Performance of EMEP Eulerian Acid Deposition Model for 1998

Transcription

1 EMEP /MSC-W Note 3/00 Date: July 2000 DET NORSKE METEOROLOGISKE INSTITUTT Norwegian Meteorological Institute Research Note no. 39 Performance of EMEP Eulerian Acid Deposition Model for 1998 Krzysztof Olendrzynski EMEP/MSC-W 2000 ISSN

2 CONTENTS Preface and Acknowledgements... 3 Introduction Recent updates to the dry deposition scheme for gases Modifications for sulfur dioxide Modifications for nitric acid, ammonia and PAN Measurement database for Model performance for 1998 data Annual scatter plots Concentrations in air Concentrations in precipitation Wet depositions Daily time series Analysis of frequency distributions of wet deposition Maps of computed concentration and deposition fields Conclusions References

3 Preface This report has been prepared for the twenty-fourth session of the Steering Body of EMEP (Cooperative Programme for Monitoring and Evaluation of the Long Range Transmission of Air Pollutants in Europe). The main objective of this report is to present the status of the development and performance of the EMEP Eulerian Acid Deposition model. The report provides a detailed description of the model performance with regard to 1998 meteorological and measurement data, and documents changes in the model since 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 Erik Berge, Asgeir Sorteberg, Hugo Jakobsen, David Simpson and Egil Støren for their valuable contributions in various parts of this work, and Leonor Tarrason for reviewing the manuscript and comments. The work could not have been completed without support from: the DNMI s meteorological section, EMEP Chemical Coordinating Centre (CCC) at the Norwegian Institute for Air Research (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 One of the main objectives of Meteorological Synthesizing Centre - West (MSC-W) of EMEP, 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 21 and 14 years, respectively for sulfur and nitrogen compounds. Two-dimensional (2-D) Lagrangian model (Eliassen and Saltbones, 1983; EMEP Summary Report, 1997) had been successfully used for routine computations until However, one important limitation of the 2-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; Jonson and Berge, 1995; Jakobsen et al., 1997; Berge and Jakobsen, 1998; Olendrzynski, 1999; Olendrzynski et. al., 2000a and 2000b). This year - for the second time - the model was applied for operational computations. Mass conservation properties of the model have been preserved and pollutant mass is conserved within 1% error for long-term computations, as required by EMEP. The results of routine calculations: country budgets and country-to-country pollution exchange, are discussed in Bartnicki (2000). The current status of the model and its performance with respect to 1997 data was extensively described in: EMEP Summary Report 1999, Olendrzynski (1999), and is available at EMEP/ MSC-W website: Therefore, only the recent updates to the model are presented here (Section 1), together with the analysis of the model performance for EMEP measurement database for used for model validation - is described in Section 2. In Section 3 annual scatter plots are presented and discussed. Also examples of frequency distributions of daily time series are given, while computed maps of annual concentration and deposition fields are presented at the end of the section. The report ends with conclusions and references. 1. Recent updates to the dry deposition scheme for gases The EMEP Eulerian acid deposition model run for 1997 showed that computed sulfur dioxide concentrations were in most cases far off the measured values (see Figure 3.1 in Olendrzynski, 1999). The problem with SO 2 concentrations was first identified by Jakobsen et al. (1997) for 1992 data (their Figure 3.1). The model generally overestimated SO 2 concentrations in source areas i.e. Central and Western Europe, and underestimated SO 2 concentrations in Scandinavia, typically a receptor area. A review of the dry deposition scheme and the subsequent modifications in the model code, led to the improved modelled air concentrations for SO 2. In Section 1.1 the modifications in the dry deposition scheme for SO 2 are briefly discussed, while the SO 2 concentration results are presented in Section 3. The modifications in the dry deposition scheme concerning other gaseous components, are described in Section 1.2. To begin with, we discuss two modifications applied to all gaseous components. In the scheme described by Jakobsen et al. (1996), the friction velocity has been computed using the following formula: - 4 -

5 u( z d)κ u *,lu = z d ln Ψ z d z m z 0, NWP + Ψ 0, lu L m NWP L NWP (1) where: u *,lu is the friction velocity for a given landuse class, u( z d) here, denotes the wind speed at the lowest model layer, z is height above the surface, d is the displacement height, κ is von Karman s constant, z 0 is the roughness length, subscript NWP indicates that the respective variable is taken from the numerical weather prediction model (NWP) here PARLAM-PS, subscript lu indicates model s landuse class, Ψ m is the similarity function for momentum. The wind speed in equation (1) was originally derived indirectly from: friction velocity of NWP, Monin-Obukhov length, and surface roughness (Jakobsen et al., 1996). This computation was at first applied to the EMEP Lagrangian model, in which the model s wind field referred to the air box extending from the surface up to the mixing height. The same computation procedure was originally applied to the Eulerian model. The uncertainty embedded in this indirect computation can be eliminated by computing the wind speed at the lowest model layer (on the average, at the height of -50m) directly from x and y components of the wind field. In that way, the unwanted situation is avoided, when computed u * becomes very small and some of the resulting surface resistances large (e.g. in-canopy resistances for forests which are inversely proportional to u * ). Such situations, in some cases, contributed to underestimation of dry deposition velocities (v d ). Another modification in the above formula, is the use of monthly mean gridcell surface roughness, which refers to the model s landuse database rather than the respective z 0,NWP from PAR- LAM-PS. The reason for this substitution is that there is inconsistency between the landuse database in PARLAM-PS and the EMEP Eulerian model. Surface roughness in PARLAM-PS (HIRLAM) is topographic with some adjustments. At high altitudes, it brings very high values unsuitable for dry deposition calculations. Instead of taking z 0,NWP, mean gridcell values of z 0 are computed and used, based on the RIVM landuse database applied in the EMEP Eulerian model. It is desirable, however, that the same landuse database is used in NWP and EMEP models. Several numerical tests have been carried out to find out how simplified methods of computing dry deposition velocities, influence computed air concentrations and depositions. In one test, single friction velocity - mean value for the gridcell - has been used instead of computing u *, separately for each landuse type (see equation (1) above, and Jakobsen et al., 1996). In another test even more radical change has been investigated. Mean single v d for the gridcell has been computed, while preserving separate - landuse type dependent - surface resistances. However, both tests have not produced better results than those presented in the following sections. 1.1 Modifications for sulfur dioxide The most important change with respect to the parameterization for SO 2, concerns the wet part of the gridcell determined by the occurrence of precipitation and cloud cover fraction. Dry deposition of sulfur dioxide to leaf stomata is cut off when leaves are covered by water from fog or rain (Wesely, 1989). Therefore, it is important to distinguish between the dry and the - 5 -

