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Classifications of winter atmospheric circulation patterns: validation of CMIP5 GCMs over Europe and the North Atlantic

Article

Abstract

Winter atmospheric circulation over the Euro-Atlantic domain and three subdomains (British Isles, Central Europe, and Eastern Mediterranean) is validated in outputs of historical runs of 32 global climate models (GCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Eight automated classifications of daily SLP patterns from five reanalysis datasets are produced for each domain in order to analyse the effect of the choices of methods and reference data on results. The results show that the ranking of GCMs fundamentally depends on which classification is used; therefore, only parallel usage of multiple classifications can provide robust rankings of models. Considering all eight classifications, three models (HadGEM2-CC, MIROC4h, and CNRM-CM5) are among the best in simulating the frequency of circulation types (CTs) over all four domains. Regardless the domain, the bias in CT frequency of the worst GCMs is larger than 50% of the frequency in the reference reanalysis dataset. Conversely, the best GCM for each domain differs from the reference reanalysis by about 10–20%, which is nearly the same result as found for the NOAA-CIRES Twentieth Century Reanalysis (version 2). The persistence of circulation is simulated better than the frequency with errors rarely exceeding 15%. The GCMs overestimate the frequency of westerly circulation over all domains (by about 7% over the British Isles, 21% over Central Europe, and almost 70% over the Eastern Mediterranean) and also cyclonic CTs, while easterly and anticyclonic CTs are typically underestimated by 30–40%.

Keywords

Global climate models Validation Atmospheric circulation Circulation classifications 

Notes

Acknowledgements

The work was funded by the Grant Agency of Charles University, Project No. 188214. We thank all climate-modelling groups for making available their GCM simulations and the PCMDI for enabling access to the data. We acknowledge the following organizations for providing their reanalysis datasets: NOAA/OAR/ESRL PSD, Boulder, Colorado for the NCEP/NCAR reanalysis and the Twentieth Century Reanalysis, version 2; ECMWF for ERA-40 and ERA-20C; and JMA for JRA-55. Thanks are also due to all developers of the COST733 software and namely Dr Andreas Philipp from the Institute of Geography, University of Augsburg, Germany for the many instructions on its usage; the Institute is also acknowledged for maintaining the software and enabling access to it. We are grateful to two reviewers, whose insight helped improve the quality of this paper.

