Table of contents

Amazon deforestation has been growing since 2012 and more recently under record rates. In fact, a new wave of rainforest destruction is on, challenging environmental agencies and policymakers. Political negligence has boosted deforestation in the Amazon, when coupled with deforestation drives that we already know about, as well as exempting environmental offenders and clearing the way to major infrastructure projects, in addition to weakening environmental agencies and command and control policies. In this letter, we share perspectives on the dynamics of deforestation alerts in the Brazilian Amazon and the action of public enforcement agencies, to draw attention to the urgency of supporting these entities for resuming the fight against deforestation. Our results reveal the few enforcement actions on deforestation alerts (1.3%) by the major environmental agency from the federal government. When compared with state government agencies, our in-depth case study showed a higher number of enforcement actions, promoting accountability for illegal deforestation in the Brazilian Amazon. It is evident that budget cuts for federal environmental agencies and changes in enforcement procedures have jeopardized actions to combat illegal deforestation. Our analysis calls for federal agencies to resume their powers, and for state agencies to recognize their role in environmental reinforcement and assigning liability. In the end, we list five key factors for reestablishing enforcement actions by public agencies for fighting deforestation and improving dissuasive effects.

In this Perspective, we suggest that research on just transitions and energy justice needs to better attend to the increasingly important trade-offs arising from issues related to speed and acceleration of low-carbon transitions. We identify and elaborate two important tensions that policymakers face when they want to simultaneously achieve both just and rapid low-carbon transitions. First, the way in which participatory processes may increase justice but slow the speed of action; and second the way in which incumbent mobilization can accelerate transitions but entrench injustices. Such an analysis shifts the focus from mapping justice dimensions to acknowledging the inevitable trade-offs and winners and losers produced by transition processes as a first step to better navigating them.

Elevated tropospheric ozone concentration ([O₃]) may substantially influence the belowground processes of the terrestrial ecosystem. Nevertheless, a comprehensive and quantitative understanding of the responses of soil C and N dynamics to elevated [O₃] remains elusive. In this study, the results of 41 peer-reviewed studies were synthesized using meta-analytic techniques, to quantify the impact of O₃ on ten variables associated with soil C and N, i.e. total C (TC, including soil organic C), total N (TN), dissolved organic C (DOC), ammonia N (NH₄⁺), nitrate N (NO₃⁻), microbial biomass C (MBC) and N (MBN), rates of nitrification (NTF) and denitrification (DNF), as well as C/N ratio. The results depicted that all these variables showed significant changes (P < 0.05) with [O₃] increased by 27.6 ± 18.7 nl l⁻¹ (mean ± SD), including decreases in TC, DOC, TN, NH₄⁺, MBC, MBN and NTF, and increases in C/N, NO₃⁻ and DNF. The effect sizes of TN, NTF, and DNF were significantly correlated with O₃ fumigation levels and experimental duration (P < 0.05). Soil pH and climate were essential in analyses of O₃ impacts on soil C and N. However, the responses of most variables to elevated [O₃] were generally independent of the O₃ fumigation method, terrestrial ecosystem type, and additional [CO₂] exposure. The altered soil C and N dynamics under elevated [O₃] may reduce its C sink capacity, and change soil N availability and thus, impact plant growth and enhance soil N losses.

Internationally, the environmental damage caused by the improper disposal of approximately 100 Mt of plastic waste per annum is of growing concern. Attempts to address this issue have generated many hundreds of scientific studies announcing the discovery of novel plastic-degrading microorganisms and their respective enzymes. On closer inspection, however, evidence remains sparse for the microbial degradation of most of the plastic polymers produced globally. We systematically surveyed the international literature to confirm how many microorganisms proposed to degrade plastics (n = 664) cause substantial (i.e. ⩾20% mass) losses of virgin polymer, rather than losses of plastic additives, filler, and/or shedding of polymer micro-fragments. We noted where degradation was only demonstrated for artificially aged polymer since physicochemical ageing procedures increase the abundance of monomers and oligomers such that they may be degraded by microbial activity. Additionally, artificial ageing may introduce functional groups to the polymer backbone, creating more locations susceptible to microbial degradation than would otherwise occur in the environment. We identified multiple studies demonstrating the effective microbial degradation of heterochain plastic polymers such as polylactic acid, polycaprolactone and polyethylene terephthalate (i.e. polymers containing elements other than carbon in the backbone structure). However, in the literature, we find no evidence for the substantial degradation of unadulterated polyethylene, polypropylene, polystyrene or polyvinyl chloride, homochain polymers which represent the overwhelming majority of global plastics production. Current research demonstrates that the pre-treatment of plastics with elevated temperature or UV-light may speed physicochemical plastic degradation, with valuable applications for downstream microbial processing. However, evidence for the microbial degradation of most plastic polymers in current circulation is lacking. We outline simple criteria that should be met before announcing the microbial degradation of plastic polymers. We hope this may help to address largely unsubstantiated expectations that microorganisms can degrade many plastic polymers in situ.

The question of what and how to measure ecological resilience has been troubling ecologists since Holling 1973s seminal paper in which he defined resilience as the ability of a system to withstand perturbations without shifting to a different state. This definition moved the focus from studying the local stability of a single attractor to which a system always converges, to the idea that a system may converge to different states when perturbed. These two concepts have later on led to the definitions of engineering (local stability) vs ecological (non-local stability) resilience metrics. While engineering resilience is associated to clear metrics, measuring ecological resilience has remained elusive. As a result, the two notions have been studied largely independently from one another and although several attempts have been devoted to mapping them together in some kind of a coherent framework, the extent to which they overlap or complement each other in quantifying the resilience of a system is not yet fully understood. In this perspective, we focus on metrics that quantify resilience following Holling's definition based on the concept of the stability landscape. We explore the relationships between different engineering and ecological resilience metrics derived from bistable systems and show that, for low dimensional ecological models, the correlation between engineering and ecological resilience can be high. We also review current approaches for measuring resilience from models and data, and we outline challenges which, if answered, could help us make progress toward a more reliable quantification of resilience in practice.

Water resources, including groundwater and prominent rivers worldwide, are under duress because of excessive contaminant and nutrient loads. To help mitigate this problem, the United States Department of Energy (DOE) has supported research since the late 1980s to improve our fundamental knowledge of processes that could be used to help clean up challenging subsurface problems. Problems of interest have included subsurface radioactive waste, heavy metals, and metalloids (e.g. uranium, mercury, arsenic). Research efforts have provided insights into detailed groundwater biogeochemical process coupling and the resulting geochemical exports of metals and nutrients to surrounding environments. Recently, an increased focus has been placed on constraining the exchanges and fates of carbon and nitrogen within and across bedrock to canopy compartments of a watershed and in river–floodplain settings, because of their important role in driving biogeochemical interactions with contaminants and the potential of increased fluxes under changing precipitation regimes, including extreme events. While reviewing the extensive research that has been conducted at DOE's representative sites and testbeds (such as the Oyster Site in Virginia, Savannah River Site in South Carolina, Oak Ridge Reservation in Tennessee, Hanford in Washington, Nevada National Security Site in Nevada, Riverton in Wyoming, and Rifle and East River in Colorado), this review paper explores the nature and distribution of contaminants in the surface and shallow subsurface (i.e. the critical zone) and their interactions with carbon and nitrogen dynamics. We also describe state-of-the-art, scale-aware characterization approaches and models developed to predict contaminant fate and transport. The models take advantage of DOE leadership-class high-performance computers and are beginning to incorporate artificial intelligence approaches to tackle the extreme diversity of hydro-biogeochemical processes and measurements. Recognizing that the insights and capability developments are potentially transferable to many other sites, we also explore the scientific implications of these advances and recommend future research directions.

This study aims to review the existing literature on the past and future effects of climate, land use, and land cover changes on hydropower generation in West Africa (WA), based on listings in the Scopus and Google Scholar databases. This review shows that several African hydropower plants have experienced repeated power disruptions over the last three decades due to climate change and variability but it is less documented how increasing land use and land cover changes around the major dams have impacted the hydrological system and the hydropower generation. In the future, the risks of hydropower in WA may not be equally distributed within a country or region. Despite uncertainties in precipitation and on impacts on streamflow and water level in major basins, climate change is likely to reduce the available water over the range of 10%–20% (15%–40%) for the RCP4.5 (RCP8.5) scenario by 2050, which may considerably affect the water demand across all sectors, including hydropower. However, in the Kainji dam (Niger River basin), models project an increase in rainfall favorable to hydropower production for both RCP4.5 and RCP8.5. In contrast, within the Black Volta sub-basin, the intensification of land use is predicted to favor runoff and, consequently, an increase in the generation of Bui hydropower in the near future, even though models predict a rainfall decrease. This increase in land use for agriculture to feed a growing population has other adverse effects that need to be assessed, namely sedimentation and siltation, which are harmful to hydropower plants. Finally, the combined impact of climate and land use changes on the efficiency of hydroelectric infrastructure in WA is not well documented, while sustainable planning and investments in the hydropower sector require consideration of the nexus between climate, land use changes, and water.

Systems models are an important tool for policy and energy planning decisions. These models generally fall into one of three modelling paradigms: energy economy, capacity expansion or power sector planning. Recent work seeks to combine these paradigms into an integrated framework to leverage the benefits of different model types. There is also interest and research in representing more system interactions to expand the modelling nexus. However, this increases model complexity and risks creating more black box models that are not well understood or trusted by users or policymakers. To understand the trade-offs and best practices of using combined models, we review current modelling practices, including an overview of the different modelling paradigms in the literature, how combined modelling has been applied to date and how the nexus has been represented in different modelling applications. Building on the literature review, we held a series of expert elicitation workshops to gain insight from energy modelling domain experts who use combined models. Finally, we encapsulate these findings and best practices into a modelling evaluation framework. We find that while there is interest and research being done in these areas, there are no set standards for how to build these types of models, resulting in a wide range of practices. Increasing model complexity to develop fully hard-linked coupled models that are also trustworthy and transparent generally requires more time and resources than is worthwhile. Instead, the focus should be on avoiding black box models by having a clear modelling purpose and developing best practices that allow for clarity and transparency. Expanding the nexus to include attributes such as biodiversity and cultural security presents a challenge and representing them as a cost is not congruent to equitable policy. These aspects could be better incorporated into analysis using stakeholder debate and citizens' assemblies.

A growing body of literature has identified methane mitigation as a key component of limiting the rate and extent of global warming. However, little is known about how methane mitigation can benefit other critical aspects of the climate system. This study explores the value of early methane mitigation in addition to carbon dioxide mitigation in helping avert an approaching and concerning climate event: the near-complete loss of Arctic summer sea ice. While drastic cuts in carbon dioxide emissions will ultimately control the fate of Arctic summer sea ice, we show that simultaneous early deployment of feasible methane mitigation measures is essential to avoiding the loss of Arctic summer sea ice this century. In fact, the benefit of combined methane and carbon dioxide mitigation on reducing the likelihood of a seasonally ice-free Arctic can be greater than the simple sum of benefits from two independent greenhouse gas policies. The extent to which methane mitigation can help preserve Arctic summer sea ice depends on the implementation timeline. The benefit of methane mitigation is maximized when all technically feasible measures are implemented within this decade, and it decreases with each decade of delay in implementation due to its influence on end-of-century temperature. A key insight is that methane mitigation substantially lowers the risk of losing Arctic summer sea ice across varying levels of concomitant carbon dioxide mitigation. This analysis provides further evidence of the value of early methane mitigation and the need to consider its benefits beyond reduced global temperature and improved air quality.

Solar photovoltaics is projected to become the dominant renewable, with much capacity being installed as ground-mounted solar parks. Land use change for solar can affect ecosystems across spatial scales and solar parks offer a unique opportunity for ecological enhancement. One compelling potential benefit beginning to be deployed by the solar industry is management for insect pollinators. Specifically, solar parks can provide refuge for pollinators through the provision of suitable habitat, potentially contributing to halting and reversing widespread declines recorded in some pollinator groups. There is scope to both manage and design solar parks for pollinators, but understanding is limited. Using a geographic information system and a process-based pollinator model, we explore how solar park management, size, shape and landscape context might impact ground-nesting bumble bee density, nest density and nest productivity inside existing solar parks and surrounding landscapes in the UK. We show that bumble bee density and nest density is driven by solar park management, with twice as many bumble bees foraging and nesting inside solar parks managed as wildflower meadows, compared to those with only wildflower margins. In comparison, solar park size, shape and landscape context have a smaller impact on bumble bee response inside solar parks. However, large, elongated resource-rich solar parks were most effective at increasing bumble bee density in surrounding landscapes, with implications for local crop pollination. Specifically, there were double the number of foraging bumble bees surrounding large solar parks managed as meadows compared to smaller parks managed as turf grass. If designed and managed optimally, solar parks therefore have the potential to boost local bumble bee density and potentially pollination services to adjacent crops. Our results demonstrate how incorporating biodiversity into solar park management and design decisions could benefit groups such as pollinators and contribute to the wider environmental sustainability of solar parks.

With the support of the Chinese National Antarctic Research Expedition, near-surface ozone (O₃) was continuously monitored at Zhongshan Station (ZOS) (69°22'12'' S, 76°21'49'' E, 18.5 m above sea level) in East Antarctica from 2008 to 2020. The seasonal and diurnal variability of near-surface O₃ at ZOS were investigated. O₃ enhancement events (OEEs) were frequently observed in the warm season (OEEs in January accounted for 23.0% of all OEEs). The OEEs at ZOS were related to the photochemical reaction processes under the influences of O₃ and solar radiation in the stratosphere and synoptic-scale air mass transport from coastal areas (Princess Elizabeth Land, Wilkes Land, and Queen Mary Land), as evidenced by the recorded wind speed, solar shortwave irradiance, and total column ozone data and the computed potential source contribution function and concentration-weighted trajectory models. The results computed by the tool Stratosphere-to-Troposphere Exchange Flux indicated that stratosphere-to-troposphere transport had no direct impact on OEEs at ZOS. Therefore, synoptic-scale air mass transport is the main cause of OEEs in Antarctica, which is consistent with previous studies. Unlike OEEs at inland Antarctic stations, which are mainly affected by air mass transport from inland plateaus, OEEs at ZOS, a coastal station, are mainly affected by air mass transport from coastal land in East Antarctica.

Nitrogen fertilizer (N_F) is a major uncertainty surrounding the greenhouse gas (GHG) emissions of lignocellulosic biofuels. N_F enhances agronomic yields and soil C inputs via plant litters, but results in soil organic carbon (SOC) decomposition, soil N₂O fluxes, and a large fossil energy footprint. Thus, whether N_F is beneficial or detrimental to the GHG mitigation of biofuels is unknown. Here, we show the potential GHG mitigation of fertilizing switchgrass (Panicum virgatum) at the N_F rate that minimizes net GHG emissions across 7.1 million ha of marginal lands in the Midwest US, with long-term production advantages surpassing emitted GHG by 0.66 Mg CO₂e ha⁻¹ yr⁻¹ on the aggregate. Marginal lands limited by poor N fertility could see a much greater benefit, but not SOC-rich lands, limited by low precipitation, or short growing seasons. The objectives of maximizing yield and minimizing GHG overlap only in a few environments, suggesting that maximum yield will reduce the climate benefit of cellulosic biofuels.

The West African Sahel has been facing for more than 30 years an increase in extreme rainfall with strong socio-economic impacts. This situation challenges decision-makers to define adaptation strategies in a rapidly changing climate. The present study proposes (i) a quantitative characterization of the trends in extreme rainfall at the regional scale, (ii) the translation of the trends into metrics that can be used by hydrological risk managers, (iii) elements for understanding the link between the climatology of extreme and mean rainfall. Based on a regional non-stationary statistical model applied to in-situ daily rainfall data over the period 1983–2015, we show that the region-wide increasing trend in extreme rainfall is highly significant. The change in extreme value distribution reflects an increase in both the mean and variability, producing a 5%/decade increase in extreme rainfall intensity whatever the return period. The statistical framework provides operational elements for revising the design methods of hydraulic structures which most often assume a stationary climate. Finally, the study shows that the increase in annual maxima of daily rainfall is more attributable to stronger storm intensities (80%) than to more frequent storm occurrences (20%), reflecting a major rainfall regime shift in comparison to those observed in the region since 1950.

Temperature extremes have increased during the past several decades and are expected to intensify under current rapid global warming over Southeast Asia (SEA). Exposure to rising temperatures in highly vulnerable regions affects populations, ecosystems, and other elements that may suffer potential losses. Here, we evaluate changes in temperature extremes and future population exposure over SEA at global warming levels (GWLs) of 2.0 °C and 3.0 °C using outputs from the Coupled Model Intercomparison Project Phase 6. Results indicate that temperature extreme indices are projected to increase over SEA at both GWLs, with more significant magnitudes at 3.0 °C. However, daily temperature ranges show a decrease. The substantial increase in total SEA population exposure to heat extremes from 730 million person–days at 2.0 °C GWL to 1200 million person–days at 3.0 °C GWL is mostly contributed by the climate change component, accounting for 48%. In addition, if global warming is restricted well below 2.0 °C, the avoided impacts in population exposure are prominent for most regions over SEA with the largest mitigation in the Philippines. Aggregate population exposure to impacts is decreased by approximately 39% at 2.0 °C GWL, while the interaction component effect, which is associated with increased population and climate change, would decrease by 53%. This indicates serious consequences for growing populations concurrent with global warming impacts if the current fossil-fueled development pathway is adhered to. The present study estimates the risks of increased temperature extremes and population exposure in a warmer future, and further emphasizes the necessity and urgency of implementing climate adaptation and mitigation strategies in SEA.

