Table of contents

Recent reports have highlighted the challenge of keeping global average temperatures below 2 °C and—even more so—1.5 °C (IPCC 2018). Fossil-fuel burning and cement production release ∼90% of all CO₂ emissions from human activities. After a three-year hiatus with stable global emissions (Jackson et al 2016; Le Quéré C et al 2018a ; IEA 2018), CO₂ emissions grew by 1.6% in 2017 to 36.2 Gt (billion tonnes), and are expected to grow a further 2.7% in 2018 (range: 1.8%–3.7%) to a record 37.1 ± 2 Gt CO₂ (Le Quéré et al 2018b). Additional increases in 2019 remain uncertain but appear likely because of persistent growth in oil and natural gas use and strong growth projected for the global economy. Coal use has slowed markedly in the last few years, potentially peaking, but its future trajectory remains uncertain. Despite positive progress in ∼19 countries whose economies have grown over the last decade and their emissions have declined, growth in energy use from fossil-fuel sources is still outpacing the rise of low-carbon sources and activities. A robust global economy, insufficient emission reductions in developed countries, and a need for increased energy use in developing countries where per capita emissions remain far below those of wealthier nations will continue to put upward pressure on CO₂ emissions. Peak emissions will occur only when total fossil CO₂ emissions finally start to decline despite growth in global energy consumption, with fossil energy production replaced by rapidly growing low- or no-carbon technologies.

The production of lithium-ion batteries (LIBs) has increased in capacity by almost eight fold in the past ten years due to growing demand for consumer electronics and electric-drive vehicles. The social and environmental implications of increased lithium demand is significant not only in the context of policy initiatives that are incentivizing electric vehicle adoption, but also because electric vehicle adoption is part of the vision of sustainability transitions that are being put forth in a variety of contexts. Any evidence that suggests that the externalities of the technology uptake are not being addressed would directly counter the intent of such initiatives. For LIBs to be fully sustainable, it is imperative that impacts along life cycle stages be adequately addressed, including lithium mineral extraction. This study investigates how the scope and focus of research in this area are changing and what drives their evolution. Based on a bibliometric analysis, we evaluate the state of research on the issues of lithium mineral extraction, use, and their impacts. The article identifies research hotspots and emerging research agendas by mapping the evolution of research focus and themes. Our analysis finds that research on the socio-environmental impacts of lithium extraction at local level has been very limited. We discuss some research directions to address the knowledge gaps in terms of specific research topics, methodologies, and broader system perspectives.

Lightning is a natural hazard, lethal and destructive on short time scales and with important climatic effects on longer time-scales (through NOx production and forest fire ignition). It is accompanied by severe weather, hail and flash flooding that often entail significant economic losses. It also poses threats to aviation safety and to renewable energy production by wind-turbines, and is known to adversely affect electric power utilities and transmission lines. Present day global trends in urbanization, land-use and energy production are mapped to climate change through several scenarios, relating future concentrations of green-house gasses in the atmosphere ('Representative Concentration Pathways' or RCPs; IPCC et al 2013 Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge: Cambridge University Press) p 1535) to the adopted energy policies and international agreements. These scenarios predict a few degrees of atmospheric warming near Earth surface, which offer significantly different climatic regimes in many regions on Earth, and also affect the intensity and frequency of atmospheric natural disasters (e.g. tropical storms). Although it is hard to precisely predict what future lightning distributions will look like, the combination of large metropolitan areas, increased population and a warmer climate almost guarantee an intensification of the human exposure to lightning hazard. We review current trends in population, urbanization and technology usage and assess their vulnerability to future lightning activity in different scenarios.

Pollution and the economy seem to have been inextricably linked throughout human history. Yet the relationship between environmental harm and economic development is complex and its understanding has been fragmented by disciplinary biases. Economists and environmental scientists have diverged on the urgency of abatement mechanisms and the marginal returns on investment on control technologies and social adaptations. The Environmental Kuznets Curve hypothesis has dominated this discourse, but is only one part of a broader pollution-economy nexus. As we consider a societal shift towards a circular economy, there is a need to consider a more integrated framework for analyzing the empirical evidence that connects pollution and economic development, and its implications for human well-being and the achievement of the sustainable development goals. This paper develops the main connections between pollution and economic development by reviewing the existing empirical evidence in the literature.

The Mongolian Plateau hosts two different governments: the Mongolian People's Republic and the Inner Mongolia Autonomous Region, a provincial-level government of the People's Republic of China. The divergence between these governments has widened in the past century, mostly due to a series of institutional changes that generated different socioeconomic and demographic trajectories. Due to its high latitude and altitude, the Plateau has been highly sensitive to the rapid changes in global and regional climates that have altered the spatial and temporal distributions of energy and water. Based on a recent workshop to synthesize findings on the sustainability of the Plateau amidst socioeconomic and environmental change, we identify five critical issues facing the social-ecological systems (SES): (1) divergent and uncertain changes in social and ecological characteristics; (2) declining prevalence of nomadism; (3) consequences of rapid urbanization in transitional economies; (4) the unsustainability of large-scale afforestation efforts in the semi-arid and arid areas of Inner Mongolia; and (5) the role of institutional changes in shaping the SES on the Plateau. We emphasize that lessons learned in Inner Mongolia are valuable, but may not always apply to Mongolia. National land management policies and regulations have long-term effects on the sustainability of SES; climate change adaptation policies and practices must be tuned to local conditions and should be central to decision-making on natural resource management and socioeconomic development pathways.

For millennia Indigenous communities worldwide have maintained diverse knowledge systems informed through careful observation of dynamics of environmental changes. Although Indigenous communities and their knowledge systems are recognized as critical resources for understanding and adapting to climate change, no comprehensive, evidence-based analysis has been conducted into how environmental studies engage Indigenous communities. Here we provide the first global systematic review of levels of Indigenous community participation and decision-making in all stages of the research process (initiation, design, implementation, analysis, dissemination) in climate field studies that access Indigenous knowledge. We develop indicators for assessing responsible community engagement in research practice and identify patterns in levels of Indigenous community engagement. We find that the vast majority of climate studies (87%) practice an extractive model in which outside researchers use Indigenous knowledge systems with minimal participation or decision-making authority from communities who hold them. Few studies report on outputs that directly serve Indigenous communities, ethical guidelines for research practice, or providing Indigenous community access to findings. Further, studies initiated with (in mutual agreement between outside researchers and Indigenous communities) and by Indigenous community members report significantly more indicators for responsible community engagement when accessing Indigenous knowledges than studies initiated by outside researchers alone. This global assessment provides an evidence base to inform our understanding of broader social impacts related to research design and concludes with a series of guiding questions and methods to support responsible research practice with Indigenous and local communities.

Increasing risks of extreme weather events are the most noticeable and damaging manifestation of anthropogenic climate change. In the aftermath of an extreme event, policymakers are often called upon to make timely and sensitive decisions about rebuilding and managing present and future risks. Information regarding whether, where, and how present day and future risks are changing is needed to adequately inform these decisions. But this information is often not available on the temporal and spatial scales decisions are made. In particular, decision makers require information about both historical changes and plausible future changes in the severity and frequency of extreme weather in a seamless way. However, applying the same methods from event attribution to future projections by defining events based on present day frequency of occurrence leads to potentially misleading estimates of future changes in a warmer climate. We demonstrate that this is fundamentally a consequence of risk ratios saturating at different values. This study investigates the circumstances under which present-day attribution frameworks become ill-suited for characterising changes in future extremes, before discussing what alternative frameworks may be more useful to inform stakeholders about what additional risks from extreme weather events will emerge in a warmer world.

We review the evidence for a putative early 21st-century divergence between global mean surface temperature (GMST) and Coupled Model Intercomparison Project Phase 5 (CMIP5) projections. We provide a systematic comparison between temperatures and projections using historical versions of GMST products and historical versions of model projections that existed at the times when claims about a divergence were made. The comparisons are conducted with a variety of statistical techniques that correct for problems in previous work, including using continuous trends and a Monte Carlo approach to simulate internal variability. The results show that there is no robust statistical evidence for a divergence between models and observations. The impression of a divergence early in the 21st century was caused by various biases in model interpretation and in the observations, and was unsupported by robust statistics.

