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.

Independent verification of anthropogenic influence on specific extreme climate events remains elusive. This study presents a framework for such verification. This framework reveals that previously published results based on a 1961–2005 attribution period frequently underestimate the influence of global warming on the probability of unprecedented extremes during the 2006–2017 period. This underestimation is particularly pronounced for hot and wet events, with greater uncertainty for dry events. The underestimation is reflected in discrepancies between probabilities predicted during the attribution period and frequencies observed during the out-of-sample verification period. These discrepancies are most explained by increases in climate forcing between the attribution and verification periods, suggesting that 21st-century global warming has substantially increased the probability of unprecedented hot and wet events. Hence, the use of temporally lagged periods for attribution—and, more broadly, for extreme event probability quantification—can cause underestimation of historical impacts, and current and future risks.

Researchers including David Lobell analyze how human-caused climate change has impacted a water deficit in Southern Africa and might contribute to a rising food security crisis in the region.

Many international policy problems, including climate change, have been characterized as global public goods. We adopt this theoretical framework to identify the baseline determinants of individual opinion about climate policy. The model implies that support for climate action will be increasing in future benefits, their timing, and the probability that a given country's contribution will make a difference while decreasing in expected costs. Utilizing original surveys in France, Germany, the United Kingdom, and the United States, we provide evidence that expected benefits, costs, and the probability of successful provision as measured by the contribution of other nations are critical for explaining support for climate action. Notably, we find no evidence that the temporality of benefits shapes support for climate action. These results indicate that climate change may be better understood as a static rather than a dynamic public goods problem and suggest strategies for designing policies that facilitate climate cooperation.

Many mountainous and high‐latitude regions have experienced more precipitation as rain rather than snow due to warmer winter temperatures. Further decreases in the annual snow fraction are projected under continued global warming, with potential impacts on flood risk. Here, we quantify the size of streamflow peaks in response to both seasonal and event‐specific rain‐fraction using stream gage observations from watersheds across the western United States. Across the study watersheds, the largest rainfall‐driven streamflow peaks are >2.5 times the size of the largest snowmelt‐driven peaks. Using a panel regression analysis of individual precipitation and snowmelt events, we show that the empirical streamflow response grows approximately exponentially as the liquid precipitation input increases, with rain‐dominated runoff leading to proportionately larger streamflow increases than snowmelt or mixed rain‐and‐snow runoff. We find that the response to changes in rain percentage is largest in the wettest watersheds, where wet antecedent conditions are important for increasing runoff efficiency. Similarly, the effect of rain percentage is larger across watersheds in the Northwest and West regions compared to watersheds in the Northern Rockies and Southwest regions. Overall, as a higher percentage of precipitation falls as rain, increases in the size of rainfall‐driven and “rain‐on‐snow”‐driven floods have the potential to more than offset decreases in the size of snowmelt‐driven floods.

The economic costs of Indonesia’s 2015 forest fires are estimated to exceed US $16 billion, with more than 100,000 premature deaths. On several days the fires emitted more carbon dioxide than the entire United States economy. Here, we combine detailed geospatial data on fire and local climatic conditions with rich administrative data to assess the underlying causes of Indonesia’s forest fires at district and village scales. We find that El Niño events explain most of the year-on-year variation in fire. The creation of new districts increases fire and exacerbates the El Niño impacts on fire. We also find that regional economic growth has gone hand-in-hand with the use of fire in rural districts. We proceed with a 30,000-village case study of the 2015 fire season on Sumatra and Kalimantan and ask which villages, for a given level of spatial fire risk, are more likely to have fire. Villages more likely to burn tend to be more remote, to be considerably less developed, and to have a history of using fire for agriculture. Although central and district level policies and regional economic development have generally contributed to voracious environmental degradation, the close link between poverty and fire at the village level suggests that the current policy push for village development might offer opportunities to reverse this trend.

Our Report draws attention to a complex but understudied issue: How will climate warming alter losses of major food crops to insect pests? Because empirical evidence on plant-insect-climate interactions is scarce and geographically localized, we developed a physiologically based model that incorporates strong and well-established effects of temperature on metabolic rates and on population growth rates. We acknowledged that other factors are involved, but the ones we analyzed are general, robust, and global (1–3).

Parmesan and colleagues argue that our model is overly simplistic and that any general model is premature. They are concerned that our model does not incorporate admittedly idiosyncratic and geographically localized aspects of plant-insect interactions. Some local effects, such as evidence that warmer winters will harm some insects but not others, were in fact evaluated in our sensitivity analyses and shown to be minor (see the Report's Supplementary Materials). Other phenomena, such as plant defenses that benefit some insects and threaten others, are relevant but are neither global nor directional. Furthermore, because Parmesan et al. present no evidence that such idiosyncratic and localized interactions will outweigh the cardinal and universally strong impacts of temperature on populations and on metabolic rates (1–3), their conclusion is subjective.

We agree with Parmesan and colleagues that the question of future crop losses is important and needs further study, that targeted experimental data are needed (as we wrote in our Report), and that our estimates are likely to be conservative (as we concluded, but for reasons different from theirs). However, we strongly disagree with their recommendation to give research priority to gathering localized experimental data. That strategy will only induce a substantial time lag before future crop losses can be addressed.

We draw a lesson from models projecting future climates. Those models lack the “complexity and idiosyncratic nature” of many climate processes, but by building from a few robust principles, they successfully capture the essence of climate patterns and trends (4). Similarly, we hold that the most expeditious and effective way to anticipate crop losses is to develop well-evidenced ecological models and use them to help guide targeted experimental approaches, which can subsequently guide revised ecological models. Experiments and models should be complementary, not sequential.