Table of contents

Governing land use to achieve sustainable outcomes is challenging, because land systems manifest complex land use spillovers—i.e. processes by which land use changes or direct interventions in land use (e.g. policy, program, new technologies) in one place have impacts on land use in another place. The ERL issue 'Focus on Leakage: informing Land-Use Governance in a Tele-coupled World' builds on discussions in an international expert workshop conducted in Berlin in November 2017 to explore innovative ways to improve our understanding of how governance interventions, new technologies and other factors can affect land-use change both directly and indirectly through spillovers. This editorial starts by clarifying the definitions and relationships between land-use spillover, indirect land use change—a form of spillover where land use change in one place is caused by land use change in another place—leakage—a form of land use spillover, which is caused by an environmental policy (e.g. a conservation or restoration intervention), and the spillover reduces the overall benefits and effectiveness of this intervention—, and land use displacement processes. We then use this terminology to summarize the individual contributions of this special issue and conclude with lessons learned as well as directions for future research.

Background. Around two-thirds of global GHG emissions are directly and indirectly linked to household consumption, with a global average of about 6 tCO₂eq/cap. The average per capita carbon footprint of North America and Europe amount to 13.4 and 7.5 tCO₂eq/cap, respectively, while that of Africa and the Middle East—to 1.7 tCO₂eq/cap on average. Changes in consumption patterns to low-carbon alternatives therefore present a great and urgently required potential for emission reductions. In this paper, we synthesize emission mitigation potentials across the consumption domains of food, housing, transport and other consumption. Methods. We systematically screened 6990 records in the Web of Science Core Collections and Scopus. Searches were restricted to (1) reviews of lifecycle assessment studies and (2) multiregional input-output studies of household consumption, published after 2011 in English. We selected against pre-determined eligibility criteria and quantitatively synthesized findings from 53 studies in a meta-review. We identified 771 original options, which we summarized and presented in 61 consumption options with a positive mitigation potential. We used a fixed-effects model to explore the role of contextual factors (geographical, technical and socio-demographic factors) for the outcome variable (mitigation potential per capita) within consumption options. Results and discussion. We establish consumption options with a high mitigation potential measured in tons of CO₂eq/capita/yr. For transport, the options with the highest mitigation potential include living car-free, shifting to a battery electric vehicle, and reducing flying by a long return flight with a median reduction potential of more than 1.7 tCO₂eq/cap. In the context of food, the highest carbon savings come from dietary changes, particularly an adoption of vegan diet with an average and median mitigation potential of 0.9 and 0.8 tCO₂eq/cap, respectively. Shifting to renewable electricity and refurbishment and renovation are the options with the highest mitigation potential in the housing domain, with medians at 1.6 and 0.9 tCO₂eq/cap, respectively. We find that the top ten consumption options together yield an average mitigation potential of 9.2 tCO₂eq/cap, indicating substantial contributions towards achieving the 1.5 °C–2 °C target, particularly in high-income context.

Erosion along high-latitude coasts has been accelerating in recent decades, resulting in land loss and infrastructure damage, threatening the wellbeing of local communities, and forcing undesired community relocations. This review paper evaluates the state of practice of current coastal stabilization measures across several coastal communities in northern high latitudes. After considering global practices and those in northern high latitude and arctic settings, this paper then explores new and potential coastal stabilization measures to address erosion specific to northern high-latitude coastlines. The challenges in constructing the current erosion control measures and the cost of the measures over the last four decades in northern high-latitude regions are presented through case histories. The synthesis shows that among the current erosion controls being used at high latitudes, revetments built with rocks have the least reported failures and are the most common measures applied along northern high-latitude coastlines including permafrost coasts, while riprap is the most common material used. For seawalls, bulkheads, and groin systems, reported failures are common and mostly associated with displacement, deflection, settlement, vandalism, and material ruptures. Revetments have been successfully implemented at sites with a wide range of mean annual erosion rates (0.3–2.4 m/year) and episodic erosion (6.0–22.9 m) due to the low costs and easy construction, inspection, and decommissioning. No successful case history has been reported for the non-engineered expedient measures that are constructed in the event of an emergency, except for the expedient vegetation measure using root-wads and willows. Soft erosion prevention measures, which include both beach nourishment and dynamically stable beaches, have been considered in this review. The effectiveness of beach nourishment in Utqiaġvik, Alaska, which is affected by permafrost, is inconclusive. Dynamically stable beaches are effective in preventing erosion, and observations show that they experience only minor damages after single storm events. The analysis also shows that more measures have been constructed on a spit (relative to bluffs, islands, barrier islands, and river mouths), which is a landform where many Alaskan coastal communities reside. The emerging erosion control measures that can potentially be adapted to mitigate coastal erosion in high-latitude regions include geosynthetics, static bay beach concept, refrigerating techniques, and biogeochemical applications. However, this review shows that there is a lack of case studies that evaluated the performance of these new measures in high-latitude environments. This paper identifies research gaps so that these emerging measures can be upscaled for full-scale applications on permafrost coasts.

Information and communication technologies (ICTs) increasingly enable employees to work from home and other locations ('teleworking'). This study explores the extent to which teleworking reduces the need to travel to work and the consequent impacts on economy-wide energy consumption. The paper provides a systematic review of the current state of knowledge of the energy impacts of teleworking. This includes the energy savings from reduced commuter travel and the indirect impacts on energy consumption associated with changes in non-work travel and home energy consumption. The aim is to identify the conditions under which teleworking leads to a net reduction in economy-wide energy consumption, and the circumstances where benefits may be outweighed by unintended impacts. The paper synthesises the results of 39 empirical studies, identified through a comprehensive search of 9000 published articles. Twenty six of the 39 studies suggest that teleworking reduces energy use, and only eight studies suggest that teleworking increases, or has a neutral impact on energy use. However, differences in the methodology, scope and assumptions of the different studies make it difficult to estimate 'average' energy savings. The main source of savings is the reduced distance travelled for commuting, potentially with an additional contribution from lower office energy consumption. However, the more rigorous studies that include a wider range of impacts (e.g. non-work travel or home energy use) generally find smaller savings. Despite the generally positive verdict on teleworking as an energy-saving practice, there are numerous uncertainties and ambiguities about its actual or potential benefits. These relate to the extent to which teleworking may lead to unpredictable increases in non-work travel and home energy use that may outweigh the gains from reduced work travel. The available evidence suggests that economy-wide energy savings are typically modest, and in many circumstances could be negative or non-existent.

Concerns over groundwater depletion and ecosystem degradation have led to the incorporation of the concept of groundwater sustainability as a groundwater policy instrument in several water codes and management directives worldwide. Because sustainable groundwater management is embedded within integrated, co-evolving hydrological, ecological, and socioeconomic systems, implementing such policies remains a challenge for water managers and the scientific community. The problem is further exacerbated when participatory processes are lacking, resulting in a communication gap among water authorities, scientists, and the broader community. This paper provides a systematic review of the concept of groundwater sustainability, and situates this concept within the calls from the hydrologic literature for more participatory and integrated approaches to water security. We discuss the definition of groundwater sustainability from both a policy and scientific perspective, tracing the evolution of this concept from safe yield, to sustainable groundwater management. We focus on the diversity of societal values related to groundwater sustainability, and the typology of the aquifer performance and governance factors. In addition, we systematically review the main components of an effective scientific evaluation of groundwater sustainability policy, which are multi-process modeling, uncertainty analysis, and participation. We conclude that effective groundwater sustainability policy implementation requires an iterative scientific evaluation that (i) engages stakeholders in a participatory process through collaborative modeling and social learning; (ii) provides improved understanding of the coevolving scenarios between surface water-groundwater systems, ecosystems, and human activities; and (iii) acknowledges and addresses uncertainty in our scientific knowledge and the diversity of societal preferences using multi-model uncertainty analysis and adaptive management. Although the development of such a transdisciplinary research approach, which connects policy, science, and practice for groundwater sustainability evaluation, is still in its infancy worldwide, we find that research towards groundwater sustainability is growing at a much faster rate than groundwater research as a whole.

Wildlife has important effects on human well-being, ranging from beneficial contributions to life threatening interactions. Here, we systematically reviewed publications of both positive and negative non-material contributions of wildlife to people (WCP) for different taxonomic groups (birds, mammals, reptiles and amphibians) and dimensions of human well-being such as health, social well-being, identity and spirituality. Overall, the majority of studies reported negative WCP, such as feelings of insecurity or injuries. However, over the last decade the number of publications on positive WCP such as good mental health, positive emotions or learning increased, mainly in the Global North. These spatial and temporal patterns may hint towards normative influences that drive the relative proportion of reported WCP. However, these normative influences are not yet well understood and future research should examine potential biases by conducting policy assessments or surveys among researchers to understand drivers and motivations behind their research questions. We found almost no joint assessments of positive and negative WCP for any wildlife species. Studies also showed taxon-specific differences in WCP outcomes, with predominantly positive WCP reported for birds and predominantly negative WCP published for mammals or reptiles. Physical health was the most dominant aspect of well-being studied and affected by WCP while other well-being dimensions such as social well-being, learning or identity were less frequently covered in the literature. Future studies should jointly evaluate positive and negative effects of wildlife on human well-being and implement multi-taxon approaches to obtain a more balanced and comprehensive understanding of WCP. These assessments of WCP will provide actionable science outcomes that will shape human-wildlife coexistence and promote human health and well-being.

