Table of contents

IPCC's 2018 Special Report is a stark and bracing reminder of climate threats. Yet literature, reportage, and public discourse reflect imbalanced risk and opportunity. Climate science often understates changes' speed and nonlinearity, but Integrated Assessment Models (IAMs) and similar studies often understate realistic mitigation options. Since ∼2010, global mitigation of fossil CO₂—including by often-uncounted modern renewable heat comparable to solar-plus-wind electricity—has accelerated to about the pace (if sustained) needed for a 2 °C trajectory. Mitigation has uncertainties, emergent properties, feasibility thresholds, and nonlinearities at least comparable to climate's, creating opportunities for aggressive action. Renewable electricity's swift uptake can now be echoed as proven integrative design can make end-use efficiency severalfold larger and cheaper, often with increasing returns (lower cost with rising quantity). Saved energy—the world's largest decarbonizer and energy 'source' (bigger than oil)—can then potentiate renewables and cut supply investments, as a few recent efficiency-centric IAMs confirm. Optimizing choices, combinations, timing, and sequencing of technologies, urban form, behavioral shifts, etc could save still more energy, money, and time. Some rigorous engineering-based national studies outside standard climate literature even imply potential 1.5 °C global trajectories cheaper than business-as-usual. A complementary opportunity—rapidly and durably abating hydrocarbon industries' deliberate upstream CH₄ releases from flares and engineered vents, by any large operator's profitably abating its own and others' emissions—could stabilize (or more) the global methane cycle and buy time to abate more CO₂. Together, these findings justify sober recalibration of the prospects for a fairer, healthier, cooler, and safer world. Supported by other disciplines, improved IAMs can illuminate this potential and support its refinement. Ambitious policies and aggressive marketplace and societal adoption of profitable new abatement opportunities need not wait for better models, but better models would help them to attract merited attention, scale faster, and turn numbing despair into collectively powerful applied hope.

Amidst declarations of planetary emergency and reports that the window for limiting climate change to 1.5 °C is rapidly closing, global average temperatures and fossil fuel emissions continue to rise. Global fossil CO₂ emissions have grown three years consecutively: +1.5% in 2017, +2.1% in 2018, and our slower central projection of +0.6% in 2019 (range of –0.32% to 1.5%) to 37 ± 2 Gt CO₂ (Friedlingstein et al 2019 Earth Syst. Sci. Data accepted), after a temporary growth hiatus from 2014 to 2016. Economic indicators and trends in global natural gas and oil use suggest a further rise in emissions in 2020 is likely. CO₂ emissions are decreasing slowly in many industrialized regions, including the European Union (preliminary estimate of −1.7% [–3.4% to +0.1%] for 2019, −0.8%/yr for 2003–2018) and United States (−1.7% [–3.7% to +0.3%] in 2019, −0.8%/yr for 2003–2018), while emissions continue growing in India (+1.8% [+0.7% to 3.7%] in 2019, +5.1%/yr for 2003–2018), China (+2.6% [+0.7% to 4.4%] in 2019, +0.4%/yr for 2003–2018), and rest of the world ((+0.5% [−0.8% to 1.8%] in 2019, +1.4%/yr for 2003–2018). Two under-appreciated trends suggest continued long-term growth in both oil and natural gas use is likely. Because per capita oil consumption in the US and Europe remains 5- to 20-fold higher than in China and India, increasing vehicle ownership and air travel in Asia are poised to increase global CO₂ emissions from oil over the next decade or more. Liquified natural gas exports from Australia and the United States are surging, lowering natural gas prices in Asia and increasing global access to this fossil resource. To counterbalance increasing emissions, we need accelerated energy efficiency improvements and reduced consumption, rapid deployment of electric vehicles, carbon capture and storage technologies, and a decarbonized electricity grid, with new renewable capacities replacing fossil fuels, not supplementing them. Stronger global commitments and carbon pricing would help implement such policies at scale and in time.

Global hydrological forecasts are now produced operationally on a daily basis. However, the lack of global river discharge observations precludes routine flood forecast evaluation, an essential step in providing more skilful and reliable forecasts. A vision is expounded for greater and more timely exchange of global river discharge observations, which would result in improved flood awareness and socioeconomic benefits in some of the World's most vulnerable countries.

Strengthening the links between research and interventions would mean new insights could be translated more quickly into actions to protect and promote population health. Doing so requires strong collaboration among funders, the research community, and stakeholders, to understand stakeholder needs, constraints, and opportunities, and to focus research questions so results are useful, useable, and used. Continuing increases in the frequency, intensity, and duration of heatwaves underscores the urgency of fostering two-way communications between researchers and those responsible for designing and implementing heat action plans, to ensure research is effectively targeted to further reduce heat-related morbidity and mortality in a changing climate.

The diversification of cropping systems encompasses different strategies that may help maintain or enhance the sustainability of agriculture. Thousands of experiments have been carried out around the world since almost five decades to evaluate and compare the performances of various diversification strategies in a wide array of agroecosystems and climates. Although these analyses have been synthesized in a growing number of meta-analyses, the information remains somewhat fragmented. A multicriteria systematic synthesis of worldwide agricultural diversification is still lacking. Here, we review all meta-analyses conducted on crop diversification strategies and produce a detailed overview of their results and of their quality. We identified and analyzed 99 meta-analyses summarizing the results of more than 3700 agronomic experiments on seven crop diversification strategies. Among these strategies, rotation and associated plant species are dominant in the literature followed by intercropping, agroforestry and landscape heterogeneity. Our analysis reveals that rotation and intercropping are associated with yield increases. Agroforestry systematically induces an improvement of biodiversity and soil quality—in particular soil organic carbon. We show that, regardless of the context, a combination of several diversification strategies outperforms any individual strategy. Our review reveals that a significant knowledge gap remains, in particular regarding water use, farmers' profitability, product quality and production stability. Few meta-analyses investigate the performance of landscape heterogeneity and of systems with species other than cereals and pulses. Additionally, we show that most of the meta-analyses studied cannot be considered fully transparent and reproducible. Their conclusions should therefore be interpreted with caution. Our systematic mapping provides a benchmark to guide and improve the relevance and reliability of future meta-analyses in agronomy.

This paper investigates the state of knowledge in quantifying the embodied greenhouse gas (GHG) emissions in rail infrastructure and develops a sketch model for estimating the GHG impact of rail infrastructure based on the literature. A literature review identified 22 publications, containing 57 case studies, at least touching on the embodied GHG for different types of rail infrastructure. The cases studies include high speed rail, intercity rail, light rail, commuter rail, heavy rail, freight, and metro rail. The paper examines the GHG impact per kilometre of rail infrastructure reported across the case studies and compares the boundaries, functional units, methods, and data used. Most studies employed process-based LCA for an attributional analysis. The embodied emissions associated with the case studies range from 0.5 to 12 700 tCO₂ km⁻¹; much of the variation is dependent on the proportion of the rail line at-grade, elevated, or in a tunnel. However, large ranges in GHG per kilometre remain after controlling for elevated and tunneled distance. Comparing the embodied emissions across the rail types was challenging, due to the large variations in system boundaries, study goals, and inventory methods adopted in the publications. This review highlights the need for standardization across the reporting of embodied GHG for rail infrastructure to better facilitate hot spot detection, engineering design and GHG policy decision making. The statistical model finds that overall ∼941(±168) tCO₂e are embodied per kilometre of rail at-grade, and tunneling has 27 (±5) times more embodied GHG per kilometre than at-grade construction. The statistical model is based on the findings of published literature and does not explicitly consider function, geometry, specifications, emphasis on whole lifecycle, legislative constraints, socio-economic factors, or the physical and environmental conditions of the construction site.

The vulnerability of social-ecological systems in sub-Saharan Africa (SSA) to climate variability and change means that there is an urgent need to better integrate weather and climate information into societal decision-making processes. Long-term climate adaptation in these regions has received increasing attention, with recent initiatives aiming to increase resilience to climate change at timescales of years to decades. Less focus has been given to weather and short-term climate information. However, users are principally interested in shorter timescales (hours to seasons) where actions can immediately reduce the impacts of severe weather events. Focusing on the priority sectors of agriculture and food security, water and disaster management, this paper uses a systematic literature review approach to analyse 61 empirical case studies drawn from academic literature and projects across SSA. We identify the main users of climate services and outline current practices and reported benefits. Barriers that impede the delivery and uptake of climate services are identified and potential strategies for overcoming them outlined based on the reporting of successful practices. Our findings show that greater capacity building of personnel working for National Meteorological and Hydrological Services and Agricultural Extension staff and reinforcing and sustaining collaboration between different stakeholders (climate scientists, hydrologists, extension workers, farmers and other user groups), are essential factors for improving the uptake and utility of weather and climate services to enhance resilience to climate shocks in SSA.

A growing number of studies provide evidence of an association between exposure to maternal air pollution during pregnancy and adverse birth outcomes including low birth weight (LBW) and preterm birth. Prevention of these health effects of air pollution is critical to reducing the adverse infant outcomes, which can have impacts throughout the life course. However, there is no consensus on whether the association between air pollution exposure and birth outcomes varies by maternal risk factors including demographic characteristics and socio-economic status (SES). Such information is vital to understand potential environmental health disparities. Our search found 859 unique studies, of which 45 studies met our inclusion criteria (January 2000–July 2019). We systematically reviewed the 45 identified epidemiologic studies and summarized the results on effect modifications by maternal race/ethnicity, educational attainment, income, and area-level SES. We considered adverse birth outcomes of preterm birth, LBW, small for gestational age (SGA), and stillbirth. Suggestive evidence of higher risk of particulate matter (PM) in infants of African–American/black mothers than infants of other women was found for preterm birth and LBW. We found weak evidence that PM risk was higher for infants of mothers with lower educational attainment for preterm birth and LBW. Due to the small study numbers, we were unable to conclude whether effect modification is present for income, occupation, and area-level SES, and additional research is needed. Furthermore, adverse birth outcomes such as SGA and stillbirth need more study to understand potential environmental justice issues regarding the impact of PM exposure during pregnancy on birth outcomes.

Carnivore and humans live in proximity due to carnivore recovery efforts and ongoing human encroachment into carnivore habitats globally. The American West is a region that uniquely exemplifies these human-carnivore dynamics, however, it is unclear how the research community here integrates social and ecological factors to examine human-carnivore relations. Therefore, strategies promoting human-carnivore coexistence are urgently needed. We conducted a systematic review on human-carnivore relations in the American West covering studies between 2000 and 2018. We first characterized human-carnivore relations across states of the American West. Second, we analyzed similarities and dissimilarities across states in terms of coexistence, tolerance, number of ecosystem services and conflicts mentioned in literature. Third, we used Bayesian modeling to quantify the effect of social and ecological factors influencing the scientific interest on coexistence, tolerance, ecosystem services and conflicts. Results revealed some underlying biases in human-carnivore relations research. Colorado and Montana were the states where the highest proportion of studies were conducted with bears and wolves the most studied species. Non-lethal management was the most common strategy to mitigate conflicts. Overall, conflicts with carnivores were much more frequently mentioned than benefits. We found similarities among Arizona, California, Utah, and New Mexico according to how coexistence, tolerance, services and conflicts are addressed in literature. We identified percentage of federal/private land, carnivore family, social actors, and management actions, as factors explaining how coexistence, tolerance, conflicts and services are addressed in literature. We provide a roadmap to foster tolerance towards carnivores and successful coexistence strategies in the American West based on four main domains, (1) the dual role of carnivores as providers of both beneficial and detrimental contributions to people, (2) social-ecological factors underpinning the provision of beneficial and detrimental contributions, (3) the inclusion of diverse actors, and (4) cross-state collaborative management.

This review addresses the causes of observed climate variations across the industrial period, from 1750 to present. It focuses on long-term changes, both in response to external forcing and to climate variability in the ocean and atmosphere. A synthesis of results from attribution studies based on palaeoclimatic reconstructions covering the recent few centuries to the 20th century, and instrumental data shows how greenhouse gases began to cause warming since the beginning of industrialization, causing trends that are attributable to greenhouse gases by 1900 in proxy-based temperature reconstructions. Their influence increased over time, dominating recent trends. However, other forcings have caused substantial deviations from this emerging greenhouse warming trend: volcanic eruptions have caused strong cooling following a period of unusually heavy activity, such as in the early 19th century; or warming during periods of low activity, such as in the early-to-mid 20th century. Anthropogenic aerosol forcing most likely masked some global greenhouse warming over the 20th century, especially since the accelerated increase in sulphate aerosol emissions starting around 1950. Based on modelling and attribution studies, aerosol forcing has also influenced regional temperatures, caused long-term changes in monsoons and imprinted on Atlantic variability. Multi-decadal variations in atmospheric modes can also cause long-term climate variability, as apparent for the example of the North Atlantic Oscillation, and have influenced Atlantic ocean variability. Long-term precipitation changes are more difficult to attribute to external forcing due to spatial sparseness of data and noisiness of precipitation changes, but the observed pattern of precipitation response to warming from station data supports climate model simulated changes and with it, predictions. The long-term warming has also led to significant differences in daily variability as, for example, visible in long European station data. Extreme events over the historical record provide valuable samples of possible extreme events and their mechanisms.

Much has been made of the potential for wind and solar generation to supply cheap, low-emissions electricity, but considerable disagreement exists as to which combinations of many potential drivers will enable deep penetration of these technologies. Most existing analyses consider limited factors in isolation, such as investment costs or energy storage, and do not provide rigorous support for understanding which combinations of factors could underpin a leading role for wind and solar. This study addresses this gap by undertaking a systematic sensitivity analysis using a state-of-the-art energy-economic model to comprehensively evaluate the relative magnitudes of five key drivers that may influence future wind and solar deployment in the United States. We find future wind and solar capital costs and carbon policy are the dominant factors, causing the average wind and solar share to vary by 38 and 31 percentage points, respectively. Transmission and storage availability have much smaller effects, causing the average share to vary by no more than 15 and 5 percentage points, respectively. No single factor unilaterally determines wind and solar deployment. The variable renewable share of electricity generation never reaches 100% nationally in any scenario even with low-cost storage, as decreasing marginal returns at higher deployments eventually outpace cost reductions. Average wind and solar shares and ranges of possible outcomes are higher in this study relative to recent multi-model comparison studies due to lower renewable costs and the potential for more stringent policies. Understanding drivers and barriers to renewable deployment has important ramifications for technology developers, infrastructure, market design, and policymakers, and this research provides insights as to which combinations of drivers lead to the greatest share of economic wind and solar deployment and why.

Many nationally determined contributions (NDCs) under the Paris Agreement follow the established practice of specifying emissions levels in tonnes of CO₂ equivalent emissions. The Global Warming Potential (GWP) is the emissions metric used most often to aggregate contributions from different greenhouse gases (GHGs). However, the climate impact of pathways expressed in this way is known to be ambiguous. For this reason, alternatives have been proposed but the ambiguity has not been quantified in the context of the Paris Agreement. Here we assess the variation in temperature using pathways consistent with the ambition of limiting temperature increases to well below 2 °C. These are taken from the IPCC Special Report on Global Warming of 1.5 °C (SR15). The CO₂ emission levels are adjusted so that the pathways all have the same total CO₂ equivalent emissions for a given emissions metric but have different proportions of short-lived and long-lived pollutants. We show that this difference affects projections by up to 0.17 °C when GWP₁₀₀ is used. Options of reducing this ambiguity include using a different emissions metric or adding supplementary information in NDCs about the emissions levels of individual GHGs. We suggest the latter on the grounds of simplicity and because it does not require agreement on the use of a different emissions metric.

The fast development of a secondary aerosol layer was observed over megacities in eastern Asia during summertime. Within three hours, from midday to early afternoon, the contribution of secondary aerosols above the planetary boundary layer (PBL) increased by a factor of three to five, and the coatings on black carbon (BC) also increased and enhanced its absorption efficiency by 50%. This tended to result from the intensive actinic flux received above the PBL which promoted photochemical reactions. The absorption of BC could be further amplified by the strong reflection of solar radiation over the cloud top across the PBL. This enhanced heating effect of BC introduced by combined processes (intensive solar radiation, secondary formation and cloud reflection) may considerably increase the temperature inversion above the PBL. This mechanism should be considered when evaluating the radiative impact of BC, especially for polluted regions receiving strong solar radiation.

China is confronted with an unprecedented water crisis regarding its quantity and quality. In this study, we quantified the dynamics of China's embodied water use and chemical oxygen demand (COD) discharge from 2010 to 2015. The analysis was conducted with the latest available water use data across sectors in primary, secondary and tertiary industries and input–output models. The results showed that (1) China's water crisis was alleviated under urbanisation. Urban consumption occupied the largest percentages (over 30%) of embodied water use and COD discharge, but embodied water intensities in urban consumption were far lower than those in rural consumption. (2) The 'new normal' phase witnessed the optimisation of China's water use structures. Embodied water use in light-manufacturing and tertiary sectors increased while those in heavy-manufacturing sectors (except chemicals and transport equipment) dropped. (3) Transformation of China's international market brought positive effects on its domestic water use. China's water use (116–80 billion tonnes (Bts))⁹ and COD discharge (3.95–2.22 million tonnes (Mts)) embodied in export tremendously decreased while its total export values (11–25 trillion CNY) soared. Furthermore, embodied water use and COD discharge in relatively low-end sectors, such as textile, started to transfer from international to domestic markets when a part of China's production activities had been relocated to other developing countries.

Despite the long-term greening trend in global vegetation identified in previous investigations, changes in the interannual variability (IAV) of vegetation greenness over time is still poorly understood. Using Global Inventory Modeling and Mapping Studies normalized difference vegetation index (NDVI) third generation data and corresponding meteorological data from 1982 to 2015, we studied the changes and drivers of the IAV of vegetation greenness as indicated by the coefficient of variation of vegetation greenness at a global scale. Dry and high-latitude areas exhibited high NDVI variability whereas humid areas exhibited relatively low NDVI variability. We detected an increase in the global IAV of vegetation greenness over time using a 15 year moving window. Spatially, we observed significant increases in the IAV of vegetation greenness in greater than 45% of vegetated areas globally and decreases in 21%. Our comparison of ecological models suggests good performance in terms of simulating spatial differences in vegetation variability, but relatively poor performance in terms of capturing changes in the IAV of vegetation greenness. Furthermore, the dominant climate variables controlling changes in the IAV of vegetation greenness were determined spatially using principal component regression and partial least squares regression. The two methods yielded similar patterns, revealing that temperature exerted the biggest influence on changes in the IAV of vegetation greenness, followed by solar radiation and precipitation. This study provides insights into global vegetation variability which should contribute to an understanding of vegetation dynamics in the context of climate change.

