Theoretical and Applied Climatology

, Volume 103, Issue 3–4, pp 427–440 | Cite as

Analysis of high-resolution simulations for the Black Forest region from a point of view of tourism climatology – a comparison between two regional climate models (REMO and CLM)

Original Paper


An analysis of climate simulations from a point of view of tourism climatology based on two regional climate models, namely REMO and CLM, was performed for a regional domain in the southwest of Germany, the Black Forest region, for two time frames, 1971–2000 that represents the twentieth century climate and 2021–2050 that represents the future climate. In that context, the Intergovernmental Panel on Climate Change (IPCC) scenarios A1B and B1 are used. The analysis focuses on human-biometeorological and applied climatologic issues, especially for tourism purposes – that means parameters belonging to thermal (physiologically equivalent temperature, PET), physical (precipitation, snow, wind), and aesthetic (fog, cloud cover) facets of climate in tourism. In general, both models reveal similar trends, but differ in their extent. The trend of thermal comfort is contradicting: it tends to decrease in REMO, while it shows a slight increase in CLM. Moreover, REMO reveals a wider range of future climate trends than CLM, especially for sunshine, dry days, and heat stress. Both models are driven by the same global coupled atmosphere–ocean model ECHAM5/MPI-OM. Because both models are not able to resolve meso- and micro-scale processes such as cloud microphysics, differences between model results and discrepancies in the development of even those parameters (e.g., cloud formation and cover) are due to different model parameterization and formulation. Climatic changes expected by 2050 are small compared to 2100, but may have major impacts on tourism as for example, snow cover and its duration are highly vulnerable to a warmer climate directly affecting tourism in winter. Beyond indirect impacts are of high relevance as they influence tourism as well. Thus, changes in climate, natural environment, demography, tourists’ demands, among other things affect economy in general. The analysis of the CLM results and its comparison with the REMO results complete the analysis performed within the project Climate Trends and Sustainable Development of Tourism in Coastal and Low Mountain Range Regions (CAST) funded by the German Federal Ministry of Education and Research (BMBF).


Heat Stress Cold Stress Regional Climate Model Thermal Comfort Snow Water Equivalent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This research study is supported by the Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung, BMBF) in the allowance of “Klimazwei” (01LS05019). Thanks to the reviewers for their very helpful comments.