6 wet fraction of a gridcell. Previous version of the dry deposition code, did not consider appropriately the influence of surface wetness. The result was a systematic underestimation of the wet fraction of a gridcell. As a result, the dry deposition velocity for SO 2 was underestimated. By correcting the code, we increased the area of wetted surfaces. This resulted in lower surface resistances, higher dry deposition velocities, and in the end, lower - closer to measured ones - SO 2 air concentrations, especially in source areas. This can be noticed by comparing respective scatter diagrams for annual mean concentration (Figure 3.1 Olendrzynski, 1999 and Figure 3.1 in this report). The effect for other gaseous components is much lesser vd_1m SO2 [cm/s] > < 0.1 Figure 1.1. Map of computed annual dry deposition velocity at 1m for SO 2 in Units: cm/s. A different factor contributed to unsatisfactory model performance at the other end of the concentration range i.e. for low values at receptor areas. It was found that underestimation of measured SO 2 concentrations at Scandinavian stations was related to overestimation of v d over water surfaces, including seas. The original RIVM/UiB/EMEP parameterization employed surface resistance equal to 10 s/m, which resulted in annually averaged dry deposition velocity at 1m, v d (1m), as high as 10 cm/s. Setting the surface resistance for water surfaces at 50 s/m (Voldner et al., 1986) lead to v d (1m) equal to 2 cm/s. This resulted in the increased transport over the water/sea areas and higher - closer to measured - concentrations at EMEP Scandinavian stations. The improvement can be noticed by comparing low concentration range at respective scatter diagrams for 1997 and The resulting, computed mean annual gridcell dry deposition velocities for SO 2, for the entire EMEP domain, are presented in Figure

7 1.2 Modifications for nitric acid, ammonia and PAN For nitric acid (HNO 3 ), surface resistances are parameterized following Wesely (1989). However, only external resistances for vegetative surfaces are computed (not stomatal, not in-canopy). Soil resistances are neglected for all snow-free surfaces. In case of snow cover over nonice surfaces, the temperature dependent surface resistances used for SO 2, are applied also for HNO 3 (Jakobsen et al., 1996). For HNO 3 over water surfaces, surface resistance was set at 25 s/m, resulting in v d (1m) equal to 4 cm/s after Barrett (1994). The original RIVM/UiB/EMEP parameterization employed surface resistance equal to 10 s/m, which resulted in annually averaged v d (1m) equal to 10 cm/s. For ammonia, like for SO 2, the surface resistance over water surfaces was increased from 10 s/ m in the RIVM scheme, to 50 s/m (Barrett; 1994) resulting in v d (1m) equal to 2 cm/s. In case of PAN, Wesely s approach for vegetative surfaces is applied (stomatal resistances only). For all other surfaces - including snow cover - the surface resistance is very large, set to 9999 s/m in the code. Figures show computed annually averaged dry deposition velocities in Europe for NO 2, HNO 3 and NH 3, respectively vd_1m NO2 [cm/s] > < 0.01 Figure 1.2. Map of computed annual dry deposition velocity at 1m for NO 2 in Units: cm/s

8 vd_1m HNO3 [cm/s] > < 0. Figure 1.3. Map of computed annual dry deposition velocity at 1m for HNO 3 in Units: cm/s vd_1m NH3 [cm/s] > < 0. Figure 1.4. Map of computed annual dry deposition velocity at 1m for NH 3 in Units: cm/s

9 2. Measurement database for 1998 The Eulerian model has been run with meteorological and emission data for Computed concentrations and depositions have been compared with available measurements in the model domain (Hjellbrekke, 2000). Altogether, 99 EMEP stations have been considered for the model validation (87 stations in 1997). These stations reported daily values of air concentrations and concentrations in precipitation. The number of days in 1998 for which measurements were performed varied from 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 2.1. Geographical positions of these stations in the model domain are shown in Figure 2.1. Compared to the 1997 evaluation (Olendrzynski; 1999) the following stations have been added: - Spain: ES1, ES3, ES4, ES5, ES6 and ES7; - France: FR13, FR14; - UK: GB36, GB37, GB38, GB43, GB45; - Ireland: IE4. On the other hand, two stations are missing in 1998: Irish IE1, and Swedish SE13. Czech stations have been renamed to CZ1 and CZ3 (CS1 and CS3 in 1997) due to the coordination of country codes within UNECE/EMEP. Figure 2.1. Geographical locations of the EMEP stations in the Eulerian model domain. The stations marked by black circles have been used for the 1998 model validation

