Table of contents

The 2017 Arctic Boreal Vulnerability Experiment Airborne Campaign (AAC) was one of the largest, most complex airborne science experiments conducted by NASA's Earth Science Division. Between April and November, the AAC involved ten aircraft in more than 200 science flights that surveyed over 4 million km² in Alaska and northwestern Canada. Many flights were coordinated with same-day ground-based measurements to link process-level studies with geospatial data products derived from satellite sensors. The AAC collected data spanning the critical intermediate space and time scales that are essential for a comprehensive understanding of scaling across the ABoVE Study Domain and ultimately extrapolation to the pan-Arctic using satellite data and ecosystem models. The AAC provided unique opportunities to validate satellite and airborne remote sensing data and data products for northern high latitude ecosystems. The science strategy coupled domain-wide sampling with L-band and P-band synthetic aperture radar (SAR), imaging spectroscopy, full waveform LIDAR, atmospheric trace gases (including carbon dioxide and methane), as well as focused studies using Ka-band SAR and solar induced chlorophyll fluorescence. Targets of interest included field sites operated by the ABoVE Science Team as well as the intensive and/or long-term sites operated by US and Canadian partners.

Relationships are the elementary forms of social life that animate structures and processes between and among individuals, groups and institutions, and are in turn transformed by them. Relationships between forest dependent peoples (FP) and state forest management institutions (FD) are central to forestry practice yet seldom the focus of research studies. Whereas decentralization and participatory institutions have received much attention in research and practice, relationships that underpin them have remained largely unaddressed. This paper utilizes an adaptation of the systematic review method to synthesize findings on the nature of this relationship in the Global South. We reviewed 135 articles published between 1997 and 2017, selected following a systematic article search and selection protocol on JSTOR and Google Scholar. History, as expected, is a living referent in shaping contemporary relations, accounting for tremendous diversity across the Global South. We identified key concepts from literature across this diversity, and synthesized them using five overlapping thematic codes: (a) asymmetries of power; (b) access to and control over productive resources; (c) knowledge, perceptions and attitudes; (d) stratification and heterogeneity; and (e) external influences. Numerical analysis of article meta-data revealed that research is attentive to the FP–FD relationship primarily in the context of decentralization or community participatory policies and projects. Well-designed policies, projects, institutions and effective individuals create opportunities for partial, temporary and symbolic transformation in the FP–FD relationship. However, structural power asymmetry between FD and FP, historically established, and reproduced through social inequalities and hierarchies, sustains. The content of social relationships overflow sector specific transformations. Reflecting on the scope of systematic review as method in synthesis of qualitative research, we found that although loss of context specificity is a disadvantage, systematic review can be productively adapted to explore neglected issues as we do in our study with relationships, through analysis of empirical data in studies with other objectives.

Reducing the social, environmental, and economic impacts of droughts and identifying pathways towards drought resilient societies remains a global priority. A common understanding of the drivers of drought risk and ways in which drought impacts materialize is crucial for improved assessments and for the identification and (spatial) planning of targeted drought risk reduction and adaptation options. Over the past two decades, we have witnessed an increase in drought risk assessments across spatial and temporal scales drawing on a multitude of conceptual foundations and methodological approaches. Recognizing the diversity of approaches in science and practice as well as the associated opportunities and challenges, we present the outcomes of a systematic literature review of the state of the art of people-centered drought vulnerability and risk conceptualization and assessments, and identify persisting gaps. Our analysis shows that, of the reviewed assessments, (i) more than 60% do not explicitly specify the type of drought hazard that is addressed, (ii) 42% do not provide a clear definition of drought risk, (iii) 62% apply static, index-based approaches, (iv) 57% of the indicator-based assessments do not specify their weighting methods, (v) only 11% conduct any form of validation, (vi) only ten percent develop future scenarios of drought risk, and (vii) only about 40% of the assessments establish a direct link to drought risk reduction or adaptation strategies, i.e. consider solutions. We discuss the challenges associated with these findings for both assessment and identification of drought risk reduction measures, and identify research needs to inform future research and policy agendas in order to advance the understanding of drought risk and support pathways towards more drought resilient societies.

The terrestrial carbon and water cycles are strongly coupled. As atmospheric carbon dioxide concentration increases, climate and the coupled hydrologic cycle are modified, thus altering the terrestrial water cycle and the availability of soil moisture necessary for plants' carbon dioxide uptake. Concomitantly, rising surface carbon dioxide concentrations also modify stomatal (small pores at the leaf surface) regulation as well as biomass, thus altering ecosystem photosynthesis and transpiration rates. Those coupled changes have profound implications for the predictions of the carbon and water cycles. This paper reviews the main mechanisms behind the coupling of the terrestrial water and carbon cycles. We especially focus on the key role of dryness (atmospheric dryness and terrestrial water availability) on carbon uptake, as well as the predicted impact of rising carbon dioxide on the water cycle. Challenges related to this coupling and the necessity to constrain it based on observations are finally discussed.

Most empirical and modeling research on soil carbon (C) dynamics has focused on those processes that control and promote C stabilization. However, we lack a strong, generalizable understanding of the mechanisms through which soil organic carbon (SOC) is destabilized in soils. Yet a clear understanding of C destabilization processes in soil is needed to quantify the feedbacks of the soil C cycle to the Earth system. Destabilization includes processes that occur along a spectrum through which SOC shifts from a 'protected' state to an 'available' state to microbial cells where it can be mineralized to gaseous forms or to soluble forms that are then lost from the soil system. These processes fall into three general categories: (1) release from physical occlusion through processes such as tillage, bioturbation, or freeze-thaw and wetting-drying cycles; (2) C desorption from soil solids and colloids; and (3) increased C metabolism. Many processes that stabilize soil C can also destabilize C, and C gain or loss depends on the balance between competing reactions. For example, earthworms may both destabilize C through aggregate destruction, but may also create new aggregates and redistribute C into mineral horizon. Similarly, mycorrhizae and roots form new soil C but may also destabilize old soil C through priming and promoting microbial mining; labile C inputs cause C stabilization through increased carbon use efficiency or may fuel priming. Changes to the soil environment that affect the solubility of minerals or change the relative surfaces charges of minerals can destabilize SOC, including increased pH or in the reductive dissolution of Fe-bearing minerals. By considering these different physical, chemical, and biological controls as processes that contribute to soil C destabilization, we can develop thoughtful new hypotheses about the persistence and vulnerability of C in soils and make more accurate and robust predictions of soil C cycling in a changing environment.

Background. Though many studies have long considered the broad social implications of climate change, researchers have only recently started to consider the gendered unevenness of the global landscape of vulnerability, exposure, and adaptive capacity to environmental stressors and shocks. Historically, policies and interventions addressing natural resource-based livelihoods have rarely considered underlying gender dynamics despite the global pervasiveness of gendered disparities in both economic opportunities and welfare outcomes. Methods/Design. Using two electronic databases, Web of Science and Scopus, we conducted a systematic review of peer-reviewed academic literature describing livelihoods policies or interventions that included documentation of gendered impacts. We focused on natural resource-based livelihoods most likely to be affected by climate change, centering on interventions targeting agriculture, fisheries and aquaculture, and forestry. Review Results/Synthesis. We identified 131 relevant articles, most of which focus on adoption or participation in interventions rather than outcomes. In general, women are less likely than men to engage with sustainable livelihoods interventions. When women do engage, some researchers have documented income and food security gains as well as improvements in environmental indicators in the short-term. However, these initiatives have also been found to increase women's labor burden without corresponding gains in income. Few studies measure longer-term effects of women's engagement on welfare and environmental outcomes, a key gap in the literature. Additionally, relatively few studies explore the intersectional impacts of initiatives, such as the added burdens of ethnicity, class, education, or other differences that modify gender disparities. Discussion. Climate change has gendered impacts on natural resource-based livelihoods. In general, existing initiatives designed to increase livelihood resilience fail to reduce gender disparities and improve women's livelihoods. Greater attention should be paid to gender when designing sustainable livelihoods policies and interventions in order to increase adoption and participation, negotiate trade-offs, improve environmental conditions, and promote broadly beneficial welfare outcomes.

Land-use change has transformed the majority of the terrestrial biosphere, impacting biodiversity, climate change, food production and provision of multiple ecosystem services. To improve our understanding of land-use change processes, the motivations and characteristics of land-use decision-makers need to be addressed more explicitly. Here, we systematically review the peer-reviewed literature between 1950 and 2018 that documents decision-making underlying land-use change processes. We found 315 publications reporting on 559 case studies worldwide that report on land-use decision-making in sufficient depth. In these cases, we identified 758 land-use decision-makers. We clustered decision-makers based on their objectives, attitudes and abilities into six distinct types: survivalist, subsistence-oriented smallholder, market-oriented smallholder, professional commercialist, professional intensifier and eco-agriculturalist. Survival and livelihood were identified as most common objectives for land-use decision makers, followed by economic objectives. We observe large differences in terms of decision-makers' attitudes towards environmental values, and particularly their financial status, while decision makers have a generally favorable attitude towards change and legislation. The majority of the documented decision-makers in the literature have only few abilities as they are poor and own small plots of land, while the wealthier decision-makers were identified to have more power and control over their decisions. Based on a representativeness analysis, we found that decision-making processes in marginal areas, such as mountainous regions, are overrepresented in existing case study evidence, while remote areas and lowlands are under-represented. These insights can help in the design of better land-use change assessments, as well as to improve policies towards sustainable land use.

Limiting mean global warming to well below 2 °C will probably require substantial negative emissions (NEs) within the 21st century. To achieve these, bioenergy plantations with subsequent carbon capture and storage (BECCS) may have to be implemented at a large scale. Irrigation of these plantations might be necessary to increase the yield, which is likely to put further pressure on already stressed freshwater systems. Conversely, the potential of bioenergy plantations (BPs) dedicated to achieving NEs through CO₂ assimilation may be limited in regions with low freshwater availability. This paper provides a first-order quantification of the biophysical potentials of BECCS as a negative emission technology contribution to reaching the 1.5 °C warming target, as constrained by associated water availabilities and requirements. Using a global biosphere model, we analyze the availability of freshwater for irrigation of BPs designed to meet the projected NEs to fulfill the 1.5 °C target, spatially explicitly on areas not reserved for ecosystem conservation or agriculture. We take account of the simultaneous water demands for agriculture, industries, and households and also account for environmental flow requirements (EFRs) needed to safeguard aquatic ecosystems. Furthermore, we assess to what extent different forms of improved water management on the suggested BPs and on cropland may help to reduce the freshwater abstractions. Results indicate that global water withdrawals for irrigation of BPs range between ∼400 and ∼3000 km³ yr⁻¹, depending on the scenario and the conversion efficiency of the carbon capture and storage process. Consideration of EFRs reduces the NE potential significantly, but can partly be compensated for by improved on-field water management.

A limited understanding of how extreme weather events affect groundwater hinders our ability to predict climate change impacts in drylands, where channel transmission losses are often the primary recharge mechanism. In this study, we investigate how potential changes to precipitation intensity and temperature will affect the water balance of a typical first-order, arid watershed located in the Chihuahuan Desert. We utilize a process-based hydrologic model driven by stochastically-downscaled simulations from a set of climate models, emissions scenarios, and future periods. Across many simulations, the average daily storm size is the primary factor that controls transmission losses with larger precipitation amounts increasing channel infiltration while simultaneously decreasing land surface evapotranspiration. Extreme events (>25 mm d⁻¹) that account for less than 30% of the annual precipitation, contribute almost 50% of the focused recharge. As a result, climatic changes leading to larger, less frequent storms will result in higher channel transmission losses in arid regions.

