21 Aug 2023
 | 21 Aug 2023
Status: this preprint is open for discussion.

ibicus: a new open-source Python package and comprehensive interface for statistical bias adjustment and evaluation in climate modelling (v1.0.1)

Fiona Raphaela Spuler, Jakob Benjamin Wessel, Edward Comyn-Platt, James Varndell, and Chiara Cagnazzo

Abstract. Statistical bias adjustment is commonly applied to climate models before using their results in impact studies. However, different methods, based on a distributional mapping between observational and model data, can change the simulated trends, as well as the spatiotemporal and inter-variable consistency of the model, and are prone to misuse if not evaluated thoroughly. Despite the importance of these fundamental issues, researchers who apply bias adjustment currently do not have the tools at hand to compare different methods or evaluate the results sufficiently to detect possible distortions. Because of this, widespread practice in statistical bias adjustment is not aligned with recommendations from the academic literature. To address the practical issues impeding this, we introduce ibicus, an open-source Python package for the implementation of eight different peer-reviewed and widely used bias adjustment methods in a common framework and their comprehensive evaluation. The evaluation framework introduced in ibicus allows the user to analyse changes to the marginal, spatiotemporal and inter-variable structure of user-defined climate indices and distributional properties, as well as any alteration of the climate change trend simulated in the model. Applying ibicus in a case study over the Mediterranean region using seven CMIP6 global circulation models, this study finds that the most appropriate bias adjustment method depends on the variable and impact studied and that even methods that aim to preserve the climate change trend can modify it. These findings highlight the importance of a use-case-specific choice of method and the need for a rigorous evaluation of results when applying statistical bias adjustment.

Fiona Raphaela Spuler et al.

Status: open (until 16 Oct 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1481', Anonymous Referee #1, 14 Sep 2023 reply
  • CC1: 'Comment on egusphere-2023-1481', Richard Chandler, 29 Sep 2023 reply
  • RC2: 'Comment on egusphere-2023-1481', Jorn Van de Velde, 02 Oct 2023 reply

Fiona Raphaela Spuler et al.

Fiona Raphaela Spuler et al.


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Short summary
Bias adjustment is commonly applied to climate models before using them to study the impacts of climate change to ensure the correspondence of models with observations at a local scale. However, this can introduce undesirable distortions in the climate model. In this paper, we present an open-source python package called ibicus to enable the comparison and detailed evaluation of bias adjustment methods to facilitate their transparent and rigorous application.