Flood exposure has been linked to shifts in population sizes and composition. Traditionally, these changes have been observed at a local level providing insight to local dynamics but not general trends, or at a coarse resolution that does not capture localized shifts. Using historic flood data between 2000-2023 across the Contiguous United States (CONUS), we identify the relationships between flood exposure and population change. We demonstrate that observed declines in population are statistically associated with higher levels of historic flood exposure, which may be subsequently coupled with future population projections. Several locations have already begun to see population responses to observed flood exposure and are forecasted to have decreased future growth rates as a result. Finally, we find that exposure to high frequency flooding (5 and 20-year return periods) results in 2-7% lower growth rates than baseline projections. This is exacerbated in areas with relatively high exposure to frequent flooding where growth is expected to decline over the next 30 years.

The core tools of science (data, software, and computers) are undergoing a rapid and historic evolution, changing what questions scientists ask and how they find answers. Earth science data are being transformed into new formats optimized for cloud storage that enable rapid analysis of multi‐petabyte data sets. Data sets are moving from archive centers to vast cloud data storage, adjacent to massive server farms. Open source cloud‐based data science platforms, accessed through a web‐browser window, are enabling advanced, collaborative, interdisciplinary science to be performed wherever scientists can connect to the internet. Specialized software and hardware for machine learning and artificial intelligence are being integrated into data science platforms, making them more accessible to average scientists. Increasing amounts of data and computational power in the cloud are unlocking new approaches for data‐driven discovery. For the first time, it is truly feasible for scientists to bring their analysis to data in the cloud without specialized cloud computing knowledge. This shift in paradigm has the potential to lower the threshold for entry, expand the science community, and increase opportunities for collaboration while promoting scientific innovation, transparency, and reproducibility. Yet, we have all witnessed promising new tools which seem harmless and beneficial at the outset become damaging or limiting. What do we need to consider as this new way of doing science is evolving?

The methodology used by the First Street Foundation Wildfire Model (FSF-WFM) to compute estimates of the 30-year, climate-adjusted aggregate wildfire hazard for the contiguous United States at 30 m horizontal resolution is presented. The FSF-WFM integrates several existing methods from the wildfire science community and implements computationally efficient and scalable modeling techniques to allow for new high-resolution, CONUS-wide hazard generation. Burn probability, flame length, and ember spread for the years 2022 and 2052 are computed from two ten-year representative Monte Carlo simulations of wildfire behavior, utilizing augmented LANDFIRE fuel estimates updated with all the available disturbance information. FSF-WFM utilizes ELMFIRE, an open-source, Rothermel-based wildfire behavior model, and multiple US Federal Government open data sources to drive the simulations. LANDFIRE non-burnable fuel classes within the wildland–urban interface (WUI) are replaced with fuel estimates from machine-learning models, trained on data from historical fires, to allow the propagation of wildfire through the WUI in the model. Historical wildfire ignition locations and NOAA’s hourly time series of surface weather at 2.5 km resolution are used to drive ELMFIRE to produce wildfire hazards representative of the 2022 and 2052 conditions at 30 m resolution, with the future weather conditions scaled to the IPCC CMIP5 RCP4.5 model ensemble predictions. Winds and vegetation were held constant between the 2022 and 2052 simulations, and climate change’s impacts on the future fuel conditions are the main contributors to the changes observed in the 2052 results. Non-zero wildfire exposure is estimated for 71.8 million out of 140 million properties across CONUS. Climate change impacts add another 11% properties to this non-zero exposure class over the next 30 years, with much of this change observed in the forested areas east of the Mississippi River. “Major” aggregate wildfire exposure of greater than 6% over the 30-year analysis period from 2022 to 2052 is estimated for 10.2 million properties. The FSF-WFM represents a notable contribution to the ability to produce property-specific, climate-adjusted wildfire risk assessments in the US.

Surface air temperature is an important variable in quantifying extreme heat, but high-resolution temporal and spatial measurement is limited by sparse climate-data stations. As a result, hyperlocal models of extreme heat involve intensive physical data collection efforts or analyze satellite-derived land-surface temperature instead. We developed a geostatistical model that integrates in situ climate-quality temperature records, gridded temperature data, land-surface temperature estimates, and spatially consistent covariates to predict monthly averaged daily maximum surface-air temperatures at spatial resolutions up to 30 m. We trained and validated the model using data from North Carolina. The fitted model showed strong predictive performance with a mean absolute error of 1.61 ${}^{\circ }$F across all summer months and a correlation coefficient of 0.75 against an independent hyperlocal temperature model for the city of Durham. We show that the proposed model framework is highly scalable and capable of producing realistic temperature fields across a variety of physiographic settings, even in areas where no climate-quality data stations are available.

Accurate representation of fire emissions and smoke transport is crucial for current and future wildfire-smoke projections. We present a flexible modeling framework for emissions sourced from the First Street Foundation Wildfire Model (FSF-WFM) to provide a national map for near-surface smoke conditions exceeding the threshold for unhealthy concentrations of particulate matter at or less than 2.5 µm, or PM_2.5. Smoke yield from simulated fires is converted to emissions transported by the National Oceanic and Atmospheric Administration’s HYSPLIT model. We present a strategy for sampling from a simulation of ~65 million individual fires, to depict the occurrence of “unhealthy smoke days” defined as 24-h average PM_2.5 concentration greater than 35.4 µg/m³ from HYSPLIT. The comparison with historical smoke simulations finds reasonable agreement using only a small subset of simulated fires. The total amount of PM_2.5 mass-released threshold of 10¹⁵ µg was found to be effective for simulating the occurrence of unhealthy days without significant computational burden.

Unhealthy air quality conditions can strongly affect long-term human health and wellbeing, yet many air quality data products focus on near real-time alerts or short-term forecasts. Understanding the full state of air quality also requires examining the longer term frequency and intensity of poor air quality at ground level, and how it might change over time. We present a new modeling framework to compute climate-adjusted estimates of air quality hazards for the contiguous United States (CONUS) at 10 km horizontal resolution. The framework blends results from statistical, machine-learning, and climate-chemistry models—including a bias-adjusted version of the EPA Community Multiscale Air Quality Model (CMAQ) time series as described in (Wilson et al., 2022)—for ground-level ozone, anthropogenic fine particulate matter (PM2.5), and wildfire smoke PM2.5 into consistent estimates of days exceeding the “unhealthy for sensitive groups” (orange colored) classification on the EPA Air Quality Index for 2023 and 2053. We find that joint PM2.5 and ozone orange+ days range from 1 day to 41 days across CONUS, with a median value of 2 days, across all years. Considering all properties across CONUS, we find that 63.5% percent are exposed to at least one orange or greater day in 2023, growing to 72.1% in 2053. For a 7-day threshold, 3.8% and 5.7% of properties are exposed in 2023 and 2053, respectively. Our results also support the identification of which parts of the country are most likely to be impacted by additional climate-related air quality risks. With growing evidence that even low levels of air pollution are harmful, these results are an important step forward in empowering individuals to understand their air quality risks both now and into the future.