10 Table 2.1. List of EMEP stations used for the model validation. (see Hjellbrekke, 2000) Station Position EMEP grid Altitude No Code Name Lat. N Lon. E i-50 j-50 (m) 1 AT2 Illmitz AT4 St. Koloman AT5 Vorhegg CH1 Jungfraujoch CH2 Payerne CH3 Tönikon CH4 Chaumont CH5 Rigi CZ1 Svratouch CZ3 Kosetice DE1 Westerland DE2 Langenbrügge DE3 Schauinsland DE4 Deuselbach DE5 Brotjacklriegel DE7 Neuglobsow DE8 Schmücke DE9 Zingst DK3 Tange DK5 Keldsnor DK8 Anholt EE9 Lahemaa EE11 Vilsandy ES1 Toledo ES2 Roquetas ES3 Logrono ES4 Noio ES5 Mahon ES6 Viznar FI4 Ähtari FI9 Utö FI17 Virolahti II FI22 Oulanka FI37 Ähtari II FR3 La Crouzille FR5 La Hague FR8 Donon FR9 Revin FR10 Morvan FR11 Bonnevaux FR12 Iraty FR13 Peyrusse_Vieille FR14 Montandon GB2 Eskdalemuir GB4 Stoke Ferry

11 Table 2.1. List of EMEP stations used for the model validation. (see Hjellbrekke, 2000) Station Position EMEP grid Altitude No Code Name Lat. N Lon. E i-50 j-50 (m) 46 GB6 Lough Navar GB7 Barcombe Mills GB13 Yarner Wood GB14 High Muffles GB15 Strath Vaich D GB16 Glen Dye GB36 Harwell GB37 Ladybower GB38 Lullington_Heath GB43 Narberth GB45 Wicken_Fen GR1 Aliartos HU2 K-puszta IE2 Turlough Hill IE3 The Burren IE4 Ridge_of_Capard IS2 Irafoss IT1 Montelibretti IT4 Ispra LT15 Preila LV10 Rucava LV16 Zoseni NL9 Kollumerwaard NL10 Vreedepeel NO1 Birkenes NO8 Skreådalen NO15 Tustervatn NO39 Kårvatn NO41 Osen NO42 Spitzbergen, Z NO55 Karasjok PL2 Jarczew PL3 Sniezka PL4 Leba PL5 Diabla Gora PT1 Braganca PT3 V. d. Castelo PT4 Monte Velho RU1 Janiskoski RU13 Pinega RU16 Shepeljovo SE2 Rörvik SE5 Bredkälen SE8 Hoburg SE11 Vavihill

12 Table 2.1. List of EMEP stations used for the model validation. (see Hjellbrekke, 2000) Station Position EMEP grid Altitude No Code Name Lat. N Lon. E i-50 j-50 (m) 91 SE12 Aspvreten SI8 Iskrba SK2 Chopok SK4 Stará Lesná SK5 Liesek SK6 Starina TR1 Cubuk YU5 Kamenicki vis YU8 Zabljak Model performance for 1998 data 3.1 Annual scatter plots 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 2.1 were selected which reported measurements for at least 274 days in 1998, i.e. 75% of days. The number of stations that fulfil the requirement varied between 29 for ammonia and ammonium, and 66 for sulfate. The same completeness or temporal coverage requirement, had been applied in previous years in case of EMEP Lagrangian acid deposition model. The results of the comparison are presented in form of scatter plots. Scatter plots illustrate the differences between calculated and measured annual concentrations and depositions for selected stations available for a given compound. Five stations (DE3, FR12, PL3, SK2 and NO42) have been excluded from the comparison due to either their location on mountain tops, or due to influence of local emission sources or boundary conditions (Hjellbrekke, 2000). The factor of two or 50% agreement between computed and measured annual concentrations lies between external dashed lines in Figures: When the station is located in the 50% 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. Similarly, the dotted lines indicate the so called 30% agreement. It is stated in the EMEP work-plan (ECE/EB, 2000), that there should be agreement with observation within +/-30%. Here, the 30% limits are defined in the following way: the ratio between computed and measured annual mean must satisfy the following relation: computed 1 ( 1 0.3) measured measured ( 1 0.3) measured 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

