Climate Dynamics

, Volume 41, Issue 7–8, pp 2061–2080 | Cite as

Current and future atmospheric circulation at 500 hPa over Greenland simulated by the CMIP3 and CMIP5 global models

  • Alexandre Belleflamme
  • Xavier Fettweis
  • Charlotte Lang
  • Michel Erpicum


The Greenland ice sheet is projected to be strongly affected by global warming. These projections are either issued from downscaling methods (such as Regional Climate Models) or they come directly from General Circulation Models (GCMs). In this context, it is necessary to evaluate the accuracy of the daily atmospheric circulation simulated by the GCMs, since it is used as forcing for downscaling methods. Thus, we use an automatic circulation type classification based on two indices (Euclidean distance and Spearman rank correlation using the daily 500 hPa geopotential height) to evaluate the ability of the GCMs from both CMIP3 and CMIP5 databases to simulate the main circulation types over Greenland during summer. For each circulation type, the GCMs are compared to three reanalysis datasets on the basis of their frequency and persistence differences. For the current climate (1961–1990), we show that most of the GCMs do not reproduce the expected frequency and the persistence of the circulation types and that they simulate poorly the observed daily variability of the general circulation. Only a few GCMs can be used as reliable forcings for downscaling methods over Greenland. Finally, when applying the same approach to the future projections of the GCMs, no significant change in the atmospheric circulation over Greenland is detected, besides a generalised increase of the geopotential height due to a uniform warming of the atmosphere.


General circulation models 500 hPa geopotential height Greenland Circulation type classification 



We acknowledge the modelling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP Working Group on Coupled Modelling (WGCM) for their role in making available the WCRP CMIP3 multi-model dataset. Support for this dataset is provided by the Office of Science, US Department of Energy.

For their role in producing, coordinating, and making available the CMIP5 model output, we acknowledge the climate modelling groups (see Table 1), the World Climate Research Programme (WCRP) Working Group on Coupled Modelling (WGCM), and the Global Organization for Earth System Science Portals (GO-ESSP).

We also wish to thank the research centres that publish their data, especially data not contained in the CMIP databases.

NCEP/NCAR Reanalysis data were provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their website at

The ECMWF ERA-40 Reanalysis data used in this study were obtained from the ECMWF Data Server (

Support for the Twentieth Century Reanalysis (20CR) Project dataset was provided by the US Department of Energy, Office of Science Innovative and Novel Computational Impact on Theory and Experiment (DOE INCITE) program, by the Office of Biological and Environmental Research (BER), and by the National Oceanic and Atmospheric Administration (NOAA) Climate Program Office.

Finally, we wish to thank the ISLV Editing and Translation Services for their support in improving the language of this manuscript.

Supplementary material

382_2012_1538_MOESM1_ESM.pdf (1.1 mb)
PDF (1092 KB)


