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Observed increases in wildfire activity across the contiguous United States (U.S.), together with continued warming and expanding development in fire-prone landscapes, highlight the need to anticipate near-term changes in fire regimes. We apply a Bayesian statistical model that integrates projected population density (SSP2) and downscaled climate simulations under a moderate emissions scenario (RCP 4.5) to estimate future wildfire occurrence, maximum fire size (using the 90th percentile of fire size distribution), and total area burned for large fires (>1000 acres) across all EPA Level III ecoregions for 2020–2060. Relative to 1984–2019, we project nationwide increases of 56% in fire occurrence and 59% in area burned, with larger increases in maximum fire size (63%) in 2020–2060. Spatial patterns vary substantially: fire occurrence increases most strongly in the eastern U.S., including regions where large fires have historically been rare, while western ecoregions experience the largest absolute increases in burned area and extreme fire size. The disproportionate growth in maximum fire size suggests that changes in fire weather will amplify extreme events beyond increases in ignition frequency alone. These projections indicate expanding wildfire risk across diverse U.S. landscapes and underscore the need for regionally tailored fire management and preparedness strategies. 

Ecosystems carry memory through the material remains of organisms. Foundation species—trees, grasses, corals, and oysters—while alive are central to ecosystem structure, and the remains of these organisms continue to influence ecological processes after death. We conducted, to our knowldge, the first continental-scale exploration of how dead foundation species influence living conspecifics, leveraging long-term experiments and observations (2 to 32 years) from 10 ecosystems (five terrestrial and five marine) across the US Long Term Ecological Research Network. We found that material legacies commonly alter demographic processes, ranging from a 50% reduction to a 12-fold increase. The ubiquity of these postmortem effects reveals an underappreciated dimension of ecological memory that shapes pathways of ecosystem resilience, reorganization, and collapse, providing a critical management tool in an era of increasing disturbance- and climate-driven mortality events. 

The most destructive and deadly wildfires in US history were also fast. Using satellite data, we analyzed the daily growth rates of more than 60,000 fires from 2001 to 2020 across the contiguous US. Nearly half of the ecoregions experienced destructive fast fires that grew more than 1620 hectares in 1 day. These fires accounted for 78% of structures destroyed and 61% of suppression costs ($18.9 billion). From 2001 to 2020, the average peak daily growth rate for these fires more than doubled (+249% relative to 2001) in the Western US. Nearly 3 million structures were within 4 kilometers of a fast fire during this period across the US. Given recent devastating wildfires, understanding fast fires is crucial for improving firefighting strategies and community preparedness. 

Global climate change and associated environmental extremes present a pressing need to understand and predict social–environmental impacts while identifying opportunities for mitigation and adaptation. In support of informing a more resilient future, emerging data analytics technologies can leverage the growing availability of Earth observations from diverse data sources ranging from satellites to sensors to social media. Yet, there remains a need to transition from research for knowledge gain to sustained operational deployment. In this paper, we present a research-to-commercialization (R2C) model and conduct a case study using it to address the wicked wildfire problem through an industry–university partnership. We systematically evaluated 39 different user stories across eight user personas and identified information gaps in public perception and dynamic risk. We discuss utility and challenges in deploying such a model as well as the relevance of the findings from this use case. We find that research-to-commercialization is non-trivial and that academic–industry partnerships can facilitate this process provided there is a clear delineation of (i) intellectual property rights; (ii) technical deliverables that help overcome cultural differences in working styles and reward systems; and (iii) a method to both satisfy open science and protect proprietary information and strategy. The R2C model presented provides a basis for directing solutions-oriented science in support of value-added analytics that can inform a more resilient future. 

Modern science depends on computers, but not all scientists have access to the scale of computation they need. A digital divide separates scientists who accelerate their science using large cyberinfrastructure from those who do not, or who do not have access to the compute resources or learning opportunities to develop the skills needed. The exclusionary nature of the digital divide threatens equity and the future of innovation by leaving people out of the scientific process while over-amplifying the voices of a small group who have resources. However, there are potential solutions: recent advancements in public research cyberinfrastructure and resources developed during the open science revolution are providing tools that can help bridge this divide. These tools can enable access to fast and powerful computation with modest internet connections and personal computers. Here we contribute another resource for narrowing the digital divide: scalable virtual machines running on public cloud infrastructure. We describe the tools, infrastructure, and methods that enabled successful deployment of a reproducible and scalable cyberinfrastructure architecture for a collaborative data synthesis working group in February 2023. This platform enabled 45 scientists with varying data and compute skills to leverage 40,000 hours of compute time over a 4-day workshop. Our approach provides an open framework that can be replicated for educational and collaborative data synthesis experiences in any data- and compute-intensive discipline.