Nonlinear increases in warm season temperatures are projected for many regions, a phenomenon we show to be associated with relative surface drying. However, negative human health impacts are physiologically linked to combinations of high temperatures and high humidity. Since the amplified warming and drying are concurrent, the net effect on humid-heat, as measured by the wet bulb temperature (T_W), is uncertain. We demonstrate that globally, on the hottest days of the year, the positive effect of amplified warming on T_W is counterbalanced by a larger negative effect resulting from drying. As a result, the largest increases in T_W and T_x do not occur on the same days. Compared to a world with linear temperature change, the drying associated with nonlinear warming dampens mid-latitude T_W increases by up to 0.5 °C, and also dampens the rise in frequency of dangerous humid-heat (T_W > 27 °C) by up to 5 d per year in parts of North America and Europe. Our results highlight the opposing interactions among temperature and humidity changes and their effects on T_W, and point to the importance of constraining uncertainty in hydrological and warm season humidity changes to best position the management of future humid-heat risks.

When released to the biosphere, mercury (Hg) is very mobile and can take millennia to be returned to a secure, long-term repository. Understanding where and when Hg was released as a result of human activities allows better quantification of present-day reemissions and future trajectories of environmental concentrations. In this work, we estimate the time-varying releases of Hg in seven world regions over the 500 year period, 1510–2010. By our estimation, this comprises 95% of all-time anthropogenic releases. Globally, 1.47 Tg of Hg were released in this period, 23% directly to the atmosphere and 77% to land and water bodies. Cumulative releases have been largest in Europe (427 Gg) and North America (413 Gg). In some world regions (Africa/Middle East and Oceania), almost all (>99%) of the Hg is relatively recent (emitted since 1850), whereas in South America it is mostly of older vintage (63% emitted before 1850). Asia was the greatest-emitting region in 2010, while releases in Europe and North America have declined since the 1970s, as recognition of the risks posed by Hg have led to its phase-out in commercial usage. The continued use of Hg in artisanal and small-scale gold mining means that the Africa/Middle East region is now a major contributor. We estimate that 72% of cumulative Hg emissions to air has been in the form of elemental mercury (Hg⁰), which has a long lifetime in the atmosphere and can therefore be transported long distances. Our results show that 83% of the total Hg has been released to local water bodies, onto land, or quickly deposited from the air in divalent (Hg^II) form. Regionally, this value ranges from 77% in Africa/Middle East and Oceania to 89% in South America. Results from global biogeochemical modeling indicate improved agreement of the refined emission estimates in this study with archival records of Hg accumulation in estuarine and deep ocean sediment.

In the face of projected increases in globalization and urbanization, there is growing recognition that cities and their hinterlands will play a pivotal role in both creating and addressing the sustainability challenges of the future. Hinterlands, the rural areas that surround cities, are connected to cities as the source of many of the ecosystem services (ES) that are used in urban areas. While much is known about the provision of multiple ES in and around a few well-studied cities, there is a limited amount of consistently measured, global-scale data about the provision of multiple ES in urban areas and their hinterlands. We mapped eight ES globally, and examined how the production of ES varied between the hinterlands (within 200 km) of 768 major city centers (population > 500 000). We found that there are seven archetypes of ES supply bundles in global hinterlands. Hinterlands near wealthy cities are specialists in regulating ES production while the poorest and most populated hinterlands are specialists in food production, with low levels of regulating and cultural ES provision. These hinterlands also experience different synergies and tradeoffs between ES, with interesting implications for landscape management. Global teleconnections have likely also played a role in the ES bundles of hinterlands, since they have allowed cities to exploit remote areas to meet their demand for ES, undermining the traditional supply-demand relationship between each city and its proximal hinterland. These results emphasize the diverse, and sometimes inequitable, ways that urbanization and globalization are influencing ES supply in the planet's most human-modified landscapes.

Climate policies targeting CO₂ emissions from fossil fuels can simultaneously reduce emissions of air pollutants and their precursors, thus mitigating air pollution and associated health impacts. Previous work has examined co-benefits of climate policy from reducing PM_2.5 in rapidly-developing countries such as China, but have not examined co-benefits from ozone and its transboundary impact for both PM_2.5 and ozone. Here, we compare the air quality and health co-benefits of China's climate policy on both PM_2.5 and ozone in China to their co-benefits in three downwind and populous countries (South Korea, Japan and the United States) using a coupled modeling framework. In a policy scenario consistent with China's pledge to peak CO₂ emissions in approximately 2030, avoided premature deaths from ozone reductions are 54 300 (95% confidence interval: 37 100–71 000) in China in 2030, nearly 60% of those from PM_2.5. Total avoided premature deaths in South Korea, Japan, and the US are 1200 (900–1600), 3500 (2800–4300), and 1900 (1400–2500), respectively. Total avoided deaths in South Korea and Japan are dominated by reductions in PM_2.5-related mortality, but ozone plays a more important role in the US. Similar to co-benefits for PM_2.5 in China, co-benefits of China's policy for ozone and for both pollutants in those downwind countries also rise with increasing policy stringency.

The ongoing pressures of climate change, as expressed by the increased intensity, duration, and frequency of temperature and precipitation events, threatens the storage of carbon in northern latitudes. One key concern is how these events will affect the production, mobilization, and export of dissolved organic carbon (DOC), the main form of aquatic carbon export in these regions. In this study, we retrospectively show contrasting effects of climate extremes over 23 years on two adjacent boreal catchments, one dominated by forest cover and the other draining a mire (wetland), despite experiencing the same extreme climate events. During the peak snowmelt, DOC concentrations ranged from 20 to 33 mg l⁻¹ in the forest catchment and 10–28 mg l⁻¹ in the mire catchment respectively, highlighting large inter-annual variation in the springtime hydrologic C export at both sites. We used climate and discharge variables to predict this variation, and found that DOC from the forested catchment, which is derived largely from riparian soils, had the highest concentrations following cold summers, dry autumns, and winters with high precipitation. By contrast, in the mire outlet, where DOC is primarily derived from decomposing peat, the highest DOC concentrations in the spring followed cold/dry winters and dry summers. Our results indicate that processes regulating stream DOC concentrations during spring in both catchments were dependent on both temperature and precipitation in multiple seasons. Together, these patterns suggest that DOC responses to climatic extremes are complex and generate variable patterns in springtime concentrations that are strongly dependent upon landscape context.

The Paris Agreement takes a bottom-up approach to tackling climate change with parties submitting pledges in the form of nationally determined contributions (NDCs). Studies show that the sum of these national pledges falls short of meeting the agreement's 2 °C target. To explore this discrepancy, we analyse individual pledges and classify them into four categories. By doing so, a lack of consistency and transparency is highlighted, which we correct for by performing a normalisation that makes pledges directly comparable. This involves calculating changes in emissions by 2030, using data for the most recent base year of 2015. We find that pledges framed in terms of absolute emission reductions against historical base years generally produce the greatest ambition, with average emission reductions of 16% by 2030. Pledges defined as GDP intensity targets perform the worst with average emission increases of 61% by 2030. We propose that a normalisation procedure of the type as we develop becomes part of the NDC process. It will allow to not only increase the transparency of pledges for policymakers and wider society, but also promote more effective NDCs upon revision as is foreseen to happen every 5 years under the 'ratcheting mechanism' of the agreement.

We develop a new index which maps relative climate change contributions to relative emergent impacts of climate change. The index compares cumulative emissions data with patterns of signal-to-noise ratios (S/N) in regional temperature (Frame et al 2017 Nat. Clim. Change7 407–11). The latter act as a proxy for a range of local climate impacts, so emergent patterns of this ratio provide an informative way of summarising the regional disparities of climate change impacts. Here we combine these with measures of regional/national contributions to climate change to develop an 'emissions-emergence index' (EEI) linking regions'/countries' contributions to climate change with the emergent regional impacts of climate change. The EEI is a simple but robust indicator which captures relative contributions to and regional impacts from climate change. We demonstrate the applicability of the EEI both for discussions of historical contributions and impacts, and for considering future relative contributions and impacts, and examine its utility in the context of existing related metrics. Finally, we show how future emissions pathways can either imply a growth or reduction of regional climate change inequalities depending on the type and compositions of socioeconomic development strategies.

Climate change will impact many economic sectors and aspects of natural and human wellbeing. Quantifying these impacts as they vary across regions, sectors, time, and social and climatological scenarios supports detailed planning, policy, and risk management. This article summarises and compares recent climate impact assessments in Europe (the JRC PESETA III project) and the USA (the American Climate Prospectus project). Both implement a multi-sector perspective combining high resolution climate data with sectoral impact and economic models. The assessments differ in their coverage of sectors and scenarios, mix of empirical and process-based methods, handling of uncertainty, and representation of damages. Despite the dissimilarities, projected relative economic impacts are comparable, with human mortality as the dominant impact category. Both studies further show a large spatial heterogeneity of impacts that may amplify pre-existing economic inequality in the EU and US, and that mitigation can considerably reduce economic impacts. The comparison highlights the various decision-points involved in interdisciplinary climate impact modelling and lessons learnt in both projects, on the basis of which we provide recommendations for further research.

Food consumption and production are separated in space through flows of food along complex supply chains. These food supply chains are critical to our food security, making it important to evaluate them. However, detailed spatial information on food flows within countries is rare. The goal of this paper is to estimate food flows between all county pairs within the United States. To do this, we develop the Food Flow Model, a data-driven methodology to estimate spatially explicit food flows. The Food Flow Model integrates machine learning, network properties, production and consumption statistics, mass balance constraints, and linear programming. Specifically, we downscale empirical information on food flows between 132 Freight Analysis Framework locations (17 292 potential links) to the 3142 counties and county-equivalents of the United States (9869 022 potential links). Subnational food flow estimates can be used in future work to improve our understanding of vulnerabilities within a national food supply chain, determine critical infrastructures, and enable spatially detailed footprint assessments.

Background. With climate change, adverse human health effects caused by heat exposure are of increasing public health concern. Forests provide beneficial ecosystem services for human health, including local cooling. Few studies have assessed the relationship between deforestation and heat-related health effects in tropical, rural populations. We sought to determine whether deforested compared to forested landscapes are associated with increased physiological heat strain in a rural, tropical environment. Methods. We analyzed data from 363 healthy adult participants from ten villages who participated in a two-by-two factorial, randomized study in East Kalimantan, Indonesia from 10/1/17 to 11/6/17. Using simple randomization, field staff allocated participants equally to different conditions to conduct a 90 min outdoor activity, representative of typical work. Core body temperature (CBT) was estimated at each minute during the activity using a validated algorithm from baseline oral temperatures and sequential heart rate data, measured using chest band monitors. We used linear regression models, clustered by village and with a sandwich variance estimator, to assess the association between deforested versus forested conditions and the number of minutes each participant spent above an estimated CBT threshold of 38.5 °C. Results. Compared to those in the forested condition (n = 172), participants in the deforested condition (n = 159) spent an average of 3.08 (95% confidence interval (CI) 0.57, 5.60) additional minutes with an estimated CBT exceeding 38.5 °C, after adjustment for age, sex, body mass index, and experiment start time, with a larger difference among those who began the experiment after 12 noon (5.17 [95% CI 2.20, 8.15]). Conclusions. In this experimental study in a tropical, rural setting, activity in a deforested versus a forested setting was associated with increased objectively measured heat strain. Longer durations of hyperthermia can increase the risk of serious health outcomes. Land use decisions should consider the implications of deforestation on local heat exposure and health as well as on forest services, including carbon storage functions that impact climate change mitigation.