Rapidly changing economics, customer preferences, and policy to address climate change and local environmental pollutants have driven increased deployment of a wide range of distributed energy resources in the U.S. electricity system. Distributed energy resources have enabled an expanded role for energy consumers and non-utility third parties to reshape system costs, drawing renewed attention to the potential of reforming electricity rate design based on the further application of cost-causal principals to improve overall system fairness and efficiency. One mechanism to move toward greater application of cost-causal rate design is dynamic pricing, which varies electricity prices across time and location to reflect costs of providing electricity to consumers under specific market conditions and grid operation conditions. While dynamic electricity pricing has penetrated some markets, and it has not been widely implemented, particularly for residential consumers. In this review article, we provide a brief summary of electricity rate design, including the possibility of introducing dynamic prices, and explain why dynamic prices are more reflective of the short-run marginal costs of electricity supply than volumetric rates. We then explore the barriers to the widespread adoption of residential dynamic pricing, emphasizing technical, economic, and political challenges. Our assessment reflects the ability of dynamic prices to engender more equitable and efficient outcomes by achieving the goal of cost-causality, and we argue that a move toward more dynamic pricing can constitute a welfare improvement over volumetric rates. However, dynamic pricing does not completely address the full set of challenges associated with rate design and, alone, is unlikely to enable the full recovery of fixed costs and the fair attribution of the positive and negative externalities of electricity provision. Therefore, electricity rate design requires tradeoffs, making it as much an art as a science. This analysis synthesizes literature across multiple fields and suggests avenues for further research.

Public support is a key determinant of whether any energy project is developed in democratic countries. In recent decades, scholars have extensively examined levels of support and opposition to energy infrastructure, often with a focus on so-called Not-in-My-Backyard (NIMBY) sentiments. As the need for energy infrastructure grows, so does the need to extract insights and lessons from this literature. In this systematic literature review, we evaluate decades of research to identify important trends in topical focus, research findings, and research design. We find a disproportionate focus on wind energy, followed by solar, fossil fuels, and transmission, with most studies conducted in the United States or United Kingdom, and that individuals are more often supportive of energy projects than they are opposed. Scholars have examined the role of many factors in understanding attitudes toward energy infrastructure, and often find knowledge, trust, and positive perceptions about the benefits of projects to be positively correlated with support for projects, although with variation across energy types. NIMBY attitudes differ widely in approach and are often plagued by problematic research designs that limit inferences and the generalizability of findings. We provide a detailed discussion of these limitations and suggest areas in which the literature can expand.

A growing number of researchers and stakeholders have started to address climate change from the bottom up: by devising scientific models, climate plans, low-carbon strategies and development policies with climate co-benefits. Little is known about the comparative characteristics of these interventions, including their relative efficacy, potentials and emissions reductions. A more systematic understanding is required to delineate the urban mitigation space and inform decision-making. Here, we utilize bibliometric methods and machine learning to meta-analyze 5635 urban case studies of climate change mitigation. We identify 867 studies that explicitly consider technological or policy instruments, and categorize these studies according to policy type, sector, abatement potential, and socio-technological composition to obtain a first heuristic of what is their pattern. Overall, we find 41 different urban solutions with an average GHG abatement potential ranging from 5.2% to 105%, most of them clustering in the building and transport sectors. More than three-fourth of the solutions are on demand side. Less than 10% of all studies were ex-post policy evaluations. Our results demonstrate that technology-oriented interventions in urban waste, transport and energy sectors have the highest marginal abatement potential, while system-wide interventions, e.g. urban form related measures have lower marginal abatement potential but wider scope. We also demonstrate that integrating measures across urban sectors realizes synergies in GHG emission reductions. Our results reveal a rich evidence of techno-policy choices that together enlarge the urban solutions space and augment actions currently considered in global assessments of climate mitigation.

Climate adaptation is a priority for Arctic regions which are witnessing some of the most rapid warming globally. Studies have documented examples of adaptation responses in the Arctic, but assessments evaluating if and how progress is being made over time remain scarce. We identify and examine adaptation progress in the Arctic using a systematic tracking methodology to compare adaptations documented during 2014–19 to those documented for the period 2004–2013 in a benchmark study by Ford et al (2014). Utilising the peer reviewed literature as out data source, we find no noticeable increase in reported adaptations across the two time periods, with the profile of adaptations undertaken remaining largely the same. The majority of documented adaptations continue to be reported in North America, are being undertaken most often in the subsistence-based hunting and fishing sector, are primarily developed in response to a combination of climatic and non-climatic stimuli, are reactive and behavioural in nature, and are mainly carried out at the individual/community scale. Climate change is observed, however, to have a more prominent role in motivating adaptation between 2014–19, consistent with intensifying climate-related exposures in the Arctic. There is limited evidence in the reported adaptations analysed that potential opportunities and benefits from the impacts of climate change are being targeted. The paper provides a general characterisation of adaptation across the Arctic and how it is evolving, and needs to be complimented in follow-up work by studies using alternative data sources on adaptation and research at national to regional scales.

To evaluate uncertainty in the transient climate response (TCR) associated with microscale deep-ocean mixing processes induced by internal tidal wave breaking, a set of idealized climate model experiments with two different implementations of deep-ocean mixing is conducted under increasing atmospheric CO₂ concentration 1% per year. The difference in TCR between the two experiments is 0.16 °C, which is about half as large as the multimodel spread of TCR in the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. The TCR difference can be attributed to the difference in the preindustrial climatological state. In the case where deep-ocean mixing works to enhance ocean stratification in the Pacific intermediate-to-deep layers, because the Pacific water mass is transported to the Southern Ocean by the Pacific meridional overturning circulation, the subsurface stratification in the Southern Ocean is also enhanced and deep wintertime convection there is suppressed. Our study shows that in this case during CO₂ increase, ocean heat uptake from the atmosphere to deeper layers is suppressed and TCR is estimated to be higher than the other case. Diminished accumulation of oceanic heat in the deep layer also leads to the sea level depression of ∼0.4 m in the Southern Ocean when atmospheric CO₂ concentration has quadrupled. Together with convective and cloud-radiative processes in the atmosphere and oceanic mesoscale processes, microscale deep-ocean mixing can be one of the major candidates in explaining uncertainty in future climate projections.

Decarbonizing the transportation sector is crucial to limiting global warming, but faces severe political feasibility challenges due to widespread opposition by those who incur the costs. With respect to private motorized vehicles, which account for the largest share of emissions from transportation, various studies show that pull measures, such as subsidies for electric vehicles (EVs) and charging infrastructure, attract more public support than push measures, such as carbon taxes or regulation to phase out fossil fuel cars. Based on a choice experiment with a large, representative sample (N = 5325) of car holders in Switzerland, we reassess and add to these findings. We empirically focus on Switzerland because its newly registered cars have the worst emissions record in Europe. First, we reassess the presumably stronger support for pull measures by studying whether such support is (negatively) affected by revealing the cost implications in terms of means for funding these policy measures. Second, a unique feature of our study is that we examine support for policies to promote EVs both amongst non-EV and EV holders. Our hypothesis is that EV holders are likely to be more supportive of such policies, even when cost implications become apparent. Our key finding is that support for pull measures, which is high amongst non-EV holders, and even higher among technology adopters (EV holders), remains stable even when policy funding is revealed. This suggests that more ambitious pull measures in this area are politically feasible, even more so as the share of EV-adopters increases. Our research also provides a methodological template for similar research in other countries.

Realistically representing the present-day characteristics of extreme precipitation has been a challenge for global climate models, which is due in part to deficiencies in model resolution and physics, but is also due to a lack of consistency in gridded observations. In this study, we use three observation datasets, including gridded rain gauge and satellite data, to assess historical simulations from sixteen Coupled Model Intercomparison Project Phase 6 (CMIP6) models. We separately evaluate summer and winter precipitation over the United States (US) with a comprehensive set of extreme precipitation indices, including an assessment of precipitation frequency, intensity and spatial structure. The observations exhibit significant differences in their estimates of area-average intensity distributions and spatial patterns of the mean and extremes of precipitation over the US. In general, the CMIP6 multi-model mean performs better than most individual models at capturing daily precipitation distributions and extreme precipitation indices, particularly in comparison to gauge-based data. Also, the representation of the extreme precipitation indices by the CMIP6 models is better in the summer than winter. Although the 'standard' horizontal-resolution can vary significantly across CMIP6 models, from ∼0.7° to ∼2.8°, we find that resolution is not a good indicator of model performance. Overall, our results highlight common biases in CMIP6 models and demonstrate that no single model is consistently the most reliable across all indices.

Background: Studies of PM_2.5 health effects are influenced by the spatiotemporal coverage and accuracy of exposure estimates. The use of satellite remote sensing data such as aerosol optical depth (AOD) in PM_2.5 exposure modeling has increased recently in the US and elsewhere in the world. However, few studies have addressed this issue in southern California due to challenges with reflective surfaces and complex terrain.