Global industrialization and urbanization processes enabled a diverse cement production boom over the past three decades, as cement is the most important building construction material. Consequently, the cement industry is the second-largest industrial CO₂ emitter (∼25% of global industrial CO₂ emissions) globally. In this study, the Global Cement Emission Database, which encompasses anthropogenic CO₂ emissions of individual production units worldwide for 1990–2019, was developed. A recently developed unit-level China Cement Emission Database was then applied to override China's data and the combination of two databases is used to reveal the unit characteristics of CO₂ emissions and ages for global cement plants, assess large disparities in national and regional CO₂ emissions, growth rates and developmental stages from 1990–2019, and identify key emerging countries of carbon emissions and commitment. This study finds that globally, CO₂ emissions from the cement industry have increased from 0.86 Gt in 1990 to 2.46 Gt in 2019 (increasing by 186%). More importantly, the large CO₂ emissions and the striking growth rates from those emerging countries, including most of the developing countries in the Asia region and the Middle East and Africa region, are clearly identified. For example, the Middle East and Africa, including mostly developing or underdeveloped countries, only represented 0.07 Gt CO₂ in 1990 (8.4% of the total), in contrast to 0.26 Gt (10.4% of the total) CO₂ in 2019, which is a 4.5% average growth rate during 1990–2019. Further, the intensive expansion of large and new facilities since 2005 in Asia and the Middle East and Africa has resulted in heavy commitment (90.1% of global commitment in 2019), and mitigation threats in the future considering their increasing emissions (the national annual growth rate can be up to >80%) and growing infrastructure construction (∼50% of clinker capacity operating ⩽10 years). Our results highlight the cement industry's development and young infrastructure in emerging economies; thus, future increasing cement demand and corresponding carbon commitment would pose great challenges to future decarbonization and climate change mitigation.

Small, low-income communities in the United States disproportionately lack access to safe drinking water (i.e. water that meets regulated quality standards). At a community level, the literature has broadly claimed that a major barrier to safe drinking water access is low technical, managerial, and financial (TMF) capacity. At a broader structural level, the environmental justice literature has shown that historical neglect of low-income communities of color has resulted in numerous water systems without the financial and political resources to meet water quality standards. This study investigates the contemporary processes by which distributive injustices persist in California's Central Valley. The study uses key informant interviews with a range of stakeholders, including employees at the state, county and community, non-profit organizations, and engineers, to understand why sustainable water quality solutions for small low-income communities remain such a challenge. The interviews are structured around a decision chain, which builds out the specific steps needed to go from a maximum contaminant level violation to remediation. The resulting decision chain makes visible the multiple steps at multiple stages with multiple actors that are needed to arrive at a solution to substandard water quality. It shows the numerous nodes at which progress can be stalled, and thus functions as a behind-the-scenes look at the (re)production of persistent inequalities. The complexity of the process shows why having the TMF capacity needed to get to a safe water system is not a reasonable expectation for most small community water systems. Inequalities are continually being produced and cemented, often by the very steps aimed towards remediation, thus making persistent disparities in safe drinking water access a de facto state-sanctioned process that compounds a discriminatory historical legacy.

For light-duty vehicles (LDVs), alternative powertrains and liquid fuels based on renewable electricity are competing options considered by policymakers and stakeholders for achieving necessary CO₂ emission reductions in the transport sector. While the urgency of climate change and the need to reach mitigation targets are well understood, system-wide implications along other sustainability dimensions need further exploration. We integrate a detailed transport system model into an integrated assessment framework and couple it with prospective life cycle impact analysis. This allows to assess different technological pathways of the European LDV fleet until 2050 for a comprehensive set of environmental and resource depletion indicators. Results indicate that greenhouse gas emissions drop significantly in all mitigation scenarios. However, impacts increase in several non-climate change impact categories even with fully renewable electricity supply. Additional impacts arise from the production of battery and fuel-cell components, and from a significant rise in electricity demand, most prominently for synthetic fuels. We consequently find that changes in mobility life-styles and in the relevant industrial processes are paramount to reduce environmental impacts from a climate-friendly LDV fleet across all categories.

The impact of rapid urbanization on the spatiotemporal pattern of short-term extreme precipitation in China remains unclear at the subnational scale. In this study, we present a general framework that measures urbanization-induced variation in hourly extreme wet season precipitation (April–October) from 1985 to 2012, with reference to a dynamic urban–rural station classification based on annual changes in urban extent. We found that urbanization in south China (<29° N) brings more extreme precipitation to urban areas than to suburbs, and reduces extreme precipitation continually over urban areas in parts of the north and northeast. Over 60% of provincial capital cities show significant changes in extreme precipitation due to urbanization, including smaller size cities separated from large urban clusters. Urbanization enhances extreme precipitation mainly in the local main part of the rainy season, which refers to May in the south (e.g. urban–rural differences of 0.70 mm h⁻¹ in Guangzhou) and July–September in the central and north (1.16 mm h⁻¹ in August of Beijing). Urbanization also increases hourly extreme precipitation at peak times in diurnal cycles. The results indicate that urbanization has caused overall more and more heterogeneous spatial patterns over China and concentrated distributions during the rainy season and peak time. These patterns warrant attention when assessing the risk of increased waterlogging and flash flooding in urban areas.

Global changes arouse large-scale fragmentation of forests, which has a profound impact on the balance of the global carbon cycle. However, the effect and process of temperate forest fragmentation on photosynthetic carbon uptake are not clear. We used remote sensing datasets to describe the degree of forest fragmentation and clarify the relationship between fragmentation and photosynthetic carbon uptake in the temperate forests of northeastern China. The results show that forest fragmentation has high spatial heterogeneity and promotes photosynthetic carbon uptake by 14% in the cold temperate zone and 10% in the middle temperate zone. Hydrothermal conditions are the dominant influencing path, explaining 60% of the variation in the cold temperate zone and 49% of the variation in the middle temperate zone. In addition, temperature is the dominant driver of the cold temperate zone, and water is the dominant driver of the middle temperate zone. Our research calls for a deeper understanding of the carbon cycle of fragmented temperate forests, and it is necessary to consider appropriate human intervention in forest management.

Greening of the Earth is observed during the past several decades and both climatic and non-climatic factors drive this process. However, the greening spatio-temporal patterns and the role of human activities such as agricultural intensification in hyper-arid regions remain unclear. This study aimed to (a) reveal the greening pattern in China's southern Xinjiang using satellite estimations of normalized difference vegetation index and leaf area index data during 1982–2019, and (b) examine the impacts of human activities in terms of land use land cover (LULC) data. Our multi-decadal analysis is ideal to reveal long-term trends and support a better understanding of the anthropogenic effects in this hyper-arid and endorheic region. The results showed that vegetation as a whole increased significantly in southern Xinjiang and the greening rate of cropland was much higher than the other LULC types. Significant greening was found over >90% of cropland, while insignificant changes and browning trends were found over nearly half the area of the other LULCs. The proportion of greening areas was more than 80% within 1 km from human-dominated areas while the proportion decreased to 40% with distances >15 km. The spatial heterogeneity of the greening indicated that, despite widely reported beneficial effects of warmer and wetter climate for a general greening trend, human activities could be the dominant factor modulating the greening rates disproportionately over different LULCs in arid and hyper-arid areas.

Ecosystems are projected to face extreme high temperatures more frequently in the near future. Various biotic coping strategies exist to prevent heat stress. Controlled experiments have recently provided evidence for continued transpiration in woody plants during high air temperatures, even when photosynthesis is inhibited. Such a decoupling of photosynthesis and transpiration would represent an effective strategy ('known as leaf or canopy cooling') to prevent lethal leaf temperatures. At the ecosystem scale, continued transpiration might dampen the development and propagation of heat extremes despite further desiccating soils. However, at the ecosystem scale, evidence for the occurrence of this decoupling is still limited. Here, we aim to investigate this mechanism using eddy-covariance data of thirteen woody ecosystems located in Australia and a causal graph discovery algorithm. Working at half-hourly time resolution, we find evidence for a decoupling of photosynthesis and transpiration in four ecosystems which can be classified as Mediterranean woodlands. The decoupling occurred at air temperatures above 35 ^∘C. At the nine other investigated woody sites, we found that vegetation CO₂ exchange remained coupled to transpiration at the observed high air temperatures. Ecosystem characteristics suggest that the canopy energy balance plays a crucial role in determining the occurrence of a decoupling. Our results highlight the value of causal-inference approaches for the analysis of complex physiological processes. With regard to projected increasing temperatures and especially extreme events in future climates, further vegetation types might be pushed to threatening canopy temperatures. Our findings suggest that the coupling of leaf-level photosynthesis and stomatal conductance, common in land surface schemes, may need be re-examined when applied to high-temperature events.

An assessment of the human impact on the global water cycle requires estimating the volume of water withdrawn for irrigated agriculture. A key parameter in this calculation is the irrigation efficiency, which corrects for the fraction of water lost between irrigation withdrawals and the crop due to management, distribution or conveyance losses. Here we show that the irrigation efficiency used in global irrigation models is flawed for it overlooks key ambiguities in partial efficiencies, irrigation technologies, the definition of 'large-scale' irrigated areas or managerial factors. Once accounted for, these uncertainties can make irrigation withdrawal estimates fluctuate by more than one order of magnitude at the country level. Such variability is larger and leads to more extreme values than that caused by the uncertainties related with climate change. Our results highlight the need to embrace deep uncertainties in irrigation efficiency to prevent the design of shortsighted policies at the river basin-water-agricultural interface.

Protecting the tropical peat swamp forests in Southeast Asia is critical for addressing global sustainability challenges such as climate change and biodiversity loss. However, more than half of these forests have been lost since 1990 due to the rapid expansion of drainage-based agriculture and forestry. Within the oil palm sector, the number of regional smallholder oil palm plantings on peat soils has risen quickly. These activities are challenging to govern and manage, due to their fragmented nature and the numerous farmers involved. It is imperative to understand the spatial distribution and drivers of the smallholder oil palm-related conversion of peat swamp forests. In contrast to existing studies based on farm surveys, we used state-of-art maps of smallholder oil palm plantings, derived from 2019 remote sensing data. Spatial data about socioeconomic and biophysical factors (e.g. mills, roads, water ways, and concessions) was then used to develop logistic regression models to investigate the relative influence of these factors. We show that the spatial patterns of smallholder oil palm plantings are distinct from those of industrial oil palm plantations, revealing the critical roles of roads, especially service roads, residential roads and tracks, in driving smallholder oil palm expansion within peatlands. We found that 90% of smallholder oil palm areas were located within 2 km of roads and 25 km of mills. The mean likelihood of a given land area being converted from peat swamp forests to smallholder oil palm declined rapidly with increasing distance from roads and mills. In addition to roads and mills, land use zones (e.g. the setting of concessions and migration settlements) and other environmental factors (e.g. precipitation and elevation) were identified as important drivers of smallholder oil palm expansion on peatland. Based on these findings, we identify priority regions for the protection of the remaining peat swamp forests in Indonesia and discuss strategies for tackling these sustainability challenges on local and global scales.

Many solar photovoltaic (PV) energy projects are currently being planned and/or developed in West Africa to sustainably bridge the increasing gap between electricity demand and supply. However, climate change will likely affect solar power generation and the atmospheric factors that control it. For the first time, the state-of-the-art CMIP climate models (CMIP6) are used to investigate the potential future evolution of solar power generation and its main atmospheric drivers in West Africa. A multi-model analyses carried out revealed a decrease of solar PV potential throughout West Africa in the 21st century, with an ensemble mean reduction reaching about 12% in southern parts of the region. In addition, the variability of future solar PV production is expected to increase with a higher frequency of lower production periods. The projected changes in the solar PV production and its variability are expected to be predominant in the June to August season. We found the decrease in the solar PV potential to be driven by a decrease of surface irradiance and an increase of near-surface air temperature. However, the decrease of the surface irradiance accounted for a substantially larger percentage of the projected solar PV potential. The decrease in surface irradiance was further linked to changes in both cloud cover and aerosol presence, although generally much more strongly for the former.

The impact of climate variability on the water requirements of crops is a key issue in a globalized world with unprecedented population and unevenly distributed water resources. Changes of hydro-climatic forcings may have significant impacts on water resources use, considering the possible effects on irrigation requirements and crop water stress. In this work, a comprehensive estimation of crop water requirements over the 1970–2019 period is presented, considering 26 main agricultural products over a 5 arcmin resolution global grid. The assessment is based on a daily-scale hydrological model considering rainfed and irrigated scenarios, driven by hydro-climatic forcings derived from ERA5, the most recent climate reanalysis product within the Climate Change Service of the Copernicus Programme. Results show the heterogeneous impact of climate variability on harvested areas of the world, quantified by water stressed days and irrigation requirement rates. Increases of irrigation requirement rates were found on more than 60% of irrigated lands, especially in regions like South Europe, North-East China, West US, Brazil and Australia, where the mean rate increased more than 100 mm yr⁻¹ from 1970s to 2010s. The daily analysis of water requirements shows that crops require significantly more days of irrigation per season, especially in Europe, Africa and South-East Asia. Statistically significant trends of water stress duration were found over 38% of rainfed croplands, while only 6% of croplands has been affected by negative trends and shorter stress duration, mainly in India, Malaysia, North Europe and coastal regions of central western Africa.

In the context of 100% renewable electricity systems, prolonged periods with persistently scarce supply from wind and solar resources have received increasing academic and political attention. This article explores how such scarcity periods relate to energy storage requirements. To this end, we contrast results from a time series analysis with those from a system cost optimization model, based on a German 100% renewable case study using 35 years of hourly time series data. While our time series analysis supports previous findings that periods with persistently scarce supply last no longer than two weeks, we find that the maximum energy deficit occurs over a much longer period of nine weeks. This is because multiple scarce periods can closely follow each other. When considering storage losses and charging limitations, the period defining storage requirements extends over as much as 12 weeks. For this longer period, the cost-optimal storage needs to be large enough to supply 36 TWh of electricity, which is about three times larger than the energy deficit of the scarcest two weeks. Most of this storage is provided via hydrogen storage in salt caverns, of which the capacity is even larger due to electricity reconversion losses (55 TWh). Adding other sources of flexibility, for example with bioenergy, the duration of the period that defines storage requirements lengthens to more than one year. When optimizing system costs based on a single year rather than a multi-year time series, we find substantial inter-annual variation in the overall storage requirements, with the average year needing less than half as much storage as calculated for all 35 years together. We conclude that focusing on short-duration extreme events or single years can lead to an underestimation of storage requirements and costs of a 100% renewable system.

The United Nations Sustainable Development Goals have highlighted the challenge posed by increasing air pollution. This study allocates PM_2.5 footprint to household consumption expenditure based on multi-regional input–output model and survey data collected from 30 000 households. The household indirect PM_2.5 footprint related to spending on food, hospital, electricity, and education rank as the top four items, plus direct PM_2.5 emissions, which in combination contribute more than 55% of total air pollution. Compared with the poor, the responsibilities for air pollution on the wealthy are more sensitive to changes in income, especially for high-end consumption categories, such as luxury goods and services, education and healthcare. Further, the wealthiest 20% of households cause 1.5 times the PM_2.5 footprint per capita than exposure to PM_2.5 emissions. The high-footprint household samples are concentrated in high-exposure areas. It is recommended that mitigation policies address inequality of PM_2.5 footprint by targeting the top 20% of footprint groups with tags of wealthy, urban resident, well-educated, small family, and apartment living.

Current global-scale models of water resources do not generally represent groundwater lateral flows and groundwater–surface water interactions. But, models that do represent groundwater in more detail are becoming available and this raises the question of how estimates of water flow, availability, and impacts might change compared to previous global estimates. In this study, we provide the first global quantification of cell-to-cell groundwater flow (GWF) using a high-resolution global-scale GWF model and compare estimated impacts of groundwater pumping using two model setups: (a) with and (b) without including cell-to-cell GWFs and realistic simulation of groundwater–surface water interactions at the global scale (simulated over 1960–2010). Results show that 40% of the land–surface cell-to-cell flows are a notable part of the cell's water budget and that globally large differences in the impact of groundwater pumping are estimatd between the two runs. Globally, simulated groundwater discharge to rivers and streams increased by a factor of 1.2–2.2 when GWFs and interactions between groundwater and surface water were included. For eight heavily pumped aquifers, estimates of groundwater depletion decrease by a factor of 1.7–22. Furthermore, our results show that GWFs and interactions between groundwater and surface water contribute to the volume of groundwater that can be pumped without causing notable changes in storage. However, in approximately 40% of the world's watersheds where groundwater is used, groundwater is being pumped notably at the expense of river flow, and in 15% of the area globally depletion is increased as a result of nearby groundwater pumping. Evaluation of the model results showed that when groundwater lateral flows and groundwater–-surface water interactions were taken into account, the indirect observations of groundwater depletion and groundwater discharge were mimicked much better than when these fluxes were not included. Based on these findings, we suggest that including GWFs in large-scale water resources assessments will benefit a realistic assessment of groundwater availability worldwide, the estimation of impacts associated with groundwater pumping, especially when one is interested in the feedback between groundwater use and groundwater and surface water availability, and the impacts of current and future groundwater uses on the hydrological system.

The needs and capacities to achieve the 17 Sustainable Development Goals (SDGs) differ across regions and nations, but little research has been done to investigate their similarities and differences. Here, we proposed using SDG bundles (i.e. groups of regions with similar performances on all individual SDGs) to classify regions when assessing SDG progress and applied the method at the provincial level in China from 2000 to 2015. Five SDG bundles with distinct characteristics were identified. The dominant bundles changed from 'poor performance for all SDGs' in 2000 to 'high scores for environmental and some social SDGs and intermediate scores for others' and 'low scores for environmental SDGs but high scores for others' in 2015, indicating the overall improvement of China's sustainable development level. However, no bundle had relatively high scores in all SDGs, implying that China has much work left to do. Changes in the SDG bundles across space and time were related to regional socioeconomic development, climate, and geographic conditions. This study sheds light on identifying regions' strengths and weaknesses in achieving all SDGs, which can inform targeted sustainability actions for regions within certain SDG bundles and promote collaborations among regions with different bundles.