Aridity is a complex concept that ideally requires a comprehensive assessment of hydroclimatological and hydroecological variables to fully understand anticipated changes. A widely used (offline) impact model to assess projected changes in aridity is the aridity index (AI) (defined as the ratio of potential evaporation to precipitation), summarizing the aridity concept into a single number. Based on the AI, it was shown that aridity will generally increase under conditions of increased CO₂ and associated global warming. However, assessing the same climate model output directly suggests a more nuanced response of aridity to global warming, raising the question if the AI provides a good representation of the complex nature of anticipated aridity changes. By systematically comparing projections of the AI against projections for various hydroclimatological and ecohydrological variables, we show that the AI generally provides a rather poor proxy for projected aridity conditions. Direct climate model output is shown to contradict signals of increasing aridity obtained from the AI in at least half of the global land area with robust change. We further show that part of this discrepancy can be related to the parameterization of potential evaporation. Especially the most commonly used potential evaporation model likely leads to an overestimation of future aridity due to incorrect assumptions under increasing atmospheric CO₂. Our results show that AI-based approaches do not correctly communicate changes projected by the fully coupled climate models. The solution is to directly analyse the model outputs rather than use a separate offline impact model. We thus urge for a direct and joint assessment of climate model output when assessing future aridity changes rather than using simple index-based impact models that use climate model output as input and are potentially subject to significant biases.

Climate change challenges societal functioning, likely requiring considerable adaptation to cope with future altered weather patterns. Machine learning (ML) algorithms have advanced dramatically, triggering breakthroughs in other research sectors, and recently suggested as aiding climate analysis (Reichstein et al 2019 Nature566 195–204, Schneider et al 2017 Geophys. Res. Lett.44 12396–417). Although a considerable number of isolated Earth System features have been analysed with ML techniques, more generic application to understand better the full climate system has not occurred. For instance, ML may aid teleconnection identification, where complex feedbacks make characterisation difficult from direct equation analysis or visualisation of measurements and Earth System model (ESM) diagnostics. Artificial intelligence (AI) can then build on discovered climate connections to provide enhanced warnings of approaching weather features, including extreme events. While ESM development is of paramount importance, we suggest a parallel emphasis on utilising ML and AI to understand and capitalise far more on existing data and simulations.

With the rapid development of urban agglomerations in the Pearl River Estuary (PRE), an increasing number of ecological problems have been exposed to the surrounding coastal zones. The timely and accurate understanding of the eco-environment in PRE has become of increasing concern. This study aimed to quantitatively evaluate multi-temporal eco-environment conditions and then detect the conflicting patterns of the eco-environment under the influence of coastal exploitation in PRE. The ecological index was derived from remote sensing images by means of principal component analysis, and the composite coastal development index was then constructed to characterize the coastal exploitation from the perspective of ecological influence, which was implemented by using the panel data analysis. This method was verified with a significant test accuracy of 0.9. Based on this, the coupling coordination patterns of the coastal economy–environment system were disclosed for all six prefectural units at both the pixel scale and the city scale. The results showed that the coupling coordination degree and inner coupling relationships in each city presented periodical characteristics, with the highest values in 2008 and the lowest values in 1988. The dominant conflict between coastal exploitation and the eco-environment in each period was capricious. This evaluation will provide a reference for decision-making in coastal zone planning and ecological red line policy to encourage the sustainable development of coastal zones.

China has been experiencing significant climate and land use changes over the past decades. The way in which these changes, particularly a warming hiatus and national ecological restoration projects that occurred almost concurrently in the late 1990s, have influenced vegetation net primary productivity (NPP), is not well documented. Here, we estimated annual and seasonal changes in China's NPP between 1982 and 2015 using the Carnegie-Ames-Stanford Approach model and examined their shifting years (SHYs) caused by the switch in climatic factors and the restoration projects. Our analyses revealed that the growth of annual, spring and summer NPP stalled in 1997 or 1998, while the trend of autumn NPP increased in 1992 at the national scale. We also showed that the changes in the NPP trends were more sensitive to the warming hiatus in spring and autumn, as well as in the temperate monsoonal region and the Tibetan Plateau, while the larger trend of autumn NPP in eastern China after the SHY was strongly coupled with increased monsoonal precipitation. Although the starting years of the restoration projects were partially consistent with the SHYs of the NPP trends, the projects were likely playing minor roles in enhancing NPP increase. Our findings can be applied for ecological risk assessment and future management of ecological restoration projects in the context of global change.

Uncertainty in model projections of future climate change arises due to internal variability, multiple possible emission scenarios, and different model responses to anthropogenic forcing. To robustly quantify uncertainty in multi-model ensembles, inter-dependencies between models as well as a models ability to reproduce observations should be considered. Here, a model weighting approach, which accounts for both independence and performance, is applied to European temperature and precipitation projections from the CMIP5 archive. Two future periods representing mid- and end-of-century conditions driven by the high-emission scenario RCP8.5 are investigated. To inform the weighting, six diagnostics based on three observational estimates are used to also account for uncertainty in the observational record. Our findings show that weighting the ensemble can reduce the interquartile spread by more than 20% in some regions, increasing the reliability of projected changes. The mean temperature change is most notably impacted by the weighting in the Mediterranean, where it is found to be 0.35 °C higher than the unweighted mean in the end-of-century period. For precipitation the largest differences are found for Northern Europe, with a relative decrease in precipitation of 2.4% and 3.4% for the two future periods compared to the unweighted case. Based on a perfect model test, it is found that weighting the ensemble leads to an increase in the investigated skill score for temperature and precipitation while minimizing the probability of overfitting.

Ocean primary production (PP), representing the uptake of inorganic carbon through photosynthesis, supports marine life and affects carbon exchange with the atmosphere. It is difficult to ascertain its magnitude, variability, and trends due to our inability to measure it directly at large scales. Yet it is paramount for understanding changes in marine health, fisheries, and the global carbon cycle. Using assimilation of ocean color satellite data into an ocean biogeochemical model, we estimate that global net ocean PP has experienced a small but significant decline −0.8 PgC y⁻¹ (−2.1%) decade⁻¹ (P < 0.05) in the 18-year satellite record from 1998 to 2015. This decline is associated with shallowing surface mixed layer depth (−2.4% decade⁻¹) and decreasing nitrate concentrations (−3.2% decade⁻¹). Relative contributions to PP by various types of ocean phytoplankton have changed, with decreases in production by intermediate-sized phytoplankton represented by chlorophytes (−14.3% decade⁻¹). This is partially compensated by increases from the unique, more nutrient-efficient, coccolithophores (8.4% decade⁻¹). Geographically, the North and Equatorial Indian Oceans are responsible for much of the decline in PP, falling 0.16 and 0.69 PgC y⁻¹ decade⁻¹, respectively. Reduced production by large, fast-growing diatoms along with chlorophytes characterizes the decline here. In contrast, increases in PP are found in the North and North Central Pacific. The increases here are led by chlorophytes in the North Pacific and the small cyanobacteria in the North Central Pacific. These results suggest that the multi-decadal satellite observational record, coupled with an underlying representation of marine biodiversity in a model, can monitor the uptake of carbon by phytoplankton and that changes, although small, are occurring in the global oceans.

The building sector consumes 75% of US electricity, offering substantial energy, cost, and CO₂ emissions savings potential. New technologies enable buildings to flexibly manage electric loads across different times of day and season in support of a low-cost, low-carbon electric grid. Assessing the value of such technologies requires an understanding of building electric load variability at a higher temporal resolution than is demonstrated in previous studies of US building efficiency potential. We adapt Scout, an open-access model of US building energy use, to characterize sub-annual variations in baseline building electricity use, costs, and emissions at the national scale. We apply this baseline in time-sensitive analyses of the energy, cost, and CO₂ emissions savings potential of various degrees of energy efficiency and flexibility, finding that efficiency continues to have strong value in a time-sensitive assessment framework while the value of flexibility depends on assumed electricity rates, measure magnitude and duration, and the amount of savings already captured by efficiency.

Cities typically exhibit higher air temperatures than their rural surroundings, a phenomenon known as the urban heat island (UHI) effect. Contrasting results are reported as to whether UHI intensity (UHII) is exacerbated or reduced during hot weather episodes (HWEs). This contrast is investigated for a four-year period from 2015 to 2018, utilising a set of observational data from high-quality meteorological stations, as well as from hundreds of crowdsourced citizen weather stations, located in the urban region of Berlin, Germany. It can be shown that if HWEs, defined here as the ten percent hottest days or nights during May–September, are identified via daytime conditions, or by night-time conditions at inner-city sites, then night-time UHII is exacerbated. However, if HWEs are identified via night-time conditions at rural sites, then night-time UHII is reduced. These differences in UHII change can be linked with prevalent weather conditions, namely radiation, cloud cover, wind speed, precipitation, and humidity. This highlights that, beside land cover changes, future changes in weather conditions due to climate change will control UHIIs, and thus heat-stress hazards in cities.

Decadal climate prediction, where climate models are initialized with the contemporaneous state of the Earth system and run for a decade into the future, represents a new source of near-term climate information to better inform decisions and policies across key climate-sensitive sectors. This paper illustrates the potential usefulness of such predictions for building a climate service for agricultural needs. In particular, we assess the forecast quality of multi-model climate predictions in estimating two user-relevant drought indices, Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Precipitation Index (SPI), at multi-annual timescales during European summer. We obtain high skill for predicting five-year average (forecast years 1–5) SPEI across Southern Europe, while for the same forecast period SPI exhibits high and significant skill over Scandinavia and its surrounding regions. In addition, an assessment of the added value of initialized decadal climate information with respect to standard uninitialized climate projections is presented. The model initialization improves the forecast skill over Central Europe, the Balkan region and Southern Scandinavia. Most of the increased skill found with initialization seems to be due to the climate forecast systems ability to improve the extended summer precipitation and potential evapotranspiration forecast, as well as their ability to adequately represent the observed effects of these climate variables on the drought indices.

The Tibetan Plateau (TP) is the largest and highest upland on Earth. Warming on the TP is faster than that in surrounding areas. Evaluating our understanding of the causes behind these changes provides a test of tools used for projections of future climate in the region. In this study, we analyse the observed changes in twelve extreme temperature indices and compare them with model simulations based on the Coupled Model Intercomparison Project Phase 5 (CMIP5). An optimal fingerprinting method is used to perform detection and attribution analyses on the changes in absolute intensity, percentile-based frequency, fixed threshold exceedances of temperature extremes and diurnal temperature ranges in the central and eastern TP. The results show that during 1958–2017 the TP has experienced increasing intensity and frequency of warm extremes and decreasing intensity and frequency of cold extremes, with almost all these changes larger than those in China and East China. The detection results and attribution analyses show that the anthropogenic (ANT) signal can be robustly detected in the trends for all extreme indices on the TP, and the natural (NAT) signal in some cases, too. The attributable contribution from ANT is estimated to be much larger than that from NAT for most indices. The study also indicates that the CMIP5 models may underestimate the magnitude of warming in some temperature extremes, especially the indices related to cold extremes. This should be kept in mind when informing adaptation decisions on the TP with projections based on the same models.

Anecdotal evidence suggests that the timing and intensity of the Central American Midsummer Drought (MSD) may be changing, while observations from limited meteorological station data and paleoclimate reconstructions show neither significant nor consistent trends in seasonal rainfall. Climate model simulations project robust future drying across the region, but internal variability is expected to dominate until the end of the century. Here we use a high-resolution gridded precipitation dataset to investigate these apparent discrepancies and to quantify the spatiotemporal complexities of the MSD. We detect spatially variable trends in MSD timing, the amount of rainy season precipitation, the number of consecutive and total dry days, and extreme wet events at the local scale. At the regional scale, we find a positive trend in the duration, but not the magnitude of the MSD, which is dominated by spatially heterogeneous trends and interannual variability linked to large-scale modes of ocean-atmosphere circulation. Although the current climate still reflects predominantly internal variability, some Central American communities are already experiencing significant changes in local characteristics of the MSD. A detailed spatiotemporal understanding of MSD trends and variability can contribute to evidence-based adaptation planning and help reduce the vulnerability of Central American communities to both natural rainfall variability and anthropogenic change.

Wetlands provide valuable ecosystem services and play a central role in global carbon cycling. Changes in rainfall and the flood-pulse are likely to disrupt the processes that maintain these landscapes; further, landscape modification may dramatically alter wetlands and promote terrestrialization. The Pantanal, South America, is the world's largest wetland due to flooding along the Upper Paraguay River. Predicting how water resources in the Pantanal may change is problematic due to a complex drainage network, resulting in the out-of-phase timing of rainfall and the flood pulse. We use remote sensing data of vegetation and climate to better understand the relationships among the rains, the flood pulse, and vegetation. Although rainfall is regionally synchronous, vegetation responses differ based on position relative to inundated areas. Away from rivers, vegetation greening occurs immediately following rainfall. Along channels, greening may lag rainfall by six months, responding closely to local flood stage. Interannual rainfall variability also impacts vegetation differently near flooded areas, with weaker, lagged responses to rainfall due to local water storage. This work suggests that the importance of flood pulse timing for vegetation productivity in inundated areas means that local conditions in wetlands may be the strongest controls on biogeochemical processes.

Increases of riverine organic carbon concentrations have been observed across the northern hemisphere over the past few decades. These increases are the result of multiple environmental drivers, but the relative importance of the drivers is still unclear. We analyzed a dataset of >10 000 observations of riverine total organic carbon (TOC) concentrations and associated water chemistry and hydrological observations from 1993 to 2017. The observations span a ∼600 km north–south gradient from 30 individual river systems in Finland. Our data show significantly increasing TOC concentrations in 25 out of 30 systems, with an average increase from 12.0 to 15.1 mg l⁻¹. The observed increase in riverine TOC concentrations led to an increase of 0.28 Mt in annual TOC load to the Baltic Sea from 1993 level to 2017 level. We analyzed the role of three putative environmental drivers of the observed TOC trends. Multiple regression analysis revealed that the most common driver was discharge, which alone explained TOC increases in 13 rivers, whereas pH and temperature were less important drivers (sole predictor in one and zero rivers, respectively). Different permutations of these three drivers were also found to be significant; the combination of discharge and pH being the most common (4 rivers). Land use was not in general linked with trends in TOC, except for the proportion of ditched land in the catchment, which was significantly correlated with increases in TOC concentration. Land use showed significant relationships with trends in discharge and pH. We also found that catchment characteristics are regulating the extent of these regional or global environmental changes causing the upward trends of riverine organic carbon.

Background: Nitrogen dioxide (NO₂) poses substantial public health risks in large cities globally. Concentrations of NO₂ shows high spatial variation, yet intra-urban measurements of NO₂ in Chinese cities are sparse. The size of Chinese cities and shortage of some datasets is challenging for high spatial resolution modelling. The aim here was to combine advantages of dispersion and land-use regression (LUR) modelling to simulate population exposure to NO₂ at high spatial resolution for health burden calculations, in the example megacity of Guangzhou. Methods: Ambient concentrations of NO₂ simulated by the ADMS-Urban dispersion model at 83 'virtual' monitoring sites, selected to span both the range of NO₂ concentration and weighting by population density, were used to develop a LUR model of 2017 annual-mean NO₂ across Guangzhou at 25 m × 25 m spatial resolution. Results: The LUR model was validated against both the 83 virtual sites (adj R²: 0.96, RMSE: 5.48 μg m⁻³; LOOCV R²: 0.96, RMSE: 5.64 μg m⁻³) and, independently, against available observations (n = 11, R^2:: 0.63, RMSE: 18.0 μg m⁻³). The modelled population-weighted long-term average concentration of NO₂ across Guangzhou was 52.5 μg m⁻³, which contributes an estimated 7270 (6960−7620) attributable deaths. Reducing concentrations in exceedance of the China air quality standard/WHO air quality guideline of 40 μg m⁻³ would reduce NO₂-attributable deaths by 1900 (1820–1980). Conclusions: We demonstrate a general hybrid modelling method that can be employed in other cities in China to model ambient NO₂ concentration at high spatial resolution for health burden estimation and epidemiological study. By running the dispersion model with alternative mitigation policies, new LUR models can be constructed to quantify policy effectiveness on NO₂ population health burden.

Bottom-up emission inventories can provide valuable information for understanding emission status and are needed as input datasets to drive chemical transport models. However, this type of inventory has the disadvantage of taking several years to be compiled because it relies on a statistical dataset. Top-down approaches use satellite data as a constraint and overcome this disadvantage. We have developed an immediate inversion system to estimate anthropogenic NO_x emissions with NO₂ column density constrained by satellite observations. The proposed method allows quick emission updates and considers model and observation errors by applying linear unbiased optimum estimations. We used this inversion system to estimate the variation of anthropogenic NO_x emissions from China and India from 2005 to 2016. On the one hand, NO_x emissions from China increased, reaching a peak in 2011 with 29.5 Tg yr⁻¹, and subsequently decreased to 25.2 Tg yr⁻¹ in 2016. On the other hand, NO_x emissions from India showed a continuous increase from 2005 to 2016, reaching 13.9 Tg yr⁻¹ in 2016. These opposing trends from 2011 to 2016 were −0.83 and +0.76 Tg yr⁻¹ over China and India, respectively, and correspond to strictly regulated and unregulated future scenarios. Assuming these trends continue after 2016, we expect NO_x emissions from China and India will be similar in 2023, with India becoming the world's largest NO_x emissions source in 2024.

A new generation of satellites for Earth observation and telecommunications are being designed and built with off the shelf components. This is driving down costs and permitting the launch of large satellite swarms with unprecedented spatial and temporal coverage. On-orbit maneuvers are commonly performed using ion thrusters. Mercury is one of the cheapest and easiest to store propellants for electric propulsion. While some mercury released in Low Earth Orbit may escape Earth's gravitational field, mercury emissions originating from many common orbital maneuvers will return to Earth. The environmental and human health implications of such releases have not been evaluated. Using an atmospheric chemical transport model, we simulate global deposition of mercury released from satellite propulsion systems. We estimate that 75% of the mercury falling back to Earth will be deposited in the world's oceans, with potentially negative implications for commercial fish and other marine life. Understanding the scale of this novel mercury source in a post-Minamata Convention world is necessary to limit ecosystem exposure to mercury contamination.

Clarifying characteristics of hazards and risks of climate change at 2 °C and 1.5 °C global warming is important for understanding the implications of the Paris Agreement. We perform and analyze large ensembles of 2 °C and 1.5 °C warming simulations. In the 2 °C runs, we find substantial increases in extreme hot days, heavy rainfalls, high streamflow and labor capacity reduction related to heat stress. For example, about half of the world's population is projected to experience a present day 1-in-10 year hot day event every other year at 2 °C warming. The regions with relatively large increases of these four hazard indicators coincide with countries characterized by small CO₂ emissions, low-income and high vulnerability. Limiting global warming to 1.5 °C, compared to 2 °C, is projected to lower increases in the four hazard indicators especially in those regions.

