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Advances in Statistical Climatology, Meteorology and Oceanography An international open-access journal on applied statistics
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Volume 2, issue 1 | Copyright
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 39-47, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  09 Jun 2016

09 Jun 2016

Calibrating regionally downscaled precipitation over Norway through quantile-based approaches

David Bolin1, Arnoldo Frigessi2,4, Peter Guttorp3,4, Ola Haug4, Elisabeth Orskaug4, Ida Scheel5, and Jonas Wallin1 David Bolin et al.
  • 1Dept. of Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden
  • 2Dept. of Biostatistics, University of Oslo, Oslo, Norway
  • 3Dept. of Statistics, University of Washington, Seattle, WA, USA
  • 4Norwegian Computing Center, Oslo, Norway
  • 5Dept. of Mathematics, University of Oslo, Oslo, Norway

Abstract. Dynamical downscaling of earth system models is intended to produce high-resolution climate information at regional to local scales. Current models, while adequate for describing temperature distributions at relatively small scales, struggle when it comes to describing precipitation distributions. In order to better match the distribution of observed precipitation over Norway, we consider approaches to statistical adjustment of the output from a regional climate model when forced with ERA-40 reanalysis boundary conditions. As a second step, we try to correct downscalings of historical climate model runs using these transformations built from downscaled ERA-40 data. Unless such calibrations are successful, it is difficult to argue that scenario-based downscaled climate projections are realistic and useful for decision makers. We study both full quantile calibrations and several different methods that correct individual quantiles separately using random field models. Results based on cross-validation show that while a full quantile calibration is not very effective in this case, one can correct individual quantiles satisfactorily if the spatial structure in the data are accounted for. Interestingly, different methods are favoured depending on whether ERA-40 data or historical climate model runs are adjusted.

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