This paper examines the scale and costs of using direct air capture (DAC) with CO₂ storage to reduce net CO₂ emissions from the US electric sector by 70% in 2050 relative to 2010. Least-cost emission and technology trajectories are generated using an optimization-based stock-and-flow model of electricity generation to meet the 70% target. The analysis finds that the 30%–44% reduction in emissions projected under a least cost business-as-usual (BAU) scenario dominated by natural gas would fall well short of the 70% reduction target at 2050. Delaying reductions in BAU emissions beyond 2030 would require deployment of DAC to achieve the 70% target. Further delays to reduce BAU emissions until 2035 would require up to 1.4 Gt CO₂ of DAC capacity to achieve the 70% target. Delaying reductions in BAU emissions beyond 2035 would require so much DAC deployment as to be implausible, placing the 70% target out of reach for most scenarios. Each year of delay in reducing CO₂ emissions beyond BAU after 2020 increases costs to achieve the 70% target. A DAC-based emissions reduction future could cost an additional 580–2015 billion USD through 2050 compared to emissions mitigation starting immediately. This translates to approximately 100–345 million USD per day of delay starting in 2020. These costs arise not just from building DAC plants, but from replacing relatively young fossil fuel plants being built today with renewables as well as for the electric power needed for DAC. These results make clear that minimizing the costs of DAC deployment depend on reducing BAU emissions as early as possible, and if done quickly enough, DAC can be avoided altogether—which reduces costs the most. Hence there should be no delay in aggressively reducing emissions from the US electric sector.

Water consumption from electricity systems can be large, and it varies greatly by region. As electricity systems change, understanding the implications for water demand is important, given differential water availability. This letter presents regional water consumption and consumptive intensities for the United States electric grid by region using a 2014 base year, based on the 26 regions in the Environmental Protection Agency's Emissions & Generation Resource Integrated Database. Estimates encompass operational (i.e. not embodied in fixed assets) water consumption from fuel extraction through conversion, calculated as the sum of induced water consumption for processes upstream of the point of generation (PoG) and water consumed at the PoG. Absolute water consumption and consumptive intensity is driven by thermal power plant cooling requirements. Regional consumption intensities vary by roughly a factor of 20. This variability is largely attributed to water consumption upstream of the PoG, particularly evaporation from reservoirs associated with hydroelectricity. Solar and wind generation, which are expected to continue to grow rapidly, consume very little water and could drive lower water consumption over time. As the electricity grid continues to change in response to policy, economic, and climatic drivers, understanding potential impacts on local water resources can inform changes.

The long-term relationship between temperature and hydroclimate has remained uncertain due to the short length of instrumental measurements and inconsistent results from climate model simulations. This lack of understanding is particularly critical with regard to projected drought and flood risks. Here we assess warm-season co-variability patterns between temperature and hydroclimate over Europe back to 850 CE using instrumental measurements, tree-ring based reconstructions, and climate model simulations. We find that the temperature–hydroclimate relationship in both the instrumental and reconstructed data turns more positive at lower frequencies, but less so in model simulations, with a dipole emerging between positive (warm and wet) and negative (warm and dry) associations in northern and southern Europe, respectively. Compared to instrumental data, models reveal a more negative co-variability across all timescales, while reconstructions exhibit a more positive co-variability. Despite the observed differences in the temperature–hydroclimate co-variability patterns in instrumental, reconstructed and model simulated data, we find that all data types share relatively similar phase-relationships between temperature and hydroclimate, indicating the common influence of external forcing. The co-variability between temperature and soil moisture in the model simulations is overestimated, implying a possible overestimation of temperature-driven future drought risks.

Subseasonal-to-seasonal (S2S) water quantity and quality forecasts are needed to support decision and policy making in multiple sectors, e.g. hydropower, agriculture, water supply, and flood control. Traditionally, S2S climate forecasts for hydroclimatic variables (e.g. precipitation) have been characterized by low predictability. Since recent next-generation S2S climate forecasts are generated using improved capabilities (e.g. model physics, assimilation techniques, and spatial resolution), they have the potential to enhance hydroclimatic predictions. Here, this is tested by building and implementing a new dynamical-statistical hydroclimatic ensemble prediction system. Dynamical modeling is used to generate S2S flow predictions, which are then combined with quantile regression to generate water quality forecasts. The system is forced with the latest S2S climate forecasts from the National Oceanic and Atmospheric Administration's Climate Forecast System version 2 to generate biweekly flow, and monthly total nitrogen, total phosphorus, and total suspended sediment loads. By implementing the system along a major tributary of the Chesapeake Bay, the largest estuary in the US, we demonstrate that the dynamical-statistical approach generates skillful flow, nutrient load, and suspended sediment load forecasts at lead times of 1–3 months. Through the dynamical-statistical approach, the system comprises a cost and time effective solution to operational S2S water quality prediction.

We test an equation for the probability of heavy 24 h precipitation amounts Pr(X > x) as a function of the wet-day frequency and the wet-day mean precipitation. The expression was evaluated against 9817 daily rain gauge records world-wide and was subsequently used to derive mathematical expressions for different rainfall statistics in terms of the wet-day frequency and the wet-day mean precipitation. This framework comprised expressions for probabilities, mean, variance, and return-values. We differentiated these statistics with respect to time and compared them to trends in number of rainy days and the mean rainfall intensity based on 1875 rain gauge records with more than 50 years of valid data over the period 1961–2018. The results indicate that there has been a general increase in the probability of precipitation exceeding 50 mm/day. The main cause for this increase has been a boost in the intensity of the rain, but there were also some cases where it has been due to more rainy days. In some limited regions there has also been an increase in Pr(X > 50 mm/day) that coincided with a decrease in the number of rainy days. We also found a general increasing trend in the variance and the 10-year return-value over 1961–2018 due to increasing wet-day frequency and wet-day mean precipitation.

The adoption of low carbon technologies needs to go hand in hand with an increased awareness of climate change and its consequences and solutions. Attitudes toward climate change are influenced by a variety of factors, most notably educational attainment and exposure to climatic events attributable to climate change. However, less is known about the effect of technology adoption on climate change beliefs and support for mitigating measures. Through a longitudinal, incentivized field experiment with Chinese households, we assess attitudes toward climate change before and after adopting efficient lighting technology. The results show differential patterns of attitudinal change: while belief in the reality of climate change and willingness to adopt energy-efficient appliances increase, support for energy taxes does not. We attribute the attitudinal change to the adoption of LED light bulbs. Further evidence suggests that experience with efficient technology, rather than knowledge acquisition, drives this change. These results highlight the importance of action-initiating behavioral intervention to complement educational programs aimed at improving knowledge.

The compact Earth system model OSCARv2.2 is used to assess the climate impact of present and future civil aviation carbon dioxide (CO₂) emissions. The impact of aviation CO₂ on future climate is quantified over the 1940–2050 period, extending some simulations to 2100 and using different aviation CO₂ emission scenarios and two background Representative Concentrations Pathways (RCP2.6 and RCP6.0) for other emission sectors. Several aviation scenarios including weak to strong mitigation options are considered with emissions ranging from 386 MtCO₂/year (Factor 2 scenario) to 2338 MtCO₂/year (ICAO based scenario) in 2050. As a reference, in 2000, the calculated impact of aviation CO₂ emissions is 9.1 ± 2 mK (0.8% of the total anthropogenic warming associated to fossil fuel emissions). In 2050, on a climate trajectory in line with the Paris Agreement limiting the global warming below 2 °C (RCP2.6), the impact of the aviation CO₂ emissions ranges from 26 ± 2 mK (1.4% of the total anthropogenic warming associated to fossil fuel emissions) for an ambitious mitigation strategy scenario (Factor 2) to 39 ± 4 mK (2.0% of the total anthropogenic warming associated to fossil fuel emissions) for the least ambitious mitigation scenario of the study (ICAO based). On the longer term, if no significant emission mitigation is implemented for the aviation sector, the associated warming could further increase and reach a value of 99.5 mK ± 20 mK in 2100 (ICAO based), which corresponds to 5.2% of the total anthropogenic warming under RCP2.6. The contribution of CO₂ is estimated to represent 36%–51% of the total aviation radiative forcing of climate including short-term climate forcers. However, due to its long residence time in the atmosphere, aviation CO₂ will have a major contribution on decadal time scales. These additional short-terms forcers are subject to large uncertainties and will be analysed in forthcoming studies.

Litter production is a fundamental ecosystem process, which plays an important role in regulating terrestrial carbon and nitrogen cycles. However, there are substantial differences in the litter production simulations among ecosystem models, and a global benchmarking evaluation to measure the performance of these models is still lacking. In this study, we generated a global dataset of aboveground litterfall production (i.e. cLitter), a benchmark as the defined reference to test model performance, by combining systematic measurements taken from a substantial number of surveys (1079 sites) with a machine learning technique (i.e. random forest, RF). Our study demonstrated that the RF model is an effective tool for upscaling local litterfall production observations to the global scale. On average, the model predicted 23.15 Pg C yr⁻¹ of aboveground litterfall production. Our results revealed substantial differences in the aboveground litterfall production simulations among the five investigated ecosystem models. Compared to the reference data at the global scale, most of models could reproduce the spatial patterns of aboveground litterfall production, but the magnitude of simulations differed substantially from the reference data. Overall, ORCHIDEE-MICT performed the best among the five investigated ecosystem models.

Large-scale land acquisitions (LSLA) in resource-rich countries came to global attention after the food and financial crises of 2008. Previous research has assessed the magnitude of these land investments in terms of land areas acquired. In this study, we analyze the trends in the evolution of LSLA by framing the latter as virtual land trade network with land transactions occurring between 2000 and 2015, in order to shed light on the development and evolution of this system. Based on an index we introduce to represent both the number of countries and size of deals, we discover three main phases of trade activity: a steady increase from 2000 until 2007 (Phase 1) followed by a peak coinciding with the food and financial crises between 2008 and 2010 (Phase 2) and concluded by a decline from 2011 to 2015 (Phase 3). We identify 73 countries that remained active in land trading during all three phases and form a core of land traders much larger than previously thought. Using network analysis methods, we group countries with similar trade patterns into categories of competitive, preferential, diversified, and occasional importers or exporters. Finally, in exploring the changes in investors and their interests in land throughout the phases, we attribute the evolution of LSLA to the different stages in the globalization and financialization of different industries. By showing that land investments seem fully integrated as investment strategies across industries we argue for the urgency of better regulation of LSLA so that they also benefit local populations without damaging the environment regardless of their primary purpose.

Tourism has been identified as a key economic sector vulnerable to climate change, yet direct empirical evidence is still lacking on the economic gain and loss of the tourism industry due to climate change. Here we find that temperature significantly affects the profits of the hotel industry with both spatial and seasonal heterogeneity. By using a rich dataset of the monthly financial records of more than 1700 hotels in 50 US states during 2016–2018 (approximately 3.2% of hotels nationally), we show that a deviation from 18 °C ∼ 20 °C in monthly averaged temperature leads to a decrease in the profit rate. The effect is triggered by fewer customers, less revenue, and higher cost per occupied room partially due to the increased usage of electricity and water. Such an effect can be lasting and is less impactful for higher chain scale hotels. In future GHG emission scenarios, climate change will lead to a loss of profit in most climate zones particularly the southern regions, with higher GHG emissions leading to a more serious effect. This study contributes to the literature on how climate change affects human activities and helps refine the relevant damage function of climate change on tourism in existing climate models.