Flooding is a function of hydrologic, climatologic, and land use characteristics. However, the relative contribution of these factors to flood risk over the long-term is uncertain. In response to this knowledge gap, this study quantifies how urbanization and climatological trends influenced flooding in the greater Houston region during Hurricane Harvey. The region—characterized by extreme precipitation events, low topographic relief, and clay-dominated soils—is naturally flood prone, but it is also one of the fastest growing urban areas in the United States. This rapid growth has contributed to increased runoff volumes and rates in areas where anthropogenic climate changes has also been shown to be contributing to extreme precipitation. To disentangle the relative contributions of urban development and climatic changes on flooding during Hurricane Harvey, we simulate catchment response using a spatially-distributed hydrologic model under 1900 and 2017 conditions. This approach provides insight into how timing, volume, and peak discharge in response to Harvey-like events have evolved over more than a century. Results suggest that over the past century, urban development and climate change have had a large impact on peak discharge at stream gauges in the Houston region, where development alone has increased peak discharges by 54% (±28%) and climate change has increased peak discharge by about 20% (±3%). When combined, urban development and climate change nearly doubled peak discharge (84% ±35%) in the Houston area during Harvey compared to a similar event in 1900, suggesting that land use change has magnified the effects of climate change on catchment response. The findings support a precautionary approach to flood risk management that explicitly considers how current land use decisions may impact future conditions under varying climate trends, particularly in low-lying coastal cities.

Large spatial and between-tree variability has recently been observed in the response of boreal forests to ongoing climate change, spanning from growth stimulation by increasing temperatures to drought limitation. To predict future responses of boreal forests, it is necessary to disentangle the drivers modulating the temperature-growth interaction. To address this issue, we established two inventory plots (at a treeline and closed-canopy forest) and assembled site chronologies in Picea glauca stands at the transition between boreal forest and tundra in Northern Quebec, Canada. In addition to site chronologies, we established a set of chronologies containing, for each year, exclusive subsets of tree-rings with specific cambial age (young/old), tree dimensions (small/large) and tree social status (dominant/suppressed). All chronologies were correlated with climatic data to identify the course of climatic conditions driving variability in tree-ring widths. Our results show that the growth of P. glauca correlates significantly with summer temperature in tree-ring formation years and during up to two prior summers. Tree-ring width is positively influenced by summer temperatures in tree-ring formation year and two years prior to tree-ring formation. In addition, climate-growth correlations indicate a negative effect of summer temperature one year before tree-ring formation at the closed-canopy forest site. The pattern of climate-growth correlations is tightly synchronized with previously published patterns of climate-reproduction correlations of P. glauca, suggesting a growth-reproduction trade-off as a possible factor modulating the response of boreal forests to summer temperatures. Climatic signal does not differ between pairs of chronologies based on subsets of cambial ages, stem dimensions or tree competition status at the treeline site. However, the response to summer temperatures one year before tree-ring formation is significant only in mature (old, large and dominant) individuals at the closed-canopy site. The inverse pattern of temperature-growth correlations during a sequence of three years challenges predictions of how boreal forests respond to climate change.

China faces severe air pollution issues due to the rapid growth of the economy, causing concerns for human physical and mental health as well as behavioral changes. Such adverse impacts can be mediated by individual avoidance behaviors such as traveling from polluted cities to cleaner ones. This study utilizes smartphone-based location data and instrumental variable regression to try and find out how air quality affects population mobility. Our results confirm that air quality does affect the population outflows of cities. An increase of 100 points in the air quality index will cause a 49.60% increase in population outflow, and a rise of 1 μg m⁻³ in PM_2.5 may cause a 0.47% rise in population outflow. Air pollution incidents can drive people to leave their cities 3 days or a week later by railway or road. The effect is heterogeneous among workdays, weekends and holidays. Our results imply that air quality management can be critical for urban tourism and environmental competitiveness.

Pre-growing season prediction of crop production outcomes such as grain yields and nitrogen (N) losses can provide insights to farmers and agronomists to make decisions. Simulation crop models can assist in scenario planning, but their use is limited because of data requirements and long runtimes. Thus, there is a need for more computationally expedient approaches to scale up predictions. We evaluated the potential of four machine learning (ML) algorithms (LASSO Regression, Ridge Regression, random forests, Extreme Gradient Boosting, and their ensembles) as meta-models for a cropping systems simulator (APSIM) to inform future decision support tool development. We asked: (1) How well do ML meta-models predict maize yield and N losses using pre-season information? (2) How many data are needed to train ML algorithms to achieve acceptable predictions? (3) Which input data variables are most important for accurate prediction? And (4) do ensembles of ML meta-models improve prediction? The simulated dataset included more than three million data including genotype, environment and management scenarios. XGBoost was the most accurate ML model in predicting yields with a relative mean square error (RRMSE) of 13.5%, and Random forests most accurately predicted N loss at planting time, with a RRMSE of 54%. ML meta-models reasonably reproduced simulated maize yields using the information available at planting, but not N loss. They also differed in their sensitivities to the size of the training dataset. Across all ML models, yield prediction error decreased by 10%–40% as the training dataset increased from 0.5 to 1.8 million data points, whereas N loss prediction error showed no consistent pattern. ML models also differed in their sensitivities to input variables (weather, soil properties, management, initial conditions), thus depending on the data availability researchers may use a different ML model. Modest prediction improvements resulted from ML ensembles. These results can help accelerate progress in coupling simulation models and ML toward developing dynamic decision support tools for pre-season management.

Direct emissions from commercial-scale composting are uncertain. We used micrometeorological methods to continuously measure greenhouse gas (CO₂, CH₄, N₂O) emissions from full composting of green waste and manure. We measured oxygen (O₂), moisture, and temperature continuously inside the composting pile, and analyzed chemical and physical characteristics of the feedstock weekly as potential drivers of emissions. Temperature, moisture, and O₂ all varied significantly by week. Feedstock porosity, C:N, and potential N mineralization all declined significantly over time. Potential net nitrification remained near zero throughout. CH₄ and CO₂ fluxes, indicators of feedstock lability, were variable, and most emissions (75% and 50% respectively) occurred during the first three weeks of composting. Total CH₄ emitted was 1.7 ± 0.32 g CH₄ kg⁻¹ feedstock, near the median literature value using different approaches (1.4 g CH₄ kg⁻¹). N₂O concentrations remained below the instrument detection. Oxygen, moisture and temperature exhibited threshold effects on CH₄ emissions. Net lifecycle emissions were negative (−690 g CO₂-e kg⁻¹), however, after considering avoided emissions and sinks. Managing composting piles to minimize methanogenesis—by maintaining sufficient O₂ concentrations, and focusing on the first three weeks—could reduce emissions, contributing to the climate change mitigation benefit of composting.

The transition from coal to natural gas and renewables in the electricity sector and the rise of unconventional shale gas extraction are likely to affect water usage throughout the US. While new natural-gas power plants use less water than coal-fired power plants, shale gas extraction through hydraulic fracturing has increased water utilization and intensity. We integrated water and energy use data to quantify the intensity of water use in the US throughout the electricity's lifecycle. We show that in spite of the rise of water use for hydraulic fracturing, during 2013–2016 the overall annual water withdrawal (8.74 × 10¹⁰ m³) and consumption (1.75 × 10⁹ m³) for coal were larger than those of natural gas (4.55 × 10¹⁰ m³, and 1.07 × 10⁹ m³, respectively). We find that during this period, for every MWh of electricity that has been generated with natural gas instead of coal, there has been a reduction of ∼1 m³ in water consumption and ∼40 m³ in water withdrawal. Examining plant locations spatially, we find that only a small proportion of net electricity generation takes place in water stressed areas, while a large proportion of both coal (37%) and natural gas (50%) are extracted in water stressed areas. We also show that the growing contribution of renewable energy technologies such as wind and solar will reduce water consumption at an even greater magnitude than the transition from coal to natural gas, eliminating much of water withdrawals and consumption for electricity generation in the US.

Dust storms are common meteorological events in arid and semi-arid regions, particularly in Southwest Iran (SWI). Here we study the relation between drought events in Iraq and dust storms in SWI between 2003 and 2018. The HYSPLIT model showed that central and southern Iraq are the main dust sources for SWI. Mean annual aerosol optical depth (AOD) analysis demonstrated that 2008 and 2009 were the dustiest years since 2003 and there is an increased frequency of summertime extreme dust events in the years 2008 and 2009. The Standardized Precipitation Evapotranspiration Index revealed that drought in Iraq significantly affects dust storms in Iran. Similarly, dramatic desiccation of Iraq wetlands has contributed to increasing fall dust events in SWI. AOD in SWI is highly correlated (−0.76) with previous-month vapor pressure deficit (VPD) over Iraq, demonstrating the potential of VPD for dust event forecasting.

Restoration of peatlands after peat extraction could be a benefit to the climate system. However a multi-year ecosystem-scale assessment of net carbon (C) sequestration is needed. We investigate the climate impact of active peatland restoration (rewetting and revegetating) using a chronosequence of C gas exchange measurements across post-extraction Canadian peatlands. An atmospheric perturbation model computed the instantaneous change in radiative forcing of CO₂ and CH₄ emissions/uptake over 500 years. We found that using emission factors specific to an active restoration technique resulted in a radiative forcing reduction of 89% within 20 years compared to IPCC Tier 1 emission factors based on a wide range of rewetting activities. Immediate active restoration achieved a neutral climate impact (excluding C losses in the removed peat) about 155 years earlier than did a 20 year delay in restoration. A management plan that includes prompt active restoration is key to utilizing peatland restoration as a climate change mitigation strategy.

Future extreme precipitation events are expected to be influenced by climate change; however, the robustness of this anthropogenically forced response in respect to projection uncertainty especially for sub-daily extremes is not fully understood. We analyze the impact of anthropogenic climate change on 3 hourly extreme precipitation with return periods ranging between 5 and 50 years over Europe using the RCA4 model ensemble simulations at 0.11° resolution. The robustness of the signals is examined based on a regionalized signal-to-noise (S2N) technique by taking the spatial pooling into account and the efficacy of the regionalization is tested by a sensitivity analysis. The results show an increasing signal in 3 hourly extreme precipitation over Europe for all seasons except summer for which a bipolar pattern (increase in the north and decrease in the south) is discerned. For the business-as-usual scenario RCP8.5, the regionalized winter 3 hourly extreme precipitation signals over 9 × 9 model grid cells are statistically significant in roughly 72%, 65%, 59% and 48% of the European area for 5, 15, 25 and 50 year return periods respectively, while 16%–21% of the area will experience significant changes in summer. The S2N values for 3 hourly extreme precipitation changes rise after the spatial pooling by about a factor of 1.4–1.7 for all seasons except summer when they decline by about a factor of 0.78. The results of sensitivity analysis reveal that the regionalization influence is sensitive—in order of decreasing importance—to season, precipitation time scale, precipitation intensity, emission scenario and model spatial resolution. The precipitation time scale is particularly important seasonally in summer and regionally in south Europe when/where short-duration convective precipitation is dominant.

Projected precipitation from climate models is used in a wide range of fields for climate change impact assessment. However, the spatial pattern of uncertainty across latitudes and the global uncertainty hotspots are not well understood despite their importance for regional adaptation planning. In this study, we describe uncertainties in projected extreme precipitation changes per K global warming across latitudes, and decompose the overall uncertainty into climate model and internal variability uncertainties. We then identify global uncertainty hotspots and discuss the broader implications. Our results show that both uncertainty sources are highly heterogeneous across latitudes, while climate model uncertainty exceeds internal variability uncertainty for all seasons and precipitation intensities. The largest difference between model and internal variability uncertainties is found in tropical regions where model uncertainty is thrice as large as internal variability uncertainty in June–July–August season and twice as large as that in the other seasons. Tropical and subtropical regions are identified as the global uncertainty hotspots, with the Sahara desert and the southern part of the Middle East being the local hotspots. The large uncertainty in the tropics and subtropics is primarily due to the convective nature of rainstorms which cannot be adequately represented by coarse-scale climate models, and also to sparse observation networks based on which climate models can be tuned and improved. The results highlight areas where future model development and improvement efforts should focus to reduce the overall uncertainties in projected precipitation extremes.

Summer temperature dominates environmental degradation and water resource availability on the Tibetan Plateau (TP), affecting glacier melting, permafrost degradation, desertification and streamflow, etc. Extending summer temperature records back before the instrumental period is fundamentally important for climatic and environmental studies over long timescales. By pooling 39 tree-ring width records from the TP that show significant (P < 0.05) correlations with the summer (June–August) minimum temperature (MinT) of the nearest grid point, we reconstructed a 366-year summer MinT record for the southern TP (STP). Reconstructed and instrumental data are highly coherent within the 1950–2010 calibration interval (R² = 0.50, P < 0.001). The reconstruction captures major temperature anomalies, such as the coldest interval of the 1810s–1820s and unprecedented warming since the 1990s. We found that the linear trends of the instrumental and reconstructed STP summer MinTs are significantly lower than those for the larger Eurasian continent over the periods 1950–2010 and 1850–1950, respectively. The lower warming rate of STP summer MinT since 1850 could be due to increased evaporative cooling, and the absence of warming enhancement factors such as snow-albedo and energy-absorbing aerosols in summer. The reconstructed summer warming rate for the STP appears to be significantly overestimated by the ensemble mean of the Coupled Model Intercomparison Project Phase 5 (CMIP5) historical simulation.

Mitigating climate change requires collective action of various sectors and on multiple scales, including individual behavioural changes among citizens. Although numerous studies have examined factors that influence individuals' mitigation behaviours, much less attention has been given to interpersonal influence. Children have been suggested to influence parents' climate change concerns; however, how the interactions between couples—typically the primary decision-makers in married-couple households—influence each other's climate change concerns has seldom been discussed. In this study, we surveyed married heterosexual couples to investigate the interdependency of husbands' and wives' motivations for behavioural change to mitigate climate change. We found that wives' psychological constructs, including climate change risk perception, self-efficacy, and gender role attitudes, demonstrated stronger effects on their husbands' motivation than did husbands' own constructs on their own motivation, whereas husbands' psychological constructs did not influence their wives' motivation. Our results suggest the importance of wives' role in motivating household climate change mitigation behaviours.

Australia has experienced regional climate trends over recent decades with consequences for agriculture and water management. We investigate the statistical significance of these trends at annual and seasonal scales using the concept of stationarity. Using long-term high quality regional-scale observations of temperature, precipitation and pan evaporation (a measure of atmospheric evaporative demand), we find that despite highly significant increases in temperature that are non-stationary, few regions of Australia have experienced annual or seasonal changes in precipitation or pan evaporation that are outside the range of observed variability over the last century. Despite a common assumption of increasing water demand under a warming climate, atmospheric evaporative demand (as measured by pan evaporation) largely remains unchanged. This is because evaporative demand depends strongly on factors other than temperature. Similarly, seasonal and annual precipitation over the last century is found to be stationary in most (but not all) regions. These findings suggest that the Australian precipitation has largely remained within the bounds of observed variability to date and emphasises the need to better account for variability in water resource management.

The African continent faces several challenges and threats: high vulnerability to climate change, the fastest population increase, the lowest degree of electrification and the need for an energy transition towards renewable energies. Solar energy constitutes a viable option for addressing these issues. In a changing climate the efficient implementation of solar capacity should rely on comprehensive information about the solar resource. Here, the newest and highest resolution regional climate simulation results are used to project the future photovoltaic and concentrated solar power potentials for Africa. We show that the high potentials for solar energy will not be reduced much throughout Africa with climate change. However, the PV solar potential is projected to decrease up to about −10% in limited areas of eastern central Africa; increases are also projected to the northwest and southern Africa (up to about +5%). These changes are mostly determined by changes in solar irradiance but in certain areas the warming is a critical factor limiting PV potential.

The Western Climate Initiative is a multilateral cap-and-trade program in California and Québec. The California climate regulator has called for cap-and-trade to deliver nearly half of the emission reductions needed to achieve the state's legally binding limit on greenhouse gas emissions in 2030, making the program the single biggest driver of the state's post-2020 policy portfolio. However, the program's supply of compliance instruments has persistently exceeded emissions subject to the program—a condition known as overallocation, which independent studies have projected may continue into the mid-2020s. If market participants purchase and bank excess compliance instruments for future use, they may be able to comply with the program's regulations while nevertheless emitting significantly in excess of the state's legally binding 2030 limit. Here, we present methods for tracking observed banking behavior on both an annual and multi-year compliance period basis. By the end of 2018, market participants had already acquired more unused compliance instruments than the regulator anticipated for 2020. The size of the private bank is now comparable to the cumulative mitigation expected from the program over the period 2021 through 2030, raising questions about the program's ability to achieve its expected reductions. Beyond diagnosing market conditions, banking metrics can also help policymakers design dynamic program reforms that increase program stringency conditional on observed market behavior deviating from expectations.

Conservation tillage is a primary tenet of conservation agriculture aimed at restoring and maintaining soil health for long-term crop productivity. Because soil degradation typically operates on century timescales, farmer adoption is influenced by near-term yield impacts and profitability. Although numerous localized field trials have examined the yield impacts of conservation tillage, their results are mixed and often unrepresentative of real-world conditions. Here, we applied a machine-learning causal inference approach to satellite-derived datasets of tillage practices and crop yields spanning the US Corn Belt from 2005 to 2017 to assess on-the-ground yield impacts at field-level resolution across thousands of fields. We found an average 3.3% and 0.74% yield increase for maize and soybeans, respectively, for fields with long-term conservation tillage. This effect was diminished in fields that only recently converted to conservation tillage. We also found significant variability in these effects, and we identified soil and weather characteristics that mediate the direction and magnitude of yield responses. This work supports soil conservation practices by demonstrating they can be used with minimal and typically positive yield impacts.

The development of renewable electricity in Africa could be massive in coming decades, as a response to the rapid rising electricity demand while complying with the Paris Agreements. This study shows that in the high-resolution climate experiments of CORDEX-AFRICA, the annual mean solar potential is expected to decrease on average by 4% over most of the continent by the end of the century, reaching up to 6% over the Horn of Africa, as a direct result of decrease in solar radiation and increase in air surface temperature. These projections are associated with large uncertainties, in particular over the Sahel and the elevated terrains of eastern Africa. While the expected decrease may affect the sizing of the numerous solar projects planned in Africa for the next decades, this study suggests that it does not endanger their viability. At last, this study indicates that the design of such projects also needs to account for the non-negligible uncertainties associated with the resource.

This study focuses on potential impacts of climate change on the early spring (March–April) temperature and its extremes in the mid-latitudes of North America, discriminated between the 1.5 °C and 2 °C levels of global warming, as projected by a suit of numerical experiments. The results suggest relatively mild seasonal average warming (0.25 °C–1.5 °C), but also an intensification of both warm and cold temperature extremes. The derived changes feature much stronger warming over the West of the United States and weak to no warming to the East, which is congruent with the ventilating effect of the intensified northerly wind over central Canada and the East of the United States. The intensified northerly component of the mid-latitude jet is likely a contributing factor to the derived increased seasonal variability of March–April temperatures over parts of Manitoba and Ontario in Canada, and the Midwest of the United States. The projected changes in temperature extremes agree to some extent with the previous studies: warm extremes intensify especially over southern mid-latitudes, while cold extremes are weakening over the north mid- to high-latitudes. However, high-resolution simulations with the Community Atmospheric Model 5 (CAM5) indicate much sharper spatial gradients, which translate into higher magnitudes and also more complex patterns of changes. Particularly, cold extremes feature not only reductions north of ∼45°N latitudes, but also a very strong intensification of cold extremes (by −4 °C for 20 year return values) in the band 25°–45°N, centered in the Midwest of the United States. While general warming and intensification of the warm extremes may accelerate the arrival of early spring, the intensifying cold extremes may increase the risk of early spring frost damage, and hence may yield a profound impact on the regional agriculture of North America. Combined with reliable information on expected temperature variability at interannual-to-decadal timescales, the background longer-term projections can help inform decision makers in the food security sector.

