT OBSERVED Figure 3.7 Computed model versus measured mean annual precipitation in Units: mm yr

17 Conc. in prec. of SO-- corr. [mg S/l/year] in SK6 YU5 IT CS3 PL SK LV1 CH3 FR8 CH CH CH5 FR9 FR11 FR1 AT5 SK AT DE8 DE7 DE DE PLPL5 DE5 DE9 DK5 FI17 YU8 DK3 NL9 FI9 SE LV16 DE1 NO1 EE9 RU NO1FI FR5. EULERIAN.3. FI IE NO8 GB RU13.3. OBSERVED mean =.51 EULERIAN mean =.7 correlation =.6 no. of stations = 5 IE NO39 IS.9.8 NO OBSERVED Figure 3.8 Computed model versus measured mean annual sulfate (corrected for sea spray) concentrations in precipitation in Units: mg(s)l -1 yr -1. Conc. in prec. of NO3- [mg N/l/year] in LV1 FR11 CH3 AT5 FR8 FR9 SK6 CH5 CH CH SK5 AT DK5 DE PL DK3 PL NO1 GB1PL5 DE DE5 CS3 DE7 DE8 NL9 DE1 DE9 SE YU FR1 FR5 NO8 GB SK FI9 FI17 GB13.3 YU8 LV16. EE9 IE. EULERIAN.1.9 GB15 GB6 IE3 FI NO1 RU OBSERVED mean =.35 EULERIAN mean =.36 correlation =.59 no. of stations = FI RU NO15 NO OBSERVED.3 Figure 3.9 Computed versus measured mean annual nitrate concentrations in precipitation in Units: mg (N)l -1 yr

18 Conc. in prec. of NH+ [mg N/l/year] in CH3 AT EULERIAN EE9 FI GB15 LV1 NO1 FR11 GB6 CH FR1 GB FR5 FI17 NO8 FR8 IE3 CH5 DE5 IT SK6 DE CH PL AT5 DE8CS3 DE7 SK5 FR9 DE DK5 DE1 SK DE9 DK3 PL5 PL NO1 LV16 FI9 GB13 YU8 IE GB1 RU16 SE YU5 NL OBSERVED mean =. EULERIAN mean =.8 correlation =.55 no. of stations = 5.6 FI.6 RU13. NO39. NO OBSERVED NO15. Figure 3.1 Computed versus measured mean annual ammonium concentrations in precipitation in Units: mg(n)l -1 yr Wet depositions The scatter plot for annual wet deposition of sulfate is shown in Figure Calculated, mean concentration over 5 stations (385. mg(s)m - yr -1 ) is merely 3% higher than the mean concentration from the observations (395.8 µg(s)m - yr -1 ), which means very good agreement. In this case, 9% of the stations can be found in the factor of two area. Correlation between measured and computed concentrations in precipitation of sulfate is good (.63) but not so good as in the case of respective air concentrations(.87; see Figure 3.3). The same conclusion applies to nitrate, and the sum of ammonia and ammonium (see below and Figures 3.5 and 3.6). The scatter plot for annual wet deposition of nitrate is shown in Figure 3.1. Calculated mean over 5 stations (189.1 mg(n)m - yr -1 ) is 3% higher than the mean concentration from the observations (68.8 mg(n)m - yr -1 ). In general, model underestimates measured values, especially for the range above 1. mg(n)m - yr -1. However, despite the significant underestimation, still 83% of the stations can be found in the factor of two area. Correlation between measured and computed depositions (.6) is similar to the one in case of sulfate. It can be noticed that although, the model on the average computes reasonable values for concentration in precipitation (Figures ), the respective computed wet depositions are on the average underestimated. A reason for that can be the underestimation of modelled total precipitation at model grid cells where the measurement stations are located. That means that although the concentrations in precipitation are on the average correct, insufficient amount of precipitation makes the total mass deposited to be underestimated