Ambient exposure to fine particulate matter (PM_2.5) is one of the top global health concerns. We estimate the PM_2.5-related health benefits of emission reduction over New York State (NYS) from 2002 to 2012 using seven publicly available PM_2.5 products that include information from ground-based observations, remote sensing and chemical transport models. While these PM_2.5 products differ in spatial patterns, they show consistent decreases in PM_2.5 by 28%–37% from 2002 to 2012. We evaluate these products using two sets of independent ground-based observations from the New York City Community Air Quality Survey (NYCCAS) Program for an urban area, and the Saint Regis Mohawk Tribe Air Quality Program for a remote area. Inclusion of satellite remote sensing improves the representativeness of surface PM_2.5 in the remote area. Of the satellite-based products, only the statistical land use regression approach captures some of the spatial variability across New York City measured by NYCCAS. We estimate the PM_2.5-related mortality burden by applying an integrated exposure-response function to the different PM_2.5 products. The multi-product mean PM_2.5-related mortality burden over NYS decreased by 5660 deaths (67%) from 8410 (95% confidence interval (CI): 4570–12 400) deaths in 2002 to 2750 (CI: 700–5790) deaths in 2012. We estimate a 28% uncertainty in the state-level PM_2.5 mortality burden due to the choice of PM_2.5 products, but such uncertainty is much smaller than the uncertainty (130%) associated with the exposure-response function.

Small-scale farming provides both food and livelihoods for the vast majority of the global poor. Thus, increasing and stabilizing farm incomes and food production in developing countries is fundamental to reducing global poverty. Policies for rural development such as improved access to non-agricultural incomes or land titling may benefit farmers, but they may also lead to farm consolidation with unintended consequences for aggregate food supply. Using a large panel dataset of rural households in Uganda, we parse apart how farm size affects the level and riskiness of agricultural incomes as well as of local food supply. Our findings indicate that while output per unit of land does decline with increasing farm size as suggested by previous literature, agricultural incomes increase with farm size. We show further that while the variance of agricultural incomes declines with increasing farm size, the variance of local food production increases with farm size. These results suggest that farmers benefit from larger farms, earning higher and more stable incomes while consumers suffer from lower and more volatile food supply.

During 2012, flash drought developed and subsequently expanded across large areas of the Central United States (US) with severe impacts to overall water resources and warm-season agricultural production. Recent efforts have yielded a methodology to detect and quantify flash drought occurrence and rate of intensification from climatological datasets via the standardized evaporative stress ratio (SESR). This study utilizes the North American Regional Reanalysis and applied the SESR methodology to quantify the spatial and temporal development and expansion of flash drought conditions during 2012. Critical results include the identification of the flash drought epicenter and subsequent spread of flash drought conditions radially outward with varying rates of intensification. Further, a comparison of the SESR analyses with surface-atmosphere coupling metrics demonstrated that a hostile environment developed across the region, which limited the formation of deep atmospheric convection, exacerbated evaporative stress, and perpetuated flash drought development and enhanced its radial spread across the Central US.

Observational evidence suggests that compared to non-forested areas, forests have a cooling effect on daytime land surface temperature (LST) and a warming effect on nighttime LST in many regions of the world, thus implying that forests dampen the diurnal temperature range. This feature is not captured by current climate models. Using the Community Land Model 5.0 (CLM5.0), we show that this diurnal behavior can be captured when accounting for biomass heat storage (BHS). The nighttime release of energy absorbed by the vegetation biomass during the day increases both nighttime LST and ambient air temperature in forested regions by more than 1 K. The daytime cooling is weaker than the nighttime warming effect, because the energy uptake by the biomass is compensated by a reduction in the turbulent heat fluxes during day. This diurnal asymmetry of the temperature response to BHS leads to a warming of daily mean temperatures, which is amplified during boreal summer warm extremes. Compared to MODIS, CLM5.0 overestimates the diurnal LST range over forested areas. The inclusion of BHS reduces this bias due to its dampening effect on diurnal LST variations. Further, BHS attenuates the negative bias in the nighttime LST difference of forest minus grassland and cropland, when compared to MODIS observations. These results indicate that it is essential to consider BHS when examining the influence of forests on diurnal temperature variations. BHS should thus be included in land surface models used to assess the climatic consequences of land use changes such as deforestation or afforestation.

Emissions from wetlands are the single largest source of the atmospheric greenhouse gas (GHG) methane (CH₄). This may increase in a warming climate, leading to a positive feedback on climate change. For the first time, we extend interactive wetland CH₄ emissions schemes to include the recently quantified, significant process of CH₄ transfer through tropical trees. We constrain the parameterisations using a multi-site flux study, and biogeochemical and inversion models. This provides an estimate and uncertainty range in contemporary, large-scale wetland emissions and their response to temperature. To assess the potential for future wetland CH₄ emissions to feedback on climate, the schemes are forced with simulated climate change using a 'pattern-scaling' system, which links altered atmospheric radiative forcing to meteorology changes. We perform multiple simulations emulating 34 Earth System Models over different anthropogenic GHG emissions scenarios (RCPs). We provide a detailed assessment of the causes of uncertainty in predicting wetland CH₄–climate feedback. Despite the constraints applied, uncertainty from wetland CH₄ emission modelling is greater that from projected climate spread (under a given RCP). Limited knowledge of contemporary global wetland emissions restricts model calibration, producing the largest individual cause of wetland parameterisation uncertainty. Wetland feedback causes an additional temperature increase between 0.6% and 5.5% over the 21st century, with a feedback on climate ranging from 0.01 to 0.11 Wm⁻² K⁻¹. Wetland CH₄ emissions amplify atmospheric CH₄ increases by up to a further possible 25.4% in one simulation, and reduce remaining allowed anthropogenic emissions to maintain the RCP2.6 temperature threshold by 8.0% on average.

Human-induced climate change poses a major threat to the reliable water supply in many highly populated regions. Here we combine hydrological and climate model simulations to evaluate risks to the water supply under projected shifts in the climate at the Paris Agreement warming levels. Modelling the primary surface water sources for Melbourne, Australia, we project that the risk of severe water supply shortage to the climate-dependent portion of the system increases substantially as global warming increases from 1.5 °C to 2.0 °C. Risks are further exacerbated by increases in water demand but substantially ameliorated by supply augmentation from desalination. We demonstrate that reductions in precipitation, rising temperature and growth in water demand combine to substantially amplify the risk of severe water supply shortage under near-term global warming in the absence of a climate-independent supply. This risk amplification is not as apparent in assessments based on meteorological drought alone. With the diminishing opportunity of meeting the 1.5 °C Paris target, our study highlights the need to accelerate greenhouse gas mitigation efforts to reduce risks to climate dependent water supply systems.

Using idealized climate model simulations, we investigate the effectiveness of black carbon (BC) aerosols in warming the planet relative to CO₂ forcing. We find that a 60-fold increase in the BC aerosol mixing ratio from the present-day levels leads to the same equilibrium global mean surface warming (∼4.1 K) as for a doubling of atmospheric CO₂ concentration. However, the radiative forcing is larger (∼5.5 Wm⁻²) in the BC case relative to the doubled CO₂ case (∼3.8 Wm⁻²) for the same surface warming indicating the efficacy (a metric for measuring the effectiveness) of BC aerosols to be less than CO₂. The lower efficacy of BC aerosols is related to the differences in the shortwave (SW) cloud feedback: negative in the BC case but positive in the CO₂ case. In the BC case, the negative SW cloud feedback is related to an increase in the tropical low clouds which is associated with a northward shift (∼7°) of the Intertropical Convergence Zone (ITCZ). Further, we show that in the BC case fast precipitation suppression offsets the surface temperature mediated precipitation response and causes ∼8% net decline in the global mean precipitation. Our study suggests that a feedback between the location of ITCZ and the interhemispheric temperature could exist, and the consequent SW cloud feedback could be contributing to the lower efficacy of BC aerosols. Therefore, an improved representation of low clouds in climate models is likely the key to understand the global climate sensitivity to BC aerosols.

Rapid warming in northern ecosystems over the past four decades has resulted in earlier spring, increased precipitation, and altered timing of plant–animal interactions, such as herbivory. Advanced spring phenology can lead to longer growing seasons and increased carbon (C) uptake. Greater precipitation coincides with greater cloud cover possibly suppressing photosynthesis. Timing of herbivory relative to spring phenology influences plant biomass. None of these changes are mutually exclusive and their interactions could lead to unexpected consequences for Arctic ecosystem function. We examined the influence of advanced spring phenology, cloud cover, and timing of grazing on C exchange in the Yukon–Kuskokwim Delta of western Alaska for three years. We combined advancement of the growing season using passive-warming open-top chambers (OTC) with controlled timing of goose grazing (early, typical, and late season) and removal of grazing. We also monitored natural variation in incident sunlight to examine the C exchange consequences of these interacting forcings. We monitored net ecosystem exchange of C (NEE) hourly using an autochamber system. Data were used to construct daily light curves for each experimental plot and sunlight data coupled with a clear-sky model was used to quantify daily and seasonal NEE over a range of incident sunlight conditions. Cloudy days resulted in the largest suppression of NEE, reducing C uptake by approximately 2 g C m⁻² d⁻¹ regardless of the timing of the season or timing of grazing. Delaying grazing enhanced C uptake by approximately 3 g C m⁻² d⁻¹. Advancing spring phenology reduced C uptake by approximately 1.5 g C m⁻² d⁻¹, but only when plots were directly warmed by the OTCs; spring advancement did not have a long-term influence on NEE. Consequently, the two strongest drivers of NEE, cloud cover and grazing, can have opposing effects and thus future growing season NEE will depend on the magnitude of change in timing of grazing and incident sunlight.

Shared stand-up electric scooters are now offered in many cities as an option for short-term rental, and marketed for short-distance travel. Using life cycle assessment, we quantify the total environmental impacts of this mobility option associated with global warming, acidification, eutrophication, and respiratory impacts. We find that environmental burdens associated with charging the e-scooter are small relative to materials and manufacturing burdens of the e-scooters and the impacts associated with transporting the scooters to overnight charging stations. The results of a Monte Carlo analysis show an average value of life cycle global warming impacts of 202 g CO₂-eq/passenger-mile, driven by materials and manufacturing (50%), followed by daily collection for charging (43% of impact). We illustrate the potential to reduce life cycle global warming impacts through improved scooter collection and charging approaches, including the use of fuel-efficient vehicles for collection (yielding 177 g CO₂-eq/passenger-mile), limiting scooter collection to those with a low battery state of charge (164 g CO₂-eq/passenger-mile), and reducing the driving distance per scooter for e-scooter collection and distribution (147 g CO₂-eq/passenger-mile). The results prove to be highly sensitive to e-scooter lifetime; ensuring that the shared e-scooters are used for two years decreases the average life cycle emissions to 141 g CO₂-eq/passenger-mile. Under our Base Case assumptions, we find that the life cycle greenhouse gas emissions associated with e-scooter use is higher in 65% of our Monte Carlo simulations than the suite of modes of transportation that are displaced. This likelihood drops to 35%–50% under our improved and efficient e-scooter collection processes and only 4% when we assume two-year e-scooter lifetimes. When e-scooter usage replaces average personal automobile travel, we nearly universally realize a net reduction in environmental impacts.

