In April 2023, a report from the President’s Council of Advisors on Science and Technology warned that “when it comes to assessing the future risks from extreme weather, America is flying blind.” Two years on, it would be overly generous to say that America is still flying at all on that front. The Trump administration has kneecapped the federal government’s already limited ability to understand the impact of climate change on the country, firing top scientists and canceling major grants and research efforts such as the National Climate Assessment. The National Oceanic and Atmospheric Administration—reportedly targeted for a 27 percent budget cut—has stopped updating its database of weather and climate disasters that cause more than $1 billion worth of damage.
As temperatures rise, wildly expensive disasters are becoming increasingly common. Through the 1980s, the United States experienced roughly three weather events per year that cost upward of a billion dollars. Over the last five years, the annual average was 24. Scientists and nonprofits are now rushing to stockpile federal climate and weather data, fearful that the administration could soon erase it entirely. That information—and the ability to reliably collect and interpret it moving forward—isn’t of interest only to researchers and green groups. Whether it comes directly from federal websites or through proprietary climate risk modeling, climate and weather data is foundational to how governments, investors, and corporations understand the future and plan to navigate it. NOAA, NASA, and other federal agencies provide the information that helps cities decide how high to build bridges and even the credit ratings that determine whether corporations and governments can finance new building projects. Federal climate data helps insurance companies determine how much to charge homeowners for new or renewed policies. The system by which all that data gets translated onto balance sheets and monthly bills was already flawed, lacking adequate accountability and coordination. As the White House declares war on climate policy, it could break down entirely.
Gathering real-time data on weather and the earth’s climate is a massive undertaking, requiring expansive networks of federal satellites, buoys, balloons, and aircraft; staff to monitor, operate, and maintain that equipment; and teams of interdisciplinary researchers to interpret and organize the information collected, align it with historical datasets, observe trends, and use supercomputers to model how the earth’s climate might behave in the future. Downstream are private companies that “downscale” that data and modeling in order to analyze how those larger patterns could affect specific assets like apartment complexes or municipal buildings. Companies offer these more boutique, “asset-level” models to customers who use them to make planning and investment decisions. First Street, for instance—among the largest of these firms—partners with Redfin and Zillow to provide information on flood risk for real estate listings.
The role federal research plays in this elaborate operation is essential. “We can’t expect the private sector to step up and replace global climate data,” said Madison Condon, an associate professor of law at Boston University who studies the market for climate risk data. Although the private-sector models can provide valuable supplements, local governments, especially—which tend to lack in-house experts to model their climate risk, and the funds to pay for those services—depend on federal research. “Town managers and local zoning commissions very regularly rely on NOAA’s sea level rise maps,” Condon explained. “City managers use the National Climate Assessment as a high-level description of what the future will look like.”
While private climate risk analysts use publicly generated data to make their models, the way those companies actually create the products they offer is proprietary. The firms operate in something of a regulatory black box and generally aren’t subject to peer review processes. Outside researchers who might want to reproduce certain models to test their accuracy generally can’t, because companies’ methodologies are protected as trade secrets. Academics have also raised serious concerns about the accuracy of translating global climate models into granular, asset-level analyses to determine whether to take out a 30-year mortgage, for example. There are very few public risk models to judge these products against, and different companies often produce divergent analyses. A 2024 Bloomberg Green analysis of two flood-risk models found that when considering the vulnerability of certain areas of Los Angeles County to a once-in-a-century flood event, the models matched just 21 percent of the time. As such information becomes an important factor for things like insurance underwriting and federal funding applications, municipal governments have a lot to lose. So do homeowners who might want to negotiate lower premiums.
Even companies in the climate risk business are wary of federal data being replicated by private companies. Frank Nutter, president of the Reinsurance Association of America, told Bloomberg in recent weeks that the private sector can’t recreate NOAA’s billion-dollar disaster database or its continuously updated maps and charts, which “are more accessible and meaningful to the public.”
Some insurers have already signaled too that relying more on private data might cause them to raise premium costs. Brian Espie—chief underwriting officer at the insurance provider Kettle—told the trade publication Insurance Business that his company “was built up around a proprietary wildfire model, and much of our data sources are from public, government-supported entities.” If those disappear, companies like Kettle will need to pay for whatever private data is available, and invest more in in-house expertise to analyze it. “Anytime you increase costs for insurers, those costs get borne … by the policyholders,” Espie added. “Over time, that is going to make insurance more expensive for homeowners and business owners.”
Breakdowns in the collection and reliability of federal data could present other, novel challenges. So-called parametric risk products, which issue payouts based on factors like wind speed or barometric pressure, have become increasingly popular with local governments, individual property owners, and reinsurance companies that provide insurance to insurers in order to offset their own risk. Without reliable and objective data, it could be difficult to determine whether those thresholds have been reached.
Private climate risk modelers are also increasingly leaning on large language models, or LLMs, in order to predict the behavior of storms and analyze risk, leading companies like Microsoft, Google, and Nvidia to roll out their own AI-branded forecasting models. While LLMs have helped spur some promising advances in predicting the weather, these products are still generally based on data generated by the public sector. If that data ceases to exist—or if it becomes outdated and unreliable due to cuts—then proprietary modeling could start to break down in ways that may not be immediately obvious to the customers who use those products, or to the researchers unable to assess how their results are tabulated. “AI is all well and good, but you still need good-quality inputs,” Condon told me. “AI can only be a supplement, not a replacement.”
Even before Trump 2.0, government researchers struggled to assess America’s vulnerability to extreme weather as temperatures rise. That’s partially because predictive modeling is based on relatively small historical datasets, and rising temperatures are changing how extreme weather behaves in unpredictable ways. Storms are slower and dump more rain, while wildfires burn hotter, faster, and at unusual times of year. In its 2023 report on extreme weather risk, the President’s Council of Advisors on Science and Technology urged that more coordination between NOAA, FEMA, and other federal agencies was needed to provide “more accurate and actionable information to guide decision-making and policy at all levels,” and remedy the “lack of high-quality estimates of extreme weather probabilities for most locations and types of events.” The council also explicitly noted the limitations of private-sector climate risk modeling, and the importance of interagency coordination for improving its reliability. “While a burgeoning industry is beginning to provide climate risk information, much of this is of questionable quality,” the report notes, “either because it has not been transparently skill-scored to show that it can predict past events, or because it relies on methods that have been shown by the academic literature to have significant bias.”
The stakes of accurate climate data are enormously high. As traditional mortgage lenders deny loans to prospective buyers in higher-risk areas, online “fintech” lenders have taken the opposite approach, offering financing on terms that may undercount the risk certain properties face from wildfires and other hazards. To offset their own risk, some lenders have also begun selling off their riskier mortgages, including in coastal areas, to Fannie Mae and Freddie Mac. A coming wave of climate-related foreclosures—which First Street predicts could soar 380 percent over the coming decade, and account for up to 30 percent of foreclosures—threatens not just housing but, potentially, the financial system more broadly.
The Trump administration is waging war on climate and extreme weather data as that information becomes increasingly valuable to the insurance and real estate sectors. With few realistic alternatives to the critical research performed by NOAA, NASA, and other federal agencies, it won’t be developers and underwriters, but homeowners and renters, who pay the steepest costs for the White House’s climate denial.