13 SO2 in air [ug S/m3] in PL2 HU2 6.0 SK6 4.0 IT1 DE8 GB4 SK4 NL10 CZ1 CZ FR13 DE7 DE4 FR9 FR8 IT4 PL5 DE2 AT2 2.0 NL9 GB14 DE5 GB IE2 SE11 DK3 FR10 GB7 LV10 DE9 CH3 RU IE3 0.8 CH5 GB2CH4 CH2 RU1 PL4 0.8 EULERIAN NO1 GB6 SE12 GB16LV16 LT15 FR5 DE1 DK5 DK8 FI22 SE2 NO55 FI17 FI37 SE8 FI9 GB OBSERVED mean = 1.16 EULERIAN mean = 1.49 correlation = 0.62 no. of stations = 58 50% error, 74% of all stations 30% error, 33% of all stations perfect fit SE OBSERVED Figure 3.1. Computed versus measured annual average sulfur dioxide concentrations in air in Units: µg(s)m -3. SO4-- in air [ug s/m3] in SK6 PL2 HU CZ1 GB7 IT1 NL10 DE8 GB4SK4 DE7 CZ3 GB14 DE2 AT2 DE9 NL9 PL5 DE4 FR9 PL4 ES3 IT4 DK5 FR5 DE1 LT15 FR8 IE4 DE5 SE11 LV10 FR13 DK8 DK3 GB13 SE2 ES4 FR10 RU16 ES1 LV16 IE2 SE8 ES7 IE3 GB2 CH2 FI17 GB16 FI9 CH5 GB6SE EULERIAN 0.2 RU1 FI37 NO8 NO1 FI22 NO55 GB OBSERVED mean = 0.77 EULERIAN mean = 0.52 correlation = 0.79 no. of stations = IS2 SE5 NO % error, 73% of all stations 30% error, 30% of all stations perfect fit NO15 NO OBSERVED Figure 3.2. Computed versus measured annual average sulfate concentrations in air in Units: µg(s)m

14 Figure 3.1 and all other scatter plots (Figures ) include EMEP stations of quality classes ranging between A (best quality; within +/- 10%) to D (lowest quality; worse than 30%). 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 updated tables with quality classes can be found in EMEP Summary Report The scatter plot for annual average surface air concentrations of sulfur dioxide, is shown in Figure 3.1. The averaged modelled concentration over 58 stations (1.49 µg(s)m -3 ) is 28% higher than the mean concentration from the observations (1.16 µg(s)m -3 ). In general, the model still overestimates concentrations in Central and Eastern European stations. The overestimation is, however, less significant compared to 1997 results (see Figure 1.1 in Olendrzynski, 1999). The improvement has been achieved mainly due to the modifications in the dry deposition scheme, described in Section 1. 74% out of 56 stations lie within the 50% lines while only 33% of stations within the 30% lines. The latter number indicates that the agreement between computed and measured annual means for SO 2 concentrations still needs improvement. For four German stations (DE4, DE5, DE6 and DE7), the model overestimates considerably the measured SO 2 concentrations. They fall outside the 50% zone. It should be mentioned, that SO 2 measurements in Germany in 1998 were carried out using TCM method, which was ranked as quality class C by CCC/NILU (Aas et al., 2000). The method is being changed presently. The scatter plot for annual average concentrations of sulfate in air, is shown in Figure 3.2. Calculated mean concentration over 66 stations (0.52 µg(s)m -3 ) is 33% lower than the mean concentration from observations (0.77 µg(s)m -3 ), which means considerable underestimation. This is the worst result among analysed air concentrations. Model underestimates concentrations in the entire concentration range, especially low values measured at three Norwegian and one Swedish station (NO15, NO39, NO41, SE5). However, still 73% of the stations are found in the 50% limits and 30% in the 30% limits. Correlation coefficient between measured and computed concentrations (0.79) is higher than for sulfur dioxide (0.62). The scatter plot for annual average concentrations of nitrogen dioxide in air, is shown in Figure 3.3. Calculated mean concentration over 50 stations (2.00 µg(n)m -3 ) is close (10% difference) to the mean concentration from the observations (2.22 µg(n)m -3 ). The agreement between model results and measurements is best among all air components. For nitrogen dioxide, 84% of stations can be found in the 50% limit, and 52% within the 30% limits. The model underestimates considerably measured values at, among others, four Spanish stations (ES1, ES3, ES4 and ES7). Relatively poor quality of measurements at those stations may help to explain the difference. Correlation coefficient between measured and computed concentrations is The scatter plot for annual average concentrations of nitric acid plus nitrate in air, is shown in Figure 3.4. Calculated mean concentration over 30 stations (0.37 µg(n)m -3 ) is in good agreement (14% lower) with the mean concentration from observations (0.43 µg(n)m -3 ). In general, model reproduces measured concentrations well, but there is underestimation of the concentrations in the range below 0.15 µg(n)m -3 at Scandinavian stations. For nitric acid plus nitrate, 63% of the stations can be found in the 50% limits and 47% within the 30% limits. Correlation coefficient between measured and computed concentrations 0.88, is highest among all scatter plots

15 NO2 in air [ug N/m3] in NL NL9 6.0 EULERIAN SE5 SE12 PL5 SE8 EE11 EE9 LV10 NO8 NO1 FI37 LV16 NO41 DE4 DE2 DE8 SE2DE5 SE11 HU2 DK8 PL2 CH4 DE9 CZ1 SK5 CZ3 DE1 DE7CH5 PL4 SK6 SK4 FI9 FI17 LT15 IT1 CH3 CH2 AT2 ES1 ES4 ES3 ES7 4.0 IT OBSERVED mean = 2.22 EULERIAN mean = 2.00 correlation = 0.63 no. of stations = NO15 NO39 FI % error, 84% of all stations 30% error, 52% of all stations perfect fit 0.1 NO OBSERVED 0.1 Figure 3.3. Computed versus measured annual average nitrogen dioxide concentrations in air in Units: µg(n)m -3. HNO3 and NO3- in air [ug N/m3] in DK5 GB PL2 HU2 DK3 0.8 SE11 DK8 CH2 0.6 PL4 PL5 0.6 SE2 0.4 LV10 GB2 ES1 ES4 LT LV16 SE12 ES7 0.2 NO8 NO1 FI FI NO41 FI OBSERVED mean = 0.43 EULERIAN mean = 0.37 correlation = 0.88 no. of stations = FI % error, 63% of all stations 30% error, 47% of all stations perfect fit SE5 NO NO15 NO OBSERVED Figure 3.4. Computed versus measured annual average nitric acid plus nitrate concentrations in air in Units: µg (N)m

