Dynamical Downscaling with Reinitializations: A Method to Generate Finescale Climate Datasets Suitable for Impact Studies

Philippe Lucas-Picher Danish Meteorological Institute, Copenhagen, Denmark, and Centre National de Recherches Météorologiques, Météo-France, Toulouse, France

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Fredrik Boberg Danish Meteorological Institute, Copenhagen, Denmark

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Jens H. Christensen Danish Meteorological Institute, Copenhagen, Denmark

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Peter Berg Rossby Centre, Swedish Meteorological and Hydrological Institute, Norrköping, Sweden, and Institute for Meteorology and Climate Research–Troposphere Research (IMK-TRO), Karlsruhe Institute of Technology, Karlsruhe, Germany

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Abstract

To retain the sequence of events of a regional climate model (RCM) simulation driven by a reanalysis, a method that has not been widely adopted uses an RCM with frequent reinitializations toward its driving field. In this regard, this study highlights the benefits of an RCM simulation with frequent (daily) reinitializations compared to a standard continuous RCM simulation. Both simulations are carried out with the RCM HIRHAM5, driven with the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim) data, over the 12-km-resolution European Coordinated Regional Climate Downscaling Experiment (CORDEX) domain covering the period 1989–2009. The analysis of daily precipitation shows improvements in the sequence of events and the maintenance of the added value from the standard continuous RCM simulation. The validation of the two RCM simulations with observations reveals that the simulation with reinitializations indeed improves the temporal correlation. Furthermore, the RCM simulation with reinitializations has lower systematic errors compared to the continuous simulation, which has a tendency to be too wet. A comparison of the distribution of wet day precipitation intensities shows similar added value in the continuous and reinitialized simulations with higher variability and extremes compared to the driving field ERA-Interim. Overall, the results suggest that the finescale climate dataset of the RCM simulation with reinitializations better suits the needs of impact studies by providing a sequence of events matching closely the observations, while limiting systematic errors and generating reliable added value. Downsides of the method with reinitializations are increased computational costs and the introduction of temporal discontinuities that are similar to those of a reanalysis.

Current affiliation: Centre ESCER, Department of Earth and Atmospheric Sciences, Université du Québec à Montréal, Montréal, Québec, Canada.

Corresponding author address: Philippe Lucas-Picher, Centre ESCER, Department of Earth and Atmospheric Sciences, Université du Québec à Montréal, Stn. Downtown, P.O. Box 8888, Montreal, QC H3C 3P8, Canada. E-mail: plp@sca.uqam.ca

Abstract

To retain the sequence of events of a regional climate model (RCM) simulation driven by a reanalysis, a method that has not been widely adopted uses an RCM with frequent reinitializations toward its driving field. In this regard, this study highlights the benefits of an RCM simulation with frequent (daily) reinitializations compared to a standard continuous RCM simulation. Both simulations are carried out with the RCM HIRHAM5, driven with the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim) data, over the 12-km-resolution European Coordinated Regional Climate Downscaling Experiment (CORDEX) domain covering the period 1989–2009. The analysis of daily precipitation shows improvements in the sequence of events and the maintenance of the added value from the standard continuous RCM simulation. The validation of the two RCM simulations with observations reveals that the simulation with reinitializations indeed improves the temporal correlation. Furthermore, the RCM simulation with reinitializations has lower systematic errors compared to the continuous simulation, which has a tendency to be too wet. A comparison of the distribution of wet day precipitation intensities shows similar added value in the continuous and reinitialized simulations with higher variability and extremes compared to the driving field ERA-Interim. Overall, the results suggest that the finescale climate dataset of the RCM simulation with reinitializations better suits the needs of impact studies by providing a sequence of events matching closely the observations, while limiting systematic errors and generating reliable added value. Downsides of the method with reinitializations are increased computational costs and the introduction of temporal discontinuities that are similar to those of a reanalysis.

Current affiliation: Centre ESCER, Department of Earth and Atmospheric Sciences, Université du Québec à Montréal, Montréal, Québec, Canada.

Corresponding author address: Philippe Lucas-Picher, Centre ESCER, Department of Earth and Atmospheric Sciences, Université du Québec à Montréal, Stn. Downtown, P.O. Box 8888, Montreal, QC H3C 3P8, Canada. E-mail: plp@sca.uqam.ca
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  • Berg, P., and Christensen J. H. , 2008: Poor man's re-analysis over Europe. WATCH Tech. Rep. 2. [Available online at http://www.eu-watch.org/publications/technical-reports/5.]

  • Berg, P., Feldmann H. , and Panitz H.-J. , 2012: Bias correction of high resolution regional climate model data. J. Hydrol., 448–449, 8092, doi:10.1016/j.jhydrol.2012.04.026.

    • Search Google Scholar
    • Export Citation
  • Christensen, J. H., and Christensen O. B. , 2007: A summary of the PRUDENCE model projections of changes in European climate by the end of the century. Climatic Change, 81, 730, doi:10.1007/s10584-006-9210-7.

    • Search Google Scholar
    • Export Citation
  • Christensen, J. H., Kjellström E. , Giorgi F. , Lenderink G. , and Rummukainen M. , 2010: Weight assignment in regional climate models. Climate Res., 44, 179194, doi:10.3354/cr00916.

    • Search Google Scholar
    • Export Citation
  • Christensen, O. B., 1999: Relaxation of soil variables in a regional climate model. Tellus, 51A, 674685.

