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

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%.

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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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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;2

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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 39:169–183

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google Scholar 

  24. Enke W, Spekat A (1997) Downscaling climate model outputs into local and regional weather elements by classification and regression. Clim Res 8:195–207

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google Scholar 

  29. Hall A (2014) Projecting regional change. Science 346:1461–1462. https://doi.org/10.1126/science.aaa0629

    Article  Google Scholar 

  30. Huth R (1996) Properties of the circulation classification scheme based on the rotated principal component analysis. Meteorol Atmos Phys 59:217–233

    Article  Google 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;2

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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 York

    Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google Scholar 

  48. Lund IA (1963) Map-pattern classification by statistical methods. J Appl Meteorol 2:56–65

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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 22:97–114

    Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google 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

    Article  Google Scholar 

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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.

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Stryhal, J., Huth, R. Classifications of winter atmospheric circulation patterns: validation of CMIP5 GCMs over Europe and the North Atlantic. Clim Dyn 52, 3575–3598 (2019). https://doi.org/10.1007/s00382-018-4344-7

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Keywords

  • Global climate models
  • Validation
  • Atmospheric circulation
  • Circulation classifications