16 The scatter plot for annual average concentrations of ammonia plus ammonium nitrate in air, is shown in Figure 3.5. Calculated mean concentration over 29 stations considered (0.89 µg(n)m -3 ) is 19% lower than the mean concentration from observations (1.10 µg(n)m -3. For ammonia plus ammonium nitrate, 69% of the stations can be found in the 50% limits and 34% within 30% limits. Here, similarly to the NO 2 case, model underestimates significantly values measured at (five) Norwegian stations. Correlation coefficient between measured and computed concentrations 0.82, is quite high. NH3 and NH4+ in air [ug N/m3] in PL2 DK3 CH2 2.0 PL5 SE11 GB14 DK5 2.0 ES3 GB2 DK8 PL LV10 ES7 SE2 LT SE12 LV EULERIAN FI37 FI9 NO1 NO41 FI17 NO OBSERVED mean = 1.10 EULERIAN mean = 0.89 correlation = 0.82 no. of stations = FI22 SE5 NO % error, 69% of all stations 30% error, 34% of all stations perfect fit RU1 NO39 NO OBSERVED Figure 3.5. Computed versus measured annual average ammonia plus ammonium concentration in air in Units: µg(n)m 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 25% of common days with measured concentrations between the model and the station. The same requirement had been used in the past, when analyzing the results of the EMEP Lagrangian model. Figure 3.6 shows the differences between measured and computed total annual precipitation. It should be noted here, that each computed value refers to the gridcell mean, while the measured value refers to a point measurement. In case of precipitation, no individual point can provide good measure for an area equivalent to the gridcell (ca to 2500km 2 ). The computed mean (1054 mm) is 18% higher than the observation mean (895 mm). This is much better agreement than the one reported for 1997 data. This improvement is the result of the significant validation effort of 3D precipitation fields in PARLAM-PS reported in Lenschow and Tsyro (2000). The correlation coefficient is only 0.48 (second worst among scatter plots). This is not surprising, given the patchy pattern and intermittence of precipitation

17 Accumulated precipitation in NO NO SK4 SK5 CH5 GB15 AT4 NO8 AT5 GB PT1 FI9 NO1 GB13 IE4 IE2 IE3 CH2 CH4 CH3 GB2 ES3 NO41 DE4 FR10 DE8 DE2 FR13 DE5 ES5 ES7 FI22 FR8 PL5 SE11 LV16 DE7 DK3 DE1 FR9 IS2 LT15 NL9 FR5 ES4 RU16 SE2 CZ1 AT2 DK8 LV10 PT3 SE5 FR14 FI4 GB14 CZ3 RU13 DK5 PL4 DE9 SK6 FI17 SE12 EE11 HU2 PL2 EE9 NO55 RU1 ES1 IT4 IT OBSERVED mean = EULERIAN mean = correlation = 0.48 no. of stations = % error, 86% of all stations 30% error, 64% of all stations perfect fit PT OBSERVED Figure 3.6. Computed model versus measured mean annual precipitation in Units: mm yr -1. The scatter plot for annual mean sulfate concentration in precipitation, is shown in Figure 3.7. Calculated, mean concentration over 56 stations (0.37 mg(s)l -1 ) is 23% lower than the mean concentration from observations (0.48 mg(s)l -1 ). In this case, 82% of the stations can be found in the 50% area and 71% within the 30% limits, which is a very good result. Computed values for stations in Ireland (3 stations) and Spain (3), are significantly underestimated and lie outside the 50% limits. Correlation coefficient between measured and computed concentrations in precipitation of sulfate (0.66) is not so good as in the case of SO 4 air concentrations (0.79). The scatter plot for annual mean concentration of nitrate in precipitation is shown in Figure 3.8. There is 22% difference between calculated mean over 59 stations (0.25 mg(n)l -1 yr -1 ) and the observation mean (0.32 mg(n)l -1 yr -1 ). For concentration in precipitation of nitrate, 90% of the stations can be found in the 50% limits, which is the second best result among scatter plots. 59% of station are within the 30% lines. Correlation coefficient between measured and computed depositions (0.76) is higher than in the case of sulfate. The scatter plot for annual mean concentration of ammonium in precipitation is shown in Figure 3.9. Calculated mean concentration in precipitation (0.31 mg(n)l -1 yr -1 ) over stations, is 16% lower than the mean over observations (0.37 mg(n)l -1 yr -1 ). In general, model reproduces measured concentrations well except for low values below 0.2 mg(n)l -1 yr -1 which tend to be underestimated. Here, like for air concentrations, model underestimates significantly values measured at (five) Norwegian stations. Nevertheless, 77% of the stations are in the 50% limits and 55% within the 30% limits. Correlation coefficient between measured and computed depositions is Although, agreement between mean computed and measured values, as well as correlation are good, the scatter of the stations within the 50% limits is larger than for air concentrations. The same remarks applies to concentrations of sulfate and nitrate in precipitation