Terrestrial gross primary productivity (GPP) is an important flux that drives the global carbon cycle. However, quantifying the trend and the control factor of GPP from the pixel level to the regional level is still a challenge. We generated monthly GPP dataset using the vegetation photosynthesis model and calculated the interannual linear trend for China during 2000–2016. The Breaks For Additive Seasonal and Trend method was applied to detect the timing of breakpoint and trends shift of monthly GPP, while boosted regression tree analysis was used to identify the most important factor and its relative influence on GPP based on gridded leaf area index (LAI), aerosol optical thickness, and NCEP-DOE Reanalysis II meteorological data. The results show that annual mean GPP was significantly (P < 0.001, R² = 0.78) increased, especially in the Loess Plateau and South China, from 2000 to 2016. The change rate of annual mean GPP declined from 18.82 g C m⁻² yr⁻¹ in 2000–2008 to 3.48 g C m⁻² yr⁻¹ in 2008–2016. About 55.4% of the breakpoints occur between 2009 and 2011 and was mainly distributed in Qinghai-Tibet Plateau, Central China, Southwestern China, and South China, and negative oriented GPP trends variation type still accounts for about 28.76%. LAI and temperature related factors generally had the highest relative influence on GPP in the north part and south part of China, respectively. Our study indicates that the ecological restoration projects and rapid urbanization have respectively induced the most obvious increase and decrease trends of GPP in China. Land cover change and climate change are the main reasons for GPP dynamics in the north and south part of China, respectively.

Plastic pollution in aquatic environments is an increasing global risk. In recent years, marine plastic pollution has been studied to a great extent, and it has been hypothesized that land-based plastics are its main source. Global modeling efforts have suggested that rivers in South East Asia are in fact the main contributors to plastic transport from land to the oceans. However, due to a lack of plastic transport observations, the origin and fate of riverine plastic waste is yet unclear. Here, we present results from a first assessment of riverine macroplastic emission from rivers and canals that run through a densely populated coastal urban city. Using a combination of field measurements, empirical relations and hydraulic modeling, we provide an estimate of total riverine plastic export originating from Jakarta, Indonesia, into the ocean. Furthermore, we provide insights in its composition, and variation in time and space. We found that most macroplastics in Jakarta consists of films and foils. We estimate that 2.1 × 10³ tonnes of plastic waste, is transported from land to sea annually, equaling 3% of the total annual unsoundly disposed plastic waste in the Jakarta area.

Deltas are resource rich, low-lying areas where vulnerability to flooding is exacerbated by natural and anthropogenically induced subsidence and geocentric sea-level rise, threatening the large populations often found in these settings. Delta 'drowning' is potentially offset by deposition of sediment on the delta surface, making the delivery of fluvial sediment to the delta a key balancing control in offsetting relative sea-level rise, provided that sediment can be dispersed across the subaerial delta. Here we analyse projected changes in fluvial sediment flux over the 21st century to 47 of the world's major deltas under 12 environmental change scenarios. The 12 scenarios were constructed using four climate pathways (Representative Concentration Pathways 2.6, 4.5, 6.0 and 8.5), three socioeconomic pathways (Shared Socioeconomic Pathways 1, 2 and 3), and one reservoir construction timeline. A majority (33/47) of the investigated deltas are projected to experience reductions in sediment flux by the end of the century, when considering the average of the scenarios, with mean and maximum declines of 38% and 83%, respectively, between 1990–2019 and 2070–2099. These declines are driven by the effects of anthropogenic activities (changing land management practices and dam construction) overwhelming the effects of future climate change. The results frame the extent and magnitude of future sustainability of major global deltas. They highlight the consequences of direct (e.g. damming) and indirect (e.g. climate change) alteration of fluvial sediment flux dynamics and stress the need for further in-depth analysis for individual deltas to aid in developing appropriate management measures.

A decarbonized future will require a transition to lower carbon fuels for personal transportation. We study consumer preferences for combustion fuels including gasoline, diesel, natural gas, and E85 (85% ethanol and 15% gasoline) using consumer choice survey data from two settings: online (n = 331) and in-person at refueling stations (n = 127). Light-duty vehicle owners were asked in a series of choice tasks to choose among fuels that varied in type, price, CO₂ emissions, and location of origin for a hypothetical vehicle that could accept all fuels. We find that the majority of gasoline and E85 users are willing to substitute towards other fuels at today's prices and attributes, while diesel users have a strong preference for diesel fuel. We also find that respondents are willing to pay on average $150/ton CO₂ avoided from fuel consumption—more than most estimates of the social cost of carbon. Thus, communicating the climate benefits from alternative fuels may be an important strategy for decarbonizing the transportation sector.

We examine how future changes in water yield and demand will affect the likelihood of water shortages and the efficacy of some of the most common methods for dealing with water shortages and meeting municipal demands, including improvements in water use efficiency and transfers of water between sectors of the economy. We find that more than 45.8 million people, primarily in the Southwest, central Great Plains, and southern California, would already be experiencing regular water shortages in the absence of groundwater mining. By 2060, that number would grow to over 136.2 million people. Among the reasons we find for increased likelihood of water shortages, reduced water yield is the most prevalent, affecting 80% of water basins in the US In the American West, nearly half of the water basins are projected to see an increase in shortages. We estimate future water withdrawals in the industrial and commercial and thermoelectric sectors will remain fairly steady, but withdrawals in the domestic and public sector are expected to rise. The Colorado River and Rio Grande regions see the largest percentage increases in projected domestic and public water use as well as the greatest percentage decreases in projected water yield. To cover new municipal demands, transfers from agriculture may be needed, in which case, significant impacts to agriculture will occur in northern New Mexico, parts of Utah, Nevada, and Washington where municipal demands are projected to grow to 25%–50% of agricultural water use. The situation is more extreme in northern Arizona and eastern Texas, where additional municipal demands are projected to be six times the amount used by agriculture.

Observational estimates of global ocean heat content (OHC) change are used to assess Earth's energy imbalance over the 20th Century. However, intercomparison studies show that the mapping methods used to interpolate sparse ocean temperature profile data are a key source of uncertainty. We present a new approach to assessing OHC mapping methods using 'synthetic profiles' generated from a state-of-the-art global climate model simulation. Synthetic profiles have the same sampling characteristics as the historical ocean temperature profile data but are based on model simulation data. Mapping methods ingest these data in the same way as they would real observations, but the resultant mapped fields can be compared to a model simulation 'truth'. We use this approach to assess two mapping methods that are used routinely for climate monitoring and initialisation of decadal forecasts. The introduction of the Argo network of autonomous profiling floats during the 2000s drives clear improvements in the ability of these methods to reconstruct the variability and spatial structure of OHC changes. At depths below 2000 m, both methods underestimate the magnitude of the simulated ocean warming signal. Temporal variability and trends in OHC are better captured in the better-observed northern hemisphere than in the southern hemisphere. At all depths, the sampling characteristics of the historical data introduces some spurious variability in the estimates of global OHC on sub-annual to multi-annual timescales. However, many of the large scale spatial anomalies, especially in the upper ocean, are successfully reconstructed even with sparse observations from the 1960s, demonstrating the potential to construct historical ocean analyses for assessing decadal predictions. The value of using accurate global covariances for data-poor periods is clearly seen. The results of this 'proof-of-concept' study are encouraging for gaining further insights into the capabilities and limitations of different mapping methods and for quantifying uncertainty in global OHC estimates.

Changes in the East Asian summer monsoon (EASM) during the mid-21st century relative to present day are simulated in two related models GOML1 and GOML2. Both models are the atmospheric components of two state-of-the-art climate models coupled to a multi-level mixed-layer ocean model, following the RCP 4.5 scenario. Both show that the EASM is enhanced due to the amplified land-sea thermal contrast. Summer precipitation over northern China is projected to increase by 5%–10% in both models mainly driven by enhancement of the monsoon circulation. Over south-eastern China the two models project different signs of precipitation change: a decrease in GOML1 with the maximum of about −1.0 mm d⁻¹ and an increase in GOML2 with a maximum of around 1.0 mm d⁻¹. Though the thermal effect of climate warming leads to a projected increase in precipitation over south-eastern China in both models, circulation changes are opposite and dominate the precipitation response. This indicates that uncertainty in changes in projected precipitation largely arises from uncertainly in projected circulation changes. The different circulation changes in the two models are likely related to differences in projected Sea Surface Temperature (SST) in the Western tropical Pacific and North Pacific. In GOML1, the SST warming in the tropical Pacific is associated with an anomalous local Hadley circulation, characterized by anomalous ascent in the tropics and southern subtropics, and anomalous descent with less precipitation over south-eastern China. In GOML2, the large decrease in the meridional SST gradient between the South China Sea and Western North Pacific is associated with an anomalous local Hadley circulation with anomalous ascent at 20°N–30°N and anomalous descent at 5°N–15°N, leading to an anti-cyclonic circulation anomaly over the South China Sea and increased precipitation over south-eastern China.

Mitigation of non-CO₂ climate forcing agents must complement the mitigation of CO₂ to achieve long-term temperature and climate policy goals. A large share of global non-CO₂ greenhouse gas emissions is attributed to agriculture, with a significant contribution related to dairy production. As demonstrated by the results of a recent USDA coordinated project, Dairy-CAP, dairy farmers can significantly reduce their greenhouse gas emissions by implementing beneficial management practices (BMPs). This study assesses the potential mitigation of projected climate change if greenhouse gases associated with the dairy subsector were reduced. To compare the performance of several mitigation measures under future climate change, we employ a fully coupled Earth system model of intermediate complexity, the MIT Earth System Model. With an interactive carbon-cycle, the model is capable of addressing important feedbacks between the climate and terrestrial biosphere impacting greenhouse gas concentrations. We illustrate the importance of ongoing mitigation efforts in the agricultural sector to reduce non-CO₂ greenhouse gas emissions towards established climate goals. If BMPs are implemented globally within the next three decades, projected warming by the end of the century can be reduced by 0.21 °C on average or 6% of total warming, with dairy farm mitigation contributing to 0.03 °C of the temperature reduction.

The spatiotemporal characteristics of temperature extremes over Antarctica remain largely unknown. Here, we use quality-controlled daily datasets from Antarctic weather stations to show that the annual maximum and minimum temperatures exhibit a decreasing pattern over Antarctica from the coast to inland regions. This feature holds for the warmest daily maximum and coldest daily minimum temperatures, which define the intensity of extremes, but not for the number of warm (cold) days measuring the frequency of extremes, which show limited dependence on latitude or elevation. During 1970–2000, the temperature extremes in the South Orkney islands and on the margins of East Antarctica show opposite trends, especially with a significant increasing and decreasing trend in warm events, respectively. During 1999–2013, the intensity and frequency of extreme temperatures decrease significantly over West Antarctica, but the trends vary greatly across sub-regions of Antarctica. Despite the limited number of stations and the potential time dependence of trends, these results not only help to decipher the climate regimes of Antarctica and fill current gaps in the map of global climate extremes, but also may guide the future design of Antarctic observational networks and be used to assess the capability of reanalysis datasets and climate models.