References

  1. Anstey JA, Davini P, Gray LJ, Woollings TJ, Butchart N, Cagnazzo C, Christiansen B, Hardiman SC, Osprey SM, Yang S (2013) Multi-model analysis of Northern Hemisphere winter blocking: model biases and the role of resolution. J Geophys Res Atmos 118:3956–3971.  https://doi.org/10.1002/jgrd.50231 CrossRefGoogle Scholar
  2. Beck C, Jacobeit J, Jones PD (2007) Frequency and within-type variations of large-scale circulation types and their effects on low-frequency climate variability in central Europe since 1780. Int J Climatol 27:473–491.  https://doi.org/10.1002/joc.1410 CrossRefGoogle Scholar
  3. Beck C, Weitnauer C, Jacobeit J (2014) Downscaling of monthly PM10 indices at different sites in Bavaria (Germany) based on circulation type classifications. Atmos Pollut Res 5:741–752.  https://doi.org/10.5094/APR.2014.083 CrossRefGoogle Scholar
  4. Beck C, Philipp A, Jacobeit J (2015) Interannual drought index variations in Central Europe related to the large-scale atmospheric circulation—application and evaluation of statistical downscaling approaches based on circulation type classifications. Theor Appl Climatol 121:713–732.  https://doi.org/10.1007/s00704-014-1267-z CrossRefGoogle Scholar
  5. Beck C, Philipp A, Streicher F (2016) The effect of domain size on the relationship between circulation type classifications and surface climate. Int J Climatol 36:2692–2709.  https://doi.org/10.1002/joc.3688 CrossRefGoogle Scholar
  6. Belleflamme A, Fettweis X, Lang C, Erpicum M (2013) Current and future atmospheric circulation at 500 hPa over Greenland simulated by the CMIP3 and CMIP5 global models. Clim Dyn 41:2061–2080.  https://doi.org/10.1007/s00382-012-1538-2 CrossRefGoogle Scholar
  7. Belleflamme A, Fettweis X, Erpicum M (2015) Do global warming-induced circulation pattern changes affect temperature and precipitation over Europe during summer? Int J Climatol 35:1484–1499.  https://doi.org/10.1002/joc.4070 CrossRefGoogle Scholar
  8. Boer GJ, McFarlane NA, Lazare M (1992) Greenhouse gas–induced climate change simulated with the CCC second-generation general circulation model. J Clim 5:1045–1077. https://doi.org/10.1175/1520-0442(1992)005<1045:GGCCSW>2.0.CO;2CrossRefGoogle Scholar
  9. Brands S, Herrera S, Fernández J, Gutiérrez JM (2013) How well do CMIP5 Earth System Models simulate present climate conditions in Europe and Africa? Clim Dyn 41:803–817.  https://doi.org/10.1007/s00382-013-1742-8 CrossRefGoogle Scholar
  10. Broderick C, Fealy R (2015) An analysis of the synoptic and climatological applicability of circulation type classifications for Ireland. Int J Climatol 35:481–505.  https://doi.org/10.1002/joc.3996 CrossRefGoogle Scholar
  11. Buehler T, Raible CC, Stocker TF (2011) The relationship of winter season North Atlantic blocking frequencies to extreme cold or dry spells in the ERA-40. Tellus A 63:212–222.  https://doi.org/10.1111/j.1600-0870.2010.00492.x CrossRefGoogle Scholar
  12. Cahynová M, Huth R (2016) Atmospheric circulation influence on climatic trends in Europe: an analysis of circulation type classifications from the COST733 catalogue. Int J Climatol 36:2743–2760.  https://doi.org/10.1002/joc.4003 CrossRefGoogle Scholar
  13. Casado MJ, Pastor MA (2016) Circulation types and winter precipitation in Spain. Int J Climatol 36:2727–2742.  https://doi.org/10.1002/joc.3860 CrossRefGoogle Scholar
  14. Casado MJ, Pastor MA, Doblas-Reyes FJ (2010) Links between circulation types and precipitation over Spain. Phys Chem Earth 35:437–447.  https://doi.org/10.1016/j.pce.2009.12.007 CrossRefGoogle Scholar
  15. Cassano JJ, Uotila P, Lynch A (2006) Changes in synoptic weather patterns in the polar regions in the twentieth and twenty-first centuries, part 1: Arctic. Int J Climatol 26:1027–1049.  https://doi.org/10.1002/joc.1306 CrossRefGoogle Scholar