  1. Anagnostopoulou C, Tolika K, Maheras P, Kutiel H, Flocas H (2008) Performance of the general circulation HadAM3P model in simulating circulation types over the Mediterranean region. Int J Climatol 28:185–203CrossRefGoogle Scholar
  2. Anagnostopoulou C, Tolika K, Maheras P (2009) Classification of circulation types: a new flexible automated approach applicable to NCEP and GCM datasets. Theoret Appl Climatol 96(1–2):3–15CrossRefGoogle Scholar
  3. Bardossy A, Caspary H-J (1990) Detection of climate change in Europe by analyzing European atmospheric circulation patterns from 1881 to 1989. Theoret Appl Climatol 42:155–167CrossRefGoogle Scholar
  4. Bardossy A, Stehlik J, Caspary H-J (2002) Automated objective classification of daily circulation patterns for precipitation and temperature downscaling based on optimized fuzzy rules. Clim Res 23:11–22CrossRefGoogle Scholar
  5. Boé J, Terray L, Martin E, Habets F (2009) Projected changes in components of the hydrological cycle in French river basins during the 21st century. Water Resourc Res 45. doi: 10.1029/2008WR007437
  6. Box J, Cappelen J, Decker D, Fettweis X, Mote T, Tedesco M, van de Wal R (2010) Greenland. In: Arctic Report Card 2010.
  7. Box J, Fettweis X, Stroeve J, Tedesco M, Hall D, Steffen K (2012) Greenland ice sheet albedo feedback: thermodynamics and atmospheric drivers. Cryosphere Discuss 6:593–634. doi: 10.5194/tcd-6-593-2012 Google Scholar
  8. Brands S, Gutierrez J, Herrera S, Cofiño A (2012) On the use of reanalysis data for downscaling. J Clim 25:2517–2526CrossRefGoogle Scholar
  9. Brinkmann W (2000) Modification of a correlation-based circulation pattern classification to reduce within-type variability of temperature and precipitation. Int J Climatol 20:839–852CrossRefGoogle Scholar
  10. Casado M, Pastor M, Doblas-Reyes F (2009) Euro-Atlantic circulation types and modes of variability in winter. Theoret Appl Climatol 96:17–29CrossRefGoogle Scholar
  11. Casado M, Pastor M (2012) Use of variability modes to evaluate AR4 climate models over the Euro-Atlantic region. Clim Dyn. doi: 10.1007/s00382-011-1077-2
  12. Compo G, Whitaker J, Sardeshmukh P, Matsui N, Allan R, Yin X, Gleason B, Vose R, Rutledge G, Bessemoulin P, Brönnimann S, Brunet M, Crouthamel R, Grant A, Groisman P, Jones P, Kruk M, Kruger A, Marshall G, Maugeri M, Mok H, Nordli Ø, Ross T, Trigo R, Wang X, Woodruff S, Worley S (2011) The twentieth century reanalysis project. Quart J R Meteorol Soc 137:1–28CrossRefGoogle Scholar
  13. 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 29:1796–1810CrossRefGoogle Scholar
  14. El-Kadi A, Smithson P (1992) Atmospheric classifications and synoptic climatology. Prog Phys Geogr 16(4):432–455CrossRefGoogle Scholar
  15. 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
  16. Fettweis X, Belleflamme A, Erpicum M, Franco B, Nicolay S (2011a) Estimation of the sea-level rise by 2100 resulting from changes in the surface mass balance of the Greenland ice sheet. In: Blanco J, Kheradmand H (eds) Climate change—Geophysical Foundations and Ecological Effects. InTech, available on:
  17. Fettweis X, Mabille G, Erpicum M, Nicolay S, Van den Broeke M (2011b) The 1958–2009 Greenland ice sheet surface melt and the mid-tropospheric atmospheric circulation. ClimDyn. doi: 10.1007/s00382-010-0772-8
  18. Fettweis X, Hanna E, Lang C, Belleflamme A, Erpicum M, Gallée H (2012) Brief communication “Important role of the mid-tropospheric atmospheric circulation in the recent surface melt increase over the Greenland ice sheet”. The Cryosphere Discuss 6:4101–4122. doi: 10.5194/tcd-6-4101-2012
  19. Franco B, Fettweis X, Erpicum M, Nicolay S (2011) Present and future climates of the Greenland ice sheet according to the IPCC AR4 models. Clim Dyn 36:1897–1918. doi: 10.1007/s00382-010-0779-1 CrossRefGoogle Scholar
  20. Gutmann E, Rasmussen R, Liu C, Ikeda K, Gochis D, Clark M, Dudhia J, Thompson G (2011) A comparison of statistical and dynamical downscaling of winter precipitation over complex terrain. J Clim. doi: 10.1175/2011JCLI4109.1
