Effect of warming temperatures on US wheat yields

Climate change is expected to increase future temperatures, potentially resulting in reduced crop production in many key production regions. Research quantifying the complex relationship between weather variables and wheat yields is rapidly growing, and recent advances have used a variety of model specifications that differ in how temperature data are included in the statistical yield equation. A unique data set that combines Kansas wheat variety field trial outcomes for 1985–2013 with location-specific weather data is used to analyze the effect of weather on wheat yield using regression analysis. Our results indicate that the effect of temperature exposure varies across the September−May growing season. The largest drivers of yield loss are freezing temperatures in the Fall and extreme heat events in the Spring. We also find that the overall effect of warming on yields is negative, even after accounting for the benefits of reduced exposure to freezing temperatures. Our analysis indicates that there exists a tradeoff between average (mean) yield and ability to resist extreme heat across varieties. More-recently released varieties are less able to resist heat than older lines. Our results also indicate that warming effects would be partially offset by increased rainfall in the Spring. Finally, we find that the method used to construct measures of temperature exposure matters for both the predictive performance of the regression model and the forecasted warming impacts on yields.The potential impact of global warming and climate change on socioeconomic outcomes has become an important and growing area of scientific study and evaluation. Separate lines of study include quantifying the likely impact of climatic change on measures of civil conflict (1–5) and agricultural land values, profitability, and/or production efficiency (6–22). Both lines of literature continue to measure, discuss, and debate the effects of warming temperature. An issue that has received much attention in both sets of literature is how best to quantify exposure to extreme temperatures. This is an important concern, as many studies rely on historical spatial and temporal variations in weather outcomes to identify the effects of weather extremes. If these historical extremes are not measured correctly, estimates of their impacts will not be credibly identified, thereby raising doubts regarding any climate change projections based on these impacts.Here we use regression analysis to estimate wheat yields as a function of observed weather variables and forecast yield impacts under a variety of weather scenarios. Our main findings are as follows. First, the effect of temperature exposure varies across the September−May growing season, with the biggest drivers of yield loss being freezing temperatures in the Fall and extreme heat in the Spring. Second, the net effect of warming on yields is negative, even after accounting for the benefits of reduced exposure to freezing temperatures. Third, there exists a tradeoff between mean yield and ability to resist extreme heat across varieties, and more-recently released varieties are less able to resist heat than older ones. Fourth, warming effects are partially offset by increased rainfall in the Spring. Fifth, the method used to construct measures of temperature exposure matters for both the predictive performance of the regression model and the forecasted warming impacts.We focus on wheat as it is one of the first domesticated food crops, forms the basic staple food of major civilizations in Europe, West Asia, and North Africa, and is the most widely planted crop globally. With a 2013 harvest of 8 million hectares, the Great Plains of the United States form the largest contiguous area of low-rainfall winter wheat in the world. Five states (Kansas, Oklahoma, Texas, Colorado, and Nebraska) produce nearly all high-quality hard red winter wheat in the United States. In 2013, Kansas production generated 378 million bushels of wheat at a value of 2.8 billion US dollars. Kansas production value represents 15% of all wheat grown in the United States.Our empirical approach uses data that combine variety-specific wheat yield observations with weather data from the exact location of the field trial. This permits two major advances for estimating the relationship between weather and wheat yield: (i) Location-specific weather data purge the results of aggregation bias that might be present in studies that use weather averages (or other aggregates) across space, and (ii) variety-specific yield responses provide information about the impact of climate on a large number of past, present, and future wheat varieties. Ref. 11 discusses limitations of gridded weather data sets, which have been used extensively because there is not often a weather station in each location of interest. Our data avoid the five pitfalls associated with gridded weather datasets (11). In addition, we find that the warming effects estimated using these field trial data are consistent with effects estimated from on-farm yield data, thereby providing external validity for the results presented here.     

Early warning of the Indian Ocean Dipole using climate network analysis

In recent years, the Indian Ocean Dipole (IOD) has received much attention in light of its substantial impacts on both the climate system and humanity. Due to its complexity, however, a reliable prediction of the IOD is still a great challenge. In this study, climate network analysis was employed to investigate whether there are early warning signals prior to the start of IOD events. An enhanced seesaw tendency in sea surface temperature (SST) among a large number of grid points between the dipole regions in the tropical Indian Ocean was revealed in boreal winter, which can be used to forewarn the potential occurrence of the IOD in the coming year. We combined this insight with the indicator of the December equatorial zonal wind in the tropical Indian Ocean to propose a network-based predictor that clearly outperforms the current dynamic models. Of the 15 IOD events over the past 37 y (1984 to 2020), 11 events were correctly predicted from December of the previous year, i.e., a hit rate of higher than 70%, and the false alarm rate was around 35%. This network-based approach suggests a perspective for better understanding and predicting the IOD.

The Indian Ocean Dipole (IOD) is a zonal dipole mode of the sea surface temperature (SST) that occurs interannually in the tropical Indian Ocean (TIO) (1, 2). A positive (negative) IOD features a below (above) normal SST off the Sumatran coast and a warming (cooling) over the western equatorial Indian Ocean. Ever since the severe floods in East Africa in 1997, which were induced by an extreme positive IOD (pIOD) event (2), the IOD has attracted much attention. Many studies showed that the IOD can affect the climate not only in the Indian Ocean rim countries but also in other more distant regions (3–5). In the past decades, great efforts have been made to reveal the mechanisms of the IOD, but the IOD prediction is still challenging, which further limits the associated seasonal climate predictions.One reason for the difficulties in predicting the IOD is that the TIO is complex, with multiple processes interacting. Previous studies reported that there are different triggers that may initiate the occurrence of the IOD, such as the El Nin˜o–Southern Oscillation (ENSO) (6, 7), the Indonesian Throughflow (8), intraseasonal disturbances (9), the subtropical IOD (10), springtime Indonesian rainfall (11), and the interhemispheric pressure gradient over the maritime continent (12). In addition, the development of the IOD in boreal summer was found to be controlled by different feedback processes in the Indian Ocean, including the thermocline–SST, cloud–radiation–SST, and evaporation–SST–wind feedbacks (1, 9, 13). As a typical atmosphere–ocean coupled mode, the IOD is thus a complex phenomenon that is sensitive to changes in multiple associated processes. The dilemma is that it is not clear which of the above-mentioned triggers play the main role in a given IOD event. For instance, ENSO events have been well recognized as a major external force to trigger IOD events via altering the Walker Circulation (4), but there are still many cases (e.g., 1996, 2012, and 2019) where an IOD event is not accompanied by an ENSO event, and the different IOD classifications (3, 14, 15) make the ENSO–IOD interactions even more complicated. The formation of the IOD was found to be associated with both the forcing outside the Indian Ocean and internal variability within the basin (16), but there is no certain physical mechanism that combines all the associated forcings and feedback processes. Moreover, as a phenomenon with quasi-biennial frequency (1, 17), two (or three) pIOD events may occur in consecutive years (18), and sometimes there may even be a few consecutive years with no remarkable IOD events. These complex characteristics increase the difficulty of the IOD prediction (19–23). In general, skillful predictions of the IOD events by climate models can only be made one season ahead (22, 23) and occasionally two or three seasons for strong events (9, 18). A rapid drop of the IOD predictive capability across the boreal winter has been well recognized as the winter predictability barrier (24), suggesting a lack of precursor signals due to the low signal-to-noise ratio. To better cope with the IOD-associated impacts, continuous efforts are thus required to improve the predictive capability for IOD events.In this study, we investigated this issue. Previous studies revealed different trigger and development mechanisms that contribute to the formation of the IOD. However, it is unclear whether there are any TIO states favorable for IOD onset, given the fact that the previously proposed triggering mechanisms do not work in all cases. It was recognized that a shallower thermocline depth in the eastern Indian Ocean is a precondition that favors the pIOD activity on decadal time scales (25, 26). Are there any preconditions on interannual or even biennial scales that may lead to an improved early warning of the IOD onset? To address these questions, we employed a recently proposed approach, the climate network analysis (27), to examine possible relationships among the grid points in the dipole regions. The climate network, as the name implies, is a network of the climate system with grid points (or stations) considered as nodes and the relations (i.e., correlations) between each pair of nodes as links (27–29). By studying climate systems in terms of climate networks, one may obtain more detailed information, including the topological structure (30, 31) and the dynamic evolutions (32, 33). A particular advantage of the climate network approach is that by taking into account all the interior grid points in the climate system, even weak signals (that could appear seemingly negligible when considered alone) contribute substantially to the overall system dynamics, eventually leading to significant effects when exhibiting cooperative behavior (34). Based on this advantage, the climate network analysis has been successfully applied in the forecasting of Atlantic Meridional Overturning Circulation (35, 36); the predicting of extreme precipitation events (37); and in particular, an early forecast of the onset of the Indian Summer Monsoon (38). By analyzing the cooperative behaviors among the interior nodes in the tropical Pacific and north Pacific, early warning signals have been detected for the onset of El Nin˜o events (39, 40) and the phase change of the Pacific Decadal Oscillation (PDO) (34). In this study, we employed this approach to investigate IOD events, especially the interactions of the SSTs between the dipole regions, to see whether there are early signals arising from the cooperative behaviors among the grid points, or in other words, to detect possible TIO states that favor the development of the IOD.     

Climate variability and density-dependent population dynamics: Lessons from a simple High Arctic ecosystem

Ecologists are still puzzled by the diverse population dynamics of herbivorous small mammals that range from high-amplitude, multiannual cycles to stable dynamics. Theory predicts that this diversity results from combinations of climatic seasonality, weather stochasticity, and density-dependent food web interactions. The almost ubiquitous 3- to 5-y cycles in boreal and arctic climates may theoretically result from bottom-up (plant–herbivore) and top-down (predator–prey) interactions. Assessing, empirically, the roles of such interactions and how they are influenced by environmental stochasticity has been hampered by food web complexity. Here, we take advantage of a uniquely simple High Arctic food web, which allowed us to analyze the dynamics of a graminivorous vole population not subjected to top-down regulation. This population exhibited high-amplitude, noncyclic fluctuations—partly driven by weather stochasticity. However, the predominant driver of the dynamics was overcompensatory density dependence in winter that caused the population to frequently crash. Model simulations showed that the seasonal pattern of density dependence would yield regular 2-y cycles in the absence of stochasticity. While such short cycles have not yet been observed in mammals, they are theoretically plausible if graminivorous vole populations are deterministically bottom-up regulated. When incorporating weather stochasticity in the model simulations, cyclicity became disrupted and the amplitude was increased—akin to the observed dynamics. Our findings contrast with the 3- to 5-y population cycles that are typical of graminivorous small mammals in more complex food webs, suggesting that top-down regulation is normally an important component of such dynamics.

