Climate Dynamics

, Volume 35, Issue 4, pp 587–600

Regional climate change patterns identified by cluster analysis

Article

Abstract

Climate change caused by anthropogenic greenhouse emissions leads to impacts on a global and a regional scale. A quantitative picture of the projected changes on a regional scale can help to decide on appropriate mitigation and adaptation measures. In the past, regional climate change results have often been presented on rectangular areas. But climate is not bound to a rectangular shape and each climate variable shows a distinct pattern of change. Therefore, the regions over which the simulated climate change results are aggregated should be based on the variable(s) of interest, on current mean climate as well as on the projected future changes. A cluster analysis algorithm is used here to define regions encompassing a similar mean climate and similar projected changes. The number and the size of the regions depend on the variable(s) of interest, the local climate pattern and on the uncertainty introduced by model disagreement. The new regions defined by the cluster analysis algorithm include information about regional climatic features which can be of a rather small scale. Comparing the regions used so far for large scale regional climate change studies and the new regions it can be shown that the spacial uncertainty of the projected changes of different climate variables is reduced significantly, i.e. both the mean climate and the expected changes are more consistent within one region and therefore more representative for local impacts.

Keywords

Regional classification Regional climate change Cluster analysis 

References

  1. Bonfils C, de Noblet-Ducoudre N, Guiot J, Bartlein P (2004) Some mechanisms of mid-holocene climate change in Europe, inferred from comparing pmip models to data. Clim Dyn 23(1):79–98 doi:10.1007/s00382-004-0425-x CrossRefGoogle Scholar
  2. Brewer S, Alleaume S, Guiot J, Nicault A (2007a) Historical droughts in mediterranean regions during the last 500 years: a data/model approach. Clim Past 3(2):355–366CrossRefGoogle Scholar
  3. Brewer S, Guiot J, Torre F (2007b) Mid-holocene climate change in Europe: a data-model comparison. Clim Past 3(3):499–512CrossRefGoogle Scholar
  4. Christensen JH, Hewitson B, Busuioc A, Chen A, Gao X, Held I, Jones R, Kolli RK, Kwon WT, Laprise R, Magaña Rueda V, Mearns L, Menéndez CG, Räisänen J, Rinke A, Sarr A, Whetton P (2007) Regional climate projections. Climate change 2007. In: Solomon S et al (eds) The physical science basis contribution of working group I to the fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, pp 847–845Google Scholar
  5. Furrer R, Knutti R, Sain SR, Nychka DW, Meehl GA (2007a) Spatial patterns of probabilistic temperature change projections from a multivariate Bayesian analysis. Geophys Res Lett 34. doi:10.1029/2006GL027754
  6. Giorgi F, Bi X (2005) Updated regional precipitation and temperature changes for the 21st century from ensembles of recent AOGCM simulations. Geophys Res Lett 32:L21,715 doi:10.1029/2005GL024288 Google Scholar
  7. Giorgi F, Francisco R (2000) Uncertainties in the prediction of regional climate change. Global change and protected areas, pp 127–139Google Scholar
  8. Giorgi F, Mearns LO (2003) Probability of regional climate change based on the reliability ensemble averaging (REA) method. Geophys Res Lett 30:1629. doi:10.1029/2003GL017130 CrossRefGoogle Scholar
  9. IPCC (2007) Climate change 2007. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) The physical science basis. Contribution of working group I to the fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, p 996Google Scholar
  10. Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comp Surv 31(3):264–323CrossRefGoogle Scholar
  11. Kaufmann L, Rousseeuw P (1990) Finding groups in data: an introduction to cluster analysis. Wiley, New YorkGoogle Scholar
  12. Kottek M, Grieser J, Beck C, Rudolf B, Rubel F (2006) World map of the Köppen–Geiger climate classification updated. Meteorologische Zeitschrift 15(3):259–263 doi:10.1127/0941-2948/2006/0130 CrossRefGoogle Scholar
  13. Meehl GA, Covey C, Delworth T, Latif M, McAvaney B, Mitchell JFB, Stouffer RJ, Taylor KE (2007) The WCRP CMIP3 multimodel dataset—a new era in climate change research. Bull Am Meteorol Soc 88:1383−1394 doi:10.1175/BAMS-88-9-1383 CrossRefGoogle Scholar
  14. 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(16):4065–4095 doi:10.1175/JCLI4175.1 CrossRefGoogle Scholar
  15. Räisänen J (2001) CO2-induced climate change in CMIP2 experiments: quantification of agreement and role of internal variability. J Clim 14(9):2088–2104CrossRefGoogle Scholar
  16. Räisänen J (2007) How reliable are climate models? Tellus Ser A Dyn Meteorol Oceanogr 59(1):2–29 doi:10.1111/j.1600-0870.2006.00211.x Google Scholar
  17. Tebaldi C, Knutti R (2007) The use of the multi-model ensemble in probabilistic climate projections. Phil Trans R Soc A 365:2053-2075 doi:10.1098/rsta.2007.2076 CrossRefGoogle Scholar
  18. Tebaldi C, Lobell DB (2008) Towards probabilistic projections of climate change impacts on global crop yields. Geophys Res Lett 35(8). doi:10.1029/2008GL033423
  19. Tebaldi C, Mearns LO, Nychka D, Smith RL (2004) Regional probabilities of precipitation change: a Bayesian analysis of multimodel simulations. Geophys Res Lett 31:L24,213 doi:10.1029/2004GL021276 CrossRefGoogle Scholar
  20. Tebaldi C, Smith RW, Nychka D, Mearns LO (2005) Quantifying uncertainty in projections of regional climate change: a Bayesian approach to the analysis of multi-model ensembles. J Clim 18:1524–1540CrossRefGoogle Scholar
  21. Wang GL (2005) Agricultural drought in a future climate: results from 15 global climate models participating in the IPCC 4th assessment. Clim Dyn 25(7–8):739–753 doi:10.1007/s00382-005-0057-9 CrossRefGoogle Scholar
  22. Wilks DS (1998) Statistical methods in the atmospheric sciences. Elsevier, New YorkGoogle Scholar
  23. Williams JW, Jackson ST, Kutzbach JE (2007) Projected distributions of novel and disappearing climates by 2100 ad. Proc Natl Acad Sci USA 104:5738–5742 doi:10.1073/pnas.0606292104 CrossRefGoogle Scholar
  24. Zhang XB, Zwiers FW, Hegerl GC, Lambert FH, Gillett NP, Solomon S, Stott PA, Nozawa T (2007) Detection of human influence on twentieth-century precipitation trends. Nature 448(7152):461–464 doi:10.1038/nature06025 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  1. 1.Institute for Atmospheric and Climate ScienceZurichSwitzerland

Personalised recommendations