Methods: We examined the factors affecting the associations with satellite AOD using a two-stage spatial statistical model. The first stage estimated the temporal PM_2.5/AOD relationships using a linear mixed effects model at 1 km resolution. The second stage accounted for spatial variation using geographically weighted regression. Goodness of fit for the final model was evaluated by comparing the daily PM_2.5 concentrations generated by cross-validation (CV) with observations. These methods were applied to a region of southern California spanning from Los Angeles to San Diego.

Results: Mean predicted PM_2.5 concentration for the study domain was 8.84 µg m⁻³. Linear regression between CV predicted PM_2.5 concentrations and observations had an R² of 0.80 and RMSE 2.25 µg m⁻³. The ratio of PM_2.5 to PM₁₀ proved an important variable in modifying the AOD/PM_2.5 relationship (β = 14.79, p ≤ 0.001). Including this ratio improved model performance significantly (a 0.10 increase in CV R² and a 0.56 µg m⁻³ decrease in CV RMSE).

Discussion: Utilizing the high-resolution MAIAC AOD, fine-resolution PM_2.5 concentrations can be estimated where measurements are sparse. This study adds to the current literature using remote sensing data to achieve better exposure data in the understudied region of Southern California. Overall, we demonstrate the usefulness of MAIAC AOD and the importance of considering coarser particles in dust prone areas.

Mountain regions are experiencing more pronounced climate change than other global land areas. How have vegetation dynamics responded to these changes and what are the implications for hydrology? To answer these questions, we examine the impacts of changes in mean air temperature (T_mean), precipitation (P) and winter snow cover extent (SCE) in the headwaters of the Yellow River basin (HYRB) on two important vegetation dynamic metrics: (i) the maximum growing-season greenness (represented by the monthly maximum NDVI); and (ii) the beginning of growing season (BGS). Satellite-derived NDVI and SCE, along with observation-based gridded climate data, show that during the past 34 years (1982–2015) the HYRB experienced widespread vegetation greening, while no significant trend in BGS was observed. Spring greenness and phenology were significantly affected by SCE change, highlighting the importance of snow-related process to spring vegetation activity. We observed a clear signal of elevation-dependent warming below 4300 m elevation, which is absent at higher elevations. Changes in NDVI and BGS are elevation-dependent, and trends inT_mean, P, and SCE with elevation play different roles in this dependence. Both observed and estimated watershed annual evapotranspiration series show increasing trends, suggesting that vegetation greening imposes positive effects on evaporative fluxes. Given steady-state and non-stationary hydrological conditions, increasing evapotranspiration should result in runoff reduction, which agrees with catchment-scale runoff observations across the HYRB. These findings represent new knowledge regarding the vegetation response to climate change in alpine environments which has important implications for the hydrology of the region and for other high-water yielding mountainous regions worldwide.

The efficiency of fertilizer conversion to harvestable products is often low in annual crops such that large amounts of nutrients are lost from fields with negative consequences for the environment. Focusing on nitrogen (N) use efficiency (NUE: the ratio of N in harvested products over the sum of all N inputs), we propose that hydrological controls can explain variations in NUE, because water mediates both the uptake of N by plants and N leaching. We assess these controls at the catchment scale, at which the water balance can be constrained by precipitation and runoff data and NUE can be quantified with census data. With this approach we test the hypotheses that a higher evaporative ratio (ET/P: the ratio of evapotranspiration over precipitation) increases N retention, thereby increasing NUE both across catchments at a given time and through time. With data from 73 catchments in the United States, encompassing a wide range of pedoclimatic conditions for the period 1988–2007, we apply a linear mixed effect model to test the effect of ET/P on NUE. Supporting our hypotheses, ET/P was positively related to NUE, and NUE increased through time. Moreover, we found an interaction between ET/P and time, such that the ET/P effect on NUE decreased in the period 1998–2007. We conclude that climatic changes that increase ET/P without negatively affecting yields, will increase N retention in the examined catchments.

Arctic tundra is a globally important store for carbon (C). However, there is a lack of reference sites characterising C exchange dynamics across annual cycles. Based on the Greenland Ecosystem Monitoring (GEM) programme, here we present 9–11 years of flux and ecosystem data across the period 2008–2018 from two wetland sites in Greenland: Zackenberg (74°N) and Kobbefjord (64°N). The Zackenberg fen was a strong C sink despite its higher latitude and shorter growing seasons compared to the Kobbefjord fen. On average the ecosystem in Zackenberg took up ∼−50 g C m⁻² yr⁻¹ (range of +21 to −90 g C m⁻² yr⁻¹), more than twice that of Kobbefjord (mean ∼−18 g C m⁻² yr⁻¹, and range of +41 to − 41 g C m⁻² yr⁻¹). The larger net carbon sequestration in Zackenberg fen was associated with higher leaf nitrogen (71%), leaf area index (140%), and plant quality (i.e. C:N ratio; 36%). Additional evidence from in-situ measurements includes 3 times higher levels of dissolved organic carbon in soils and 5 times more available plant nutrients, including dissolved organic nitrogen (N) and nitrates, in Zackenberg. Simulations using the soil-plant-atmosphere ecosystem model showed that Zackenberg's stronger CO₂ sink could be related to measured differences in plant nutrients, and their effects on photosynthesis and respiration. The model explained 69% of the variability of net ecosystem exchange of CO₂, 80% for photosynthesis and 71% for respiration over 11 years at Zackenberg, similar to previous results at Kobbefjord (73%, 73%, and 50%, respectively, over 8 years). We conclude that growing season limitations of plant phenology on net C uptake have been more than counterbalanced by the increased leaf nutrient content at the Zackenberg site.

Over the period 2012–2016, the state of California in the United States (U.S.) experienced a drought considered to be one of the worst in state history. Drought's direct impacts on California's electric power sector are understood. Extremely low streamflow manifests as reduced hydropower availability, and if drought is also marked by elevated temperatures, these can increase building electricity demands for cooling. Collectively, these impacts force system operators to increase reliance on natural gas power plants, increasing market prices and emissions. However, previous investigations have relied mostly on ex post analysis of observational data to develop estimates of increases in costs and carbon dioxide (CO₂) emissions due to the 2012–2016 drought. This has made it difficult to control for confounding variables (e.g. growing renewable energy capacity, volatile natural gas prices) in assessing the drought's impacts. In this study, we use a power system simulation model to isolate the direct impacts of several hydrometeorological phenomena observed during the 2012–2016 drought on system wide CO₂ emissions and wholesale electricity prices in the California market. We find that the impacts of drought conditions on wholesale electricity prices were modest (annual prices increased by $0–3 MWh⁻¹, although much larger within-year increases are also observed). Instead, it was an increase in natural gas prices, punctuated by the 2014 polar vortex event that affected much of the Eastern U.S., which caused wholesale electricity prices to increase during the drought. Costs from the drought were very different for the state's three investor owned utilities. Overall, we find that increased cooling demands (electricity demand) during the drought may have represented a larger economic cost ($3.8 billion) than lost hydropower generation ($1.9 billion). We also find the potential for renewable energy to mitigate drought-cased increases in CO₂ emissions to be negligible, standing in contrast to some previous studies.

Revealing the determinants and associated impacts of the transboundary pollution caused by trade is a critical issue when promoting the joint control among regions. This interdisciplinary study utilizes physical, economic and epidemiological methods to explore the anthropogenic PM_2.5 related mortality driven by interprovincial trade within China and its determinants. The results showed that 68% of the mortality flow in China was from the central and north plain area, with 29% occurring within these regions and 39% flowing to other eastern and western provinces. The high death intensity resulting from higher exports of heavily polluted agricultural and heavy industry products dominated the trade surplus of PM_2.5 mortality for the central and northern plains of China; these bring an imbalanced economic return for these regions, with only 43% of the value added generated in interprovincial trade being retained in these regions. Our study provides a more comprehensive picture of how atmospheric pollution deaths were caused by domestic trade within China, which may facilitate the multilateral pollution mitigation actions from an environmentally economic balanced perspective.

Most studies of irrigation as an anthropogenic climate forcing focus on its cooling effects. However, irrigation also increases humidity, and so may not ameliorate humid heat and its extremes. We analyzed global climate model results over hot locations and seasons at high temporal resolution to estimate the impact of irrigation on humid heat extremes, quantified as different percentiles of wet-bulb temperature ($T_\mathrm{w}$), under contemporary conditions. We found that although irrigation reduced temperature, the median and higher percentiles of $T_\mathrm{w}$  on average did not decrease. Increases in $T_\mathrm{w}$  percentile values and increases in frequency of dangerous $T_\mathrm{w}$  of several days per year due to irrigation were found in some densely populated regions, including the central United States and the Middle East, while the Ganges basin saw reduced $T_\mathrm{w}$. Changes in $T_\mathrm{w}$  were partly associated with the differential regional impacts of irrigation on moisture transport. These results underline the importance of considering impacts of climate forcings on humidity as well as temperature in evaluating associated effects on heat extremes.