Theory suggests that contrasting population dynamics result from details in the pattern of density dependence, including its strength, whether it acts instantly or with a delay, and how it interacts with deterministic (seasonal) and stochastic (weather) components of the prevailing or changing climate (1–5). Studies of small rodents have contributed much to elucidating the different facets of density-dependent and density-independent population dynamics (2, 4). A central topic has been what sort of density dependence yields the high-amplitude, multiannual population cycles for which voles and lemmings have become so renowned (6–9). Based on time series analyses, delayed density dependence is considered to be a main determinant of population cycles (see refs. 4, 10 for reviews), although overcompensatory direct density dependence appears to be an alternative in some settings (11). As rodent cycles are most prevalent in northern ecosystems with profound climatic seasonality (refs. 6, 7, 12, but see refs. 13, 14), several studies have emphasized that annual density dependence ought to be decomposed into its seasonal components (15–17)—both to accurately account for the density-dependent structure that underlies the observed dynamics and to identify the season-specific biotic mechanisms that cause density dependence. Considering seasonal dynamics is also crucial to assessing the role of climatic change and weather stochasticity because both differ between summer and winter (15, 18). The role of climate forcing is now also emphasized by the recent collapses and dampening of population cycles in several ecosystems that appear to be associated with ongoing climate change (15, 19, 20).Linking density dependence to the biotic mechanisms that causally generate the diversity of population dynamics patterns seen in small mammals has proved to be challenging. Most rodent populations are imbedded in complex food webs and, hence, simultaneously subjected to a multitude of biotic interactions that could cause the different facets of density-dependent population growth. For instance, density dependence may result from both top-down and bottom-up trophic interactions as well as intrinsic population mechanisms (11, 21, 22). While field experiments have helped pinpoint some mechanisms (23–27), they have been too short term to be conclusive with respect to what generates different patterns of multiannual population dynamics.Here, we apply an approach that has proved useful for unraveling the effects of density dependence and weather stochasticity in herbivorous large mammals (e.g., refs. 28–30), namely to target populations that are found in exceptionally simple biotic settings. Hence, our study targets a High Arctic population of the graminivorous (grass-eating) East European vole (Microtus levis) in a food web that lacks significant top-down regulation (i.e., predation). By combining statistical analyses of long-term, high-quality live-trapping data with simulations of a population model parameterized from these data, we 1) estimate the seasonal density dependence and resultant population dynamics (e.g., cyclic or noncyclic) that emerge in such a simple biotic setting and 2) assess how climatic seasonality and weather stochasticity in terms of rain-on-snow (ROS) events in winter impinge on such density-dependent population dynamics. Finally, we point out how the insights from this unique case study shed light on the longstanding puzzle about what generates population cycles and how ongoing climate change may influence these cycles.     

Climate challenges,vulnerabilities, and food security

This paper identifies rare climate challenges in the long-term history of seven areas, three in the subpolar North Atlantic Islands and four in the arid-to-semiarid deserts of the US Southwest. For each case, the vulnerability to food shortage before the climate challenge is quantified based on eight variables encompassing both environmental and social domains. These data are used to evaluate the relationship between the “weight” of vulnerability before a climate challenge and the nature of social change and food security following a challenge. The outcome of this work is directly applicable to debates about disaster management policy.Managing disasters, especially those that are climate-induced, calls for reducing vulnerabilities as an essential step in reducing impacts (1–8). Exposure to environmental risks is but one component of potential for disasters. Social, political, and economic processes play substantial roles in determining the scale and kind of impacts of hazards (1, 8–12). “Disasters triggered by natural hazards are not solely influenced by the magnitude and frequency of the hazard event (wave height, drought intensity etc.), but are also rather heavily determined by the vulnerability of the affected society and its natural environment” (ref. 1, p. 2). Thus, disaster planning and relief should address vulnerabilities, rather than returning a system to its previous condition following a disaster event (6).Using archaeologically and historically documented cultural and climate series from the North Atlantic Islands and the US Southwest, we contribute strength to the increasing emphasis on vulnerability reduction in disaster management. We ask whether there are ways to think about climate uncertainties that can help people build resilience to rare, extreme, and potentially devastating climate events. More specifically, we ask whether vulnerability to food shortfall before a climate challenge predicts the scale of impact of that challenge. Our goal is both to assess current understandings of disaster management and to aid in understanding how people can build the capability to increase food security and reduce their vulnerability to climate challenges.We present analyses of cases from substantially different regions and cultural traditions that show strong relationships between levels of vulnerability to food shortage before rare climate events and the impact of those events. The patterns and details of the different contexts support the view that vulnerability cannot be ignored. These cases offer a long-term view rarely included in studies of disaster management or human and cultural well-being (for exceptions, see refs. 13 and 14). This long time frame allows us to witness changes in the context of vulnerabilities and climate challenges, responding to a call for more attention to “how human security changes through time, and particularly the dynamics of vulnerability in the context of multiple processes of change” (ref. 10, p. 17).     

Long-term exposure to elevated CO2 enhances plant community stability by suppressing dominant plant species in a mixed-grass prairie

Climate controls vegetation distribution across the globe, and some vegetation types are more vulnerable to climate change, whereas others are more resistant. Because resistance and resilience can influence ecosystem stability and determine how communities and ecosystems respond to climate change, we need to evaluate the potential for resistance as we predict future ecosystem function. In a mixed-grass prairie in the northern Great Plains, we used a large field experiment to test the effects of elevated CO₂, warming, and summer irrigation on plant community structure and productivity, linking changes in both to stability in plant community composition and biomass production. We show that the independent effects of CO₂ and warming on community composition and productivity depend on interannual variation in precipitation and that the effects of elevated CO₂ are not limited to water saving because they differ from those of irrigation. We also show that production in this mixed-grass prairie ecosystem is not only relatively resistant to interannual variation in precipitation, but also rendered more stable under elevated CO₂ conditions. This increase in production stability is the result of altered community dominance patterns: Community evenness increases as dominant species decrease in biomass under elevated CO₂. In many grasslands that serve as rangelands, the economic value of the ecosystem is largely dependent on plant community composition and the relative abundance of key forage species. Thus, our results have implications for how we manage native grasslands in the face of changing climate.Ecologists have long recognized the importance of climate in shaping plant communities across spatial and temporal scales (1). Together, precipitation and temperature characterize the distribution of terrestrial biomes across the globe. As climate changes, some biomes will be more vulnerable to temperature increase (2) or altered precipitation (3), whereas others will be more resistant (4–6). Ecological stability, the maintenance of community structure and function despite climatic fluctuation or disturbance (7–9), includes two components: resistance [lack of change despite perturbation (9)] and resilience [return to a previous state following a perturbation (10–13)]. Diversity (14) and productivity (11, 15) can both influence community stability (16) and dampen responses to environmental perturbation (5, 9, 17, 18). What remains unclear is how stability and resistance respond to predicted changes in climate.Multiple climate change factors simultaneously impact plant performance, community structure, and productivity (4, 19, 20). For example, elevated CO₂ can improve water use efficiency and increase plant productivity (21–23), but warming can reduce it, counteracting the positive water-saving effects of elevated CO₂ (24). In addition, plant species and functional groups that differ in photosynthetic pathway often have contrasting responses to elevated CO₂, warming, and altered precipitation. Furthermore, the effects of individual climate change factors may be additive (25, 26), subadditive (4, 24, 27), or antagonistic (27, 28). As a result, the performance of a given species or functional group depends on interactions among CO₂, temperature, and soil characteristics that influence plant water availability at the community level.Globally, both elevated CO₂ and warming are expected to lead to pronounced changes in vegetation distribution and structure (25, 29, 30). In North American grasslands, warming is expected to promote C₄ dominance, dampening the ability of these areas to show large responses to elevated CO₂ (25). Because responses to climate change differ among individual plant species and depend on community context (31–33), the resultant community dynamics are difficult to predict. In addition, plant responses to climate manipulations can shift over time. Our earlier work in a mixed-grass prairie shows that in the first 3 y of the Prairie Heating and CO₂ Enrichment (PHACE) experiment, both C₃ and C₄ grass production benefited from elevated CO₂ conditions (34). However, long-term studies of CO₂ enrichment show that plant responses can diminish over time (22, 35), including the responses of dominant grass species in our mixed-grass prairie (36). To accurately characterize the trajectory of species responses and predict the interacting impacts of global climate change on plant community structure and function, long-term experiments are necessary.Grasslands in the northern Great Plains are experiencing rapid climate change, with average annual temperatures increasing by 2.6 °C over the last century and winter and spring temperatures increasing more rapidly than summer temperatures (37). Grasslands are extensively grazed, and moisture availability (timing and amount of rainfall) affects grassland productivity to support domestic and native herbivores (3, 38). Compared with other regions, precipitation change is expected to be relatively modest, but there is a general consensus that even if annual precipitation change is small, precipitation timing will become increasingly variable (37) and the number of extreme precipitation events will also increase (39–41). When coupled with rising temperatures, water limitation will increase (42), potentially reducing rangeland productivity (43). Because the timing of water availability regulates grassland productivity and community dynamics (3, 44), variation in background climate may promote or reduce the resistance of grasslands to climate change. The economic value of the ecosystem is largely dependent on the plant community and the relative abundance of key forage grass species (45). Thus, changes in grassland productivity can have clear economic impacts for ranching and managing wildlife (46).To understand how climate change influences plant community dynamics and stability (namely, resistance to interannual shifts in precipitation), we quantified the impacts of experimentally imposed elevated CO₂, warming, and summer irrigation on plant community composition and aboveground biomass production over 8 y in a northern mixed-grass prairie in southeastern Wyoming. Species that dominate biomass production are expected to respond to changes in climate most directly (47), whereas subdominant species may respond to climate change directly and indirectly through their interactions with the dominant species (6, 48, 49). Thus, we quantified climate change effects on the entire community and on dominant and subdominant community members separately. We addressed three questions: (i) Do the effects of climate change on plant community composition and productivity depend on temperature and precipitation variation? (ii) Do dominant and subdominant components of the plant community respond differently to climate change? and (iii) What is the influence of climate change on community composition and biomass stability?     