As climatic changes continue to drive increases in the frequency and severity of forest fires, it is critical to understand all of the factors influencing the risk of forest fire. Using a spatial dataset of areas burnt over a 65 year period in a 528 343 ha study area, we examined three possible drivers of flammability dynamics. These were: that forests became more flammable as fine biomass (fuel) returned following disturbance (H1), that disturbance increased flammability by initiating dense understorey growth that later self-thinned (H2), and that climatic effects were more important than either of these internal dynamics (H3). We found that forests were unlikely to burn for a short 'young' period (5–7 years) following fire, very likely to burn as the regrowing understorey became taller and denser (regrowth period), then after a total post-disturbance period of 43–56 years (young + regrowth periods), fire became unlikely and continued to decrease in likelihood (mature period). This trend did not change as the climate warmed, although increases in synoptic variability (mean changes in synoptic systems per season) had a pronounced effect on wildfire likelihood overall. Young forest and regrowth forest became increasingly likely to burn in years of greater synoptic variability and the time taken for forests to mature increased, but in years with the most severe synoptic variability, mature forests were the least likely to burn. Our findings offer an explanation for fire behaviour in numerous long-term studies in diverse forest types globally and indicate that, even in the face of a warming climate, 'ecologically-cooperative' approaches may be employed that reinforce rather than disrupt natural ecological controls on forest fire. These range from traditional indigenous fire knowledge, to modern targeting of suppression resources to capitalise on the benefits of self-thinning, and minimise the extent of dense regrowth in the landscape.

Negative trade-offs between food production and biodiversity and the positive functional diversity–productivity relationships are potentially conflicting paradigms that are frequently evoked in conservation and sustainability science and management. While the complementary niches of species could potentially increase fisheries yields, stark food-diversity trade-offs have been proposed for wild-caught fisheries. Nevertheless, this first evaluation of stock biomass, yields, and species relationships in 115 coral reef locations in the Western Indian Ocean found that management for multispecies-maximum sustained yield (MMSY) will increase both food production and numbers of species relative to open access fisheries. A precipitous loss of >50% of species did not occur until >70% of the fishable and target biomasses was depleted. At MMSY, 6%–15% of total predicted number of fish species were lost indicating a need for other conservation mechanisms. These patterns occurred because the best-fit to the yield-numbers of species relationship was either a saturation or convex parabolic relationship. Fishing at MMSY in coral reefs should provide considerable diversity required to support many ecosystem services. Low biomass and overfishing were common and around 80% of studied locations were losing ∼2.0–2.5 tons km⁻² yr⁻¹ and 15%–40% of their species relative to MMSY.

The recent European Union Ship Recycling Regulation and other existing conventions aimed to reduce harmful environmental and health impacts of ship shipbreaking, may push the shipbreaking industry further to South Asian countries, where ecosystem and public health are threatened due to the lack of monitoring for dirty beaching methods for ship breaking. Such unsustainable patterns may continue to expand due to the mismatch of economic beneficiaries and environmental costs in the shipbreaking industry, the ineffectiveness of existing conventions and regulations, and the prospect of a large number of ships to be dismantled in the near future. Our study focuses on these emerging issues and raises the urgency of joint actions for the shipbreaking industry.

The leading mode of wintertime atmospheric variability over the North Atlantic-North Eurasia sector is dominated by the North Atlantic Oscillation (NAO) and accounts for more than one third of the total variability. This study explores the influences of the leading mode on decadal climate variability of Northern Eurasia. We focus on the little-explored decadal covariations of surface air temperature (SAT), snowfall, snow water equivalent (SWE) and snow cover over the region, using extensive model output from the Coupled Model Intercomparison Project sixth phase. Recent decadal trends (−0.92σ per decade) in the leading mode identified, are found to be largely a manifestation of internal climate variability (at least two thirds from the most conservative estimate). These internally-generated decadal trends strongly contributed to recent trends in SAT, snowfall, SWE and snow cover over Eurasia. External forcings should have played a minor role over Eurasia as they usually suggest opposite decadal trends to those observed. An exception is found for snowfall and SWE in east Eurasia, for which external forcings may have driven a large part of the recent upward trends, equally as important as the NAO-dominated mode. This points to a complex interplay between internally-generated and externally-forced climate variability over Northern Eurasia. Model discrepancies are identified in reproducing the linkages between the leading mode and the Eurasian surface climate variability. The internally-generated variability of this leading mode thus represents a large source of uncertainty in future decadal climate projections over Eurasia and, due to the memory effects of snow, also in modelling springtime climate variability.

Extreme events can lead to crop yield declines, resulting in financial losses and threats to food security, and the frequency and intensity of such events is projected to increase. As global gridded crop models (GGCMs) are commonly used to assess climate change impacts on agricultural yields, there is a need to understand whether these models are able to reproduce the observed yield declines. We evaluated 13 GGCMs from the Inter-Sectoral Impact Model Intercomparison Project and compared observed and simulated impact of past droughts and heatwaves on yields for four crops (maize, rice, soy, wheat). We found that most models detect but underestimate the impact of droughts and heatwaves on yield. Specifically, the drought signal was detected by 12 of 13 models for maize and all models for wheat, while the heat signal was detected by eleven models for maize and six models for wheat. To investigate whether the difference between simulated and observed yield declines is due to a misrepresentation of simulated exposure to heat or water scarcity (i.e. misrepresentation of growing season), we analysed the relationship between average discrepancies between observed and simulated yield losses, and average simulated exposure to extreme weather conditions across all crop models. We found a positive correlation between simulated exposure to heat and model performance for heatwaves, but found no correlation for droughts. This suggests that there is a systematic underestimation of yield responses to heat and drought and not only a misrepresentation of exposure. Assuming that performance for the past indicates models' capacity to project future yield impacts, models likely underestimate future yield decline from climate change. High-quality temporally and spatially resolved observational data on growing seasons will be highly valuable to further improve crop models' capacity to adequately respond to extreme weather events.

Climate change is expected to exacerbate the urban heat island (UHI) effect in cities worldwide, increasing the risk of heat-related morbidity and mortality. Solar reflective 'cool pavement' is one of several mitigation strategies that may counteract the negative effects of the UHI effect. An increase in pavement albedo results in less heat absorption, which results in reduced surface temperatures (T_surface). Near surface air temperatures (T_air) could also be reduced if cool pavements are deployed at sufficiently large spatial scales, though this has never been confirmed by field measurements. This field study is the first to conduct controlled measurements of the impacts of neighborhood-scale cool pavement installations. We measured the impacts of cool pavement on albedo, T_surface, and T_air. In addition, pavement albedo was monitored after installation to assess its degradation over time. The field site (∼0.64 km²) was located in Covina, California; ∼30 km east of Downtown Los Angeles. We found that an average pavement albedo increase of 0.18 (from 0.08 to 0.26) corresponded to maximum neighborhood averaged T_surface and T_air reductions of 5 °C and 0.2 °C, respectively. Maximum T_surface reductions were observed in the afternoon, while minimum reductions of 0.9 °C were observed in the morning. T_air reductions were detected at 12:00 local standard time (LST), and from 20:00 LST to 22:59 LST, suggesting that cool pavement decreases T_air during the daytime as well as in the evening. An average albedo reduction of 30% corresponded to a ∼1 °C reduction in the T_surface cooling efficacy. Although we present here the first measured T_air reductions due to cool pavement, we emphasize that the tradeoffs between T_air reductions and reflected shortwave radiation increases are still unclear and warrant further investigation in order to holistically assess the efficacy of cool pavements, especially with regards to pedestrian thermal comfort.

Rapid urbanization, population growth, and other intensive human activities have greatly altered natural hydrological conditions and matter cycling, which are the main causes of water quality deterioration in North China's rivers. With the help of a 15-year (2005–2019) dataset of river water quality (1043 records from nine sites), this study investigated the spatiotemporal water quality patterns in the Yongding River Basin (YRB) in North China using a new water quality index (WQI-DET), which has been customized for China's water quality classification scheme. Our results showed that the river water quality of the YRB has significantly improved due to the decreased surface runoff and an abrupt change of WQI-DET was observed in 2011. The elimination of anoxic conditions and the mitigation of nitrogen and phosphorus resulting from the construction of wastewater treatment plants and the improvement of treatment capacity are the main reasons for the improvement in river water quality. We also found that eutrophication is still not completely eradicated because of the high concentrations of NH₄⁺ and total phosphorus. Our study suggests that for rivers in which runoff has decreased sharply, the water quality could be improved significantly by wastewater treatment facilities. At present, for the YRB, more effort is needed to eliminate eutrophication and dried-up river sections and thereby finally improve the river ecosystem. We concluded that more attention and effort should be given to river hydrological conditions, specific river ecological characteristics, and the increasingly important non-point source pollutants during the design of river restoration measures in North China.

The Arabian Peninsula exhibits extreme hot summers and has one of the world's largest population growths. We use satellite observations and reanalysis as well as climate model projections to analyze morning and evening land surface temperatures (LSTs), to refer to processes at the surface, and wet bulb temperatures (WBTs) to measure human heat stress. We focus on three regions: the Persian Gulf and Gulf of Oman, the inland capital of Saudi Arabia, Riyadh and the irrigated agricultural region in Al-Jouf, Saudi Arabia. This study shows that the time of day is important when studying LST and WBT, with current and future WBT higher in the early summer evenings. It also shows that the effect of humidity brought from waterbodies or through irrigation can significantly increase heat stress. Over the coasts of the Peninsula, humidity decreases LST but increases heat stress via WBT values higher than 25 °C in the evening. Riyadh, located in the heart of the Peninsula has lower WBT of 15 °C–17.5 °C and LST reaching 42.5 °C. Irrigation in the Al-Jouf province decreases LST by up to 10° with respect to its surroundings, while it increases WBT by up to 2.5°. Climate projections over the Arabian Peninsula suggest that global efforts will determine the survivability in this region. The projected increase in LST and WBT are +6 °C and +4 °C, respectively, in the Persian Gulf and Riyadh by the end of the century, posing significant risks on human survivability in the Peninsula unless strict climate mitigation takes place.

Urban runoff and wastewater/sewage input are majorly responsible for the contamination of urban streams. In streams where wastewater input is not a considerable input, the importance of urban runoff as a mechanism of contaminant transport and delivery from urban surfaces to receiving waters is even more apparent. Extensive studies on two such streams in Southern Ontario, Canada yielded data on the occurrence and levels of multiple contaminant groups (polycyclic aromatic hydrocarbons and quinones, benzotriazoles (BTs), BT ultraviolet stabilizers, organophosphate esters, herbicides) and the influence of factors such as temperature, rainfall characteristics, and land use. Here, we collectively examined the data from these studies to identify any trends and further insights. Using concentration-discharge relationships, we found that the transport dynamics of many particle-bound compounds are strikingly similar to each other, and to that of suspended solids in which they were quantified, suggesting a single, predominant source. Similar urban to rural ratios across compound groups and strong correlations with road density further support the existence of a dominant source and point to traffic as this source, respectively. Although road traffic had not previously been implicated as a major source of many of the investigated compound groups, their uses suggest that traffic-related sources are very plausible. Overall, this work highlights that traffic is a major source of a surprisingly wide array of organic contaminants to urban surfaces, and subsequently to nearby streams.

The phenology of tropical forests is tightly related to climate conditions. In the Amazon, the seasonal greening of forests is conditioned by solar radiation and rainfall. Yet, increasing anthropogenic pressures (e.g. logging and wildfires), raise concerns about the impacts of forest degradation on the functioning of forest ecosystems, especially in a climate change context. In this study, we relied on remote sensing data to assess the contribution of solar radiation and precipitation to forest greening in mature and fire degraded forests, with a focus on the 2015 drought event. Our results showed that forest greening is more dependent on water resources in degraded forests than in mature forests. As a consequence, the expected increase in drought episodes and associated fire occurrences under climate change could lead to a long-term drying of tropical forests.

Enabling crop flowering within an optimal calendar window minimises long-term risk of abiotic stress exposure, improving prospects for attaining potential yield. Here, we define the optimal flowering period (OFP) as the calendar time in which long-term risk of frost, water and heat stress are collectively minimised. Using the internationally-renowned farming systems model Agricultural Systems Production Systems sIMulator, we characterised combined effects of climate change and extreme climatic events on the OFPs of barley, durum wheat, canola, chickpeas, fababean and maize from 1910 to 2021. We generate response surfaces for irrigated and dryland conditions using a range of representative sowing times for early and late maturity genotypes. Global warming truncated crop lifecycles, shifting forward flowering of winter crops by 2–43 d in dryland environments, and by −6–19 d in environments with irrigation. Alleviation of water stress by irrigation delayed OFPs by 3–25 d or 11–30 d for early and late maturity winter crops, respectively, raising average yields of irrigated crops by 44%. Even so, irrigation was unable to completely negate the long-term yield penalty caused by the climate crisis; peak yields respectively declined by 24% and 13% for rainfed and irrigated crops over the 111 years simulation duration. We conclude with two important insights: (a) use of irrigation broadens OFPs, providing greater sowing time flexibility and likelihood of realising potential yields compared with dryland conditions and (b), the most preferable maturity durations for irrigated winter and summer crops to maximise potential yields are early-sown long-season (late) and later-sown short-season (early) maturity types, respectively.

The sea surface temperature inter-hemispheric dipole (SSTID) is an important variability mode of global SST anomalies, characterized by an anti-phase variation of SST between the two hemispheres. In this study, the decadal variation of the northern hemisphere summer monsoon (NHSM) is found to be strongly regulated by the SSTID, with positive (negative) phases of the SSTID corresponding to the strengthening (weakening) of NHSM. Both observation and SST-forced atmospheric model simulations suggest that the SSTID related thermal forcing modulates the NHSM by causing planetary-scale atmospheric circulation adjustments. Positive SSTID events lead to coherent increase (decrease) of surface air temperature over the entire northern (southern) hemisphere, increasing the inter-hemispheric thermal contrast (ITC). As sea level pressure changes are just opposite to air temperature, the increase of ITC enhances the inter-hemispheric pressure gradient (southern hemisphere minus northern hemisphere), leading to the strengthening of summer monsoonal circulation and the increase of monsoon rainfall in the northern hemisphere.

Accumulating evidence on the impact of climate change on droughts, highlights the necessity for developing effective adaptation and mitigation strategies. Changes in future drought risk and severity in Australia are quantified by analyzing nine Coupled Model Intercomparison Project Phase 6 climate models. Historic conditions (1981–2014) and projections for mid-century (2015–2050) and end-century (2051–2100) from four shared socioeconomic pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) are examined. Drought events are identified using both the standardized precipitation index and the standardized precipitation evapotranspiration index. The spatial-temporal evolution of droughts is addressed by quantifying the areal extent of regions under moderate, severe and extreme drought from historic to end-century periods. Drought characteristics derived from the models are used to develop severity–duration–frequency curves using an extreme value analysis method based on ordinary events. Under SSP5-8.5, a tenfold increase in the area subject to extreme droughts is projected by the end of the century, while a twofold increase is projected under SSP1-2.6. Increase in extreme droughts frequency is found to be more pronounced in the southern and western regions of Australia. For example, frequency analysis of 12 month duration droughts for the state of South Australia indicates that, under SSP5-8.5, drought severities currently expected to happen on average only once in 100 years could happen as often as once in 3 years by the end of the century, with a 33 times higher risk (from 1% to 33%), while under SSP1-2.6, the increase is fivefold (1%–5%). The significant difference in the increase of drought risk between the two extreme scenarios highlights the urge to reduce greenhouse gases emission in order to avoid extreme drought conditions to become the norm by the end of the century.

Although successful sustainability transitions depend on public support, we still know little about citizens' opinions on climate solutions. Existing research often focuses on the problem perception of climate change rather than analyzing attitudes toward specific climate solutions. Studies also largely use closed questions to assess public opinion, posing a problem of ecological validity. Here, we address these gaps by leveraging data from a large-scale public consultation process, the "Grand Débat National", launched by the French government in response to the Yellow Vest movement in 2019. Combining structural topic modelling, dictionary-based text analysis and qualitative coding, we map the salience and directionality of public opinion on climate solutions. We find that consultation participants perceive climate change as the most salient environmental problem. Transforming the transport and energy sectors is the most supported solution for addressing climate change. For these two sectors, substitution-based climate solutions - as opposed to sufficiency- or efficiency-based measures - are most salient. For instance, participants stress the need to expand public transport infrastructure and switch to renewable energy technologies for power generation. Our findings demonstrate a strong public consensus on most substitution-based climate solutions, except for the role of cars and nuclear energy. While most participants do not link climate solutions to specific policy instruments, we find preferences for authority-based instruments in the context of phasing out polluting technologies, and treasury-based instruments for supporting innovation and phasing in low carbon technologies.

Temperature impacts on crop yield are known to be dependent on concurrent precipitation conditions and vice versa. To date, their confounding effects, as well as the associated uncertainties, are not well quantified at the global scale. Here, we disentangle the separate and confounding effects of temperature and precipitation on global maize yield under 25 climate scenarios. Instead of relying on a single type of crop model, as pursued in most previous impact assessments, we utilize machine learning, statistical and process-based crop models in a novel approach that allows for reasonable inter-method comparisons and uncertainty quantifications. Through controlling precipitation, an increase in warming of 1 °C could cause a global yield loss of 6.88%, 4.86% or 5.61% according to polynomial regression, long short-term memory (LSTM) and process-based crop models, respectively. With a 10% increase in precipitation, such negative temperature effects could be mitigated by 3.98%, 1.05% or 3.10%, respectively. When temperature is fixed at the baseline level, a 10% increase in precipitation alone could lead to a global yield growth of 0.23%, 1.43% or 3.09% according to polynomial regression, LSTM and process-based crop models, respectively. Further analysis demonstrates substantial uncertainties in impact assessment across crop models, which show a larger discrepancy in predicting temperature impacts than precipitation effects. Overall, global-scale assessment is more uncertain under drier conditions than under wet conditions, while a diverse uncertainty pattern is found for the top ten maize producing countries. This study highlights the important role of climate interactions in regulating yield response to changes in a specific climate factor and emphasizes the value of using both machine learning, statistical and process crop models in a consistent manner for a more realistic estimate of uncertainty than would be provided by a single type of model.

