Superensembles of raw and bias-adjusted regional climate models for Mediterranean region, Turkey


INTERNATIONAL JOURNAL OF CLIMATOLOGY, vol.42, no.4, pp.2566-2585, 2022 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 42 Issue: 4
  • Publication Date: 2022
  • Doi Number: 10.1002/joc.7381
  • Journal Indexes: Science Citation Index Expanded, Scopus, Academic Search Premier, IBZ Online, PASCAL, Agricultural & Environmental Science Database, Aquatic Science & Fisheries Abstracts (ASFA), Artic & Antarctic Regions, CAB Abstracts, Compendex, Environment Index, Geobase, Greenfile, INSPEC, Pollution Abstracts, Veterinary Science Database
  • Page Numbers: pp.2566-2585
  • Keywords: bias-adjusted RCM, climate change impact assessment, Mediterranean region, multi-model ensembles, RCM, superensemble, SEASONAL CLIMATE, MULTIMODEL ENSEMBLE, MULTIPLE GCMS, CHANGE IMPACT, PRECIPITATION, SIMULATIONS, WEATHER, FORECASTS


For regional-scale studies on climate change and relevant impact assessment, the projections of regional climate models (RCMs) are used due to their advantage of high resolution and better representation of the local climate relative to the global climate models. However, direct use of RCM outputs is prone to uncertainties and biases that may significantly diminish the accuracy of results. EURO-COordinated Regional Downscaling EXperiment (CORDEX) initiative that is a part of the global Coordinated Regional Downscaling Experiment Project provides high-resolution RCM projections for the European domain and bias-adjusted regional projections under the "CORDEX-Adjust" Project for climate change impact assessment studies. This study aims to perform a multi-model analysis of precipitation data using bias-adjusted and raw/non-bias adjusted CORDEX RCMs to obtain an evaluation of their representativeness for local climate conditions and adequacy to be used for climate change impact assessment. For this purpose, the analysis focuses on four CORDEX RCMs and their 12 bias-adjusted versions generated with cumulative distribution function transformation, quantile mapping, and distribution-based scaling methodologies. For the analysis in total, 16 hindcast results of raw and bias-adjusted RCMs, and three superensembles (SEs) generated through multiple linear regression are compared for their performance regarding their goodness of fit to the ground-based precipitation monitoring data from eight meteorological stations in the Mediterranean region in Turkey. The analysis verified that the skill of individual simulations including the bias-adjusted outputs is significantly variable in spatial and temporal means. On the other hand, SE formed by using all 16 hindcast outputs has the highest skill for the representation of variability in precipitation in time as well as for the reproduction of annual climatology at all stations, although potential drawbacks concerning seasonality and the range of anomaly may still exist which might be significant depending on the specific aim of impact assessment.