This work reviews the literature on an alleged global warming 'pause' in global mean surface temperature (GMST) to determine how it has been defined, what time intervals are used to characterise it, what data are used to measure it, and what methods used to assess it. We test for 'pauses', both in the normally understood meaning of the term to mean no warming trend, as well as for a 'pause' defined as a substantially slower trend in GMST. The tests are carried out with the historical versions of GMST that existed for each pause-interval tested, and with current versions of each of the GMST datasets. The tests are conducted following the common (but questionable) practice of breaking the linear fit at the start of the trend interval ('broken' trends), and also with trends that are continuous with the data bordering the trend interval. We also compare results when appropriate allowance is made for the selection bias problem. The results show that there is little or no statistical evidence for a lack of trend or slower trend in GMST using either the historical data or the current data. The perception that there was a 'pause' in GMST was bolstered by earlier biases in the data in combination with incomplete statistical testing.

We review the capabilities and costs of various lofting methods intended to deliver sulfates into the lower stratosphere. We lay out a future solar geoengineering deployment scenario of halving the increase in anthropogenic radiative forcing beginning 15 years hence, by deploying material to altitudes as high as ∼20 km. After surveying an exhaustive list of potential deployment techniques, we settle upon an aircraft-based delivery system. Unlike the one prior comprehensive study on the topic (McClellan et al 2012 Environ. Res. Lett.7 034019), we conclude that no existing aircraft design—even with extensive modifications—can reasonably fulfill this mission. However, we also conclude that developing a new, purpose-built high-altitude tanker with substantial payload capabilities would neither be technologically difficult nor prohibitively expensive. We calculate early-year costs of ∼$1500 ton⁻¹ of material deployed, resulting in average costs of ∼$2.25 billion yr⁻¹ over the first 15 years of deployment. We further calculate the number of flights at ∼4000 in year one, linearly increasing by ∼4000 yr⁻¹. We conclude by arguing that, while cheap, such an aircraft-based program would unlikely be a secret, given the need for thousands of flights annually by airliner-sized aircraft operating from an international array of bases.

The Renewable Fuel Standard (RFS) initially set ambitious goals for US cellulosic biofuel production and, although the total renewable fuel volume reached 80% of the established target for 2017, the cellulosic fuel volume reached just 5% of the original goal. This shortfall has, in part, been ascribed to the hesitance of farmers to plant the high-yielding, low-input perennial biomass crops identified as otherwise ideal feedstocks. Policy and market uncertainty also hinder investment in capital-intensive new cellulosic biorefineries. This study combines remote sensing land use data, yield predictions, a fine-resolution geospatial modeling framework, and a novel facility siting algorithm to evaluate the potential for near-term scale-up of cellulosic fuel production using a combination of lower-risk annual feedstocks more familiar to US farmers: corn stover and biomass sorghum. Potential strategies include expansion or retrofitting of existing corn ethanol facilities and targeted construction of new facilities in resource-rich areas. The results indicate that, with a maximum 10% conversion of pastureland and cropland to sorghum in suitable regions, more than 80 of the 214 existing corn ethanol biorefineries could be retrofitted or expanded to accept cellulosic feedstocks and an additional 71 new biorefineries could be built. The resulting land conversion for bioenergy sorghum totals to 4.5% of US cropland and 3.7% of pastureland. If this biomass is converted to ethanol, the total increase in annual production could be 17 billion gallons, just over the original RFS 2022 cellulosic biofuel production target and equivalent to 12% of US gasoline consumption.

Recent rates of deforestation on private lands in Australia rival deforestation hotspots around the world, despite conservation policies in place to avert deforestation. This study uses causal impact estimation techniques to determine if a controversial conservation policy—the Vegetation Management Act (VMA)—has successfully reduced deforestation of remnant trees in the Brigalow Belt South, a 21.6 Mha biodiversity hotspot in Queensland. We use covariate matching to determine the regulatory effect of the policy on deforestation rates over time, compared to two counterfactual scenarios representing upper and lower estimates of policy impact. The VMA significantly reduced the rate of remnant deforestation in the highest impact scenario, saving 17, 729 ± 1733 ha during 2000–2016. In the lowest scenario, 'panic clearing' before and after enactment of the VMA minimized the amount of remnant forests saved and may have marginally increased deforestation relative to the counterfactual (−404 ± 617 ha). At peak effectiveness, the VMA successfully counteracted the amount of remnant deforestation during 2010–2012, but this only represents 4.78% of the 371, 252 ha of remnant forests cleared in the bioregion since enactment in 1999. Thus, while deforestation rates in the region have substantially reduced since the policy was enacted, our results of positive yet limited direct regulatory impact suggests the policy's effectiveness is strongly confounded by other deforestation drivers, like changing socio-economic or climate conditions, as well as new social signals provoked by the policy. The mechanisms through which the policy influences deforestation behavior must be further investigated to ensure real, desirable change is achieved.

The transition to electric vehicles is an important strategy for reducing greenhouse gas emissions from passenger cars. Modelling future pathways helps identify critical drivers and uncertainties. Global integrated assessment models (IAMs) have been used extensively to analyse climate mitigation policy. IAMs emphasise technological change processes but are largely silent on important social and behavioural dimensions to future technological transitions. Here, we develop a novel conceptual framing and empirical evidence base on social learning processes relevant for vehicle adoption. We then implement this formulation of social learning in IMAGE, a widely-used global IAM. We apply this new modelling approach to analyse how technological learning and social learning interact to influence electric vehicle transition dynamics. We find that technological learning and social learning processes can be mutually reinforcing. Increased electric vehicle market shares can induce technological learning which reduces technology costs while social learning stimulates diffusion from early adopters to more risk-averse adopter groups. In this way, both types of learning process interact to stimulate each other. In the absence of social learning, however, the perceived risks of electric vehicle adoption among later-adopting groups remains prohibitively high. In the absence of technological learning, electric vehicles remain relatively expensive and therefore is only an attractive choice for early adopters. This first-of-its-kind model formulation of both social and technological learning is a significant contribution to improving the behavioural realism of global IAMs. Applying this new modelling approach emphasises the importance of market heterogeneity, real-world consumer decision-making, and social dynamics as well as technology parameters, to understand climate mitigation potentials.

This study assesses the flood characteristics (timing, magnitude and frequency) in the pre-industrial and historical periods, and analyzes climate change impacts on floods at the warming levels of 1.5, 2.0 and 3.0 K above the pre-industrial level in four large river basins as required by the Paris agreement. Three well-established hydrological models (HMs) were forced with bias-corrected outputs from four global climate models (GCMs) for the pre-industrial, historical and future periods until 2100. The long pre-industrial and historical periods were subdivided into multiple 31-year subperiods to investigate the natural variability. The mean flood characteristics in the pre-industrial period were derived from the large ensemble based on all GCMs, HMs and 31-year subperiods, and compared to the ensemble means in the historical and future periods. In general, the variance of simulated flood characteristics is quite large in the pre-industrial and historical periods. Mostly GCMs and HMs contribute to the variance, especially for flood timing and magnitude, while the selection of 31-year subperiods is an important source of variance for flood frequency. The comparison between the ensemble means shows that there are already some changes in flood characteristics between the pre-industrial and historical periods. There is a clear shift towards earlier flooding for the Rhine (1.5 K scenario) and Upper Mississippi (3.0 K scenario). The flood magnitudes show a substantial increase in the Rhine and Upper Yellow only under the 3.0 K scenario. The floods are projected to occur more frequently in the Rhine under the 1.5 and 2.0 K scenarios, and less frequently in the Upper Mississippi under all scenarios.

For the Northern Hemisphere extratropics, changes in the mid-latitude storm tracks are key to understanding the impacts of climate warming, but projections of their future location in current climate models are affected by large uncertainty. Here, we show that in spite of this uncertainty in the atmospheric circulation response to warming, by analysing the behaviour of the storms (or extratropical cyclones) themselves, projections of change in the number of the most intensely precipitating extratropical cyclones are substantial and consistent across models. In particular, we show large increases in the frequency of extreme extratropical cyclones (those above the present day 99th percentile of precipitation intensity) by the end of the century. In both Europe and North America, these intensely precipitating extratropical cyclones are projected to more than triple in number by the end of the century unless greenhouse gas emissions are mitigated. Such changes in extratropical cyclone behaviour may have major impacts on society given intensely precipitating extratropical cyclones are responsible for many large-scale flooding events, and associated severe economic losses, in these regions.