This paper uses over 30 million individual-level trips in federal recreation locations to investigate the impact of short-term temperature shocks on outdoor recreation activities. Our results show that in the short term, a $1\ ^{\circ}$C temperature increase during the last six months increases the total trip duration by 1.197 d (or a 4.12% increase) and the total number of trips by 0.472 (or a 5.44% increase) at the zipcode-month level. The positive effect is primarily driven by the increased number of trips and more in-state travel. We find that the impact of temperature on the number of recreation trips generally increases under a higher temperature. When the monthly temperature is below $5\ ^{\circ}$C, the temperature increase will reduce the number of trips as individuals in low-temperature regions are likely to reduce travel when the temperature gets warmer.

Hundreds of projects to reduce emissions from deforestation and forest degradation and enhance carbon stocks (REDD+) are implemented globally, many by non-governmental organizations (NGOs) or for-profit companies. Yet, at the global level, the Paris Agreement focuses on jurisdictional (national and subnational) REDD+. We ask: (1) How much can REDD+ projects contribute to achieving national and international climate objectives? (2) What are the issues in integrating REDD+ projects into national carbon accounting? Our snapshot of 377 REDD+ projects covering 53 million ha in 56 countries is based on data from the International Database on REDD+ Projects (ID-RECCO) supplemented with new data on projects' accounting methods. The number of new REDD+ projects declined steadily from 45 new projects in 2011 to five in 2019. We examined 161 certified projects that started between 2007 and 2017; 96 of these could sell carbon credits in voluntary carbon markets by 2020 and spent on average 4.7 (± 2.4) years between project start and sales in voluntary carbon markets. Globally, REDD+ projects claim to reduce an average of 3.67 tCO2e/ha annually. This figure - combined with projects limited coverage - implies that projects need to be upscaled more than 40x to fulfil the potential contribution of tropical and subtropical forests towards limiting global warming to well below 2oC. Compared to the national carbon accounting methods, most projects in Colombia, Indonesia and Peru (63 of 86) use at least one different carbon accounting parameter. Carbon accounting inconsistencies across levels need to be addressed. Overall, the argument for REDD+ projects lies in the emissions reductions they can achieve, diversifying participation in REDD+ and providing non-carbon benefits to local communities, potentially leading to broader support for climate action.

The Red Sea is surrounded by a diverse mixture of climates and is spanned by opposite hydrologic end-uses and geopolitical states. Unique water supply management challenges on both sides (related to agricultural and trans-boundary conflict in East Africa, and to groundwater depletion in the Arabian Peninsula) are made more severe by a rising demand, which underscores the importance of understanding shifts in rainfall supply to aid effective action. In this study, we characterize the relative importance of rainfall intensities to annual rainfall, the onset and duration of wet seasons, and the (statistically significant) trends in each of these over the region from 1981 through 2020 using daily gridded (0.05°) precipitation estimates. Results show that heavy rainfall (above 20 mm d⁻¹) does not necessarily benefit annual totals, as the wettest regions are shaped by moderate (between 5 and 20 mm d⁻¹) rainfall coupled with prolonged wet seasons. Observed trends in annual rainfall are underlain by interactions between shifting wet season lengths and rainfall intensities. Wet season length increases for 26% of the region, dampening the inherent drying resulting from shifts toward less-intense rainfall, and bolstering the inherent wetting from shifts toward more-intense rainfall. Regions shifting toward less- (more-)intense rainfall without an expanding wet season generally show negative (insignificant) rainfall trends. This reveals an important control that wet-day frequency has over wet-day intensity alone in shaping annual rainfall changes. We emphasize that the large-scale distribution of these shifts and their regional importance should punctuate cooperative efforts in sustainable resource management and transboundary governance.

The North Atlantic exhibits temperature variations on multidecadal time scales, summarized as the Atlantic multidecadal variability (AMV). The AMV plays an essential role for regional climate and is a key driver of the low-frequency variability in Northern Europe. This study analyzed the interaction between the atmosphere and the ocean using Coupled Model Intercomparison Project 6 (CMIP6) control runs. The results showed that the physical mechanisms underlying decadal or longer time scales differ among CMIP6 models, which allowed them to be sorted into two clusters. For the first cluster, a significant coherence between the North Atlantic Oscillation (NAO) and the AMV was found. Further, it showed a strong negative NAO response and decreasing precipitation over Northern Europe. In contrast, the second cluster showed no significant coherence between NAO and AMV. This non-coherent cluster developed a low-pressure anomaly in the subpolar gyre and showed increasing precipitation over Europe. Differences in the northward extension of the Atlantic meridional overturning circulation (AMOC) between the two clusters were identified and linked to the different atmospheric responses. Our findings have important implications for European climate, since predictions of an increase or decrease in precipitation over Northern Europe will be model-dependent.

Recent advances in citizen weather station (CWS) networks, with data accessible via crowd-sourcing, provide relevant climatic information to urban scientists and decision makers. In particular, CWS can provide long-term measurements of urban heat and valuable information on spatio-temporal heterogeneity related to horizontal heat advection. In this study, we make the first compilation of a quasi-climatologic dataset covering six years (2015–2020) of hourly near-surface air temperature measurements obtained via 1560 suitable CWS in a domain covering south-east England and Greater London. We investigated the spatio-temporal distribution of urban heat and the influences of local environments on climate, captured by CWS through the scope of Local Climate Zones (LCZ)—a land-use land-cover classification specifically designed for urban climate studies. We further calculate, for the first time, the amount of advected heat captured by CWS located in Greater London and the wider south east England region. We find that London is on average warmer by about 1.0 ^∘C–1.5 ^∘C than the rest of south-east England. Characteristics of the southern coastal climate are also captured in the analysis. We find that on average, urban heat advection (UHA) contributes to 0.22 ± 0.96 ^∘C of the total urban heat in Greater London. Certain areas, mostly in the centre of London are deprived of urban heat through advection since heat is transferred more to downwind suburban areas. UHA can positively contribute to urban heat by up to 1.57 ^∘C, on average and negatively by down to −1.21 ^∘C. Our results also show an important degree of inter- and intra-LCZ variability in UHA, calling for more research in the future. Nevertheless, we already find that UHA can impact green areas and reduce their cooling benefit. Such outcomes show the added value of CWS when considering future urban design.

The Andean snowpack is an important source of water for many communities. As other snow-covered regions around the world, the Andes are sensitive to black carbon (BC) deposition from fossil fuel and biomass combustion. BC darkens the snow surface, reduces the albedo, and accelerates melting. Here, we report on measurements of the BC content conducted by using the meltwater filtration (MF) technique in snow samples collected across a transect of more than 2500 km from the mid-latitude Andes to the southern tip of South America. Addressing some of the key knowledge gaps regarding the effects of the BC deposition on the Andean snow, we identified BC-impacted areas, assessed the BC-related albedo reduction, and estimated the resulting snow losses. We found that BC concentrations in our samples generally ranged from 2 to 15 ng g⁻¹, except for the nearly BC-free Patagonian Icefields and for the BC-impacted sites nearby Santiago (a metropolis of 6 million inhabitants). We estimate that the seasonal snowpack shrinking attributable to the BC deposition ranges from 4 mm water equivalent (w.e.) at relatively clean sites in Patagonia to 241 mm w.e. at heavily impacted sites close to Santiago.

A shift from fossil fuel to renewable energy is crucial in limiting global temperature increase to 2 °C above preindustrial levels. However, renewable energy technologies, solar photovoltaics, wind turbines, and electric vehicles are metal-intensive, and the mining and smelting processes to obtain the needed metals are emission-intensive. We estimate the future PM_2.5 emissions from mining and smelting to meet the metal demand of renewable energy technologies in two climate pathways to be 0.3–0.6 Tg yr⁻¹ in the 2020–2050 period, which are projected to contribute 10%–30% of total anthropogenic primary PM_2.5 combustion emissions in many countries. The concentration of mineral reserves in a few regions means the impacts are also regionally concentrated. Rapid decarbonization could lead to a faster reduction of overall anthropogenic PM_2.5 emissions but also could create more unevenness in the distributions of emissions relative to where demand occurs. Options to reduce metal-related PM_2.5 emissions by over 90% exist and are well understood; introducing policy requiring their installation could avoid emission hotspots.

Analysis of forest disturbance patterns in relation to precipitation seasonality is important for understanding African tropical forest dynamics under changing climate conditions and different levels of human activities. Newly available radar-based forest disturbance information now enables an investigation of the intra-annual relationship between precipitation and forest disturbance in a spatially and temporally explicit manner, especially in the tropics, where frequent cloud cover hinders the use of optical-based remote sensing products. In this study, we applied cross-correlation on monthly precipitation and forest disturbance time series for 2019 and 2020 at a 0.5° grid in the African rainforest. We used the magnitude of the correlation and time lag to assess the intra-annual relationship between precipitation and forest disturbance, and introduced accessibility proxies to analyse the spatial variation of the relationship. Results revealed that a significant negative correlation between forest disturbance and precipitation dominates the study region. We found that significant negative correlations appear on average closer to settlements with overall smaller variations in travel time to settlements compared to grid cells with non-significant and significant positive correlation. The magnitude of the negative correlation increases as the travel time to settlements increases, implying that forest disturbances in less accessible areas are more affected by precipitation seasonality and that in particular human-induced disturbance activities are predominantly carried out in the drier months. Few areas showed a significant positive correlation, mainly resulting from natural causes such as flooding. These new insights in the interaction between forest disturbance, precipitation and accessibility provide a step forward in understanding the complex interactions that underlie the complexity of forest loss patterns that we can increasingly capture with Earth Observation approaches. As such, they can support forest conservation and management in coping with climate change induced changes of precipitation patterns in African rainforest countries.

Policy makers have long been interested in detecting 'high-emitters', a supposedly small fraction of vehicles that make disproportionally large contributions to total fleet emissions. However, existing identification schemes often exclusively rely on snapshot measurements (i.e. emissions within less than a second), and thus simply identify vehicles with high instantaneous emissions, instead of vehicles with high average emissions over a driving period as regulated by emission standards. We design a comprehensive scheme to address this challenge by combining fleetwide remote sensing measurements with detailed second-by-second emission measurements from individual vehicles. We first determine the trip-average NO_x emission rates of individual vehicles in a Euro-5 diesel fleet measured across European locations; this allows, second, to calculate the fraction and emission contributions of high-emitters based on trip-average emission. We demonstrate that the identification of high-emitters is quite uncertain as long as it is based on single snapshots only; but 80% of the high-emitters can be identified with over 75% precision with five or more repeated measurements of the same vehicle. Compared to the conventional detection schemes, our scheme can increase the identified high-emitters and associated emission reductions by over 140%. Our method is validated and shown to be superior to the conventional interpretation of snapshot measurements.

The North Atlantic subpolar gyre influences the climate in many different ways. Here, we identified that it is also responsible for a recent extreme event of Arctic Ocean freshwater export west of Greenland. A shift in climate regimes occurred in the mid-2000s, with a significant negative trend in the dynamic sea level in the subpolar gyre since then. We found that the dynamic sea level drop induced a strong increase in freshwater export west of Greenland, in particular from 2015 to 2017, when the sea level was close to the minimum. Sea ice melting and atmospheric variability in the Arctic had only a small contribution to this event. As the exported water from the Arctic Ocean has low salinity and constituents of chemical tracers very different from those in the North Atlantic, such events might have impacts on the North Atlantic ecosystem and the climate as well. Our study suggests that such events might be predictable if the subpolar gyre sea level has certain predictability.

Restoration of fire-prone forests can promote resiliency to disturbances, yet such activities may reduce biomass stocks to levels that conflict with climate mitigation goals. Using a set of large-scale historical inventories across the Sierra Nevada/southern Cascade region, we identified underlying climatic and biophysical drivers of historical forest characteristics and projected how restoration of these characteristics manifest under future climate. Historical forest conditions varied with climate and site moisture availability but were generally characterized by low tree density (∼53 trees ha⁻¹), low live basal area (∼22 m² ha⁻¹), low biomass (∼34 Mg ha⁻¹), and high pine dominance. Our predictions reflected broad convergence in forest structure, frequent fire is the most likely explanation for this convergence. Under projected climate (2040–2069), hotter sites become more prevalent, nearly ubiquitously favoring low tree densities, low biomass, and high pine dominance. Based on these projections, this region may be unable to support aboveground biomass >40 Mg ha⁻¹ by 2069, a value approximately 25% of current average biomass stocks. Ultimately, restoring resilient forests will require adjusting carbon policy to match limited future aboveground carbon stocks in this region.

Africa's economic and population growth prospects are likely to increase energy and water demands. This quantitative study shows that energy decarbonisation pathways reduce water withdrawals (WWs) and water consumption (WC) relative to the baseline scenario. However, the more aggressive decarbonisation pathway (1.5 °C) leads to higher overall WWs than the 2.0 °C scenario but lower WC levels by 2065. By 2065, investments in low-carbon energy infrastructure increase annual WWs from 1% (52 bcm) in the 2.0 °C to 2% (85 bcm) in the 1.5 °C scenarios of total renewable water resources in Africa compared to 3% (159 bcm) in the baseline scenario with lower final energy demands in the mitigation scenarios. WC decreases from 1.2 bcm in the 2.0 °C to 1 bcm in the 1.5 °C scenario, compared to 2.2 bcm in the baseline scenario by 2065, due to the lower water intensity of the low-carbon energy systems. To meet the 1.5 °C pathway, the energy sector requires a higher WW than the 2.0 °C scenario, both in total and per unit of final energy. Overall, these findings demonstrate the crucial role of integrated water-energy planning, and the need for joined-up carbon policy and water resources management for the continent to achieve climate-compatible growth.

British Columbia's interior forests (∼400 000 km²) have experienced severe cumulative disturbance from harvesting, wildfires, and mountain pine beetle (MPB). Estimating their impacts on carbon dynamics is critical for effective forest management and climate-change mitigation strategies. This study quantifies the magnitude of historical cumulative forest disturbances and models the effect on regional carbon stocks and emissions using the Carbon Budget Model of the Canadian Forest Service from 1980 to 2018. The study region has been a sustained carbon source since 2003, with an estimated net biome production of −18.6 ± 4.6 gC m⁻² yr⁻¹ from 2003 to 2016, dropping to −90.4 ± 8.6 gC m⁻² yr⁻¹ in 2017 and 2018 due to large-scale wildfires. MPB affected areas emitted an estimated 268 ± 28 Mt C from 2000 to 2018. Harvesting transferred an estimated 153 ± 14 Mt C to forest products and these areas also emitted 343 ± 27 Mt C in 2000–2018. Areas disturbed by wildfire from 2000 to 2018 generated an estimated 100 ± 8 Mt C of emissions, 73% of which were from 2017 and 2018. Of the area burned between 2014 and 2018, 38% had been previously affected by MPB, highlighting landscape-level interactions of cumulative forest disturbance. Approximately half of decomposition carbon emissions from disturbances in 2000–2018 were calculated as incremental to the decomposition that would have otherwise occurred without MPB disturbance. The average net primary production was reduced by 10% to 335 ± 31 gC m⁻² yr⁻¹ from 2000 to 2018. We conclude that cumulative forest disturbance has driven the region's forests to become a sustained carbon source over the past two decades. While MPB and harvesting were dominant and consistent drivers, recent severe wildfires have prolonged and strengthened the carbon source. Increased disturbances, driven in part by climate change, may limit the ability of regional forests to meet national carbon emission reduction targets.

Inter-basin water transfer (IBT) is widely used to mitigate water shortage at the cost of compromising water availability in water-exporting regions. Yet, we do not know how efficient are the IBTs in alleviating inter-regional water stress in a changing climate and water supply-demand context. From a socio-hydrological perspective, we here quantify the efficiency of more than 200 IBTs across the United States by a Stress Relief Index that measures the impact of water redistribution on the overall water stress level. Based on the assumption that an IBT-induced increase and reduction in water availability would respectively constitute a positive and negative impact on regional water security, we show that 29% of the IBTs could be considered socially inefficient by 2010 as they shift water stress from water-receiving to water-exporting and downstream regions. Future stress escalations induced by growing population, declining runoff, and increasing demands for energy production and irrigation will alter IBT efficiency disproportionately. The inefficient IBTs would amount to 32% and 35% by the end of the 21st century under the scenarios of representative concentration pathway (RCP) 4.5 and RCP8.5, with 7 ∼ 16 IBTs reaching a tipping point that their role in the water system could switch from alleviating to aggravating the overall water stress. Our results indicate that the evolving climatic and socioeconomic status can largely affect transfer efficiency, highlighting the need of basin-level adaptation strategies for sustainable use of the IBTs.

