An intercomparison of a large ensemble of statistical downscaling methods over Europe: results from the VALUE perfect predictor cross-validation experiment

Research output: Contribution to journalArticle

Authors

  • J. M. Gutiérrez
  • D. Maraun
  • R. Huth
  • E. Hertig
  • R. Benestad
  • O. Roessler
  • J. Wibig
  • R. Wilcke
  • S. Kotlarski
  • D. San Martín
  • S. Herrera
  • J. Bedia
  • A. Casanueva
  • R. Manzanas
  • M. Iturbide
  • M. Vrac
  • M. Dubrovsky
  • J. Ribalaygua
  • J. Pórtoles
  • O. Räty
  • J. Räisänen
  • B. Hingray
  • D. Raynaud
  • M. J. Casado
  • P. Ramos
  • T. Zerenner
  • M. Turco
  • T. Bosshard
  • P. Štěpánek
  • J. Bartholy
  • R. Pongracz
  • D. E. Keller
  • A. M. Fischer
  • R. M. Cardoso
  • P. M. M. Soares
  • B. Czernecki
  • C. Pagé

Colleges, School and Institutes

External organisations

  • Meteorology Group, Instituto de Física de Cantabria; CSIC-University of Cantabria; Santander Spain
  • Wegener Center for Climate and Global Change; University of Graz; Graz Austria
  • Institute of Atmospheric Physics; Czech Academy of Sciences; Prague Czech Republic
  • Institute of Geography; University of Augsburg; Augsburg Germany
  • The Norwegian Meteorological Institute; Osla Norway
  • Department of Geography/Oeschger Centre for Climate Change Research; University of Bern; Bern Switzerland
  • Department of Meteorology and Climatology; University of Lodz; Lodz Poland
  • Rossby Centre; Swedish Meteorological and Hydrological Institute; Norrköping Sweden
  • Federal Office of Meteorology and Climatology MeteoSwiss; Zurich Switzerland
  • Predictia Intelligent Data Solutions, SME; Madrid Spain
  • Meteorology Group, Departamento de Matemática Aplicada y Computación; University of Cantabria; Santander Spain
  • Laboratoire des Sciences du Climat et de l'Environnement (LSCE-IPSL/CNRS); Paris France
  • Global Change Research Institute, Czech Academy of Sciences; Brno Czech Republic
  • Fundación Para la Investigación del Clima (FIC); Madrid Spain
  • University of Helsinki (UHEL); Helsinki Finland
  • Université Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE; Grenoble France
  • Agencia Estatal de Meteorología, AEMET, C/ Leonardo Prieto Castro, 8, Madrid, 28071 Spain
  • Meteorological Institute; University of Bonn; Bonn Germany
  • Department of Applied Physics; University of Barcelona; Barcelona Spain
  • Swedish Meteorological and Hydrological Institute (SMHI); Norrköping Sweden
  • Eötvös Loránd University (ELU); Budapest Hungary
  • Center for Climate Systems Modeling (C2SM), ETH Zurich; Zurich Switzerland
  • Instituto Dom Luiz; Faculdade de Ciências, Universidade de Lisboa (IDL); Lisboa Portugal
  • Adam Mickiewicz University; Poznań Poland
  • CECI, Université de Toulouse, CNRS, Cerfacs; Toulouse France
  • Department of Physical Geography and Geoecology, Faculty of Science; Charles University; Prague Czech Republic

Abstract

VALUE is an open European collaboration to intercompare downscaling approaches for climate change research, focusing on different validation aspects (marginal, temporal, extremes, spatial, process‐based, etc.). Here we describe the participating methods and first results from the first experiment, using “perfect” reanalysis (and reanalysis‐driven regional climate model (RCM)) predictors to assess the intrinsic performance of the methods for downscaling precipitation and temperatures over a set of 86 stations representative of the main climatic regions in Europe. This study constitutes the largest and most comprehensive to date intercomparison of statistical downscaling methods, covering the three common downscaling approaches (perfect prognosis, model output statistics—including bias correction—and weather generators) with a total of over 50 downscaling methods representative of the most common techniques.

Overall, most of the downscaling methods greatly improve (reanalysis or RCM) raw model biases and no approach or technique seems to be superior in general, because there is a large method‐to‐method variability. The main factors most influencing the results are the seasonal calibration of the methods (e.g., using a moving window) and their stochastic nature. The particular predictors used also play an important role in cases where the comparison was possible, both for the validation results and for the strength of the predictor–predictand link, indicating the local variability explained. However, the present study cannot give a conclusive assessment of the skill of the methods to simulate regional future climates, and further experiments will be soon performed in the framework of the EURO‐CORDEX initiative (where VALUE activities have merged and follow on).

Finally, research transparency and reproducibility has been a major concern and substantive steps have been taken. In particular, the necessary data to run the experiments are provided at http://www.value‐cost.eu/data and data and validation results are available from the VALUE validation portal for further investigation: http://www.value‐cost.eu/validationportal.

Details

Original languageEnglish
Pages (from-to)3750-3785
Number of pages36
JournalInternational Journal of Climatology
Volume39
Issue number9
Early online date23 Mar 2019
Publication statusPublished - Jul 2019

Keywords

  • Bias adjustment, CORDEX, Downscaling, Model output statistics, Perfect prognosis, Reproducibility, Validation, Weather generators

ASJC Scopus subject areas