Fog and rain in the Amazon

The diurnal and seasonal water cycles in the Amazon remain poorly simulated in general circulation models, exhibiting peak evapotranspiration in the wrong season and rain too early in the day. We show that those biases are not present in cloud-resolving simulations with parameterized large-scale circulation. The difference is attributed to the representation of the morning fog layer, and to more accurate characterization of convection and its coupling with large-scale circulation. The morning fog layer, present in the wet season but absent in the dry season, dramatically increases cloud albedo, which reduces evapotranspiration through its modulation of the surface energy budget. These results highlight the importance of the coupling between the energy and hydrological cycles and the key role of cloud albedo feedback for climates over tropical continents.Tropical forests, and the Amazon in particular, are the biggest terrestrial CO₂ sinks on the planet, accounting for about 30% of the total net primary productivity in terrestrial ecosystems. Hence, the climate of the Amazon is of particular importance for the fate of global CO₂ concentration in the atmosphere (1). Besides the difficulty of estimating carbon pools (1–3), our incapacity to correctly predict CO₂ fluxes in the continental tropics largely results from inaccurate simulation of the tropical climate (1, 2, 4, 5). More frequent and more intense droughts in particular are expected to affect the future health of the Amazon and its capacity to act as a major carbon sink (6–8). The land surface is not isolated, however, but interacts with the weather and climate through a series of land−atmosphere feedback loops, which couple the energy, carbon, and water cycles through stomata regulation and boundary layer mediation (9).Current General Circulation Models (GCMs) fail to correctly represent some of the key features of the Amazon climate. In particular, they (i) underestimate the precipitation in the region (10, 11), (ii) do not reproduce the seasonality of either precipitation (10, 11) or surface fluxes such as evapotranspiration (12), and (iii) produce errors in the diurnal cycle and intensity of precipitation, with a tendency to rain too little and too early in the day (13, 14). In the more humid Western part of the basin, surface incoming radiation, evapotranspiration, and photosynthesis all tend to peak in the dry season (15–17), whereas GCMs simulate peaks of those fluxes in the wet season (10, 11). Those issues might be related to the representation of convection (1, 2, 4, 5, 13, 14) and vegetation water stress (6–8, 15–17) in GCMs.We here show that we can represent the Amazonian climate using a strategy opposite to GCMs in which we resolve convection and parameterize the large-scale circulation (Methods). The simulations lack many of the biases observed in GCMs and more accurately capture the differences between the dry and wet season of the Amazon in surface heat fluxes and precipitation. Besides top-of-the-atmosphere insolation, the simulations require the monthly mean temperature profile as an input. We demonstrate that this profile, whose seasonal cycle itself is a product of the coupled ocean−land−atmosphere dynamics, mediates the seasonality of the Amazonian climate by modulating the vertical structure of the large-scale circulation in such a way that thermal energy is less effectively ventilated in the rainy season.     

Environmental tipping points significantly affect the cost?benefit assessment of climate policies

Most current cost−benefit analyses of climate change policies suggest an optimal global climate policy that is significantly less stringent than the level required to meet the internationally agreed 2 °C target. This is partly because the sum of estimated economic damage of climate change across various sectors, such as energy use and changes in agricultural production, results in only a small economic loss or even a small economic gain in the gross world product under predicted levels of climate change. However, those cost−benefit analyses rarely take account of environmental tipping points leading to abrupt and irreversible impacts on market and nonmarket goods and services, including those provided by the climate and by ecosystems. Here we show that including environmental tipping point impacts in a stochastic dynamic integrated assessment model profoundly alters cost−benefit assessment of global climate policy. The risk of a tipping point, even if it only has nonmarket impacts, could substantially increase the present optimal carbon tax. For example, a risk of only 5% loss in nonmarket goods that occurs with a 5% annual probability at 4 °C increase of the global surface temperature causes an immediate two-thirds increase in optimal carbon tax. If the tipping point also has a 5% impact on market goods, the optimal carbon tax increases by more than a factor of 3. Hence existing cost−benefit assessments of global climate policy may be significantly underestimating the needs for controlling climate change.Tipping points in the climate system (1) and in ecosystems (2, 3) could be crossed in a changing climate. The resulting impacts are expected to reduce the environmental goods and services provided to humanity by the climate and by ecosystems (4). Some of those impacts will be on goods that have direct market value, such as the food produced from agricultural ecosystems. Other impacts will be on services that do not involve any production processes of market goods but can still directly affect human well-being through, e.g., health effects, changes in physical comfort, sensory satisfaction, or spiritual fulfillment—making them nonmarket impacts (5).Environmental tipping points can occur at a range of spatial scales (6), from global-scale tipping points in the climate system, such as a reorganization of the Atlantic Meridional Overturning Circulation (1, 7), to ecosystem-scale tipping points, such as sudden lake eutrophication (2). Here we consider the idealized case of an instantaneous tipping point that occurs on a sufficient scale to impact the global economy. Such a tipping point could come from a physical tipping element in the climate system, such as the West African or Indian monsoons (1), which in turn impacts humans and ecosystems, or it could come from a more biological tipping element such as a major biome (1). For example, widespread dieback of forests has been observed in Canada (8, 9), both boreal and tropical biomes are thought to exhibit multiple stable states (10–12), and abrupt forest dieback has been forecast in both the Amazon and boreal regions in future (1, 7). There is even speculation that an abrupt and irreversible shift of ecosystems could occur on a planetary scale (3, 4). Whether they themselves tip or they are impacted by tipping in a more physical part of the climate system, ecosystems and the goods and services they provide carry significant market and nonmarket values (5) (as presumably do the goods and services provided by more physical parts of the climate system).Predicting when tipping points will occur is inherently uncertain (1, 2), because they occur in imperfectly understood complex systems, which are subject to stochastic environmental variability (as well as deterministic forcing), meaning that their time of tipping can never be forecast precisely (13). The representation of such risk and uncertainty is recognized as an unresolved issue for the estimation of the social cost of carbon (14–16).Here we explore how the risk of stochastically uncertain environmental tipping points that have nonmarket, or both market and nonmarket, impacts affects the cost−benefit assessment of climate change policies. The majority of attempts to assess the economic implications of the impacts of climate change concentrate on market impacts, whose estimation can draw information from market statistics (approaches and limitations of which are discussed in, e.g., refs. 15 and 17). Most integrated assessment models (IAMs) also include nonmarket impacts, but they tend to discount these future impacts without accounting for increases in their relative price as environmental goods and services become scarcer (18, 19). The damages in IAMs are also often smooth functions of temperature that do not account for abrupt and irreversible impacts from tipping points. Finally, many IAMs are deterministic, failing to consider uncertainty surrounding the impacts of climate change.Each of these three weaknesses has been addressed individually in existing studies. The limited substitutability of ecosystem services has been shown to increase the welfare impact of these nonmarket losses, as discussed by Hoel and Sterner (18) and Sterner and Persson (19). The prospect of irreversible, environmental tipping points has been shown to produce a precautionary optimal management response in many cases (20–22). Stochastic uncertainty surrounding climate change damages has been shown to generally increase the optimal level of mitigation (23, 24). Furthermore, the combination of stochastic uncertainty and abrupt, irreversible patterns of climate change has been shown to increase optimal levels of mitigation (25, 26). Here, we address the three issues simultaneously, analyzing how the stochastic component of climate change risk interacts with the limited substitutability of environmental goods and services, under irreversible tipping.     

Growing impact of wildfire on western US water supply

Streamflow often increases after fire, but the persistence of this effect and its importance to present and future regional water resources are unclear. This paper addresses these knowledge gaps for the western United States (WUS), where annual forest fire area increased by more than 1,100% during 1984 to 2020. Among 72 forested basins across the WUS that burned between 1984 and 2019, the multibasin mean streamflow was significantly elevated by 0.19 SDs (P < 0.01) for an average of 6 water years postfire, compared to the range of results expected from climate alone. Significance is assessed by comparing prefire and postfire streamflow responses to climate and also to streamflow among 107 control basins that experienced little to no wildfire during the study period. The streamflow response scales with fire extent: among the 29 basins where >20% of forest area burned in a year, streamflow over the first 6 water years postfire increased by a multibasin average of 0.38 SDs, or 30%. Postfire streamflow increases were significant in all four seasons. Historical fire–climate relationships combined with climate model projections suggest that 2021 to 2050 will see repeated years when climate is more fire-conducive than in 2020, the year currently holding the modern record for WUS forest area burned. These findings center on relatively small, minimally managed basins, but our results suggest that burned areas will grow enough over the next 3 decades to enhance streamflow at regional scales. Wildfire is an emerging driver of runoff change that will increasingly alter climate impacts on water supplies and runoff-related risks.

Recent declines in soil moisture, streamflow, and reservoir storage signal the precariousness of water supplies in the western United States (WUS) and the urgency of managing associated risks (1, 2). Declining WUS water supplies are qualitatively consistent with modeled trends due to anthropogenic climate change (3, 4), but projections are uncertain due to not only climate but also the complexity of vegetation responses to climate change and associated disturbances such as wildfire (5–9). In addition to transpiration and interception, which directly divert moisture from runoff, vegetation also affects hydrology by shaping soil depth and structure and by modulating turbulent energy fluxes that alter snowpack and evaporation (10). In addition to direct effects on vegetation, wildfires can further affect streamflow by promoting water repellency and soil erosion (11–13). Given that the headwater areas of major WUS rivers are generally forested, altered forest cover or ecosystem water demand could potentially affect water resources at regional scales.In recent decades, the annual forest area burned in the WUS has risen rapidly, in step with climate trends toward warming and drying (14–20). In general, forest disturbances such as wildfire are known to temporarily enhance streamflow (21–23), although cases of postdisturbance streamflow declines, especially in arid areas, have also been documented (21, 24, 25). The likelihood that rapid increases in regional forest fire activity will continue (26, 27) suggests that wildfire may increasingly impact water resources in the water-limited WUS (6). Yet, the duration and seasonality of postdisturbance increases in runoff are unknown, raising the question of whether increased forest fire activity will meaningfully affect water availability in the WUS.Here we use stream gauge records from 179 river basins in the WUS to assess the strength, duration, and seasonality of postfire changes in streamflow and whether increasing forest fire activity is likely to have a detectable effect on regional streamflow.     

