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Towards a fair comparison of statistical and dynamical downscaling in the framework of the EURO-CORDEX initiative

Author

Listed:
  • A. Casanueva

    (Universidad de Cantabria)

  • S. Herrera

    (Universidad de Cantabria)

  • J. Fernández

    (Universidad de Cantabria)

  • J.M. Gutiérrez

    (CSIC-Universidad de Cantabria)

Abstract

Both statistical and dynamical downscaling methods are well established techniques to bridge the gap between the coarse information produced by global circulation models and the regional-to-local scales required by the climate change Impacts, Adaptation, and Vulnerability (IAV) communities. A number of studies have analyzed the relative merits of each technique by inter-comparing their performance in reproducing the observed climate, as given by a number of climatic indices (e.g. mean values, percentiles, spells). However, in this paper we stress that fair comparisons should be based on indices that are not affected by the calibration towards the observed climate used for some of the methods. We focus on precipitation (over continental Spain) and consider the output of eight Regional Climate Models (RCMs) from the EURO-CORDEX initiative at 0.44∘ resolution and five Statistical Downscaling Methods (SDMs) —analog resampling, weather typing and generalized linear models— trained using the Spain044 observational gridded dataset on exactly the same RCM grid. The performance of these models is inter-compared in terms of several standard indices —mean precipitation, 90th percentile on wet days, maximum precipitation amount and maximum number of consecutive dry days— taking into account the parameters involved in the SDM training phase. It is shown, that not only the directly affected indices should be carefully analyzed, but also those indirectly influenced (e.g. percentile-based indices for precipitation) which are more difficult to identify. We also analyze how simple transformations (e.g. linear scaling) could be applied to the outputs of the uncalibrated methods in order to put SDMs and RCMs on equal footing, and thus perform a fairer comparison.

Suggested Citation

  • A. Casanueva & S. Herrera & J. Fernández & J.M. Gutiérrez, 2016. "Towards a fair comparison of statistical and dynamical downscaling in the framework of the EURO-CORDEX initiative," Climatic Change, Springer, vol. 137(3), pages 411-426, August.
  • Handle: RePEc:spr:climat:v:137:y:2016:i:3:d:10.1007_s10584-016-1683-4
    DOI: 10.1007/s10584-016-1683-4
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    References listed on IDEAS

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    1. Boris Orlowsky & Sonia Seneviratne, 2012. "Global changes in extreme events: regional and seasonal dimension," Climatic Change, Springer, vol. 110(3), pages 669-696, February.
    2. Francisco Estrada & Víctor Guerrero & Carlos Gay-García & Benjamín Martínez-López, 2013. "A cautionary note on automated statistical downscaling methods for climate change," Climatic Change, Springer, vol. 120(1), pages 263-276, September.
    3. R. E. Benestad & D. Nychka & L. O. Mearns, 2012. "Spatially and temporally consistent prediction of heavy precipitation from mean values," Nature Climate Change, Nature, vol. 2(7), pages 544-547, July.
    4. Qunying Luo & Li Wen & John McGregor & Bertrand Timbal, 2013. "A comparison of downscaling techniques in the projection of local climate change and wheat yields," Climatic Change, Springer, vol. 120(1), pages 249-261, September.
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