18 Conc. in prec. of SO4-- corr. [mg S/l/year] in SK FI4 HU2 SK5 SK4 PL5 CZ3 LV10 DE7 DK5 DE8 PL4 DE2 DE9 DE5 DK3 FI9 LV16 NO1 FI17 DE4 AT5 NL9 FR9 DE1 FR8 SE2 FR13 RU16 AT4 CH3 CH4 CH2 FR10 RU13 GB2NO8 CH5 ES1 NO41 NO55 FR14 ES7 PL2 AT EE FI22 FR5 IE2IE3 ES5 0.2 EULERIAN IS PT3 IE OBSERVED mean = 0.48 EULERIAN mean = 0.37 correlation = 0.66 no. of stations = NO15 NO % error, 82% of all stations 30% error, 71% of all stations perfect fit OBSERVED Figure 3.7. 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 IE3 DE2 FR9 SK6 NL9 DE7 0.4 HU2 CZ3 DE5 DK5 PL2 DE8 DE9 CH3 GB14 FR8 FR13 AT5 DE4 AT2 DE1 DK3 0.3 CH4 CH2 LV10 AT4 SK5 CH5 PL4 PL5 FR10 SE2 FR14 NO1 ES4 GB2 SK4 FI9 ES7 ES1 LV EE9 GB13 NO8FR5 IE2 FI17 EULERIAN GB6 GB15 RU13 IE4 FI4 NO41 ES5 RU OBSERVED mean = 0.32 EULERIAN mean = 0.25 correlation = 0.76 no. of stations = FI22 NO % error, 90% of all stations 30% error, 59% of all stations perfect fit NO NO OBSERVED Figure 3.8. Computed versus measured mean annual nitrate concentrations in precipitation in Units: mg (N)l -1 yr

19 Conc. in prec. of NH4+ [mg N/l/year] in CH GB6 CH4 CH2 DE5PL2 0.6 FR14 CH5 SK6 AT4 DE2 NL9 FR9 DK3DE8 DK5 DE7 CZ3 FR8 FR13 DE1 PL5 AT2 AT5 DE9 0.4 FR10 ES4 SK5 DE4 HU2 IE3 GB2 LV10SK4 IE2 GB14 ES1 PL4 FR5SE2 GB13 LV NO1 ES7 EE9 FI9 PT3 IE4 NO8 FI17 EULERIAN GB15 ES5 FI4 NO41 RU OBSERVED mean = 0.37 EULERIAN mean = 0.31 correlation = 0.63 no. of stations = 0.02 FI22 NO55 RU % error, 77% of all stations 30% error, 55% of all stations perfect fit 0.01 NO39 NO OBSERVED Figure 3.9. 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 sulfur is shown in Figure Calculated, mean concentration over 56 stations (388 mg(s)m -2 yr -1 ) is merely 9% lower than the mean from observations (425 mg(s)m -2 yr -1 ), which means fairly good agreement. Like for air concentrations, model underestimates significantly the measured values at three Irish stations. Still, % of the stations can be found in the 50% area, and 61% within the 30% area. Correlation coefficient between measured and computed concentrations is the lowest among all scatter plots (0.33). Lower correlation coefficients for wet deposition compared to the respective air concentrations are also found for oxidized and reduced nitrogen (see below and Figures 3.4 and 3.5). The scatter plot for annual wet deposition of oxidized nitrogen is shown in Figure Calculated mean over 59 stations (263 mg(n)m -2 yr -1 ) is merely 6% lower than the mean from observations (281 mg(n)m -2 yr -1 ). Despite considerable scatter, 95% of the stations are within the 50% error limits - the best result among the scatter plots. The respective figure for the 30% limits is 63%. Correlation coefficient between measured and computed depositions is 0.65). The scatter plot for annual wet deposition of reduced nitrogen, is shown in Figure There is little difference (2%) between calculated mean (334 mg(n)m -1 yr -1 ) over stations and the mean from observations (329 mg(n)m -1 yr -1 ). The scatter is, however, not so little. 77% of the stations are within the 50% limits and merely 55% within the 30% limits. Like for the respective air concentrations, model underestimates significantly the measured values at Norwegian stations. Correlation coefficient between measured and computed depositions is

20 Wet deposition of SO4-- corr. [mg S/m2/year] in SK5 SK SK FI9 FI22 FI4 NO41 ES7 RU13 CH4 CH2 CH3 AT4 AT5 PL2 DE7 DE2 AT2 CZ3 CH5 HU2 LV10DE4 LV16 DE5 NO8 DK3 FR13 NL9 FR9 DK5 PL4 FR8 DE9DE1 FR10 GB2 SE2 RU16 FI17 FR5 FR14 PL5 ES5 NO1 DE8 IE3 EE9 IE4 IE IS2 NO15 NO55 NO39 ES1 PT3 OBSERVED mean = EULERIAN mean = correlation = 0.33 no. of stations = 56 50% error, % of all stations 30% error, 61% of all stations perfect fit OBSERVED Figure Computed versus measured annual wet deposition of sulfate in Units: mg(s)m -2 yr -1. Wet deposition of NO3- [mg N/m2/year] in SK5 AT5 CH5 DE2 AT GB6 CH3 DE7 FR9 NL9 SK4 DE4 FR13 FR8 CH4 CH2 FR10DK3 SK6 CZ3 DK5 AT2 PL5 GB14 DE9 GB2 HU2 LV10 PL2 GB13 SE2 FR14 ES7 ES4 IE2 PL4 LV16 IE3 FR5 DE1 DE5 NO8 DE8 0 NO EULERIAN 90 GB15 NO39 FI9 IE4 NO41 EE9 ES1 FI4 FI17 RU16 ES5 90 OBSERVED mean = EULERIAN mean = correlation = 0.65 no. of stations = RU13 NO15 FI % error, 95% of all stations 30% error, 63% of all stations perfect fit NO OBSERVED Figure Computed versus measured annual wet deposition of nitrate in Units: mg(n)m -2 yr

