Abstract/Summary

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, as well as 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.