Achieving the goals of the Paris Agreement and related sustainability initiatives will require halving of global greenhouse gas emissions each decade from now on through to 2050, when net zero emissions should be achieved. To reach such significant reductions requires a rapid and strategic scaling of existing and emerging technologies and practices, coupled with economic and social transformations and novel governance solutions. Here we present a new 'Powers of 10' (P10) logarithmic framework and demonstrate its potential as a practical tool for decision makers and change agents at multiple scales to inform and catalyze engagement and actions, complementing and adding nuance to existing frameworks. P10 assists in identifying the suitable cohorts and cohort ranges for rapidly deploying climate and sustainability actions between a single individual and the globally projected ∼ 10 billion persons by 2050. Applying a robust dataset of climate solutions from Project Drawdown's Plausible scenario that could cumulatively reduce greenhouse gas emissions by 1051 gigatons (Gt) against a reference scenario (2190 Gt) between 2020 and 2050, we seek to identify a 'sweet spot' where these climate and sustainability actions are suitably scaled. We suggest that prioritizing the analyzed climate actions between community and urban scales, where global and local converge, can help catalyze and enhance individual, household and local practices, and support national and international policies and finances for rapid sustainability transformations.

High river temperatures, or 'thermal extremes', can cause fish mortality and thermoelectric powerplant derating. Under climate change, projected higher air temperature and stronger surface energy fluxes will lead to increased water temperatures, exacerbating thermal extremes. However, cold hypolimnetic releases from thermally stratified reservoirs can depress tailwater temperatures and therefore alleviate thermal extremes. Thermal extremes are more harmful when they coincide with low flows, which we refer to as 'hydrologic hot-dry events'. To assess multi-sectoral impacts of climate change over large regions, we evaluate thermal events according to three impact attributes: duration (D), intensity (I), and severity (S). We apply an established model framework to simulate streamflow and stream temperature over the southeastern US regulated river system. We quantify climate change impacts (by the 2080s under RCP8.5) by comparing historical and future periods and quantify regulation impacts by comparing unregulated and regulated model setups. We find that climate change will exacerbate thermal extremes (all three metrics) in both unregulated and regulated model setups, albeit less in the regulated setup. Thermal mitigation from reservoir regulation will be stronger under climate change, decreasing the three metrics compared to the unregulated case. Even so, thermal extremes in the regulated setup will still be more severe under climate change, and only 12.2%, 19.7%, and 26.0% of D, I, and S can be mitigated by reservoirs. Despite stronger reservoir stratification, the number of regulated river segments that experience simultaneous high temperature and low flow events (hydrologic hot-dry events) will increase by 21.4% by the 2080s under RCP8.5. These events will have a median annual duration of 10.3 day/year, over 10 times the historical value.

Accurate representation of crop responses to climate is critically important to understand impacts of climate change and variability in food systems. We use Random Forest (RF), a diagnostic machine learning tool, to explore the dependence of yield on climate and technology for maize, sorghum and soybean in the US plains. We analyze the period from 1980 to 2016 and use a panel of county yields and climate variables for the crop-specific developmental phases: establishment, critical window (yield potential definition) and grain filling. The RF models accounted for between 71% to 86% of the yield variance. Technology, evaluated through the time variable, accounted for approximately 20% of the yield variance and indicates that yields have steadily increased. Responses to climate confirm prior findings revealing threshold-like responses to high temperature (yield decrease sharply when maximum temperature exceed 29 °C and 30 °C for maize and soybean), and reveal a higher temperature tolerance for sorghum, whose yield decreases gradually as maximum temperature exceeds 32.5 °C. We found that sorghum and soybean responded positively to increases in cool minimum temperatures. Maize yield exhibited a unique and negative response to low atmospheric humidity during the critical phase that encompasses flowering, as well as a strong sensitivity to extreme temperature exposure. Using maize as a benchmark, we estimate that if warming continues unabated through the first half of the 21st century, the best climatic conditions for rainfed maize and soybean production may shift from Iowa and Illinois to Minnesota and the Dakotas with possible modulation by soil productivity.

Despite broad consensus on the benefits of a nexus approach to multi-sector planning, actual implementation in government and other decision-making institutions is still rare. This study presents an approach to conducting integrated energy-water-land (EWL) planning, using Uruguay as an example. This stakeholder-driven study focuses on assessing the EWL nexus implications of actual planned policies aimed at strengthening three of Uruguay's key exports (beef, soy, and rice), which account for more than 40% of total national export revenue. Five scenarios are analyzed in the study: a reference scenario, a climate impacts scenario, and three policy scenarios. The three policy scenarios include measures such as increasing the intensity of beef production while simultaneously decreasing emissions, increasing irrigated soybean production, and improving rice yields. This study supplements previous sector-specific planning efforts in Uruguay by conducting the first stakeholder-driven integrated multi-sector assessment of planned policies in Uruguay using a suite of integrated modeling tools. Key insights from the study are: as compared to a reference scenario, improving beef productivity could lead to cropland expansion (+30%) and significant indirect increases in water requirements (+20%); improving rice yields could lead to increases in total emissions (+3%), which may partially offset emissions reductions from other policies; expanding irrigated soy could have the least EWL impacts amongst the policies studied; and climate-driven changes could have significantly less impact on EWL systems as compared to human actions. The generalizable insights derived from this analysis are readily applicable to other countries facing similar multi-sector planning challenges. In particular, the study's results reinforce the fact that policies often have multi-sector consequences, and thus policies can impact one another's efficacy. Thus, policy design and implementation can benefit from coordination across sectors and decision-making institutions.

The quantities of greenhouse gas emissions and the activity of functional microbes in coastal soils receiving nutrient-rich wastewaters from mariculture activities have seldom been reported. We investigated the effects of wastewater discharge resulting from dredging shrimp pond sediment on the soil fluxes of methane (CH₄) and nitrous oxide (N₂O) in intertidal areas and on the functional microorganisms and physio-chemical characteristics of soil. The temporal variations in gas fluxes and soil characteristics following wastewater discharge were also evaluated with the tidal regime on the day of discharge taken into account. The results showed that wastewater discharge immediately resulted in higher levels of ammonia (NH₄⁺-N) deposited and N₂O emissions from the soil at the discharge site than at the non-discharge site, while the CH₄ flux was not affected. The increase in N₂O flux lasted for a longer time when the discharge was performed during a neap tide day than when it was performed during a spring tide day. Wastewater discharge also increased the abundance of ammonia-oxidizing bacterial (AOB) amoA genes and nosZ genes in soil rather than increasing the abundance of narG and nirK genes. The pattern of temporal variations between the N₂O flux and soil NH₄⁺–N content was similar to that between the flux and the AOB-amoA gene abundance, suggesting that bacterial nitrification was important for N₂O production in soil receiving the dredging wastewater. The results suggest that the wastewater discharge impacts nitrogen metabolism processes and causes a significant N₂O emission problem; therefore, pollutant management is essential in shrimp culturing activities to reduce greenhouse gas emissions into the atmosphere.

California has experienced devastating autumn wildfires in recent years. These autumn wildfires have coincided with extreme fire weather conditions during periods of strong offshore winds coincident with unusually dry vegetation enabled by anomalously warm conditions and late onset of autumn precipitation. In this study, we quantify observed changes in the occurrence and magnitude of meteorological factors that enable extreme autumn wildfires in California, and use climate model simulations to ascertain whether these changes are attributable to human-caused climate change. We show that state-wide increases in autumn temperature (∼1 °C) and decreases in autumn precipitation (∼30%) over the past four decades have contributed to increases in aggregate fire weather indices (+20%). As a result, the observed frequency of autumn days with extreme (95th percentile) fire weather—which we show are preferentially associated with extreme autumn wildfires—has more than doubled in California since the early 1980s. We further find an increase in the climate model-estimated probability of these extreme autumn conditions since ∼1950, including a long-term trend toward increased same-season co-occurrence of extreme fire weather conditions in northern and southern California. Our climate model analyses suggest that continued climate change will further amplify the number of days with extreme fire weather by the end of this century, though a pathway consistent with the UN Paris commitments would substantially curb that increase. Given the acute societal impacts of extreme autumn wildfires in recent years, our findings have critical relevance for ongoing efforts to manage wildfire risks in California and other regions.

Cities around the world are taking action to limit greenhouse gas emissions through ambitious climate targets and climate action plans. These strategies are likely to simultaneously improve local air quality, leading to public health and monetary co-benefits. We quantify and monetarily value the health impacts of eliminating emissions from the City of Boston, and in doing so, highlight the importance of considering health impacts alongside environmental impacts of local climate action. We simulated at a 4 km resolution how the elimination of anthropogenic emissions from the City of Boston would impact air quality within a 120 km by 120 km study domain. We then estimated how this change in air quality would impact a number of annual health outcomes, as well as the associated monetary savings. We found that eliminating anthropogenic emissions from Boston would result in a decline in PM_2.5 concentration across the entire study region ranging from 8.5 µg m⁻³ in Boston to less than 1 µg m⁻³ elsewhere in the domain. In addition, we estimate that summer ozone would increase for the Greater Boston Area and areas west, and decrease elsewhere. The monetary impact of the change in air quality on health is estimated to be a $2.4 billion per year savings across the full domain and $1.7 billion within Suffolk County only, about 1.4% of the gross domestic product of the county. These monetary impacts are driven primarily by reduced incidence of mortality. We estimate that 288 deaths would be avoided per year across the study domain from eliminating Boston anthropogenic emissions, about six deaths avoided, annually, per 100 000 people. Within Suffolk County, we estimate that 47 deaths would be avoided per 100 000 people, around 16% of all-cause premature mortality. We also found a net decrease in cardiovascular and respiratory illness. Across the study domain, these health benefits would be disproportionately conferred upon people of color.