Global agriculture is challenged to increase soil carbon sequestration and reduce greenhouse gas emissions while providing products for an increasing population. Growing crop production could be achieved through higher yield per hectare (i.e. intensive farming) or more hectares (extensive farming), which however, have different ecological and environmental consequences. Multiple lines of evidence indicate that expanding cropland for additional production may lead to loss of vegetation and soil carbon, and threaten the survival of wildlife. New concerns about the impacts of extensive farming have been raised for the US Corn Belt, one of the world's most productive regions, as cropland has rapidly expanded northwestward unto grasslands and wetlands in recent years. Here we used a process-based ecosystem model to distinguish and quantify how natural drivers as well as intensive and extensive farming practices have altered grain production, soil carbon storage, and agricultural carbon footprint in the US Western Corn Belt since 1980. Compared to the period 1980–2005, we found that cropland expansion more than tripled in the most recent decade (2006–2016), becoming a significant factor contributing to growing grain production. Land use change in this period led to a soil carbon loss of 90.8 ± 14.7 Tg (1 Tg = 10¹² g). As a result, grain production in this region shifted from carbon neutral to a carbon loss of 2.3 kg C kg⁻¹ grain produced. The enlarging negative carbon footprint (ΔC/ΔP) indicates the major role that cropland expansion has had on the carbon cost of grain production in this region. Therefore, we should be more cautious to pursue high crop production through agricultural cropland conversion, particularly in those carbon-rich soils.

The enhanced vegetation growth by climate warming plays a pivotal role in amplifying the seasonal cycle of atmospheric CO₂ at northern lands (>50° N) since 1960s. However, the correlation between vegetation growth, temperature and seasonal amplitude of atmospheric CO₂ concentration have become elusive with the slowed increasing trend of vegetation growth and weakened temperature control on CO₂ uptake since late 1990s. Here, based on in situ atmospheric CO₂ concentration records from the Barrow observatory site, we found a slowdown in the increasing trend of the atmospheric CO₂ amplitude from 1990s to mid-2000s. This phenomenon was associated with the paused decrease in the minimum CO₂ concentration ([CO₂]_min), which was significantly correlated with the slowdown of vegetation greening and growing-season length extension. We then showed that both the vegetation greenness and growing-season length were positively correlated with spring but not autumn temperature over the northern lands. Furthermore, such asymmetric dependences of vegetation growth upon spring and autumn temperature cannot be captured by the state-of-art terrestrial biosphere models. These findings indicate that the responses of vegetation growth to spring and autumn warming are asymmetric, and highlight the need of improving autumn phenology in the models for predicting seasonal cycle of atmospheric CO₂ concentration.

Predictions of future food supply under climate change rely on projected crop yield trends, which are typically based upon retrospective empirical analyses of historical yield gains. However, the estimation of these trends is difficult given the evolving impact of agricultural technologies and confounding influences such as weather. Here, we evaluate the effect of climate change on United States (US) maize yields in light of the productivity gains associated with the period of rapid adoption of genetically engineered (GE) seeds. We find that yield gains on the order of those experienced during the adoption of GE maize are needed to offset climate change impacts under the business-as-usual scenario, and that smaller gains, such as those associated with the pre-GE era in the 1980s and early 90s, would likely imply yield reductions below current levels. Although this study cannot identify the biophysical drivers of past and future maize yields, it helps contextualize the yield growth requirements necessary to counterbalance projected yield losses under climate change. Outside of the US, our findings have important implications for regions lagging in the adoption of new technologies which could help offset the detrimental effects of climate change.

In the period 2015–2017, the Western Cape region has suffered from three consecutive years of below average rainfall—leading to a prolonged drought and acute water shortages, most prominently in the city of Cape Town. After testing that the precipitation deficit is the primary driver behind the reduced surface water availability, we undertake a multi-method attribution analysis for the meteorological drought, defined in terms of a deficit in the 3 years running mean precipitation averaged over the Western Cape area. The exact estimate of the return time of the event is sensitive to the number of stations whose data is incorporated in the analysis but the rarity of the event is unquestionable, with a return time of more than a hundred years. Synthesising the results from five different large model ensembles as well as observed data gives a significant increase by a factor of three (95% confidence interval 1.5–6) of such a drought to occur because of anthropogenic climate change. All the model results further suggest that this trend will continue with future global warming. These results are in line with physical understanding of the effect of climate change at these latitudes and highlights that measures to improve Cape Town's resilience to future droughts are an adaptation priority.

Future anthropogenic aerosol and greenhouse gas emissions determine climate change in China, which influences crop growth and food production. However, very few studies have investigated their combined climate impacts on crop yields. Here, we apply a process-based modeling approach to examine potential climatic impacts of air pollution controls on maize yields in China for two future scenarios in the 2030s. The model suggests that reducing aerosol pollution emissions increases radiation, temperature and precipitation. Increased radiation and precipitation enhance yields while higher temperature reduces yields. These contrasting climate effects offset each other, leading to varied spatial responses in yields. Following the current legislation emission scenario, maize yield declines by 2.3% because air pollution shows only moderate reductions and the higher future temperature exerts the dominant detrimental impacts. In contrast, with the maximum technically feasible reduction scenario, the maize yield is projected to increase by 4.4% relative to the current level, because the benefit of increased radiation and precipitation outweighs the detrimental impacts of warming. Our results suggest that stringent aerosol pollution regulations can help mitigate maize yield losses in China due to the future climate warming.

In Europe, offshore wind farms have a capacity of 16 GW, with 71% installed at the North Sea. These wind farms represent an additional source of turbulence and may influence the stratification of the marine boundary layer. We present aircraft measurements and simulations showing an impact on temperature and humidity at hub height in the order of 0.5 K and 0.5 g kg⁻¹ even 60 km downwind of a wind farm cluster. We extend these simulations to explore a realistic future scenario, suggesting wakes in potential temperature and water vapor propagating more than 100 km downwind. Such impacts of wind farms are only observed in case of a strong stable stratification at rotor height, allowing wind farms to mix warmer air downward.

Previous studies have documented that surface wind speed (u) has been increasing over the ocean but decreasing over land for the past several decades. The decreasing u at the surface over land has been referred to as terrestrial stilling. A plausible hypothesis for terrestrial stilling is an increase in surface roughness associated with changes in land surface (e.g. enhanced vegetation growth, landscape fragmentation or urbanization). One of the most widespread land surface changes is enhanced vegetation leaf area index (LAI) known as greening, particularly over the middle to high latitudes of the Northern Hemisphere where strong stilling is observed from weather station data. In this study, we examine the hypothesis that enhanced vegetation LAI is a key driver of global terrestrial stilling. We first characterized the trend in u over the ocean using long-term satellite altimeter measurements, and the trend in u over land using continuous wind records from 4305 in situ meteorological stations. We then performed initial condition ensemble Atmospheric Model Intercomparison Project-type simulations using two state-of-the-art Earth system models (IPSL-CM and CESM) to isolate the response of u to the historical increase in LAI (representing the greening) for the period 1982–2011. Both models, forced with observed sea surface temperature and sea ice and with LAI from satellite observation, captured the observed strengthening of Pacific trade winds and Southern Ocean westerly winds. However, these simulations did not reproduce the weakening of surface winds over land as significantly as it appears in the observations (−0.006 m s⁻¹ versus −0.198 m s⁻¹ during 1982–2011), indicating that enhanced LAI (greening) is not a dominant driver for terrestrial stilling.

