• Medientyp: E-Artikel
  • Titel: Evaluation of New CORDEX Simulations Using an Updated Köppen–Trewartha Climate Classification
  • Beteiligte: Remedio, Armelle Reca; Teichmann, Claas; Buntemeyer, Lars; Sieck, Kevin; Weber, Torsten; Rechid, Diana; Hoffmann, Peter; Nam, Christine; Kotova, Lola; Jacob, Daniela
  • Erschienen: MDPI AG, 2019
  • Erschienen in: Atmosphere, 10 (2019) 11, Seite 726
  • Sprache: Englisch
  • DOI: 10.3390/atmos10110726
  • ISSN: 2073-4433
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  • Beschreibung: A new ensemble of climate and climate change simulations covering all major inhabited regions with a spatial resolution of about 25 km, from the WCRP CORDEX COmmon Regional Experiment (CORE) Framework, has been established in support of the growing demands for climate services. The main objective of this study is to assess the quality of the simulated climate and its fitness for climate change projections by REMO (REMO2015), a regional climate model of Climate Service Center Germany (GERICS) and one of the RCMs used in the CORDEX-CORE Framework. The CORDEX-CORE REMO2015 simulations were driven by the ECMWF ERA-Interim reanalysis and the simulations were evaluated in terms of biases and skill scores over ten CORDEX Domains against the Climatic Research Unit (CRU) TS version 4.02, from 1981 to 2010, according to the regions defined by the Köppen–Trewartha (K–T) Climate Classification types. The REMO simulations have a relatively low mean annual temperature bias (about ± 0.5 K) with low spatial standard deviation (about ± 1.5 K) in the European, African, North and Central American, and Southeast Asian domains. The relative mean annual precipitation biases of REMO are below ± 50 % in most domains; however, spatial standard deviation varies from ± 30 % to ± 200 %. The REMO results simulated most climate types relatively well with lowest biases and highest skill score found in the boreal, temperate, and subtropical regions. In dry and polar regions, the REMO results simulated a relatively high annual biases of precipitation and temperature and low skill. Biases were traced to: missing or misrepresented processes, observational uncertainty, and uncertainties due to input boundary forcing.
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