Climate Risk Decisions Demand Trust
Climate risk research now directly influences real-world decisions such as where people live, what gets insured, how financial risk is priced, and how public resources are allocated. As these outputs move from “informational” to “decision-grade,” stakeholders need to understand the assumptions made, the data used, and how the results were produced to judge whether a projection is fit for purpose. Pollack and colleagues argue that transparency and reusability are not optional ideals in this setting; they are core requirements for credible climate risk management.
Complex Pipelines Make Validation Hard
Many climate risk estimates are not a single model output but an interconnected pipeline that can include climate simulations, downscaling, hazard modeling, exposure data, and vulnerability or damage functions. Each stage can introduce uncertainty and hard-to-detect artifacts, and validation can be constrained by limited, inconsistent, or inaccessible observations—especially for rare extremes and rapidly changing conditions. When that pipeline is opaque, it becomes difficult to determine why models disagree, which assumptions drive outcomes, or whether results are calibrated in a way that makes sense for the decision at hand.
What Transparency And Reusability Mean In Practice
In the Perspective, transparency and reusability are treated as practical levers for improving climate risk science rather than abstract virtues. Transparency focuses on making key choices legible, including data provenance, methodological steps, and evaluation logic. Reusability focuses on making the work usable by others, so they can rerun analyses, stress-test assumptions, benchmark methods, and build on prior work without rebuilding the entire workflow from scratch.
Why Code And Data Matter For Reproducibility
A major reason code and data sharing matters is that computational results are often inseparable from the digital artifacts that produced them. The broader research community has emphasized that computational reproducibility generally requires access to the original code, data, and documentation needed to regenerate results using the same workflow. In parallel, the FAIR principles highlight that research outputs should be structured so they are findable, accessible, interoperable, and reusable, which is especially relevant for multi-step climate risk pipelines.
How Often Climate Risk Studies Share What’s Needed
Pollack et al. point to a striking gap between the norms of decision-grade evidence and the reality of published climate risk research. In their review of highly influential work, they report that only about 4% of the most-cited peer-reviewed climate risk studies in recent years fully shared both data and code, a common minimum standard for enabling independent verification and meaningful reuse. In a field where results feed into high-stakes decisions, that shortfall becomes a bottleneck for quality control, learning, and trust.
When Models Disagree, Opacity Blocks Learning
Model disagreement is not automatically a problem; uncertainty is inherent in climate risk. The problem arises when disagreements cannot be traced to specific methodological or data choices. The Dartmouth summary highlights an example where two flood hazard models in Los Angeles agreed on only about 24% of properties in the current 100-year floodplain, and limited access to historical data made it difficult to determine which model was more accurate. When evaluation data, processing steps, and assumptions are not visible, users cannot tell whether differences reflect genuine uncertainty or avoidable inconsistencies.
Why Trust Can Erode Quickly In Public-Facing Uses
As climate risk information becomes more consumer-facing and market-shaping, scrutiny increases, and tolerance for “black box” methods declines. The paper’s discussion, echoed in the Dartmouth write-up, notes how confidence can unravel when stakeholders cannot evaluate how scores or projections were generated, and it points to the broader reputational and adoption risks that follow from insufficient transparency in high-impact contexts.
What Transparent Workflows Enable For Science And Practice
The benefits of transparency and reusability are not limited to checking a box for openness. They support clearer scrutiny of assumptions, faster identification of errors, and stronger benchmarking across methods. They also reduce duplicated effort by enabling shared foundations—datasets, tools, and workflows—that other teams can extend rather than rebuild. Over time, this accelerates learning about uncertainty, improves comparability across models, and supports more defensible communication of what projections can and cannot say.
Practical Steps That Raise The Floor Quickly
A central message of the Perspective is that meaningful progress does not always require revolutionary change. Concrete steps include journals enforcing or strengthening sharing policies, researchers depositing code and data in durable repositories when possible, using persistent identifiers for software and datasets, and writing clear availability statements that explain what can be shared and what cannot, along with the reasons. Even when full sharing is not feasible, explicit documentation of constraints can still improve interpretability and appropriate use.
Investment And Governance Are Part Of The Solution
The authors also emphasize that durable change requires more than individual effort. Building transparent, reusable climate risk science at scale requires investment in infrastructure, training, and support; it also requires careful governance of data rights, privacy, and equity considerations. In other words, the goal is not “open everything,” but rather to be transparent and reusable by design, with guardrails that align with the ethical, legal, and practical realities of climate risk work.
From Principles To Practice In Climate Risk Management
Climate risk science is being asked to inform decisions under deep uncertainty about rare and evolving extremes, often with imperfect observational baselines. In that world, transparency and reusability are essential for building projections that can be evaluated, compared, improved, and responsibly applied. Pollack and colleagues’ call is ultimately a call to normalize scientific practices that make climate risk knowledge more testable and more usable, strengthening both the science and the decisions it supports.
(Source: A.B. Pollack, L. Auermuller, C.D. Burleyson, J. Campbell, M. Condon, C. Cooper, M. Coronese, S. Dangendorf, J. Doss-Gollin, P. Hegde, C. Helgeson, R.E. Kopp, J. Kwakkel, C. Lesk, J. Mankin, R.E. Nicholas, J. Rice, S. Roth, V. Srikrishnan, M. Scheeler, N. Tuana, C. Vernon, M. Zhao, & K. Keller, Unlocking the benefits of transparent and reusable science for climate risk management, Proc. Natl. Acad. Sci. U.S.A. 123 (3) e2422157123, https://doi.org/10.1073/pnas.2422157123 (2026).)
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