Modelling Near Future Regional Climate Change for Germany and Africa

  • Hans-Jürgen Panitz
  • Peter Berg
  • Gerd Schädler
  • Giorgia Fosser
Conference paper


The scope of regional climate simulations carried out at the Institute for Meteorology and Climate Research (IMK) of Karlsruhe Institute of Technology (KIT) using the regional climate model (RCM) COSMO-CLM (CCLM) has been extended during the last years. From the focus first on Southwest Germany the area of interest has then been extended to the whole of Germany for the assessment of changes in flood risk for medium and small mountainous river catchments (CEDIM project:, and within the frame of CORDEX (Coordinated Regional climate Downscaling Experiment, also to the whole African continent. CORDEX aims to provide a framework to evaluate and benchmark RCMs and to design a set of experiments to produce climate projections for use in impact and adaption studies and as input to the IPCC 5th Assessment Report (AR5).

Within the CEDIM-project “Flood hazard in a changing climate” an ensemble of 14 CCLM simulations have been created at the HLRS facilities. They have been driven by three realizations of the global ECHAM5 model and one realization of the Canadian model CGCM3, all using the A1B emission scenario. Although the ensemble spans a large range of values, there is a general pattern of increasing temperatures throughout the year, and also increases in precipitation for most of the year, except for summer when a decrease is more likely. Related to the NEC SX-8 an aggregated computing time of 1145 node-days have been needed for all simulations.

The efforts within the CORDEX framework began with a series of sensitivity runs in order to identify a model configuration suitable for Africa. The simulations have been driven by ERA-Interim reanalysis. Based on the experiences gained from the sensitivity runs evaluation simulations have been carried out for the 20 years period from 1989 until 2008 for two different horizontal resolutions, 0.44 Deg and 0.22 Deg. The evaluation of the results is still in progress. However, first comparisons between the results of various RCMs, including CCLM, participating in the CORDEX project show a rather wide intra-model spread of the results, and also rather large discrepancies to climatic observations. The High Performance Computing (HPC) demands for the two simulations (0.44 Deg and 0.22 Deg) were 14 node-days and 86 node-days, respectively.

Within KLIWA (Klimaveränderung und Wasserwirtschaft; a project has recently been initiated to assess the impact of climate change on soil erosion in Southern Germany. The work will focus on modelling extreme precipitation events at higher spatial and temporal resolution (2.8 km, 1 km; 1 hour, 5 minutes) for the recent past (ca. 1970–2000) and near future (2011–2050). The purpose is to evaluate what the added values of very high spatial and temporal resolution are and how the high resolution affects the precipitation statistics as well as the changes in erosion-related extreme precipitation events in the future.

Further large demands for HPC will also be necessary within the program MIKLIP (Mittelfristige Klimaprognosen) initiated and recently approved by the Federal Ministry for Science and Education (BMBF). IMK will participate in joint projects which aim at regional decadal predictions of climate for Central Europe and the West African monsoon region.