Global food production and current reliance on meat-based diets requires a large share of natural resource use and causes widespread environmental pollution including phosphorus (P). Transitions to less animal-intensive diets address a suite of sustainability goals, but their impact on society's wastewater P burden is unclear. Using the UK as our example, we explored historical diet changes between 1942 and 2016, and how shifting towards plant-based diets might impact the P burden entering wastewater treatment works (WWTW), and subsequent effluent P discharge to receiving water bodies. Average daily per capita P intake declined from its peak in 1963 (1599 mg P pp⁻¹ d⁻¹) to 1354 mg P pp⁻¹ d⁻¹ in 2016. Since 1942, the contribution of processed foods to total P consumption has increased from 21% to 52% in 2016, but consumption of total animal products has not changed significantly. Scenario analysis indicated that if individuals adopted a vegan diet or a low-meat ('EAT- Lancet') diet by 2050, the P burden entering WWTW increased by 17% and 35%, respectively relative to baseline conditions in 2050. A much lower P burden increase (6%) was obtained with a flexitarian diet. An increasing burden of P to WWTW threatens greater non-compliance with regulatory targets for P discharge to water, but also presents an opportunity to the wastewater industry to recycle P in the food chain, and reduce reliance on finite phosphate rock resources. Sustainable diets that reduce food system P demand pre-consumption could also provide a source of renewable fertilizers through enhanced P recovery post-consumption and should be further explored.

Alaskan wildfires are becoming more frequent and severe, but very little is known regarding exposure to wildfire smoke, a risk factor for respiratory and cardiovascular illnesses. We estimated long-term, present-day and future exposure to wildfire-related fine particulate matter (PM_2.5) across Alaska for the general population and subpopulations to assess vulnerability using observed data for the present day (1997–2010), modelled estimates for the present day (1997–2001), and modelled estimates for the future (2047–2051). First, we assessed wildfire-PM_2.5 exposure by estimating monthly-average wildfire-specific PM_2.5 levels across 1997–2010 for 158 Alaskan census tracts, using atmospheric transport modelling based on observed area-burned data. Second, we estimated changes in future (2047–2051) wildfire-PM_2.5 exposure compared to the present-day (1997–2001) by estimating the monthly-average wildfire-specific PM_2.5 levels for 29 boroughs/census areas (county-equivalent areas), under the Intergovernmental Panel on Climate Change (IPCC) A1B scenario from an ensemble of 13 climate models. Subpopulation risks for present and future exposure levels were estimated by summing area-weighted exposure levels utilizing the 2000 Census and State of Alaska's population projections. We assessed vulnerability by several subpopulation characteristics (e.g. race/ethnicity, urbanicity). Wildfire-PM_2.5 exposure levels during 1997–2010 were highest in interior Alaska during July. Among subpopulations, average summer (June-August) exposure levels for urban dwellers and African-American/Blacks were highest at 9.1 µg m⁻³ and 10 µg m⁻³, respectively. Estimated wildfire-PM_2.5 varied by Native American tribe, ranging from average summer levels of 2.4 µg m⁻³ to 13 µg m⁻³ for Tlingit-Haida and Alaskan Athabascan tribes, respectively. Estimates indicate that by the mid-21st century, under climate change, almost all of Alaska could be exposed to increases of 100% or more in levels of wildfire-specific PM_2.5 levels. Exposure to wildfire-PM_2.5 likely presents a substantial public health burden in the present day for Alaska communities, with different impacts by subpopulation. Under climate change, wildfire smoke could pose an even greater public health risks for most Alaskans.

Before Cyclone Amphan took place in 2020, Cyclone Fani (May 2019) is the strongest pre-monsoon cyclone in the Bay of Bengal (BOB) since 1991, killing 90 people in eastern India and Bangladesh while causing US$1.81 billion of damages. Fani developed during a period of high concentration of anthropogenic aerosols in the BOB with abnormally high sea surface temperature (SST), thereby presenting an opportunity to understand the compound effects of atmospheric aerosols and regional climate warming on a tropical cyclone. A quantitative attribution analysis was conducted using the Weather Research and Forecasting model with chemistry (WRF-Chem) run at the convection-permitting (4 km) grid spacing, accompanied by an ensemble of coarser-resolution simulations to quantify the uncertainty. The removal of post-1990 trends in the tropospheric variables and SST from WRF-Chem's initial conditions (IC) and boundary conditions (BC, including the lateral and lower boundary conditions) resulted in a reduction of cyclone precipitation by about 51% during the 5 d of April 28-May 2. The removal of tropospheric warming shows approximately twice as strong an effect on Fani (39% reduction in precipitation) as that of SST warming (22% reduction). When aerosol's direct and indirect effects were removed from the simulations, i.e., no aerosol influence on radiation and cloud microphysics, Fani initially strengthened but later weakened, as measured by geopotential height and precipitation amounts. These results suggest that aerosol and its interaction with the atmosphere acted to mitigate the strengthening effect of anthropogenic warming on Fani, but was not strong enough to entirely counteract it. Although the ensemble of coarser simulations appears to overestimate Cyclone Fani in terms of precipitation, the direction of the effects is in agreement with that obtained from the 4 km simulations. Given the increasing anthropogenic aerosols in the BOB, future attribution studies using more sophisticated dynamical aerosol models on BOB tropical cyclones are urged.

Concurrent extreme events, i.e. multi-variate extremes, can be associated with strong impacts. Hence, an understanding of how such events are changing in a warming climate is helpful to avoid some associated climate change impacts and better prepare for them. In this article, we analyse the projected occurrence of hot, dry, and wet extreme events' clusters in the multi-model ensemble of the 6th phase of the Coupled Model Intercomparison Project (CMIP6). Changes in 'extreme extremes', i.e. events with only 1% probability of occurrence in the current climate are analysed, first as univariate extremes, and then when co-occurring with other types of extremes (i.e. events clusters) within the same week, month or year. The projections are analysed for present-day climate (+1 °C) and different levels of additional global warming (+1.5 °C, +2 °C, +3 °C). The results reveal substantial risk of occurrence of extreme events' clusters of different types across the globe at higher global warming levels. Hotspot regions for hot and dry clusters are mainly found in Brazil, i.e. in the Northeast and the Amazon rain forest, the Mediterranean region, and Southern Africa. Hotspot regions for wet and hot clusters are found in tropical Africa but also in the Sahel region, Indonesia, and in mountainous regions such as the Andes and the Himalaya.

Environmental changes induced by ongoing anthropogenic activities have caused severe lake degradation. Because of the lack of long-term records, few studies have investigated the change in Wuhan lakes, and the effect of human activities on regional lake changes prior to 1973 has not been systematically studied yet. Therefore, in this study, historical maps and Landsat images were combined to track these changes from the 1920s to 2015. Three phases could be identified over the nearly 100-year study period. The most dramatic lake reduction (−21.53 km² yr⁻¹) occurred during Phase II (1950s–1980s) rather than Phase III (after the 1980s), as indicated by previous studies; the decreased lake area in Phase II was almost double that in Phase III. This reduction could be attributed to major hydraulic engineering projects during Phase II based on the watershed-scale analysis. In addition, land-use conversion over the past 45 years was used to quantify the impact of human exploitation on lakes. The shrinkage of lakes was predominately driven by agricultural activities, such as reclamation (39.2%) and aquaculture development (29.0%), and urbanization was a secondary driving force (19.8%), despite the rapid economic development of Wuhan. This study therefore provides a practical guide for lake protection in other areas similar to Wuhan.

The availability of seasonal weather forecast information in Africa has potential to provide advanced early warning of rainfall variability, informing preparedness actions to minimise adverse impacts. Obtaining accurate forecast information for the spatial scales at which decisions are made is vital. Here we examine the impact of spatial scales on the utility of seasonal rainfall forecasts in Africa. Using observations alongside seasonal forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF), we combine measures of local representativity and skill to assess optimal spatial scales for anticipating local rainfall conditions. The results reveal regions where spatial aggregation of gridded forecast data improves the quality of information provided at the local scale, and regions where forecasts have useful skill without aggregation. More generally this study presents a novel approach for evaluating the utility of forecast information which is applicable both globally and at all timescales.

We quantify the heavily oil-dominated WEF nexus in three Gulf Cooperation Council (GCC) countries (Kuwait, Qatar and Saudi Arabia) across spatial scales and over time, using available empirical data at the national level, and explore the exposure to nexus stresses (groundwater depletion) in other countries through virtual water trade. At the domestic scale, WEF trade-offs are fairly limited; while all sectors require considerable amounts of energy, the requirements for water and food production are modest compared to other uses. At the international scale, revenues from oil exports in the GCC allow the region to compensate for low food production and scarce water availability. This dependency is dynamic over time, increasing when oil prices are low and food prices are high. We show how reducing domestic trade-offs can lead to higher exposure internationally, with rice imports originating in regions where groundwater is being depleted. However, Saudi Arabia's increased wheat imports, after reversing its food self-sufficiency policy, have had limited effects on groundwater depletion elsewhere. Climate change mitigation links the WEF nexus to the global scale. While there is great uncertainty about future international climate policy, our analysis illustrates how implementation of measures to account for the social costs of carbon would reduce the oil and gas revenues available to import food and desalinate water in the GCC.