Climate change (CC) clearly impacts food production, but risks on the climatic suitability of agricultural areas for vegetable crops, their pests and associated natural enemies are largely unexplored. Tomato, one of the most important vegetables in the world, is grown mostly outdoors, and may be severely impacted by CC. Farmers cultivating tomatoes need to adapt to an increase in the potential for outbreaks of pests favoured by CC and disruption of biological control, yet, no attempt has been made to simultaneously evaluate CC effects on a crop-pest-natural enemy system for tomato or any other crop. Here, we modelled the suitability of areas equipped with irrigation facilities (AEI) in 2050 for tomato, the two-spotted spider mite, Tetranychus urticae, a mite pest of tomato among more than 200 crops, and its key predator Phytoseiulus persimilis. We evaluated the suitability of AEI for tomato production under a 1.6 °C warming by 2050, within the targets of the Paris agreement. Projections show that climatic conditions become unsuitable for tomato production on 30%–100% of AEI for seven out of the 29 top tomato producing countries of the world. Model predictions suggest that two-spotted spider mite potential for outbreaks would increase substantially in nine countries in Europe, Africa and Asia, while biological control failures would occur globally. Model results have a significant relationship with growth rates for the three species measured in outdoor experiments, and farmer/expert perceptions on two-spotted spider mite outbreak severity captured via interviews. The expansion of AEI in other agricultural areas in the sub-Saharan Africa may offset losses of suitable land. However, several nations in the Middle East and South Asia with prevalent small scale agriculture would experience devastating impacts because of the unsuitability of conditions for tomato production and the potential increase in two-spotted spider mite outbreaks.

The economic impacts of disasters can reach far beyond the affected regions through interconnected transboundary trade flows. As quantification of these indirect impacts is complex, most disaster risk models focus on the direct impacts on assets and people in the impacted region. This study explicitly includes the indirect effects via regional economic interdependencies to model economic disaster losses on a continental scale, exemplified by river flooding in Europe. The results demonstrate that economic implications go beyond the direct damages typically considered. Moreover, we find that indirect losses can be offset by up to 60% by economic actors through finding alternative suppliers and markets within their existing trade relations. Towards the future, increases in economic flood losses (up to 350%) can be expected for all global warming scenarios. Indirect losses rise by 65% more compared to direct asset damages due to the increasing size of future flood events, making it more difficult to offset losses through alternative suppliers and markets. On a sectoral level, future increases in losses are highest for commercial services (∼980%) and public utilities (∼580%). As the latter are predominately affected through cascading effects, this highlights how interdependencies between economic actors could amplify future disaster losses.

Global climate change affects residential heating and cooling demand that further contributes to carbon dioxide (CO₂) emissions. The spatio-temporal changes in magnitude and distribution of the demands in China are poorly understood. In addition, few studies have focused on the future impact of climate change on long term residential CO₂ emissions in China. Here we investigate regional changes in CO₂ emissions calculated from degree-days. Our results show that heating degree-days (HDD), cooling degree-days (CDD) and their associated CO₂ emissions all have large spatio-temporal variability. We find that average durations of HDD and CDD are predicted to be 34 days shorter and 63 days longer by the end of century (2071–2100) than history (1976–2005). CO₂ emissions from residential cooling and heating are predicted to increase 218% and decrease 30% in China by end-century, respectively. We further examine the CO₂ emissions from residential heating and cooling in five cities representative of five contrasting architectural climate zones in China. The CO₂ emissions from heating of these cities are projected to decrease by end-century: 26% in Harbin, 32% in Beijing, 43% in Shanghai, 42% in Kunming, and 61% in Shenzhen. The CO₂ emissions from cooling of these cities all increase by end-century: 436% in Harbin, 215% in Beijing, 223% in Shanghai, 765% in Kunming, and 149% in Shenzhen.

Simulating the implications of Brexit on the UK's emissions embodied in trade with a multi-region input–output table exposes the benefits of European integration. Under 2014 trade volumes, technologies and energy mixes, a hard Brexit—reverting to a trade pattern between the UK and the EU prior to the European Internal Market (EIM)—would imply a rise of about 0.215Gt of CO₂eq in the UK's emissions embodied in imports. This is equivalent to a 38% rise in UK's imported emissions in 2014 and roughly equal to the territorial emissions of the Netherlands in 2017. Substituting imports from the EU with those from the Rest of the World (RoW), under the same conditions, implies adding 0.35 kg of CO₂eq, on average, to each dollar of activity imported in the UK. This underlines the emission benefits of an integrated European market abiding to common environmental standards and climate policies. Filling the gap in imports lost from the UK to the EU by stepping up production within the EIM would result in an extra 0.012Gt of CO₂eq, a rather small increase when compared to the additional emissions in the UK's imports following Brexit. Should the EU reallocate the lost imports from the UK to the RoW, a total of 0.128Gt of CO₂eq would be added to the EIM imports. This exposes the environmental benefits in terms of emissions in keeping UK trade closely linked to the EU and the important role that Single Member States can play indirectly on EU's import emissions. In terms of emissions embodied in trade, the sum of the EU market is, paradoxically and for the better, less than the sum of its individual parts.

Intensity-duration-frequency (IDF) curves usefully quantify extreme precipitation over various durations and return periods for engineering design. Unfortunately, sparse, infrequent, or short observations hinder the creation of robust IDF curves in many locations. This paper presents the first global, multi-temporal (1–360 h) dataset of generalized extreme value (GEV) parameters at 31 km resolution dubbed PXR-2 (Parametrized eXtreme Rain). Using these data we generalize site-specific studies to show that that GEV parameters typically scale robustly with event duration (r² > 0.88). Thus, we propose a universal IDF formula that allows estimates of rainfall intensity for a continuous range of durations (PXR-4). This parameter scaling property opens the door to estimating sub-daily IDF from daily records. We evaluate this characteristic for selected global cities and a high-density rain gauge network in the United Kingdom. We find that intensities estimated with PXR-4 are within ±20% of PXR-2 for durations ranging between 2 and 360 h. PXR is immediately usable by earth scientists studying global precipitation extremes and a promising proof-of-concept for engineers designing infrastructure in data-scarce regions.

This paper presents an evaluation of the global and regional consequences of climate change for heat extremes, water resources, river and coastal flooding, droughts, agriculture and energy use. It presents change in hazard and resource base under different rates of climate change (representative concentration pathways (RCP)), and socio-economic impacts are estimated for each combination of RCP and shared socioeconomic pathway. Uncertainty in the regional pattern of climate change is characterised by CMIP5 climate model projections. The analysis adopts a novel approach using relationships between level of warming and impact to rapidly estimate impacts under any climate forcing. The projections provided here can be used to inform assessments of the implications of climate change. At the global scale all the consequences of climate change considered here are adverse, with large increases under the highest rates of warming. Under the highest forcing the global average annual chance of a major heatwave increases from 5% now to 97% in 2100, the average proportion of time in drought increases from 7% to 27%, and the average chance of the current 50 year flood increases from 2% to 7%. The socio-economic impacts of these climate changes are determined by socio-economic scenario. There is variability in impact across regions, reflecting variability in projected changes in precipitation and temperature. The range in the estimated impacts can be large, due to uncertainty in future emissions and future socio-economic conditions and scientific uncertainty in how climate changes in response to future emissions. For the temperature-based indicators, the largest source of scientific uncertainty is in the estimated magnitude of equilibrium climate sensitivity, but for the indicators determined by precipitation the largest source is in the estimated spatial and seasonal pattern of changes in precipitation. By 2100, the range across socio-economic scenario is often greater than the range across the forcing levels.

Forest cover loss in the tropics is well known to cause warming at deforested sites, with maximum temperatures being particularly sensitive. Forest loss causes warming by altering local energy balance and surface roughness, local changes that can propagate across a wide range of spatial scales. Consequently, temperature increases result from not only changes in forest cover at a site, but also by the aggregate effects of non-local forest loss. We explored such non-local warming within Brazil's Amazon and Cerrado biomes, the region with the world's single largest amount of forest loss since 2000. Two datasets, one consisting of in-situ air temperature observations and a second, larger dataset consisting of ATs derived from remotely-sensed observations of land surface temperature, were used to quantify changes in maximum temperature due to forest cover loss at varying length-scales. We considered undisturbed forest locations (1 km² in extent), and forest loss trends in annuli ('halos'), located 1–2 km, 2–4 km, 4–10 km and 10–50 km from these undisturbed sites. Our research finds significant and substantial non-local warming, suggesting that historical estimates of warming due to forest cover loss under-estimate warming or mis-attribute warming to local change, where non-local changes also influence the pattern of temperature warming.

The main obstacle to making the transportation sector ecologically more sustainable is political feasibility. Effective policy-interventions usually encounter strong public opposition as they interfere in costly ways with people's daily lives, unveiling a dilemma between political feasibility and environmental policy effectiveness. Evidencing the existence of this dilemma, the literature on attitudes towards different policy instrument types maintains that so-called push measures are less supported by citizens than pull measures, and that market-based instruments tend to be less supported than non-market instruments. While these findings may uphold when considering single policy instruments, whether they continue to do so when considering policy-packages, that is, simultaneously implemented policy-interventions consisting of several policy instruments, remains unclear. To identify politically feasible and effective policy-packages aimed at greening the transportation sector we use choice experiments with representative samples of citizens from China, Germany, and the USA (N = 4'876). Contrary to existing literature, we find that public support does not necessarily depend on the instrument type but rather on specific policy design and is highly context dependent. Moreover, despite significant differences between the three country contexts considered, various combinations of policy measures appear to be both potentially effective and supported by most citizens. Altogether, these results suggest that carefully bundled policy-packages may allow governments to employ instruments that would not be politically feasible if introduced in isolation.

Total CO₂ emissions from the United States power sector increased over the period 1990–2005, but peaked soon after, and by 2015 they had declined by 20% compared to 2005. This study analyzes the supply-side drivers of the increasing trend up until 2005 as well as the factors across US states that enabled significant reductions in the following decade. Using index decomposition analysis, we show that the two main factors driving the CO₂ decrease were natural gas substituting for coal and petroleum, and large increases in renewable energy generation (primarily wind)—which were responsible for 60% and 30% of the decline respectively since 2005. Both effects were concentrated in states where low natural gas prices or a combination of federal tax credits, state energy policies, decreasing costs of renewables, and advantageous wind conditions drove significant reductions of CO₂ emissions—resulting in the overall national emissions decline.

Atmospheric CO₂ observations have the potential to monitor regional fossil fuel emission (FFCO₂) changes to support carbon mitigation efforts such as the Paris Accord, but they must contend with the confounding impacts of the natural carbon cycle. Here, we quantify trend detection time and magnitude in gridded total CO₂ fluxes—the sum of FFCO₂ and natural carbon fluxes—under an idealized assumption that monthly total CO₂ fluxes can be perfectly resolved at a 2°×2° resolution. Using Coupled Model Intercomparison Project 5 (CMIP5) 'business-as-usual' emission scenarios to represent FFCO₂ and simulated net biome exchange (NBE) to represent natural carbon fluxes, we find that trend detection time for the total CO₂ fluxes at such a resolution has a median of 10 years across the globe, with significant spatial variability depending on FFCO₂ magnitude and NBE variability. Differences between trends in the total CO₂ fluxes and the underlying FFCO₂ component highlight the role of natural carbon cycle variability in modulating regional detection of FFCO₂ emission trends using CO₂ observations alone, particularly in the tropics and subtropics where mega-cities with large populations are developing rapidly. Using CO₂ estimates alone at such a spatiotemporal resolution can only quantify fossil fuel trends in a few places—mostly limited to arid regions. For instance, in the Middle East, FFCO₂ can explain more than 75% of the total CO₂ trends in ∼70% of the grids, but only ∼20% of grids in China can meet such criteria. Only a third of the 25 megacities we analyze here show total CO₂ trends that are primarily explained (>75%) by FFCO₂. Our analysis provides a theoretical baseline at a global scale for the design of regional FFCO₂ monitoring networks and underscores the importance of estimating biospheric interannual variability to improve the accuracy of FFCO₂ trend monitoring. We envision that this can be achieved with a fully integrated carbon cycle assimilation system with explicit constraints on FFCO₂ and NBE, respectively.