  • Christensen, O. B., Drews M. , Christensen J. H. , Dethloff K. , Ketelsen K. , Hebestadt I. , and Rinke A. , 2006: The HIRHAM Regional Climate Model version 5. DMI Tech. Rep. 0617. [Available online at http://www.dmi.dk/dmi/tr06-17.pdf.]

  • Dai, A., Xin L. , and Hsu K.-L. , 2007: The frequency, intensity, and diurnal cycle of precipitation in surface and satellite observations over low- and mid-latitudes. Climate Dyn., 29, 727744, doi:10.1007/s00382-007-0260-y.

    • Search Google Scholar
    • Export Citation
  • de Ela, R., Laprise R. , and Denis B. , 2002: Forecasting skill limits of nested, limited-area models: A perfect model approach. Mon. Wea. Rev.,130, 2006–2023.

  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553597, doi:10.1002/qj.828.

    • Search Google Scholar
    • Export Citation
  • Giorgi, F., 2006: Regional climate modeling: Status and perspectives. J. Phys., 139, 101118, doi:10.1051/jp4:2006139008.

  • Giorgi, F., Jones C. , and Asrar G. , 2009: Addressing climate information needs at the regional level: The CORDEX framework. WMO Bull., 58, 175183.

    • Search Google Scholar
    • Export Citation
  • Harding, R., and Coauthors, 2011: WATCH: Current knowledge of the terrestrial Global Water Cycle. J. Hydrometeor., 12, 11491156.

  • Haylock, M. R., Hofstra N. , Klein Tank A. M. G. , Klok E. J. , Jones P. D. , and New M. , 2008: A European daily high-resolution gridded data set of surface temperature and precipitation for 1950–2006. J. Geophys. Res., 113, D20119, doi:10.1029/2008JD010201.

    • Search Google Scholar
    • Export Citation
  • Herrera, S., Gutiérrez J. M. , Ancell R. , Pons M. R. , Frías M. D. , and Fernández J. , 2012: Development and analysis of a 50-year high-resolution daily gridded precipitation dataset over Spain (Spain02). Int. J. Climatol.,32, 74–85, doi:10.1002/joc.2256.

  • Kotlarski, S., Hagemann S. , Krahe P. , Podzun R. , and Jacob D. , 2012: The Elbe River flooding 2002 as seen by an extended regional climate model. J. Hydrol., 472–473, 169183, doi:10.1016/j.jhydrol.2012.09.020.

    • Search Google Scholar
    • Export Citation
  • Lenderink, G., 2010: Exploring metrics of extreme daily precipitation in a large ensemble of regional climate model simulations. Climate Res., 44, 151166.

    • Search Google Scholar
    • Export Citation
  • Lo, J. C.-F., Yang Z.-L. , and Pielke R. A. Sr., 2008: Assessment of three dynamical climate downscaling methods using the Weather Research and Forecasting (WRF) model. J. Geophys. Res., 113, D09112, doi:10.1029/2007JD009216.

    • Search Google Scholar
    • Export Citation
  • Lucas-Picher, P., Caya D. , de Elía R. , and Laprise R. , 2008: Investigation of regional climate models' internal variability with a ten-member ensemble of 10-year simulations over a large domain. Climate Dyn., 31, 927940, doi:10.1007/s00382-008-0384-8.

    • Search Google Scholar
    • Export Citation
  • Lucas-Picher, P., and Coauthors, 2011: Can regional climate models represent the Indian monsoon? J. Hydrometeor., 12, 849868.

  • Pan, Z., Takle E. , Gutowski W. , and Turner R. , 1999: Long simulation of regional climate as a sequence of short segments. Mon. Wea. Rev., 127, 308321.

    • Search Google Scholar
    • Export Citation
  • Piani, C., Haerter J. O. , and Coppola E. , 2010: Statistical bias correction for daily precipitation in regional climate models over Europe. Theor. Appl. Climatol., 99, 187192, doi:10.1007/S00704-009-0134-9.

    • Search Google Scholar
    • Export Citation
  • Qian, J.-H., Seth A. , and Zebiak S. , 2003: Reinitialized versus continuous simulations for regional climate downscaling. Mon. Wea. Rev.,131, 2857–2874.

  • Rummukainen, M., 2010: State-of-the-art with regional climate models. Wiley Interdiscip. Rev. Clim Change, 1, 8296, doi:10.1002/wcc.8.

    • Search Google Scholar
    • Export Citation
  • Stahl, K., Tallaksen L. M. , Gudmundsson L. , and Christensen J. H. , 2011: Streamflow data from small basins: A challenging test to high resolution regional climate modeling. J. Hydrometeor., 12, 900912.

    • Search Google Scholar
    • Export Citation
  • Vidal, J.-P., Martin E. , Franchistéguy L. , Baillon M. , and Soubeyroux J.-M. , 2010: A 50-year high-resolution atmospheric reanalysis over France with the Safran system. Int. J. Climatol., 30, 16271644, doi:10.1002/joc.2003.

    • Search Google Scholar
    • Export Citation
  • von Storch, H., Langenberg H. , and Feser F. , 2000: A spectral nudging technique for dynamical downscaling purposes. Mon. Wea. Rev., 128, 36643673.

    • Search Google Scholar
    • Export Citation
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