Intercomparison of statistical and dynamical downscaling models under the EURO- and MED-CORDEX initiative framework: present climate evaluations

Abstract

Given the coarse spatial resolution of General Circulation Models, finer scale projections of variables affected by local-scale processes such as precipitation are often needed to drive impacts models, for example in hydrology or ecology among other fields. This need for high-resolution data leads to apply projection techniques called downscaling. Downscaling can be performed according to two approaches: dynamical and statistical models. The latter approach is constituted by various statistical families conceptually different. If several studies have made some intercomparisons of existing downscaling models, none of them included all those families and approaches in a manner that all the models are equally considered. To this end, the present study conducts an intercomparison exercise under the EURO- and MED-CORDEX initiative hindcast framework. Six Statistical Downscaling Models (SDMs) and five Regional Climate Models (RCMs) are compared in terms of precipitation outputs. The downscaled simulations are driven by the ERAinterim reanalyses over the 1989–2008 period over a common area at 0.44° of resolution. The 11 models are evaluated according to four aspects of the precipitation: occurrence, intensity, as well as spatial and temporal properties. For each aspect, one or several indicators are computed to discriminate the models. The results indicate that marginal properties of rain occurrence and intensity are better modelled by stochastic and resampling-based SDMs, while spatial and temporal variability are better modelled by RCMs and resampling-based SDM. These general conclusions have to be considered with caution because they rely on the chosen indicators and could change when considering other specific criteria. The indicators suit specific purpose and therefore the model evaluation results depend on the end-users point of view and how they intend to use with model outputs. Nevertheless, building on previous intercomparison exercises, this study provides a consistent intercomparison framework, including both SDMs and RCMs, which is designed to be flexible, i.e., other models and indicators can easily be added. More generally, this framework provides a tool to select the downscaling model to be used according to the statistical properties of the local-scale climate data to drive properly specific impact models.

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Notes

  1. 1.

    http://prudence.dmi.dk/.

  2. 2.

    http://www.ensembles-eu.org.

  3. 3.

    http://ensembles-eu.metoffice.com.

  4. 4.

    http://www.ecad.eu.

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Acknowledgments

The authors are thankful to all the RCM data providers, especially to R. Vautard (IPSL) and A. Colette (INERIS) for the WRF-IPSL-INERIS44 EURO-CORDEX run and Météo-France/CNRM (A. Alias, S. Somot) for the CNRM-ALADIN52 MED-CORDEX run. The MED-CORDEX simulations used in this work are downloaded from the MED-CORDEX data portal (www.medcordex.eu/medcordex.php). This work has been partially funded by the Spanish Ministry of Education and Science and the European Regional Development Fund, through Grant CGL2007-66440-C04-02. We also thank F. Blondot (HSM) who, in collaboration with Julie Carreau, helped us for the predictors selection. All the estimations and simulations for the stochastic and the TF models have been done with the R-package “VGAM” (Yee 2010). Special thanks are due to Thomas Yee, the “VGAM” package author for his help. The MOS model has been computed thanks to the R-package CDFt (Michelangeli et al. 2009). This work has been supported by the ANR StaRMIP project, the ANR REMEMBER project and the REMedHE GICC project. It is a contribution to the HyMeX program (HYdrological cycle in The Mediterranean EXperiment) through INSU-MISTRALS support and the MED-CORDEX program. It was supported by the IPSL group for regional climate and environmental studies, with granted access to the HPC resources of IDRIS (under allocation i2011010227). It is a contribution to the CORDEX-ESD initiative (http://wcrp-cordex.ipsl.jussieu.fr/index.php/community/cordex-esd) and to the COST Action VALUE (http://www.value-cost.eu/, Maraun et al. 2015).

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Appendix: Technical features

Appendix: Technical features

See Table 6.

Table 6 The main R features to reproduce the simulations are indicated in this table

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Vaittinada Ayar, P., Vrac, M., Bastin, S. et al. Intercomparison of statistical and dynamical downscaling models under the EURO- and MED-CORDEX initiative framework: present climate evaluations. Clim Dyn 46, 1301–1329 (2016). https://doi.org/10.1007/s00382-015-2647-5

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Keywords

  • Statistical downscaling
  • Dynamical downscaling
  • CORDEX
  • Precipitation
  • Intercomparison