Advertisement

Modelling Near Future Regional Climate Change for Germany and Africa

  • H.-J. PanitzEmail author
  • G. Fosser
  • R. Sasse
  • A. Sehlinger
  • H. Feldmann
  • G. Schädler
Conference paper

Abstract

The modelling of future regional climate change for Germany and Africa using the regional climate model COSMO-CLM (CCLM) comprises basic studies on how temperature and precipitation are affected in general, but also specific impact studies whose results can be used for the planning of adaptation and mitigation measures. For Africa ERA-Interim driven simulations have been carried out within the CORDEX framework. Evaluation studies using two different horizontal grid spacings (0.44 and 0.22) did not show significant differences in the results. Furthermore, these simulations have been used to perform the transition from NEC computing systems to the new CRAY XE6, and to investigate the impact of this change on the model results, which is very small. The impact of likely heavier summer rainfall on soil erosion in southern Germany is investigated within the KLIWA project “Bodenabtrag durch Wassererosion in Folge von Klimaveränderungen”. The corresponding simulations are performed with very high horizontal resolution (7, 2.8, and 1km). Results show an added-value of the convection-permitting scale (2.8km) in comparison with coarser spatial resolution (7km). The high–resolution simulation also represents well the frequency of high and very high precipitation days. In order to answer the important question how to account for uncertainties in regional climate projections, it is necessary to understand the sensitivity of the model results to various processes, e.g. the initialisation of soil moisture, and to create multi-member ensembles of climate simulations. The latter aspect is tackled within the Helmholtz Climate Initiative REKLIM (Regional Climate Change). In order to capture uncertainties related to the positioning of synoptic systems, a multi-member ensemble of climate simulations is generated by introducing small shifts to the large-scale atmospheric forcing provided by low-resolution global climate models (GCMs). The shifted atmospheric fields are then used to drive CCLM simulations at 50km resolution. These shifts have a considerable effect on the CCLM results, in particular during hydrological summer. Thus, the ensemble generation using the shifting method and, moreover, the usage of different GCMs are valuable for an improved representation of present climate conditions and projecting regional climate change. Soil moisture is a crucial component in the atmospheric water cycle, due to the long-term memory effect of the deep soil and its feedbacks with the atmosphere. Although observational data on the temporal and heterogeneous 3-dimensional spatial distribution of soil moisture is sparse, such information is necessary to initialise climate models–especially for climate forecasts. Sensitivity studies varying the initial soil moisture distribution have been performed with COSMO-CLM. The model was able to reproduce the observations with respect to the temporal evolution of a drought index for the major European regions. Even after several years effects of the soil initialisation could be found. The effect was most pronounced in areas with continental characteristics or at high latitudes (Scandinavia, Eastern Europe or the Mediterranean) and less towards the Atlantic.

Keywords

Regional Climate Model Regional Climate Change Ensemble Spread Regional Climate Model Simulation Effective Drought Index 
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.

