Statistical bias adjustment and the usability of climate information: a perspective on strategies and underlying assumptions

Statistical bias adjustment of climate models has become widespread practice to bridge the usability gap of climate information for impact studies and other societal applications. However, the application of bias adjustment offers potential for misuse and comes with several fundamental issues which have been highlighted in the literature. In this tension between widespread use and fundamental issues, different strategies for the application of statistical bias adjustment have developed, ranging from selecting a consistent bias adjustment method across applications to ensure comparability, to applying an ensemble of available methods in a given case study. In this contribution, we examine the specific methodological assumptions of different approaches to bias adjustment, such as the relevance and potential for trend preservation, and propose an evaluative framework based on recent literature in philosophy of science to assess the understanding of usability underlying different approaches to bias adjustment. We find that both methodological assumptions about bias adjustment, as well as the understanding of usability in the context of climate information determine the choice of bias adjustment strategy in current practice. For example, global application of a bias adjustment method generates information that is salient and credible and thus usable mostly for the purpose of model intercomparison, whilst local adaptation improves credibility, but compromises on the ease-of-use. With neither the methodological assumptions nor the understanding of what usable climate information is and who it is generated for often explicitly stated in practice, we hope to contribute to enhanced methodological practice and reflection through this discussion.