The benefits of the 1987 Montreal Protocol in reducing chlorofluorocarbon emissions, repairing the stratospheric ozone hole, shielding incoming UV radiation, reducing the incidence of skin cancer and mitigating negative ecosystem effects are all well documented. Projected future climate impacts have also been described, mainly focused on a reduced impact of the mid-latitude jet as the ozone hole gradually repairs. However, there is little appreciation of the surface warming that has been avoided as a result of the Montreal Protocol, despite CFCs being potent greenhouse gases. Instead, the issue of ozone depletion and climate change are often thought of as two distinct problems, even though both ozone and CFCs impact Earth's radiation budget. Here we show that a substantial amount of warming has been avoided because of the Montreal Protocol, even after factoring in the surface cooling associated with stratospheric ozone depletion. As of today, as much as 1.1 °C warming has been avoided over parts of the Arctic. Future climate benefits are even stronger, with 3 °C–4 °C Arctic warming and ∼1 °C global average warming avoided by 2050; corresponding to a ∼25% mitigation of global warming. The Montreal Protocol has thus not only been a major success in repairing the stratospheric ozone hole, it has also achieved substantial mitigation of anthropogenic climate change both today and into the future.

Climate changes are expected to progressively increase extreme wildfire frequency in forests. Finding past analogs for periods of extreme biomass burning would provide valuable insights regarding what the effects of warming might be for tree species distribution, ecosystem integrity, atmospheric greenhouse gas balance, and human safety. Here, we used a network of 42 lake-sediment charcoal records across a ∼2000 km transect in eastern boreal North America to infer widespread periods of wildfire activity in association with past climate conditions. The reconstructed fluctuations in biomass burning are broadly consistent with variations in ethane concentration in Greenland polar ice cores. Biomass burning fluctuations also significantly co-varied with Greenland temperatures estimated from ice cores, at least for the past 6000 years. Our retrospective analysis of past fire activity allowed us to identify two fire periods centered around 4800 and 1100 BP, coinciding with large-scale warming in northern latitudes and having respectively affected an estimated ∼71% and ∼57% of the study area. These two periods co-occurred with widespread decreases in mean fire-return intervals. The two periods are likely the best analogs for what could be anticipated in terms of impacts of fire on ecosystem services provided by these forests in coming decades.

Dependence between extreme rainfall and storm surge can have significant implications for coastal floods, which are often caused by joint occurrence of these flood drivers (through pluvial or fluvial processes). The effect of multiple drivers leading to a compound flood event poses higher risk than those caused by a single flood-driving process. There is strong evidence that compound floods caused by joint occurrence of extreme storm surge and heavy rainfall are related to meteorological forcing (e.g. large scale pressure systems and wind) and climate phenomena (e.g. the El Niño Southern Oscillation or ENSO). Therefore, understanding how climate phenomena affect the co-occurrence of coastal flood drivers is an important step towards understanding future coastal flood risk under climate change. Here we examine the impact of one of the most important climate phenomena—ENSO—on dependence between storm surge and rainfall in Australia, using both observed surge and modelled surge from a linked ocean-climate model—the Regional Ocean Modeling System. Our results show that ENSO has a significant impact on the dependence between extreme rainfall and storm surge, thus flood risk resulted from these drivers. The overall dependence is largely driven by La Niña in Australia, with increased dependence observed during La Niña along most of the Australian coastline. However, there can be increased dependence during El Niño in some locations. The results demonstrate dependence is contributed by unequally-weighted mechanisms due to the interaction between climate phenomena and local features, indicating the need for greater understanding of composition of compound flood risk. Where climate phenomena are anticipated to change into the future, it is possible to use integrated process-driven models to establish a better understanding of whether extremes are more likely to co-occur and exacerbate compound flood risk.

The directionality of the response of gross primary productivity (GPP) to climate has been shown to vary across the globe. This effect has been hypothesized to be the result of the interaction between multiple bioclimatic factors, including environmental energy (i.e. temperature and radiation) and water availability. This is due to the tight coupling between water and carbon cycling in plants and the fact that temperature often drives plant water demand. Using GPP data extracted from 188 sites of FLUXNET2015 and observation-driven terrestrial biosphere models (TBMs), we disentangled the confounding effects of temperature, precipitation and carbon dioxide on GPP, and examined their long-term effects on productivity across the globe. Based on the FLUXNET2015 data, we observed a decline in the positive effect of temperature on GPP, while the positive effects of precipitation and CO₂ were becoming stronger during 2000–2014. Using data derived from TBMs between 1980 and 2010 we found similar effects globally. The modeled data allowed us to investigate these effects more thoroughly over space and time. In arid regions, the modeled response to precipitation increased since 1950, approximately 30 years earlier than in humid regions. We further observed the negative effects of summer temperature on GPP in arid regions, suggesting greater aridity stress on productivity under global warming. Our results imply that aridity stress, triggered by rising temperatures, has reduced the positive influence of temperature on GPP, while increased precipitation and elevated CO₂ may alleviate negative aridity impacts.

Over the past decades, the Peruvian Amazon has experienced a rapid change in forest cover due to the expansion of agriculture and extractive activities. This study uses spectral mixture analysis (SMA) in a cloud-computing platform to map forest loss within and outside indigenous territories, protected areas, mining concessions, and reforestation concessions within the Madre de Dios Region in Peru. The study area is focused on key areas of forest loss in the western part of the Tambopata National Reserve and surrounding the Malinowski River. Landsat 8 Operational Land Imager and Landsat 7 Enhanced Thematic Mapper Plus surface reflectance data spanning 2013–2018 were analyzed using cloud-based SMA to identify patterns of forest loss for each year. High-resolution Planet Dove (3m) and RapidEye (5m) imagery were used to validate the forest loss map and to identify the potential drivers of loss. Results show large areas of forest loss, especially within buffer zones of protected areas. Forest loss also appears in the Kotsimba Native Community within a 1 km buffer of the Malinowski River. In addition to gold mining, agriculture and pasture fields also appear to be major drivers of forest loss for our study period. This study also suggests that gold mining activity is potentially not restricted to the legal mining concession areas, with 49% of forest loss occurring outside the mining concessions. Overall accuracy obtained for the forest loss analysis was 96%. These results illustrate the applicability of a cloud-based platform not only for land use land cover change detection but also for accessing and processing large datasets; the importance of monitoring not only forest loss progression in the Madre de Dios, which has been increasing over the years, especially within buffer zones, but also its drivers; and reiterates the use of SMA as a reliable change detection classification approach.

Hurricane Harvey devastated large parts of the US Gulf Coast in 2017, and its floodwaters posed a number of threats to the environment and human health. In particular, an estimated 375 000 Texas residents experienced issues related to the provision of safe drinking water at the peak of the hurricane. In this study, physical, chemical, and biological water quality was monitored in two drinking water systems in Texas following Hurricane Harvey to understand the relationship between water quality parameters and changes in the drinking water microbiota. Results show initial surges in total organic carbon, trihalomethanes, and bacterial concentrations in finished water immediately following Hurricane Harvey. Microbial community analyses highlight the dependence of the distribution system microbiota on distribution system characteristics (i.e. water age), raw water quality, and disinfectant residual, among other factors. While both systems had problems maintaining disinfectant residual in the weeks following the hurricane, stabilization of water quality occurred over time. Overall, this study provides an understanding of the challenges associated with maintaining drinking water quality in the wake of a natural disaster and can be used to better prepare drinking water managers and engineers to combat changing weather patterns in the future.

The imperative to decarbonize long-haul, heavy-duty trucking for mitigating both global climate change as well as air pollution is clear. Given recent developments in battery and ultra-fast charging technology, some of the prominent barriers to electrification of trucking are dissolving rapidly. Here we shed light on a significant yet less-understood barrier, which is the general approach to retail electricity pricing. We show that this is a near term pathway to $0.06/kWh charging costs that will make electric trucking substantially cheaper than diesel. This pathway includes (i) reforming demand charges to reflect true, time-varying system costs; (ii) avoiding charging during a few specific periods (<45 h in a year) when prices are high; and (iii) achieving charging infrastructure utilization of 33% or greater. However, without reforming demand charges and low utilization of charging infrastructure, charging costs more than quadruple (to $0.28/kWh). We also illustrate that a substantial share of current trucking miles within select large regions of the United States can be reliably electrified without constraining electricity generation capacity as it exists today. Using historical hourly electricity price and load data for last 10 years and future projections in Texas and California, we show that electricity demand is at least 10% lower than yearly peak demand for at least 15 h on any given day. In sum, with electricity rates that closely reflect actual power system costs of serving off-peak trucking load, we show that electric trucks can provide overwhelming cost savings over diesel trucks. For reference, at diesel prices of $3.16/gal and charging costs of $0.06/kWh (inclusive of amortized charging station infrastructure costs), an electric truck's fuel cost savings are $251 000 (NPV), providing net savings of $61 000 (18% of lifetime diesel fuel cost) over the truck's lifetime at battery price of $170/kWh, or up to $148 000 (44% of lifetime diesel fuel cost) at a battery price of $100/kWh (figure 1).

Biodiversity is rapidly declining globally and targeted efforts are needed to mitigate the loss of species. Conventional conservation efforts have focused on establishing protected areas and restoring degraded lands in order to maintain current conditions or restore ecosystems to a pre-damaged state. However, as the climate changes, the current bioclimatic zones will be re-distributed globally. Historical distribution patterns may no longer serve as an effective guide for supporting biodiversity under climate change. In response to these challenges, this study proposes a spatially explicit strategy for biodiversity conservation that takes climate change into account using bioclimatic classification. The bioclimatic classification maps of Northeast Asia (NEA) were constructed for three historical time periods (the 1980s, 1990s, and 2000s) and two future time periods (the 2050s and 2070s) using five general circulation models (GCMs) in representative concentration pathway (RCP 8.5) scenarios. It was predicted that, in general, zones are shifting north, and some zones are expanding or shrinking rapidly. Based on an analysis of latitudinal and areal change for each zone, the bioclimate vulnerability index (BVI) and naturality index (NI) were developed to quantify the impact of environmental change. As a result of the BVI analysis, the distribution of vulnerable zones is expected to shift northward and expand. As is evident with the increased vulnerability of the subarctic region caused by the expansion of the temperate climate, the extent of vulnerable zones will increase. Also, the southern regions of NEA are becoming vulnerable due to the transformation of the temperate zone to a more subtropical zone. Quadrant graphs based on the BVI and NI were created to present appropriate strategies for each zone. Our proposed framework shows that conservation strategies should be modified based on the changes in the relative position of each zone over time.

The minilateral approach of a climate club of countries has been suggested as an intermediate phase in a transition towards a global agreement that enforces national climate policies through harmonization. To garner critical mass, we propose an extended club configuration including sub-national states or provinces, resulting in a multi-level club. This would allow considerable contributions from important emitters like the US to be brought on board, relevant given its intended withdrawal from the Paris Agreement. We elaborate this idea and clarify potential roles of participants at distinct levels. The concept is operationalized by developing a method for identifying suitable entities at each level that uses a set of likelihood-of-involvement indicators capturing existing carbon dependence, public opinion, government policy and climate coalition membership. Application at the national level identifies a subset of seven of the highest emitting countries representing 21% of global emissions. This rises to 51% assuming that China, the dominant global emitter, could be enticed into the group. Given that US involvement remains unlikely for now, we illustrate selection at the sub-national level for US states. Here, an initial group of 21 states appear as potential members, jointly accounting for 36% of national emissions. An additional group, representing a further 34% of emissions, are potentially receptive to enticement via trade dependencies on four key countries identified within the group of national members. Accordingly, some 70% of US emissions, representing 11% of global totals, may be subject to climate club involvement via a combination of these pathways. While the implementation of such a club requires various political and legal hurdles to be overcome, the ongoing threat of climate change and inadequacies of the Paris Agreement suggest that novel solutions of this kind deserve serious attention from scientists and politicians.

While daily extreme precipitation intensities increase with global warming on average at approximately the same rate as the availability of water vapor (∼7%/°C), a debated topic is whether sub-daily extremes increase more. Modelling at convection-permitting scales has been deemed necessary to reproduce extreme summer precipitation at local scale. Here we analyze multi-model ensembles and apply a 3 km horizontal resolution model over four regions across Europe (S. Norway, Denmark, Benelux and Albania) and find very good agreement with observed daily and hourly summer precipitation extremes. Projections show that daily extreme precipitation intensifies compared to the mean in all regions and across a wide range of models and resolutions. Hourly and 10 min extremes intensify at a higher rate in nearly all regions. Unlike most recent studies, we do not find sub-daily precipitation extremes increasing much more than 7%/°C, even for sub-hourly extremes, but this may be due to robust summer drying over large parts of Europe. However, the absolute strongest local daily precipitation event in a 20 year period will increase by 10%–20%/°C. At the same time, model projections strongly indicate that summer drying will be more pronounced for extremely dry years.

Plastic pollution in the marine environment is an urgent global environmental challenge. Land-based plastics, emitted into the ocean through rivers, are believed to be the main source of marine plastic litter. According to the latest model-based estimates, most riverine plastics are emitted in Asia. However, the exact amount of global riverine plastic emission remains uncertain due to a severe lack of observation. Field-based studies are rare in numbers, focused on rivers in Europe and North America and used strongly varying data collection methods. We present a harmonized assessment of floating macroplastic transport from observations at 24 locations in rivers in seven countries in Europe and Asia. Visual counting and debris sampling were used to assess (1) magnitude of plastic transport, (2) the spatial distribution across the river width, and (3) the plastic polymer composition. Several waterways in Indonesia and Vietnam contain up to four orders of magnitude more plastic than waterways in Italy, France, and The Netherlands in terms of plastic items per hour. We present a first transcontinental overview of plastic transport, providing observational evidence that, for the sampled rivers, Asian rivers transport considerably more plastics towards the ocean. New insights are presented in the magnitude, composition, and spatiotemporal variation of riverine plastic debris. We emphasize the urgent need for more long-term monitoring efforts. Accurate data on riverine plastic debris are extremely important to improve global and local modeling approaches and to optimize prevention and collection strategies.

While there is considerable agreement in the scientific community about the intensification of tropical cyclones (TCs) in a warming world, that consensus does not exist for TC frequency. In order to shed new light on this uncertainty, we classified the global oceans into three pools based on SST percentiles: the (a) warm (≥90th percentiles), (a) moderate (65th–90th percentiles) and (c) cool (<65th percentiles) pools, and found that TC frequency increases significantly over the cool SST pool but decreases in the warm and moderate SST pools. The differences in TC frequency change is large among the three pools, contrasting to the small trend differences of TC intensity.

Long range atmospheric transport is an important pathway for the spread of plant pathogens, such as rust fungi which can devastate cereal crop health and food security worldwide. In recent years, serious concern has been caused by the evolution of new virulent races of Puccinia graminis f. sp. tritici, a pathogen causing wheat stem rust that can result in close to 100% yield losses on susceptible wheat cultivars in favourable weather conditions. We applied an Earth system model to compare the suitability of the current climate and a business-as-usual climate scenario (RCP 8.5) for 2100 for wheat stem rust. Although there are large uncertainties in modelling changes in disease spread, we focus in this paper on the changes which are likely to be robust to model assumptions. We show that the warmer climate with lower relative humidity and enhanced turbulence will lead to ∼40% increase in the urediniospore emitting potential of an infected field as global average. The main predicted changes in the atmospheric long-range transport include reduced connections between Europe, Africa and South Asia, and increased frequency of spores crossing the mid-latitude oceans. Due to reduction in subfreezing conditions, the overwintering areas of the fungus will expand. On the other hand, projected drier conditions will reduce substantially the probability of an infection starting from deposited spores, except in irrigated fields.

The ecology in the Murray–Darling Basin in Australia is threatened by water scarcity due to climate change and the over-extraction and over-use of natural water resources. Ensuring environmental flows and sustainable water resources management is urgently needed. Seawater desalination offers high potential to deliver water in virtually unlimited quantity. However, this technology is energy-intensive. In order to prevent desalination becoming a driver of greenhouse gases, the operation of seawater desalination with renewables is increasingly being considered. Our study examines the optimisation of the operation of a 100% renewable energy grid by integrating seawater desalination plants and pipelines as a variable load. We use a GIS-based renewable energy load-shifting model and show how both technologies create synergy effects. First, we analyse what quantity of water is missing in the basin in the long run. We determine locations for seawater desalination plants and pipelines to distribute the water into existing storages in the Murray–Darling Basin. Second, we design a pipeline system and calculate the electricity needed to pump the water from the plants to the storages. Third, we use the combined renewable energy load-shifting model. We minimise the total cost of the energy system by shifting energy demand for water production to periods of high renewable energy availability. Our calculations show that in such a system, the unused spilt electricity can be reduced by at least 27 TWh. The electricity system's installed capacity and levelised cost of electricity can be reduced by up to 29%, and 43% respectively. This approach can provide an annual net economic benefit of $22.5 bn. The results illustrate that the expansion of seawater desalination capacity for load-shifting is economically beneficial.

Addressing emissions of non-CO₂ greenhouse gases (GHGs) is an integral part of efficient climate change mitigation and therefore an essential part of climate policy. Metrics are used to aggregate and compare emissions of short- and long-lived GHGs and need to account for the difference in both magnitude and persistence of their climatic effects. Different metrics describe different approaches and perspectives, and hence yield different numerical estimates for aggregated GHG emissions. When interpreting GHG emission reduction targets, being mindful of the underlying metrical choices thus proves to be essential. Here we present the impact a recently proposed GHG metric related to the concept of CO₂ forcing-equivalent emissions (called GWP*) would have on the internal consistency and environmental integrity of the Paris Agreement. We show that interpreting the Paris Agreement goals in a metric like GWP* that is significantly different from the standard metric used in the IPCC Fifth Assessment Report can lead to profound inconsistencies in the mitigation architecture of the Agreement. It could even undermine the integrity of the Agreement's mitigation target altogether by failing to deliver net-zero CO₂ emissions and therewith failing to ensure warming is halted. Our results indicate that great care needs to be taken when applying new concepts that appear scientifically favourable to a pre-existing climate policy context.

The Paris Agreement requires measurement of the progress made on adaptation. Tracking the progress made by governments through analysis of policies provides insight into the goals and means to achieve adaptation targets. Here we show the current state-of-the-art in public adaptation planning affecting 136 of the largest coastal port urban agglomerations, covering 68 countries. We identify 226 adaptation policies: 88 at national level, 57 at regional/state level and 81 at city/metropolitan level. This set of adaptation policies can be considered the latest, most up-to-date database of governmental and public-led adaptations. Our analyses show that (1) in one half of cases, there is no evidence of policy implementation, (2) in almost 85% of cases, planned adaptation actions are not driven by present or future climatic impacts or risks, and (3) formal adaptation planning is relatively recent and is concentrated in more developed areas and countries.