Climate change and physical disturbance cause similar community shifts in biological soil crusts

Biological soil crusts (biocrusts)—communities of mosses, lichens, cyanobacteria, and heterotrophs living at the soil surface—are fundamental components of drylands worldwide, and destruction of biocrusts dramatically alters biogeochemical processes, hydrology, surface energy balance, and vegetation cover. Although there has been long-standing concern over impacts of physical disturbances on biocrusts (e.g., trampling by livestock, damage from vehicles), there is increasing concern over the potential for climate change to alter biocrust community structure. Using long-term data from the Colorado Plateau, we examined the effects of 10 y of experimental warming and altered precipitation (in full-factorial design) on biocrust communities and compared the effects of altered climate with those of long-term physical disturbance (>10 y of replicated human trampling). Surprisingly, altered climate and physical disturbance treatments had similar effects on biocrust community structure. Warming, altered precipitation frequency [an increase of small (1.2 mm) summer rainfall events], and physical disturbance from trampling all promoted early successional community states marked by dramatic declines in moss cover and increases in cyanobacteria cover, with more variable effects on lichens. Although the pace of community change varied significantly among treatments, our results suggest that multiple aspects of climate change will affect biocrusts to the same degree as physical disturbance. This is particularly disconcerting in the context of warming, as temperatures for drylands are projected to increase beyond those imposed as treatments in our study.The potential for ecological state transitions in response to global change, particularly transitions that promote feedbacks to terrestrial biogeochemical cycling, is a growing concern (1–6). Anticipating state transitions in terrestrial ecosystems largely hinges on understanding the response of primary producers to warming temperatures, altered precipitation patterns, and novel disturbance regimes (4–7). In arid and semiarid ecosystems (drylands), a substantial portion of primary production can take place in biological soil crusts (biocrusts; Fig. 1) (8, 9), which are communities of mosses, lichens, cyanobacteria, and heterotrophs living at the soil surface that can constitute up to 70% of dryland ground cover (10–12).Open in a separate windowFig. 1.Biocrusts can locally regulate ecosystem processes and cover large portions of dryland ecosystems as in A (photo by Bill Bowman). Biocrusts are sensitive to physical disturbances from vehicles and trampling by livestock or people as depicted in B, which shows an experimentally trampled plot (foreground) bordered by undisturbed biocrust (background).Biocrust organisms are adapted to limited moisture and low nutrient conditions and respond rapidly to pulsed, dynamic environmental conditions (13–16). Due to their extensive cover and rapid responses to even small inputs of moisture and nutrients, biocrusts often locally regulate soil hydrology and the cycling of soil carbon (9, 12, 17–21) and nitrogen (9, 22–27). However, the same traits that adapt biocrust organisms to pulsed environmental conditions also make them potentially vulnerable to anticipated changes in climate (28–30). Given their significant influence on ecosystem processes, understanding how biocrust communities will respond to changing climate and disturbance regimes is essential for predicting ecological state changes in drylands—which cover roughly 40% of the Earth’s terrestrial surface and hold an estimated 25% of global organic soil carbon (31).Biocrusts are bound together by filamentous strands of cyanobacteria, which glue soil particles together to form the characteristic soil shields for which the communities are renowned (10). Although this structure results in resistance to wind shear stress, it does not provide much resistance to physical disturbance. Not surprisingly, both the physical and biotic structures of biocrust are highly sensitive to a range of disturbances, such as vehicle traffic and trampling by humans and livestock (32). Disturbance-induced changes in biocrust community structure and subsequent variation across successional states are well-characterized and are remarkably similar globally: Physical disturbance typically transforms later-successional communities of lichens and mosses to early-successional communities dominated by cyanobacteria (10, 32). This successional resetting significantly impacts ecosystem processes, including large changes in carbon and nitrogen cycling (11, 24, 25, 33). Alarmingly, some experimental work suggests biocrust communities may also be highly sensitive to changing climate. Specifically, increased temperatures have been reported to reduce lichen cover in semiarid Spain (12, 34), and altered precipitation patterns promoted rapid moss mortality (35) and greater variability in cyanobacterial species composition and abundance (36, 37) in a cool desert of the Colorado Plateau. These changes in biocrusts due to climate manipulations also impact ecosystem processes (12, 35, 38).Despite concern over potential climate change-induced shifts in biocrust community structure and the impacts on ecosystem processes, available reports of biocrust responses to climate manipulations are based on relatively short experimental time spans, often less than 3 y in duration (e.g., 12, 34–36, 39). In addition to the lack of long-term data on biocrust responses to climate change, it is not yet known how climate change impacts on biocrust communities will compare with large, continued threats from physical disturbances due to development, agriculture, and other human activities (32, 40, 41). Understanding the magnitude of threats to biocrusts from both climate change scenarios and novel disturbance regimes is thus a necessary step for predicting future ecological states and developing comprehensive management plans in drylands.We compared the effects of warming temperatures and altered precipitation patterns on biocrust community structure using data from 10 y (autumn 2005 to autumn 2014) of biocrust community surveys, completed in a full-factorial climate manipulation experiment (control, warming, watering, warming + watering) on the Colorado Plateau, Utah. We simultaneously examined the impact of 15 y annual physical disturbance on biocrust community structure within a replicated human-trampling experiment in a site with vegetation and biocrust communities similar to those in our climate manipulation study (Fig. 1). Finally, we compared the effects of climate manipulation treatments and physical disturbance from trampling on the relative cover of three biotic groups that typically dominate biocrusts of our study region: cyanobacteria, mosses, and lichens. Our goals were, first, to compare the responses of biocrust communities to increased temperature versus increased frequency of small precipitation events, both of which are forecast by climate models for the Colorado Plateau (28, 29, 42) and, second, to compare the effects of climate manipulations to the effects of physical disturbance on biocrust community structure, with a focus on understanding potential variation in responses across different fractions of the community.     

Footprint of greenhouse forcing in daily temperature variability

Changes in mean climatic conditions will affect natural and societal systems profoundly under continued anthropogenic global warming. Changes in the high-frequency variability of temperature exert additional pressures, yet the effect of greenhouse forcing thereon has not been fully assessed or identified in observational data. Here, we show that the intramonthly variability of daily surface temperature changes with distinct global patterns as greenhouse gas concentrations rise. In both reanalyses of historical observations and state-of-the-art projections, variability increases at low to mid latitudes and decreases at northern mid to high latitudes with enhanced greenhouse forcing. These latitudinally polarized daily variability changes are identified from internal climate variability using a recently developed signal-to-noise-maximizing pattern-filtering technique. Analysis of a multimodel ensemble from the Coupled Model Intercomparison Project Phase 6 shows that these changes are attributable to enhanced greenhouse forcing. By the end of the century under a business-as-usual emissions scenario, daily temperature variability would continue to increase by up to a further 100% at low latitudes and decrease by 40% at northern high latitudes. Alternative scenarios demonstrate that these changes would be limited by mitigation of greenhouse gases. Moreover, global changes in daily variability exhibit strong covariation with warming across climate models, suggesting that the equilibrium climate sensitivity will also play a role in determining the extent of future variability changes. This global response of the high-frequency climate system to enhanced greenhouse forcing is likely to have strong and unequal effects on societies, economies, and ecosystems if mitigation and protection measures are not taken.

The effect of anthropogenic greenhouse gas emissions on mean climatic conditions is well understood. Theory, observational, and modeling work all demonstrate that average temperatures increase as a result of elevated greenhouse gas concentrations (1). However, it is also of considerable importance to natural and human systems whether changes in the temporal variability of climatic conditions have accompanied historical global warming and whether they will do so in the future (2–5). A more variable climate implies greater uncertainty and greater frequency of extremes, both of which constitute more damaging conditions.The variability of climate from one year to the next has received considerable attention. Large-scale climatic oscillations, such as the El Niño Southern Oscillation and the Indian Ocean Dipole, are dominant determinants of interannual variability (6–8) and have been shown to exhibit more frequent extremes under enhanced greenhouse forcing within comprehensive climate models (9–11), results that are supported by paleoclimatic evidence (12). Identifying a response in interannual temperature variability has been less conclusive. Some studies have attributed recent summer temperature extremes to greater interannual variability, both regionally (13) and globally (14), but there is still debate as to the extent of the role of interannual variability (15–17). Some regional trends in interannual temperature variability have been identified (17–21), but there is no consensus between observations and climate models (22).Here, we focus on variability of temperature at a higher frequency (daily), which a growing body of econometric literature has identified as an important determinant of societal outcomes, including human health (23–27), agriculture (28–30), and economic growth (31). The effect of enhanced greenhouse gas concentrations on the daily variability of temperature is therefore of wide societal importance and a critical component of the impact of anthropogenic climate change.Decreases in daily temperature variability at northern mid to high latitudes have been detected in observations (32–34) and agree well with predictions from comprehensive climate models (34–36) and physical reasoning (34, 35). Previous generations of climate models have also suggested that daily variability may increase during European summer (37) and across the tropics (36, 38), but these predictions have not yet been detected in observations or confirmed in state-of-the art climate models. This paper unifies these works by presenting a global analysis of changes in subseasonal, daily temperature variability under enhanced greenhouse forcing in both reanalyses of historical observations (National Oceanographic and Atmospheric Administration [NOAA] 20th Century Reanalysis Version 3 and the European Centre for Medium-Range Weather Forecasts Reanalysis 5 [ERA-5]) and the latest generation of comprehensive climate models (Coupled Model Intercomparison Project phase 6 [CMIP-6]). Daily temperature variability refers to the intramonthly SD of daily surface temperature from hereon. We consider changes in daily variability in boreal winter (“DJF”), boreal summer (“JJA”), and across the year (“annual”) to both assess the season specific mechanisms identified in previous work and to provide an aggregated overview of variability changes.     

