Submitted as: methods for assessment of models
21 Feb 2022
Submitted as: methods for assessment of models | 21 Feb 2022
Status: this preprint is currently under review for the journal GMD.

MIdAS—MultI-scale bias AdjuStment

Peter Berg1,, Thomas Bosshard1,, Wei Yang1,, and Klaus Zimmermann1, Peter Berg et al.
  • 1Swedish Meteorological and Hydrological Institute, Folkborgsvägen 17, 601 76 Norrköping, Sweden
  • These authors contributed equally to this work.

Abstract. Bias adjustment is the practice of statistically transforming climate model data in order to reduce systematic deviations from a reference data set, typically some sort of observations. There are numerous proposed methodologies to perform the adjustments – ranging from simple scaling approaches to advanced multi-variate distribution based mapping. In practice, the actual bias adjustment method is a small step in the application, and most of the processing handles reading, writing and linking different data sets. These practical processing steps become especially heavy with increasing model domain size and resolution in both time and space. Here, we present a new implementation platform for bias adjustment, which we call MIdAS (MultI-scale bias AdjuStment). MIdAS is a modern code implementation that supports features such as: modern Python libraries that allow efficient processing of large data sets at computing clusters, state-of-the-art bias adjustment methods based on quantile mapping, "day-of-year" based adjustments to avoid artificial discontinuities, and also introduces cascade adjustment in time and space. The MIdAS platform has been set up such that it will continually support development of methods aimed towards higher resolution climate model data, explicitly targeting cases where there is a scale mismatch between data sets. The paper presents a comparison of different quantile mapping based bias adjustment methods and the subsequently chosen code implementation for MIdAS. A current recommended setup of the MIdAS bias adjustment is presented and evaluated in a pseudo-reference setup for regions around the world. Special focus is put on preservation of trends in future climate projections, and it is shown that the cascade adjustments perform better than the standard quantile mapping implementations, and often similar to more advanced trend preserving methods. The code is available from Berg et al. (2021).

Peter Berg et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-6', Jorn Van de Velde, 31 Mar 2022
  • RC2: 'Comment on gmd-2022-6', Faranak Tootoonchi, 11 Apr 2022
  • RC3: 'Comment on gmd-2022-6', Joel Fiddes, 19 Apr 2022
  • AC1: 'final author comments on gmd-2022-6', Peter Berg, 13 May 2022

Peter Berg et al.

Data sets

MIdAS: Bias adjustment intercomparison and evaluation scripts Peter Berg, Thomas Bosshard, and Wei Yang

Model code and software

MIdAS (MultI-scale bias AdjuStment) Peter Berg, Thomas Bosshard, Wei Yang, and Klaus Zimmermann

Peter Berg et al.


Total article views: 500 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
388 97 15 500 7 5
  • HTML: 388
  • PDF: 97
  • XML: 15
  • Total: 500
  • BibTeX: 7
  • EndNote: 5
Views and downloads (calculated since 21 Feb 2022)
Cumulative views and downloads (calculated since 21 Feb 2022)

Viewed (geographical distribution)

Total article views: 457 (including HTML, PDF, and XML) Thereof 457 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 20 May 2022
Short summary
When performing impact analysis with climate models, one is often confronted with the issue that the models have significant bias. Commonly, the modelled climatological temperature deviates from observed climate by a few degrees, or it rains excessively in the model. MIdAS employs a novel statistical model to translate the model climatology toward that observed, using novel methodologies and modern tools. The coding platform allows opportunities to develop methods for high-resolution models.