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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/gmd-2019-131
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-131
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: methods for assessment of models 01 Jul 2019

Submitted as: methods for assessment of models | 01 Jul 2019

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A revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

A new bias-correction method for precipitation over complex terrain suitable for different climate states

Patricio Velasquez1,2, Martina Messmer1,2,3, and Christoph C. Raible1,2 Patricio Velasquez et al.
  • 1Climate and Environmental Physics Institute, University of Bern, Switzerland
  • 2Oeschger Centre for Climate Change Research, University of Bern, Switzerland
  • 3School of Earth Sciences, The University of Melbourne, Melbourne, Victoria, Australia

Abstract. This work presents a new bias-correction method for precipitation that considers orographic characteristics, which makes it flexible to be used under highly different climate conditions, e.g., glacial conditions. The new bias-correction and its performance are presented for Switzerland using a regional climate simulation under perpetual 1990 conditions at 2-km resolution driven by a simulation performed with a global climate model. Comparing the regional simulations with observations, we find a strong seasonal and height dependence of the bias in precipitation commonly observed in regional climate modelling over complex terrain. Thus, we suggest a 3-step correction method consisting of (i) a separation into different orographic characteristics, (ii) correction of low intensity precipitation, and finally (iii) the application of empirical quantile mapping, which is applied to each month separately. Testing different orographic characteristics shows that separating in 400-m height-intervals provides the overall most reasonable correction of the biases in precipitation and additionally at the lowest computational costs. The seasonal precipitation bias induced by the global climate model is fully corrected, whereas some regional biases remain, in particular positive biases in winter over mountains and negative biases in winter and summer in deep valleys and Ticino. The biases over mountains are difficult to judge, as observations over complex terrain are afflicted with uncertainties, which may be more than 30 % above 1500 m a.s.l. A rigorous cross validation, which trains the correction method with independent observations from Germany, Austria and France, exhibits a similar performance compared to just using Switzerland as training and verification region. This illustrates the robustness of the new method. Thus, the new bias-correction provides a flexible tool which is suitable in studies where orography strongly changes, e.g., during glacial times.

Patricio Velasquez et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Patricio Velasquez et al.

Data sets

Code and Dataset P. Velasquez, M. Messmer, and C. C. Raible https://doi.org/10.5281/zenodo.3243797

Model code and software

Code and Dataset P. Velasquez, M. Messmer, and C. C. Raible https://doi.org/10.5281/zenodo.3243797

Patricio Velasquez et al.

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Short summary
This work presents a new bias-correction method for precipitation that considers orographic characteristics, which can be used in studies where the latter strongly changes. The 3-step correction method consists of a separation into orographic characteristics, correction of low intensity precipitation and the application of empirical quantile mapping. The seasonal bias induced by the global climate model is fully corrected. A rigorous cross validation illustrates the method's robustness.
This work presents a new bias-correction method for precipitation that considers orographic...
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