Understanding population exposure to precipitation-related extreme events is important for effective climate change adaptation and mitigation measures. We analyze extreme precipitation using indices (EPIs), including consecutive dry days (CDD), annual total precipitation, simple daily intensity, and the number of extremely wet days, under the past and future climatic conditions over East Africa. The exposure of the East African population to these extreme events at 1.5 °C and 2.0 °C global warming levels (GWLs) is analyzed based on Climate Model Intercomparison Project phase 6 models. Exposure is computed from extremely wet and dry days (R95p and CDD, respectively). Under both GWLs, EPIs (except CDD) averaged over East Africa are projected to increase under the Shared Socio-economic Pathways (SSP)2-4.5 and SSP5-8.5 scenarios. The largest increase in wet events will likely occur in eastern and northern Kenya. The results also reveal an intensification of precipitation extremes over Burundi, Rwanda, and some parts of Uganda. However, small changes are expected over most parts of Kenya and Tanzania. Examination of population exposure to EPIs shows that the most prominent and net intense occurrence is over Burundi, Rwanda, and some parts of Uganda. In contrast, less change is noted to occur over vast parts of Kenya and Tanzania. Meanwhile, limiting the warming target to less than 1.5 °C but not more than 2.0 °C has 37% (44.2%) and 92% (4%) less impact on the occurrence of EPIs for R95p (CDD) under SSP2-4.5 (SSP5-8.5) scenarios, respectively. The study establishes that future exposure is predominantly driven by changes in population compared to other factors such as climate or concurrent changes in climate and population (the nonlinear interaction effect). For instance, climate effects are anticipated to contribute ∼10.6% (12.6%) of the total change in population exposure under 1.5 °C (2.0 °C) warming levels, while population and interaction effects are expected to contribute ∼77.4% (71.9%) and 12% (15.5%), respectively, under 1.5 °C (2.0 °C) scenarios. Interestingly, the projected changes in regional exposure due to the interaction effects under SSP2-4.5 are greater than the climate effect, while the reverse pattern is observed under SSP5-8.5. For example, under SSP5-8.5, climate effects for 1.5 °C and 2.0 °C are larger (after population effect) with ∼3.8 × 10⁵ (15.7%) and ∼6.1 × 10⁵ (17.5%) billion person-mm, respectively. The high exposure noted over East Africa calls for a shift in policies to instate suitable adaptation measures to cushion the already vulnerable population.

Large-ensemble climate model simulations can provide deeper understanding of the characteristics and causes of extreme events than historical observations, due to their larger sample size. However, adequate evaluation of simulated 'unseen' events that are more extreme than those seen in historical records is complicated by observational uncertainties and natural variability. Consequently, conventional evaluation and correction methods cannot determine whether simulations outside observed variability are correct for the right physical reasons. Here, we introduce a three-step procedure to assess the realism of simulated extreme events based on the model properties (step 1), statistical features (step 2), and physical credibility of the extreme events (step 3). We illustrate these steps for a 2000 year Amazon monthly flood ensemble simulated by the global climate model EC-Earth and global hydrological model PCR-GLOBWB. EC-Earth and PCR-GLOBWB are adequate for large-scale catchments like the Amazon, and have simulated 'unseen' monthly floods far outside observed variability. We find that the realism of these simulations cannot be statistically explained. For example, there could be legitimate discrepancies between simulations and observations resulting from infrequent temporal compounding of multiple flood peaks, rarely seen in observations. Physical credibility checks are crucial to assessing their realism and show that the unseen Amazon monthly floods were generated by an unrealistic bias correction of precipitation. We conclude that there is high sensitivity of simulations outside observed variability to the bias correction method, and that physical credibility checks are crucial to understanding what is driving the simulated extreme events. Understanding the driving mechanisms of unseen events may guide future research by uncovering key climate model deficiencies. They may also play a vital role in helping decision makers to anticipate unseen impacts by detecting plausible drivers.

This study examines coupled relationships among clouds, atmospheric circulation, and sea ice in Antarctic winter. We find that the wave-3 pattern dominates the leading covariability mode among cloud, atmospheric circulation, and sea ice. Both horizontal transport and vertical motion contribute to cloud formation, resulting in maximum cloud anomalies spatially between maximum meridional wind and pressure anomalies in the coupled system. The radiative effect of the clouds related to the wave-3 pattern can generate sea ice anomalies up to 12 cm thick in one month in the Amundsen Sea. It also strengthens the sea ice anomalies that are directly induced by low-level atmospheric circulation anomalies. In addition, the radiative forcing of the leading cloud mode in the lower troposphere is suppressed by the dynamic and thermodynamic effects of the circulation anomalies. These discoveries provide a better understanding of Antarctica's interactive processes, and also offer physical evidence for climate model validations.

Ecologically unequal exchange arises if more developed economies ('core') shift the environmental burden of their consumption and capital accumulation to less developed economies ('periphery'/'semi-core'). Here we demonstrate that human populations in core regions can benefit from the use of products containing toxic chemicals while transferring to the periphery the risk of human and ecological exposure to emissions associated with manufacturing and waste disposal. We use a global scale substance flow analysis approach to quantify the emissions of polybrominated diphenyl ethers (PBDEs), a group of flame retardants added to consumer products, that are embodied in the trade of chemicals, products and wastes between seven world regions over the 2000–2020 time period. We find that core regions have off-loaded PBDE emissions, mostly associated with the disposal of electrical and electronic waste (e-waste), to semi-core and peripheral regions in mainland China and the Global South. In core regions this results in small emissions that mostly occur during the product use phase, whereas in peripheral regions emissions are much higher and dominated by end of life disposal. The transfer of toxic chemical emissions between core and periphery can be quantified and should be accounted for when appraising the costs and benefits of global trade relationships.

In the twenty-first century, the effects of sea-level rise (SLR) and more intense tropical cyclones (TCs) are increasing compound coastal inundation worldwide. To facilitate the adaptation efforts being made by coastal communities, here, we use a coastal surge-wave model together with a novel statistical approach to incorporate the six joint probability density functions (PDFs) of five landfall TC parameters and SLR values, instead of the traditional five-parameter approach, which considers the five PDFs of TCs with prescribed SLR values as boundary conditions. The five-parameter approach determines the 1% annual chance of coastal inundation by conducting numerous sets of surge-wave simulations, each for a different SLR scenario, for the future TC ensemble. The six-parameter approach, however, uses a future TC and SLR ensemble to conduct only one set of surge-wave simulations without the subjective selection of an SLR scenario, and is much less uncertain and much more efficient. In this paper, we focus on the 1% risk of inundation in a large coastal flood plain in southwest Florida by incorporating intensifying TCs and accelerating SLR under a representative concentration pathway 8.5 climate scenario in 2100. The 1% risk of inundation determined by the six-parameter approach is comparable to that obtained from the traditional approach forced with the expected SLR value in 2100. The total inundation volume, total inundation area, average inundation height, and maximum inundation height are expected to dramatically increase by (5.7, 2.4, 2.6, and 2.5) times, respectively, compared to their 1982–2009 values. The coastal inundations caused by TCs and SLR are found to interact nonlinearly over the coastal flood plain. Near the coast, TCs account for 70%–80% of the total 1% inundation risk for 1 m of SLR and 30%–70% for 2 m of SLR. Therefore, future inundation analyses must consider TCs and their nonlinear interaction with SLR-induced inundation. These findings will inform local communities and help them to develop coastal adaptation plans.

Cropland area and cropping frequency play very crucial roles in determining regional food production. However, rapid urbanization accompanied by declining surplus-agricultural labor force has greatly altered patterns of agriculture land use and cropping frequency. Due to lack of continuous cropland and cropland-use intensity maps, our knowledge is still limited to understand whether the urbanization process must have a negative effect for changes in cropland-use intensity. Herein, we took the North China Plain (NCP), both the largest winter wheat and summer maize rotation area and rapidly urbanized area in China, as the study area, and used 250 m moderate resolution imaging spectroradiometer NDVI anomaly data, the correlation of NDVI time series in two neighboring years and machine learning algorithms to investigate spatiotemporal patterns and trends of cropland area and cropping frequency change over the NCP from 2000 to 2019. Results showed a significantly decreased cropland area observed since 2004 (slope = 783.8 km² a⁻¹, p < 0.01), while area of double-season cropping presented a relatively steady trend (slope = 446.9 km² a⁻¹, p = 0.335). As expected, decreased croplands were mainly occupied by urban and built-up land expansion, however, existing cropland-use intensity was yet improved. Patterns and trends of double-season cropping types were varied spatially. Particularly, the area of winter wheat and summer maize rotation presented a significantly increasing trend (slope = 3423.3 km² a⁻¹, p < 0.01). Furthermore, the respective area of winter wheat and summer maize both displayed significantly increasing trends with slope of 2953.8 and 2874.9 km² a ⁻¹(p < 0.01) in entire period. Land-use and grain subsidy policies are considered as largely responsible for this phenomenon. These satellite-observed findings highlight that positive land-use policies and managements will be helpful for profitably keeping/improving the harvest area.

Islands are uniquely vulnerable to extreme weather events and food insecurity, and have additional response challenges due to their limited landmasses and economies, isolation, colonial legacies, and high dependence of food imports. Domestic farmers have a key role in producing food for island communities like Puerto Rico, which can safeguard food security when food importation may be challenging. Nevertheless, in the context of disaster, farmers themselves may be vulnerable to food insecurity and unable to contribute to domestic markets. This paper examines Puerto Rican farmers households' food security in the aftermath of 2017's Hurricane Maria using a social-ecological lens. Survey data from 405 farmers gathered eight months after Maria, coupled with biophysical data from the hurricane's impacts (winds, rains, and landslides), were analyzed. Overall, 69% of farmers experienced at least one month of food insecurity in the aftermath of Hurricane Maria, and 38% reported persistent food insecurity (three months or more). A multinomial logistic regression suggests that biophysical impacts, but especially social factors, such as age and constraint access to external sources of support, are linked with persistent food insecurity. This suggests that the biophysical impacts of the hurricane interact with existing infrastructure and social resources to affect farmer vulnerability and the food environment in different ways. Thus, strengthening adaptive capacity in multiple domains can help farmers and vulnerable populations better navigate the disruptions faced during disasters to alleviate food insecurity.

Paleo-climate proxy records documenting sea-ice extent are important sources of information to assess the time of emergence and magnitude of on-going changes in the Arctic Ocean and better predict future climate and environmental evolution in that region. In this study, a suite of geochemical tracers including total organic carbon (TOC), total nitrogen (TN), carbon/nitrogen ratio (C/N), stable isotope composition of organic carbon and nitrogen (δ¹³C, δ¹⁵N), and phytoplankton biomarkers (highly branched isoprenoids (HBIs) and sterols) were measured in a marine sediment core to document the sea-ice variability in the Chukchi Sea since the beginning of the Industrial Era. The downcore profile of the sea-ice proxy HBIs suggests a transition from extensive sea ice in the late 19th century to Marginal Ice Zone (MIZ) in AD 1930–1990s and then moderate sea-ice cover since 1990s. Rising of all HBI abundances between AD 1865–1875 indicate a transient retreat of summer ice edge off the shelf followed by a return to near-perennial sea ice till 1920–1930 as revealed by the absence of HBIs and brassicasterol. Sea ice retreat occurred again in AD 1920–1930 and followed by colder decades in 1940s–1960s before a sustained decline since the 1990s. The downcore profile of C/N, δ¹³C of organic matter and sterols indicates a gradual increase of terrigenous inputs accelerating during the most recent decades likely due to enhanced fluvial run-off and sediment transport by sediment-laden sea ice. Concomitantly, increasing δ¹⁵N values suggest limited nutrient utilization due to enhanced stratification of the surface ocean caused by increased freshening. The role of the Arctic oscillation (AO), the Pacific decadal oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO) are discussed to explore potential drivers of the observed sea-ice changes.

Human activities both aggravate and alleviate streamflow drought. Here we show that aggravation is dominant in contrasting cases around the world analysed with a consistent methodology. Our 28 cases included different combinations of human-water interactions. We found that water abstraction aggravated all drought characteristics, with increases of 20%–305% in total time in drought found across the case studies, and increases in total deficit of up to almost 3000%. Water transfers reduced drought time and deficit by up to 97%. In cases with both abstraction and water transfers into the catchment or augmenting streamflow from groundwater, the water inputs could not compensate for the aggravation of droughts due to abstraction and only shift the effects in space or time. Reservoir releases for downstream water use alleviated droughts in the dry season, but also led to deficits in the wet season by changing flow seasonality. This led to minor changes in average drought duration (−26 to +38%) and moderate changes in average drought deficit (−86 to +369%). Land use showed a smaller impact on streamflow drought, also with both increases and decreases observed (−48 to +98%). Sewage return flows and pipe leakage possibly counteracted the effects of increased imperviousness in urban areas; however, untangling the effects of land use change on streamflow drought is challenging. This synthesis of diverse global cases highlights the complexity of the human influence on streamflow drought and the added value of empirical comparative studies. Results indicate both intended and unintended consequences of water management and infrastructure on downstream society and ecosystems.

A shelterbelt is an important measure to protect farmland and increase crop yield. However, how a shelterbelt structure affects crop yield is still unclear due to the difficulties accessing sufficient data from traditional field observations. To address this problem, we developed an innovative framework to estimate the shelterbelt structure and crop yield profile at a regional scale based on Google Earth and Sentinel-2 data. Using this method, we quantified the impact of the shelterbelt structure on the corn yield at 302 shelterbelts in the Northeast Plain of China. Generally, the corn yield increased (by 2.41% on average) within a distance of 1.2–15 times the tree height from the shelterbelt. Such an effect was particularly prominent within a distance of two to five times the tree height, where the corn yield was significantly increased by up to 4.63%. The structure of the shelterbelt has a significant effect on the magnitude of increase in yield of the surrounding corn. The increment of corn yields with high-, medium-high-, medium- and low-width-gap grade shelterbelt were 2.01%, 2.21%, 1.99%, and 0.91%, respectively. The medium-high grade shelterbelt achieved the largest yield increase effect. The location of the farmland relative to the shelterbelt also affected the yield, with a yield increase of 2.39% on the leeward side and 1.89% on the windward side, but it did not change the relationship between the yield increase effect and the shelterbelt structure. Our findings highlight the optimal shelterbelt structure for increasing corn yield, providing practical guidance on the design and management of farmland shelterbelts for maximizing yield.

On 14 August 2021, rain fell on the peak of Greenland for the first time on record. The atmospheric circulation and water vapour transport responsible for the rain were investigated. A high-pressure ridge favoured southwesterly advection of warm and moist air, the intrusion of which contributed to the rainfall. At the same time, Summit station observed above-freezing temperatures, which was the third time in a decade, after summers 2012 and 2019. The previous two warm events also included influxes of moisture, but no rainfall. Comparison between them and the 2021 event show different atmospheric pressure fields and water vapour transports. In 2021, the moisture from the southwest ascended the sloping ice sheet, whereas in the prior events moisture was transported from the southeast in smaller amounts. The sufficient supply of warm and moist air was the key factor in the 2021 rain event.

Satellite observations since the early 1980s have revealed a trend of 'Earth greening' across global terrestrial ecosystems. Dryland vegetation is more sensitive to climate change and human activities. China's drylands are among the largest in extent worldwide, and large-scale ecological restoration of these areas has been implemented since the late 1970s, which has resulted in more complicated but still poorly quantified vegetation dynamics. To figure out the vegetation dynamics and associated driving forces, we provide an assessment of the vegetation dynamics from 1982 to 2015 using the CO₂ fertilization effect function, principal component regression, Residual Trend analysis, and Breaks For Additive Seasonal and Trend methods based on the ERA5 climate factors and GIMMS 3.1 normalized difference vegetation index datasets. This study shows that anthropogenic impacts and CO₂ fertilization have jointly led to vegetation greening in China's drylands since the 1980s, and ecological restoration has accelerated this greening since the 2000s. The results show that the vegetation greening in China's drylands (41.51% of the study area, +0.60 × 10⁻³ yr⁻¹) is mainly driven by CO₂ fertilization (+0.55 × 10⁻³ yr⁻¹) and anthropogenic activities (+0.12 × 10⁻³ yr⁻¹). The anthropogenic effects are especially higher on the Loess Plateau (+1.01 × 10⁻³ yr⁻¹) and the Three-North region (+0.23 × 10⁻³ yr⁻¹). The vegetation dynamics shifts in 6.73% (31.64 Mha) of China's drylands were directly attributed to anthropogenic impacts around the 2000s. When the anthropogenic effect was intensified, the vegetation dynamics shifted from no change to greening and vice versa, which significantly intensified the vegetation greening since the 1980s. These results capture the processes of ecological programs and provide an assessment of the effects of ecological restoration. This work provides a credible attribution of the vegetation greenness dynamics and trend shifts in China's drylands, thus facilitating a better understanding of regional environmental change and management.

The global ocean is warming and has absorbed 90% of the Earth Energy Imbalance over 2010–2018 leading to global mean sea level rise. Both ocean heat content (OHC) and sea level trends show large regional deviations from their global means. Both quantities have been estimated from in-situ observations for years. However, in-situ profile coverage is spatially uneven, leading to uncertainties when assessing both OHC and sea level trends, especially at regional scale. Recently, a new possible driver of regional sea level and OHC trends has been highlighted using eddy-permitting ensemble ocean simulations over multiple decades: non-linear ocean processes produce chaotic fluctuations, which yield random contributions to regional decadal OHC and sea level trends. In-situ measurements capture a combination of the atmospherically-forced response and this intrinsic ocean variability. It is therefore important to understand the imprint of the chaotic ocean variability recorded by the in-situ measurement sampling in order to assess its impact and associated uncertainty on regional budgets. A possible approach to investigate this problem is to use a set of synthetic in-situ-like profiles extracted from an ensemble of forced ocean simulations started from different states and integrated with the same atmospheric forcing. Comparisons between the original ensemble outputs and the remapped, subsampled, in-situ-like profiles elucidate the contribution of chaotic ocean variability to OHC and regional sea level trends. Our results show that intrinsic variability may be large in eddy-active regions in the gridded model outputs, and remains substantial when using the in-situ sampling-based estimates. Using the latter, the same result is also found on large scales, for which atmospheric forcing has been identified as the main driver. Our results suggest accounting for this intrinsic ocean variability when assessing regional OHC and sea level trend budgets on decadal time scales.