INAUGURAL ARTICLE by a Recently Elected Academy Member:Lagging adaptation to warming climate in Arabidopsis thaliana

If climate change outpaces the rate of adaptive evolution within a site, populations previously well adapted to local conditions may decline or disappear, and banked seeds from those populations will be unsuitable for restoring them. However, if such adaptational lag has occurred, immigrants from historically warmer climates will outperform natives and may provide genetic potential for evolutionary rescue. We tested for lagging adaptation to warming climate using banked seeds of the annual weed Arabidopsis thaliana in common garden experiments in four sites across the species’ native European range: Valencia, Spain; Norwich, United Kingdom; Halle, Germany; and Oulu, Finland. Genotypes originating from geographic regions near the planting site had high relative fitness in each site, direct evidence for broad-scale geographic adaptation in this model species. However, genotypes originating in sites historically warmer than the planting site had higher average relative fitness than local genotypes in every site, especially at the northern range limit in Finland. This result suggests that local adaptive optima have shifted rapidly with recent warming across the species’ native range. Climatic optima also differed among seasonal germination cohorts within the Norwich site, suggesting that populations occurring where summer germination is common may have greater evolutionary potential to persist under future warming. If adaptational lag has occurred over just a few decades in banked seeds of an annual species, it may be an important consideration for managing longer-lived species, as well as for attempts to conserve threatened populations through ex situ preservation.Rapid climate change has already caused species range shifts and local extinctions (1) and is predicted to have greater future impacts (2). As the suitable climate space for a species shifts poleward (3), populations previously well adapted to the historical climate in a particular region may experience strong selection to adapt to rapidly warming local temperatures (4–10). Rapid evolutionary response to climate change has already been observed (11, 12), but it remains unclear whether evolutionary response can keep pace with rapidly changing local adaptive optima (6, 8, 13–15). If local adaptation is slower than the rate of climate change, the average fitness of local populations may decline over time (7, 14, 16, 17), possibly resulting in local extinctions and range collapse at the warmer margin. Where such lag exists, we expect that local seeds banked for conservation may no longer be well adapted to their sites of origin (18). However, such adaptational lag may be mitigated by migration or gene flow from populations in historically warmer sites if those populations are better adapted to current conditions in a site than local populations (8, 13, 19, 20). Although adaptational lag has been predicted (4–6, 8, 14, 15, 19, 21, 22), the distinctive signature of mismatch between local population performance and current climate optima has not yet been explicitly demonstrated in nature.Despite evidence for local adaptation in many organisms (23), there have been few explicit tests for the role of specific climate factors in shaping local fitness optima (4, 9, 13). Such tests require growing many genotypes from populations spanning a range of climates in common gardens across a species’ range to decouple climate of origin from geographic variation in other selective factors (4, 6, 14). If adaptation to local climate has occurred, then genotypes from climates similar to each planting site are expected to have high fitness in that site relative to genotypes from dissimilar climates (6). However, if local adaptive optima have shifted with rapid warming trends over the last 50 y, we expect that banked genotypes from historically warmer climates will have higher fitness within a site than banked genotypes of local origin (6, 21, 22).We tested for lagging adaptation to climate using Arabidopsis thaliana, a naturally inbreeding annual species that inhabits a broad climate space across its native Eurasian range (24). A. thaliana exhibits strong circumstantial evidence of climate adaptation, including geographic clines in ecologically important life-history traits (25–28) and in candidate genes associated with these traits (29, 30), as well as genome-wide associations of single nucleotide polymorphisms with climatic factors (31–34). To test explicitly for local adaptation to climate we measured the lifetime fitness of more than 230 accessions from banked seeds originating from a broad range of climates in replicated field experiments in four sites across the species’ native climate range (Fig. 1). We observed that genotypes originating in historically warmer climates outperformed local genotypes, particularly at the northern range limit.Open in a separate windowFig. 1.Map of common garden sites and sites of origin of the 241 native A. thaliana accessions represented in our experiments.     

From the Cover: Climate and topography control the size and flux of sediment produced on steep mountain slopes

Weathering on mountain slopes converts rock to sediment that erodes into channels and thus provides streams with tools for incision into bedrock. Both the size and flux of sediment from slopes can influence channel incision, making sediment production and erosion central to the interplay of climate and tectonics in landscape evolution. Although erosion rates are commonly measured using cosmogenic nuclides, there has been no complementary way to quantify how sediment size varies across slopes where the sediment is produced. Here we show how this limitation can be overcome using a combination of apatite helium ages and cosmogenic nuclides measured in multiple sizes of stream sediment. We applied the approach to a catchment underlain by granodiorite bedrock on the eastern flanks of the High Sierra, in California. Our results show that higher-elevation slopes, which are steeper, colder, and less vegetated, are producing coarser sediment that erodes faster into the channel network. This suggests that both the size and flux of sediment from slopes to channels are governed by altitudinal variations in climate, vegetation, and topography across the catchment. By quantifying spatial variations in the sizes of sediment produced by weathering, this analysis enables new understanding of sediment supply in feedbacks between climate, tectonics, and mountain landscape evolution.The interplay of climate and life drives weathering on mountain slopes (1–4), converting intact bedrock into mobile sediment particles ranging in size from clay to boulders (5, 6). Water, wind, and biota sweep these particles across slopes under the force of gravity and erode them into channels, where they serve as tools that cut into underlying bedrock during transport downstream (7). Both the size and flux of particles eroded from slopes into channels can influence incision into bedrock (8, 9), which in turn governs the pace of erosion from slopes where the sediment is produced (10, 11). The relationships between sediment production, hillslope erosion, and channel incision imply that they are central to feedbacks that drive mountain landscape evolution (12). When channel incision and hillslope erosion are relatively fast, sediment particles spend less time exposed to weathering on slopes (13) and thus may be coarser when they enter the channel (14), promoting faster incision into bedrock (7). Integrated over time, channel incision and hillslope erosion generate topography (15), imposing altitudinal gradients in precipitation, temperature, and hillslope form (16), and thus ultimately influencing erosion (17), weathering (1), and the sizes of sediment produced on slopes (2). Thus, the size and erosional flux of sediment may both depend on and regulate rates of channel incision into bedrock via feedbacks spanning a range of scales and processes.Feedbacks between climate, erosion, and tectonics have been widely studied (8, 16, 18–23). However, understanding the role of sediment size remains a fundamental challenge (6–9, 12), due to a lack of methods for quantifying how the size distributions of sediment particles vary across the slopes where sediment is produced from bedrock by weathering and erosion (5, 6). Here we show how to overcome this limitation using a combination of tracing methods on multiple sediment sizes collected from streams in steep landscapes. Results from the Sierra Nevada, California, enable new understanding of connections between climate, mountain topography, and sediment supply.     

Environmental outcomes of the US Renewable Fuel Standard

The Renewable Fuel Standard (RFS) specifies the use of biofuels in the United States and thereby guides nearly half of all global biofuel production, yet outcomes of this keystone climate and environmental regulation remain unclear. Here we combine econometric analyses, land use observations, and biophysical models to estimate the realized effects of the RFS in aggregate and down to the scale of individual agricultural fields across the United States. We find that the RFS increased corn prices by 30% and the prices of other crops by 20%, which, in turn, expanded US corn cultivation by 2.8 Mha (8.7%) and total cropland by 2.1 Mha (2.4%) in the years following policy enactment (2008 to 2016). These changes increased annual nationwide fertilizer use by 3 to 8%, increased water quality degradants by 3 to 5%, and caused enough domestic land use change emissions such that the carbon intensity of corn ethanol produced under the RFS is no less than gasoline and likely at least 24% higher. These tradeoffs must be weighed alongside the benefits of biofuels as decision-makers consider the future of renewable energy policies and the potential for fuels like corn ethanol to meet climate mitigation goals.

Bioenergy is an essential component of most proposed pathways to reduce anthropogenic greenhouse gas (GHG) emissions and limit global warming to 1.5 or 2 °C by middle to late century (1–6). Liquid biofuels may contribute to bioenergy’s share of climate mitigation by displacing petroleum-based fuels with those generated from modern-day plants (7, 8). The GHG benefits of such substitution, however, are dependent on several factors including whether biofuel production invokes additional plant growth (9–12), the extent to which combusted plants (typically crops) are replaced in the food system (13–15), and the degree to which biofuel production directly and indirectly alters patterns of land use and management (2, 16–20). Because land use changes (LUCs) and other consequences induced by biofuels have the potential to cause significant novel GHG emissions and modify other ecosystem services and disservices (21–26), accurately estimating and accounting these outcomes is critical for the formation of effective climate and environmental policy (27–29).The United States is the world leader in biofuel production by volume and generated 47% of global output over the last decade under the purview of its Renewable Fuel Standard (RFS) (30). First enacted in 2005 and greatly expanded in 2007, the RFS requires that biofuels be blended into the transportation fuel supply at annually increasing increments. Volume targets exist for several advanced biofuel types including biomass-based diesel and those made from cellulosic feedstocks. However, the vast majority (∼87%) of the mandate to date has been fulfilled by conventional renewable fuels, specifically corn grain ethanol (30, 31), such that the potential benefits of its more advanced fuel requirements have not yet materialized (32–34).To comply with the policy’s GHG reduction goals, the RFS requires conventional renewable fuels to generate life cycle GHG savings of at least 20% relative to gasoline. Upon enactment, the policy’s regulatory analysis projected that life cycle emissions of corn ethanol production would just clear the 20% threshold by 2022, even when emissions from LUC were included (35). At the time, most LUC emissions were projected to occur internationally. Since the initial RFS policy-making, however, observations of widespread land conversion and resultant GHG emissions within the United States have also emerged (36–39).Heightened demand for crops for use as biofuel feedstocks and the associated changes to landscapes may also engender broader environmental disservices upon ground and surface waters, soil resources, and other ecosystem components (40–44). The magnitudes of such effects are highly uncertain, however, as they ultimately depend upon unpredictable behaviors throughout the supply chain—from field to refinery—making it difficult to forecast impacts. As such, public policy-making and support for biofuels has needed to rely on widely varying projections of anticipated effects—a quandary that could potentially misguide strategies for climate change mitigation and environmental protection (27, 28, 45).The RFS legislation contains several environmental safeguards to try to prevent perverse outcomes including periodic scientific review of the conservation impacts of the program and opportunities to adjust annual fuel volumes if the program creates severe environmental harm (31). Although the most recent program review identified that biofuels may in fact be contributing to land conversion and subsequent declines in water quality, these impacts have not been causally attributed to biofuels or the RFS (32). Likewise, volume requirements for specific fuel types have not been revised based on environmental performance (31). Given the United States’ leading role in biofuel production, understanding the outcomes of the RFS has direct ramifications not only for national environmental quality and global climate change but also for policy-making around the world as governments seek to modify or develop their own biofuel policies to meet climate and clean energy goals.Here we assess the effects of the RFS on US land and water resources during the first 8 y of the policy’s implementation (2008 to 2016) by integrating econometric analyses with observed changes in agricultural land use and models of biophysical impacts. We analyze how demand from the RFS affected corn, soybean, and wheat prices and how these price shocks influenced the areas planted to specific crops and cropland overall. We then assess how these changes affected key environmental indicators including nitrate leaching, phosphorus runoff, soil erosion, and GHG emissions. For all estimates, we compare outcomes under the 2007 RFS to a business-as-usual (BAU) counterfactual scenario in which ethanol production satisfies only the volume required by the initial 2005 version of the policy, equivalent to the amount needed for reformulated gasoline under the 1990 Clean Air Act. We apply our models only domestically, such that any environmental effects that occur outside the United States would be additional.Our analyses show a modest change in the use of US agricultural land for crop production due to the RFS, which led to sizable increases in associated environmental impacts including nitrate leaching, phosphorus runoff, and soil erosion. While improvements in production efficiency have likely reduced the carbon intensity of corn ethanol since inception of the RFS, the previously underestimated emissions from US land conversion attributable to the policy are enough to fully negate or even reverse any GHG advantages of the fuel relative to gasoline. Our findings thereby underscore the importance of including such LUCs and environmental effects when projecting and evaluating the performance of renewable fuels and associated policies.     