  1. Beniston M (2003) Climatic change in mountain regions: a review of possible impacts. Clim Change 59:5–31CrossRefGoogle Scholar
  2. Böhm U, Kücken M, AhrensW BA, Hauffe D, Keuler K, Rockel B, Will A (2006) CLM – the climate version of LM: brief description and long-term application. COSMO Newsletter 6:225–235Google Scholar
  3. Branković C, Srnec L, Pataračić M (2010) An assessment of global and regional climate change based on the EH5OM climate model ensembles. Clim Change 98:21–49CrossRefGoogle Scholar
  4. Brown RD, Mote PW (2009) The response of northern hemisphere snow cover to a changing climate. J Clim 22:2124–2145CrossRefGoogle Scholar
  5. Crossley JF, Polcher J, Cox PM, Gedney N, Planton S (2000) Uncertainties linked to land-surface processes in climate change simulations. Clim Dyn 16:949–961CrossRefGoogle Scholar
  6. De Freitas CR (1990) Recreation climate assessment. Int J Climatol 10:89–103CrossRefGoogle Scholar
  7. De Freitas CR (2003) Tourism climatology: evaluating environmental information for decision making and business planning in the recreation and tourism sector. Int J Biometeorol 48:45–54CrossRefGoogle Scholar
  8. Demuzere M, Werner M, van Lipzig NPM, 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
  9. Déqué M, Rowell DP, Lüthi D, Giorgi F, Christensen JH, Rockel B, Jacob D, Kjellström E, de Castro M, van den Hurk B (2007) An intercomparison of regional climate simulations for Europe: assessing uncertainties in model projections. Clim Change 81:53–70CrossRefGoogle Scholar
  10. Endler C, Matzarakis A (2010a) Climatic potential for tourism in the Black Forest, Germany – winter season. Int J Biometeorol. doi: 10.1007/s00484-010-0342-0
  11. Endler C, Matzarakis A (2010b) Climate and tourism in the Black Forest during the warm season. Int J Biometeorol. doi: 10.1007/s00484-010-0323-3 Google Scholar
  12. Endler C, Oehler K, Matzarakis A (2010) Vertical gradient of climate change and climate tourism conditions in the Black Forest. Int J Biometeorol 54:45–61CrossRefGoogle Scholar
  13. Feldmann H, Früh B, Schädler G, Panitz H-J, Keuler K, Jacob D, Lorenz P (2008) Evaluation of the precipitation for South-western Germany from high resolution simulations with regional climate models. Met Z 17(4):455–465CrossRefGoogle Scholar
  14. Giorgi F, Hewitson B, Christensen JH, Hulme M, von Storch H, Whetton P, Jones R, Mearns L, Fu C (2001) Regional climate information—evaluation and projections. In: Houghton J et al (eds) Climate change 2001: the scientific basis. Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, CambridgeGoogle Scholar
  15. Hagemann S, Arpe K, Roeckner E (2006) Evaluation of the hydrological cycle in the ECHAM5 model. J Climate 19:3810–3827CrossRefGoogle Scholar
  16. Han J, Roads JO (2004) U.S. climate sensitivity simulated with NCEP regional spectral model. Clim Change 62:115–154CrossRefGoogle Scholar
  17. Höppe P (1999) The physiological equivalent temperature – a universal index for the biometeorological assessment of the thermal environment. Int J Biometeorol 43:71–75CrossRefGoogle Scholar
  18. IPCC (2001) Climate change 2001: the scientific basis. In: Houghton JT et al (eds) Contribution of the Working Group I to the third assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, CambridgeGoogle Scholar
  19. Jacob D (2001) A note on the simulation of the annual and inter-annual variability of the water budget over the Baltic Sea drainage basin. Meteorol Atmos Phys 77:61–73CrossRefGoogle Scholar
  20. Jacob D, Podzun R (1997) Sensitivity studies with the regional climate model REMO. Meteorol Atmos Phys 63:119–129CrossRefGoogle Scholar
  21. Jacob D, van der Hurk BJJM, Andræ U, Elgered G, Graham L-P, Fortelius C, Jackson SD, Kartens U, Chr K, Lindau R, Podzun R, Roeckel B, Rubel F, Sass BH, Smith RNB, Yang X (2001) A comprehensive model inter-comparison study investigating the water budget during the BALTEX PIDCAP period. Meteorol Atmos Phys 77:19–43CrossRefGoogle Scholar
  22. Jacob D, Bärring L, Christensen OB, Christensen JH, de Castro M, Déqué M, Giorgi F, Hagemann S, Hirschi M, Jones R, Kjellström E, Lenderink G, Rockel B, Sánchez E, Schär C, Seneviratne SI, Somot S, van Ulden A, van den Hurk B (2007) An inter-comparison of regional climate models for Europe: model performance in present-day climate. Clim Change 81:31–52CrossRefGoogle Scholar
  23. Jacob D, Göttel H, Kotlarski S, Lorenz P, Sieck K (2008) Klimaauswirkungen und Anpassung in Deutschland. Phase 1: Erstellung regionaler Klimaszenarien für Deutschland. Climate Change 11/2008, UBA-FBNr: 000969 Förderkennzeichen: 20441138, Umweltbundesamt. Dessau-RoßlauGoogle Scholar
