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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2019-36
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-36
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Development and technical paper 07 Mar 2019

Development and technical paper | 07 Mar 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0)

Stefan Lange Stefan Lange
  • Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany

Abstract. In this paper I present new methods for bias adjustment and statistical downscaling that are tailored to the requirements of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). In comparison to their predecessors, the new methods allow for a more robust bias adjustment of extreme values, preserve trends more accurately across quantiles, and facilitate a clearer separation of bias adjustment and statistical downscaling. The new statistical downscaling method is stochastic and better at adjusting spatial variability than the old interpolation method. Improvements in bias adjustment and trend preservation are demonstrated in a cross-validation framework.

Stefan Lange
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Status: open (until 02 May 2019)
Status: open (until 02 May 2019)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Stefan Lange
Model code and software

ISIMIP3BASD S. Lange https://doi.org/10.5281/zenodo.2549632

Stefan Lange
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Short summary
Compared to their predecessors, the new ISIMIP methods for bias adjustment and statistical downscaling allow for a more robust bias adjustment of extreme values and spatial variability, preserve trends more accurately across quantiles, and facilitate a clearer separation of bias adjustment and statistical downscaling.
Compared to their predecessors, the new ISIMIP methods for bias adjustment and statistical...
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