  16. Cattiaux J, Douville H, Peings Y (2013a) European temperatures in CMIP5: origins of present-day biases and future uncertainties. Clim Dyn 41:2889–2907.  https://doi.org/10.1007/s00382-013-1731-y CrossRefGoogle Scholar
  17. Cattiaux J, Douville H, Ribes A, Chauvin F, Plante C (2013b) Towards a better understanding of changes in wintertime cold extremes over Europe: a pilot study with CNRM and IPSL atmospheric models. Clim Dyn 40:2433–2445.  https://doi.org/10.1007/s00382-012-1436-7 CrossRefGoogle Scholar
  18. Compo GP et al (2011) The twentieth century reanalysis project. Q J R Meteorol Soc 137:1–28.  https://doi.org/10.1002/qj.776 CrossRefGoogle Scholar
  19. Crane RG, Barry RG (1988) Comparison of the MSL synoptic pressure patterns of the Arctic as observed and simulated by the GISS general circulation model. Meteorol Atmos Phys 39:169–183CrossRefGoogle Scholar
  20. Davini P, Cagnazzo C (2014) On the misinterpretation of the North Atlantic Oscillation in CMIP5 models. Clim Dyn 43:1497–1511.  https://doi.org/10.1007/s00382-013-1970-y CrossRefGoogle Scholar
  21. Demuzere M, Werner M, Van Lipzig N, Roeckner E (2009) An analysis of present and future ECHAM5 pressure fields using a classification of circulation patterns. Int J Climatol 29:1796–1810.  https://doi.org/10.1002/joc.1821 CrossRefGoogle Scholar
  22. Demuzere M, Kassomenos P, Philipp A (2011) The COST733 circulation type classification software: an example for surface ozone concentrations in Central Europe. Theor Appl Climatol 105:143–166.  https://doi.org/10.1007/s00704-010-0378-4 CrossRefGoogle Scholar
  23. Dunn-Sigouin E, Son SW (2013) Northern Hemisphere blocking frequency and duration in the CMIP5 models. J Geophys Res Atmos 118:1179–1188.  https://doi.org/10.1002/jgrd.50143 CrossRefGoogle Scholar
  24. Enke W, Spekat A (1997) Downscaling climate model outputs into local and regional weather elements by classification and regression. Clim Res 8:195–207CrossRefGoogle Scholar
  25. Finnis J, Cassano J, Holland M, Uotila P (2009a) Synoptically forced hydroclimatology of major Arctic watersheds in general circulation models; Part 1: the Mackenzie River Basin. Int J Climatol 29:1226–1243.  https://doi.org/10.1002/joc.1753 CrossRefGoogle Scholar
  26. Finnis J, Cassano J, Holland M, Uotila P (2009b) Synoptically forced hydroclimatology of major Arctic watersheds in general circulation models; Part 2: Eurasian watersheds. Int J Climatol 29:1244–1261.  https://doi.org/10.1002/joc.1769 CrossRefGoogle Scholar
  27. Fleig AK, Tallaksen LM, Hisdal H, Stahl K, Hannah DM (2010) Inter-comparison of weather and circulation type classifications for hydrological drought development. Phys Chem Earth 35:507–515.  https://doi.org/10.1016/j.pce.2009.11.005 CrossRefGoogle Scholar
  28. Gibson PB, Uotila P, Perkins-Kirkpatrick SE, Alexander LV, Pitman AJ (2016) Evaluating synoptic systems in the CMIP5 climate models over the Australian region. Clim Dyn 47:2235–2251.  https://doi.org/10.1007/s00382-015-2961-y CrossRefGoogle Scholar
  29. Hall A (2014) Projecting regional change. Science 346:1461–1462.  https://doi.org/10.1126/science.aaa0629 CrossRefGoogle Scholar
  30. Huth R (1996) Properties of the circulation classification scheme based on the rotated principal component analysis. Meteorol Atmos Phys 59:217–233CrossRefGoogle Scholar
  31. Huth R (1997) Continental-scale circulation in the UKHI GCM. J Clim 10:1545–1561. https://doi.org/10.1175/1520-0442(1997)010<1545:CSCITU>2.0.CO;2CrossRefGoogle Scholar
  32. Huth R (2000) A circulation classification scheme applicable in GCM studies. Theor Appl Climatol 67:1–18.  https://doi.org/10.1007/s007040070012 CrossRefGoogle Scholar
  33. Huth R (2010) Synoptic-climatological applicability of circulation classifications from the COST733 collection: first results. Phys Chem Earth 35:388–394.  https://doi.org/10.1016/j.pce.2009.11.013 CrossRefGoogle Scholar
  34. Huth R, Pokorná L, Bochníček J, Hejda P (2006) Solar cycle effects on modes of low-frequency circulation variability. J Geophys Res 111:D22107.  https://doi.org/10.1029/2005JD006813 CrossRefGoogle Scholar