References

  1. 1.
    Panitz, H.-J., P. Berg, G. Schädler, and G. Fosser (2012): Modelling Regional Climate Change for Germany and Africa, In: High Performance Computing in Science and Engineering ’11 [W. E. Nagel, D. Kröner, M. Resch (Eds.)]. DOI: 10.1007/978-3-642-23869-7, Springer Berlin Heidelberg New York 2012, pp 503–512.Google Scholar
  2. 2.
    Giorgi, F., C. Jones, and G. R. Asrar (2009): Adressing climate information needs at the regional level: the CORDEX framework. WMO Bulletin 58 (3)- July 2009, 175–183.Google Scholar
  3. 3.
    Baldauf, M., A. Seifert, J. Förstner, D. Majewski, and M. Raschendorfer (2011): Operational Convective-Scale Numerical Weather Prediction with the COSMO Model: Description and Sensitivities. Mon. Wea. Rev, 139, 2011, 3887–3905, DOI: 10.1175/MWR-D-10-05013.1.Google Scholar
  4. 4.
    Doms, G. and U. Schättler (2002): A description of the nonhydrostatic regional model LM, Part I: Dynamics and Numerics. COSMO Newsletter, 2, 225–235.Google Scholar
  5. 5.
    Meissner, C. 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
  6. 6.
    Rockel, B., A. Will, and A. Hense (2008): Regional climate modelling with COSMO-CLM (CCLM). Meteorologische Zeitschrift, 17, 4, 2008, 347–348, DOI: 10.1127/0941-2948/2008/0309, special issue, ISSN 0941–2948.Google Scholar
  7. 7.
    Meissner, C., 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
  8. 8.
    Schädler, G., P. Berg, D. Düthmann, H. Feldmann, J. Ihringer, H. Kunstmann, J. Liebert, B. Merz, I. Ott, and S. Wagner (2012): Flood Hazards in a Changing Climate. Project Report, pp 83. Centre for Disaster Management and Rsik Reduction Technology (CEDIM), http://www.cedim.de/download/Flood_Hazards_in_a_Changing_Climate.pdf
  9. 9.
    Uppala, S. M., P. W. Kållberg, A. J. Simmons, U. Andrae, V. Da Costa Bechtold, M. Fiorino, J. K. Gibson, J. Haseler, A. Hernandez, G. A. Kelly, X. Li, K. Onogi, S. Saarinen, N. Sokka, R. P. Allan, E. Andersson, K. Arpe, M. A. Balmaseda, A. C. M. Beljaars, L. Berg, J. Van De Bidlot, N. Bormann, S. Caires, F. Chevallier, A. Dethof, M. Dragosavac, M. Fisher, M. Fuentes, S. Hagemann, E. Hólm, B. J. Hoskins, L. Isaksen, P. A. E. M. Janssen, R. Jenne, A. P. Mcnally, J.–F. Mahfouf, J.–J. Morcrette, N. A. Rayner, R. W. Saunders, P. Simon, A. Sterl, K. E. Trenberth, A. Untch, D. Vasiljevic, P. Viterbo, and J. Woollen (2005): The ERA-40 re-analysis. Q. J. R. Meteorol. Soc., 131, 2961–3012.Google Scholar
  10. 10.
    Roeckner, G., G. Baeuml, L. Bonaventura, R. Brokopf, M. Esch, M. Giorgetta, S. Hagemann, I. Kirchner, L. Kornblueh, E. Manzini, A. Rhodin, U. Schlese, U. Schulzweida, and A. Tompkins (2003): The atmospheric general circulation model ECHAM 5. PART I: Model description. Technical Report 349, pp 127, Max-Planck-Institut für Meteorologie, Bundesstr. 55, D-20146 Hamburg, Germany.Google Scholar
  11. 11.
    Steiner H., U. Riediger, and A. Gratzki (2011): The HYRAS data set - A high resolution gridded reference data set covering Germany and neighbouring river basins. EMS Annual Meeting Abstracts, Vol. 8, EMS2011-589, 2011.Google Scholar
  12. 12.
    Schlüter, I. and G. Schädler (2010): Sensitivity of Heavy Precipitation Forecasts to Small Modifications of Large-Scale Weather Patterns for the Elbe River. J. Hydrometeor, 11, 770–780.CrossRefGoogle Scholar
  13. 13.
    Haylock, M. R., N. Hofstra, A. M. G. Klein Tank, E. J. Klok, P. D. Jones, and M. New (2008): A European daily high-resolution gridded dataset of surface temperature and precipitation. J. Geophys. Res, 113, D20119.CrossRefGoogle Scholar
  14. 14.
    Seneviratne, S., E. Corti, E. L. Davin, M. Hirschi, E. B. Jaeger, I. Lehner, B. Orlowsky, and A. J. Teuling (2010): Investigating soil moisture-climate interactions in a changing climate: A review. Earth-Sci. Rev., 99, 125–161.CrossRefGoogle Scholar
  15. 15.
    Kerr, Y. H. (2007): Soil moisture from space: Where are we? Hydrogeol. J., 15, 117–120.CrossRefGoogle Scholar
  16. 16.
    Byun, H. R. and D. A.Wilhite (1999): Objective Quantification of Drought Severity and Duration. J. Climate, 12, 2747–2756.CrossRefGoogle Scholar
  17. 17.
    Simmons, A., S. Uppala, D. Dee, and Sh. Kobayashiera (2006): New ECMWF reanalysis products from 1989 onwards. ECMWF Newsletter No. 110, Winter 2006/07, 25–35.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • H.-J. Panitz
    • 1
    Email author
  • G. Fosser
    • 1
  • R. Sasse
    • 1
  • A. Sehlinger
    • 1
  • H. Feldmann
    • 1
  • G. Schädler
    • 1
  1. 1.Institut für Meteorologie und KlimaforschungKarlsruher Institut für Technologie (KIT)Eggenstein-LeopoldshafenGermany

Personalised recommendations