One near-term expression of climate change is increased occurrence and intensity of extreme heat events. The evolution of extreme heat risk in cities depends on the interactions of large-scale climate change with regional dynamics and urban micro-climates as well as the distribution and demographic characteristics of people who live and work within these micro-climate areas. Here we use California as a testbed where we employ a suite of high-resolution (1.5 km) future regional climate simulations coupled with a satellite-driven urban canopy model and a spatially explicit population projection to investigate the interacting effects of climate change, population growth, and urban heat mitigation measures, such as cool roofs, on exposure to extreme heat events. We find that climate change and population growth reinforce with one another to drive substantial increases in future exposure to heat extremes, which are poised to become more frequent, longer, and more intense. Exposure to events analogous to historic high-mortality extreme heat waves increases by 3.5–6 folds. Widespread implementation of cool roofs can offset a substantial fraction (51%–100%) of the increased heat exposure and associated building energy demand owing to climate change in urbanized regions.

Anthropogenic CO₂ emission from fossil fuel combustion has major impacts on the global climate. The Orbiting Carbon Observatory 2 (OCO-2) observations have previously been used to estimate individual power plant emissions with a Gaussian plume model assuming constant wind fields. The present work assesses the feasibility of estimating power plant CO₂ emission using high resolution chemistry transport model simulations with OCO-2 observation data. In the new framework, 1.33 km Weather Research and Forecasting-Chem (WRF)-Chem simulation results are used to calculate the Jacobian matrix, which is then used with the OCO-2 XCO₂ data to obtain power plant daily mean emission rates, through a maximum likelihood estimation. We applied the framework to the seven OCO-2 observations of near mid-to-large coal burning power plants identified in Nassar et al (2017 Geophys. Res. Lett. 44, 10045–53). Our estimation results closely match the reported emission rates at the Westar power plant (Kansas, USA), with a reported value of 26.67 ktCO₂/day, and our estimated value at 25.82–26.47 ktCO₂/day using OCO-2 v8 data, and 22.09–22.80 ktCO₂/day using v9 data. At Ghent, KY, USA, our estimations using three versions (v7, v8, and v9) range from 9.84–20.40 ktCO₂/day, which are substantially lower than the reported value (29.17 ktCO₂/day). We attribute this difference to diminished WRF-Chem wind field simulation accuracy. The results from the seven cases indicate that accurate estimation requires accurate meteorological simulations and high quality XCO₂ data. In addition, the strength and orientation (relative to the OCO-2 ground track) of the XCO₂ enhancement are important for accurate and reliable estimation. Compared with the Gaussian plume model based approach, the high resolution WRF-Chem simulation based approach provides a framework for addressing varying wind fields, and possible expansion to city level emission estimation.

The context in which trees and forests grow in cities is highly variable and influences the provision of ecological, social, and economic benefits. Understanding the spatial extent, structure, and composition of forests is necessary to guide urban forest policy and management, yet current forest assessment methodologies vary widely in scale, sampling intensity, and focus. Current definitions of the urban forest include all trees growing in the urban environment, and have been translated to the design of urban forest assessments. However, such broad assessments may aggregate types of urban forest that differ significantly in usage and management needs. For example, street trees occur in highly developed environments, and are planted and cared for on an individual basis, whereas forested natural areas often occur in parkland, are managed at the stand level, and are primarily sustained by natural processes such as regeneration. We use multiple datasets for New York City to compare the outcomes from assessments of the entire urban forest, street trees, and forested natural areas. We find that non-stratified assessments of the entire urban forest are biased towards abundant canopy types in cities (e.g. street trees) and underestimate the condition of forested natural areas due to their uneven spatial arrangement. These natural areas account for one quarter of the city's tree canopy, but represent the majority of trees both numerically and in terms of biomass. Non-stratified assessments of urban forest canopy should be modified to accurately represent the true composition of different urban forest types to inform effective policy and management.

During the last deglaciation (18–8 kyr BP), shelf flooding and warming presumably led to a large-scale decomposition of permafrost soils in the mid-to-high latitudes of the Northern Hemisphere. Microbial degradation of old organic matter released from the decomposing permafrost potentially contributed to the deglacial rise in atmospheric CO₂ and also to the declining atmospheric radiocarbon contents (Δ¹⁴C). The significance of permafrost for the atmospheric carbon pool is not well understood as the timing of the carbon activation is poorly constrained by proxy data. Here, we trace the mobilization of organic matter from permafrost in the Pacific sector of Beringia over the last 22 kyr using mass-accumulation rates and radiocarbon signatures of terrigenous biomarkers in four sediment cores from the Bering Sea and the Northwest Pacific. We find that pronounced reworking and thus the vulnerability of old organic carbon to remineralization commenced during the early deglaciation (∼16.8 kyr BP) when meltwater runoff in the Yukon River intensified riverbank erosion of permafrost soils and fluvial discharge. Regional deglaciation in Alaska additionally mobilized significant fractions of fossil, petrogenic organic matter at this time. Permafrost decomposition across Beringia's Pacific sector occurred in two major pulses that match the Bølling-Allerød and Preboreal warm spells and rapidly initiated within centuries. The carbon mobilization likely resulted from massive shelf flooding during meltwater pulses 1A (∼14.6 kyr BP) and 1B (∼11.5 kyr BP) followed by permafrost thaw in the hinterland. Our findings emphasize that coastal erosion was a major control to rapidly mobilize permafrost carbon along Beringia's Pacific coast at ∼14.6 and ∼11.5 kyr BP implying that shelf flooding in Beringia may partly explain the centennial-scale rises in atmospheric CO₂ at these times. Around 16.5 kyr BP, the mobilization of old terrigenous organic matter caused by meltwater-floods may have additionally contributed to increasing CO₂ levels.

The use of limited organic resources to build resilience to drought in semi-arid regions was investigated using systems modelling. The study focused on Halaba in Ethiopia, drawing on biophysical and socio-economic data obtained from a survey of farms before, during and after the 2015/16 El Niño event. Using a simplified weather dataset to remove noise from weather fluctuations, a ten yearly El Niño was demonstrated to cause significant long-term degradation of soil, reducing crop yields by 9%–14% and soil carbon by 0.5%–4.1%; more frequent droughts would increase this impact. Farmers in Halaba usually apply manures to soils untreated. Counteracting the impact of El Niño on soil degradation is possible by increasing application of untreated manure, but would result in a small net cost due to loss of dung as fuel. By composting manure its recalcitrance increases, allowing soil degradation to be counteracted without cost. The best option investigated, in terms of both food and fuel security, for households with access to water and finances needed for anaerobic digestion (500–2000 US$), is to use manure to produce biogas and then apply the nutrient-rich bioslurry residue to the soil. This will result in a significant benefit of over 5000 US$ per decade from increased crop production and saved fuel costs. However, many households are limited in water and finances; in that situation, the much cheaper pyrolysis cook-stove (50 US$) can provide similar economic benefits without the need for water. The biochar residue from pyrolysis is highly recalcitrant, but pyrolysis results in loss of nutrients, so may result in lower yields than other uses of manures. This may be countered by using biochar to capture nutrients from elsewhere in the farm, such as from animal housing or compost pits; more work is needed to quantify the impact of treated biochar on crop yields.

El Niño events generate periods of relatively low precipitation, low cloud cover and high temperature over the rainforests of Southeast Asia, but their impact on tree physiology remains poorly understood. Here we use remote sensing and functional trait approaches—commonly used to understand plant acclimation to environmental fluctuations—to evaluate rainforest responses to an El Niño event at a site in northern Borneo. Spaceborne measurements (i.e. normalised difference vegetation index calculated from Moderate Resolution Imaging Spectroradiometer data) show the rainforest canopy greened throughout 2015, coinciding with a strengthening of the El Niño event in Sabah, Malaysia, then lost greenness in early 2016, when the El Niño was at its peak. Leaf chemical and structural traits measured for mature leaves of 65 species (104 branches from 99 tree canopies), during and after this El Niño event revealed that chlorophyll and carotenoid concentrations were 35% higher in mid 2015 than in mid 2016. Foliar concentrations of the nutrients N, P, K and Mg did not vary, suggesting the mineralisation and transportation processes were unaffected by the El Niño event. Leaves contained more phenolics, tannins and cellulose but less Ca and lignin during the El Niño event, with concentration shifts varying strongly among species. These changes in functional traits were also apparent in hyperspectral reflectance data collected using a field spectrometer, particularly in the shortwave infrared region. Leaf-level acclimation and leaf turnover could have driven the trait changes observed. We argue that trees were not water limited in the initial phase of the El Niño event, and responded by flushing new leaves, seen in the canopy greening trend and higher pigment concentrations (associated with young leaves); we argue that high evaporative demand and depleted soil water eventually caused leaves to drop in 2016. However, further studies are needed to confirm these ideas. Time-series of vegetation dynamics obtained from space can only be understood if changes in functional traits, as well as the quantity of leaves in canopies, are monitored on the ground.

Introduction. Switching from polluting (e.g. wood, crop waste, coal) to clean (e.g. gas, electricity) cooking fuels can reduce household air pollution exposures and climate-forcing emissions. While studies have evaluated specific interventions and assessed fuel-switching in repeated cross-sectional surveys, the role of different multilevel factors in household fuel switching, outside of interventions and across diverse community settings, is not well understood. Methods. We examined longitudinal survey data from 24 172 households in 177 rural communities across nine countries within the Prospective Urban and Rural Epidemiology study. We assessed household-level primary cooking fuel switching during a median of 10 years of follow up (∼2005–2015). We used hierarchical logistic regression models to examine the relative importance of household, community, sub-national and national-level factors contributing to primary fuel switching. Results. One-half of study households (12 369) reported changing their primary cooking fuels between baseline and follow up surveys. Of these, 61% (7582) switched from polluting (wood, dung, agricultural waste, charcoal, coal, kerosene) to clean (gas, electricity) fuels, 26% (3109) switched between different polluting fuels, 10% (1164) switched from clean to polluting fuels and 3% (522) switched between different clean fuels. Among the 17 830 households using polluting cooking fuels at baseline, household-level factors (e.g. larger household size, higher wealth, higher education level) were most strongly associated with switching from polluting to clean fuels in India; in all other countries, community-level factors (e.g. larger population density in 2010, larger increase in population density between 2005 and 2015) were the strongest predictors of polluting-to-clean fuel switching. Conclusions. The importance of community and sub-national factors relative to household characteristics in determining polluting-to-clean fuel switching varied dramatically across the nine countries examined. This highlights the potential importance of national and other contextual factors in shaping large-scale clean cooking transitions among rural communities in low- and middle-income countries.