Implementation of socially acceptable and environmentally desirable solutions to soil erosion challenges is often limited by (1) fundamental gaps between the evidence bases of different disciplines and (2) an implementation gap between science-based recommendations, policy makers and practitioners. We present an integrated, interdisciplinary approach to support co-design of land management policy tailored to the needs of specific communities and places in degraded pastoral land in the East African Rift System. In a northern Tanzanian case study site, hydrological and sedimentary evidence shows that, over the past two decades, severe drought and increased livestock have reduced grass cover, leading to surface crusting, loss of soil aggregate stability, and lower infiltration capacity. Infiltration excess overland flow has driven (a) sheet wash erosion, (b) incision along convergence pathways and livestock tracks, and (c) gully development, leading to increased hydrological connectivity. Stakeholder interviews in associated sedenterising Maasai communities identified significant barriers to adoption of soil conservation measures, despite local awareness of problems. Barriers were rooted in specific pathways of vulnerability, such as a strong cattle-based cultural identity, weak governance structures, and a lack of resources and motivation for community action to protect shared land. At the same time, opportunities for overcoming such barriers exist, through openness to change and appetite for education and participatory decision-making. Guided by specialist knowledge from natural and social sciences, we used a participatory approach that enabled practitioners to start co-designing potential solutions, increasing their sense of efficacy and willingness to change practice. This approach, tested in East Africa, provides a valuable conceptual model around which other soil erosion challenges in the Global South might be addressed.

In recent years, cities across the United States have devoted considerable attention and resources to developing greenhouse gas (GHG) inventories and climate action plans (CAPs). Using integrated metropolitan-level GHG estimates from publicly available national datasets, we explore the implications of inventory scope and boundary choices for 41 metropolitan areas across the United States. We quantify emissions from 'under-reported' activities (i.e. emissions from industrial processes and from transportation between urban and suburban areas) and 'under-reported' geographies (i.e. emissions from all activities occurring within the metropolitan area, but outside the city limits), and find that, in most cases, these 'under-reported' emissions constitute a considerable portion of total metropolitan emissions. Given the important role local CAPs continue to play in national-level GHG reduction efforts, there appears to be much to gain from continuing to expand the scope and boundaries of local-level GHG accounting and reduction actions. This analysis helps illustrate why transitions toward policies at the regional (as opposed to the city level) may be warranted, as well as highlights some key issues that may arise as local-level GHG policies continue to evolve and expand. For example, if local decision-makers choose to expand the scope and/or scale of their policies, GHG reduction plans may warrant substantial alterations to baseline emission levels, targeted annual emission reduction rates, overall emission target levels, or the number of years needed to achieve a desired emission reduction. Ultimately, the manner in which these policies evolve will determine their overall contribution to national and international climate mitigation efforts.

To contribute to the debate over globalization and the environment we ask the question: what is the impact of trade openness on the nutrient use of nations? We address this question by using econometric methods to quantify the causal relationship between the trade openness and the nutrient use of nations on a global scale. In our empirical analysis we go beyond a cross-sectional analysis. We exploit time-series variation for an unbalanced panel of countries that spans the time period 2001–2014 (1027 total observations). By using a panel data analysis we are able to use fixed effects and better control for unobservable heterogeneity. We also explicitly consider how the openness of a country to trade may interact with its comparative advantage which determines its relative specialization in production, and hence its export strength as well as its import needs. We find that trade openness on average does not significantly impact nutrient use. However, there is evidence that as countries become more open and more capital abundant their nutrient use is reduced. This finding is in line with previous research that shows that trade openness does not have a negative impact on the environment. Our findings have both scientific and policy relevance as we strive to untangle causal relationships in the global food supply chain and determine its environmental impacts.

On agricultural frontiers, minimal regulation and potential windfall profits drive opportunistic land use that often results in environmental damage. Cannabis, an increasingly decriminalized agricultural commodity in many places throughout the world, may now be creating new agricultural frontiers. We examined how cannabis frontiers have boomed in northern California, one of the United States' leading production areas. From 2012–2016 cannabis farms increased in number by 58%, cannabis plants increased by 183%, and the total area under cultivation increased by 91%. Growth in number of sites (80%), as well as in site size (56% per site) contributed to the observed expansion. Cannabis expansion took place in areas of high environmental sensitivity, including 80%–116% increases in cultivation sites near high-quality habitat for threatened and endangered salmonid fish species. Production increased by 40% on steep slopes, sites more than doubled near public lands, and increased by 44% in remote locations far from paved roads. Cannabis farm abandonment was modest, and driven primarily by farm size, not location within sensitive environments. To address policy and institutions for environmental protection, we examined state budget allocations for cannabis regulatory programs. These increased six-fold between 2012–2016 but remained very low relative to other regulatory programs. Production may expand on frontiers elsewhere in the world, and our results warn that without careful policy and institutional development these frontiers may pose environmental threats, even in locations with otherwise robust environmental laws and regulatory institutions.

Forests dominate carbon (C) exchanges between the terrestrial biosphere and the atmosphere on land. In the long term, the net carbon flux between forests and the atmosphere has been significantly impacted by changes in forest cover area and structure due to ecological disturbances and management activities. Current empirical approaches for estimating net ecosystem productivity (NEP) rarely consider forest age as a predictor, which represents variation in physiological processes that can respond differently to environmental drivers, and regrowth following disturbance. Here, we conduct an observational synthesis to empirically determine to what extent climate, soil properties, nitrogen deposition, forest age and management influence the spatial and interannual variability of forest NEP across 126 forest eddy-covariance flux sites worldwide. The empirical models explained up to 62% and 71% of spatio-temporal and across-site variability of annual NEP, respectively. An investigation of model structures revealed that forest age was a dominant factor of NEP spatio-temporal variability in both space and time at the global scale as compared to abiotic factors, such as nutrient availability, soil characteristics and climate. These findings emphasize the importance of forest age in quantifying spatio-temporal variation in NEP using empirical approaches.

Water availability is one of the most critical issues facing southern California. Consequently, the role and management of intact watersheds on public lands that supply water are paramount. We undertake the first regional study of climate impacts on hydrological services (runoff, recharge, and climatic water deficit) across the four national forests of southern California—the Angeles, Los Padres, Cleveland and San Bernardino. We assess the exposure, sensitivity, and vulnerability of water resources by comparing current conditions (1981–2010) to mid-century (2040–2069) and end-of-century (2070–2099) using three general circulation models (GCMs) under RCP8.5. Half of the study area is projected to exceed 2015's drought conditions in 10%–30% of the years between now and end-of-century under the moderate GCM (CCSM4), and one-third of the area is projected to exceed 2015 in 50% of the years under the hotter, drier projection (MIROC-ESM). Under a moderate projection, mean runoff increased by 1.2× by the end-of-century for three of the national forests, while mean recharge decreased by 0.9× across all forests. Projected end-of-century climatic water deficit increased on average 1.1× across the four forests. We assessed the vulnerability of watersheds by comparing the projected mean change between current and future climates with the current inter-annual variability using three categories of vulnerability. Under the moderate projection, one-third of the 385 watersheds were moderately vulnerable to changes in runoff and recharge (+/−0.2 to +/−1× the standard deviation of current inter-annual variability) and ∼12 watersheds were highly vulnerable, suggesting an era of new hydrological conditions by the end-of-century. Half of the Forest Service's priority management watersheds had moderate or high vulnerability for runoff and recharge. Spatial data on hydrological services and their vulnerability can directly assist in climate-smart planning, allowing tradeoffs to be assessed between proposed management actions and their effect on hydrological services.

Agroforestry systems comprise trees and crops, or trees and pastures within the same field. Globally, they cover approximately 1 billion hectares of land and contribute to the livelihoods of over 900 million people. Agroforestry systems have the capacity to sequester large quantities of carbon (C) in both soil and biomass. However, these systems have not yet been fully considered in the approach to C accounting developed by the Intergovernmental Panel on Climate Change, largely due to the high diversity of agroforestry systems and scarcity of relevant data. Our literature review identified a total of 72 scientific, peer-reviewed articles associated with biomass C storage (50) and with soil organic carbon (SOC) (122), containing a total of 542 observations (324 and 218, respectively). Based on a synthesis of the reported observations, we are presenting a set of Tier 1 coefficients for biomass C storage for each of the eight main agroforestry systems identified, including alley cropping, fallows, hedgerows, multistrata, parklands, shaded perennial-crop, silvoarable and silvopastoral systems, disaggregated by climate and region. Using the same agroforestry classification, we are presenting a set of stock change factors (F_LU) and SOC accumulation/loss rates for three main land use changes (LUCs): cropland to agroforestry; forest to agroforestry; and grassland to agroforestry. Globally, the mean SOC stock change factors (± confidence intervals) were estimated to be 1.25 ± 0.04, 0.89 ± 0.07, and 1.19 ± 0.10, for the three main LUCs, respectively. However, these average coefficients hide huge disparities across and within different climates, regions, and types of agroforestry systems, highlighting the necessity to adopt the more disaggregated coefficients provided herein. We encourage national governments to synthesize data from local field experiments to generate country-specific factors for more robust estimation of biomass and SOC storage.

