Submitted as: model experiment description paper 09 Jul 2021

Submitted as: model experiment description paper | 09 Jul 2021

Review status: this preprint is currently under review for the journal GMD.

A Norwegian Approach to Downscaling

Rasmus E. Benestad Rasmus E. Benestad
  • The Norwegian Meteorological Institute, Henrik Mohns plass 1, 0313 Oslo, Norway

Abstract. A description of a comprehensive geoscientific downscaling model strategy is presented outlining an approach that has evolved over the last 20 years, together with an explanation for its development, its technical aspects, and evaluation scheme. This effort has resulted in an open-source and free R-based tool, 'esd', for the benefit of sharing and improving the reproducibility of the downscaling results. Furthermore, a set of new metrics was developed as an integral part of the downscaling approach which assesses model performance with an emphasis on regional information for society (RifS). These metrics involve novel ways of comparing model results with observational data and have been developed for downscaling large multi-model global climate model ensembles. A literature search suggests that this comprehensive downscaling strategy and evaluation scheme are not widely used within the downscaling community. In addition, a new convention for storing large datasets of ensemble results that provides fast access to information and drastically saves data volume is explained.

Rasmus E. Benestad

Status: open (until 30 Sep 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Rasmus E. Benestad

Data sets

A Norwegian approach to downscaling Rasmus Benestad

Model code and software

A Norwegian approach to downscaling Rasmus Benestad

Video supplement

A comprehensive downscaling strategy for climate change adaptation Rasmus Benestad

Rasmus E. Benestad


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Short summary
A Norwegian approach for deriving regional climate information through downscaling is presented. It is unique and involves a different set to techniques compared to the wider community but give more robust results. We estimate the statistical properties of daily temperature and precipitation and the results are based on large sets of simulations with global climate models.