A practical approach to continuously monitor and provide real-time solar energy prediction can help support reliable renewable energy supply and relevant energy security systems. In this study on the Korean Peninsula, contemporaneous solar radiation images obtained from the Communication, Ocean and Meteorological Satellite (COMS) Meteorological Imager (MI) system, were used to design a convolutional neural network and a long short-term memory network predictive model, ConvLSTM. This model was applied to predict one-hour ahead solar radiation and spatially map solar energy potential. The newly designed ConvLSTM model enabled reliable prediction of solar radiation, incorporating spatial changes in atmospheric conditions and capturing the temporal sequence-to-sequence variations that are likely to influence solar driven power supply and its overall stability. Results showed that the proposed ConvLSTM model successfully captured cloud-induced variations in ground level solar radiation when compared with reference images from a physical model. A comparison with ground pyranometer measurements indicated that the short-term prediction of global solar radiation by the proposed ConvLSTM had the highest accuracy [root mean square error (RMSE) = 83.458 W · m⁻², mean bias error (MBE) = 4.466 W · m⁻², coefficient of determination (R²) = 0.874] when compared with results of conventional artificial neural network (ANN) [RMSE = 94.085 W · m⁻², MBE = −6.039 W · m⁻², R² = 0.821] and random forest (RF) [RMSE = 95.262 W · m⁻², MBE = −11.576 W · m⁻², R² = 0.839] models. In addition, ConvLSTM better captured the temporal variations in predicted solar radiation, mainly due to cloud attenuation effects when compared with two selected ground stations. The study showed that contemporaneous satellite images over short-term or near real-time intervals can successfully support solar energy exploration in areas without continuous environmental monitoring systems, where satellite footprints are available to model and monitor solar energy management systems supporting real-life power grid systems.

Vegetation biomass is a key and active component of the carbon cycle. Though China's vegetation biomass in recent decades has been widely investigated, only two studies have quantitatively assessed its century-scale changes so far and reported totally opposite trends. This study provided the first multi-model estimates of China's vegetation biomass change for the 20th century and its responses to historical changes in environmental and anthropogenic factors, based on simulations evaluated with the field observations from 3757 inventory plots in China and bias-corrected using machine learning (Gaussian process regression). A significant decline in vegetation biomass over the 20th century was shown by bias-corrected simulations from the six Dynamic Global Vegetation models (DGVMs) with trends ranging from −32.48 to −11.10 Tg C yr^–1 and a mean trend of −17.74 Tg C yr^–1. Land use and land cover change (LULCC) was primarily responsible for the simulated downward trend (−50.71 to −24.28 Tg C yr^–1), while increasing atmospheric CO₂ concentration lead to increased vegetation biomass (+9.27 to + 13.37 Tg C yr^–1). Climate change had limited impacts on the long-term trend (−3.75 to + 5.06 Tg C yr^–1). This study highlights the importance of LULCC for historical reconstruction and future projection of vegetation biomass over China. It also suggests that the incorrect change in China's forest area for 1980–2000 in the LULCC dataset used as model input data of many existing and ongoing model intercomparison projects (MIPs) has likely led to inaccurate estimations of historical vegetation biomass changes in China.

The American West confronts the challenge of fulfilling indigenous claims to water within the context of increasingly scarce and variable water supplies. 170 of 226 American Indian reservations have unresolved water claims that potentially exceed the region's hydrological capacity, generating uncertainty for tribes and off-reservation water users. To help resolve key uncertainties about dispute origins and outcomes, we construct a complete and novel dataset on Indian water settlements and reservation characteristics which we then analyze using a bargaining framework from economics. We find that rapid off-reservation population growth, water scarcity, and large anticipated water entitlements catalyze disputes. When more users are involved in the negotiations, transaction costs delay settlement, increasing water insecurity. We use our findings to predict allocations for 25 ongoing water right negotiations. These estimates help bound the uncertainty facing water managers throughout the American West.

This study explores the variability of tropical cyclone (TC) intensification rates (IRs) in the postmonsoon Bay of Bengal (BoB) for the satellite period of 1980–2015. It is found that both number of rapid intensification (RI) events and magnitude of IRs show a robust increase, with a northeastward shift of intensification events. Analyses show that the temporal variability of sea surface temperature dominated the IR variability during 1980–1997. However, the thick barrier layer in the northern BoB was considerably responsible for IR variability during 1998–2015, which significantly contributed to the IR increase. Due to more intensification events occurring over the northeastern region in two recent decades, the thick barrier layer with strong salinity stratification in the northern BoB limits TC-induced sea surface cooling and in turn favors TC intensification. This study has an important implication that air–sea coupled climate model need to realistically simulate upper ocean salinity variability on projecting TC intensity change over the BoB.

Crop loss and ensuing social crises can be detrimental for the agriculture-driven economy of India. Though some studies identify country-wide increasing temperatures as the dominant factor for crop loss, the agro-climatic diversity within the country necessitates an understanding of the influence of climate variability on yields at regional scales. We report a complex interplay among rainfall, temperature and cropping choices, with a focus on the drought-prone Marathwada region in Maharashtra. Our analysis based on observations, as well as statistical and process-based modelling experiments, and temperature projections of 1.5 °C and 2 °C warmer worlds show that for the two major cropping seasons, rainfall deficit is the primary cause of crop failure, as compared to rising temperatures. The gradual shift from drought-resilient food crops, such as sorghum and pearl-millet to water-intensive cash crops such as sugarcane in recent years, is seemingly responsible for aggravating this crisis. Our findings warrant strategies promoting drought-resilient food crops, that will be useful, not only for mitigating the immediate agrarian crisis, but also for curbing impending threats to food security in the region under future climate change.

In the face of climate change, it is important to estimate changes in key ecosystem properties such as plant biomass and gross primary productivity (GPP). Ground truth estimates and especially experiments are performed at small spatial scales (0.01–1 m²) and scaled up using coarse scale satellite remote sensing products. This will lead to a scaling bias for non-linearly related properties in heterogeneous environments when the relationships are not developed at the same spatial scale as the remote sensing products. We show that unmanned aerial vehicles (UAVs) can reliably measure normalized difference vegetation index (NDVI) at centimeter resolution even in highly heterogeneous Arctic tundra terrain. This reveals that this scaling bias increases most at very fine resolution, but UAVs can overcome this by generating remote sensing products at the same scales as ecological changes occur. Using ground truth data generated at 0.0625 m² and 1 m² with Landsat 30 m scale satellite imagery the resulting underestimation is large (8.9%–17.0% for biomass and 5.0%–9.7% for GPP⁶⁰⁰) and of a magnitude comparable to the expected effects of decades of climate change. Methods to correct this upscaling bias exist but rely on sub-pixel information. Our data shows that this scale-dependency will vary strongly between areas and across seasons, making it hard to derive generalized functions compensating for it. This is particularly relevant to Arctic greening with a predominantly heterogeneous land cover, strong seasonality and much experimental research at sub-meter scale, but also applies to other heterogeneous landscapes. These results demonstrate the value of UAVs for satellite validation. UAVs can bridge between plot scale used in ecological field investigations and coarse scale in satellite monitoring relevant for Earth System Models. Since future climate changes are expected to alter landscape heterogeneity, seasonally updated UAV imagery will be an essential tool to correctly predict landscape-scale changes in ecosystem properties.

Biodiversity has been devastated globally in the past hundred years, largely because of land conversion and agricultural intensification. Conversion of tropical forest to oil palm plantations is one of the greatest per unit area contributors to biodiversity loss in Southeast Asia. Concerned consumers, mainly from developed countries, have begun demanding sustainable palm oil in response to these issues. More 'biodiversity-friendly' oil palm production is also in demand, similar to that of other commodity crops (e.g. coffee, cacao). However, farming practices that improve biodiversity are thought to reduce yield, leading to increased pressure to clear more forest, resulting in further biodiversity loss. Here, we explore relationships between oil palm yield and avian biodiversity. To gather data on yields and agricultural inputs, we interviewed smallholders in Selangor, Peninsular Malaysia. We also quantified bird species richness, feeding guild diversity, abundance, and vegetation structure in smallholdings. We found that smallholdings with high yields were characterised by high species richness and feeding guild diversity, but low bird abundance. Our empirical results show the benefits to both yield and avian biodiversity of a wildlife-friendly strategy in smallholdings. We encourage the integration of farming practices with management that improves biodiversity to reconcile oil palm production and nature conservation.

