Bias Adjustment and the Question of Usable Climate Information: Methodological Assumptions and Value Judgments

Abstract Statistical bias adjustment has become a common practice to increase the relevance of climate model outputs for impact studies and other societal applications. However, the application of bias adjustment raises fundamental issues identified in the literature, calling into question the credibility of the adjusted climate information. In the attempt to address the usability gap of climate model output despite these unresolved issues, different approaches to bias adjustment have emerged—from applying a single consistent method across studies, selecting the most suitable method for a given use case, to employing an ensemble of bias adjustment methods. This paper examines how these approaches rest on both methodological assumptions and implicit value judgments about what constitutes usable climate information and for whom it is produced. Building on recent literature in the philosophy of science, we propose a framework for evaluating the usability of climate projections in the context of bias adjustment and apply this framework to evaluate the different approaches to bias adjustment. To evaluate the credibility of the adjusted climate information, the paper provides a detailed discussion of two key methodological assumptions underlying different approaches, the interpretation of performance differences of bias adjustment methods and changes to the climate model trend and ensemble through bias adjustment. Through this perspective, we aim to situate bias adjustment in the discussion around usable climate information and the production of climate services, while offering a practical discussion of assumptions for climate impact researchers and climate service practitioners working with bias adjustment methods. Significance Statement Statistical bias adjustment of climate model output has become common practice but raises fundamental issues unresolved in the literature. Informed by the development of the software package ibicus for the comparison and evaluation of bias adjustment methods, this perspective provides both a technical discussion of methodological assumptions of prevalent approaches to bias adjustment and a philosophical reflection on the associated interpretations of usable climate information. Both of these aspects inform the approach to bias adjustment chosen in practice. We argue that the discussion of both technical assumptions and implicit value judgments conducted here is important to guide future method development and can serve as a practical guide to users of bias adjustment and organizations who aim to provide actionable climate services.

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Publication Details

Journal
Bulletin of the American Meteorological Society
Published
2025-11-26
DOI
https://doi.org/10.1175/bams-d-25-0022.1
Citations
1
Primary Topic
Climate change impacts on agriculture
Type
article
Field-Weighted Citation Impact
3.03
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article

Bias Adjustment and the Question of Usable Climate Information: Methodological Assumptions and Value Judgments

Jakob Benjamin Wessel, Julie Jebeile, Theodore G. Shepherd, Fiona R. Spuler et al.
1 citations
Bulletin of the American Meteorological Society
Climate change impacts on agriculture
3.03
article

Bias Adjustment and the Question of Usable Climate Information: Methodological Assumptions and Value Judgments

Jakob Benjamin Wessel, Julie Jebeile, Theodore G. Shepherd, Fiona R. Spuler, Jakob Zscheischler
article en
1 citations

Abstract

Abstract Statistical bias adjustment has become a common practice to increase the relevance of climate model outputs for impact studies and other societal applications. However, the application of bias adjustment raises fundamental issues identified in the literature, calling into question the credibility of the adjusted climate information. In the attempt to address the usability gap of climate model output despite these unresolved issues, different approaches to bias adjustment have emerged—from applying a single consistent method across studies, selecting the most suitable method for a given use case, to employing an ensemble of bias adjustment methods. This paper examines how these approaches rest on both methodological assumptions and implicit value judgments about what constitutes usable climate information and for whom it is produced. Building on recent literature in the philosophy of science, we propose a framework for evaluating the usability of climate projections in the context of bias adjustment and apply this framework to evaluate the different approaches to bias adjustment. To evaluate the credibility of the adjusted climate information, the paper provides a detailed discussion of two key methodological assumptions underlying different approaches, the interpretation of performance differences of bias adjustment methods and changes to the climate model trend and ensemble through bias adjustment. Through this perspective, we aim to situate bias adjustment in the discussion around usable climate information and the production of climate services, while offering a practical discussion of assumptions for climate impact researchers and climate service practitioners working with bias adjustment methods. Significance Statement Statistical bias adjustment of climate model output has become common practice but raises fundamental issues unresolved in the literature. Informed by the development of the software package ibicus for the comparison and evaluation of bias adjustment methods, this perspective provides both a technical discussion of methodological assumptions of prevalent approaches to bias adjustment and a philosophical reflection on the associated interpretations of usable climate information. Both of these aspects inform the approach to bias adjustment chosen in practice. We argue that the discussion of both technical assumptions and implicit value judgments conducted here is important to guide future method development and can serve as a practical guide to users of bias adjustment and organizations who aim to provide actionable climate services.

Bulletin of the American Meteorological SocietyVol. 107(1)
University of Bern (CH), Centre National de la Recherche Scientifique (FR), Oeschger Centre for Climate Change Research (CH), Helmholtz Centre for Environmental Research (DE), Norsk Hydro (Germany) (DE), University of Exeter (GB), Centre National de Recherches Météorologiques (FR), Météo-France (FR), The Alan Turing Institute (GB), University of Reading (GB), Technische Universität Dresden (DE)
Openalex Percentile: Top 7%
Climate change impacts on agriculture
3.03
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