Regional Climate Model High Performance Computing Extreme Precipitation Event Global Circulation Model Regional Climate Simulation 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    H.-J. Panitz, G. Schädler, and H. Feldmann (2010): Modelling Regional Climate Change in Southwest Germany. In: High Performance Computing in Science and Engineering ’09 [W. E. Nagel, D. Kröner, M. Resch (Eds.)]. doi: 10.1007/978-3-642-04665-0, Springer Berlin Heidelberg New York 2010, pp. 429–441. CrossRefGoogle Scholar
  2. 2.
    P. Berg, H.-J. Panitz, G. Schädler, H. Feldmann, and Ch. Kottmeier (2010): Downscaling Climate Simulations for Use in Hydrological Modeling of Medium-Sized River Catchments. In: High Performance Computing on Vector Systems 2010 [M. Resch, K. Benkert, X. Wang, M. Galle, W. Bez, H. Kobayashi, S. Roller (Eds.)]. doi: 10.1007/978-3-642-11851-7, Springer Berlin Heidelberg New York 2010, pp. 163–170. CrossRefGoogle Scholar
  3. 3.
    P. Berg, H.-J. Panitz, G. Schädler, H. Feldmann, and Ch. Kottmeier (2011): Modelling Regional Climate Change in Germany. In: High Performance Computing in Science and Engineering ’10 [W. E. Nagel, D. Kröner, M. Resch (Eds.)]. doi: 10.1007/978-3-642-15748-6, Springer Berlin Heidelberg New York 2010, pp. 467-478. Google Scholar
  4. 4.
    F. Giorgi, C. Jones, and G. R. Asrar (2009): Addressing climate informationneeds at the regional level: The CORDEX framework WMO Bulletin, 58 (3), July 2009, 175–183. Google Scholar
  5. 5.
    IPCC (2007): Summary for Policymakers. 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 [S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor and H. L. Miller (Eds.)]. Cambridge University Press Cambridge, United Kingdom and New York, NY, USA. Google Scholar
  6. 6.
    N. Nakicenovic et al. (2000). Special Report on Emissions Scenarios: A Special Report of Working Group III of the Intergovernmental Panel on Climate Change, Cambridge University Press Cambridge, U.K., 599 pp. Available online at: Google Scholar
  7. 7.
    R. H. Moss, J. A. Edmonds, K. A. Hibbard, M. R. Manning, St. K. Rose, D. P. van Vuuren, T. R. Carter, S. Emori, M. Kainuma, T. Krma, G. A. Meehl, J. F. B. Mitchell, N. Nakicenovic, K. Riahi, St. J. Smith, R. J. Stouffer, A. M. Thomson, J. P. Weyant, and T. J. Wilbanks (2010): The next generation of scenarios for climate change research and assessment. Nature, 463, 11 February 2010, 747–756, doi: 10.1038/nature08823. CrossRefGoogle Scholar
  8. 8.
    E. Röckner (2006a): IPCC-AR4 MPI-ECHAM5_T63L31 MPI-OM_GR1.5L40 20C3M run no.1: Atmosphere 6 HOUR values MPImet/MaD Germany. World Data Center for Climate. [doi: 10.1594/WDCC/EH5-T63L31_OM-GR1.5L40_20C_1_6H].
  9. 9.
    E. Röckner (2006b): IPCC-AR4 MPI-ECHAM5_T63L31 MPI-OM_GR1.5L40 20C3M run no.2: atmosphere 6 HOUR values MPImet/MaD Germany. World Data Center for Climate. [doi: 10.1594/WDCC/EH5-T63L31_OM-GR1.5L40_20C_2_6H].
  10. 10.
    E. Röckner (2006c): IPCC-AR4 MPI-ECHAM5_T63L31 MPI-OM_GR1.5L40 20C3M run no.3: atmosphere 6 HOUR values MPImet/MaD Germany. World Data Center for Climate. [doi: 10.1594/WDCC/EH5-T63L31_OM-GR1.5L40_20C_3_6H].
  11. 11.
    N.A. McFarlane, J. F. Scinocca, M. Lazare, R. Harvey, D. Verseghy, and J. Li (2005): The CCCma third generation atmospheric general circulation model. CCCma Internal Rep., 25 pp. Google Scholar
  12. 12.
    J. F. Scinocca, N. A. McFarlane, M. Lazare, J. Li, and D. Plummer, 2008: The CCCma third generation AGCM and its extension into the middle atmosphere. Atmos. Chem. and Phys., 8, 7055–7074. CrossRefGoogle Scholar
  13. 13.
    G. Doms and U. Schättler (2002): A description of the nonhydrostatic regional model LM, Part I: Dynamics and Numerics. COSMO Newsletter, 2, 225–235. Google Scholar
  14. 14.
    C. Meissner and G. Schädler (2007): Modelling the Regional Climate of Southwest Germany: Sensitivity to Simulation Setup. In: High Performance Computing in Science and Engineering ’07 [W. E. Nagel, D. Kröner, M. Resch (Eds.)]. ISBN 978-3-540-74738-3, Springer Berlin Heidelberg New York. Google Scholar
  15. 15.
    A. Hense, A. Will, and B. Rockel (2008): Regional climate modelling with COSMO-CLM (CCLM). Meteorologische Zeitschrift, 17, 4, 2008, special issue, ISSN 0941-2948. CrossRefGoogle Scholar
  16. 16.
    C. Meissner, G. Schädler, H.-J. Panitz, H. Feldmann, and Ch. Kottmeier (2009): High resolution sensitivity studies with the regional climate model COSMO-CLM. Meteorologische Zeitschrift, 18, 543–557, doi: 10.1127/0941-2948/20090400. CrossRefGoogle Scholar
  17. 17.
    G. Roeckner, G. Baeuml, L. Bonaventura, R. Brokopf, M. Esch, M. Giorgetta, S. Hagemann, I. Kirchner, L. Kornblueh, E. Manzini, A. Rhodin, U. Schlese, U. Schulzweida, A. Tompkins (2003): The atmospheric general circulation model ECHAM 5. PART I: Model description. Technical Report 349, Max-Planck-Institut für Meteorologie, Bundesstr. 55, D-20146 Hamburg, Germany Google Scholar
  18. 18.
    B. Efron, and R. J. Tibshirani (1993): An Introduction to the Bootstrap. Chapman & Hall, New York. zbMATHGoogle Scholar
  19. 19.
    A. Simmons, S. Uppala, D. Dee, Sh. Kobayashiera (2006): New ECMWF reanalysis products from 1989 onwards. ECMWF Newsletter 110, Winter 2006/07, 25–35. Google Scholar
  20. 20.
    P. J. Lawrence, and Th. N. Chase (2007): Representing a new MODIS consistent land surface in the Community Land Model (CLM3.0). J. Geophys. Res., 112, G01023, doi: 10.1029/2006JG000168, 2007. CrossRefGoogle Scholar
  21. 22.
    G. J. Huffman, R. F. Adler, D. T. Bolvin, E. J. Nelkin, D. B. Wolff, G. Gu, Y. Hong, K. P. Bowman, E. F. Stocker (2007): The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales. J. Hydrometeorol., 8, 38–55. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hans-Jürgen Panitz
    • 1
  • Peter Berg
    • 1
  • Gerd Schädler
    • 1
  • Giorgia Fosser
    • 1
  1. 1.Institut für Meteorologie und KlimaforschungKarlsruhe Institut für Technologie (KIT)KarlsruheGermany

Personalised recommendations