Recent dramatic and deadly increases in global wildfire activity have increased attention on the causes of wildfires, their consequences, and how risk from wildfire might be mitigated. Here we bring together data on the changing risk and societal burden of wildfire in the United States. We estimate that nearly 50 million homes are currently in the wildland–urban interface in the United States, a number increasing by 1 million houses every 3 y. To illustrate how changes in wildfire activity might affect air pollution and related health outcomes, and how these linkages might guide future science and policy, we develop a statistical model that relates satellite-based fire and smoke data to information from pollution monitoring stations. Using the model, we estimate that wildfires have accounted for up to 25% of PM_2.5 (particulate matter with diameter <2.5 μm) in recent years across the United States, and up to half in some Western regions, with spatial patterns in ambient smoke exposure that do not follow traditional socioeconomic pollution exposure gradients. We combine the model with stylized scenarios to show that fuel management interventions could have large health benefits and that future health impacts from climate-change–induced wildfire smoke could approach projected overall increases in temperature-related mortality from climate change—but that both estimates remain uncertain. We use model results to highlight important areas for future research and to draw lessons for policy.

Precipitation extremes have increased in many regions of the United States, suggesting that climate change may be exacerbating the cost of flooding. However, the impact of historical precipitation change on the cost of US flood damages remains poorly quantified. Applying empirical analysis to historical precipitation and flood damages, we estimate that approximately one-third (36%) of the cost of flood damages over 1988 to 2017 is a result of historical precipitation changes. Climate models show that anthropogenic climate change has increased the probability of heavy precipitation associated with these costs. Our results provide information quantifying the costs of climate change, and suggest that lower levels of future warming would very likely reduce flooding losses relative to the current global warming trajectory.

Cloud computing and freely available, high-resolution satellite data have enabled recent progress in crop yield mapping at fine scales. However, extensive validation data at a matching resolution remain uncommon or infeasible due to data availability. This has limited the ability to evaluate different yield estimation models and improve understanding of key features useful for yield estimation in both data-rich and data-poor contexts. Here, we assess machine learning models’ capacity for soybean yield prediction using a unique ground-truth dataset of high-resolution (5 m) yield maps generated from combine harvester yield monitor data for over a million field-year observations across the Midwestern United States from 2008 to 2018. First, we compare random forest (RF) implementations, testing a range of feature engineering approaches using Sentinel-2 and Landsat spectral data for 20- and 30-m scale yield prediction. We find that Sentinel-2-based models can explain up to 45% of out-of-sample yield variability from 2017 to 2018 (r² = 0.45), while Landsat models explain up to 43% across the longer 2008–2018 period. Using discrete Fourier transforms, or harmonic regressions, to capture soybean phenology improved the Landsat-based model considerably. Second, we compare RF models trained using this ground-truth data to models trained on available county-level statistics. We find that county-level models rely more heavily on just a few predictors, namely August weather covariates (vapor pressure deficit, rainfall, temperature) and July and August near-infrared observations. As a result, county-scale models perform relatively poorly on field-scale validation (r² = 0.32), especially for high-yielding fields, but perform similarly to field-scale models when evaluated at the county scale (r² = 0.82). Finally, we test whether our findings on variable importance can inform a simple, generalizable framework for regions or time periods beyond ground data availability. To do so, we test improvements to a Scalable Crop Yield Mapper (SCYM) approach that uses crop simulations to train statistical models for yield estimation. Based on findings from our RF models, we employ harmonic regressions to estimate peak vegetation index (VI) and a VI observation 30 days later, with August rainfall as the sole weather covariate in our new SCYM model. Modifications improved SCYM’s explained variance (r² = 0.27 at the 30 m scale) and provide a new, parsimonious model.