From the Cover: Strategic protection of landslide vulnerable mountains for biodiversity conservation under land-cover and climate change impacts

Natural disasters impose huge uncertainty and loss to human lives and economic activities. Landslides are one disaster that has become more prevalent because of anthropogenic disturbances, such as land-cover changes, land degradation, and expansion of infrastructure. These are further exacerbated by more extreme precipitation due to climate change, which is predicted to trigger more landslides and threaten sustainable development in vulnerable regions. Although biodiversity conservation and development are often regarded as having a trade-off relationship, here we present a global analysis of the area with co-benefits, where conservation through expanding protection and reducing deforestation can not only benefit biodiversity but also reduce landslide risks to human society. High overlap exists between landslide susceptibility and areas of endemism for mammals, birds, and amphibians, which are mostly concentrated in mountain regions. We identified 247 mountain ranges as areas with high vulnerability, having both exceptional biodiversity and landslide risks, accounting for 25.8% of the global mountainous areas. Another 31 biodiverse mountains are classified as future vulnerable mountains as they face increasing landslide risks because of predicted climate change and deforestation. None of these 278 mountains reach the Aichi Target 11 of 17% coverage by protected areas. Of the 278 mountains, 52 need immediate actions because of high vulnerability, severe threats from future deforestation and precipitation extremes, low protection, and high-population density and anthropogenic activities. These actions include protected area expansion, forest conservation, and restoration where it could be a cost-effective way to reduce the risks of landslides.

Land-cover/land-use changes, such as deforestation, agriculture expansion, and urbanization, are among the biggest drivers of biodiversity loss (1–3). These changes not only result in the decline of wildlife populations and fragmentation of habitats, but they also increase environmental risks, such as erosion of fertile soil and increases in avalanches, landslides, and flooding, especially in mountainous areas (4, 5). Landslides are one of the most prevalent natural hazards and are caused by changes in slope stability resulting from undercutting, changes in water saturation, or loss of woody vegetation (6). They have become more frequent because of anthropogenic activities and land-cover/land-use changes (7–9). Deforestation, infrastructure construction, and mining triggered about 16% of fatal landslides from 2004 to 2016 (10). Landslides cause direct and indirect damages worth billions of dollars each year across the world, contributing to 17% of the fatalities due to natural hazards (11). From 2006 to 2015, landslides alone accounted for 27.6% of the geological disasters worldwide and caused casualties of 9,477 people, threatening the livelihood of local communities (12).While land-cover changes exacerbate the conditions for landslide activities, precipitation is the primary trigger for landslides; one expected to increase in importance under climate change (13, 14). Either high-intensity, short-duration rainfall or prolonged rain at relatively low intensities can trigger landslides, creating a rainfall intensity–duration relationship for identifying possible areas of risk (15). Both model studies and historical records show an increase in precipitation extremes, such as heavy rainfall, flooding, and droughts with climate warming due to increases in the saturation vapor pressure of water (16–19). Consequently, the total precipitation from extreme events doubles per degree of warming, mainly because of increasing event frequencies (19). With this greater frequency and magnitude of heavy precipitation (20), landslides are expected to increase (21). For example, High Mountain Asia is predicted to experience a 30 to 70% increase in landslide activity because of the intensification of precipitation extremes (22). This geohazard poses one of the greatest threats of climate change to human safety and development with potentially huge economic losses (14).It is crucial to understand the relationship between environmental risks, biodiversity, and the potential for sustainable development. This is true especially for mountain communities, where many people are in poverty and have low resilience and adaptive capacity (6, 22–24). About 24% of the global land area is mountains with 12% of the world’s population (25). Local communities directly depend on mountainous resources for their livelihoods and well-being (26). Residents in mountain areas are among the most economically vulnerable populations because of inaccessibility to markets and high risks from natural disasters, such as earthquakes, flash floods, and the impacts of climate change (25). However, the common development pattern relies on land-use changes and infrastructure building, which could pose higher environmental risks in mountainous areas than in lowland regions. For example, the expansion of road networks degrades the slope stability and further increases the susceptibility to landslides (27–29).Mountain building driven by plate tectonics and volcanism provokes many geological disasters, including earthquakes, landslides, and volcanic movements (5, 30), but it also creates the landscape and climatic variations that drive species diversification. Mountains harbor an exceptionally large portion of the world’s biodiversity, including more than 85% of the world’s amphibian, bird, and mammal species (31). The interaction between speciation, coexistence, and persistence of species has resulted in high-species richness and endemism in most mountains, especially in the tropics (31). Nonetheless, habitat loss and degradation continue to threaten biodiversity in mountain regions, with increasing anthropogenic activities such as logging, livestock grazing, and agriculture expansion with a higher rate of land abandonment than lowland areas (32). Instead of trade-offs between conservation and development (33–35), we suggest that, with careful conservation planning and priority setting, a compatible outcome can be achieved. We identify vulnerable mountain regions for conservation that have both high biodiversity and landslide susceptibility. Since future landslide activity is expected to increase in areas with 1) increased deforestation and 2) increased regional precipitation due to climate change (36, 37), we also identified emerging vulnerable mountains in the future as priorities for conservation using land-use projections and climate scenarios. The emerging strategy of nature-based solutions (NBS) promotes nature as the means for providing solutions to societal challenges (38, 39). Here, we present a potential NBS and identify key areas with the greatest potential to reduce landslide risks while also protecting biodiversity through expanding protected areas, reducing deforestation, and restoring forests.     

Robust increases in severe thunderstorm environments in response to greenhouse forcing

Although severe thunderstorms are one of the primary causes of catastrophic loss in the United States, their response to elevated greenhouse forcing has remained a prominent source of uncertainty for climate change impacts assessment. We find that the Coupled Model Intercomparison Project, Phase 5, global climate model ensemble indicates robust increases in the occurrence of severe thunderstorm environments over the eastern United States in response to further global warming. For spring and autumn, these robust increases emerge before mean global warming of 2 °C above the preindustrial baseline. We also find that days with high convective available potential energy (CAPE) and strong low-level wind shear increase in occurrence, suggesting an increasing likelihood of atmospheric conditions that contribute to the most severe events, including tornadoes. In contrast, whereas expected decreases in mean wind shear have been used to argue for a negative influence of global warming on severe thunderstorms, we find that decreases in shear are in fact concentrated in days with low CAPE and therefore do not decrease the total occurrence of severe environments. Further, we find that the shift toward high CAPE is most concentrated in days with low convective inhibition, increasing the occurrence of high-CAPE/low-convective inhibition days. The fact that the projected increases in severe environments are robust across a suite of climate models, emerge in response to relatively moderate global warming, and result from robust physical changes suggests that continued increases in greenhouse forcing are likely to increase severe thunderstorm occurrence, thereby increasing the risk of thunderstorm-related damage.There now is considerable evidence that the occurrence and intensity of climate extremes have been increasing in recent decades, and that continued global warming likely will amplify these changes (1). However, the response of severe thunderstorms has remained a prominent uncertainty (e.g., ref. 1). Given the substantial damage caused by heavy rainfall, high winds, hail, and tornadoes, uncertainty about potential changes in the frequency, distribution, and intensity of severe thunderstorms poses considerable challenges for climate impacts assessment.This uncertainty arises from at least three sources (e.g., refs. 2–5). First, there is no reliable, independent, long-term record of severe thunderstorms—and particularly tornadoes—with which to systematically analyze variability and trends (2–5). Second, theoretical arguments and climate model experiments both predict conflicting influences of the large-scale—or “environmental”—conditions that support severe thunderstorms (e.g., refs. 2–4 and 6–9). Third, a suite of processes important for the realization of individual storms in the real atmosphere has remained mostly inaccessible in climate model experiments because of deficiencies in model development and/or computational resources (e.g., refs. 3 and 10–12).Despite these theoretical and technical challenges, both explicit and implicit modeling approaches have been used. Explicit approaches use horizontal and vertical resolutions that literally permit an explicit representation of deep convective storms and their associated characteristics (e.g., refs. 10–14). However, to date, explicit climate change experiments have been limited to short integrations of a single model (13, 14) or simulations of individual events over relatively small computational domains (12). Alternatively, implicit approaches examine the atmospheric environments that are known to support severe thunderstorm formation in the current climate (15). For example, Trapp et al. (6, 7) suggested that increasing convective available potential energy (CAPE) overcomes decreasing vertical wind shear to increase the total occurrence of severe thunderstorm environments over much of the continental United States. Although similar implicit approaches have concluded that global warming enhances conditions that support severe convection (8, 9, 16), expectations of decreasing shear continue to create uncertainty about the severe thunderstorm response (e.g., refs. 2, 3, and 8).Here, we use the implicit approach to analyze severe thunderstorm environments in the Coupled Model Intercomparison Project, Phase 5 (CMIP5) global climate model ensemble, which offers a unique multimodel dataset of subdaily 3D atmospheric variables (17). We focus on representative concentration pathway (RCP)8.5, which covers the full range of 21st century radiative forcing and global warming spanned by the illustrative RCPs (18), thereby allowing us to probe the response to both low and high levels of forcing. We define a severe thunderstorm day using the product of vertical wind shear (over a 6-km layer; S06) and CAPE, as suggested by Brooks et al. (15) and modified by Trapp et al. (6, 7) (Materials and Methods). Our criteria apply to a generic severe thunderstorm environment that might result in hail, damaging “straight-line” surface winds, and/or tornadoes (6, 15). The additional existence of strong shear within the lowest atmospheric levels (e.g., 1-km shear; S01) often is considered a key ingredient toward tornado generation (e.g., ref. 19); therefore, we consider S01 as well. We focus our analysis on the continental United States, which exhibits a globally unique confluence of peak severe thunderstorm activity (3, 15), peak density of observations (3, 15), and acute losses to severe thunderstorm events (20–22).     