Simplified assumptions regarding the relationship between per capita income and emissions are oftentimes utilized to generate future emission scenarios in integrated assessment models (IAMs). One such relationship is an environmental Kuznets curve (EKC), where emissions first increase, then decline with income growth. However, current knowledge about this relationship lacks the specificity needed for each sector and pollutant pairing, which is important for future emission scenarios. To fill this knowledge gap, we analyze the historical relationship between per capita income and emissions of SO₂, CO₂, and black carbon (BC) utilizing widely-used global, country-level emission inventories for the following four sectors: power, industry, residential, and transportation. Based on a modeling setup using long-term growth rates, emissions of SO₂ from the power and industrial sectors, as well as CO₂ from the industrial and the residential sectors, largely follow an EKC pattern. Income-emission trajectories for SO₂ and CO₂ from other sectors, and those for BC from all sectors, do not show an EKC, however. Results across different global inventories were variable, indicating that uncertainties within historical emission trajectories persist. Nonetheless, these results demonstrate that long-term income-emission trajectories of air pollutants are both sector and pollutant specific. Future reference trajectories of SO₂ and BC from three IAMs show earlier estimates of turnover incomes and faster rates of emission declines when compared to historical data. Users of future emission scenarios derived using EKC assumptions should consider the underlying uncertainties in such projections in light of this historical analysis.

Global warming has been shown to affect weather and climate extremes, such as droughts, floods, windstorms, cold waves, and heat waves. A number of studies have focused on the variability of different characteristics of these extremes, including their frequency, spatial extent, and severity. Recently, the study of compound extremes, defined by the co-occurrence of multiple events with extreme impacts, has attracted much attention. The compound dry and hot extreme is one type of compound extreme and may lead to detrimental impacts on the society and ecosystem. Most previous studies have focused on changes in the frequency or spatial extent of compound dry and hot extremes, while assessments of changes in the severity of compound extremes are lacking. This study evaluated changes in the severity of compound dry and hot extremes at the global scale, based on the Standardized Dry and Hot Index (SDHI). A significant increase in the severity of compound dry and hot extremes (or decrease of the SDHI value) during the warm season was found in western US, northern South America, western Europe, Africa, western Asia, southeastern Asia, southern India, northeastern China and eastern Australia. Moreover, a significant temporal increase in the average severity of the hottest month over global land areas was also observed. Results from this study highlight the increased severity of compound dry and hot extremes over global land areas and call for improved efforts on assessing the impact of compound extremes under global warming.

Quality energy consumption data are important for many types of analysis, and global data sets estimate trends of county level energy consumption, derived from country reported data and regional reports. We present a novel basis for informing uncertainty in energy data by quantifying the changes in reported energy consumption as countries update their previously reported data. We use 17 editions of the British Petroleum World Energy Statistics (2001–2017) to evaluate how reported energy consumption is revised over time in aggregate coal, oil, and natural gas consumption data. We find that 70% of non-zero data points are adjusted by an average of 1.3% of a country's total fossil fuel use in the year after their first publication. Earlier data points are revised less often, but almost half of historical trends contain some revisions in later years. The size and rate of data revisions vary over countries and fuels: coal data points have larger, less frequent revisions while oil data points have smaller more frequent revisions, with natural gas in between. A k-means cluster analysis was performed to group together countries with similar revision patterns. These groups span income, economy classification, OECD membership, and regions. Standard country groupings, therefore, do not predict the extent to which a country's energy data has undergone revisions in the past.

The degree of physical-biogeochemical equilibration of the climate system determines for how long global warming will continue after anthropogenic CO₂ emissions have ceased. The physical part of this equilibration process is quantified by the realized warming fraction (RWF), but RWF estimates differ strongly between different climate models. Here we analyze the RWF spread and its physical causes in three model ensembles: 1. an ensemble of comprehensive climate models, 2. an ensemble of reduced-complexity models, and 3. an observationally constrained parameter ensemble of the Bern3D-LPX reduced-complexity model. We show that RWF is generally lower in models with higher equilibrium climate sensitivity. The RWF uncertainty from applying different extrapolation methods for climate sensitivity is substantial, but smaller than the inter-model spread in the three ensembles. We decompose the inter-model spread of RWF using a diagnostic global energy balance model, to compare the spread contribution by the climate sensitivity to contributions by other physical quantities: the efficiency and efficacy of ocean heat uptake, and the effective radiative forcing. In the ensembles of the comprehensive climate models and the Bern3D-LPX model, the spread of the RWF is mostly determined by the spread of the climate sensitivity; for the reduced-complexity models, the spread contribution by the ocean heat uptake efficiency is dominant. Compared to the comprehensive models, the reduced-complexity models have a lower range of climate sensitivities and lower, more unitary ocean heat uptake efficacies, resulting in higher RWF. However, by tuning such models to higher climate sensitivities, they can also achieve RWF values in the lower range of comprehensive models, as demonstrated for Bern3D-LPX. This suggests that reduced-complexity models remain useful tools for future climate change projections, but should employ a range of climate sensitivity tunings to account for the uncertainty in both the long-term warming and the RWF.

Since 2015 the greater Cape Town area (∼3.7 million people) has been experiencing the worst drought of the last century. The combined effect of this prolonged dry period with an ever-growing demand for water culminated in the widely publicized 'Day Zero' water crisis. Here we show how: (i) consecutive significant decreases in rainfall during the last three winters led to the current water crisis; (ii) the 2015–2017 record breaking drought was driven by a poleward shift of the Southern Hemisphere moisture corridor; (iii) a displacement of the jet-stream and South Atlantic storm-track has imposed significantly drier conditions to this region. Decreasing local rainfall trends are consistent with an expansion of the semi-permanent South Atlantic high pressure, and reflected in the prevalence of the positive phase of the Southern Annular Mode. Large-scale forcing mechanisms reveal the intensification and migration of subtropical anticyclones towards the mid-latitudes, highlighting the link between these circulation responses and the record warm years during 2015–2017 at the global scale.

Empirical and anecdotal reports suggest that muskrat are in decline across North America, including in the Peace-Athabasca Delta ('Delta'), Canada, one of the largest inland deltas in the world and part of a World Heritage Site with 'in Danger' status pending. Muskrat are a key ecological indicator in the Delta. We investigate whether the large-scale loss of critical habitat over the past half-century could be driving a decline in muskrat abundance in the Delta. To do this, we use the Landsat record (1972–2017) to construct a 46 year record of inundation, and compare changes in the extent of critical habitat to the survey record for muskrat (1970–2016) over this 5500 km² region. Results show that the declines in critical habitat and muskrat numbers in the Delta are synchronous: ∼1450 km² of temporarily inundated regions that support critical habitat have diminished by ∼10 km² yr⁻¹ over the past 46 years, while the muskrat population density (houses/km²) has also declined and is significantly related to critical habitat area (km²) (R² = 0.60, P = 0.0001). These findings have implications for the Delta, a Ramsar Wetland of International Importance in part for its role as a habitat for nearly 200 species of birds, many of which rely on the aquatic habitat considered here. Our results further suggest that the loss of wetland habitat is a primary driver of the decline of muskrat across the species' native range.

The agro-food system perturbs the nitrogen (N) cycle through its N loads to the environment. The present study focused on food-related consumer-level N loads in Japan from 1961–2015, with a particular focus on food loss and protein overconsumption. Gender and age differences were also analyzed. Consumer-level food loss was negligible until the 1970s, when it began to slowly increase, accounting for an average of 13.2% of the annual net supply during 2011–2015. Japanese people have consumed more protein than the World Health Organization's recommended intake since 1961. Protein overconsumption increased until the mid-1990s, when it began to decrease, but it still accounted for an average of 32.3% of total annual protein consumption during 2011–2015. The national mean of food N footprints (total release of reactive N into the environment related to individual food consumption) in the same period was 18.3 kg N capita^–1 yr^–1, of which food loss accounted for 4% and protein overconsumption for 37%. The food N footprint of each sex/age class varied from 16.0–21.6 kg N capita^–1 yr^–1, males had a larger footprint in each age class. Seven scenarios to reduce the N footprints were evaluated; a scenario that included halving protein overconsumption, livestock meat consumption, and food loss was estimated to reduce the food N footprint by 31%. Thus, there is room for reducing consumer-induced N loads to the environment. Campaigns aimed at boosting healthy and environmentally friendly diets should consider the diverse consumption patterns of different sex and age classes.