  35. Huth R, Beck C, Philipp A, Demuzere M, Ustrnul Z, Cahynová M, Kyselý J, Tveito OE (2008) Classifications of atmospheric circulation patterns. Recent advances and applications. Ann N Y Acad Sci 1146:105–152.  https://doi.org/10.1196/annals.1446.019 CrossRefGoogle Scholar
  36. Huth R, Beck C, Tveito OE (2010) Classifications of atmospheric circulation patterns—theory and applications—preface. Phys Chem Earth 35:307–308.  https://doi.org/10.1016/j.pce.2010.06.005 CrossRefGoogle Scholar
  37. Huth R, Beck C, Kučerová M (2016) Synoptic-climatological evaluation of the classifications of atmospheric circulation patterns over Europe. Int J Climatol 36:2710–2726.  https://doi.org/10.1002/joc.4546 CrossRefGoogle Scholar
  38. James PM (2006) An assessment of European synoptic variability in Hadley Centre Global Environmental models based on an objective classification of weather regimes. Clim Dyn 27:215–231.  https://doi.org/10.1007/s00382-006-0133-9 CrossRefGoogle Scholar
  39. Jones PD, Harpham C, Briffa KR (2013) Lamb weather types derived from reanalysis products. Int J Climatol 33:1129–1139.  https://doi.org/10.1002/joc.3498 CrossRefGoogle Scholar
  40. Kalnay E et al. (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77:437–470.  https://doi.org/10.1175/1520-0477(1996)077%3C0437:TNYRP%3E2.0.CO;2 CrossRefGoogle Scholar
  41. Kassomenos P (2010) Synoptic circulation control on wild fire occurrence. Phys Chem Earth 35:544–552.  https://doi.org/10.1016/j.pce.2009.11.008 CrossRefGoogle Scholar
  42. Kaufman L, Rousseeuw PJ (1990) Finding groups in data: an introduction to cluster analysis. Wiley Series in probability and mathematical statistics: applied probability and statistics. Wiley, New YorkGoogle Scholar
  43. Kobayashi S et al (2015) The JRA-55 Reanalysis: General specifications and basic characteristics. J Meteorol Soc Japan 93:5–48.  https://doi.org/10.2151/jmsj.2015-001 CrossRefGoogle Scholar
  44. Kröner N, Kotlarski S, Fischer E, Lüthi D, Zubler E, Schär C (2017) Separating climate change signals into thermodynamic, lapse-rate and circulation effects: theory and application to the European summer climate. Clim Dyn 48:3425–3440.  https://doi.org/10.1007/s00382-016-3276-3 CrossRefGoogle Scholar
  45. Kučerová M, Beck C, Philipp A, Huth R (2017) Trends in frequency and persistence of atmospheric circulation types over Europe derived from a multitude of classifications. Int J Climatol 37:2502–2521.  https://doi.org/10.1002/joc.4861 CrossRefGoogle Scholar
  46. Lapp S, Byrne J, Kienzle S, Townshend I (2002) Linking global circulation model synoptics and precipitation for western North America. Int J Climatol 22:1807–1817.  https://doi.org/10.1002/joc.851 CrossRefGoogle Scholar
  47. Lorenzo MN, Ramos AM, Taboada JJ, Gimeno L (2011) Changes in present and future circulation types frequency in northwest Iberian Peninsula. PLoS ONE 6:e16201.  https://doi.org/10.1371/journal.pone.0016201 CrossRefGoogle Scholar
  48. Lund IA (1963) Map-pattern classification by statistical methods. J Appl Meteorol 2:56–65CrossRefGoogle Scholar
  49. Lupikasza E (2010) Relationships between occurrence of high precipitation and atmospheric circulation in Poland using different classifications of circulation types. Phys Chem Earth 35:448–455.  https://doi.org/10.1016/j.pce.2009.11.012 CrossRefGoogle Scholar
  50. Lynch A, Uotila P, Cassano JJ (2006) Changes in synoptic weather patterns in the polar regions in the twentieth and twenty-first centuries, part 2: Antarctic. Int J Climatol 26:1181–1199.  https://doi.org/10.1002/joc.1306 CrossRefGoogle Scholar
  51. McKendry IG, Steyn DG, McBean G (1995) Validation of synoptic circulation patterns simulated by the Canadian climate centre general circulation model for western North America. Atmos-Ocean 33:809–825.  https://doi.org/10.1080/07055900.1995.9649554 CrossRefGoogle Scholar