21 By comparing the respective scatter diagrams for wet depositions of oxidized and reduced nitrogen for 1997 (Olendrzynski, 1999) and those for 1998, a considerable improvement can be noticed. The significant underestimations for 1997 data, are no longer seen in the 1998 data. The main reason for the improvement is better precipitation data. Let us recall that the modelled 1997 precipitation data were underestimated at most EMEP stations. For wet sulfur deposition, good results from 1997 are maintained in 1998 data. The fairly good agreement with 1997 observed values was the result of compensation of errors. Overestimation, on the average, of concentration in precipitation (see Figure 3.8 in Olendrzynski, 1999) together with underestimation of total precipitation amount, lead to reasonable agreement for wet deposition. In 1998 results, we have quite opposite situation: overestimation, on the average, of precipitation, underestimation of concentration in precipitation, and good agreement with observations for wet deposition. Wet deposition of NH4+ [mg N/m2/year] in CH3 CH5 AT4 CH4 CH SK5 AT5 DE2 DE5 NL9 DE8 SK4FR14 FR9 FR13DK3 DE7 FR10PL5 SK6 FR8 DE4 DK5 DE1 PL2 IE3AT2 CZ3 IE2 ES4 GB2 DE ES7 GB6 GB13 LV10 HU2 GB14 FR5 SE2PL4 LV16 NO1 NO8 IE4 200 ES1 PT3 EULERIAN FI9 GB15 FI4 EE9 NO41 ES5 RU16 FI17 OBSERVED mean = 328. EULERIAN mean = correlation = 0.55 no. of stations = FI22 RU13 NO39 50% error, 77% of all stations 30% error, 55% of all stations perfect fit 20 NO15 20 NO OBSERVED Figure Computed versus measured annual wet deposition of ammonium in Units: mg(n)m -2 yr Daily time series Analysis of frequency distributions for wet depositions In this section, computed daily precipitation amounts and wet depositions are compared with daily measurements at EMEP stations in Europe. Fifty-five stations have been considered, stations that satisfy the temporal coverage requirements, and that have been used for annual scatter diagrams in the proceeding section. The 55 stations have been grouped in the following way: - Northern Europe, 11 stations in Finland, Norway and Sweden: FI4, FI9, FI17, FI22, NO1, NO8, NO15, NO39, NO41, NO55 (see Table 2.1. for details) - Eastern Europe, 13 stations in Czech Republic, Estonia, Hungary, Latvia, Poland, Russia and

22 Slovakia: CZ3, EE9, HU2, LV10, LV16, PL2, PL4, PL5, RU13, RU16, SK4, SK5, SK6 - Western Europe, 27 stations in Austria, Switzerland, Germany, Denmark, France, Great Britain, Ireland, and the Netherlands: AT2, AT4, AT5, CH2, CH3, CH4, CH5, DE1, DE2, DE4, DE5, DE7, DE8, DE9, DK3, DK5, FR5, FR8, FR9, FR10, FR13, FR14, GB2, IE2, IE3, IE4, NL9 - Southern Europe, 4 stations in Spain and Portugal: ES1, ES5, ES7 and PT3 As we can see, there is fairly good amount of EMEP stations in Northern and Central Europe, but only few stations in the Mediterranean region. In addition, the quality of measurements is, on the average, lower in southern Europe compared to the rest of the continent (EMEP Summary Report 2000, Aas et al., 2000). Five variables have been included in the analysis: number of days with precipitation per precipitation range, precipitation amount per precipitation range, and wet depositions per precipitation ranges: of sulfur, oxidized nitrogen and reduced nitrogen. Precipitation is expressed as mm/day while wet deposition is given as mgs(or N) m -2 day -1. It should be noted here that point measurements at stations are compared with the corresponding gridcell values in the model (roughly 50 km 50 km). 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. Figure 3.13 shows the comparison of measured and computed by meteorological model PAR- LAM-PS (Lenschow and Tsyro, 2000) number of days per precipitation range in Northern Europe. These are cumulative data for eleven stations listed above. The gridcell values provided by PARLAM-PS, are the input data to the EMEP Eulerian acid deposition model. The daily precipitation totals have been computed based on 3-hour values. The figure shows that the meteorological model overestimates the number of days with precipitation for all precipitation ranges at EMEP stations in Scandinavia. In other words, it rains/snows in the respective model gridcell more often than in the respective EMEP station(s). This may be the feature of the meteorological model and/or the effect of comparing point measurements with fairly large gridcell area (roughly 2500 km -2 ). Graphs for Western and Eastern Europe (not shown here) look very similar to Figure For Western Europe the agreement between measured and computed values for precipitation range exceeding 10mm/day, is better than in the case of Northern Europe Number of days with precipitation per precipitation range: Northern Europe 500 Number of days with precipitation precipitation [mm] Figure Comparison of measured and computed by PARLAM-PS number of days per precipitation range: Northern Europe