Biomass is a crucial option of substituting fossil fuels to reduce emissions, and bioenergy with carbon capture and storage (BECCS) allows for obtaining net-negative emissions. We explore the role of biomass in China's long-term mitigation toward the Paris climate goals in light of three narratives and five mitigation scenarios, modeling by a refined Global Change Assessment Model. While presenting a limited contribution to achieving China's Nationally Determined Contribution (NDC), biomass plays an important role in China's post-NDC mitigation toward the Paris climate goals. All the assessed scenarios call for extensive biomass use, accounting for 6.5%–28% of China's 2100 primary energy in our three 2 °C scenarios and 15%–30% in our two 1.5 °C scenarios. The exact biomass deployment trajectories tend to depend greatly on how China envisages national mitigation paces and BECCS strategies. For either 2 °C or 1.5 °C, a smaller negative-emission narrative, which means a more rapid immediate decarbonization of the energy system toward mid-century, depends on larger bioenergy in medium-to-long-term. Delaying short- and medium-term ambition delays bioenergy applications but requires far more in the second half of the century to create greater negative emissions via BECCS. Moving from 2 °C toward 1.5 °C features higher and earlier bioenergy deployments and meaningfully increasing BECCS volumes and biofuel shares in China's energy system. Consequently, the Chinese stockholders might be ready to make a decision on to what degree biomass and BECCS enter the sphere of China's energy and climate policies, which will greatly influence not only national biomass roadmap but also mid-century mitigation targets.

Severe haze during the winter season has been troubling the citizens of Beijing over the past few decades, and the trend seems to be continuing. However, occasionally such as the winter of 2017 (2017/12–2018/2), one would be amazed to see unusually few hazy days throughout the winter that brings memories of a long past. It is controversial to say whether such a nowadays-rare event is a result of policy-driven emission cuts or an opportunity brought about by natural climate variability. This paper investigates the probability of such anomalous atmospheric circulation events in winter from a climate perspective. Based on updated observations, only three winters during the past 38 years are found to be similar to that of 2017. These events were accompanied by a strong Siberian High to the north and cold anomalies in the mid-lower troposphere in association with a strong East Asian Trough, which favored the strengthening of northwesterly winds and effective ventilation. The occurrences of such favorable winter circulation anomalies are found to have decreased by about 50% from the 1st to the 2nd half of the 20th century. A further 60% [11.4%, 89.3%] reduction between 1951–2000 and 2050–2099 is projected by climate models under the IPCC's Representative Concentration Pathway 8.5 scenario. Without serious cuts in pollution emissions, winters are projected to be dismal for the 20 million people of Beijing to a possible future under global warming.

Data centers are energy intensive buildings that have grown in size and number to meet the increasing demands of a digital economy. This paper presents a bottom-up model to estimate data center electricity demand in the United States over a 20 year period and examines observed and projected electricity use trends in the context of changing data center operations. Results indicate a rapidly increasing electricity demand at the turn of the century that has significantly subsided to a nearly steady annual electricity use of about 70 billion kWh in recent years. While data center workloads continue to grow exponentially, comparable increases in electricity demand have been avoided through the adoption of key energy efficiency measures and a shift towards large cloud-based service providers. Alternative projections from the model illustrate the wide range in potential electricity that could be consumed to support data centers, with the US data center workload demand estimated for 2020 requiring a total electricity use that varies by about 135 billion kWh, depending on the adoption rate of efficiency measures during this decade. While recent improvements in data center energy efficiency have been a success, the growth of data center electricity use beyond 2020 is uncertain, as modeled trends indicate that the efficiency measures of the past may not be enough for the data center workloads of the future. The results show that successful stabilization of data center electricity will require new innovations in data center efficiency to further decouple electricity demand from the ever-growing demand for data center services.

The deposition of dust on snow accelerates melt by perturbing snow albedo, directly by darkening the snow surface and indirectly by enhancing snow grain growth. The snow darkening process impacts hydrology by shifting runoff timing and magnitude. Dust on snow deposition has been documented in the Wasatch Mountains, snowmelt from which accounts for up to 80% of surface water supply for Salt Lake City, UT, but the impact on snow melt has not yet been investigated. Here, we present a case study of a dust event observed in the Wasatch (13–14th April, 2017), sampled coincidentally in the air and at the snow surface at an instrumented high elevation site (Atwater Study Plot, Alta, UT). Atmospheric backtrajectory modeling, the results of which were supported by measurements, showed that dust originated predominantly from the west: the Great Salt Lake Desert and the Great Salt Lake (GSL) dry lake bed. The deposited dust mass accounted for ∼50% of the season total dust loading in snow, and daily mean radiative forcing of 20–50 W m⁻² accelerated snow melt by approximately 25%. This has important implications for The Greatest Snow on Earth^®, and snow water resources; the water level of the GSL has been declining, exposing dry lake beds, and there are no legal water rights or protections to maintain lake levels or mitigate dust emission.

Water is needed for hydroelectric generation and to cool thermoelectric power plants. This dependence on water makes electricity generation vulnerable to droughts. Furthermore, because power sector CO₂ emissions amount to approximately one third of total US emissions, droughts could influence the inter-annual variability of state- and national-scale emissions. However, the magnitude of drought-induced changes in power sector emissions is not well understood, especially in the context of climate mitigation policies. Using multivariate linear regressions, we find that droughts are positively correlated to increases in electricity generation from natural gas in California, Idaho, Oregon, and Washington; and from coal in Colorado, Montana, Oregon, Utah, Washington, and Wyoming. Using a statistical model, we estimate that this shift in generation sources led to total increases in regional emissions of 100 Mt of CO₂, 45 kt of SO₂, and 57 kt of NO_x from 2001 to 2015, most of which originated in California, Oregon, Washington, and Wyoming. The CO₂ emissions induced by droughts in California, Idaho, Oregon, and Washington amounted to 7%–12% of the total CO₂ emissions from their respective power sectors, and the yearly rates were 8%–15% of their respective 2030 yearly targets outlined in the Clean Power Plan (CPP). Although there is uncertainty surrounding the CPP, its targets provide appropriate reference points for climate mitigation goals for the power sector. Given the global importance of hydroelectric and thermoelectric power, our results represent a critical step in quantifying the impact of drought on pollutant emissions from the power sector—and thus on mitigation targets—in other regions of the world.

Strong winter warming has dominated recent patterns of climate change along the Arctic Coastal Plain (ACP) of northern Alaska. The full impact of arctic winters may be best manifest by freshwater ice growth and the extent to which abundant shallow ACP lakes freeze solid with bedfast ice by the end of winter. For example, winter conditions of 2016–17 produced record low extents of bedfast ice across the ACP. In addition to high air temperatures, the causes varied from deep snow accumulation on the Barrow Peninsula to high late season rainfall and lake levels farther east on the ACP. In contrast, the previous winter of 2015–16 was also warm, but low snowpack and high winds caused relatively thick lake ice to develop and corresponding high extents of bedfast ice on the ACP. This recent comparison of extreme variation in lake ice responses between two adjacent regions and years in the context of long-term climate and ice records highlights the complexity associated with weather conditions and climate change in the Arctic. Recent observations of maximum ice thickness (MIT) compared to simulated MIT from Weather Research and Forcing (Polar-WRF) model output show greater departure toward thinner ice than predicted by models, underscoring this uncertainty and the need for sustained observations. Lake ice thickness and the extent of bedfast ice not only indicate the impact of arctic winters, but also directly affect sublake permafrost, winter water supply for industry, and overwinter habitat availability. Therefore, tracking freshwater ice responses provides a comprehensive picture of winter, as well as summer, weather conditions and climate change with implications to broader landscape, ecosystem, and resource responses in the Arctic.