Around the world today, the magnitude and rates of environmental, social, and economic change are undermining the sustainability of many rural societies that rely directly on natural resources for their livelihoods. Sustainable development efforts seek to promote livelihood adaptations that enhance food security and reduce social-ecological vulnerability, but these efforts are hampered by the difficulty of understanding the complexity and dynamism of rural livelihood systems. Disparate research avenues are strengthening our ability to grapple with complexity. But we are only just beginning to find ways to simultaneously account for problematic complexities, including multiscalar feedbacks in the ecosystems that that support livelihoods, the heterogeneous benefits garnered by different segments of society, and the complex contingencies that constrain people's decisions and capacities to adapt. To provide a more nuanced analysis of the dynamics of transformation in rural livelihood systems, we identified key complementarities between four different research approaches, enabling us to integrate them in a novel research framework that can guide empirical and modeling research on livelihood adaptation. The framework capitalizes upon parallel concepts of sequentiality in (1) ecosystem services and (2) livelihood adaptation scholarship, then incorporates principles from (3) adaptation in social-ecological systems research to account for the dynamism inherent in these often rapidly-transforming systems. Lastly, we include advances in (4) agent-based modeling, which couples human decisions and land use change and provides tools to incorporate complex social-ecological feedbacks in simulation studies of livelihood adaptation. Here we describe the new Ecosystem Services—Livelihood Adaptation (ESLA) framework, explain how it links the contributing approaches, and illustrate its application with two case studies. We offer guidance for its implementation in empirical and modeling research, and conclude with a discussion of current challenges in sustainability science and the contributions that could be gained through research guided by the ESLA framework.

To improve air quality, China has been increasing the stringency of national emission standards for industrial sectors. However, from the projected air quality and associated health impacts it is not known if the updated emission limits have been completely followed. Here, we develop emission inventories based on the emission limits at different stages and use the Weather Research and Forecasting with Chemistry (WRF-Chem) model and health impact function to determine the potential air quality and health impacts for different scenarios. We found that full implementation of the special emission standards would decrease PM_2.5 concentrations by over 25% in April, July and October, and decrease O₃ concentrations in July by 10%–20%. Overall, the number of PM_2.5-related premature deaths could be reduced by 0.15 million (95% confidence level 0.13–0.17 million) if special emission limits were met and that O₃-related premature deaths would decrease by 18oo (95% confidence level 500–3000). However, even with these special emission limits, a greater than 25% increase in O₃ levels occurred in northern China during October, January and April, leading to an increase in O₃-related premature deaths in northern provinces which would offset the decrease in provincial PM_2.5-related mortality by 10%–20%. These findings indicate that emissions of volatile organic compounds (VOCs) should be reduced to avoid increases in O₃ in VOC-limited provinces. Despite the increase in O₃ in certain provinces, improvement of industrial emission standards could greatly limit PM_2.5 pollution and reduce the associated health burden. Therefore, we suggest that government should accelerate full implementation of strict emission standards in order to improve air quality.

With ongoing global warming, the changes in EA-WNPSM rainfall—feeding over two billion people in East Asia and the Indochina Peninsula—projected by the Coupled Model Intercomparison Project Phase 5 (CMIP5) models show remarkable and unidentified inter-model spread. Here, we reveal the leading inter-model spread of EA-WNPSM changes in 28 CMIP5 models is related to a 'dry north–wet south' dipole in East Asia and a wet Indochina and WNP. This spread pattern of EA-WNPSM changes is induced by the spread of sea surface temperature changes in the equatorial western Pacific, and can be further traced back to an apparent discrepancy among the state-of-the-art models in simulating the tropical Pacific rainfall. An air-sea coupling processes involved with summer background circulation contribute to this robust spread pattern of EA-WNPSM changes. We can constrain the EA-WNPSM rainfall changes based on the current-future relationship and observation that there should be more rainfall increase in North China and the Korean Peninsula and less increase in South China, the Indochina Peninsula and WNP, relative to previous multi-model ensemble projections.

Recent research has quantified the contributions of CO₂ and CH₄ emissions traced to the products of major fossil fuel companies and cement manufacturers to global atmospheric CO₂, surface temperature, and sea level rise. This work has informed societal considerations of the climate responsibilities of these major industrial carbon producers. Here, we extend this work to historical (1880–2015) and recent (1965–2015) acidification of the world's ocean. Using an energy balance carbon-cycle model, we find that emissions traced to the 88 largest industrial carbon producers from 1880–2015 and 1965–2015 have contributed ∼55% and ∼51%, respectively, of the historical 1880–2015 decline in surface ocean pH. As ocean acidification is not spatially uniform, we employ a three-dimensional ocean model and identify five marine regions with large declines in surface water pH and aragonite saturation state over similar historical (average 1850–1859 to average 2000–2009) and recent (average 1960–1969 to average of 2000–2009) time periods. We characterize the biological and socioeconomic systems in these regions facing loss and damage from ocean acidification in the context of climate change and other stressors. Such analysis can inform societal consideration of carbon producer responsibility for current and near-term risks of further loss and damage to human communities dependent on marine ecosystems and fisheries vulnerable to ocean acidification.

Global warming is driving environmental change in the Arctic. However, our current understanding of this change varies strongly among different environmental disciplines and is limited by the number and distribution of field sampling locations. Here, we use a quantitative framework based on multivariate statistical modeling to present the current state of sampling across environmental disciplines in the Arctic. We utilize an existing database of georeferenced Arctic field studies to investigate how sampling locations and citations of disciplines are distributed across Arctic topographical, soil and vegetation conditions, and highlight critical regions for potential new research areas in different disciplines. Continuous permafrost landscapes, and the northernmost Arctic bioclimatic zones are studied and cited the least in relation to their extent in many disciplines. We show that the clusters of sampling locations and citations are not uniform across disciplines. Sampling locations in Botany and Biogeochemistry cover environmental gradients the best, and Microbiology, Meteorology, Geosciences And Geographic Information Systems/remote Sensing/Modeling have the worst coverage. We conclude that across all disciplines, more research is needed particularly in the Canadian Arctic Archipelago, northern Greenland, central and eastern Siberia, and in some disciplines, in Canadian mainland, central Alaska, western Siberia and northern Taimyr region. We provide detailed maps of potential new sampling locations for each environmental discipline that consider multiple variables simultaneously. These results will help prioritize future research efforts, thus increasing our knowledge about the Arctic environmental change.

Indonesia is the world's second largest producer and third largest consumer of seafood. Fish is therefore essential to the nation, both financially and nutritionally. Overfishing and the effects of climate change will, however, limit future landings of capture fisheries, so any increases in future seafood production will need to come from aquaculture. The ecological effects of aquaculture are dependent upon the choice of species, management, and where it is sited. In the present study we use life cycle assessment (LCA) to evaluate how possible interventions and innovations can mitigate environmental impacts related to the aquaculture sector's growth. The mitigation potential of six interventions were also quantified, namely (1) FCR reductions for whiteleg shrimp, carp, and tilapia; (2) sustainable intensification of milkfish and Asian tiger shrimp polyculture; (3) shifting groupers from whole fish diets to pellets; (4) favoring freshwater finfish over shrimp; (5) renewable electricity; and (6) reduced food waste and improved byproduct utilization. If all six interventions are implemented, we demonstrate that global warming, acidification, eutrophication, land occupation, freshwater use, and fossil energy use could be reduced by between 28% and 49% per unit of fish. The addition of many innovations that could not be quantified in the present study, including innovative feed ingredients, suggest that production could double within the current environmental footprint. This does not, however, satisfy the expected 3.25-fold increase under a business-as-usual scenario, neither does it satisfy the government's growth targets. We therefore also explore possible geographical areas across Indonesia where aquaculture expansions and ecological hotspots may conflict. Conclusively, we advocate more conservative production targets and investment in more sustainable farming practices. To accelerate the implementation of these improvements, it will be central to identify the most cost-effective aquaculture interventions.

An Earth system model (ESM) was used to investigate the effect of reaching the target of 1.5 °C warming (relative to preindustrial levels) after overshooting to the 2 °C level with respect to selected global environment indicators. Two scenarios were compared that diverged after reaching the 2 °C level: one stayed at the 2 °C level, and the other cooled to the 1.5 °C level. Unlike the internationally coordinated model intercomparison projects, the scenarios were developed for a specific climatic model with emissions and land use scenarios consistent with socioeconomic projections from an integrated assessment model. The ESM output resulted in delayed realization of the 1.5 °C and 2 °C targets expected for 2100. The cumulative CO₂ emissions for 2010−2100 (2300) were 358 (−53) GtCO₂ in the 2 °C scenario and −337 (−936) GtCO₂ in the 1.5 °C scenario. We examined the effect of overshooting on commonly used indicators related to surface air temperature, sea surface temperature and total ocean heat uptake. Global vegetation productivity at 2100 showed around a 5% increase in the 2 °C scenario without overshooting compared with the 1.5 °C scenario with overshooting, considered to be caused by more precipitation and stronger CO₂ fertilization. A considerable difference was found between the two scenarios in terms of Arctic sea ice, whereas both scenarios indicated few corals would survive past the 21st century. The difference in steric sea level rise, reflecting total cumulative ocean heat uptake, between the two scenarios was <2 cm in 2100, and around 9 cm in 2300 in the Pacific Island region. A large overshoot may reduce the eventual difference between targets (i.e. 1.5 °C in contrast to 2 °C), particularly in terms of the indicators related to total ocean heat uptake, and to sensitive biological thresholds.

Reducing agricultural greenhouse gas (GHG) emissions, which contribute approximately 24% of global emissions, is important to efficiently achieve the goals of the Paris Agreement; however, most mitigation options have focused on industrialized, not pastoral farming practices. New Zealand (NZ) has ambitious GHG reduction targets, but biogenic emissions from the agricultural sector are nearly half of total annual emissions and hence must be an integral part of the solution. We use a national-level economic land use model to estimate the benefits and costs of implementing land-based GHG emissions reduction practices that are currently available and/or under development. Results indicate the cost and effectiveness of modeled practices are highly variable, with individual practices capable of reducing gross GHGs by 2% to 62%. Methane inhibitors are estimated to be highly effective but costly, while targeted urine patch treatments are cheap but less effective. Afforestation and methane vaccines cost less than $50/tCO₂e and could reduce NZ's GHG emissions by at least 20%. Using a mix of current and emerging mitigation practices to achieve reduction targets ranging from 10% to 50% could cost an average of $14 to $76/tCO₂e, potentially much less than estimates for achieving similar targets from NZ's energy and transportation sectors. Sensitivity analysis indicates that commercialization of an effective methane vaccine at a cost that is typical of other livestock vaccines is critical to achieving a 50% gross emissions reduction target. Without it, a large portion of land could be left fallow. The practices and technologies evaluated in this paper are not unique to New Zealand and could be adopted globally, thereby helping other nations achieve their climate mitigation goals more cost-effectively.

Offshore wind is one of the most important sources of renewable energy. Therefore, it is crucial to assess how this resource will evolve over the 21st century in the context of a changing climate. The North African Coastal Low-Level Jet (CLLJ) region, which encompasses offshore areas from Northwest Morocco to Senegal, has an enormous wind-harvesting potential, as it provides a strong, persistent alongshore flow. In the current study, the present climate wind energy potential is featured for two heights (100 and 250 m). More importantly, the climate change impact on the wind energy density in the region is also depicted. For this purpose, the newest and highest-resolution regional climate simulations available are used, which include two ROM simulations (uncoupled and coupled) at 25 km resolution and 19 CORDEX-Africa runs at 50 km resolution. Historical and future (under the RCP4.5 and RCP8.5 scenarios) simulations are used for the periods 1976–2005 and 2070–2099, respectively. Overall, the results show that the annual wind energy density is projected to increase slightly in the northern offshore areas (<+10%) and decrease in the southern ones (>−10%). In close connection to the projected changes for the seasonal changes of the CLLJ system, in the further north regions (downwind Cap Ghir), the spring season shows the largest increases of wind energy, up to +20%, while in the offshore western Sahara, an increase of wind energy is projected in all seasons. A decrease of wind energy is expected for the southern areas.

The spring sensible heating (SH) over the Tibetan Plateau (TP) serves as a huge 'air pump', significantly influencing the Asian summer monsoon, has experienced a decreasing trend. However, it remains unclear whether this decline will continue. Therefore, we here examine the long-term trends of spring SH over the central and eastern TP (CETP) based on a meteorological station-based calculated SH dataset, and CMIP6 multi-model simulations. These two sources confirmed the previous finding that the SH peaks in May. Further, we find that the declining SH was replaced by a fast recovery after approximate 2000 in the station-based SH. This is to some extent verified by the historical simulations of CMIP6 models. Importantly, CMIP6 future projections suggest that this increasing trend will continue, and get stronger with higher radiative forcing from SSP126 to SSP585. Mechanism analysis indicates that the previous decreasing trend in SH was mainly caused by the decline of 10 m wind speed, while the recent and future increasing trend results from the rising ground-air temperature difference. We suggest that this increasing trend of spring SH over the CETP may serve as an alternative driver for the enhancement of the East Asian summer monsoon in the future.

Near-constancy of the transient climate response to cumulative carbon emissions (TCRE) facilitates the development of future emission pathways compatible with temperature targets. However, most studies have explored TCRE under scenarios of temperature increase. We used an Earth system model (MIROC-ESM) to examine TCRE in scenarios with increasing and stable CO₂ concentrations, as well as overshoot pathways in which global mean temperatures peak and decline. Results showed that TCRE is stable under scenarios of increasing or stable CO₂ concentration at an atmospheric CO₂ concentration (pCO₂) double the pre-industrial level. However, in the case of overshoot pathways and a stable pCO₂ scenario at a quadrupled pCO₂ level, the TCRE increases by 10%–50%, with large increases over a short period just after pCO₂ starts to decrease. During the period of pCO₂ increase, annual ocean heat uptake (OHU) and ocean carbon storage (C_O) (or cumulative ocean carbon uptake from the start of the experiment) exhibit similar changes, resulting in a stable TCRE. During the pCO₂ decrease period, after a sudden TCRE increase when pCO₂ starts to decrease, the OHU decreases and C_O increases (relative to the pCO₂ increase period) balance each other out, resulting in a stable TCRE. In overshoot pathways, the temperature distribution when the global mean temperature anomaly cools to 1.5 °C reveals small warming over land and large warming over the oceans relative to the 1% per annum pCO₂ increasing scenario, particularly in some high-latitude areas of both hemispheres. The increase in TCRE with overshoot pathways decreases the carbon budget for the temperature anomaly targets in such scenarios. Our analysis showed a 16%–35% decrease in the remaining carbon budget for the 1.5 °C global warming target, in comparison with the reference scenario with a 1% per year pCO₂ increase, for pathways peaking at the doubled pCO₂ level followed by decline to the pre-industrial level.

An aerosol layer in the upper troposphere and lower stratosphere over the Asian summer monsoon (ASM) regions, namely, the Asian tropopause aerosol layer (ATAL), has been observed based on satellite remote sensing and in situ measurements; however, its source is still under debate. In August 2018, an experimental campaign over the Tibetan Plateau at Golmud (GLM, 36.48 °N, 94.93 °E) was performed, during which a balloon-borne Portable Optical Particle Counter was used to measure the aerosol particle profile. Backward-trajectory simulations were conducted with the Massive-Parallel Trajectory Calculations model to investigate the possible sources and transport pathways of the observed particles. The in situ measurements showed a robust ATAL around the tropopause, 16 km above sea level, with a maximum aerosol number density of 35 cm⁻³ and a maximum aerosol mass concentration of 0.15 μg m⁻³ for particles with diameters between 0.14 and 3 μm. The aerosol particles in the ATAL are mostly smaller than 0.25 μm in diameter, accounting for 98% of all aerosol particles detected. The backward-trajectory analysis revealed that the air parcels arrived at the altitude of the ATAL through two separate pathways: (1) the uplift below the 360 K isentropic surface, where air parcels were first elevated to the upper troposphere and then joined the ASM anticyclonic circulation; and (2) the quasi-horizontal transport along the anticyclonic circulation, located approximately between the 360 and 420 K isentropic surfaces. The complex transport pathways may aggravate the challenge of analyzing the composition of the ATAL, and further observation campaigns are required to extend our knowledge.

Reducing carbon dioxide (CO₂) emissions through a reliance on natural gas can create a hidden commitment to methane (CH₄) leakage mitigation. While the quantity of CH₄ leakage from natural gas has been studied extensively, the magnitude and timing of the CH₄ mitigation required to meet climate policy goals is less well understood. Here we address this topic by examining the case of US electricity under a range of baseline natural gas leakage rate estimates and emissions equivalency metrics for converting CH₄ to CO₂-equivalent emissions. We find that CH₄ emissions from the power sector would need to be reduced by 30%–90% from today's levels by 2030 in order to meet a CO₂-equivalent climate policy target while continuing to rely on natural gas. These CH₄ emissions reductions are greater than the required CO₂ reductions under the same policy. Alternatively, expanding carbon-free sources more rapidly could meet the 2030 target without reductions in natural gas leakage rates. The results provide insight on an important policy choice in regions and sectors using natural gas, between emphasizing a natural gas supply chain clean-up effort or an accelerated transition toward carbon-free energy sources.

The Paris Agreement set long-term global climate goals to pursue stabilization of the global mean temperature increase at below 2 °C (the so-called 2 °C goal). Individual countries submitted their own short-term targets, mostly for the year 2030. Meanwhile, the UN's sustainable development goals (SDGs) were designed to help set multiple societal goals with respect to socioeconomic development, the environment, and other issues. Climate policies can lead to intended or unintended consequences in various sectors, but these types of side effects rarely have been studied in China, where climate policies will play an important role in global greenhouse gas emissions and sustainable development is a major goal. This study identified the extent to which climate policies in line with the 2 °C goal could have multi-sectoral consequences in China. Carbon constraints in China in the 2Deg scenario are set to align with the global 2 °C target based on the emissions per capita convergence principle. Carbon policies for NDC pledges as well as policies in China regarding renewables, air pollution control, and land management were also simulated. The results show that energy security and air quality have co-benefits related to climate policies, whereas food security and land resources experienced negative side effects (trade-offs). Near-term climate actions were shown to help reduce these trade-offs in the mid-term. A policy package that included food and land subsidies also helped achieve climate targets while avoiding the adverse side effects caused by the mitigation policies. The findings should help policymakers in China develop win–win policies that do not negatively affect some sectors, which could potentially enhance their ability to take climate actions to realize the global 2 °C goal within the context of sustainable development.

Future fine particulate matter (PM_2.5) concentrations and resulting health impacts will be largely determined by factors such as energy use, fuel choices, emission controls, state and national policies, and demographics. In this study, a human-earth system model is used to estimate PM_2.5 mortality costs (PMMC) due to air pollutant emissions from each US state over the period 2015 to 2050, considering current major air quality and energy regulations. Contributions of various socioeconomic and energy factors to PMMC are quantified using the Logarithmic Mean Divisia Index. National PMMC are estimated to decrease 25% from 2015 to 2050, driven by decreases in energy intensity and PMMC per unit consumption of electric sector coal and transportation liquids. These factors together contribute 68% of the decrease, primarily from technology improvements and air quality regulations. States with greater population and economic growth, but with fewer clean energy resources, are more likely to face significant challenges in reducing future PMMC from their emissions. In contrast, states with larger projected decreases in PMMC have smaller increases in population and per capita GDP, and greater decreases in electric sector coal share and PMMC per unit fuel consumption.