  21. Hanna E, Huybrechts P, Steffen K, Cappelen J, Huff R, Shuman C, Irvine-Fynn T, Wise S, Griffiths M (2008) Increased runoff from melt from the Greenland ice sheet: a response to global warming. J Clim 21:331–341CrossRefGoogle Scholar
  22. Hanna E, Cappelen J, Fettweis X, Huybrechts P, Luckman A, Ribergaard MH (2009) Hydrologic response of the Greenland ice sheet: the role of oceanographic warming, Hydrol Process (special issue: Hydrol Effect Shrink Cryosphere) 23(1):7–30. doi: 10.1002/hyp.7090
  23. Huth R (2000) A circulation classification scheme applicable in GCM studies. Theoret Appl Climatol 67:1–18CrossRefGoogle Scholar
  24. Huth R, Ustrnul Z, Dittmann E, Bissolli P, Pasqui M, James P (2007) Inventory of circulation classification methods and their applications in Europe within the COST 733 action. In: Proceedings from the 5th annual meeting of the European Meteorological Society, Session AW8 Weather types classifications, Utrecht, pp 9–15Google Scholar
  25. Kalkstein L, Tan G, Skindlov J (1987) An evaluation of three clustering procedures for use in synoptic climatological classification. J Clim Appl Meteorol 26:717–730CrossRefGoogle Scholar
  26. Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Leetmaa A, Reynolds B, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo K, Ropelewski C, Wang J, Jenne R, Joseph D (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77:437–471CrossRefGoogle Scholar
  27. Kirchhofer W (1973) Classification of European 500mb patterns. Arbeitsbericht der Schweizerischen Meteorologischen Zentralanstalt, 43p, GenevaGoogle Scholar
  28. Kysely J, Huth R (2006) Changes in atmospheric circulation over Europe detected by objective and subjective methods. Theoret Appl Climatol 85:19–36CrossRefGoogle Scholar
  29. Lund IA (1963) Map-pattern classification by statistical methods. J Appl Meteorol 2:56–65CrossRefGoogle Scholar
  30. Masson D, Knutti R (2011) Spatial-scale dependence of climate model performance in the CMIP3 ensemble. J Clim 24:2680–2692CrossRefGoogle Scholar
  31. Meehl G, Stocker T, Collins W, Friedlingstein P, Gaye A, Gregory J, Kitoh A, Knutti R, Murphy J, Noda A, Raper S, Watterson I, Weaver A, Zhao Z-C (2007) Global climate projections. In: Climate change 2007: the physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt K, Tignor M, Miller H (eds)]. Cambridge University Press, Cambridge, UK, New York, NYGoogle Scholar
  32. Moss R, Edmonds J, Hibbard K, Manning M, Rose S, van Vuuren D, Carter T, Emori S, Kainuma M, Kram T, Meehl G, Mitchell J, Nakicenovic N, Riahi K, Smith S, Stouffer R, Thomson A, Weyant J, Wilbanks T (2010) The next generation of scenarios for climate change research and assessment. Nature 463. doi: 10.1038/nature08823
  33. Mote TL (1998a) Mid-tropospheric circulation and surface melt on the Greenland Ice Sheet. Part I: atmospheric teleconnections. Int J Climatol 18:111–130CrossRefGoogle Scholar
  34. Mote TL (1998b) Mid-tropospheric circulation and surface melt on the Greenland Ice sheet. Part II: synoptic climatology. Int J Climatol 18:131–146CrossRefGoogle Scholar
  35. Overland J, Wang M, Bond N, Walsh J, Kattsov V, Chapman W (2011) Considerations in the selection of global climate models for regional climate projections: the Arctic as a case study. J Clim 24:1583–1597CrossRefGoogle Scholar
  36. Pastor M, Casado M (2012) Use of circulation types classifications to evaluate AR4 climate models over the Euro-Atlantic region. Clim Dyn. doi: 10.1007/s00382-012-1449-2
  37. Philipp A, Della-Marta P, Jacobeit J, Fereday D, Jones P, Moberg A, Wanner H (2007) Long-term variability of daily North Atlantic-European pressure patterns since 1850 classified by simulated annealing clustering. J Clim 20:4065–4095CrossRefGoogle Scholar