Sparse gauge networks in Sub-Saharan Africa (SSA) limit our ability to identify changing precipitation extremes with in situ observations. Given the potential for satellite and satellite-gauge precipitation products to help, we investigate how daily gridded gauge and satellite products compare for seven core climate change precipitation indices. According to a new gauge-only product, the Rainfall Estimates on a Gridded Network (REGEN), there were notable changes in SSA precipitation characteristics between 1950 and 2013 in well-gauged areas. We examine these trends and how these vary for wet, intermediate, and dry areas. For a 31 year period of overlap, we compare REGEN data, other gridded products and three satellite products. Then for 1998–2013, we compare a set of 12 satellite products. Finally, we compare spatial patterns of 1983–2013 trends across all of SSA. Robust 1950–2013 trends indicate that in well-gauged areas extreme events became wetter, particularly in wet areas. Annual totals decreased due to fewer rain days. Between 1983 and 2013 there were positive trends in average precipitation intensity and annual maximum 1 d totals. These trends only represent 15% of SSA, however, and only one tenth of the main wet areas. Unfortunately, gauge and satellite products do not provide consensus for wet area trends. A promising result for identifying regional changes is that numerous satellite products do well at interannual variations in precipitation totals and number of rain days, even as well as some gauge-only products. Products are less accurate for dry spell length and average intensity and least accurate for annual maximum 1 d totals. Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (3B42-V7) and Climate Hazards center Infrared Precipitation with Stations (CHIRPS v2.0) ranked highest for multiple indices. Several products have seemingly unrealistic trends outside of the well-gauged areas that may be due to influence of non-stationary systematic biases. Social media abstract. Sparse data show increasing Africa rainfall extremes and satellite products fill some missing pieces.

Urban mobility is the main source of air pollution in Europe and accounts for 25% of greenhouse gas emissions. In order to address this, a range of interventions and policies are being implemented across major European cities and studies in sustainable urban transport have proliferated. One such mitigation strategy involves redesigning urban form through 'hard' traffic policies, with a view of decreasing emission levels and therefore mitigating the effects of air pollution and climate change. However, efforts to assess public response to such interventions and the effectiveness of policy instruments in promoting sustainable travel in cities remain sparse. The city of Potsdam, Germany implemented a trial traffic measure aimed at reducing motorized traffic and promoting the use of bicycles and public transport systems. This study analysed data from 3553 survey participants who responded to a survey conducted prior to the implementation of the traffic measure. We aimed to identify mobility behaviours and underlying attitudes within the context of a 'hard' policy instrument, in order to obtain insight into the opportunities to more effectively define policy priorities that improve air quality and upscale climate mitigation. An exploratory cluster analysis identified four groups, characterised by mobility habits, their attitudes towards the measure, and general level of environmental concern. By identifying and understanding the differing attitudes and perceptions across population groups we are able to highlight group-specific barriers and opportunities, as well as potential transition pathways to encourage more sustainable transportation use. This study exemplifies how context can help to further shape mobility group typologies, identify policy-related priorities useful for decision-makers and assess the feasibility of policy instruments to facilitate a transformation towards more sustainable cities.

Africa is projected to add one billion urban residents by 2050. Yet developing sustainable solutions to tackle the host of challenges posed by rapid urban population growth is stymied by a lack municipality-level population data across the continent. To fill this gap, we intersect volunteered urban settlement data from OpenStreetMap with five synthetic gridded population datasets to estimate the how Africa's urban population is distributed among over 4750 individual urban settlements across Africa. We assess how urban settlement distributions changed from 2000 to 2015 within and between countries and across moisture zones. To this end, we construct urban settlement Lorenz curves to calculate change in Gini coefficients and test the degree to which Africa's urban settlements distributions fit power law distributions exhibited by Zipf's law. Our results reveal that 77%–85% of urban settlements in Africa have fewer than 100 000 people and that at least 50% of Africa's urban population live in urban settlements with fewer than 1 million residents. Across almost all African countries, the distribution of urban population shifted towards larger cities between 2000 and 2015. However, in arid regions, our results indicate that small- and medium-sized urban settlements are absorbing a greater share of urban population growth compared to large urban settlements. While our urban population estimates vary across gridded population datasets and differ from United Nations estimates, this is the first paper to measure urban population across Africa using a consistent methodology to identify urban settlement populations. Unlike UN urban population data for Africa, our results can readily be incorporated with geolocated environmental, public health, and economic data to support efforts to monitor United Nations Sustainable Development Goals related to urban sustainability, poverty reduction, and food security across Africa's ever-growing urban settlements.

Climate change is impacting forested ecosystems worldwide, particularly in the Northern Hemisphere where warming has increased at a faster rate than the rest of the globe. As climate warms, trembling aspen (Populus tremuloides) is expected to become more successful in northern boreal forests because of its current presence in drier areas of North America. However, large-scale productivity decline of aspen has recently been documented throughout the United States and Canada as a result of drought and insect outbreaks. We used tree ring measurements (basal area increment (BAI) and stable carbon isotopes (δ¹³C)) and remote sensing indices of vegetation productivity (NDVI) to study the impact of climate and damage by the aspen epidermal leaf miner (Phyllocnistis populiella) on aspen productivity and physiology in interior Alaska. We found that productivity decreased with greater leaf mining and was not sensitive to growing season (GS) moisture availability. Although productivity decreased during high leaf mining years, it recovered to pre-outbreak levels during years of low insect damage, suggesting a degree of resilience to P. populiella mining. Climate and leaf mining interacted to influence tree ring δ¹³C, with greater leaf mining resulting in decreased δ¹³C when GS moisture availability was low. We also found that NDVI was negatively associated with leaf mining, and positively correlated with BAI and the δ¹³C decrease corresponding to mining. This suggests that NDVI is capturing not only variations in productivity, but also changes in physiology associated with P. populiella. Overall, these findings indicate that the indirect effects of P. populiella mining have a larger impact on aspen productivity and physiology than climate under current conditions, and is essential to consider when assessing growth, physiology and NDVI trends in interior Alaska.

To deal with climate change, cities must reduce their greenhouse gas (GHG) emissions and at the same time mitigate climate impacts associated with the physical infrastructure of the built environment. One strand of literature demonstrates that compact cities of sufficient density result in lower GHG emissions in the transport and the buildings sectors compared to sprawled cities. Another strand of literature, however, reveals that compactness hinders climate adaptation by amplifying the urban heat island (UHI) effect. As a result, mitigation and adaptation objectives of cities appear to contradict each other. Here, we develop a geometrical optimization framework and model of a three-dimensional city that minimizes this conflict. It makes use of the observation that low-carbon efficient transport can be realized via linear public transport axes, and that GHG emissions and UHI effects scale differently with varying geometric properties, thus enabling design that reflects both the economics and the climate of cities. We find that star-shaped cities, in contrast to radially symmetric cities, are well suited to alleviate the problematic trade-off. We also demonstrate that urban design considerations depend on transport fuel prices. The results are of particular importance for city planners of rapidly urbanizing cities in Asia and Africa who still have the potential to shape urban layout.

State and local policy-makers in the US have shown interest in transitioning electricity systems toward renewable energy sources and in mitigating harmful air pollution. However, the extent to which sub-national renewable energy policies can improve air quality remains unclear. To investigate this issue, we develop a systemic modeling framework that combines economic and air pollution models to assess the projected sub-national impacts of Renewable Portfolio Standards (RPSs) on air quality and human health, as well as on the economy and on climate change. We contribute to existing RPS cost-benefit literature by providing a comprehensive assessment of economic costs and estimating economy-wide changes in emissions and their impacts, using a general equilibrium modeling approach. This study is also the first to our knowledge to directly compare the health co-benefits of RPSs to those of carbon pricing. We estimate that existing RPSs in the 'Rust Belt' region generate a health co-benefit of $94 per ton CO₂ reduced ($2-477/tCO₂) in 2030, or 8¢ for each kWh of renewable energy deployed (0.2–40¢ kWh⁻¹) in 2015 dollars. Our central estimate is 34% larger than total policy costs. We estimate that the central marginal benefit of raising renewable energy requirements exceeds the marginal cost, suggesting that strengthening RPSs increases net societal benefits. We also calculate that carbon pricing delivers health co-benefits of $211/tCO₂ in 2030, 63% greater than the health co-benefit of reducing the same amount of CO₂ through an RPS approach.

This study examines the performance of the policy of community management for rural groundwater supply in Africa. Across the continent, policies that promote community management have dominated the rural water supply sector for decades. As a result, hundreds of thousands of village-level committees have been formed to manage community boreholes equipped with handpumps. With a significant proportion of these handpumps non-functional at any one time, increasing effort is targeted toward understanding the interacting social and physical determinants of this 'hidden crisis'. We conducted a survey of community management arrangements across six hundred sites in rural Ethiopia, Malawi, and Uganda, examining the extent to which management capacity is related to borehole functionality whilst accounting for a range of contextual variables. The capacity of water management arrangements (WMAs) was assessed according to four dimensions: finance system; affordable maintenance and repair; decision making, rules, and leadership; and external support. The survey reveals that 73.3% of WMAs have medium or high capacity. However, we found no strong relationship between the capacity of the WMA and the functionality of the borehole. Of the four management dimensions, affordable maintenance and repair was the best predictor of borehole functionality. However, the capacity of this dimension was seen to be the lowest overall, with 61.9% of sites weak or non-existent. Our results provide very limited support for the policy of community management, and we suggest that evidence alone has not accounted for its persistence over decades. After a short historical analysis, we conclude that explanation for the endurance of this model can be found in the nexus between evidence, ideology, and policy. We argue that it is this same nexus that will likely ensure the popularity of community management for some time to come, despite new ideas and evidence to the contrary.

Numerous negative carbon isotope excursions (nCIEs) in the geologic record occurring over 10⁴–10⁵ years are interpreted as episodes of massive carbon release. nCIEs help to illuminate the connection between past carbon cycling and climate variability. Theoretically, the size of a nCIE can be used to determine the mass of carbon released, provided that the carbon source is known or other environmental changes such as temperature or ocean pH can be constrained. A simple isotopic mass balance equation often serves as a first order estimate for the mass of carbon input, but this approach ignores the effects of negative carbon cycle-climate feedbacks. Here we show, using 432 earth system model simulations, that the mass of carbon release and associated environmental impacts for a nCIE of a given size and carbon source depend on the onset duration of that nCIE: the longer the nCIE onset duration, the greater the required carbon input in order to counterbalance the input of ¹³C-enriched carbon through carbonate compensation and weathering feedbacks. On timescales >10³ years, these feedbacks remove carbon from the atmosphere so that the relative rise in atmospheric CO₂ decreases with the nCIE onset duration. Consequently, the impacts on global temperature, surface ocean pH and saturation state are reduced if the nCIE has a long onset duration. The framework provided here demonstrates how constraints on the total nCIE duration and relative shape—together determining the onset duration—affect the interpretation of sedimentary nCIEs. Finally, we evaluate selected well-studied nCIEs, including the Eocene Thermal Maximum 2 (∼54 Ma), the Paleocene–Eocene Thermal Maximum (∼56 Ma), and the Aptian Oceanic Anoxic Event (∼120 Ma), in the context of our model-based framework and show how modeled environmental changes can be used to narrow down the most likely carbon emissions scenarios.