A fundamental societal concern in energy system transitions is the distribution of benefits and costs across populations. A recent transition, the US shale gas boom, has dramatically altered the domestic energy outlook and global markets; however, the social equity implications have not been meaningfully assessed and accounted for in public and private decision making. In this study, we develop and demonstrate a systematic approach to quantify the multi-dimensional equity state of an energy system, with a focus on the shale gas boom in the Appalachian basin. We tailor variants of standard equity metrics as well as develop new empirical and analytical methods and metrics to assess spatial, temporal, income, and racial equity as it relates to air quality, climate change, and labor market impacts across the natural gas supply chain. We find moderate to high spatial inequities with respect to the distribution of production (Gini coefficient (η) = 0.93), consumption for electric power generation (η = 0.68), commercial, industrial, and residential end use (η = 0.72), job creation (η = 0.72), and air pollution-related deaths (η = 0.77), which are largely driven by geographically-fixed natural gas abundance and demand. Air quality impacts are also regressive, such that mortality risk induced by natural gas activity generally increases as income decreases; for example, mortality risk (m) (in units of premature mortality per 100 000 people) for the lowest income class (<$15 000; m = 0.22 in 2016) is higher (18%–31%) than for the highest income class (>$150 000; m = 0.27 in 2016). These risks are higher for white (m = 0.30 in 2016) than non-white (m = 0.16 in 2016) populations, which is largely a result of the demographics of rural communities within the vicinity of natural gas development. With respect to local labor market impacts within producing counties, we find marginal declines in income inequality (2.8% ± 1.0%) and poverty rates (9.9% ± 1.7%) during the boom, although household income increases for the wealthiest and decreases for the poorest. At a systems-level, there is an implied air quality-employment tradeoff of 3 (<1 to 7) job-years created per life-year lost; this tradeoff varies spatially (−1100 to 4400 life-years lost minus job-years created), wherein the job benefit outweighs the air quality costs in most producing counties whereas in all other counties the reverse is true. We also observe temporal inequities, with air quality and employment impacts following the boom-and-bust cycle, while climate impacts are largely borne by future generations. Cross-impact elasticities (ε), which measure the sensitivity between different types of impacts, reveal that employment increases are sensitive to and coupled with increases in air and climate impacts (ε = 1.1 and ε = 1.3, respectively). The metrics applied here facilitate the evaluation and design of countervailing policies and systems that explicitly account for social inequities mediated through energy infrastructure, supply, and demand. For example, in future energy system transition, such equity metrics can be used to facilitate decisions related to the siting of low-carbon infrastructure such as transmission lines and wind turbines and the phase-out of fossil fuel infrastructure, as well as to demonstrate changes in distributional tradeoffs such as the decoupling of environmental and employment effects.

Robust assessments of ecosystem stability are critical for informing conservation and management decisions. Tidal marsh ecosystems provide vital services, yet are globally threatened by anthropogenic alterations to physical and biological processes. A variety of monitoring and modeling approaches have been undertaken to determine which tidal marshes are likely to persist into the future. Here, we conduct the most robust comparison of marsh metrics to date, building on two foundational studies that had previously and independently developed metrics for marsh condition. We characterized pairs of marshes with contrasting trajectories of marsh cover across six regions of the United States, using a combination of remote-sensing and field-based metrics. We also quantified decadal trends in marsh conversion to mudflat/open water at these twelve marshes. Our results suggest that metrics quantifying the distribution of vegetation across an elevational gradient represent the best indicators of marsh trajectories. The unvegetated to vegetated ratio and flood-ebb sediment differential also served as valuable indicators. No single metric universally predicted marsh trajectories, and therefore a more robust approach includes a suite of spatially-integrated, landscape-scale metrics that are mostly obtainable from remote sensing. Data from surface elevation tables and marker horizons revealed that degrading marshes can have higher rates of vertical accretion and elevation gain than more intact counterparts, likely due to longer inundation times potentially combined with internal recycling of material. A high rate of elevation gain relative to local sea-level rise has been considered critical to marsh persistence, but our results suggest that it also may serve as a signature of degradation in marshes that have already begun to deteriorate. This investigation, with rigorous comparison and integration of metrics initially developed independently, tested at a broad geographic scale, provides a model for collaborative science to develop management tools for improving conservation outcomes.

Interannual variations in the flux of carbon dioxide (CO₂) between the land surface and the atmosphere are the dominant component of interannual variations in the atmospheric CO₂ growth rate. Here, we investigate the potential to predict variations in these terrestrial carbon fluxes 1–10 years in advance using a novel set of retrospective decadal forecasts of an Earth system model. We demonstrate that globally-integrated net ecosystem production (NEP) exhibits high potential predictability for 2 years following forecast initialization. This predictability exceeds that from a persistence or uninitialized forecast conducted with the same Earth system model. The potential predictability in NEP derives mainly from high predictability in ecosystem respiration, which itself is driven by vegetation carbon and soil moisture initialization. Our findings unlock the potential to forecast the terrestrial ecosystem in a changing environment.

River runoff is a key attribute of the land surface, that additionally has a strong influence on society by the provision of freshwater. Yet various environmental factors modify runoff levels, and some trends could be detrimental to humanity. Drivers include elevated CO₂ concentration, climate change, aerosols and altered land-use. Additionally, nitrogen deposition and tropospheric ozone changes influence plant functioning, and thus runoff, yet their importance is less understood. All these effects are now included in the JULES-CN model. We first evaluate runoff estimates from this model against 42 large basin scales, and then conduct factorial simulations to investigate these mechanisms individually. We determine how different drivers govern the trends of runoff over three decades for which data is available. Numerical results suggest rising atmospheric CO₂ concentration is the most important contributor to the global mean runoff trend, having a significant mean increase of +0.18 ± 0.006 mm yr⁻² and due to the overwhelming importance of physiological effects. However, at the local scale, the dominant influence on historical runoff trends is climate in 82% of the global land area. This difference is because climate change impacts, mainly due to precipitation changes, can be positive (38% of global land area) or negative (44% of area), depending on location. For other drivers, land use change leads to increased runoff trends in wet tropical regions and decreased runoff in Southeast China, Central Asia and the eastern USA. Modelling the terrestrial nitrogen cycle in general suppresses runoff decreases induced by the CO₂ fertilization effect, highlighting the importance of carbon–nitrogen interactions on ecosystem hydrology. Nitrogen effects do, though, induce decreasing trend components for much of arid Australia and the boreal regions. Ozone influence was mainly smaller than other drivers.

Despite intense discussions on the recent boom of mid-latitude wintertime cold extremes, co-variations of warm and cold extremes, i.e. winter temperature volatility, has garnered substantially less attention. Apart from using temperature extremes' frequency and intensity, we also define 'temperature whiplash', which depicts rapid switches between warm and cold extremes, to measure winter temperature volatility in China. Results show that Northeast-, Northwest-, Southwest-, Southeast-China and the Yangtze River Valley have experienced increasingly volatile winters after 1980, co-occurring with precipitous decline in Arctic sea-ice. This enhanced volatility has a strong expression in significant increases in temperature whiplash events, with some hotspots also seeing both warm and cold extremes become more frequent and/or intense. An observation-based detection analysis highlights the dominance of intrinsic atmospheric variability over both anthropogenic warming and sea-ice decline during 1980–2018 in driving winters in China to be more volatile over this period.

Urban form in both two- (2D) and three-dimensions (3D) has significant impacts on local and global environments. Here we developed the largest global dataset characterizing 2D and 3D urban growth for 478 cities with populations of one million or larger. Using remote sensing data from the SeaWinds scatterometer for 2001 and 2009, and the Global Human Settlement Layer for 2000 and 2014, we applied a cluster analysis and found five urban growth typologies: stabilized, outward, mature upward, budding outward, upward and outward. Budding outward is the dominant typology worldwide, per the largest total area. Cities characterized by upward and outward growth are few in number and concentrated primarily in China and South Korea, where there has been a large increase in high-rises during the study period. With the exception of East Asia, cities within a geographic region exhibit remarkably similar patterns of urban growth. Our results show that every city exhibits multiple urban growth typologies concurrently. Thus, while it is possible to describe a city by its dominant urban growth typology, a more accurate and comprehensive characterization would include some combination of the five typologies. The implications of the results for urban sustainability are multi-fold. First, the results suggest that there is considerable opportunity to shape future patterns of urbanization, given that most of the new urban growth is nascent and low magnitude outward expansion. Second, the clear geographic patterns and wide variations in the physical form of urban growth, within country and city, suggest that markets, national and subnational policies, including the absence of, can shape how cities grow. Third, the presence of different typologies within each city suggests the need for differentiated strategies for different parts of a single city. Finally, the new urban forms revealed in this analysis provide a first glimpse into the carbon lock-in of recently constructed energy-demanding infrastructure of urban settlements.

We present allometric-scaling relationships between non-point-source emissions of air pollutants and settlement population, using 3030 urban settlements in Great Britain (home to ca. 80% of the population of that region). Sub-linear scalings (slope < 1.0; standard error on slope ∼0.01; r² > 0.6) were found for the oxides of nitrogen (NO_x) and microscopic airborne particles (PM₁₀ and PM_2.5). That is, emissions of these pollutants from larger cities are lower per capita than would be expected when compared to the same population dispersed in smaller settlements. The scalings of traffic-related emissions are disaggregated into a component due to under-use of roads in small settlements and a fraction due to congestion in large settlements. We use these scalings of emissions, along with a scaling related to urban form, to explain quantitatively how and why urban airshed-average air pollutant concentrations also scale with population. Our predicted concentration scaling with population is strongly sub-linear, with a slope about half that of the emissions scaling, consistent with satellite measurements of NO₂ columns over large cities across Europe. We demonstrate that the urban form of a particular settlement can result in the airshed-average air pollution of that settlement being much larger or smaller than expected. We extend our analysis to predict that the likelihood of occurrence of local air pollution hotspots will scale super-linearly with population, a testable hypothesis that awaits suitable data. Our analysis suggests that coordinated management of emissions and urban form would strongly reduce the likelihood of local pollutant hotspots occurring whilst also ameliorating the urban heat island effect under climate change.

The increased spring rainfall intensity and amounts observed recently in the US Midwest poses additional risk of nitrate (NO₃) leaching from cropland, and contamination of surface and subsurface freshwater bodies. Several individual strategies can reduce NO₃ loading to freshwater ecosystems (i.e. optimize N fertilizer applications, planting cover crops, retention of active cycling N), but the potential for synergistic interactions among N management practices has not been fully examined. We applied portfolio effect (PE) theory, a concept originally developed for financial asset management, to test whether implementing multiple N management practices simultaneously produces more stable NO₃ leaching mitigation outcomes than what would be predicted from implementing each practice independently. We analyzed simulated data generated using a validated process-based cropping system model (APSIM) that covers a range of soils, weather conditions, and management practices. Results indicated that individual management practices alone explained little of the variation in drainage NO₃ loads but were more influential in the amount of residual soil NO₃ at crop harvest. Despite this, we observed a general stabilizing effect from adopting well-designed multi-strategy approaches for both NO₃ loads and soil NO₃ at harvest, which became more pronounced in years with high spring rainfall. We use the PE principle to design multi-strategy management to reduce and stabilize NO₃ leaching, which resulted in 9.6% greater yields, 15% less NO₃ load, and 61% less soil NO₃ at harvest than the baseline typical management. Our results make the case for applying the PE to adapt NO₃ leaching mitigation to increased climate variability and change, and guide policy action and on-the-ground implementation.

Background. Recent studies on temperature-related mortality burden generally found higher cold-related deaths than heat-related deaths. In the future, it is anticipated that global warming will, on one hand result in larger heat-related mortality but on the other hand lead to less cold-related mortality. Thus, it remains unclear whether the net change in temperature-related mortality burden will increase in the future under climate change.

Objectives. We aimed to quantify the impact of climate change on heat-, cold-, and the total temperature-related (net change) mortality burden taking into account the future demographic changes across five districts in Bavaria, Germany by the end of the 21st century.

Methods. We applied location-specific age-specific exposure-response functions (ERFs) to project the net change in temperature-related mortality burden during the future period 2083–2099 as compared to the baseline period 1990–2006. The projections were under different combinations of five climate change scenarios (assuming a constant climate, Representative Concentration Pathway [RCP] 2.6, RCP4.5, RCP6.0, and RCP8.5) and six population projection scenarios (assuming a constant population, Shared Socio-economic Pathway [SSP] 1, SSP2, SSP3, SSP4, and SSP5). Our projections were under the assumption of a constant vulnerability of the future population. We furthered compared the results with projections using location-specific overall all-age ERFs, i.e. not considering the age-effect and population aging.

Results. The net temperature-related mortality for the total population was found to increase significantly under all scenarios of climate and population change with the highest total increments under SSP5-RCP8.5 by 19.61% (95% empirical CI (eCI): 11.78, 30.91). Under the same scenario for age ≥ 75, the increment was by 30.46% (95% eCI: 18.60, 47.74) and for age <75, the increment was by 0.28% (95% eCI: −2.84, 3.24). Considering the combination SSP2-RCP2.6, the middle-of-the road population and the lowest climate change scenario, the net temperature-related mortality for the total population was found to still increase by 9.33% (95% eCI: 5.94, 12.76). Contrastingly, the mortality projection without consideration of an age-effect and population aging under the same scenario resulted in a decrease of temperature-related deaths by −0.23% (95% eCI −0.64, 0.14), thus showing an underestimation of temperature-related mortality. Furthermore, the results of climate-only effect showed no considerable changes, whereas, the population-only effect showed a high, up to 17.35% (95% eCI: 11.46, 22.70), increment in the net temperature-related deaths.

Conclusion. The elderly population (age ≥ 75), highly vulnerable to both heat and cold, is projected to be about four folds the younger population (age < 75) in the future. Thus, the combined effect of global warming and population aging results in an increase in both the heat- and the cold-related deaths. The population-effect dominates the climate-effect. Mitigation and age-specific adaptation strategies might greatly reduce the temperature-related mortality burden in the future.

The interdisciplinary nature of conservation problems is increasingly being incorporated into research, raising fundamental questions about the relative importance of the different types of knowledge and data. Although there has been extensive research on the development of methods and tools for conservation planning, especially spatial planning, comparatively little is known about the relative importance of ecological versus non-ecological data for prioritization, or the likely return on investment of incorporating better data. We demonstrate a simple approach for (1) quantifying the sensitivity of spatial planning results to different ecological and non-ecological data layers, and (2) estimating the potential gains in efficiency from incorporating additional data. Our case study involves spatial planning for coastal squeeze, a process by which development blocks coastal ecosystems from moving landward in response to sea-level rise. We show that incorporating spatial data on landowners' likelihood of selling had little effect on identifying relative priorities but drastically changed the outlook for whether conservation goals could be achieved. Better data on the costs of conservation actions had the greatest potential to improve the efficiency of spatial planning, in some cases generating more than an order of magnitude greater cost savings compared to ecological data. Our framework could be applied to other systems to guide the development of spatial planning and to identify general rules of thumb for the importance of alternative data sources for conservation problems in different socio-ecological contexts.

Carbon dioxide (CO₂) evasion from streams greatly contributes to global carbon fluxes. Despite this, the temporal dynamics of CO₂ and its drivers remain poorly understood to date. This is particularly true for high-altitude streams. Using high-resolution time series of CO₂ concentration and specific discharge from sensors in twelve streams in the Swiss Alps, we studied over three years the responsiveness of both CO₂ concentration and evasion fluxes to specific discharge at annual scales and at the scale of the spring freshet. On an annual basis, our results show dilution responses of the streamwater CO₂ likely attributable to limited supply from sources within the catchment. Combining our sensor data with stable isotope analyses, we identify the spring freshet as a window where source limitation of the CO₂ evasion fluxes becomes relieved. CO₂ from soil respiration enters the streams during the freshet thereby facilitating CO₂ evasion fluxes that are potentially relevant for the carbon fluxes at catchment scale. Our study highlights the need for long-term measurements of CO₂ concentrations and fluxes to better understand and predict the role of streams for global carbon cycling.

Multifunctionality refers to the capacity of an area to supply multiple ecosystem functions or services. While many conceptual and methodological advances have focused on defining and quantifying multifunctionality, the challenge of dealing with cross-scale dynamics of multifunctionality remains open. This study proposes a new way of measuring multifunctionality across spatial scales, illustrated with a European-wide dataset of 18 ecosystem services. Our assessment captures not only the diversity of ecosystem services supplied within each municipality (alpha-multifunctionality), but also the unique contribution of each municipality to the regional ecosystem service diversity (beta-multifunctionality). This cross-scale analysis helps better understanding the spatial distribution of ecosystem services, which is required to design management and policies at the right scale. Our analysis shows that alpha-multifunctionality follows a latitudinal gradient across Europe and strongly decreases towards the city centers of metropolitan areas. By relating alpha- and beta-multifunctionality to land use intensity, we show that low-intensity management systems support higher ecosystem multifunctionality across Europe. Municipalities of low alpha-multifunctionality often contribute significantly to regional multifunctionality, by providing ecosystem services of a specific value to the region. Our method to measure both alpha- and beta-multifunctionality thus provides a new way to inform reconciliation of competing land uses when maximizing alpha-multifunctionality is not reasonable.

Humanity must find ways to feed an expanding human population. This requires maintaining the productivity of agricultural land, although much of it is increasingly degraded. Many trillions of dollars will be needed to address land degradation globally, but there has been little discussion about how to sustainably finance major global initiatives such as the UN Decade of ecosystem restoration. We suggest that existing financing instruments (government grants and commercial loans) have two limitations. First, the size of the problem is so substantial that contemporary approaches have no real prospects of adequately addressing the issue. Second, even if grants and loans had the potential in terms of prospective magnitude, in many instances they would be inequitable, regressive and/or have high risks for farm properties. We examine an alternate financing instrument, revenue-contingent loans (RCL), which potentially has subsidy-reducing properties for government budgets, and thus the capacity for more broadly-based public sector engagement with agricultural land remediation. RCL can substantially diminish borrowing risks and hardship for farm properties. Unlike commercial debt, repayments under RCL are not time-based but instead occur when a farm business can afford to make them. This is important as remediation of farmland degradation can be a medium to long-term process, and hence loan repayments need to parallel the time that it takes for the benefits of restoration to accrue to a farm business. Using income data from Australian agriculture, we illustrate empirically the repayment effects of a hypothetical RCL, focusing on the consequences for a government's budget, the time stream of repayments for farms differing in revenue streams, and the benefits of income-smoothing. Our results underscore the potential benefits of RCL for the financing of land restoration investments, for both farmers and taxpayers. RCL could be made operational without significant costs to government budgets and within acceptable time frames.