  38. Philipp A, Bartholy J, Beck C, Erpicum M, Esteban P, Fettweis X, Huth R, James P, Jourdain S, Kreienkamp F, Krennert T, Lykoudis S, Michalides S, Pianko K, Post P, Rassilla Álvarez D, Schiemann R, Spekat A, Tymvios F. S (2010) COST733CAT—a database of weather and circulation type classifications. Phys Chem Earth 35(9–12):360–373Google Scholar
  39. Plaut G, Simonnet E (2001) Large-scale circulation classification, weather regimes, and local climate over France, the Alps and Western Europe. Clim Res 17:303–324CrossRefGoogle Scholar
  40. Randall D, Wood R, Bony S, Colman R, Fichefet T, Fyfe J, Kattsov V, Pitman A, Shukla J, Srinivasan J, Stouffer R, Sumi A, Taylor K (2007) Climate models and their evaluation. In: Climate change 2007: the physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt K, Tignor M, Miller H (eds)]. Cambridge University Press, Cambridge, UK, New York, NYGoogle Scholar
  41. Reifen C, Toumi R (2009) Climate projections: past performance no guarantee of future skill? Geophys Res Lett 36. doi: 10.1029/2009GL038082
  42. Schuenemann K, Cassano J (2009) Changes in synoptic weather patterns and Greenland precipitation in the 20th and 21st centuries: 1. Evaluation of late 20th century simulations from IPCC models, J Geophys Res 114. doi: 10.1029/2009JD011705
  43. Schuenemann K, Cassano J (2010) Changes in synoptic weather patterns and Greenland precipitation in the 20th and 21st centuries: 2. Analysis of 21st century atmospheric changes using self-organizing maps. J Geophys Res 115. doi: 10.1029/2009JD011706
  44. Stoner AM, Hayhoe K, Wuebbles D (2009) Assessing general circulation model simulations of atmospheric teleconnection patterns. J Clim 22:4348–4372CrossRefGoogle Scholar
  45. Tedesco M, Serreze M, Fettweis X (2008) Diagnosing the extreme surface melt event over southwestern Greenland in 2007. The Cryosphere 2:159–166CrossRefGoogle Scholar
  46. Uppala, SM, Kallberg PW, Simmons AJ, Andrae U, da Costa Bechtold V, Fiorino M, Gibson JK, Haseler J, Hernandez A, Kelly GA, Li X, Onogi K, Saarinen S, Sokka N, Allan RP, Andersson E, Arpe K, Balmaseda MA, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Caires S, Chevallier F, Dethof A, Dragosavac M, Fisher M, Fuentes M, Hagemann S, Holm E, Hoskins B, Isaksen L, Janssen PAEM, Jenne R, McNally AP, Mahfouf J-F, Morcrette J-J, Rayner NA, Saunders RW, Simon P, Sterl A, Trenberth KE, Untch A, Vasiljevic D, Viterbo P, Woollen J (2005) The ECMWF re-analysis. Quart J R Meteorol Soc 131:2961–3012. doi: 10.1256/qj.04.176 CrossRefGoogle Scholar
  47. Vautard R, Yiou P (2009) Control of recent European surface climate change by atmospheric flow. Geophys Res Lett 36. doi: 10.1029/2009GL040480
  48. Walsh J, Chapman W, Romanovsky V, Christensen J, Stendel M (2008) Global climate model performance over Alaska and Greenland. J Clim 21:6156–6174CrossRefGoogle Scholar
  49. Wilby R, Wigley T (2000) Precipitation predictors for downscaling: observed and general circulation model relationships. Int J Climatol 20:641–661CrossRefGoogle Scholar
  50. Yarnal B, Comrie A, Frakes B, Brown D (2001) Developments and prospects in synoptic climatology. Int J Climatol 21(15):1923–1950CrossRefGoogle Scholar
  51. Yoshimori M, Abe-Ouchi A (2011) Sources of spread in multi-model projections of the Greenland ice-sheet surface mass balance. J Clim. doi: 10.1175/2011JCLI4011.1
  52. Zorita E, Hughes J, Lettemaier D, von Storch H (1995) Stochastic characterization of regional circulation patterns for climate model diagnosis and estimation of local precipitation. J Clim 8:1023–1042CrossRefGoogle Scholar
  53. Zorita E, von Storch H (1999) The analog method as a simple statistical downscaling technique: comparison with more complicated methods. J Clim 12:2474–2489CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Alexandre Belleflamme
    • 1
  • Xavier Fettweis
    • 1
  • Charlotte Lang
    • 1
  • Michel Erpicum
    • 1
  1. 1.Laboratory of Climatology and TopoclimatologyUniversity of LiègeLiègeBelgium

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