Dartmouth Researchers Call for More Transparency in Climate-Risk Science

Homes damaged by Hurricane Michael in Jacksonville, Florida in 2018. (Photo by Glenn Fawcett)

"When data and code are difficult or impossible to access, it slows down climate-risk science—especially in application areas that require integrating many areas of expertise, methods, and tools," said Adam Pollack, former postdoctoral engineering researcher at Dartmouth and lead author on the study, who recently became an assistant professor at the University of Iowa.

Although big-picture conclusions of climate research remain well-established, demand for more detailed climate-risk projections has surged, along with public scrutiny of how they are produced and used. For example, millions of prospective US homebuyers have used individual property-level flood-risk estimates in housing decisions. Amid widespread concerns about non-transparent data, however, Zillow's recent decision to stop displaying property-level climate-risk scores on listings underscores how quickly trust can break down when methods are hard to evaluate.

The paper also explains why transparency and reusability matter so much in climate-risk research. Projections often combine multiple steps, including integrating weather prediction into risk estimation, and many of those steps face fundamental challenges around data validation and calibration. The authors cite an example of two flood hazard models for Los Angeles that had only 24% agreement about which properties fall within the current 100-year floodplain. Limited access to historical data made it difficult to determine which was the more accurate model.

"Transparency and reusability are not abstract ideals in climate-risk management," said Dartmouth Engineering Professor Klaus Keller, co-author on the study. "They are crucial tools to reveal key assumptions, support benchmarking, identify errors, and help users understand the limits of risk projections."

The paper argues that more open and reusable non-commercial research can benefit the entire ecosystem—both public and private-sector—by making it easier to benchmark products, communicate uncertainty honestly, and reduce duplication.

"There is no zero-sum game between trustworthy, publicly available science and private-sector innovation," said Justin Mankin, associate professor of geography at Dartmouth, and co-author on the study. "Transparency actually fosters responsible innovation, positioning scrutiny of assumptions, hidden biases, and comparison across tools and providers."

Among the paper's near-term, low-cost recommendations: journals should enforce (or revise) the sharing standards they already state, researchers should deposit data and code in persistent repositories when possible (with thoughtful exceptions for privacy and sensitivity), cite datasets and software with DOIs, and write clear data/code availability statements, especially when some materials cannot be shared.

Beyond these 'low-hanging fruit,' the authors also argue that realizing the full potential of transparent and reusable climate-risk science—including navigating equity tradeoffs, respecting data rights and privacy, and expanding benefits for early-career scientists—will require that institutions, funders, and professional societies make sustained investments in infrastructure, training, and support.

"I hope our call-to-action helps make open and reusable climate-risk science the default," said Samantha Roth, postdoctoral engineering researcher at Dartmouth and co-author on the study. "I believe many early-career climate-risk scientists are eager for this training no matter what career they ultimately pursue."