23 On the other hand, in Southern Europe (Figure 3.14), modelled data are significantly overestimated for precipitation ranges below 1mm/day, and underestimated for precipitation ranges exceeding 20 mm. The latter may be caused by the underestimation of convective precipitation by PARLAM-PS, which is quite important in lower latitudes in Europe. However, the merely four EMEP stations in Southern Europe do not give a representative picture for the region. Number of days with precipitation per precipitation range: Southern Europe 1 Number of days with precipitation precipitation [mm] Figure Comparison of measured and computed by PARLAM-PS number of days per precipitation range: Southern Europe. Precipitation amount per precipitation range: Northern Europe Precipitation amount [mm] precipitation [mm] Figure Comparison of measured and computed by PARLAM-PS precipitation amount per precipitation range: Northern Europe. Figures 3.15 and 3.16 demonstrate the agreement between modelled and measured precipitation amounts per precipitation ranges. In Northern Europe, measured total precipitation amount for the 11 stations is mm, while the modelled value is 133 mm (37% higher). The modelled overestimation of precipitation is present at almost all precipitation ranges. Similar situation is for Eastern Europe (not shown). For Western Europe (not shown) the overestimation occurs for the range below 10 mm/day, while the total for 27 stations mm exceeds the respective measured total by 11% (27878 mm). A different situation is for the Southern Europe (Figure 3.16). The modelled values are underestimated for ranges 5-7 mm/day and 20- mm/ day. The modelled total for 4 stations: 3477 mm is 15% higher than the measured total (3036 mm)

24 Precipitation amount per precipitation range: Southern Europe 0 Precipitation amount [mm] precipitation [mm] Figure Comparison of measured and computed by PARLAM-PS precipitation amount per precipitation range: Southern Europe. Sulfate deposition per precipitation range: Northern Europe 0 0 Deposition [mg S/m2] precipitation [mm] Figure Comparison of measured and computed wet deposition of sulfur per precipitation range: Northern Europe. Sulfate deposition per precipitation range: Southern Europe Deposition [mg S/m2] precipitation [mm] Figure Comparison of measured and computed wet deposition of sulfur per precipitation range: Southern Europe

25 Despite - on the average - rather poor agreement between measured and modelled precipitation amounts at EMEP stations in Northern, Eastern and Western Europe, the modelled annual wet deposition of sulfur agrees fairly well with respective measurements (Figure 3.10). The modelled annual totals: 2969 mg(s)/m 2, 7353 mg(s)/m 2 and mg(s)/m 2 for Northern, Eastern and Western Europe, respectively, agree well with the respective measured totals: 3003, 5851 and mg(s)/m 2. In Southern Europe the modelled total: 707 mg(s)/m 2 accounts for only 43% of the measured 1654 mg(s)/m 2. However, when one looks at the agreement for various precipitation ranges (Figures 3.17 and 3.18), the following conclusion can be drawn. For Northern, Eastern and Western Europe (the last two not shown) the modelled deposition is generally overestimated for precipitation ranges below 10 mm/day. For ranges exceeding 10 mm/day the opposite is true. The two errors compensate each other resulting in reasonable annual totals. For Southern Europe, the underestimation of measurements begins at the 2 mm/day range. It seems that the model does not perform satisfactory for cases of convective precipitation which are more common throughout the year in Southern Europe compared to middle and high latitudes. Oxidized N deposition per precipitation range: Northern Europe Deposition [mg N/m2] precipitation [mm] Figure Comparison of measured and computed wet deposition of oxidized nitrogen per precipitation range: Northern Europe. Oxidized N deposition per precipitation range: Southern Europe 150 Deposition [mg N/m2] precipitation [mm] Figure Comparison of measured and computed wet deposition of oxidized nitrogen per precipitation range: Southern Europe. The results for wet deposition of oxidized nitrogen (Figures 3.19 and 3.20) are similar to those

26 for sulfur for Western and Eastern Europe (not shown). The good agreement for annual totals: measured 3351 mg(n)/m 2 and mg(n)/m 2 for Eastern and Western Europe respectively, versus 3250 mg(n)/m 2 and mg(n)/m 2 modelled, is due to the compensation of errors: model overestimation for low and model underestimation for high precipitation intensities. For Northern and Southern Europe the model underestimates the measured values: 18 and 610 mg(n)/m 2, respectively for Northern and Southern Europe, versus measured 2420 and 797 mg(n)/m 2, respectively. The results for wet deposition of reduced nitrogen (Figures 3.21 and 3.22) are - once again - similar to those for sulfur for Western and Eastern Europe (not shown). The good agreement for annual totals: measured 3726 mg(n)/m 2 and 116 mg(n)/m 2 for Eastern and Western Europe respectively, versus 3753 mg(n)/m 2 and mg(n)/m 2 modelled, is again the result of compensation of: model overestimation for low and model underestimation for high precipitation intensities. This time the good agreement for annual totals is also shown for Southern Europe (Figure 3.22). The totals are 533 and 551 mg(n)/m 2 for measured and modelled values respectively. For Northern Europe, the model considerably underestimates the measured values: 1123, versus measured 2471 mg(n)/m 2, respectively. Reduced N deposition per precipitation range: Northern Europe Deposition [mg N/m2] precipitation [mm] Figure 3.21 Comparison of measured and computed wet deposition of reduced nitrogen per precipitation range: Northern Europe. Reduced N deposition per precipitation range: Southern Europe Deposition [mg N/m2] precipitation [mm] Figure 3.22 Comparison of measured and computed wet deposition of reduced nitrogen per precipitation range: Southern Europe