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Estimates of global economic damage caused by carbon dioxide (CO 2 ) emissions can inform climate policy 1 – 3 . The social cost of carbon (SCC) quantifies these damages by characterizing how additional CO 2 emissions today impact future economic outcomes through altering the climate 4 – 6 . Previous estimates have suggested that large, warming-driven increases in energy expenditures could dominate the SCC 7 , 8 , but they rely on models 9 – 11 that are spatially coarse and not tightly linked to data 2 , 3 , 6 , 7 , 12 , 13 . Here we show that the release of one ton of CO 2 today is projected to reduce total future energy expenditures, with most estimates valued between −US$3 and −US$1, depending on discount rates. Our results are based on an architecture that integrates global data, econometrics and climate science to estimate local damages worldwide. Notably, we project that emerging economies in the tropics will dramatically increase electricity consumption owing to warming, which requires critical infrastructure planning. However, heating reductions in colder countries offset this increase globally. We estimate that 2099 annual global electricity consumption increases by about 4.5 exajoules (7 per cent of current global consumption) per one-degree-Celsius increase in global mean surface temperature (GMST), whereas direct consumption of other fuels declines by about 11.3 exajoules (7 per cent of current global consumption) per one-degree-Celsius increase in GMST. Our finding of net savings contradicts previous research 7 , 8 , because global data indicate that many populations will remain too poor for most of the twenty-first century to substantially increase energy consumption in response to warming. Importantly, damage estimates would differ if poorer populations were given greater weight 14 . Using global data, econometrics and climate science to estimate the damages induced by the emission of one ton of carbon dioxide, climate change is projected to increase electricity spending but reduce overall end-use energy expenditure.

Disaster losses are increasing and evidence is mounting that climate change is driving up the probability of extreme natural shocks 1 – 3 . Yet it has also proved politically expedient to invoke climate change as an exogenous force that supposedly places disasters beyond the influence of local and national authorities 4 , 5 . However, locally determined patterns of urbanization and spatial development are key factors to the exposure and vulnerability of people to climatic shocks 6 . Using high-resolution annual data, this study shows that, since 1985, human settlements around the world—from villages to megacities—have expanded continuously and rapidly into present-day flood zones. In many regions, growth in the most hazardous flood zones is outpacing growth in non-exposed zones by a large margin, particularly in East Asia, where high-hazard settlements have expanded 60% faster than flood-safe settlements. These results provide systematic evidence of a divergence in the exposure of countries to flood hazards. Instead of adapting their exposure, many countries continue to actively amplify their exposure to increasingly frequent climatic shocks. Analysis of high-resolution annual data shows that global human settlements have expanded continuously and rapidly into flood zones, with those in the most hazardous zones increasing by 122% from 1985 to 2015.

Climate extremes are escalating under anthropogenic climate change 1 . Yet, how this translates into unprecedented cumulative extreme event exposure in a person’s lifetime remains unclear. Here we use climate models, impact models and demographic data to project the number of people experiencing cumulative lifetime exposure to climate extremes above the 99.99th percentile of exposure expected in a pre-industrial climate. We project that the birth cohort fraction facing this unprecedented lifetime exposure to heatwaves, crop failures, river floods, droughts, wildfires and tropical cyclones will at least double from 1960 to 2020 under current mitigation policies aligned with a global warming pathway reaching 2.7 °C above pre-industrial temperatures by 2100. Under a 1.5 °C pathway, 52% of people born in 2020 will experience unprecedented lifetime exposure to heatwaves. If global warming reaches 3.5 °C by 2100, this fraction rises to 92% for heatwaves, 29% for crop failures and 14% for river floods. The chance of facing unprecedented lifetime exposure to heatwaves is substantially larger among population groups characterized by high socioeconomic vulnerabilities. Our results call for deep and sustained greenhouse gas emissions reductions to lower the burden of climate change on current young generations. Climate models, impact models and demographic data are used to estimate the number of people projected to experience unprecedented lifetime exposure to extreme climate events across multiple dimensions, including birth year, warming scenario and vulnerability.

