Lost in Projection: Uncertainty is Misrepresented in Climate Risk and Vulnerability Assessments.

Authors

Patrick Curran, Kendrick Hardaway, Tom Logan

Published in

Risk analysis : an official publication of the Society for Risk Analysis. Volume 46. Issue 9. Pages e70331.

Abstract

In practice, many climate change risk assessments fail to capture the true depth of uncertainty. Despite widespread scientific recognition of deep uncertainty (large ranges of possibility) in climate conditions, this nuance is often lost in translation to policy and planning contexts; instead, conditions are presented as single projections. This means that communities are making large-scale infrastructure investments and long-term policy commitments based on false precision, leaving them unprepared for climate surprises or potentially wasting resources and disrupting communities unnecessarily by overadapting. To examine how climate uncertainties are represented and accounted for in adaptation planning, we conducted a structured review of 39 climate risk and vulnerability assessments from across the world, using sea level rise as a case study. These documents inform policy that guides billions of dollars in infrastructure investments and shape community preparedness strategies. Our analysis reveals that only 54% of these documents correctly represent sea level rise as deeply uncertain. This issue is compounded when making decisions; 71% of decisions were made by misapplying scenarios as individual projections to plan for, rather than as a tool for exploring potential future conditions, directly contradicting their intended use. This demonstrates a gap between scientific understanding of climate uncertainty and planning practice. Addressing this gap requires improved uncertainty communication, moving beyond just quantifying uncertainty to also characterizing uncertainty. This must be done in conjunction with the support of decision makers to incorporate a stronger understanding of uncertainty into planning by using tools designed specifically for decision making in deeply uncertain environments.

PMID:
42687213
Bibliographic data and abstract were imported from PubMed on 03 Sep 2026.

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