Microplastics are a major environmental challenge, being ubiquitous and persistent as to represent a new component in all marine environments. As any biogenic particle, microplastics provide surfaces for microbial growth and biofilm production, which largely consists of carbohydrates and proteins. Biofilms influence microbial activity and modify particle buoyancy, and therefore control the fate of microplastics at sea. In a simulated 'plastic ocean', three mesocosms containing oligotrophic seawater were amended with polystyrene microbeads and compared to three control mesocosms. The evolution of organic matter, microbial communities and nutrient concentrations was monitored over 12 days. The results indicated that microplastics increased the production of organic carbon and its aggregation into gel particulates. The observed increase of gel-like organics has implications on the marine biological pump as well as the transport of microplastics in the ocean.

Electricity generation output forecasts for wind farms across Europe use numerical weather prediction (NWP) models. These forecasts influence decisions in the energy market, some of which help determine daily energy prices or the usage of thermal power generation plants. The predictive skill of power generation forecasts has an impact on the profitability of energy trading strategies and the ability to decrease carbon emissions. Probabilistic ensemble forecasts contain valuable information about the uncertainties in a forecast. The energy market typically takes basic approaches to using ensemble data to obtain more skilful forecasts. There is, however, evidence that more sophisticated approaches could yield significant further improvements in forecast skill and utility. In this letter, the application of ensemble forecasting methods to the aggregated electricity generation output for wind farms across Germany is investigated using historical ensemble forecasts from the European Centre for Medium-Range Weather Forecasting (ECMWF). Multiple methods for producing a single forecast from the ensemble are tried and tested against traditional deterministic methods. All the methods exhibit positive skill, relative to a climatological forecast, out to a lead time of at least seven days. A wind energy trading strategy involving ensemble data is implemented and produces significantly more profit than trading strategies based on single forecasts. It is thus found that ensemble spread is a good predictor for wind electricity generation output forecast uncertainty and is extremely valuable at informing wind energy trading strategy.

The increasing levels of variable renewable electricity (VRE) generation—such as wind and solar power—will create important opportunities for the charging of electric vehicle (EV) batteries during low-cost hours with a lot of VRE generation and for the discharge of EV batteries back to the grid (i.e. vehicle-to-grid; V2G) during high-cost hours. This study investigates how different EV charging strategies influence the cost-competitiveness of generation and storage technologies other than EV batteries in the electricity system, using a regional electricity system investment and dispatch model. The charging requirements of the EVs, which are used as an input to the optimisation model, are derived from the yearly driving patterns of 426 vehicles measured with global positioning system. The study is carried out for four regions in Europe with different conditions for wind, solar and hydro power generation. The results show that optimised EV charging with V2G can: (i) reduce investments in peak power capacity in all the regions investigated; (ii) reduce the need for short-term and long-term storage technologies other than EV batteries (i.e. stationary batteries and hydrogen storage); and (iii) stimulate increased shares of solar and wind power generation, as compared to direct charging in some regions (mainly Hungary). This study also shows that EV battery capacities as low as 30 kWh, which are connected to the grid only at their home location, can to a large extent contribute with flexibility to the electricity system in the way mentioned. The present study also investigates the influences of different shares of the fleet participating in V2G, and shows that the additional benefits for the electricity system level off when approximately 24% of the vehicle fleet participates in V2G.

A global transformation in semi-arid cropping systems is occurring as dryland (non-irrigated) farmers in semi-arid regions shift from crop rotations reliant on year-long bare fallows, called summer fallow, to more intensively cropped systems. Understanding the rate of cropping system intensification at the landscape scale is critical to estimating the economic and environmental implications of this movement. Here, we use high-resolution satellite data to quantify dryland cropping patterns from 2008 to 2016 in the US High Plains. We use these estimates to scale up our previous field-level research in this region on soil carbon, herbicide use, yields, and profitability. Over the nine year study period, the High Plains witnessed a profound shift in cropping systems, as the historically dominant wheat-fallow system was replaced by more intensified rotations as the dominant systems by land area. Out of the 4 million hectares of non-irrigated cropland in the study area, this shift coincided with a 0.5 million-hectare decline in summer fallow and a concurrent increase in alternative (non-wheat) crops. We estimate that, from 2008 to 2016, these patterns resulted in a 0.53 Tg (9%) increase in annual grain production, 80 million USD (10%) increase in annual net farm operating income, substantial reductions in herbicide use, and an increase in C sequestration that corresponds to greenhouse gas reductions of 0.32 million metric tons of CO₂ equivalents per year (MMTCO₂e yr⁻¹). We project each of these implications to a scenario of potential maximum 100% intensification and estimate that, relative to 2016 levels, herbicide use would be reduced by more than half, grain production would increase by 25%, net operating income would increase by 223 million USD (26%), and greenhouse gases would be reduced by an additional 0.8 MMTCO₂e yr⁻¹. The scale of cropping intensification in the High Plains and its environmental and economic impacts has important implications for other regions undergoing similar transformations, and for policy that can either support or hinder these shifts toward more sustainable cropping systems.

Weather shocks, such as heatwaves, droughts, and excess rainfall, are a major cause of crop yield losses and food insecurity worldwide. Statistical or process-based crop models can be used to quantify how yields will respond to these events and future climate change. However, the accuracy of weather-yield relationships derived from crop models, whether statistical or process-based, is dependent on the quality of the underlying input data used to run these models. In this context, a major challenge in many developing countries is the lack of accessible and reliable meteorological datasets. Gridded weather datasets, derived from combinations of in situ gauges, remote sensing, and climate models, provide a solution to fill this gap, and have been widely used to evaluate climate impacts on agriculture in data-scarce regions worldwide. However, these reference datasets are also known to contain important biases and uncertainties. To date, there has been little research to assess how the choice of reference datasets influences projected sensitivity of crop yields to weather. We compare multiple freely available gridded datasets that provide daily weather data over the Indian sub-continent over the period 1983–2005, and explore their implications for estimates of yield responses to weather variability for key crops grown in the region (wheat and rice). Our results show that individual gridded weather datasets vary in their representation of historic spatial and temporal temperature and precipitation patterns across India. We show that these differences create large uncertainties in estimated crop yield responses and exposure to variability in growing season weather, which in turn, highlights the need for improved consideration of input data uncertainty in statistical studies that explore impacts of climate variability and change on agriculture.

Although tropical forests harbour most of the terrestrial carbon and biological diversity on Earth they continue to be deforested or degraded at high rates. In Amazonia, the largest tropical forest on Earth, a sixth of the remaining natural forests is formally dedicated to timber extraction through selective logging. Reconciling timber extraction with the provision of other ecosystem services (ES) remains a major challenge for forest managers and policy-makers. This study applies a spatial optimisation of logging in Amazonian production forests to analyse potential trade-offs between timber extraction and recovery, carbon storage, and biodiversity conservation. Current logging regulations with unique cutting cycles result in sub-optimal ES-use efficiency. Long-term timber provision would require the adoption of a land-sharing strategy that involves extensive low-intensity logging, although high transport and road-building costs might make this approach economically unattractive. By contrast, retention of carbon and biodiversity would be enhanced by a land-sparing strategy restricting high-intensive logging to designated areas such as the outer fringes of the region. Depending on management goals and societal demands, either choice will substantially influence the future of Amazonian forests. Overall, our results highlight the need for revaluation of current logging regulations and regional cooperation among Amazonian countries to enhance coherent and trans-boundary forest management.

Low nitrogen (N) fertilization is a dominant cause of malnutrition in Africa, but the spatial and temporal variability of N cycling patterns in Africa remain unclear. This study is the first to perform a detailed analysis of the N cycling patterns of 52 African countries from 1961 to 2016. We calculated the N use efficiency (NUE) in crop production, country-specific N fertilization trends, and the impacts of N fertilization on human protein demand and the environment. Over the past five decades, total N input to African croplands increased from 20 to 35 kg N ha⁻¹ yr⁻¹, while the application of synthetic N fertilizers (SNF) increased from 4.0 to 15 kg N ha⁻¹ yr⁻¹. N contributions from animal manure and biological N fixation remained lower than 10 kg N ha⁻¹ yr⁻¹ and 20 kg N ha⁻¹ yr⁻¹, respectively. The total N crop production increased from 15 to 22 kg N ha⁻¹ yr⁻¹ from 1961 to 2016. Total N surplus in Africa increased from 5 to 13 kg N ha⁻¹ yr⁻¹, while estimated gaseous losses increased from 4.0 to 11 kg N ha⁻¹ yr⁻¹. However, NUE declined from 74% to 63% during the past five decades, and protein consumption increased from 2.99 to 3.78 kg N capita⁻¹ yr⁻¹. These results suggest that Africa suffers from extremely low N input and that N loss is increasing in agricultural land. We recommend the implementation of an effective N management strategy incorporating the use of locally available organic material along with the balanced application of SNF. Such measures will require effective policy development and cooperation between all stakeholders.

High elevation alpine ecosystems—the 'water towers of the world'—provide water for human populations around the globe. Active geomorphic features such as glaciers and permafrost leave alpine ecosystems susceptible to changes in climate which could also lead to changing biogeochemistry and water quality. Here, we synthesize recent changes in high-elevation stream chemistry from multiple sites that demonstrate a consistent and widespread pattern of increasing sulfate and base cation concentrations or fluxes. This trend has occurred over the past 30 years and is consistent across multiple sites in the Rocky Mountains of the United States, western Canada, the European Alps, the Icelandic Shield, and the Himalayas in Asia. To better understand these recent changes and to examine the potential causes of increased sulfur and base cation concentrations in surface waters, we present a synthesis of global records as well as a high resolution 33 year record of atmospheric deposition and river export data from a long-term ecological research site in Colorado, USA. We evaluate which factors may be driving global shifts in stream chemistry including atmospheric deposition trends and broad climatic patterns. Our analysis suggests that recent changes in climate may be stimulating changes to hydrology and/or geomorphic processes, which in turn lead to accelerated weathering of bedrock. This cascade of effects has broad implications for the chemistry and quality of important surface water resources.

Ground-level ozone, which forms photochemically in the atmosphere from precursor emissions of oxides of nitrogen (NO_x) and volatile organic compounds, is a criteria pollutant that harms human health and public welfare. For a representative summer episode, premature mortality and potential productivity losses (PPLs) of selected crops and trees attributable to ozone exposure have been quantified using ozone fields from the Community Multiscale Air Quality (CMAQ) model. We applied exposure-response models for the increased risk of premature mortality due to long-term exposure to ozone over a theoretical minimum risk exposure level (TMREL) and for the reduced accumulation of vegetative biomass for four crop species and eleven tree species using the W126 metric designed to capture impacts on plants. To elucidate which emissions contributed to these disbenefits, we applied adjoint-based sensitivity analysis, which efficiently estimates sensitivities of concentration-based metrics with respect to numerous emissions parameters simultaneously. The adjoint of CMAQ was applied to the continental US to calculate the influence of spatially-resolved ozone precursor emissions on the annual average, domain-wide daily maximum 8 h average over the TMREL (elevated MDA8), premature mortality attributable to exposure to ozone above the TMREL, and PPLs. These quantities provide the impact in terms of the percent reduction in precursor emissions. Additionally, locations where similar percent reductions in ozone precursor emissions would impact one or more endpoints greater than average have been identified. NO_x emissions were found to contribute most to the three metrics. The distinct spatial patterns of emissions influences on public welfare disbenefits as compared to the elevated MDA8 and premature mortality suggest that the current regulatory averaging time motivates different emissions control strategies than those that could most directly protect public welfare.

Wildfires are altering ecosystems globally as they change in frequency, size, and severity. As wildfires change vegetation structure, they also alter moisture inputs and energy fluxes which influence snowpack and hydrology. In unburned forests, snow has been shown to accumulate more in small clearings or in stands with low to moderate forest densities. Here we investigate whether peak snowpack varies with burn severity or percent overstory tree mortality post-fire in a mid-latitude, subalpine forest. We found that peak snowpack across the burn severity gradients increased 15% in snow-water equivalence (SWE) and 17% in depth for every 20% increase in overstory tree mortality due to burn severity. Snowpack quantity varied greatly between the two winter seasons sampled in this study with 114% more snow in 2016 versus 2015, yet the effect of burn severity on snowpack remained consistent. These data support previous studies showing increases in peak snow depth and SWE in burned forests but for the first time provides novel insights into how snow depth and SWE change as a function of burn severity. We conclude that changes not only in the frequency and size of wildfires, but also in the severity, can alter peak snow depth and SWE, with important potential implications for watershed hydrology.

In East Africa, accurate grain yield predictions can help save lives and protect livelihoods. Regional grain yield forecasts can inform decisions regarding the availability and prices of key staples, food aid, and large humanitarian responses. Here, we use earth observation (EO) products to develop and evaluate subnational grain yield forecasts for 56 regions located in two severely food insecure countries: Kenya and Somalia. We identify, for a given region and time of year, which, if any, product is the best indicator for end-of-season maize yields. Our analysis seeks to inform a real-world situation in which analysts have access to multiple regularly updated EO data products, but predictive skill corresponding to each may vary across these regions and throughout the season. We find that the most accurate predictions can be made for high-producing areas, but that the relationship between production and forecast accuracy diminishes in areas with yields averaging greater than one metric ton per hectare. However, while forecast accuracy is highest in high production areas, in many of these regions, the forecast accuracy of models using EO products is not better than a set of baseline models that do not use EO products. Overall, we find that rainfall is the best indicator in low-producing regions and that other EO products work best in areas where yields are relatively consistent, but production is still limited by environmental factors.

Latin America and the Caribbean (LAC) has the least carbon-intensive electricity sector of any region in the world, as hydropower remains the largest source of electricity. But are current plans consistent with the international climate change goals laid out in the Paris Agreement? In this paper, we assess committed CO₂ emissions from existing and planned power plants in LAC. Those are the carbon emissions that would result from the operation of fossil-fueled power plants during their typical lifetime. Committed emissions from existing power plants are close to 6.9 Gt of CO₂. Building and operating all power plants that are announced, authorized, being procured, or under construction would result in 6.7 Gt of CO₂ of additional commitments (for a total of 13.6 Gt of CO₂). Committed emissions are above average IPCC assessments of cumulative emissions from power generation in LAC consistent with international temperature targets. To meet average carbon budgets from IPCC, 10%–16% of existing fossil-fueled power plants would need to be closed before the end of their technical lifespan. Our results suggest that building more fossil-fueled power plants in the region could jeopardize the achievement of the Paris Agreement temperature targets.

Wildfire in boreal permafrost peatlands causes a thickening and warming of the seasonally thawed active layer, exposing large amounts of soil carbon to microbial processes and potential release as greenhouse gases. In this study, conducted in the discontinuous permafrost zone of western Canada, we monitored soil thermal regime and soil respiration throughout the 2016 growing season at an unburned peat plateau and two nearby peat plateaus that burned 16 and 9 years prior to the study. Maximum seasonal soil temperature at 40 cm depth was 4 °C warmer in the burned sites, and active layers were ∼90 cm thicker compared to the unburned site. Despite the deeper and warmer seasonally thawed active layer, we found higher soil respiration in the unburned site during the first half of the growing season. We partitioned soil respiration into contribution from shallow and deep peat using a model driven by soil temperatures at 10 and 40 cm depths. Cumulative estimated deep soil respiration throughout the growing season was four times greater in the burned sites than in the unburned site, 32 and 8 g C m⁻² respectively. Concurrently, cumulative shallow soil respiration was estimated to be lower in the burned than unburned site, 49 and 80 g C m⁻² respectively, likely due to the removal of the microbially labile soil carbon in the shallow peat. Differences in deep contribution to soil respiration were supported by radiocarbon analysis in fall. With effects of wildfire on soil thermal regime lasting for up to 25 years in these ecosystems, we conclude that increased loss of deep, old, soil carbon during this period is of similar magnitude as the direct carbon losses from combustion during wildfire and thus needs to be considered when assessing overall impact of wildfire on carbon cycling in permafrost peatlands.

Most of the growth in agricultural output in the last thirty years comes from increases in the efficiency with which both land and non-land inputs are used. Recent work calls for a better understanding of whether this efficiency, known as total factor productivity (TFP), contributes to a more sustainable food system. Key to this understanding is the documented phenomenon that, instead of saving lands, the introduction of technologies that improve agricultural productivity encourage cropland expansion. We extend the results of a recently published econometric model of cross-country cropland change and TFP growth to explore the extent to which improvements in technology were associated with lower greenhouse emissions from land conversion to agriculture as well as with lower land conversion pressures in biodiversity-rich biomes. We focus on the decade of 2001–2010, a period in which our sample of 70 countries (≈75% of global croplands) experienced net land contraction. Except in sub-Saharan Africa and South and East Asia, regional TFP growth was associated with regional land expansion, thus confirming the existence of Jevons paradox in most regions of the world. However, such expansion was more than offset by indirect land use effects stemming from increases in productivity somewhere else. These indirect effects are far from trivial. In the absence of TFP growth, our estimates suggest that ≈125 Mha would have been needed to satisfy demand, half of which are in the four most biodiverse biomes of the world; estimated land use emissions from the ensuing changes in land use range from a lower bound of 17 Gt CO2eq to an upper bound of 84 Gt CO2eq, depending on whether the expansion would have occurred on pasturelands or forest, in contrast to the ≈1 to 15 Gt CO2eq imputed to observed cropland expansion. Our projections of the land needed to satisfy projected growth in TFP per capita during 2018–2023 indicate that current rates of TFP growth are insufficient to prevent further land expansion, reversing in most cases the in-sample trends in land contraction observed during 2001–2010.

In this study, we present a modeling approach that investigates how much cultivable land was required to supply a society and whether societies were in need when environmental conditions deteriorated. The approach is implemented for the North-Eastern Peloponnese and is based upon the location of Late Helladic IIIB (1300–1200 BCE) archaeological sites, an assessment of their sizes, and a proposed diet of the people. Based on these information, the areal requirement of each site is calculated and mapped. The results show that large sites do not have sufficient space in their surroundings in order to supply themselves with the required food resources and thus they depended on supplies from the hinterland. Dry climatic conditions aggravate the situation. This indicates that potential societal crisis are less a factor of changing environmental conditions or a shortage of arable land but primarily caused by socio-economic factors.