  52. McKendry IG, Stahl K, Moore RD (2006) Synoptic sea-level pressure patterns generated by a general circulation model: comparison with types derived from NCEP/NCAR re-analysis and implications for downscaling. Int J Climatol 26:1727–1736.  https://doi.org/10.1002/joc.1337 CrossRefGoogle Scholar
  53. Meehl GA, Covey C, Taylor KE, Delworth T, Stouffer RJ, Latif M, McAvaney B, Mitchell JFB (2007) The WCRP CMIP3 multimodel dataset: a new era in climate change research. Bull Am Meteorol Soc 88:1383–1394.  https://doi.org/10.1175/BAMS-88-9-1383 CrossRefGoogle Scholar
  54. Palm V, Sepp M, Truu J, Ward RD, Leito A (2017) The effect of atmospheric circulation on spring arrival of short- and long-distance migratory bird species in Estonia. Boreal Env Res 22:97–114Google Scholar
  55. Pastor MA, Casado MJ (2012) Use of circulation types classifications to evaluate AR4 climate models over the Euro-Atlantic region. Clim Dyn 39:2059–2077.  https://doi.org/10.1007/s00382-012-1449-2 CrossRefGoogle Scholar
  56. Perez J, Menendez M, Mendez FJ, Losada IJ (2014) Evaluating the performance of CMIP3 and CMIP5 global climate models over the north-east Atlantic region. Clim Dyn 43:2663–2680.  https://doi.org/10.1007/s00382-014-2078-8 CrossRefGoogle Scholar
  57. Pfahl S (2014) Characterising the relationship between weather extremes in Europe and synoptic circulation features. Nat Hazards Earth Syst Sci 14:1461–1475.  https://doi.org/10.5194/nhess-14-1461-2014 CrossRefGoogle Scholar
  58. Philipp A, Della-Marta PM, Jacobeit J, Fereday DR, Jones PD, Moberg A, Wanner H (2007) Long-term variability of daily North Atlantic–European pressure patterns since 1850 classified by simulated annealing clustering. J Clim 20:4065–4095.  https://doi.org/10.1175/JCLI4175.1 CrossRefGoogle Scholar
  59. Philipp A, Bartholy J, Beck C, Erpicum M, Esteban P, Fettweis R, Huth R, James P, Jourdain S, Kreienkamp F, Krennert T, Lykoudis S, Michaelides S, Pianko K, Post P, Rasilla Álvarez D, Schiemann R, Spekat A, Tymvios FS (2010) Cost733cat—a database of weather and circulation type classifications. Phys Chem Earth 35:360–373.  https://doi.org/10.1016/j.pce.2009.12.010 CrossRefGoogle Scholar
  60. Philipp A, Beck C, Huth R, Jacobeit J (2016) Development and comparison of circulation type classifications using the COST 733 dataset and software. Int J Climatol 36:2671–2809.  https://doi.org/10.1002/joc.3920 CrossRefGoogle Scholar
  61. Plavcová E, Kyselý J (2012) Atmospheric circulation in regional climate models over Central Europe: links to surface air temperature and the influence of driving data. Clim Dyn 39:1681–1695.  https://doi.org/10.1007/s00382-011-1278-8 CrossRefGoogle Scholar
  62. Plavcová E, Kyselý J (2013) Projected evolution of circulation types and their temperatures over Central Europe in climate models. Theor Appl Climatol 114:625–634.  https://doi.org/10.1007/s00704-013-0874-4 CrossRefGoogle Scholar
  63. Poli P et al (2016) ERA-20C: An atmospheric reanalysis of the twentieth century. J Clim 29:4083–4097.  https://doi.org/10.1175/JCLI-D-15-0556.1 CrossRefGoogle Scholar
  64. Rohrer M, Croci-Maspoli M, Appenzeller C (2017) Climate change and circulation types in the Alpine region. Meteorol Z 26:83–92.  https://doi.org/10.1127/metz/2016/0681 CrossRefGoogle Scholar
  65. Rust HW, Vrac M, Lengaigne M, Sultan B (2010) Quantifying differences in circulation patterns based on probabilistic models: IPCC AR4 multimodel comparison for the North Atlantic. J Clim 23:6573–6589.  https://doi.org/10.1175/2010JCLI3432.1 CrossRefGoogle Scholar
  66. Schiemann R, Frei C (2010) How to quantify the resolution of surface: an, example for alpine precipitation. Phys Chem Earth 35:403–410.  https://doi.org/10.1016/j.pce.2009.09.005 CrossRefGoogle Scholar
  67. Schoof JT, Pryor SC (2006) An evaluation of two GCMs: simulation of North American teleconnection indices and synoptic phenomena. Int J Climatol 26:267–282.  https://doi.org/10.1002/joc.1242 CrossRefGoogle Scholar