19 Wet deposition of SO-- corr. [mg S/m/year] in SK5 SK YU SK YU AT5 PL5 FR1 DE CH3 AT PL LV16 NO1 DE8 DE5 NO8 IT 5 LV1 DK3 CH5 FR11 FR9 CS3 3 FI9 NO1 FI17 CH CH DE7 EE9 DK5 IE FR8 PL DE RU16 SE GB DE1 DE9 NL9 3 OBSERVED mean = EULERIAN mean = 385. correlation =.63 no. of stations = 5 FI FR5 IE3 FI RU13 NO NO IS OBSERVED 8 Figure 3.11 Computed versus measured annual wet deposition of sulfate in Units: mg(s)m - yr -1. Wet deposition of NO3- [mg N/m/year] in SK5 5 3 LV1 NO8 SK YU8 AT AT5 3 DE5 NO1 SK6 CH3 DE1 DE8 DK3 FR9 FR1 GB13 DE DE GB IE FR11CH5 FR8 NL9 PL5 SE DK5 DE7 GB1 LV16 CH CH PL CS3 PL IE3 FR5 DE9 YU5 EULERIAN GB15 EE9 FI9 FI17 GB6 RU16 NO OBSERVED mean = 68.8 EULERIAN mean = correlation =.6 no. of stations = 5 6 FI NO15 NO39 3 FI 3 RU OBSERVED Figure 3.1 Computed versus measured annual wet deposition of nitrate in Units: mg(n)m - yr

20 The scatter plot for annual wet deposition of mean of ammonia and ammonium is shown in Figure There is a large difference (8%) between calculated mean (3.98 mg(n)m -1 yr - 1 ) over 5 stations and the mean from the observations (338.7 mg(n)m -1 yr -1 ). As in the case of nitrate, the model underestimates measured depositions, especially for the range above 1. mg(n)m - yr -1. For wet deposition of ammonia and ammonium nitrate, 7% of the stations can be found in the factor of two area. Correlation between measured and computed depositions is.58. Wet deposition of NH+ [mg N/m/year] in SK5 8 6 SK AT 6 DK5 CH5 DE5 CH3 SK6 AT5 YU8 FR1 DE8 CH FR11 CH DE PL FR9 DE GB FR8 DK3 PL5 NO1 DE1 IE NO8 DE7 YU5 LV1 LV16 GB13 CS3 SE NL9 IT PL GB6 DE9 FI17 FR5 GB1 IE3 EULERIAN EE9 GB15 FI FI9 NO1 NO39 RU OBSERVED mean = EULERIAN mean = 3.98 correlation =.58 no. of stations = 5 FI NO15 RU13 1 NO OBSERVED 1 Figure 3.13 Computed versus measured annual wet deposition of ammonium in Units: mg(n)m - yr

21 . Comparison of measured and computed daily results In this section, computed daily concentrations are compared with daily measurements at EMEP stations in Europe. To present all available graphs - several hundreds - would be a formidable task. Therefore, a reasonable selection must be made. Below, we give examples which we think are typical for describing model behavior for a given component/location combination. For each of the combinations, there are stations for which model performs better than in the displayed graphs, and there are stations where the model performance is poorer. Five components are included in the analysis: sulfur dioxide, nitrogen dioxide, sulfate, sum of nitric acid and nitrate, and sum of ammonia and ammonium. The first two components are gases while the last two are measured as gases plus aerosols. Sulfate is measured as aerosol. In all graphs the units are: µgs(or N) m -3. Before we turn to the computed daily values, one important limitation has to be revoked. EMEP Eulerian acid deposition model has not been designed to simulate day-to-day variations. Its main objective is to compute long-term (monthly, annual) mean concentrations and accumulated depositions. Long-term transboundary fluxes and country budgets constitute another important part of model output. To model daily concentrations more precisely, one would need much better input data (e.g. daily variations of emissions on a grid cell basis) and finer horizontal resolution. The former is very difficult or almost impossible to obtain. Introduction of the latter would result in a very complicated model. Then, the required computation time would make long-term calculations practically impossible, at least in the operational mode. Point measurements at stations are compared with the grid cell values in the model (roughly 5 km 5 km, in the horizontal direction). For gases the computed values are scaled from the lowest model layer (approximately 5 m) to the height of 1m. Measurements can vary significantly even for locations close to each other. Local topographical effects and local emission sources may influence measurements and result in poor representativeness of a given station. For each component we selected three different stations: one located in northern part of Europe, one in middle latitudes, and one in southern Europe. One possibility would be to take the same locations for presentation of various pollutants. However, while there are stations fulfilling this requirement in the northern and Central Europe, no such station exists in the South. Therefore, to better illustrate the model performance different stations have been selected for different components. For both Virolahti II and Cubuk 11 (Figure.1), there is a fairly good agreement between calculated and measured SO concentrations. The seasonal variations in the observations are well captured by the model, especially at Virolahti II. As one could expect not all peaks are well simulated, but the model dynamics and its response to rapid concentration changes in winter and spring looks satisfactory. For Neuglobsow in Germany the agreement is not good. Model overestimates concentrations throughout the year. As discussed in Section 3, this is typical model behavior in Central and Western Europe, where calculated SO concentrations are usually overestimated