In this study, we aimed to reconstruct spring (April–June) sea ice changes in the western Arctic Ocean over recent centuries (ca. the last 250 years) by measuring biomarker distributions in a multicore (ARA01B-03MUC) retrieved from the Chukchi Shelf region and to evaluate outcomes against known or modelled estimates of sea ice conditions. Specifically, we analyzed for the Arctic sea ice proxy IP₂₅ and assessed the suitability of a further highly branched isoprenoid (HBI) lipid (HBI III), epi-brassicasterol, and dinosterol as complementary biomarkers for use with the so-called phytoplankton marker-IP₂₅ index (PIP₂₅; P_IIIIP₂₅, P_BIP₂₅, and P_DIP₂₅, respectively). The presence of IP₂₅ throughout core ARA01B-03MUC confirms the occurrence of seasonal sea ice at the study site over recent centuries. From a semi-quantitative perspective, all three PIP₂₅ indices gave different trends, with some dependence on the balance factor c, a term used in the calculation of the PIP₂₅ index. P_IIIIP₂₅-derived spring sea ice concentration (SpSIC) estimates using a c value of 0.63, determined previously from analysis of Barents Sea surface sediments, were likely most reliable, since SpSIC values were high throughout the record (SpSIC > 78%), consistent with the modern context for the Chukchi Sea and the mean SpSIC record of the 41 CMIP5 climate models over recent centuries. P_BIP₂₅-based SpSIC estimates were also high (SpSIC 108%−127%), albeit somewhat over-estimated, when using a c value of 0.023 obtained from a pan-Arctic distribution of surface sediments. In contrast, P_DIP₂₅ values using a pan-Arctic c value of 0.11, and PIP₂₅ data based on the mean biomarker concentrations from ARA01B-03MUC, largely underestimated sea ice conditions (SpSIC as low as 13%), and exhibited poor agreement with instrumental records or model outputs. On the other hand, P_BIP₂₅ values using a c factor based on mean IP₂₅ and epi-brassicasterol concentrations exhibited a decline towards the core top, which resembled recent decreasing changes in summer sea ice conditions for the Chukchi Sea; however, further work is needed to test the broader spatial generality of this observation.

Many rapidly urbanizing desert cities (RUDC) around the globe experience an acute risk of flooding. To reduce this risk, properly engineered flood control structures (FCS) must account for sediment accumulation as well as flood waters. While the Phoenix area, USA, uses regional data from non-urban, non-desert watersheds to generate sediment yield rates, the proposed desired outcome for RUDCs is to base FCS on data related to urbanization. Wolman (1967 Geogr. Ann. A 49 385–95) recognized that sediment yields spike during a relatively short period of bare-ground exposure associated with urban growth, followed by surface sealing resulting in a great reduction in sediment yield. This research presents a new analysis of empirical data where two regression models provide estimates of a more realistic sediment accumulation for arid regions and also urbanization of a desert cities: (i) linear regression between drainage area and sediment yield based on a compilation of more than 150 global sediment yield data for warm desert (BWh Köppen‐Geiger) climate; and (ii) linear regression relating percent urban growth with sediment yield using available data on urbanization-generated sediment associated with growth of a desert city. The new model can be used to predict the realistic sediment accumulation for helping provide data where few data exists in urbanizing parts of arid Africa, southwest Asia, and India.

Urbanization causes tremendous pressure on biodiversity and ecosystems at the global scale. China is among the countries undergoing the fastest urban expansion. For a long time, ecological environment protection has not been a priority in China's urban planning process. Current urban growth optimization research has some limitations regarding the selection of more scientific ecological constraint indicators and the interaction between urban expansion and ecological factors. This paper at first aimed to establish a reasonable comprehensive biodiversity constraint indicator based on the indicators of net primary productivity, habitat connectivity and habitat quality, and then conducted a case study in Beijing and compared biodiversity loss and urban growth patterns under different developing situations. The integrated valuation of ecosystem services and trade-offs model and GIS-related methods were used to obtain biodiversity and ecological spatial distribution layers. Then an ecological priority-oriented urban growth simulation method was proposed to search for minimum biodiversity loss. The results showed that the important biodiversity security areas were mostly distributed in the western part of the study area and that the ecological degradation in 2000 had a radial pattern and was well in line with the urban construction and ring road distribution patterns. Meanwhile, biodiversity loss with the biodiversity constraint was much less than actual urban growth in 2000–2010. Under the guidance of ecological optimization, urban growth in the research results reflects decentralized and multi-center spatial development characteristics. This type of urban growth not only provides a new model for breaking the inertia of urban sprawl but also proposes 'biodiversity security' as an applicable regulatory tool for urban planning and space governance reforming.

Many studies have used time series of satellite-derived vegetation indices to identify so-called greening and browning trends across the northern high-latitudes and to suggest that the productivity of Arctic-Boreal ecosystems is changing in response to climate forcing at local and continental scales. However, disturbances that alter land cover are prevalent in Arctic-Boreal ecosystems, and changes in Arctic-Boreal land cover, which complicate interpretation of trends in vegetation indices, have mostly been ignored in previous studies. Here we use a new land cover change dataset derived from Landsat imagery to explore the extent to which land cover and land cover change influence trends in the normalized difference vegetation index (NDVI) over a large (3.76 M km²) area of NASA's Arctic Boreal Vulnerability Experiment, which spans much of northwestern Canada and Alaska. Between 1984 and 2012, 21.2% of the study domain experienced land cover change and 42.7% had significant NDVI trends. Land cover change occurred in 27.6% of locations with significant NDVI trends during this period and resulted in greening and browning rates 48%–128% higher than in areas of stable land cover. While the majority of land cover change areas experienced significant NDVI trends, more than half of areas with stable land cover did not. Further, the extent and magnitude of browning and greening trends varied substantially as a function of land cover class and land cover change type. Forest disturbance from fire and timber harvest drove over one third of statistically significant NDVI trends and created complex mosaics of recent forest loss (as browning) and post-disturbance recovery (as greening) at both landscape and continental scale. Our results demonstrate the importance of land cover changes in highly disturbed high-latitude ecosystems for interpreting trends of NDVI and productivity across multiple spatial scales.

While there are obstacles to the exchange of long-term high temporal resolution precipitation data, there have been fewer barriers to the exchange of so-called 'indices'. These are derived from daily and sub-daily data and measure aspects of precipitation frequency, duration and intensity that could be used for the study of extremes. This paper outlines the history of the rationale and use of these indices, the types of indices that are frequently used and the advantages and pitfalls in analysing them. Moving forward, satellite precipitation products are now showing the potential to provide global climate indices to supplement existing products using longer-term in situ gauge records but we suggest that to advance this area differences between data products, limitations in satellite-based estimation processes, and the inherent challenges of scale need to be better understood.

Cattle ranching accounts for 44% of the greenhouse gas (GHG) emissions from the land use sector in Brazil. In response, Brazil has proposed a massive pasture restoration program that aspires to make ranching more competitive while at the same time reducing associated GHG emissions. Pasture restoration, however, is only one of several intensification options that could be employed to achieve these goals. Here we analyze potential production, economic return and GHG emissions from an intensification strategy based mainly on pasture restoration and compare its productive, economic and GHG emissions performances with intensification options more focused on supplemental feeding (grain-feed supplementation of grazing animals and animal finishing in feedlots). To this end, we developed a multi-sectoral, deterministic simulation model of the ranching system and applied it to Mato Grosso state, the largest producer and earliest adopter of intensive production. To account for GHG emissions, we performed a life cycle analysis of a complete beef production cycle. Our results show that an intensification strategy focused more heavily on pasture restoration does reduce GHG emissions but produces the least favorable economic and GHG emissions outcomes when compared with a range of supplemental feeding alternatives. In view of these results, Brazil should seek a more diversified strategies for cattle intensification in its climate mitigation policy.

The Arctic is warming at twice the rate of the global mean. This warming could further stimulate methane (CH₄) emissions from northern wetlands and enhance the greenhouse impact of this region. Arctic wetlands are extremely heterogeneous in terms of geochemistry, vegetation, microtopography, and hydrology, and therefore CH₄ fluxes can differ dramatically within the metre scale. Eddy covariance (EC) is one of the most useful methods for estimating CH₄ fluxes in remote areas over long periods of time. However, when the areas sampled by these EC towers (i.e. tower footprints) are by definition very heterogeneous, due to encompassing a variety of environmental conditions and vegetation types, modelling environmental controls of CH₄ emissions becomes even more challenging, confounding efforts to reduce uncertainty in baseline CH₄ emissions from these landscapes. In this study, we evaluated the effect of footprint variability on CH₄ fluxes from two EC towers located in wetlands on the North Slope of Alaska. The local domain of each of these sites contains well developed polygonal tundra as well as a drained thermokarst lake basin. We found that the spatiotemporal variability of the footprint, has a significant influence on the observed CH₄ fluxes, contributing between 3% and 33% of the variance, depending on site, time period, and modelling method. Multiple indices were used to define spatial heterogeneity, and their explanatory power varied depending on site and season. Overall, the normalised difference water index had the most consistent explanatory power on CH₄ fluxes, though generally only when used in concert with at least one other spatial index. The spatial bias (defined here as the difference between the mean for the 0.36 km² domain around the tower and the footprint-weighted mean) was between ∣51∣% and ∣18∣% depending on the index. This study highlights the need for footprint modelling to infer the representativeness of the carbon fluxes measured by EC towers in these highly heterogeneous tundra ecosystems, and the need to evaluate spatial variability when upscaling EC site-level data to a larger domain.

A growing number of cities are investing in green infrastructure to foster urban resilience and sustainability. While these nature-based solutions are often promoted on the basis of their multifunctionality, in practice, most studies and plans focus on a single benefit, such as stormwater management. This represents a missed opportunity to strategically site green infrastructure to leverage social and ecological co-benefits. To address this gap, this paper builds on existing modeling approaches for green infrastructure planning to create a more generalizable tool for comparing spatial tradeoffs and synergistic 'hotspots' for multiple desired benefits. I apply the model to three diverse coastal megacities: New York City, Los Angeles (United States), and Manila (Philippines), enabling cross-city comparisons for the first time. Spatial multi-criteria evaluation is used to examine how strategic areas for green infrastructure development across the cities change depending on which benefit is prioritized. GIS layers corresponding to six planning priorities (managing stormwater, reducing social vulnerability, increasing access to green space, improving air quality, reducing the urban heat island effect, and increasing landscape connectivity) are mapped and spatial tradeoffs assessed. Criteria are also weighted to reflect local stakeholders' desired outcomes as determined through surveys and stakeholder meetings and combined to identify high priority areas for green infrastructure development. To extend the model's utility as a decision-support tool, an interactive web-based application is developed that allows any user to change the criteria weights and visualize the resulting hotspots in real time. The model empirically illustrates the complexities of planning green infrastructure in different urban contexts, while also demonstrating a flexible approach for more participatory, strategic, and multifunctional planning of green infrastructure in cities around the world.

Reviewing recent social science research on the western United States from multiple disciplines, we present a state-of-the-art synthesis for scholars and policymakers focused on the socioecological future of this distinctive region. We address four core topics: (1) Migration and population change, focusing on the movements of people across the US West, and the ways that these population shifts are both shaped by and shaping the rise of 'New West' economies. (2) Environmental governance, synthesizing work on non-federal government institutions' interactions with the environment, including local/regional government agencies, Indigenous nations, and non-governmental organizations—all of which shape environmental quality and resource access for communities. (3) Place, culture, and belonging, which concerns how people find meaning in their environment and locate their sense of place in the region given changing social and natural landscapes. (4) Research methodologies, with a specific focus on blending cutting-edge machine learning, and social network approaches with well-established ethnographic, demographic, and survey-based methods. We then map out a future interdisciplinary agenda for the policy-relevant study of social and environmental change in the US West. Our approach stresses the importance of mixed method social research and a robust understanding of how culture, values, and identities intersect with ecological changes on landscapes to shape the well-being of people and ecosystems.

Climate shocks are predicted to increase in magnitude and frequency as the climate changes, notably impacting poor and vulnerable communities across the Tropics. The urgency to better understand and improve communities' resilience is reflected in international agreements such as the Paris Agreement and the multiplication of adaptation research and action programs. In turn, the need for collecting and communicating evidence on the climate resilience of communities has increasingly drawn questions concerning how to assess resilience. While empirical case studies are often used to delve into the context-specific nature of resilience, synthesizing results is essential to produce generalizable findings at the scale at which policies are designed. Yet datasets, methods and modalities that enable cross-case analyses that draw from individual local studies are still rare in climate resilience literature. We use empirical case studies on the impacts of El Niño on smallholder households from five countries to test the application of quantitative data aggregation for policy recommendation. We standardized data into an aggregated dataset to explore how key demographic factors affected the impact of climate shocks, modeled as crop loss. We find that while cross-study results partially align with the findings from the individual projects and with theory, several challenges associated with quantitative aggregation remain when examining complex, contextual and multi-dimensional concepts such as resilience. We conclude that future exercises synthesizing cross-site empirical evidence in climate resilience could accelerate research to policy impact by using mixed methods, focusing on specific landscapes or regional scales, and facilitating research through the use of shared frameworks and learning exercises.

Social, technological and climatic changes will transform the way energy is consumed over the 21st century, with important implications for energy networks and greenhouse gas emissions. Here, we develop a method to efficiently explore climate-energy interactions under various scenarios of climate, urban infrastructure and technological change. We couple the Urban Climate and Energy Model with the Conformal Cubic Atmospheric Model as a full-height single column driven with a series of global climate model simulations in an ensemble approach. The framework is evaluated against observations, then a series of century-scale simulations are undertaken to examine projected climate change impacts on electricity and gas demand in the temperate/ oceanic climate of Melbourne, Australia. With air-conditioning ownership remaining at early 21st century levels, and in the absence of other changes, climate change under radiative forcing RCP 8.5 increases peak electricity demand by 10%, and decreases peak gas demand by 22% between 2000 and 2100. However, if projected increases in air-conditioning ownership are considered, peak electricity demand increases by 84%, surpassing peak gas demand in the second half of the century. These findings highlight the complex nature of changes facing energy networks. Changes will be location and scenario dependent.

The consistent and robust assessment of ecosystem carbon stocks remains central to developing and monitoring climate change mitigation strategies. Here, we investigate the dynamics of forest ecosystem carbon stocks in the conterminous United States between 1907 and 2012 at national and regional levels. We build upon timber volume records from historical forest inventories and use a modelling approach to include all relevant pools, e.g. soil carbon, to derive a comprehensive long-term dataset. We find a consistent increase in forest carbon stocks across the country, from 27 PgC in 1907 to 39 PgC in 2012, with persistent regional variations between western and eastern United States, signalling pronounced land use and land management legacy effects. We identify additional potential to increase forest C sinks in both west and east, on diverging levels. Extended forest C stocks stem from forest biomass thickening i.e. increases in biomass C densities, rather than forest area expansion. Our study reflects the first such effort to collectively understand the effects of environmental change and land management on contemporary biomass C stocks at the national level, and critically engages with ongoing initiatives towards assessing the potential for carbon sequestration in forest ecosystems.

Biases in climatological and extreme precipitation estimates are assessed for 11 global observational datasets constructed with merged satellite measurements and/or rain gauge networks. Specifically, the biases in extreme precipitation are contrasted with mean-state biases. Extreme precipitation is defined by a 99th percentile threshold (R99p) on a daily, 1° × 1° grid for 50 °S–50 °N. The spatial pattern of extreme precipitation lacks distinct features such as the ITCZ that is evident in the global climatological map, and the climatology and extremes share little in common in terms of the spatial characteristics of inter-product biases. The time series also exhibit a larger spread in the extremes than in the climatology. Further, when analysed from 2001 to 2013, they show relatively consistent decadal stability in the climatology over ocean while the dispersion is larger for the extremes over ocean. This contrast is not observed over land. Overall, the results suggest that the inter-product biases apparent in the climatology are a poor predictor of the extreme-precipitation biases even in a qualitative sense.

Wildfire risk is a defining environmental challenge throughout much of the American West, as well as in other regions where complex social and ecological dynamics defy simple policy or management solutions. In such settings, diverse forms of land use, livelihoods, and accompanying values provide the conditions for trade-offs (e.g. between protecting homes from uncontrollable fires and restoring low-severity fire to ecosystems as a natural disturbance process). Addressing wildfire risk requires grappling with these trade-offs at multiple levels—given the need for action by individuals as well as by large and diverse stakeholder groups—and under conditions of considerable complexity. We evaluated how individual and collective perception of trade-offs varies as a function of complexity through analysis of the cognitive maps—representations of perceived causal relationships among factors that structure an individual's understanding of a system—of 111 stakeholders in the Eastern Cascades Ecoregion of central Oregon. Bayesian statistical analysis revealed a strong tendency against perception of trade-offs in individual maps, but not in a collective map that resulted from the aggregation of all individual cognitive maps. Furthermore, we found that lags (the number of factors that mediated the effect of an action on multiple valued outcomes) limited perception of trade-offs. Each additional intervening factor decreased the likelihood of a trade-off by approximately 52% in individual cognitive maps and by 10% in the collective cognitive map. However, the heterogeneity of these factors increased the likelihood of perception of trade-offs, particularly among individual cognitive maps, for which each unit increase of the Shannon diversity index translated into a 20-fold increase in the likelihood of perception of trade-offs. Taken together, these results suggest that features of complexity have distinct effects on individual—and collective-level perception of trade-offs. We discuss implications for wildfire risk decision-making in central Oregon and in other complex wildfire-prone social-ecological systems.

Many studies have reported that the Arctic is greening; however, we lack an understanding of the detailed patterns and processes that are leading to this observed greening. The normalized difference vegetation index (NDVI) is used to quantify greening, which has had largely positive trends over the last few decades using low spatial resolution satellite imagery such as AVHRR or MODIS over the pan-Arctic region. However, substantial fine scale spatial heterogeneity in the Arctic makes this large-scale investigation hard to interpret in terms of vegetation and other environmental changes. Here we focus on one area of the northern Alaskan Arctic using high spatial resolution (4 m) multispectral satellite imagery from DigitalGlobe^™ to analyze the greening trend near Utqiaġvik (formerly known as Barrow) over 14 years from 2002 to 2016. We found that tundra vegetation has been greening (τ = 0.65, p = 0.01, NDVI increase of 0.01 yr⁻¹) despite no overall change in vegetation community composition. The greening is most closely correlated to the number of thawing degree days (R² = 0.77, F = 21.5, p < 0.001) which increased in a similar linear trend over the 14 year study period (1.79 ± 0.50 days per year, p < 0.01, τ = −0.56). This suggests that in this Arctic ecosystem, greening is occurring due to a lengthening growing season that appears to stimulate plant productivity without any significant change in vegetation communities. We found that vegetation communities in wetter locations greened about twice as fast as those found in drier conditions supporting the hypothesis that these communities respond more strongly to warming. We suggest that in Arctic environments, vegetation productivity may continue to rise, particularly in wet areas.

A critical frontier of water management in the western US is the challenge of cross-scale interactions. It is difficult to establish clear governance boundaries and collectively act when basins are interconnected, surface water and groundwater flows are interrelated, and urban and rural water demands are increasingly affected by regional and international telecoupling. Changing climate, snowpack, and rainfall, peri-urbanization, and shifting economics of rural landscapes further increase sustainable governance challenges. Using a lens of cross-scale interactions drawn from the social-ecological literature, we develop a set of conceptual frames for socio-hydrology that highlight: (1) spatial and temporal mismatches, (2) telecoupled flows, and (3) networked and nested systems. Using the exemplary case of Central Arizona, we explore nesting of the system within the larger western socio-hydrological system (SHS), impacts of changing Colorado River policies, such as the Drought Contingency Plan, and emerging institutional arrangements between the State of Arizona, agricultural communities, and Tribal Nations. We conclude with a set of questions that inform analyses of cross-scale, multi-level governance within social-ecological systems. Without grappling with the dynamics and interconnectedness of SHSs, we cannot sustainably manage water in an increasingly arid West.