  68. Shepherd TG (2014) Atmospheric circulation as a source of uncertainty in climate change projections. Nat Geosci 7:703–708.  https://doi.org/10.1038/ngeo2253 CrossRefGoogle Scholar
  69. Sheridan SC, Lee CC (2011) The self-organizing map in synoptic climatological research. Prog Phys Geogr 35:109–119.  https://doi.org/10.1177/0309133310397582 CrossRefGoogle Scholar
  70. Stefan S, Necula C, Georgescu F (2010) Analysis of long-range transport of particulate matters in connection with air circulation over Central and Eastern part of Europe. Phys Chem Earth 35:523–529.  https://doi.org/10.1016/j.pce.2009.12.008 CrossRefGoogle Scholar
  71. Stryhal J, Huth R (2017) Classifications of winter Euro-Atlantic circulation patterns: an intercomparison of five atmospheric reanalyses. J Clim 30:7847–7861.  https://doi.org/10.1175/JCLI-D-17-0059.1 CrossRefGoogle Scholar
  72. Stryhal J, Huth R (2018) Trends in winter circulation over the British Isles and central Europe in twenty-first century projections by 25 CMIP5 GCMs. Clim Dyn.  https://doi.org/10.1007/s00382-018-4178-3 Google Scholar
  73. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bull Am Meteorol Soc 93:485–498.  https://doi.org/10.1175/BAMS-D-11-00094.1 CrossRefGoogle Scholar
  74. Tveito OE (2010) An assessment of circulation type classifications for precipitation distribution in Norway. Phys Chem Earth 35:395–402.  https://doi.org/10.1016/j.pce.2010.03.044 CrossRefGoogle Scholar
  75. Tveito OE, Huth R (2016) Circulation-type classifications in Europe: results of the COST 733 Action. Int J Climatol 36:2671–2672.  https://doi.org/10.1002/joc.4768 CrossRefGoogle Scholar
  76. Uppala SM et al (2005) The ERA-40 re-analysis. Q J R Meteorol Soc 131(612):2961–3012.  https://doi.org/10.1256/qj.04.176 CrossRefGoogle Scholar
  77. Ustrnul Z, Czekierda D, Wypych A (2010) Extreme values of air temperature in Poland according to different atmospheric circulation classifications. Phys Chem Earth 35:429–436.  https://doi.org/10.1016/j.pce.2009.12.012 CrossRefGoogle Scholar
  78. Valverde V, Pay MT, Baldasano JM (2015) Circulation-type classification derived on a climatic basis to study air quality dynamics over the Iberian Peninsula. Int J Climatol 35:2877–2897.  https://doi.org/10.1002/joc.4179 CrossRefGoogle Scholar
  79. van Ulden AP, van Oldenborgh GJ (2006) Large-scale atmospheric circulation biases and changes in global climate model simulations and their importance for climate change in Central Europe. Atmos Chem Phys 6:863–881.  https://doi.org/10.5194/acp-6-863-2006 CrossRefGoogle Scholar
  80. Vial J, Osborn TJ (2012) Assessment of atmosphere-ocean general circulation model simulations of winter northern hemisphere atmospheric blocking. Clim Dyn 39:95–112.  https://doi.org/10.1007/s00382-011-1177-z CrossRefGoogle Scholar
  81. Wójcik R (2015) Reliability of CMIP5 GCM simulations in reproducing atmospheric circulation over Europe and the North Atlantic: a statistical downscaling perspective. Int J Climatol 35:714–732.  https://doi.org/10.1002/joc.4015 CrossRefGoogle Scholar
  82. Wood JL, Harrison S, Turkington TAR, Reinhardt L (2016) Landslides and synoptic weather trends in the European Alps. Clim Change 136:297–308.  https://doi.org/10.1007/s10584-016-1623-3 CrossRefGoogle Scholar
  83. Zappa G, Shaffrey LC, Hodges KI (2013) The ability of CMIP5 models to simulate North Atlantic extratropical cyclones. J Clim 26:5379–5396.  https://doi.org/10.1175/JCLI-D-12-00501.1 CrossRefGoogle Scholar

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© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Physical Geography and Geoecology, Faculty of ScienceCharles UniversityPrague 2Czech Republic
  2. 2.Institute of Atmospheric PhysicsCzech Academy of SciencesPrague 4Czech Republic

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