As climate change leads to increased frequency and severity of drought in many agricultural regions, a prominent adaptation goal is to reduce the drought sensitivity of crop yields. Yet many of the sources of average yield gains are more effective in good weather, leading to heightened drought sensitivity. Here we consider two empirical strategies for detecting changes in drought sensitivity and apply them to maize in the United States, a crop that has experienced myriad management changes including recent adoption of drought-tolerant varieties. We show that a strategy that utilizes weather-driven temporal variations in drought exposure is inconclusive because of the infrequent occurrence of substantial drought. In contrast, a strategy that exploits within-county spatial variability in drought exposure, driven primarily by differences in soil water storage capacity, reveals robust trends over time. Yield sensitivity to soil water storage increased by 55% on average across the US Corn Belt since 1999, with larger increases in drier states. Although yields have been increasing under all conditions, the cost of drought relative to good weather has also risen. These results highlight the difficulty of simultaneously raising average yields and lowering drought sensitivity.

We study a program that funded 39,000 Jewish households in New York City to leave enclave neighborhoods circa 1910. Compared to their neighbors with the same occupation and income score at baseline, program participants earned 4 percent more ten years after removal, and these gains persisted to the next generation. Men who left enclaves also married spouses with less Jewish names, but they did not choose less Jewish names for their children. Gains were largest for men who spent more years outside of an enclave. Our results suggest that leaving ethnic neighborhoods could facilitate economic advancement and assimilation into the broader society, but might make it more difficult to retain cultural identity.

Research has consistently identified firearm availability as a risk factor for suicide. However, existing studies are relatively small in scale, estimates vary widely, and no study appears to have tracked risks from commencement of firearm ownership.

Recent dramatic and deadly increases in global wildfire activity have increased attention on the causes of wildfires, their consequences, and how risk from fire might be mitigated. Here we bring together data on the changing risk and societal burden of wildfire in the US. We estimate that nearly 50 million homes are currently in the wildland-urban interface in the US, a number increasing by 1 million houses every 3 years. Using a statistical model that links satellite-based fire and smoke data to pollution monitoring stations, we estimate that wildfires have accounted for up to 25% of PM_2.5 in recent years across the US, and up to half in some Western regions. We then show that ambient exposure to smoke-based PM_2.5 does not follow traditional socioeconomic exposure gradients. Finally, using stylized scenarios, we show that fuels management interventions have large but uncertain impacts on health outcomes, and that future health impacts from climate-change-induced wildfire smoke could approach projected overall increases in temperature-related mortality from climate change. We draw lessons for research and policy.

Floods and other climate hazards pose a widespread and growing threat to housing and infrastructure around the world. By incorporating climate risk into asset prices, markets can discourage excessive development in hazardous areas. However, the extent to which markets actually price these risks remains poorly understood. Here we measure the effect of information about flood risk on residential property values in the United States. Using multiple empirical approaches and two decades of sales data covering the universe of homes in the US, we find little evidence that housing markets fully price information about flood risk in aggregate. However, the price penalty for flood risk is larger for commercial buyers and in states where sellers must disclose information about flood risk to potential buyers, suggesting that policies to improve risk communication could influence market outcomes. Our findings indicate that floodplain homes in the US are currently overvalued by a total of $34B, raising concerns about the stability of real estate markets as climate risks become more salient and severe.

California has experienced devastating autumn wildfires in recent years. These autumn wildfires have coincided with extreme fire weather conditions during periods of strong offshore winds coincident with unusually dry vegetation enabled by anomalously warm conditions and late onset of autumn precipitation. In this study, we quantify observed changes in the occurrence and magnitude of meteorological factors that enable extreme autumn wildfires in California, and use climate model simulations to ascertain whether these changes are attributable to human-caused climate change. We show that state-wide increases in autumn temperature (~1 ˚C) and decreases in autumn precipitation (~30%) over the past four decades have contributed to increases in aggregate fire weather indices (+20%). As a result, the observed frequency of autumn days with extreme (95th percentile) fire weather – which we show are preferentially associated with extreme autumn wildfires – has more than doubled in California since the early 1980s. We further find an increase in the climate model-estimated probability of these extreme autumn conditions since ~1950, including a long-term trend toward increased same-season co-occurrence of extreme fire weather conditions in northern and southern California. Our climate model analyses suggest that continued climate change will further amplify the number of days with extreme fire weather by the end of this century, though a pathway consistent with the UN Paris commitments would substantially curb that increase. Given the acute societal impacts of extreme autumn wildfires in recent years, our findings have critical relevance for ongoing efforts to manage wildfire risks in California and other regions.