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weather@home 2: validation of an improved global–regional climate modelling system

Research output: Contribution to journalLetter

  • Benoit Guillod
  • Richard Jones
  • Andy Bowery
  • Karsten Haustein
  • Neil Massey
  • Daniel M. Mitchell
  • Friederike Otto
  • Sarah Sparrow
  • Peter Uhe
  • David Wallom
  • Simon Wilson
  • Myles Allen
Original languageEnglish
JournalGeoscientific Model Development
Issue number5
DateAccepted/In press - 22 Apr 2017
DatePublished (current) - 5 May 2017


Extreme weather events can have large impacts on society and, in many regions, are expected to change in frequency and intensity with climate change. Owing to the relatively short observational record, climate models are useful tools as they allow for generation of a larger sample of extreme events, to attribute recent events to anthropogenic climate change, and to project changes in such events into the future. The modelling system known as weather@home, consisting of a global climate model (GCM) with a nested regional climate model (RCM) and driven by sea surface temperatures, allows one to generate a very large ensemble with the help of volunteer distributed computing. This is a key tool to understanding many aspects of extreme events. Here, a new version of the weather@home system (weather@home 2) with a higher-resolution RCM over Europe is documented and a broad validation of the climate is performed. The new model includes a more recent land-surface scheme in both GCM and RCM, where subgrid-scale land-surface heterogeneity is newly represented using tiles, and an increase in RCM resolution from 50 to 25 km. The GCM performs similarly to the previous version, with some improvements in the representation of mean climate. The European RCM temperature biases are overall reduced, in particular the warm bias over eastern Europe, but large biases remain. Precipitation is improved over the Alps in summer, with mixed changes in other regions and seasons. The model is shown to represent the main classes of regional extreme events reasonably well and shows a good sensitivity to its drivers. In particular, given the improvements in this version of the weather@home system, it is likely that more reliable statements can be made with regards to impact statements, especially at more localized scales.

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