The ongoing trade war between the United States and China is having profound impacts on the global economy. As recent studies have found substantial amounts of carbon dioxide and air pollution embedded in the global supply chains, the Sino–US trade war may also affect emissions and health burdens worldwide, which remains poorly understood. Here, we estimate the potential changes in gross domestic product (GDP), anthropogenic emissions and particulate matter (PM_2.5) related premature deaths worldwide under two Sino–US trade war scenarios. We find that for the US and China, the trade war would reduce their GDP and, less significantly, emissions and mortality, suggesting that the trade war is not an effective means of environmental protection. The trade war would increase both GDP and mortality in many developing regions, because of their increased production of goods targeted in the Sino–US trade war. Surprisingly, Western Europe and Latin America and Caribbean would have higher GDP but lower emissions and mortality, an economic and environmental win-win outcome as a net result of the complex changes in the global supply chains. Neighbour regions of the US and China such as Canada, Japan and Korea would also have higher GDP but lower mortality, because of reduced atmospheric transboundary transport from the US and China overcompensating for increased local emissions of these neighbours. The complex consequences of the Sino–US trade war highlight the strong inter-regional and economic-environmental linkage in support of a global collaborative strategy to foster economic growth and environmental protection.

Nearly one-sixth of U.S. river basins are unable to consistently meet societal water demands while also providing sufficient water for the environment. Water scarcity is expected to intensify and spread as populations increase, new water demands emerge, and climate changes. Improving water productivity by meeting realistic benchmarks for all water users could allow U.S. communities to expand economic activity and improve environmental flows. Here we utilize a spatially detailed database of water productivity to set realistic benchmarks for over 400 industries and products. We assess unrealized water savings achievable by each industry in each river basin within the conterminous U.S. by bringing all water users up to industry- and region-specific water productivity benchmarks. Some of the most water stressed areas throughout the U.S. West and South have the greatest potential for water savings, with around half of these water savings obtained by improving water productivity in the production of corn, cotton, and alfalfa. By incorporating benchmark-meeting water savings within a national hydrological model (WaSSI), we demonstrate that depletion of river flows across Western U.S. regions can be reduced on average by 6.2–23.2%, without reducing economic production. Lastly, we employ an environmentally extended input-output model to identify the U.S. industries and locations that can make the biggest impact by working with their suppliers to reduce water use 'upstream' in their supply chain. The agriculture and manufacturing sectors have the largest indirect water footprint due to their reliance on water-intensive inputs but these sectors also show the greatest capacity to reduce water consumption throughout their supply chains.

Managing plastics has become a focal issue of the Anthropocene. Developments in plastic materials have made possible many of the technologies and conveniences that define our modern life. Yet, plastics are accumulating in landfills and natural environments, impacting resource utilization and ecosystem function. Solutions to these rising problems will require action and coordination across all stages of plastics value chains. Here, we offer the first contemporary plastics material flow by resin type through the US economy, encompassing 2017 production, sales, use markets and end-of-life management. This roadmap, while sourced from disparate and incomplete data, provides stakeholders with a system-scale context for understanding challenges, opportunities and implications of future interventions. More than three-quarters of the plastics reaching end of life went to landfill, and less than 8% was recycled. Packaging was the largest defined use market for plastics, but two thirds of the plastic put into use in 2017 went into other markets, including consumer products, electronics, buildings and transportation. In nearly all uses, increased coordination between material and product innovation and design and end-of-life recovery and recycling are needed. Alignment of technology, policy and market drivers will be necessary to reduce plastic waste and improve the circularity of plastic materials.

The over one million agricultural workers in the United States (U.S.) are amongst the populations most vulnerable to the health impacts of extreme heat. Climate change will further increase this vulnerability. Here we estimate the magnitude and spatial patterns of the growing heat exposure and health risk faced by U.S. crop workers and assess the effect of workplace adaptations on mitigating that risk. We find that the average number of days spent working in unsafe conditions will double by mid-century, and, without mitigation, triple by the end of it. Increases in rest time and the availability of climate-controlled recovery areas can eliminate this risk but could affect farm productivity, farm worker earnings, and/or labor costs much more than alternative measures. Safeguarding the health and well-being of U.S. crop workers will therefore require systemic change beyond the worker and workplace level.

Intra-annual variability of tree-ring oxygen stable isotopes (δ¹⁸O) can record seasonal climate variability and a tree's ecophysiological response to it. Variability of sub-annual tree-ring δ¹⁸O maxima and minima, which usually occur in different parts of the growing season, may exhibit different climatic signals and can help in understanding past seasonal moisture conditions, especially in Asian monsoon areas. We developed minimum and maximum tree-ring δ¹⁸O series based on sub-annual tree-ring δ¹⁸O measurements of Pinus massoniana at a humid site in southeastern China. We found that interannual variability in minimum tree-ring δ¹⁸O is primarily controlled by the July–September soil water supply and source water δ¹⁸O, whereas the maximum latewood tree-ring δ¹⁸O is primarily controlled by the relative humidity (RH) in October. The maximum of variability of earlywood tree-ring δ¹⁸O records the RH of October of the previous year. We used minimum and maximum tree-ring δ¹⁸O to develop two reconstructions (1900–2014) of seasonal moisture availability. The summer soil water supply (July–September self-calibrated Palmer drought severity index) and the RH in fall show contrasting trends, which may be related to late-growing seasonal warming leading to a high vapor capacity and high atmospheric moisture. Our findings are valuable for research that aims to explore seasonal moisture changes under anthropogenic climate change and the ecological implications of such contrasting trends.

The storm tracks are a major driver of regional extreme weather events. Using the daily output of reanalysis and a latest generation ensemble seasonal forecasting system, this study examines the interannual variability and predictability of the boreal winter storm tracks in the North Pacific and North Atlantic. In both basins, the leading mode of storm track variability describes a latitudinal shifting of the climatological storm tracks. The shifting mode is closely connected with the extratropical large-scale teleconnection patterns (i.e. Pacific-North America teleconnection and North Atlantic Oscillation).

The main predictability source for the shifting mode of the North Pacific storm tracks are the ENSO-related sea surface temperature anomalies. Assessment of the seasonal prediction skill further shows that the shifting mode of the North Pacific storm tracks is in general better predicted than that of the North Atlantic storm tracks likely due to stronger ENSO effects.

Our analyses also find that, through the modulations of ENSO and the subtropical jet, the shifting mode of the North Pacific storm tracks exhibit a mid-to-late winter predictability enhancement. During El Niño phases, the North Pacific subtropical jet shifts equatorward and becomes strongest in mid-to-late winter, which dominates the upper-level flow and guides the storm track most equatorward. We argue that the intensification and equatorward shift of the North Pacific subtropical jet in mid-to-late winter of El Niño years provide the main reason for the increased mid-to-late winter predictability for the storm tracks. The results imply that good representation of the background subtropical jet in models is important for winter climate prediction of storm tracks.

Humans pose a major threat to many species through land-use change in virtually every habitat. However, the extent of this threat is largely unknown for invertebrates due to challenges with investigating their distributions at large scales. This knowledge gap is particularly troublesome for soil macrofauna because of the critical roles many of these organisms perform as ecosystem engineers. We used a combination of high-resolution airborne Light Detection and Ranging and deep learning models to map the distribution of the ecologically important termite genus Macrotermes across a South African savanna land-use gradient, quantifying the effects of land-use change on patterns of mound densities, heights and spatial patterning. Despite significant anthropogenic alteration to landscapes, termite mounds persisted and shared a number of similarities to mounds in untransformed areas. Mean mound height was not substantially reduced in transformed landscapes, and over-dispersion of mounds at localized scales was conserved. However, mound densities were partially reduced, and height distributions in transformed areas differed to those in protected areas. Our findings suggest that mound-building termites persist even in areas of relatively high human disturbance, but also highlight important differences in termite distributions that could lead to reductions in ecosystem services provided by termites in human-modified landscapes. The persistence of at least half of mounds in human-modified landscapes could serve as starting points for savanna restoration.

Climate services that can anticipate crop yields can potentially increase the resilience of food security in the face of climate change. These services are based on our understanding of how crop yield anomalies are related to climate anomalies, yet the share of global crop yield variability explained directly by climate factors is largely variable between regions. In Europe, France has been a major crop producer since the beginning of the 20th Century. Process based and statistical approaches to model crop yields driven by observed climate have proven highly challenging in France. This is especially due to a high regional diversity in climate but also due to environmental and agro-management factors. An additional level of uncertainty is introduced if these models are driven by seasonal-to-decadal surface climate predictions due to their low performances before the harvesting months of both wheat and maize in western Europe. On the other hand, large scale circulation patterns can possibly be better predicted than the regional surface climate, which offers the opportunity to rely on large scale circulation patterns as an alternative to local climate variables. This method assumes a certain degree of stationarity in the relationships between large scale patterns, surface climate variables, and crop yield anomalies. However, such an assumption was never tested, because of the lack of suitable long-term data. This study uses a unique dataset of subnational crop yields in France covering more than a century. By calibrating and comparing statistical models linking large scale circulation patterns and observed surface climate variables to crop yield anomalies, we can demonstrate that the relationship between large scale patterns and surface variables relevant for crop yields is not stationary. Therefore, large scale circulation pattern based crop yield forecasting methods can be adopted for seasonal predictions provided that regression parameters are constantly updated. However, the statistical crop yield models based on large-scale circulation patterns are not suitable for decadal predictions or climate change impact assessments at even longer time-scales.