Climate change mitigation policies have usually considered forest-based actions as cheap and fast options to reduce CO₂ concentration in the atmosphere and slow down global warming. Most economic analyses, however, have ignored the effects of these actions on land surface albedo and the resulting effect on energy balance and temperature. This study estimates the marginal cost of forest mitigation associated with both carbon sequestration and albedo change, by introducing regional and forest-specific albedo information in a global dynamic forestry model. Our analysis indicates that traditional forest sequestration policies have underestimated the costs of climate mitigation, driving forest-based actions in regions where subsequent changes in albedo are significant. To reduce this inefficiency, this paper proposes a novel approach where both carbon sequestration and albedo effect are incorporated into pricing. Our results suggest that, under the same carbon price path, the integrative policy provides greater net global mitigation in absolute terms and per hectare of forest, and thus it is more efficient and less intrusive than the traditional policy.

Global change has been linked to significant increases in tree mortality in the world's forests. Reduced tree longevity through increased growth rates has been suggested as one of the mechanisms responsible for the temporal increases in tree mortality, but this idea has not been directly tested. Here we explicitly defined two testable hypotheses: (i) the probability of ageing driven tree mortality increases with global change and (ii) the mortality probability associated with global change is higher for faster growing trees. To test these hypotheses, we examined the temporal changes of tree mortality probability in 539 permanent sample plots monitored from 1960–2009, with ages greater than 100 years at initial censuses, across the boreal region of Alberta, Canada. As expected, we found an overall temporal increase in tree mortality probability, indicating a loss in tree longevity with global change. We also found that trees with faster lifetime growth rates experienced higher temporal increases in mortality probability compared to slower growing trees. An analysis of the responses of tree mortality probability to increasing atmospheric carbon dioxide and temperature and decreases in water availability indicated that increasing atmospheric carbon dioxide and decreasing water availability were the major drivers of declining longevity. Our results suggest that tree longevity may further decline with the expected increase of atmospheric carbon dioxide and decreasing water availability in the region.

The ability to harmonize data sources with varying temporal, spatial, and ecosystem measurements (e.g. forest structure to soil organic carbon) for creation of terrestrial carbon baselines is paramount to refining the monitoring of terrestrial carbon stocks and stock changes. In this study, we developed and examined the short- (5 years) and long-term (30 years) performance of matrix models for incorporating light detection and ranging (LiDAR) strip samples and time-series Landsat surface reflectance high-level data products, with field inventory measurements to predict aboveground biomass (AGB) dynamics for study sites across the eastern USA—Minnesota (MN), Maine (ME), Pennsylvania-New Jersey (PANJ) and South Carolina (SC). The rows and columns of the matrix were stand density (i.e. number of trees per unit area) sorted by inventory plot and by species group and diameter class. Through model comparisons in the short-term, we found that average stand basal area (B) predicted by three matrix models all fell within the 95% confidence interval of observed values. The three matrix models were based on (i) only field inventory variables (inventory), (ii) LiDAR and Landsat-derived metrics combined with field inventory variables (LiDAR + Landsat + inventory), and (iii) only Landsat-derived metrics combined with field inventory variables (Landsat + inventory), respectively. In the long term, predicted AGB using LiDAR + Landsat + inventory and Landsat + inventory variables had similar AGB patterns (differences within 7.2 Mg ha⁻¹) to those predicted by matrix models with only inventory variables from 2015–2045. When considering uncertainty derived from fuzzy sets all three matrix models had similar AGBs (differences within 7.6 Mg ha⁻¹) by the year 2045. Therefore, the use of matrix models enabled various combinations of LiDAR, Landsat, and field data, especially Landsat data, to estimate large-scale AGB dynamics (i.e. central component of carbon stock monitoring) without loss of accuracy from only using variables from forest inventories. These findings suggest that the use of Landsat data alone incorporating elevation (E), plot slope (S) and aspect (A), and site productivity (C) could produce suitable estimation of AGB dynamics (ranging from 67.1–105.5 Mg ha⁻¹ in 2045) to actual AGB dynamics using matrix models. Such a framework may afford refined monitoring and estimation of terrestrial carbon stocks and stock changes from spatially explicit to spatially explicit and spatially continuous estimates and also provide temporal flexibility and continuity with the Landsat time series.

Bioplastic production is a small but fast growing sector in the global bioeconomy, which may benefit from public support measures in the future as governments seek to promote more sustainable consumption patterns. Here we assess the potential net economy-wide impacts of a 5% bioplastic target relative to current plastic consumption in the main producing regions. We compare two alternative policy strategies to achieve the target in a general equilibrium framework that allows for substitution between conventional and bio-based plastics: a subsidy on bioplastics versus a tax on fossil-based plastic consumption. Our study is the first to quantify global greenhouse gas (GHG) emissions from an increased demand for bioplastics on a global scale, produced from arable crops, considering both direct and indirect land use change (LUC). The tax provokes a contraction of all sectors that employ plastics, which leads to a drop of 0.07% in global real GDP, whereas the subsidy has no significant effect on the global economy. Both tax and subsidy reduce world demand for petroleum products, by 0.37% and 0.07%, respectively, boosting demand for sugar- and starch-based feedstock in the bioplastic industry. This leads to emissions from LUC globally, which correspond to a carbon payback time of 22 years on average, with the associated annual abatement costs of over US$2000 per tonne of CO₂-eq. The tax has greater GHG reduction potential in bioplastic producing regions but generates greater economic and environmental spillover effects in countries that do not enforce the target. Results show that promoting bioplastic consumption is not a cost-effective strategy for climate change mitigation if based on conventional feedstock, due to market-mediated GHG emissions from LUC. Bioplastics are not necessarily more sustainable than conventional polymers just because they are bio-based, although further assessment of potential environmental gains associated with biodegradability and recyclability is desirable.

A wide range of research shows that nutrient availability strongly influences terrestrial carbon (C) cycling and shapes ecosystem responses to environmental changes and hence terrestrial feedbacks to climate. Nonetheless, our understanding of nutrient controls remains far from complete and poorly quantified, at least partly due to a lack of informative, comparable, and accessible datasets at regional-to-global scales. A growing research infrastructure of multi-site networks are providing valuable data on C fluxes and stocks and are monitoring their responses to global environmental change and measuring responses to experimental treatments. These networks thus provide an opportunity for improving our understanding of C-nutrient cycle interactions and our ability to model them. However, coherent information on how nutrient cycling interacts with observed C cycle patterns is still generally lacking. Here, we argue that complementing available C-cycle measurements from monitoring and experimental sites with data characterizing nutrient availability will greatly enhance their power and will improve our capacity to forecast future trajectories of terrestrial C cycling and climate. Therefore, we propose a set of complementary measurements that are relatively easy to conduct routinely at any site or experiment and that, in combination with C cycle observations, can provide a robust characterization of the effects of nutrient availability across sites. In addition, we discuss the power of different observable variables for informing the formulation of models and constraining their predictions. Most widely available measurements of nutrient availability often do not align well with current modelling needs. This highlights the importance to foster the interaction between the empirical and modelling communities for setting future research priorities.

Climate change has been implicated in the widespread 'greening' of the arctic in recent decades. However, differences in arctic greening patterns among satellite platforms and recent reports of decreased rate of greening or of browning have made attributing arctic greening trends to a warming climate challenging. Here, we compared MODIS greening trends to those predicted by the coupled carbon and nitrogen model (CCaN); a mass balance carbon and nitrogen model that was driven by MODIS surface temperature and climate. CCaN was parameterized using model-data fusion, where model predictions were ecologically constrained with historical ecological ground and satellite based data. We found that, at long temporal and large spatial scales, MODIS greening trends were consistent with ecological and biogeochemical data from arctic tundra. However, at smaller spatial scales, observations and CCaN greening trends differed in the location, extent, and magnitude of greening. CCaN was unable to capture the high rates of MODIS greening in northern wetlands, and the patchy MODIS browning in the southern portion of the North Slope. This model-data disagreement points to disturbance and its legacy impacts on land cover as an important mechanism for understanding greening trends on the North Slope of Alaska.