The expansion of wind power poses distinct and varied geographic challenges to a sustainable energy transition. However, current knowledge of its land use impacts and synergies is limited by reliance on static characterizations that overlook the role of turbine technology and plant design in mediating interactions with the environment. Here, we investigate how wind technology development and innovation have shaped landscape interactions with social and ecological systems within the United States and contribute to evolving land area requirements. This work assesses trends in key land use facets of wind power using a holistic set of metrics to establish an evidence base that researchers, technology designers, land use managers, and policymakers can use in envisioning how future wind-intensive energy systems may be jointly optimized for clean energy, social, and environmental objectives. Since 2000, we find dynamic land occupancy patterns and regional trends that are driven by advancing technology and geographic factors. Though most historical U.S. wind deployment has been confined to the temperate grassland biome in the nation's interior, regional expansion has implicated diverse land use and cover types. A large percentage of the typical wind plant footprint (∼96% to $> \,$99%) is not directly impacted by permanent physical infrastructure, allowing for multiple uses in the spaces between turbines. Surprisingly, turbines are commonly close to built structures. Moreover, rangeland and cropland have supported 93.4% of deployment, highlighting potential synergies with agricultural lands. Despite broadly decreasing capacity densities, offsetting technology improvements have stabilized power densities. Land use intensity, defined as the ratio of direct land usage to lifetime power generation of wind facilities, has also trended downwards. Although continued deployment on disturbed lands, and in close proximity to existing wind facilities and other infrastructure, could minimize the extent of impacts, ambitious decarbonization trajectories may predispose particular biomes to cumulative effects and risks from regional wind power saturation. Increased land-use and sustainability feedback in technology and plant design will be critical to sustainable management of wind power.

PM_2.5 emissions from the transportation sector are a source of haze pollution in China, to which, however, less attention is paid by society. The decoupling relationships between PM_2.5 emissions and economic growth from the transportation sector in the eastern, central, and western regions of China from 2010 to 2017 are analyzed by using the Tapio decoupling model. On this basis, in the transportation sector, socioeconomic factors influencing PM_2.5 emissions and effective means of controlling PM_2.5 emissions are studied by using a logarithmic mean Divisia index model. The results indicate that: (a) in China's transportation sector, the decoupling relationships of the two aspects in the eastern, central, and western regions show an N-shaped trend, that is, the rate of change in PM_2.5 emissions from the transportation sector gradually exceeds that of economic development. The strong decoupling changes into an expansive coupling in the eastern and central regions, while the strong decoupling becomes an expansive negative decoupling in the western region. (b) Economic growth and population growth mainly contribute to the increase of PM_2.5 emissions. Improvements of the energy structure and a decrease in transport intensity are the main factors driving a reduction in PM_2.5 emissions. (c) Due to regional differences in the 'rebound effect' and 'technological effect', technological progress has increased PM_2.5 emissions from the transportation sector in the central region, while reduced such emissions in the eastern and western regions. This research provides targeted policy reference for regional governance of PM_2.5 emissions from the transportation sector.

With climate change threatening agricultural productivity and global food demand increasing, it is important to better understand which farm management practices will maximize crop yields in various climatic conditions. To assess the effectiveness of agricultural practices, researchers often turn to randomized field experiments, which are reliable for identifying causal effects but are often limited in scope and therefore lack external validity. Recently, researchers have also leveraged large observational datasets from satellites and other sources, which can lead to conclusions biased by confounding variables or systematic measurement errors. Because experimental and observational datasets have complementary strengths, in this paper we propose a method that uses a combination of experimental and observational data in the same analysis. As a case study, we focus on the causal effect of crop rotation on corn (maize) and soybean yields in the Midwestern United States. We find that, in terms of root mean squared error, our hybrid method performs 13% better than using experimental data alone and 26% better than using the observational data alone in the task of predicting the effect of rotation on corn yield at held-out experimental sites. Further, the causal estimates based on our method suggest that benefits of crop rotations on corn yield are lower in years and locations with high temperatures whereas the benefits of crop rotations on soybean yield are higher in years and locations with high temperatures. In particular, we estimated that the benefit of rotation on corn yields (and soybean yields) was 0.85 t ha⁻¹ (0.24 t ha⁻¹) on average for the top quintile of temperatures, 1.03 t ha⁻¹ (0.21 t ha⁻¹) on average for the whole dataset, and 1.19 t ha⁻¹ (0.16 t ha⁻¹) on average for the bottom quintile of temperatures. This association between temperatures and rotation benefits is consistent with the hypothesis that the benefit of the corn-soybean rotation on soybean yield is largely driven by pest pressure reductions while the benefit of the corn-soybean rotation on corn yields is largely driven by nitrogen availability.

Can countries reorient their productive capacity to become more environmentally friendly and inclusive? To investigate this question this paper uses a standard Input-Output modeling framework and data from 141 countries and regions to construct a new global dataset of employment, value-added, greenhouse gas (GHG) emissions (disaggregated into CO₂ and Non-CO₂ elements), and air pollution (including nine categories of air pollutants such as PM_2.5) multipliers from supply side investments. We find that many of the traditional sectors in agriculture and industry have large employment multipliers, but also generate male dominant, lower skill employment, and tend to have higher emission multipliers. It is in economies dominated by these sectors that trade-offs to a 'greener' transition will emerge most sharply. However, we find a substantial heterogeneity in outcomes, so even in these economies, there exist other sectors with high employment multipliers and low emissions, including sectors that are more conducive to female employment. In addition, we find a high correlation between industries that generate GHG emissions, which cause long term climate impacts, and those that generate air pollution, which have immediate harmful impacts on human health, suggesting that policies could be designed to simultaneously confer longer climate benefits with immediate health improvements. Our results confirm some of the findings from recent research and shed new light on opportunities for greening economies.

In recent decades, the Barents Sea has warmed more than twice as fast as the rest of the Arctic in winter, but the exact causes behind this amplified warming remain unclear. In this study, we quantify the wintertime Barents Sea warming (BSW, for near-surface air temperature) with an average linear trend of 1.74 °C decade⁻¹ and an interdecadal change around 2003 based on a surface energy budget analysis using the ERA5 reanalysis dataset from 1979–2019. Our analysis suggests that the interdecadal change in the wintertime near-surface air temperature is dominated by enhanced clear-sky downward longwave radiation (CDLW) associated with increased total column water vapor. Furthermore, it is found that a mode of atmospheric variability over the North Atlantic region known as the Barents oscillation (BO) strongly contributed to the BSW with a stepwise jump in 2003. Since 2003, the BO turned into a strengthened and positive phase, characteristic of anomalous high pressure over the North Atlantic and South of the Barents Sea, which promoted two branches of heat and moisture transport from southern Greenland along the Norwegian Sea and from the Eurasian continent to the Barents Sea. This enhanced the water vapor convergence over the Barents Sea, resulting in BSW through enhanced CDLW. Our results highlight the atmospheric circulation related to the BO as an emerging driver of the wintertime BSW through enhanced meridional atmospheric heat and moisture transport over the North Atlantic Ocean.

Water hyacinth is an aquatic free-floating plant that is highly invasive, to the extent that it is now present in most freshwater bodies in sub-tropical and tropical regions worldwide. Due to the ecological and socio-economic damages these plants can cause, monitoring their spatial coverage and seasonality is key for development of timely and efficient mitigation measures. Hyacinth patches are sufficiently large to be detectable in high-resolution satellite imagery, allowing for monitoring using freely available remote sensing data collected by platforms such as Sentinel-2. In this study, we estimated water hyacinth coverage and seasonal dynamics over three years (2018–2020) for the Saigon river, Vietnam. Using a Naïve Bayes classifier, hyacinth coverage was mapped in Sentinel-2 imagery with an accuracy of 91%. We show that the dry season (December-May) corresponds to highest water hyacinth abundance, with maximum coverage in February. Lower rainfall and relative humidity were found to be highly correlated (r = −0.56 and r = −0.64, respectively) with higher hyacinth cover. We also detected substantial interannual variability: annual means in hyacinth coverage varied by a factor of five between the 2018/2019 and 2020 yearly averages, with peak cover occurring in February 2020. The percentage of Saigon river covered by hyacinths over the entire study area peaked at 14% and reached as much as 24% for the upstream section. This confirms the prevalence of these invasive plants in the region, and the growing threat to river navigability and biodiversity. Our study provides an openly available automated workflow for long-term monitoring of hyacinth coverage, which can be scaled-up and extended to other freshwater systems. As such, it provides a step for building a large-scale monitoring tool of this highly invasive species, which may also be used for designing mitigation and reduction strategies of hyacinth and the pollutants it carries along.

With the goal of informing local food-action planning, this paper develops the first county-level database detailing agrifood consumption and production across 3114 counties in the United States. The database covers 12 070 food items that comprise the entire diet, mapping them to the production demand of 95 agrifood commodities. Agrifood demand is delineated further into fresh and processed components, along with characterization of animal feed, and compared with local food production to yield the current local agrifood capacity (CLC). CLC results are shown for individual agrifoods and for aggregated categories (e.g., on average, 0.03 for fruits and nuts, 0.24 for vegetables, 0.31 for non-meat animal products) across all US counties. CLC results for the entire diet find that a large proportion of US counties can be self-sufficient in individual agrifood commodities (ranging from <0.5% of counties for agrifoods like hops, papayas, and artichokes to 59% of counties for beef), and 23% of US counties can supply over half of their total human dietary demand through local production, but only 9% of the US population resides in these counties. Such granular, subnational baselines are essential to inform future goal-setting for urban agriculture.

High levels of crop species diversity are considered beneficial. However, increasing diversity might be difficult because of environmental constraints and the reliance on a few major crops for most food supply. Here we introduce a theoretical framework of hierarchical levels of crop diversity, in which the environmental requirements of crops limit potential diversity, and the demand for agricultural products further constrain attainable crop diversity. We estimated global potential, attainable, and current crop diversity for grid cells of 86 km². To do so, we first estimated cropland suitability values for each of 171 crops, with spatial distribution models to get estimations of relative suitability and with a crop model to estimate absolute suitability. We then used a crop allocation algorithm to distribute the required crop area to suitable cropland. We show that the attainable crop diversity is lower in temperate and continental areas than in tropical and coastal regions. The diversity gap (the difference between attainable and current crop diversity) is particularly large in most of the Americas and relatively small in parts of Europe and East Asia. By filling these diversity gaps, crop diversity could double on 84% of the world's agricultural land without changing the aggregate amount of global food produced. It follows that while there are important regional differences in attainable diversity, specialization of farms and regions is the main reason for low levels of local crop diversity across the globe, rather than our high reliance on a few crops.

Anthropogenic fossil fuel burning increases atmospheric carbon dioxide (CO₂) concentration, which is adjusting the climate system. The direct impact of rising CO₂ levels and climate feedback alters the terrestrial carbon stores. Land stores are presently increasing, offsetting a substantial fraction of CO₂ emissions. Less understood is how this human-induced carbon cycle perturbation interacts with other terrestrial biogeochemical cycles. These connections require quantification, as they may eventually suppress land fertilisation, and so fewer emissions are allowed to follow any prescribed future global warming pathway. Using the new Joint UK Land Environment Simulator-CN large-scale land model, which contributed to Coupled Model Intercomparison Project Phase 6 as the land component of the UK Earth System Model v1 climate model, we focus on how the introduction of the simulated terrestrial nitrogen (N) cycle modulates the expected evolution of vegetation and soil carbon pools. We find that the N-cycle suppresses, by approximately one-third, any future gains by the global soil pool when compared to calculations without that cycle. There is also a decrease in the vegetation carbon gain, although this is much smaller. Factorial simulations illustrate that N suppression tracks direct CO₂ rise rather than climate change. The finding that this CO₂-related effect predominantly influences soil carbon rather than vegetation carbon, we explain by different balances between changing carbon uptake levels and residence times. Finally, we discuss how this new generation of land models may gain further from emerging point knowledge held by the detailed ecological modelling community.

Carbon accounting is important for quantifying the sources of greenhouse gas (GHG) emissions that are driving climate change, and is increasingly being used to guide policy, investment, business, and regulatory decisions. The current practice for accounting emissions from consumed electricity, guided by standards like the GHG protocol, uses annual-average grid emission factors, although previous studies have shown that grid carbon intensity varies across seasons and hours of the day. Previous case studies have shown that annual-average carbon accounting can bias emission inventories, but none have shown that this bias is substantial or widespread. This study addresses this gap by calculating emission inventories for thousands of residential, commercial, industrial, and agricultural facilities across the US, and explores the magnitude and direction of this bias compared to hourly accounting of emissions. Our results show that annual-average accounting can over- or under-estimate carbon inventories as much as 35% in certain settings but result in effectively no bias in others. Bias will be greater in regions with high variation in carbon intensity, and for end-users with high variation in their electricity consumption across hours and seasons. As variation in carbon intensity continues to grow with growing shares of variable and intermittent renewable generation, these biases will only continue to worsen in the future. In most cases, using monthly-average emission factors does not substantially reduce bias compared to annual averages. Thus, the authors recommend that hourly accounting be adopted as the best practice for emissions inventories of consumed electricity.

Climate change has the potential to impact headwater streams in the Arctic by thawing permafrost and subsequently altering hydrologic regimes and vegetation distribution, physiognomy and productivity. Permafrost thaw and increased subsurface flow have been inferred from the chemistry of large rivers, but there is limited empirical evidence of the impacts to headwater streams. Here we demonstrate how changing vegetation cover and soil thaw may alter headwater catchment hydrology using water budgets, stream discharge trends, and chemistry across a gradient of ground temperature in northwestern Alaska. Colder, tundra-dominated catchments shed precipitation through stream discharge, whereas in warmer catchments with greater forest extent, evapotranspiration (ET) and infiltration are substantial fluxes. Forest soils thaw earlier, remain thawed longer, and display seasonal water content declines, consistent with greater ET and infiltration. Streambed infiltration and water chemistry indicate that even minor warming can lead to increased infiltration and subsurface flow. Additional warming, permafrost loss, and vegetation shifts in the Arctic will deliver water back to the atmosphere and to subsurface aquifers in many regions, with the potential to substantially reduce discharge in headwater streams, if not compensated by increasing precipitation. Decreasing discharge in headwater streams will have important implications for aquatic and riparian ecosystems.

Few studies have used empirical evidence of past adaptation to project temperature-related excess mortality under climate change. Here, we assess adaptation in future projections of temperature-related excess mortality by employing evidence of shifting minimum mortality temperatures (MMTs) concurrent with climate warming of recent decades. The study is based on daily non-external mortality and daily mean temperature time-series from 11 Spanish cities covering four decades (1978–2017). It employs distributed lag non-linear models (DLNMs) to describe temperature-mortality associations, and multivariate mixed-effect meta-regression models to derive city- and subperiod-specific MMTs, and subsequently MMT associations with climatic indicators. We use temperature projections for one low- and one high-emission scenario (ssp126, ssp370) derived from five global climate models. Our results show that MMTs have closely tracked mean summer temperatures (MSTs) over time and space, with meta-regression models suggesting that the MMTs increased by 0.73 °C (95%CI: 0.65, 0.80) per 1 °C rise in MST over time, and by 0.84 °C (95%CI: 0.76, 0.92) per 1 °C rise in MST across cities. Future projections, which include adaptation by shifting MMTs according to observed temporal changes, result in 63.5% (95%CI: 50.0, 81.2) lower heat-related excess mortality, 63.7% (95%CI: 30.2, 166.7) higher cold-related excess mortality, and 11.2% (95%CI: −5.5, 39.5) lower total temperature-related excess mortality in the 2090s for ssp370 compared to estimates that do not account for adaptation. For ssp126, assumptions on adaptation have a comparatively small impact on excess mortality estimates. Elucidating the adaptive capacities of societies can motivate strengthened efforts to implement specific adaptation measures directed at reducing heat stress under climate change.

Solar geoengineering has been suggested as a means to cool the planet and ameliorate climate impacts in the Arctic. However, few studies approach this idea from the viewpoint of Arctic communities. We explore the substantive rationale for public engagement with solar geoengineering research, including the premises that: (a) evaluation of local impacts by communities can generate better knowledge about what modeling results mean; and (b) ideas and questions surfaced in public discussions can contribute to and shape scientific research. We convened focus groups in Finnish Lapland, conducted scientific analysis of climate model output on albedo modification based upon the discussions, and returned a year later to discuss the results. The increased granularity of scientific information highlighted the limited scientific basis for decisions, which turned the discussions back towards questions of ethics and justice. We conclude that while there are serious limitations to global public decision-making on climate intervention, in the absence of formal governance, co-producing research could act as one de facto form of governance.

Sectoral coverage that plays a critical role in operationalizing the emission trading scheme (ETS), has gained substantive attention. Despite the insightful views on sectoral coverage from the emission reduction potential or carbon leakage, previous studies overlook the cost-effectiveness of ETS in the sense that the varying marginal contributions of each sector to reducing emission abatement costs (EACs) (which is defined as marginal cost savings, MCSs) remain underexplored. To fill this gap, this paper proposes a costs-oriented approach for sectoral coverage (COASCO), which ranks the sectors by estimating and comparing their MCSs. Taking China's climate targets by 2030 as an example, we conduct an empirical study that implements the COASCO method to explore the impacts of sectoral coverage on China's EACs. Our analysis demonstrates that, while coverage extension generally reduces China's EACs, a small sectoral coverage can already lead to a substantial decline in the national EACs. The results underpin the Pareto principle that covering six sectors (i.e. Electricity production, Metallurgy, Transport and storage, Petroleum and gas, Nonmetal mining) out of 29 can reduce China's EACs by over 80% compared to covering Electricity production only. Although coverage extension may reduce the differences in EACs between sectors and improve market activation, extending the sectoral coverage probably gives rise to the number of big carbon traders, which thenincreases the risks of market manipulation. As a result, covering those six sectors can reach a balance between ETS market activation and risks. By providing a generalized and systematic framework for determining the sectoral coverage, this study makes it possible to minimize the total EACs associated with any sectoral coverages, thus assisting policymakers in fulfilling China's latest ambitious goals of reaching carbon peaking by 2030 and carbon neutrality by 2060 in a cost-effective manner.