Land change is a cause and consequence of global environmental change 1 , 2 . Changes in land use and land cover considerably alter the Earth’s energy balance and biogeochemical cycles, which contributes to climate change and—in turn—affects land surface properties and the provision of ecosystem services 1 – 4 . However, quantification of global land change is lacking. Here we analyse 35 years’ worth of satellite data and provide a comprehensive record of global land-change dynamics during the period 1982–2016. We show that—contrary to the prevailing view that forest area has declined globally 5 —tree cover has increased by 2.24 million km 2 (+7.1% relative to the 1982 level). This overall net gain is the result of a net loss in the tropics being outweighed by a net gain in the extratropics. Global bare ground cover has decreased by 1.16 million km 2 (−3.1%), most notably in agricultural regions in Asia. Of all land changes, 60% are associated with direct human activities and 40% with indirect drivers such as climate change. Land-use change exhibits regional dominance, including tropical deforestation and agricultural expansion, temperate reforestation or afforestation, cropland intensification and urbanization. Consistently across all climate domains, montane systems have gained tree cover and many arid and semi-arid ecosystems have lost vegetation cover. The mapped land changes and the driver attributions reflect a human-dominated Earth system. The dataset we developed may be used to improve the modelling of land-use changes, biogeochemical cycles and vegetation–climate interactions to advance our understanding of global environmental change 1 – 4 , 6 . Satellite data for the period 1982–2016 reveal changes in land use and land cover at global and regional scales that reflect patterns of land change indicative of a human-dominated Earth system.

Category 4 landfalling hurricane Harvey poured more than a metre of rainfall across the heavily populated Houston area, leading to unprecedented flooding and damage. Although studies have focused on the contribution of anthropogenic climate change to this extreme rainfall event 1 – 3 , limited attention has been paid to the potential effects of urbanization on the hydrometeorology associated with hurricane Harvey. Here we find that urbanization exacerbated not only the flood response but also the storm total rainfall. Using the Weather Research and Forecast model—a numerical model for simulating weather and climate at regional scales—and statistical models, we quantify the contribution of urbanization to rainfall and flooding. Overall, we find that the probability of such extreme flood events across the studied basins increased on average by about 21 times in the period 25–30 August 2017 because of urbanization. The effect of urbanization on storm-induced extreme precipitation and flooding should be more explicitly included in global climate models, and this study highlights its importance when assessing the future risk of such extreme events in highly urbanized coastal areas. Modelling the contribution of urbanization to the impacts associated with hurricane Harvey in August 2017 shows that urbanization worsens rainfall and flooding.

Climate change threatens global food systems 1 , but the extent to which adaptation will reduce losses remains unknown and controversial 2 . Even within the well-studied context of US agriculture, some analyses argue that adaptation will be widespread and climate damages small 3 , 4 , whereas others conclude that adaptation will be limited and losses severe 5 , 6 . Scenario-based analyses indicate that adaptation should have notable consequences on global agricultural productivity 7 , 8 – 9 , but there has been no systematic study of how extensively real-world producers actually adapt at the global scale. Here we empirically estimate the impact of global producer adaptations using longitudinal data on six staple crops spanning 12,658 regions, capturing two-thirds of global crop calories. We estimate that global production declines 5.5 × 10 14  kcal annually per 1 °C global mean surface temperature (GMST) rise (120 kcal per person per day or 4.4% of recommended consumption per 1 °C; P  < 0.001). We project that adaptation and income growth alleviate 23% of global losses in 2050 and 34% at the end of the century (6% and 12%, respectively; moderate-emissions scenario), but substantial residual losses remain for all staples except rice. In contrast to analyses of other outcomes that project the greatest damages to the global poor 10 , 11 , we find that global impacts are dominated by losses to modern-day breadbaskets with favourable climates and limited present adaptation, although losses in low-income regions losses are also substantial. These results indicate a scale of innovation, cropland expansion or further adaptation that might be necessary to ensure food security in a changing climate. Analysis of data on six stable crops, capturing two-thirds of global crop calories, allows estimation of agricultural impacts and the potential of global producer adaptations to reduce output losses owing to climate change.