22 Virolahti II- FI17: SO in air Neuglobsow- DE 7: SO in air Cubuk11- TR1: SO in air Figure.1 Comparison of measured and computed SO concentrations in air at Virolahti II, Neuglobsow and Cubuk11. Solid line - observations, dotted line - model results. Units: µg(s) m

23 Vavihill - SE11: SO in air Payerne - CH: SO in air Aliartos - GR1: SO in air Figure. Comparison of measured and computed SO concentrations in air at Vavihill, Payerne and Aliartos. Solid line - observations, dotted line - model results. Units: µg (S) m

24 Birkenes - NO1: NO in air Montelibretti - IT1: NO in air Westerland - DE 1: NO in air Figure.3 Comparison of measured and computed NO concentrations in air at Birkenes, Montelibretti and Westerland. Solid line - observations, dotted line - model results. Units: µg(n)m

25 Virolahti II - FI17: HNO 3 and NO 3 - in air Preila - LT15: HNO 3 and NO 3 - in air K-puszta - HU: HNO 3 and NO 3 - in air Figure. Comparison of measured and computed nitric acid and nitrate concentrations in air at Virolahti II, Preila and K-puszta. Solid line - observations, dotted line - model results. Units: µg(n)m

26 Oulanka - FI: Ammonia and ammonium in air Jarczew - PL: Ammonia and ammonium in air Iskrba - SI8: Ammonia and ammonium in air Figure.5 Comparison of measured and computed ammonia and ammonium concentrations in air at Oulanka, Jarczew Birkenes and Iskrba. Solid line - observations, dotted line - model results. Units: µg(n)m

27 In case of SO measurements (Figure.), reasonable agreement is seen in all three graphs. At Swedish station Vavihill, the episodes of high concentrations associated with advection from central Europe are well represented. Also at Payerne (Switzerland) the model fairly well captures the day-to-day variations in all seasons except winter, when the computed values are too low. The agreement between measured and computed values is not so good at Aliartos (Greece). The agreement for other stations in southern Europe - not shown here - is generally worse than in central and northern Europe. This can be explained by the gaps and poorer quality of emission data in southern Europe. Also the density of the measurement network is lower as well as the lower - on the average - quality of measurements estimated by NILU during inter-laboratory comparisons (Hjellbrekke, 1998). NO concentrations (Figure.3).are well reproduced in all three stations: Birkenes (Norway), Montelibretti (Italy) and Westerland (Germany). As in the case of SO, good agreement is typical for most of Scandinavian stations and those located in Central and Western Europe. There is a clear seasonal course in the results at Westerland. Higher concentrations are computed for the colder period of the year. This is the result of the seasonal variations of emissions. According to NILU (Hjellbrekke, 1998), the main reason for the relatively large variations of NO concentrations at Montelibretti, is the influence of local NO x emission sources. In case of HNO 3 and NO 3 - observations (Figure.), a better agreement is for the Finish station Virolahti II and Lithuanian Preila, than for the Hungarian K-Puszta. The seasonal trend at K- Puszta is captured by the model, but its amplitude in winter is underestimated. At Virolahti, the episodes of high concentrations are - on the average - well simulated by the model. It is not the case at Preila. The graphs for the sum of ammonia and ammonium (Figure.5), show considerable day to day variations. Model simulates the variations quite well at Oulanka (Finland) and in the second half of the year at Jarczew (Poland). At Iskrba (Slovenia) model simulates significant variations on a daily basis, but the variations do not exactly follow the observed ones. The computed values in the colder part of the year are underestimated. The graphs presented in this section give an insight into the model behaviour on a daily basis. The dynamics of concentration changes is frequently well captured by the model. This is particularly evident at stations in Scandinavia on occasions of long-range transport from the continental Europe. The agreement between model results and observations becomes worse when moving towards southern Europe. This conclusions holds for all five components analysed here. More plots are of this type are presented in the Appendix of the EMEP/MSC-W Report 1/99. Plots for all stations and all components can be found at:

28 5. Maps of computed concentration and deposition fields for 1997 The results of the EMEP Eulerian model run for 1997 are presented in this section as concentration and deposition maps. Annual average air concentrations of sulfur dioxide, sulfate, nitrogen dioxide, nitric acid + nitrate, and ammonia + ammonium are shown in Figures 5.1, 5., 5.3, 5. and 5.5, respectively. Annual depositions of total sulfur, oxidized nitrogen and reduced nitrogen are shown in Figures 5.6, 5.7 and 5.8, respectively. In the map of computed annual mean sulfur dioxide air concentration at the surface (Figure 5.1), one can clearly see the influence of the distribution of emission sources in Europe (Mylona, 1999). Southern Italy around Sicily is the location with the absolute maximum of the computed concentrations, the same where the largest single source of both natural and anthropogenic emissions - Mt. Etna - is. A large area with high concentrations exceeding 1 µg(s)/ m 3 is the so called Black Triangle region (southern Poland, eastern Germany and northern part of the Czech Republic), where also the largest sources of anthropogenic emissions of sulfur dioxide are. Outside Black triangle, there are several other large emission sources (e.g southern U.K., northern Yugoslavia, Bulgaria, Belgium, Kola Peninsula) which are reflected in the concentration map. Overall concentration pattern computed by the Eulerian model is similar to the concentration pattern produced by the Lagrangian model for 1996 (see Appendix C of the Numerical Addendum to EMEP/MSC-W Report 1/98). However, because of the better spatial resolution of the Eulerian model (5 km instead of 15 km), more details are visible in the map computed by this model. In addition, due to fully three-dimensional structure of the Eulerian model, certain climatological and terrain features appear clearly only in the maps produced by the Eulerian model. For example, the presence of Alps appears as an area with significantly lower concentrations in the Eulerian map. This and other similar features can be seen when comparing all concentration and deposition fields produced by the two models. Computed surface concentration field for sulfates (sum of SO and ammonium sulfate) (Figure 5.) is smoother than the one for sulfur dioxide. This is because unlike SO which is emitted, sulfates are mainly produced in the atmosphere - as a result of SO oxidation. The influence of emissions is not so pronounced in this case, however, regions with higher sulfate concentrations follow, to some extent, the regions with high emissions of sulfur dioxide. Concentrations exceeding 5 µg(s)/m 3 are found in southern Poland, northern Czech Republic, south-western Germany ( Black Triangle ), northern Yugoslavia, parts of Britain and Bulgaria. Map of computed annual average surface concentrations of nitrogen dioxide is shown in Figure 5.3. Again, as in the case of sulfur dioxide, concentration pattern for nitrogen dioxide is driven by the distribution of NO x emissions in Europe. However, the location of maxima is a bit different compared to maps for SO and sulfates. A large area with high concentrations above 5 µg(n)/m 3 covers northern Germany, the Netherlands, part of Belgium and southern United Kingdom. Similar concentrations are also found in southern Poland and south-western Germany. Computed annual average surface concentrations of nitric acid + nitrate are shown in Figure 5.. Here, we note high concentrations exceeding µg(n)/m 3 in and around the English Channel. Another large area of high concentrations is in the northern Italy and off the south-western coast of Spain