Variables describing the abiotic environment (e.g. climate, topography or biogeographic history) have a long tradition of use as predictors of tree species richness patterns. However, these variables may capture variations in richness related to climate, but not those that are related to soil type or forest disturbance. Canopy structure has previously been shown to provide information on the variation of tree species richness, with richness generally increasing with larger canopy heights and denser foliage. The use of canopy structure is increasingly relevant with the availability of such data from the Global Ecosystem Dynamics Investigation (GEDI), a lidar mission onboard the International Space Station. In this analysis we show that GEDI canopy structure explains up to 66% of the variation in tree species richness in natural forests without a history of recent disturbance across the globe. However, this portion overlaps with the variation (up to 80%) explained by environmental and biogeographical variables. Our results show that relationships between tree species richness on one side and climate and canopy structure on the other side are not as straightforward as we initially expected, and should be further investigated across both natural and disturbed forests.

The human-earth system is confronted with the challenge of providing a range of resources for a growing and more prosperous world population while simultaneously reducing environmental degradation. The sustainable development goals and the planetary boundaries define targets to manage this challenge. Many of these are linked to the land system, such as biodiversity, water, food, nutrients and climate, and are strongly interconnected. A key question is how measures can be designed in the context of multi-dimensional sustainability targets to exploit synergies. To address this, a nexus approach is adopted that acknowledges the interconnectedness between the important sub-systems water, land, food, and climate. This study quantifies synergies and trade-offs from ambitious interventions in different components of this water-land-fod-climate nexus at the global scale. For this purpose, a set of six harmonized scenarios is simulated with the MAgPIE and IMAGE models. The multi-model approach improves robustness of the results while shedding light on variations coming from different modelling approaches. Our results show that measures in the food component towards healthy diets with low meat consumption have synergies with all other nexus dimensions: Increased natural land improving terrestrial biodiversity (+4% to +8%), lower greenhouse gas emissions from land (−45% to −58%), reduced irrigation water withdrawals to protect or restore hydrological environmental flows (−3% to −24%), and reductions in nitrogen surpluses (−23% to −35%). Climate mitigation measures in line with the Paris Agreement have trade-offs with the water and food components of the nexus, as they adversely affect irrigation water withdrawals (+5% to +30% in 2050 compared to reference scenario) and food prices (+1% to +20%). The analysis of a scenario combining all measures reveals how certain measures are in conflict while others reinforce each other. This study provides an example of a nexus approach to scenario analysis providing input to the next generation of pathways aiming to achieve multiple dimensions of sustainable development.

Overwintering fires are a historically rare phenomenon but may become more prevalent in the warming boreal region. Overwintering fires have been studied to a limited extent in boreal North America; however, their role and contribution to fire regimes in Siberia are still largely unknown. Here, for the first time, we quantified the proportion of overwintering fires and their burned areas in Yakutia, eastern Siberia, using fire, lightning, and infrastructure data. Our results demonstrate that overwintering fires contributed to 3.2 ± 0.6% of the total burned area during 2012–2020 over Yakutia, compared to 31.4 ± 6.8% from lightning ignitions and 51.0 ± 6.9% from anthropogenic ignitions (14.4% of the burned area had unknown cause), but they accounted for 7.5 ± 0.7% of the burned area in the extreme fire season of 2020. In addition, overwintering fires have different spatiotemporal characteristics than lightning and anthropogenic fires, suggesting that overwintering fires need to be incorporated into fire models as a separate fire category when modelling future boreal fire regimes.

Many climate subsystems are thought to be susceptible to tipping—and some might be close to a tipping point. The general belief and intuition, based on simple conceptual models of tipping elements, is that tipping leads to reorganization of the full (sub)system. Here, we explore tipping in conceptual, but spatially extended and spatially heterogenous models. These are extensions of conceptual models taken from all sorts of climate system components on multiple spatial scales. By analysis of the bifurcation structure of such systems, special stable equilibrium states are revealed: coexistence states with part of the spatial domain in one state, and part in another, with a spatial interface between these regions. These coexistence states critically depend on the size and the spatial heterogeneity of the (sub)system. In particular, in these systems the crossing of a tipping point not necessarily leads to a full reorganization of the system. Instead, it might lead to a reorganization of only part of the spatial domain, limiting the impact of these events on the system's functioning.

This paper presents the projected changes in daytime-nighttime compound heat waves (HWs) (i.e. concurrent occurrence of HWs both in daytime and nighttime) and associated population exposure in China under the shared socioeconomic pathway (SSP)2-4.5 and SSP5-8.5 scenarios based on the Coupled Model Intercomparison Project phase 6 simulations. A comparison with the changes in daytime HWs (i.e. occurring only in daytime) or nighttime HWs (i.e. occurring only in nighttime) is also conducted. The results generally indicate an aggravated risk of compound HWs in China in the future under warmer scenarios. On the national average, the compound HWs are projected to increase persistently toward the end of the 21st century, with larger increase under SSP5-8.5 than that under SSP2-4.5. The greatest changes occur in northwest China and southern China. Compared with the daytime or nighttime HWs, the projected increase of compound HWs is the greatest. Accordingly, the proportion of compound HWs to the total HW events tends to increase and that of daytime HWs tends to decrease toward the end of the 21st century. The substantial increases in the frequency of compound HWs are expected to cause a significant increase in population exposure across the entire country. The projected increase of nationally averaged population exposure is 12.2-fold (7.9-fold) of the current in the mid-century (2046–2065) and further enhances to 16.3-fold (12.4-fold) in the end-century (2081–2100) under SSP5-8.5 (SSP2-4.5). The largest increases are distributed in western China and southern China. These findings raise the necessity and urgency for policy-makers and the public to develop measurements to address compound HW risks.

The Laurentian Great Lakes system is a major global sink for plastic debris. An area of 10 m² on each of sixty-six Great Lakes beaches was sampled for large micro-, meso- and macroplastic items. A total of 21 592 plastic items were collected and categorized. Pre-production plastic pellets were the most abundant debris type, accounting for 58.3% of the total count. The remaining 42.7% of the debris items are the focus of this study. Detailed, multi-step characterization was performed with the plastics being categorized using physical identification, known usage, and Fourier transform infrared spectroscopy (FTIR). Values of 805.5 items m⁻² at Baxter Beach in Sarnia, Ontario, Canada, and 688.1 items m⁻² at Bronte Beach in Oakville, Ontario, Canada are the highest of all sampling locations. Sampling sites on only three beaches contained no plastic debris: Bay City in Michigan, U.S.A., Presque Isle State Park in Erie, Pennsylvania, U.S.A. and Pebble Beach in Marathon, Ontario, Canada. The plastic items sampled were mainly large microplastics (68.4% of total) with a total of 1477.5 items m⁻², followed by mesoplastics (27.3% of total) with 598.8 items m⁻², and macroplastics (4.3% of total) with 91.9 items m⁻². By mass, the microplastic fraction accounted for 25.61 g m² (14.1%), the mesoplastic for 47.06 g m² (25.9%), and macroplastic for 109.3 g m² (60.1%). A total of 3004 items were determined as specific polymers based on physical properties, known polymer usage, Resin Identification Code, and FTIR. A total of 1227 plastic items (40.8% of total) were identified as expanded polystyrene. The 49 most common items, excluding pellets, were scored using a matrix scoring technique to determine their potential general origin. It was determined that these items mostly originated from shoreline and urban sources, whereas pellets originated from the plastics industry.

Natural forest regeneration is a key component of global ecosystem restoration scenarios. Regenerated forests, however, may not persist and a better understanding of the drivers of forest persistence is critical to ensure the success of restoration efforts. We used 35 years of detailed land cover maps to quantify forest regeneration and study the drivers of regenerated forest persistence and longevity in the Brazilian Atlantic Forest, a restoration hotspot. We mapped over 4.47 Mha of native forest regenerated in the region between 1985 and 2019, of which, two thirds persisted until 2019 (3.1 Mha). However, mean age of ephemeral (i.e. cleared before 2019) forest regeneration was only 7.9 years, suggesting a rapid turnover of regrowing forests under certain conditions. Regenerated forests had greater longevity and probability of persistence in steeper slopes, close to rivers and existing forests, near permanent agriculture, and in areas with higher Gross Development Product and agricultural yield, but were less likely to persist in areas with higher rural-urban population ratios. Regeneration occurred predominantly in pasturelands and areas of shifting agriculture, but it was also less likely to persist within these dynamic landscapes. Specific public policies should stimulate forest regeneration in areas of consolidated agriculture, where forest permanence tends to be higher. The ephemerality of forest regeneration in the Brazilian Atlantic Forest highlights the importance of favorable conditions and policies that promote second-growth forest persistence in tropical regions. Conservation of regenerating forests is critical for meeting national and global restoration and climate mitigation goals.

In Uganda, upgrading smallholder agriculture is a necessary step to achieve the interlinked sustainable development goals of hunger eradication, poverty reduction and land degradation neutrality. However, targeting the right restoration practices and estimate their cost-benefit at the national scale is difficult given the highly contextual nature of restoration practices and the diversity of small-scale interventions to be adopted. By analysing the context-specific outcomes of 82 successful case studies on different sustainable land and water management (SLWM) in Uganda, we estimated that out-scaling of existing successful practices to 75% of agricultural land would require a one-time investment of US$ 4.4 billion from smallholders. Our results show that, besides the many social and environmental benefit commonly associated to SLWM, a wide outscale of SLWM could generate US$ 4.7 billion every year, once the practices are fully operational. Our context-specific estimates highlight the profitability of investing in smallholder farming to achieve the sustainable development goals in Uganda, with geographical differences coming from specific social-ecological conditions. This study can guide sustainable intensification development by targeting the most suitable SLWM practices and plan for adequate financial support from government, investors and international development aids to smallholder farming.

Spatio-temporal inventory of natural hazards is a challenging task especially in rural or remote areas in the Global South where data collection at regional scale is difficult. Citizen science, i.e. involvement of no-experts in collecting information and co-creation of knowledge with experts to solve societal and environmental problems, has been suggested as a viable approach to tackle this bottleneck, although the reliability of the resulting data is often questioned. Here we analyse an inventory of geo-hydrological hazards (landslides and floods) reported by a network of citizen scientists in the Rwenzori Mountains, Uganda, established since 2017. We assess the precision, sensitivity and potential biases affecting this citizen science-based hazard inventory. We compare the citizen science-based records with two independent inventories, one collected through systematic fieldwork and another by PlanetScope satellite imagery mapping for the period between May 2019 and May 2020. The precision of the geo-observer data is higher (99% and 100% for landslides and floods, respectively) than that of satellite-based data (44% and 84%, respectively) indicative of fewer false positives in the former inventory. Also, citizen scientists have a higher sensitivity in reporting landslides (51%) compared to satellite imagery (39%) in addition to being able to report the events a few days after the occurrence. In contrast, the sensitivity of satellite-based flood detection is higher than that of citizen scientists. The probability of landslide events being reported by citizen scientists depends both on citizen scientists and hazard specific features (impact, landslide-citizen scientist home distance, landslide-road access distance and altitude). Although satellite imagery mapping could result in a spatially less biased inventory, small landslides are often missed while shallow ones can easily be confused with freshly cleared vegetation. Also, in a dominantly cloudy environment, it can take several days to weeks before a cloud-free satellite image can be obtained. In summary, the typically rapid response time of citizen scientists can result in faster information with high reliability at the risk of missing out almost half of the occurrences. Citizen scientists also provide more data on impact and type of land use, something difficult to achieve using satellite imagery. Working with farmers at village level as citizen scientists can facilitate covering a wider geographical area while reducing the area monitored by each citizen scientist at the same time.

The contemporary fire regime of southern Amazonian forests has been dominated by interactions between droughts and sources of fire ignition associated with deforestation and slash-and-burn agriculture. Until recently, wildfires have been concentrated mostly on private properties, with protected areas functioning as large-scale firebreaks along the Amazon's agricultural frontier. However, as the climate changes, protected forests have become increasingly flammable. Here, we have quantified forest degradation in the Território Indígena do Xingu (TIX), an iconic area of 2.8 million hectares where over 6000 people from 16 different ethnic Indigenous groups live across 100 villages. Our main hypothesis was that forest degradation, defined here as areas with lower canopy cover, inside the TIX is increasing due to pervasive sources of fire ignition, more frequent extreme drought events, and changing slash-and-burn agricultural practices. Between 2001 and 2020, nearly 189 000 hectares (∼7%) of the TIX became degraded by recurrent drought and fire events that were the main factors driving forest degradation, particularly in seasonally flooded forests. After three fire events, the probability of forest loss was higher in seasonally flooded areas (63%) compared to upland areas (41%). Given the same fire frequency, areas that have not suffered with extreme droughts showed a 24% lower probability of forest loss compared to areas that experienced three drought events. Distance from villages and human density also had a marked effect on forest cover loss, which was generally higher in areas close to the largest villages. In one of the most culturally diverse Indigenous lands of the Amazon, in a landscape highly threatened by deforestation, our findings demonstrate that climate change may have already exceeded the conditions to which the system has adapted.

Plastic pollution is a critical environmental concern. There is a growing focus on this transboundary issue, and a corresponding increase in public and government awareness. Understanding the key factors associated with litter and mismanaged waste on land will help to predict where and how waste enters the environment, providing opportunities for low cost, effective interventions. There exist only a few large-scale datasets with which such analyses can be conducted. To fill this knowledge gap we analysed a national, designed survey dataset of litter in the environment from Keep Australia Beautiful (2007–2017). We found that debris decreased significantly, with a nearly 6% decrease over the decade. Using generalised additive model modelling of 17 653 surveys at 983 sites around Australia, we found that site type, land use, state, population, and socio-economic status had the strongest relationships (in decreasing order) with litter distribution. Higher levels of litter were found in economically and socially disadvantaged neighbourhoods. Site types related to transitory human use such as highways and carparks, had more litter than areas with higher aesthetic or cultural value such as beaches, parks, and residential neighbourhoods. Sites that were sources of litter, such as shopping centres and retail strips, also had elevated litter counts, as did surveys near waterways. This enhanced understanding of the factors that influence litter deposition will help to craft more effective policy solutions, and can also improve our models of litter loads on land, and subsequent input to the ocean.

Increasing fire impacts across North America are associated with climate and vegetation change, greater exposure through development expansion, and less-well studied but salient social vulnerabilities. We are at a critical moment in the contemporary human-fire relationship, with an urgent need to transition from emergency response to proactive measures that build sustainable communities, protect human health, and restore the use of fire necessary for maintaining ecosystem processes. We propose an integrated risk factor that includes fire and smoke hazard, exposure, and vulnerability as a method to identify 'fires that matter', that is, fires that have potentially devastating impacts on our communities. This approach enables pathways to delineate and prioritise science-informed planning strategies most likely to increase community resilience to fires.

Climate change is expected to increase both the frequency and the intensity of climate extremes, consequently increasing the risk of forest role transition from carbon sequestration to carbon emission. These changes are occurring more rapidly in the Alps, with important consequences for tree species adapted to strong climate seasonality and short growing season. In this study, we aimed at investigating the responses of a high-altitude Larix decidua Mill. forest to heat and drought, by coupling ecosystem- and tree-level measurements. From 2012 to 2018, ecosystem carbon and water fluxes (i.e. gross primary production, net ecosystem exchange, and evapotranspiration) were measured by means of the eddy covariance technique, together with the monitoring of canopy development (i.e. larch phenology and normalized difference vegetation index). From 2015 to 2017 we carried out additional observations at the tree level, including stem growth and its duration, direct phenological observations, sap flow, and tree water deficit. Results showed that the warm spells in 2015 and 2017 caused an advance of the phenological development and, thus, of the seasonal trajectories of many processes, at both tree and ecosystem level. However, we did not observe any significant quantitative changes regarding ecosystem gas exchanges during extreme years. In contrast, in 2017 we found a reduction of 17% in larch stem growth and a contraction of 45% of the stem growth period. The growing season in 2017 was indeed characterized by different drought events and by the highest water deficit during the study years. Due to its multi-level approach, our study provided evidence of the independence between C-source (i.e. photosynthesis) and C-sink (i.e. tree stem growth) processes in a subalpine larch forest.

Alaska has diverse boreal ecosystems across heterogeneous landscapes driven by a wide range of biological and geomorphic processes associated with disturbance and successional patterns under a changing climate. To assess historical patterns and rates of change, we quantified the areal extent of ecotypes and the biophysical factors driving change through photo-interpretation of 2200 points on a time-series (∼1949, ∼1978, ∼2007, ∼2017) of geo-rectified imagery for 22 grids across central Alaska. Overall, 68.6% of the area had changes in ecotypes over ∼68 years. Most of the change resulted from increases in upland and lowland forest types, with an accompanying decrease in upland and lowland scrub types, as post-fire succession led to mid- and late-successional stages. Of 17 drivers of landscape change, fire was by far the largest, affecting 46.5% of the region overall from 1949 to 2017. Fire was notably more extensive in the early 1900s. Thermokarst nearly doubled from 3.9% in 1949 to 6.3% in 2017. Riverine ecotypes covered 7.8% area and showed dynamic changes related to channel migration and succession. Using past rates of ecotype transitions, we developed four state-transition models to project future ecotype extent based on historical rates, increasing temperatures, and driver associations. Ecotype changes from 2017 to 2100, nearly tripled for the driver-adjusted RCP6.0 temperature model (30.6%) compared to the historical rate model (11.5%), and the RCP4.5 (12.4%) and RCP8.0 (14.7%) temperature models. The historical-rate model projected 38 ecotypes will gain area and 24 will lose area by 2100. Overall, disturbance and recovery associated with a wide range of drivers across the patchy mosaic of differing aged ecotypes led to a fairly stable overall composition of most ecotypes over long intervals, although fire caused large temporal fluctuations for many ecotypes. Thermokarst, however, is accelerating and projected to have increasingly transformative effects on future ecotype distributions.