Forest ecosystems depend on their capacity to withstand and recover from natural and anthropogenic perturbations (that is, their resilience) 1 . Experimental evidence of sudden increases in tree mortality is raising concerns about variation in forest resilience 2 , yet little is known about how it is evolving in response to climate change. Here we integrate satellite-based vegetation indices with machine learning to show how forest resilience, quantified in terms of critical slowing down indicators 3 – 5 , has changed during the period 2000–2020. We show that tropical, arid and temperate forests are experiencing a significant decline in resilience, probably related to increased water limitations and climate variability. By contrast, boreal forests show divergent local patterns with an average increasing trend in resilience, probably benefiting from warming and CO 2 fertilization, which may outweigh the adverse effects of climate change. These patterns emerge consistently in both managed and intact forests, corroborating the existence of common large-scale climate drivers. Reductions in resilience are statistically linked to abrupt declines in forest primary productivity, occurring in response to slow drifting towards a critical resilience threshold. Approximately 23% of intact undisturbed forests, corresponding to 3.32 Pg C of gross primary productivity, have already reached a critical threshold and are experiencing a further degradation in resilience. Together, these signals reveal a widespread decline in the capacity of forests to withstand perturbation that should be accounted for in the design of land-based mitigation and adaptation plans.

Climate change has led to concerns about increasing river floods resulting from the greater water-holding capacity of a warmer atmosphere. These concerns are reinforced by evidence of increasing economic losses associated with flooding in many parts of the world, including Europe. Any changes in river floods would have lasting implications for the design of flood protection measures and flood risk zoning. However, existing studies have been unable to identify a consistent continental-scale climatic-change signal in flood discharge observations in Europe, because of the limited spatial coverage and number of hydrometric stations. Here we demonstrate clear regional patterns of both increases and decreases in observed river flood discharges in the past five decades in Europe, which are manifestations of a changing climate. Our results-arising from the most complete database of European flooding so far-suggest that: increasing autumn and winter rainfall has resulted in increasing floods in northwestern Europe; decreasing precipitation and increasing evaporation have led to decreasing floods in medium and large catchments in southern Europe; and decreasing snow cover and snowmelt, resulting from warmer temperatures, have led to decreasing floods in eastern Europe. Regional flood discharge trends in Europe range from an increase of about 11 per cent per decade to a decrease of 23 per cent. Notwithstanding the spatial and temporal heterogeneity of the observational record, the flood changes identified here are broadly consistent with climate model projections for the next century, suggesting that climate-driven changes are already happening and supporting calls for the consideration of climate change in flood risk management.

The threats of old are still the dominant drivers of current species loss, indicates an analysis of IUCN Red List data by Sean Maxwell and colleagues.

Crop production is a large source of atmospheric ammonia (NH 3 ), which poses risks to air quality, human health and ecosystems 1 – 5 . However, estimating global NH 3 emissions from croplands is subject to uncertainties because of data limitations, thereby limiting the accurate identification of mitigation options and efficacy 4 , 5 . Here we develop a machine learning model for generating crop-specific and spatially explicit NH 3 emission factors globally (5-arcmin resolution) based on a compiled dataset of field observations. We show that global NH 3 emissions from rice, wheat and maize fields in 2018 were 4.3 ± 1.0 Tg N yr −1 , lower than previous estimates that did not fully consider fertilizer management practices 6 – 9 . Furthermore, spatially optimizing fertilizer management, as guided by the machine learning model, has the potential to reduce the NH 3 emissions by about 38% (1.6 ± 0.4 Tg N yr −1 ) without altering total fertilizer nitrogen inputs. Specifically, we estimate potential NH 3 emissions reductions of 47% (44–56%) for rice, 27% (24–28%) for maize and 26% (20–28%) for wheat cultivation, respectively. Under future climate change scenarios, we estimate that NH 3 emissions could increase by 4.0 ± 2.7% under SSP1–2.6 and 5.5 ± 5.7% under SSP5–8.5 by 2030–2060. However, targeted fertilizer management has the potential to mitigate these increases. A machine learning model for generating crop-specific and spatially explicit NH 3 emission factors globally shows that global NH 3 emissions in 2018 were lower than previous estimates that did not fully consider fertilizer management practices.