In this study, we propose a new Arctic climate change indicator based on the strength of the Arctic halocline, a porous barrier between the cold and fresh upper ocean and ice and the warm intermediate Atlantic Water of the Arctic Ocean. This indicator provides a measure of the vulnerability of sea ice to upward heat fluxes from the ocean interior, as well as the efficiency of mixing affecting carbon and nutrient exchanges. It utilizes the well-accepted calculation of available potential energy (APE), which integrates anomalies of potential density from the surface downwards through the surface mixed layer to the base of the halocline. Regional APE contrasts are striking and show a strengthening of stratification in the Amerasian Basin (AB) and an overall weakening in the Eurasian Basin (EB). In contrast, Arctic-wide time series of APE is not reflective of these inter-basin contrasts. The use of two time series of APE—AB and EB—as an indicator of Arctic Ocean climate change provides a powerful tool for detecting and monitoring transition of the Arctic Ocean towards a seasonally ice-free Arctic Ocean. This new, straightforward climate indicator can be used to inform both the scientific community and the broader public about changes occurring in the Arctic Ocean interior and their potential impacts on the state of the ice cover, the productivity of marine ecosystems and mid-latitude weather.

Systematic errors in forecast near-surface air temperature (SAT) still constitute a considerable problem for numerical weather prediction (NWP) at high latitudes. Numerous studies in the past have attempted to reduce this problem through recalibration of physical parameterization schemes and better approximation of the surface energy budget. The errors, however, remain despite notable improvements in the overall weather forecast performance. This study looks at the problem from a different perspective. It analyzes asymmetries in the SAT forecast errors. The study reveals a statistical pattern of warm SAT biases under cold weather conditions and cold SAT biases under warm weather conditions. The largest errors were found in shallow atmospheric boundary layers (ABLs). The study attributes the problem to the modeled excessive ABL thickness in northern Eurasia (the NEFI region). The ABL thickness is considered as a scaling factor controlling the efficacy of the applied surface heating. Too thick an ABL damps the magnitude and agility of the SAT response. The study utilized the operational model SL-AV of the Russian Hydrometeorological Centre. Two turbulence schemes were evaluated in the northern European and western Siberian regions of Russia against observations from 73 meteorological stations. The pTKE (old) scheme is based on the local balance of the turbulence characteristics. The TOUCANS (new) scheme incorporated the total turbulence energy equations in an energy-flux balance approach. Neither scheme uses the ABL thickness as a prognostic parameter. The study reveals that the SAT errors are consistent with the damped response of temperature and reduced agility of temperature fluctuations in too thick ABLs. The TOUCANS scheme did not improve those features, probably because it links the turbulent fluxes and the ABL thickness. The SAT errors in shallow ABLs persist in the new scheme. This study emphasizes the need for a closer look at the ABL thickness in the NWP models.

Atmospheric nitrogen (N) pollution is considered responsible for a substantial decline in plant species richness and for altered community structures in terrestrial habitats worldwide. Nitrogen affects habitats through direct toxicity, soil acidification, and in particular by favoring fast-growing species. Pressure from N pollution is decreasing in some areas. In Europe (EU28), overall emissions of NO_x declined by more than 50% while NH₃ declined by less than 30% between the years 1990 and 2015, and further decreases may be achieved. The timescale over which these improvements will affect ecosystems is uncertain. Here we use 23 European forest research sites with high quality long-term data on deposition, climate, soil recovery, and understory vegetation to assess benefits of currently legislated N deposition reductions in forest understory vegetation. A dynamic soil model coupled to a statistical plant species niche model was applied with site-based climate and deposition. We use indicators of N deposition and climate warming effects such as the change in the occurrence of oligophilic, acidophilic, and cold-tolerant plant species to compare the present with projections for 2030 and 2050. The decrease in N deposition under current legislation emission (CLE) reduction targets until 2030 is not expected to result in a release from eutrophication. Albeit the model predictions show considerable uncertainty when compared with observations, they indicate that oligophilic forest understory plant species will further decrease. This result is partially due to confounding processes related to climate effects and to major decreases in sulphur deposition and consequent recovery from soil acidification, but shows that decreases in N deposition under CLE will most likely be insufficient to allow recovery from eutrophication.

Warm periods in Earth's history tend to cool more slowly than cool periods warm. Here we explore initial differences in how the global ocean takes up and gives up heat and carbon in forced rapid warming and cooling climate scenarios. We force an intermediate-complexity earth system model using two atmospheric CO₂ scenarios. A ramp-up (1% per year increase in atmospheric CO₂ for 150 years) starts from an average global CO₂ concentration of 285 ppm to represent warming of an icehouse climate. A ramp-down (1% per year decrease in atmospheric CO₂ for 150 years) starts from an average global CO₂ concentration of 1257 ppm to represent cooling of a greenhouse climate. Atmospheric CO₂ is then held constant in each simulation and the model is integrated an additional 350 years. The ramp-down simulation shows a weaker response of surface air temperature to changes in radiative forcing relative to the ramp-up scenario. This weaker response is due to a relatively large and fast release of heat from the ocean to the atmosphere. This asymmetry in heat exchange in cooling and warming scenarios exists mainly because of differences in the response of the ocean circulation to forcing. In the ramp-up, increasing stratification and weakening of meridional overturning circulation slows ocean heat and carbon uptake. In the ramp-down, cooling accelerates meridional overturning and deepens vertical mixing, accelerating the release of heat and carbon stored at depth. Though idealized, our experiments offer insight into differences in ocean dynamics in icehouse and greenhouse climate transitions.

The Arctic Monitoring and Assessment Program (AMAP 2017) report identifies the Arctic as the largest regional source of land ice to global sea-level rise in the 2003–2014 period. Yet, this contextualization ignores the longer perspective from in situ records of glacier mass balance. Here, using 17 (>55 °N latitude) glacier and ice cap mass balance series in the 1971–2017 period, we develop a semi-empirical estimate of annual sea-level contribution from seven Arctic regions by scaling the in situ records to GRACE averages. We contend that our estimate represents the most accurate Arctic land ice mass balance assessment so far available before the 1992 start of satellite altimetry. We estimate the 1971–2017 eustatic sea-level contribution from land ice north of ∼55 °N to be 23.0 ± 12.3 mm sea-level equivalent (SLE). In all regions, the cumulative sea-level rise curves exhibit an acceleration, starting especially after 1988. Greenland is the source of 46% of the Arctic sea-level rise contribution (10.6 ± 7.3 mm), followed by Alaska (5.7 ± 2.2 mm), Arctic Canada (3.2 ± 0.7 mm) and the Russian High Arctic (1.5 ± 0.4 mm). Our annual results exhibit co-variability over a 43 year overlap (1971–2013) with the alternative dataset of Marzeion et al (2015 Cryosphere9 2399–404) (M15). However, we find a 1.36× lower sea-level contribution, in agreement with satellite gravimetry. The IPCC Fifth Assessment report identified constraining the pre-satellite era sea-level budget as a topic of low scientific understanding that we address and specify sea-level contributions coinciding with IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) 'present day' (2005–2015) and 'recent past' (1986–2005) reference periods. We assess an Arctic land ice loss of 8.3 mm SLE during the recent past and 12.4 mm SLE during the present day. The seven regional sea-level rise contribution time series of this study are available from AMAP.no.

An analysis by Sterman et al (2018 Environ. Res. Lett. 13 015007) suggests that use of wood for bioenergy production results in a worse climate outcome than from using coal. However, many of the assumptions on which their primary wood bioenergy scenario is based are not realistic and therefore are not informative. Assumptions of uncharacteristically long rotations for southern pine plantations, no utilization of wood for longer-duration products, and a single harvest over 100 years understate the carbon performance of current forest management practices. We provide references that support realistic modeling of forest carbon dynamics that are reflective of current practice and therefore more informative.

We respond to Prisley et al's (2018 Environ. Res. Lett.13 128002) critique of Sterman et al (2018 Environ. Res. Lett.13 015007), which found that using wood to produce electricity can worsen climate change at least through 2100, even if wood displaces coal. The result arises because (1) wood generates more CO₂/kWh than coal, creating an initial carbon debt; (2) regrowth of harvested land can remove CO₂ from the atmosphere, but takes time and is not certain; and (3) until the carbon debt is repaid, atmospheric CO₂ is higher, increasing radiative forcing and worsening climate change long after the initial carbon debt is repaid by new growth. We correct several errors in Prisley et al's critique, and show that our results are robust to the harvest and land management practices they prefer.