  24. Kropp J, Scholze M (2009) Climate change information for effective adaptation – a practitioner’s manual. Deutsche Gesellschaft für Technische Zusammenarbeit GmbH(GTZ) Climate Protection Programme, EschbornGoogle Scholar
  25. Machenhauer B, Windelband M, Botzet M, Christensen JH, Déqué M, Jones RG, Ruti PM, Visconti G (1998) Validation and analysis of regional present-day climate and climate change simulations over Europe. Max Planck Institut für Meteorologie Report 275, MPI, Hamburg, GermanyGoogle Scholar
  26. Marsland GA, Haak H, Jungclaus JH, Latif M, Röske F (2003) The Max Planck Institute global/sea-ice model with orthogonal curvilinear coordinates. Ocean Model 5:91–127CrossRefGoogle Scholar
  27. Matzarakis A, Rutz F, Mayer H (2007) Modelling radiation fluxes in simple and complex environment—application of the RayMan model. Int J Biometeorol 51:323–334CrossRefGoogle Scholar
  28. PCMDI (2007) IPCC model output. Available online at
  29. PRUDENCE (2007) Prediction of regional Scenarios and Uncertainties for Defining European Climate Change Risks and Effects: The PRUDENCE Project. Climatic Change 81 Supplement 1:1-371Google Scholar
  30. Reichler T, Kim J (2008) How well do coupled models simulate today’s climate? Bull Am Meteorol Soc 89:304–311CrossRefGoogle Scholar
  31. Rial JA, Pielke RA, Beniston M, Claussen M, Canadell J, Cox P, Held H, de Noblet-Ducoudré N, Prinn R, Reynolds JF, Salas JD (2004) Nonlinearities, feedbacks and critical thresholds within the earth’s climate system. Clim Change 65:11–38CrossRefGoogle Scholar
  32. Rind D (2008) The consequences of not knowing low- and high-latitude climate sensitivity. Bull Am Meteorol Soc 89:855–864CrossRefGoogle Scholar
  33. Rockel B, Will A, Hense A (2008) The regional climate model COSMO-CLM (CCLM), Editorial. Met Z 12(4):347–348CrossRefGoogle Scholar
  34. Roeckner E, Arpe K, Bengtsson L, Christoph M, Claussen M, Dümenil L, Esch M, Giorgetta M, Schlese U, Schulzweida U (1996) The atmospheric general circulation model ECHAM4: model description and simulation of present-day climate. Max Planck Institute for Meteorology, Report No. 218. HamburgGoogle Scholar
  35. Roeckner E, Bäuml G, Bonaventura L, Brokopf R, Esch M, Giorgetta M, Hagemann S, Kirchner I, Kornblueh L, Manzini E, Rhodin A, Schlese U, Schultzweida U, Tompkins A (2003) The atmospheric general circulation model ECHAM5. Part I: model description. Max Planck Institute for Meteorology, Report No. 349. HamburgGoogle Scholar
  36. Roesch A, Roeckner E (2008) Assessment of snow cover and surface albedo in the ECHAM5 general circulation model. J Climate 19:3828–3843CrossRefGoogle Scholar
  37. Rowell DP (2006) A demonstration of the uncertainty in projection of the UK climate change resulting from regional climate model formulation. Clim Change 79:243–257CrossRefGoogle Scholar
  38. Schröter D, Zebisch M, Grothmann T (2005) Climate change in Germany – vulnerability and adaptation of climate-sensitive sectors. Klimastatusbericht DWD 2005:44–56Google Scholar
  39. Sillmann J, Roeckner E (2008) Indices for extreme events in projections of anthropogenic climate change. Clim Change 86:83–104CrossRefGoogle Scholar
  40. Steppeler J, Doms G, Schaettler U, Bitzer HW, Gassmann A, Damrath U, Gregoric G (2003) Meso-gamma scale forecasts using the nonhydrostatic model LM. Meteorol Atmos Phys 82:75–96CrossRefGoogle Scholar
  41. Sturm M, Holmgren J, Liston GE (1995) A seasonal snow cover classification system for local to global applications. J Climate 8:1261–1283CrossRefGoogle Scholar
  42. USGS (2004) Shuttle radar topography mission, 30 arc second scene SRTM_GTOPO_u30_n090e020, unfilled unfinished 2.0, Global Land Cover Facility, University of Maryland, College Park, Maryland, July 2004Google Scholar
  43. Van der Linden P, Mitschell JFB (2009) ENSEMBLES: climate change and its impacts: summary of research and results from the ENSEMBLES projection. Final report. Met Office Hadley Centre, ExeterGoogle Scholar
  44. 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 6:863–881CrossRefGoogle Scholar
  45. Walkenhorst O, Stock M (2009) Regionale Klimaszenarien für Deutschland. Eine Leseanleitung. E-Paper der ARL, Nr. 6. Akademie für Raumforschung und Landesplanung, HannoverGoogle Scholar
  46. Wang G (2005) Agricultural drought in a future climate: results from 15 global climate models participating in the IPCC 4th assessment. Clim Dyn 25:739–753CrossRefGoogle Scholar
  47. Witmer U (1986) Erfassung, Bearbeitung und Kartierung von Schneedaten in der Schweiz. Geographica Bernesia G25Google Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.Meteorological InstituteAlbert-Ludwigs-University of FreiburgFreiburgGermany

Personalised recommendations