A ramp-up of bioenergy supply is vital in most climate change mitigation scenarios. Using abandoned land to produce perennial grasses is a promising option for near-term bioenergy deployment with minimal trade-offs to food production and the environment. The former Soviet Union (fSU) experienced substantial agricultural abandonment following its dissolution, but bioenergy potentials on these areas and their water requirements are still unclear. We integrate a regional land cover dataset tailored towards cropland abandonment, an agro-ecological crop yield model, and a dataset of sustainable agricultural irrigation expansion potentials to quantify bioenergy potentials and water requirements on abandoned land in the fSU. Rain-fed bioenergy potentials are 3.5 EJ yr⁻¹ from 25 Mha of abandoned land, with land-sparing measures for nature conservation. Irrigation can be sustainably deployed on 7–18 Mha of abandoned land depending on water reservoir size, thereby increasing bioenergy potentials with rain-fed production elsewhere to 5.2–7.1 EJ yr⁻¹. This requires recultivating 29–33 Mha combined with 30–63 billion m³ yr⁻¹ of blue water withdrawals. Rain-fed productive abandoned land equals 26%–61% of the projected regional fSU land use for dedicated bioenergy crops in 2050 for 2 °C future scenarios. Sustainable irrigation can bring productive areas up to 30%–80% of the projected fSU land requirements. Unraveling the complex interactions between land availability for bioenergy and water use at local levels is instrumental to ensure a sustainable bioenergy deployment.

Research on plastic pollution has rapidly expanded in recent years and has led to the discovery of vast amounts of microplastics floating on the surface of subtropical oceanic gyres. However, the distribution of floating plastic in the ocean is still poorly constrained, and there is a lack of information from a few meters from the coastline where the largest plastic emissions take place. Here, we provide a comprehensive study on the loads of plastic debris in the coastal surface waters of the NW Mediterranean Sea using data from 124 manta trawl deployments collected along 7 months by citizen scientists. Our results reveal that pollution by microplastics in the nearshore is likely subject to seasonal variations associated to a combination of hydrodynamic and anthropogenic pressures. The high proportions of microplastics found indicate that potential breakdown of plastics in the nearshore may take place in line with previous works. We prove that citizen science is a powerful tool in plastic research to monitor microplastics in the nearshore as it provides scientifically meaningful results while stimulating citizen engagement. Future studies may benefit from targeting specific scientific open questions by using the citizen science methodological approach presented here.

The spatial mapping of social-ecological system (SES) archetypes constitutes a fundamental tool to operationalize the SES concept in empirical research. Approaches to detect, map, and characterize SES archetypes have evolved over the last decade towards more integrative and comparable perspectives guided by SES conceptual frameworks and reference lists of variables. However, hardly any studies have investigated how to empirically identify the most relevant set of indicators to map the diversity of SESs. In this study, we propose a data-driven methodological routine based on multivariate statistical analysis to identify the most relevant indicators for mapping and characterizing SES archetypes in a particular region. Taking Andalusia (Spain) as a case study, we applied this methodological routine to 86 indicators representing multiple variables and dimensions of the SES. Additionally, we assessed how the empirical relevance of these indicators contributes to previous expert and empirical knowledge on key variables for characterizing SESs. We identified 29 key indicators that allowed us to map 15 SES archetypes encompassing natural, mosaic, agricultural, and urban systems, which uncovered contrasting land sharing and land sparing patterns throughout the territory. We found synergies but also disagreements between empirical and expert knowledge on the relevance of variables: agreement on their widespread relevance (32.7% of the variables, e.g. crop and livestock production, net primary productivity, population density); relevance conditioned by the context or the scale (16.3%, e.g. land protection, educational level); lack of agreement (20.4%, e.g. economic level, land tenure); need of further assessments due to the lack of expert or empirical knowledge (30.6%). Overall, our data-driven approach can contribute to more objective selection of relevant indicators for SES mapping, which may help to produce comparable and generalizable empirical knowledge on key variables for characterizing SESs, as well as to derive more representative descriptions and causal factor configurations in SES archetype analysis.

The overgrowth of reactive nitrogen emissions (Nr, all species of nitrogen except N₂ gas) is a major cause of environmental pollution, especially in rapidly urbanizing regions. The nitrogen footprint (NF) indicator has been widely used to assess Nr losses occurring from the consumption of food and energy. We undertake the first attempt to apply NF methods to explore the spatial-temporal NF characteristics of major urban agglomerations in China between 2000 and 2019, and find that the highest level of annual NF (average 3868 Gg N yr⁻¹) was produced by the Yangtze River Delta urban agglomeration (YRDUA), followed by the Beijing–Tianjin–Hebei urban agglomeration (BTHUA) (average 2657 Gg N yr⁻¹). Their NF growth rates showed similar downward trends during the study period, while the Pearl River Delta urban agglomeration (PRDUA) (average 1528 Gg N yr⁻¹) retained a higher growth rate. The average proportions of food NF (FNF) in BTHUA, YRDUA and PRDUA were 57.64%, 68.64% and 66.79%, respectively. Compared to the FNF, the energy NF gradually plays a more important role in China's urban agglomerations compared to other countries. Analysis of the underlying drivers showed that an increasing urbanization rate boosted the NF of YRDUA, and rising GDP per capita significantly contributed to the NF growths of BTHUA and PRDUA. Through scenario analysis, we found that shifting to healthy dietary patterns and a partial substitution of fossil fuels with clean energy, as well as improvements in rural wastewater treatment, could contribute to NF reductions by 2030. The largest potential NF reduction is predicted in PRDUA (29% reduction), followed by YRDUA (23% reduction) and BTHUA (18% reduction). The energy reduction scenario is considered to be the most realistic in reducing the NF. We demonstrate the potential of the NF as a tool for the assessment of sustainable development in urban agglomeration, which may prove instructive for broader research on sustainable Nr management.

Wildfire is an integral part of the Earth system, but at the same time it can pose serious threats to human society and to certain types of terrestrial ecosystems. Meteorological conditions are a key driver of wildfire activity and extent, which led to the emergence of the use of fire danger indices that depend solely on weather conditions. The Canadian Fire Weather Index (FWI) is a widely used fire danger index of this kind. Here, we evaluate how well the FWI, its components, and the climate variables from which it is derived, correlate with observation-based burned area (BA) for a variety of world regions. We use a novel technique, according to which monthly BA are grouped by size for each Global Fire Emissions Database (GFED) pyrographic region. We find strong correlations of BA anomalies with the FWI anomalies, as well as with the underlying deviations from their climatologies for the four climate variables from which FWI is estimated, namely, temperature, relative humidity, precipitation, and wind. We quantify the relative sensitivity of the observed BA to each of the four climate variables, finding that this relationship strongly depends on the pyrographic region and land type. Our results indicate that the BA anomalies strongly correlate with FWI anomalies at a GFED region scale, compared to the strength of the correlation with individual climate variables. Additionally, among the individual climate variables that comprise the FWI, relative humidity and temperature are the most influential factors that affect the observed BA. Our results support the use of the composite fire danger index FWI, as well as its sub-indices, the Build-Up Index (BUI) and the Initial Spread Index (ISI), comparing to single climate variables, since they are found to correlate better with the observed forest or non-forest BA, for the most regions across the globe.

Recent studies have shown that temperature and precipitation in the Mediterranean are expected to change, contributing to longer and more intense summer droughts that even extend out of season. In connection to this, the frequency of forest fire occurrence and intensity will likely increase. In the present study, the changes in future fire danger conditions are assessed for the different regions of Greece using the Canadian fire weather index (FWI). Gridded future climate output as estimated from three regional climate models from the Coordinated Regional Downscaling Experiment are utilized. We use three representative concentration pathways (RCPs) consisting of an optimistic emissions scenario where emissions peak and decline beyond 2020 (RCP2.6), a middle-of-the-road scenario (RCP4.5) and a pessimistic scenario, in terms of mitigation where emissions continue to rise throughout the century (RCP8.5). Based on established critical fire FWI threshold values for Greece, the future change in days with critical fire danger were calculated for different areas of Greece domains. The results show that fire danger is expected to progressively increase in the future especially in the high-end climate change scenario with southern and eastern regions of Greece expected to have up to 40 additional days of high fire danger relative to the late 20th century, on average. Crete, the Aegean Islands, the Attica region, as well as parts of Peloponnese are predicted to experience a stronger increase in fire danger.

The commons literature focuses heavily on rules and the behavior of resource users but places less emphasis on the returns to individual effort. However, for most resource settings, market conditions and associated resource prices are key drivers of exploitation effort. In a globalized world, import competition can strongly influence the incentives for individual resource users, a topic largely unexplored in the commons literature. Import competition is especially salient for seafood, one of the most internationally traded food groups. We analyze the US shrimp market, which was once dominated by domestic catches but is now mostly supplied by imports. For domestic producers (users of the commons), lower revenues result, while US consumers eat more shrimp at lower prices. Globalization changed the sources of price risk and compensation that domestic producers face and altered incentives to exploit the commons. In a market dominated by domestic supply shocks, the price response to a shock moderates the effect on revenue and effort. In a market dominated by imports, domestic shocks are buffered by import adjustments, while price movements are determined by global shocks. Despite losses for the domestic fishery, globalization creates new incentives to coordinate effort and capture price premiums determined in the global market.

Foundation species have disproportionately large impacts on ecosystem structure and function. As a result, future changes to their distribution may be important determinants of ecosystem carbon (C) cycling in a warmer world. We assessed the role of a foundation tussock sedge (Eriophorum vaginatum) as a climatically vulnerable C stock using field data, a machine learning ecological niche model, and an ensemble of terrestrial biosphere models (TBMs). Field data indicated that tussock density has decreased by ∼0.97 tussocks per m² over the past ∼38 years on Alaska's North Slope from ∼1981 to 2019. This declining trend is concerning because tussocks are a large Arctic C stock, which enhances soil organic layer C stocks by 6.9% on average and represents 745 Tg C across our study area. By 2100, we project that changes in tussock density may decrease the tussock C stock by 41% in regions where tussocks are currently abundant (e.g. −0.8 tussocks per m² and −85 Tg C on the North Slope) and may increase the tussock C stock by 46% in regions where tussocks are currently scarce (e.g. +0.9 tussocks per m² and +81 Tg C on Victoria Island). These climate-induced changes to the tussock C stock were comparable to, but sometimes opposite in sign, to vegetation C stock changes predicted by an ensemble of TBMs. Our results illustrate the important role of tussocks as a foundation species in determining future Arctic C stocks and highlight the need for better representation of this species in TBMs.

One of the greatest challenges for land managers is to maintain a multitude of ecosystem services while reducing hazards posed by wildfires, insect outbreaks, and other disturbances accelerating due to climate change. In response to limited available resources and improved technical abilities, natural resource managers are increasingly using geospatial data to plan and evaluate their management actions. Large amounts of public resources are invested in research and development to improve geospatial datasets, yet there is limited knowledge about the specific data types and data characteristics that clients (e.g. land managers) prefer. Our overall objective was to investigate what geospatial data characteristics are preferred by natural resource professionals to monitor and manage forests and fuels across large landscapes. We performed an online survey and collected supplemental data at a subsequent workshop during the 2020 Operational Lidar Inventory meeting to investigate preferred data use and data characteristics of data users of the Pacific Northwest. Our online survey was completed by 69 respondents represented by managers and natural resource professionals from tribal/state, federal, academic, and industry/consulting entities. We found that metrics related to species composition, total biomass/timber volume, and vegetation height were the most preferred attributes, yet preference differed slightly by employment type. From the workshop we found that metric preferences depend upon which management priorities are central to the management application. There was preference for data with Landsat pixel-level (30 m) spatial resolution, annual temporal resolution, and at regional spatial extents. To maintain viable ecosystem services in the long term, it is important to understand the metrics and their data characteristics that are most useful. We conclude that our study is a useful way to understand (a) how to improve the data utility for the users (clients) and (b) the development and investment needs for the data developers and funders.

Sea ice can attenuate Southern Ocean swell before it reaches Antarctic ice shelves and imposes flexural stresses, which promote calving of outer ice-shelf margins and influence ice shelf stability. An algorithm is developed to identify sea ice-free corridors that connect the open Southern Ocean to Antarctic ice shelves from daily satellite sea ice concentration data between September 1979 and August 2019. Large swell in the corridors available to impact the ice shelves is extracted from spectral wave model hindcast data. For a selection of ice shelves around the Antarctic coastline, corridors are assessed in terms of duration and areal extent. The availability of large swell to impact certain ice shelves through the corridors is evaluated from spectral wave data for daily statistical properties and the number of large swell days per year. Results integrated over a large number of ice shelves are used to assess overall trends. Large variations are found between individual ice shelves for both corridors and available swell, with contrasting trends between the West and East Antarctic Ice Sheet. The findings indicate ice shelves likely to experience prolonged periods of appreciable outer margin flexure due to large swell action, such as the Fimbul, Shackleton and Ross Ice Shelves, which could exacerbate climate-driven weakening and decreasing buttressing capacity, with implications for sea-level rise.

Groundwater discharge is an important mechanism through which fresh water and associated solutes are delivered to the ocean. Permafrost environments have traditionally been considered hydrogeologically inactive, yet with accelerated climate change and permafrost thaw, groundwater flow paths are activating and opening subsurface connections to the coastal zone. While warming has the potential to increase land-sea connectivity, sea-level change has the potential to alter land-sea hydraulic gradients and enhance coastal permafrost thaw, resulting in a complex interplay that will govern future groundwater discharge dynamics along Arctic coastlines. Here, we use a recently developed permafrost hydrological model that simulates variable-density groundwater flow and salinity-dependent freeze-thaw to investigate the impacts of sea-level change and land and ocean warming on the magnitude, spatial distribution, and salinity of coastal groundwater discharge. Results project both an increase and decrease in discharge with climate change depending on the rate of warming and sea-level change. Under high warming and low sea-level rise scenarios, results show up to a 58% increase in coastal groundwater discharge by 2100 due to the formation of a supra-permafrost aquifer that enhances freshwater delivery to the coastal zone. With higher rates of sea-level rise, the increase in discharge due to warming is reduced to 21% as sea-level rise decreased land-sea hydraulic gradients. Under lower warming scenarios for which supra-permafrost groundwater flow was not established, discharge decreased by up to 26% between 1980 and 2100 for high sea-level rise scenarios and increased only 8% under low sea-level rise scenarios. Thus, regions with higher warming rates and lower rates of sea-level change (e.g. northern Nunavut, Canada) will experience a greater increase in discharge than regions with lower warming rates and higher rates of sea-level change. The magnitude, location and salinity of discharge have important implications for ecosystem function, water quality, and carbon dynamics in coastal zones.

An increasing concentration of nitrous oxide (N₂O) in the global atmosphere can perturb the ecological balance, affecting the climate and human life. South Asia, one of the world's most populous regions, is a hotspot for N₂O emission. Although agriculture traditionally dominated the region, economic activities are rapidly shifting towards industry and energy services. These activites may become the largest emitters of N₂O in future. Yet, few attempts have been made to estimate long-term direct N₂O emission from fuel combustion for the different energy-consuming sectors in the South Asian region. Therefore, the present study developed a comprehensive sectoral N₂O emission inventory for South Asian countries for the time period of 1990–2017, with projections till 2041. It revealed that the average N₂O emission from fuel combustion in the South Asia region is about 40.96 Gg yr⁻¹ with a possible uncertainty of ±12 Gg yr⁻¹, showing an increase of more than 100% from 1990 to 2017. Although India is the major contributor, with an average of 34 Gg yr⁻¹ of N₂O emissions, in terms of growth, small countries like Bhutan and Maldives are dominating other South Asian countries. Sector-wise, the residential sector contributed a maximum emission of 14.52 Gg yr⁻¹ of N₂O but this is projected to reduce by more than 50% by 2041. This is because of the successful promotion of cleaner fuels like liquefied petroleum gas over more polluting fuelwood. Power generation contributed 9.43 Gg yr⁻¹of N₂O emissions, exhibiting a maximum growth of 395%, followed by road transport (289%) and industry (231%). Future N₂O emissions from transport, power and industry are projected to rise by 2.8, 3.3, and 23.9 times their 2017 estimates, respectively, due to the incapability of current policies to combat rising fossil fuel consumption. Mitigation options, such as replacing diesel and compressed natural gas vehicles with electricity-driven vehicles, can decelerate N₂O emissions to 45% by 2041 for road transport. A 41% reduction is possible by displacing coal with renewables in the power and industry sectors. Overall, the South Asian contribution to global N₂O emissions has enlarged from 2.7% in 1990 to 5.7% in 2007–2016, meaning there is an urgent need for N₂O emission mitigation in the region.

China is the largest producer of synthetic ammonia, accounting for one-third of the world's total production. Ammonia is mainly used to produce fertilizer and is also considered as a potential fuel and new energy carrier for the future. Concomitantly, the ammonia industry is the largest energy consumer and CO₂ emitter in China's chemical industry. In this study, we developed the MESSAGEix-ammonia model with detailed process descriptions to evaluate the energy-saving and emission reduction potential that can be generated by energy efficiency (EE) improvement, as well as the transition path and emission characteristics in the context of deep emission reduction. Results show that the cost-effective EE measures implemented under the EE scenario could reduce fresh water, fuel coal, and electricity consumptions by 7%, 25%, and 16%, as well as reduce CO₂, PM_2.5, SO₂, and NOx emissions by 33%, 24%, 24%, and 24%, respectively, by 2060. Regarding the exploration of the deep de-carbonization path, carbon capture and storage technology (CCS) increases the CO₂ reduction potential to 62%, but it requires additional electricity. Meanwhile, electrolysis technology not only saves additional fresh water and fuel coal, but also reduces CO₂, PM_2.5, SO₂, and NOx by 80%, 84%, 86%, and 84%, respectively. Furthermore, the integration of electrolysis technology and CCS can bring 98% carbon emission reduction, which is close to net-zero emission status. With the development of renewable electricity, sufficient, clean, and affordable electricity can be provided for electrolysis devices. Our recommendation to policy makers is that electrolysis of water to produce ammonia using renewable electricity is a feasible deep de-carbonization pathway.