Substantial increase in concurrent droughts and heatwaves in the United States

A combination of climate events (e.g., low precipitation and high temperatures) may cause a significant impact on the ecosystem and society, although individual events involved may not be severe extremes themselves. Analyzing historical changes in concurrent climate extremes is critical to preparing for and mitigating the negative effects of climatic change and variability. This study focuses on the changes in concurrences of heatwaves and meteorological droughts from 1960 to 2010. Despite an apparent hiatus in rising temperature and no significant trend in droughts, we show a substantial increase in concurrent droughts and heatwaves across most parts of the United States, and a statistically significant shift in the distribution of concurrent extremes. Although commonly used trend analysis methods do not show any trend in concurrent droughts and heatwaves, a unique statistical approach discussed in this study exhibits a statistically significant change in the distribution of the data.Heatwaves cause severe damage to society and the environment (1), with impacts on human health, air quality, and vegetation (2, 3). In 2003, for example, European countries faced an unprecedented heatwave, which in turn caused unusually high ozone concentrations (3) and severe health problems, particularly in France, where 15,000 extra deaths occurred (3–5). United Nations Environment Programme considers the European heatwave the world’s most costly weather-related disaster in 2003. Impacts were exacerbated because the region was in a drought (6).Heatwaves have a variety of direct, indirect, immediate, and delayed impacts, including higher water loss via evapotranspiration, lower yields of grains and other agricultural products (7), increased energy consumption, a decrease in efficiency of power plants (8), air pollution, and adverse effects on human health (3, 6). Heatwaves have also contributed to an increase in the duration, size, and intensity of wildfires, causing economic losses and catastrophic environmental impacts (8).Droughts also have pronounced impacts on society and the environment, such as significant reductions in gross primary productivity, leading to shortages in food production and increases in global food prices (2). The annual economic damage caused by droughts is estimated to be approximately $7 billion globally (9), with potential impacts on livestock, transportation by river, hydropower production, bioenergy, and energy consumption (8, 10–12).Extreme climatic events can occur simultaneously, exacerbating environmental and societal impacts. Environmental hazards often result from a combination of climatic events (13, 14) over a range of spatial and temporal scales (15, 16). A wildfire, for example, may occur on a hot, dry, and windy day, although each of these individual conditions may not necessarily be extreme by themselves (16). In the Intergovernmental Panel on Climate Change special report on managing the risks of extreme events and disasters, the combination of multiple climate extreme events is termed a compound event (14, 16). Most analyses of climate and weather extremes typically tend to focus on a single climatic condition; however, this univariate approach may underestimate the effects of concurrent and compound extremes (16).Sustained precipitation deficit in summer can be a contributory factor to hot summer days (17). Heatwaves reduce the total energy transfer to the atmosphere, resulting in a decrease in convective precipitation (7). This in turn causes a soil−precipitation feedback loop that tends to extend or intensify drought conditions (7). The interaction between precipitation and temperature has been widely recognized in numerous studies (18, 19). Heatwaves concurrent with droughts can intensify individual impacts of heatwaves or drought on society, the environment, and the global economy (19, 20). Studies suggest that changes in the relationship between precipitation and temperature may be more important than the changes in each of the variables individually (16, 21). This study investigates changes in concurrent droughts and heatwaves in the United States using several different statistical techniques.A heatwave is typically defined as a period of consecutive extremely hot days (22, 23), such as five consecutive days with temperature above the 90th percentile. Here, we use the 85th, 90th, and 95th percentiles of the warm season (May–October) temperature as extreme thresholds, and three heatwave durations (3 d, 5 d, and 7 d). A 5-d heatwave with a 90th percentile threshold is defined as five consecutive days with the maximum temperature exceeding the 90th percentile of the long-term climatology for that month. In this study, meteorological droughts are defined as precipitation deficits relative to the climatology using the Standardized Precipitation Index (SPI) (24). Throughout this study, a drought is defined as an event that leads to SPI < −0.8 (approximately the 20th percentile precipitation). We use daily temperature and monthly precipitation information to identify historical droughts and heatwaves in the United States (see Data).     

Higher sea surface temperature in the Indian Ocean during the Last Interglacial weakened the South Asian monsoon

Addressing and anticipating future South Asian monsoon changes under continuing global warming is of critical importance for the food security and socioeconomic well-being of one-quarter of the world’s population. However, climate model projections show discrepancies in future monsoon variability in South Asian monsoon domains, largely due to our still limited understanding of the monsoon response to warm climate change scenarios. Particularly, climate models are largely based on the assumption that higher solar insolation causes higher rainfall during similar warm climatic regimes, but this has not been verified by proxy data for different interglacial periods. Here, we compare Indian summer monsoon (ISM) variability during the Last Interglacial and Holocene using a sedimentary leaf wax δD and δ¹³C record from the northern Bay of Bengal, representing the Ganges–Brahmaputra–Meghna (G-B-M) river catchment. In combination with a seawater salinity record, our results show that ISM intensity broadly follows summer insolation on orbital scales, but ISM intensity during the Last Interglacial was lower than during the Holocene despite higher summer insolation and greenhouse gas concentrations. We argue that sustained warmer sea surface temperature in the equatorial and tropical Indian Ocean during the Last Interglacial increased convective rainfall above the ocean but dampened ISM intensity on land. Our study demonstrates that besides solar insolation, internal climatic feedbacks also play an important role for South Asian monsoon variability during warm climate states. This work can help to improve future climate model projections and highlights the importance of understanding controls of monsoonal rainfall under interglacial boundary conditions.

The South Asian monsoon, also known as Indian summer monsoon (ISM), is one of the world’s most sensitive weather systems (1, 2). It is also one of the most critical weather systems for human livelihood. The water and food security of billions of people on the Indian subcontinent and adjacent areas is under pressure by increased weather anomalies and extreme monsoonal rainfall events (1). Despite the far-reaching consequences, prediction of ISM behavior under climate warming scenarios remains a key challenge for both global and regional climate models (3–5). Due to inconsistencies in model projections, debates center on whether the ISM will weaken or strengthen in a warming climate (2–4, 6). In this regard, a major challenge is our incomplete understanding of the extent to which ISM intensity responds to rapidly changing climatic factors—such as rising sea surface temperature (SST), elevated atmospheric greenhouse gas (GHG) concentrations, changing vegetation cover, and decreasing ice sheets and sea ice cover—and their interactive dynamics in a warming climate (1, 2, 5, 7, 8). Several proxy records from stalagmites and marine sediment archives on and around the Indian subcontinent have extended instrumental records and helped to identify monsoon response and variability to various climate forcings (9–12). However, these studies have mainly focused on distinct climatic transitions, namely, between glacial and interglacial periods. While studies showed that the monsoon was generally stronger during warm interglacials and interstadials compared to cold glacials and stadials, the varying degree of monsoonal rainfall intensity between different warm periods—when climatic boundary conditions were fairly similar—is often overlooked. Obtaining and comparing climatic information from different warm periods is therefore highly relevant for constraining uncertainties in model projections for a future warming climate (13).At orbital time scales, changes in incoming solar insolation are regarded the most prominent control for the difference between overall glacial and interglacial monsoon rainfall intensity because past fluctuations of monsoon strength coincide remarkably well with changes in the Earth’s precessional cycle (5, 8, 9, 14–17). Higher summer insolation leads to enhanced atmospheric humidity, wind circulation, and land–sea thermal gradients, which ultimately increase precipitation (8, 18). Although it is generally assumed that higher insolation during warmer interglacials also results in higher ISM rainfall intensity, solar modulation of monsoon intensity and variation during different interglacials has remained largely unexplored. The last interglacial period, commonly correlated with marine isotope stage (MIS) 5e, can be considered a good analog for future climate scenarios because ice sheets at that time were much smaller, while temperatures and sea level were higher than at present (19–21). Compared to the Last Interglacial, the present interglacial period, the Holocene, also underwent comparable changes in orbital configurations, although the magnitude of boreal summer insolation change was weaker at precessional perihelion conditions (22, 23). Because of the higher boreal summer insolation and global SST (by 1 to 2 °C) and the lower ice volume during the Last Interglacial than during the Holocene (24, 25), fully coupled global ocean–atmosphere climate models predict higher monsoon rainfall intensity during the Last Interglacial (ca. 130 to 115 ka) compared to the Holocene (11.6 ka to present) (13, 17, 18). However, despite such model predictions, no direct evidence for higher ISM intensity during the Last Interglacial than during the Holocene has been presented so far.Last Interglacial rainfall reconstructions for the Bay of Bengal branch of the ISM are typically based on stalagmite stable oxygen isotope (δ¹⁸O) records, which show varying magnitudes of δ¹⁸O changes at different sites on the Indian subcontinent (9, 11). However, great caution should be taken in equating stalagmite δ¹⁸O values with rainfall amounts because these values can also be influenced by various moisture sources, water circulation through underground networks, and the influence of climatic conditions on stalagmite formation (8, 11, 14, 26). Another qualitative precipitation proxy, δ¹⁸O of planktonic foraminifera, implies that the surface water salinity in the northern Bay of Bengal was slightly lower during the Holocene than during the Last Interglacial (27). This indicates that freshwater input from the catchment of the Ganges–Brahmaputra–Meghna (G-B-M) river system and ultimately ISM rainfall intensity during the Holocene might have been higher or at least similar despite higher insolation and higher SST in the tropical Indian Ocean during the Last Interglacial (27). Since salinity is not a direct measure of the rainfall amount but rather a proxy for freshwater runoff (28, 29), which could also be driven by mountain glacier melt, applying additional hydrological proxies at the same location can help to elucidate differences in ISM intensity between the two interglacial periods (8).To test whether monsoon rainfall was indeed higher during the Last Interglacial than during the Holocene, we provide regional proxy records of ISM rainfall and vegetation changes in the G-B-M river catchment obtained from a marine sediment core from the northern Bay of Bengal that spans the last ∼130 kyr at submillennial-scale resolution for the Holocene and MIS 5e. This sediment core covers the last two interglacial periods and six precessional cycles, allowing us to scrutinize the relationship between insolation and rainfall intensity for the Bay of Bengal branch of the ISM. In particular, we establish records of the stable hydrogen and carbon isotope composition (δD and δ¹³C) of sedimentary leaf wax lipids, namely, long-chain n-alkanes, that have been extracted from the sediments. Studies of modern surface sediments in the northern Bay of Bengal and moisture source modeling, as well as a continuous ∼18-kyr-long marine sediment record, have confirmed that the δD and δ¹³C of long-chain n-alkanes are reliable proxies for ISM rainfall amount and vegetation changes in the G-B-M catchment (10, 30, 31). To complement our compound-specific stable isotope records, we compare these with published proxy data from the same sediment core (27) as well as with other Asian paleomonsoon proxy records.     

