Designing and describing climate change impact attribution studies: a guide to common approaches

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Colin J Carlson , Dann Mitchell , Tamma Carleton, Matthew Chersich, Rory Gibb, Torre Lavelle, Megan Lukas-Sithole, Michelle North, Catherine Lippi, Mark New, Sadie Jane Ryan, Stella Shumba, Christopher Trisos


Impact attribution is an emerging transdisciplinary sub-discipline of detection and attribution, focused on the social, economic, and ecological impacts of climate change. Here, we provide an overview of common end-to-end frameworks in impact attribution, focusing on examples relating to the human health impacts of climate change. We propose a typology of study designs based on whether researchers choose to focus on long-term trends or specific events; whether they compare climate scenarios by estimating impact probabilities, or only focus on the difference in impact distributions; and whether they choose to split climate change attribution and impact estimation into separate analytical steps (and often, separate studies). We map four common study designs onto this typology, and discuss their relative strengths in terms of both inferential rigor and science communication potential. We conclude by discussing a handful of related and emerging approaches, and discuss how methodological innovations in impact attribution are continuing to advance our understanding of the climate crisis.



Climate, Earth Sciences, Ecology and Evolutionary Biology, Environmental Public Health, Environmental Studies, Human Geography, Physical and Environmental Geography, Physical Sciences and Mathematics, Probability, Public Health, Spatial Science, Statistical Methodology, Statistical Models, Statistics and Probability


detection and attribution, human health, impact attribution, probabilistic event attribution, storylines, human-caused climate change


Published: 2024-01-06 01:24

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CC BY Attribution 4.0 International

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