Increased variability of the western Pacific subtropical high under greenhouse warming

An anomalous strengthening in western Pacific subtropical high (WPSH) increases moisture transport from the tropics to East Asia, inducing anomalous boreal summer monsoonal rainfall, causing extreme weather events in the densely populated region. Such positive WPSH anomalies can be induced by central Pacific (CP) cold sea-surface temperature (SST) anomalies of an incipient La Niña and warm anomalies in the Indian and/or the tropical Atlantic Ocean, both promoting anticyclonic anomalies over the northwestern Pacific region. How variability of the WPSH, its extremity, and the associated mechanisms might respond to greenhouse warming remains elusive. Using outputs from 32 of the latest climate models, here we show an increase in WPSH variability translating into a 73% increase in frequency of strong WPSH events under a business-as-usual emission scenario, supported by a strong intermodel consensus. Under greenhouse warming, response of tropical atmosphere convection to CP SST anomalies increases, as does the response of the northwestern Pacific anticyclonic circulation. Thus, climate extremes such as floods in the Yangtze River Valley of East China associated with WPSH variability are likely to be more frequent and more severe.

The western Pacific subtropical high (WPSH) is an anticyclonic system hovering over the middle and lower troposphere of the northwestern Pacific, strongest in boreal summer (1, 2) (SI Appendix, Fig. S1A). The southerly winds in the west flank of the system transport moisture from the tropics to East Asia and collide with dry and cold flows from the north (2–4). These winds influence meiyu (Baiu in Japan and Changma in Korea) and an associated elongated rain belt that usually starts moving northward from southern China in May before its seasonal southward withdrawal in August, dominating variability of East Asian summer rainfall (3–6). The WPSH undergoes strong interannual variability (SI Appendix, Fig. S1B), exerting a severe impact on the boreal summer climate over the densely populated region of East Asia (2, 6–8).A strong WPSH event occurs when the WPSH undergoes a westward intensification, influencing regional climate and leading to anomalous rainfall and inducing severe floods (4, 9). For example, during the 2020 strong WPSH event, floods in the Yangtze River Valley of East China caused hundreds of deaths, millions of hectares of crops destroyed, and tens of billions in economic damage (9, 10). Further back, in 1998, a strong WPSH contributed to river floods in East China that killed thousands and affected more than 200 million people (11).Previous research examined observed WPSH variability, impact, and change over the period from 1979 to 2017 (12) or response of future climatological WPSH to greenhouse warming in models (13–17). There is an upward trend of the observed WPSH, which is expressed in a leading mode, but whether the WPSH variability above the trend has changed is not clear (12). Other studies found either a stronger or little changed mean WPSH under greenhouse warming (12–17). However, how WPSH variability that rides on the mean state, frequency of strong WPSH anomalies above the mean change, and associated mechanisms will change under future greenhouse warming remain largely unknown.Multiple processes from ocean basins affect variability of the WPSH (5, 8, 18–22). One process is an atmospheric response to sea-surface temperature (SST) variability in the central Pacific (CP) (Niño4 region, 5°S-5°N, 160°E-150°W), where cool anomalies of an incipient La Niña develop in boreal summer (June, July, and August, JJA) (23); an incipient La Niña suppresses CP local convection, generating a westward propagated atmospheric Rossby wave that strengthens the WPSH (8, 24–29). An enhanced convection over the Maritime Continent associated with the La Niña strengthens the WPSH by modulating local Hadley circulation with increased anticyclonic circulation over the northwestern Pacific (30–32). Anomalous warming of the tropical North Atlantic is also involved in driving positive WPSH anomalies by inducing CP cold SST anomalies and suppressed convection (33–35).Another process involves the Indian Ocean, where boreal summer warming induced by a previous-year El Niño triggers warm Kelvin waves emanating into the tropical western Pacific, inducing an anomalous anticyclone at the low troposphere of the northwest Pacific (36, 37). Although the Indian Ocean mechanism can operate by itself with a residual warm condition of the decaying El Niño in the equatorial Pacific, it is in part incorporated in the impact of CP SST variability as the previous-year El Niño transitions to an incipient La Niña.Because of the vast impact, how variability of the WPSH might change under greenhouse warming is an important issue. Assuming that greenhouse warming has an impact, one expects that observed WPSH variability of the past 42 y (1979 to 2020) should already be impacted. However, due to the short length of observational data and strong internal variability (38), whether change between the latter half (2000 to 2020) and former half (1979 to 1999) period has emerged out of internal variability is not clear. As such, we examine how WPSH variability might change under long-term further increasing greenhouse warming by comparing simulated WPSH variability between two 100-y periods of the 20th and 21st centuries. Over a longer period, impact of greenhouse warming is more detectable because the influence from internal variability is weaker and the climate change signal is larger (39, 40). Below we show that WPSH variability increases under long-term global warming, in turn suggesting that part of the observed change in WPSH variability is driven by greenhouse warming.     

Thermodynamics of formation of coffinite,USiO4

Coffinite, USiO₄, is an important U(IV) mineral, but its thermodynamic properties are not well-constrained. In this work, two different coffinite samples were synthesized under hydrothermal conditions and purified from a mixture of products. The enthalpy of formation was obtained by high-temperature oxide melt solution calorimetry. Coffinite is energetically metastable with respect to a mixture of UO₂ (uraninite) and SiO₂ (quartz) by 25.6 ± 3.9 kJ/mol. Its standard enthalpy of formation from the elements at 25 °C is −1,970.0 ± 4.2 kJ/mol. Decomposition of the two samples was characterized by X-ray diffraction and by thermogravimetry and differential scanning calorimetry coupled with mass spectrometric analysis of evolved gases. Coffinite slowly decomposes to U₃O₈ and SiO₂ starting around 450 °C in air and thus has poor thermal stability in the ambient environment. The energetic metastability explains why coffinite cannot be synthesized directly from uraninite and quartz but can be made by low-temperature precipitation in aqueous and hydrothermal environments. These thermochemical constraints are in accord with observations of the occurrence of coffinite in nature and are relevant to spent nuclear fuel corrosion.In many countries with nuclear energy programs, spent nuclear fuel (SNF) and/or vitrified high-level radioactive waste will be disposed in an underground geological repository. Demonstrating the long-term (10⁶–10⁹ y) safety of such a repository system is a major challenge. The potential release of radionuclides into the environment strongly depends on the availability of water and the subsequent corrosion of the waste form as well as the formation of secondary phases, which control the radionuclide solubility. Coffinite (1), USiO₄, is expected to be an important alteration product of SNF in contact with silica-enriched groundwater under reducing conditions (2–8). It is also found, accompanied by thorium orthosilicate and uranothorite, in igneous and metamorphic rocks and ore minerals from uranium and thorium sedimentary deposits (2, 4, 5, 8–16). Under reducing conditions in the repository system, the uranium solubility (very low) in aqueous solutions is typically derived from the solubility product of UO₂. Stable U(IV) minerals, which could form as secondary phases, would impart lower uranium solubility to such systems. Thus, knowledge of coffinite thermodynamics is needed to constrain the solubility of U(IV) in natural environments and would be useful in repository assessment.In natural uranium deposits such as Oklo (Gabon) (4, 7, 11, 12, 14, 17, 18) and Cigar Lake (Canada) (5, 13, 15), coffinite has been suggested to coexist with uraninite, based on electron probe microanalysis (EPMA) (4, 5, 7, 11, 13, 17, 19, 20) and transmission electron microscopy (TEM) (8, 15). However, it is not clear whether such apparent replacement of uraninite by a coffinite-like phase is a direct solid-state process or occurs mediated by dissolution and reprecipitation.The precipitation of USiO₄ as a secondary phase should be favored in contact with silica-rich groundwater (21) [silica concentration >10⁻⁴ mol/L (22, 23)]. Natural coffinite samples are often fine-grained (4, 5, 8, 11, 13, 15, 24), due to the long exposure to alpha-decay event irradiation (4, 6, 25, 26) and are associated with other minerals and organic matter (6, 8, 12, 18, 27, 28). Hence the determination of accurate thermodynamic data from natural samples is not straightforward. However, the synthesis of pure coffinite also has challenges. It appears not to form by reacting the oxides under dry high-temperature conditions (24, 29). Synthesis from aqueous solutions usually produces UO₂ and amorphous SiO₂ impurities, with coffinite sometimes being only a minor phase (24, 30–35). It is not clear whether these difficulties arise from kinetic factors (slow reaction rates) or reflect intrinsic thermodynamic instability (33). Thus, there are only a few reported estimates of thermodynamic properties of coffinite (22, 36–40) and some of them are inconsistent. To resolve these uncertainties, we directly investigated the energetics of synthetic coffinite by high-temperature oxide melt solution calorimetry to obtain a reliable enthalpy of formation and explored its thermal decomposition.     

Low-cost solution to the grid reliability problem with 100% penetration of intermittent wind,water, and solar for all purposes

This study addresses the greatest concern facing the large-scale integration of wind, water, and solar (WWS) into a power grid: the high cost of avoiding load loss caused by WWS variability and uncertainty. It uses a new grid integration model and finds low-cost, no-load-loss, nonunique solutions to this problem on electrification of all US energy sectors (electricity, transportation, heating/cooling, and industry) while accounting for wind and solar time series data from a 3D global weather model that simulates extreme events and competition among wind turbines for available kinetic energy. Solutions are obtained by prioritizing storage for heat (in soil and water); cold (in ice and water); and electricity (in phase-change materials, pumped hydro, hydropower, and hydrogen), and using demand response. No natural gas, biofuels, nuclear power, or stationary batteries are needed. The resulting 2050–2055 US electricity social cost for a full system is much less than for fossil fuels. These results hold for many conditions, suggesting that low-cost, reliable 100% WWS systems should work many places worldwide.Worldwide, the development of wind, water, and solar (WWS) energy is expanding rapidly because it is sustainable, clean, safe, widely available, and, in many cases, already economical. However, utilities and grid operators often argue that today’s power systems cannot accommodate significant variable wind and solar supplies without failure (1). Several studies have addressed some of the grid reliability issues with high WWS penetrations (2–21), but no study has analyzed a system that provides the maximum possible long-term environmental and social benefits, namely supplying all energy end uses with only WWS power (no natural gas, biofuels, or nuclear power), with no load loss at reasonable cost. This paper fills this gap. It describes the ability of WWS installations, determined consistently over each of the 48 contiguous United States (CONUS) and with wind and solar power output predicted in time and space with a 3D climate/weather model, accounting for extreme variability, to provide time-dependent load reliably and at low cost when combined with storage and demand response (DR) for the period 2050–2055, when a 100% WWS world may exist.