Despite similar evolution of Niño3.4 index for three strong El Niño events in the tropical Pacific in 1982–1983, 1997–1998 and 2015–2016, divergent sea surface temperature anomalies (SSTAs) were observed in the North Atlantic (NA) in spring following El Niño peak. Strong teleconnection occurred for the first two events in 1982–1983 and 1997–1998, leading to a negative phase of a North Atlantic Oscillation-like circulation over the extratropical NA, and thus a positive tripolar SSTA pattern in the NA. But the teleconnection was weak for the case of 2015–2016 El Niño, the SSTA in spring 2016 in NA being mainly created and maintained by the preconditioning of the NA basin and local atmosphere-ocean interactions. The salient difference among the three events resides in their ways to operate the teleconnection linking the central-eastern equatorial Pacific and the subtropical eastern North Pacific to the extratropical NA, which would be a key to explain the different impacts of the three exceptional El Niño events. It is furthermore shown that the reduced anomalous westerlies over Central America along 30° N around the peak time of 2015–2016 El Niño play a role of inhibition for an efficient Rossby wave energy propagation from the tropics, eastward into the Gulf of Mexico and northward into midlatitudes in the western NA.

Understanding how net primary production (NPP) and its allocation respond to climate warming is of fundamental importance in predicting ecosystem carbon (C) cycle and C-climate feedback. Especially, the optimal partitioning theory suggests that plants preferentially allocate photosynthates toward the above- or below-ground parts to acquire the limiting resources to maximize their growth rate and optimize resource use under environmental change. However, it remains elusive on how NPP allocation changes and regulates community water-use efficiency (WUEc) under climate warming. In this study, we conducted a manipulative warming experiment with three levels of warming treatments (control, + 1.5 °C and + 2.5 °C) to explore the response of NPP allocation and its regulation on WUEc in an alpine meadow. Results showed that above-ground NPP (ANPP) and below-ground NPP (BNPP) responded differently to warming. On average, W1.5 (+ 1.5 °C) and W2.5 (+ 2.5 °C) treatments increased BNPP by 28.98% and 33.28% and increased NPP by 20.05% and 38.70%, respectively, across 4 years. Whereas no consistent warming effect on ANPP was observed across years. The fraction of BNPP to total NPP (f_BNPP) responded positively to warming under low ambient temperature and community biomass while it responded negatively under high ambient temperature and community biomass. Notably warming-induced changes in f_BNPP negatively correlated with warming-induced changes in WUEc. These results suggested that warming effect on NPP allocation was largely contextually dependent and implied important biological regulation on WUEc. The emerging trade-offs between NPP allocation and WUEc reflect adaptation strategy of plant community under climate change.

Many cities are developing mitigation plans in an effort to reduce the population health impacts from expected future increases in the frequency and intensity of heat waves. To inform heat mitigation and adaptation planning, information is needed on the extent to which available mitigation strategies, such as reflective and green roofs, could result in significant reductions in heat exposure. Using the Weather Research and Forecasting (WRF) model, we analysed the impact of green and cool (reflective) roofs on the urban heat island (UHI) and temperature-related deaths in the Greater Boston area (GBA) and New England area (NEA) in summer and winter. In the GBA, green and cool roofs reduced summertime population-weighted temperature by 0.35 °C and 0.40 °C, respectively. In winter, green roofs did not affect temperature, whereas cool roofs caused a temperature reduction of 0.40 °C. In the NEA, the cooler summers induced by green and cool roofs were estimated to reduce the heat-related mortality rates by 0.21% and 0.17%, respectively, compared to baseline. Cool-roof-induced temperature reduction in winter could increase the cold-related mortality rate by 0.096% compared to baseline. These results suggest that both green and cool roofing strategies have the potential to reduce the impact of heat on premature deaths. Additionally, the differing effects in winter suggest the need for a careful consideration of health trade-offs in choosing heat island mitigation strategies.

The relationships between strong earthquakes, landslides, and vegetation destruction and the process of post-seismic recovery in tectonically active alpine valley areas have not been adequately documented. Here we show detailed pollen study results from a swamp located near the epicenter of the 1933 M 7.5 Diexi earthquake in eastern Qinghai-Tibetan Plateau (QTP) to reveal the impact of earthquake on vegetation, and the post-seismic recovery process. Based on ²¹⁰Pb-¹³⁷Cs age model, the seismic event layer is well constrained. The earthquake event corresponds stratigraphically to a zone with the lowest pollen concentrations, the lowest pollen diversity, and a high frequency of non-arboreal pollen. Elaeagnaceae scrubs rapidly developed in post-seismic landscape recovery processes, which is important for reducing soil erosion and landslide activities. Natural ecological recovery is slow due to increasing human activities and historical climatic fluctuations.

Urban boundaries, an essential property of cities, are widely used in many urban studies. However, extracting urban boundaries from satellite images is still a great challenge, especially at a global scale and a fine resolution. In this study, we developed an automatic delineation framework to generate a multi-temporal dataset of global urban boundaries (GUB) using 30 m global artificial impervious area (GAIA) data. First, we delineated an initial urban boundary by filling inner non-urban areas of each city. A kernel density estimation approach and cellular-automata based urban growth modeling were jointly used in this step. Second, we improved the initial urban boundaries around urban fringe areas, using a morphological approach by dilating and eroding the derived urban extent. We implemented this delineation on the Google Earth Engine platform and generated a 30 m resolution global urban boundary dataset in seven representative years (i.e. 1990, 1995, 2000, 2005, 2010, 2015, and 2018). Our extracted urban boundaries show a good agreement with results derived from nighttime light data and human interpretation, and they can well delineate the urban extent of cities when compared with high-resolution Google Earth images. The total area of 65 582 GUBs, each of which exceeds 1 km², is 809 664 km² in 2018. The impervious surface areas account for approximately 60% of the total. From 1990 to 2018, the proportion of impervious areas in delineated boundaries increased from 53% to 60%, suggesting a compact urban growth over the past decades. We found that the United States has the highest per capita urban area (i.e. more than 900 m²) among the top 10 most urbanized nations in 2018. This dataset provides a physical boundary of urban areas that can be used to study the impact of urbanization on food security, biodiversity, climate change, and urban health. The GUB dataset can be accessed from http://data.ess.tsinghua.edu.cn.

The impact of the Arctic on midlatitude weather and climate is still in scientific debate. The observation-based analysis, however, shows frequent concurrences of Arctic warming with extreme cold in the midlatitudes, and vice versa. This teleconnection could aid in seasonal climate forecasts for the midlatitudes. This study assessed the forecast skill of Arctic temperature and the Arctic-midlatitude teleconnection patterns in operational seasonal climate forecast models based on their wintertime forecast archives. Further, the impact of the Arctic-midlatitude teleconnection on the midlatitude forecast skill is evaluated. The results revealed that most climate forecast models have the capability to simulate the overall pattern of Arctic-midlatitude teleconnection for both the eastern Eurasian and North American regions. However, this is little converted to practical forecast skill in midlatitude likely due to poor capabilities in forecasting Arctic temperatures. Idealized analysis (assuming a perfect forecast of Arctic temperature) showed that considerable forecasting improvements could be achieved, and further improvements are possible with accurate simulations of the Arctic and its teleconnection patterns. These results highlight the importance of better predictions of the Arctic conditions in seasonal forecasts that are not just limited to their own region but extend to midlatitude weather and climate as well.

Recent extreme fire seasons in California have prompted utilities such as Pacific Gas and Electric to pre-emptively de-energize portions of the electrical grid during periods of extreme fire weather to reduce the risk of powerline-related fire ignitions. The policy was deployed in 2019, resulting in 12 million person-days of power outages and widespread societal disruption. Retrospective weather and vegetation moisture data highlight hotspots of historical risk across northern California. We estimate an average of 1.6 million person-days of de-energization per year, based on recent historical climate conditions and assuming publicly stated utility de-energization thresholds. We further estimate an additional 70% increase in the population affected by de-energization when vegetation remains abnormally dry later into autumn—suggesting that climate change will likely increase population vulnerable to de-energization. Adaptation efforts to curtail fire risk can be beneficial, but efforts to prepare affected populations, modernize the grid, and refine decision-making surrounding such policies have high potential to reduce the magnitude of negative externalities experienced during the 2019 de-energization events.

Climate warming leads to crop yield loss. Although investigations have shown the region-specific effect of climate warming on maize yield in China, the determinants of this region-specific effect are poorly known. Using county-level data from 1980 to 2010 for China, we investigated the dependence of yield change under climate warming on soil indigenous nutrients. Analysis of the data indicated an average decrease of 2.6% in maize yield for 1 °C warming. Warming-related yield loss occurred mostly in western China, the North China Plain, and the southwest region of Northeast China. By contrast, climate warming did not decline maize yield in the northern region of Northeast China, south, and southwest China. Summer maize is more sensitive to warming than spring maize. A 1 °C warming resulted in an average loss of 3.3% for summer maize and 1.8% for spring maize. The region-specific change in yield can be well quantified by a combination of soil indigenous total nitrogen (STN), available phosphorus (SAP), and available potassium (SAK). Under climate warming, maize yields in regions with high STN generally increased, while the risk of yield reduction appeared in regions with high SAK. Areas that were vulnerable (defined as a yield loss higher than 1% for a 1 °C increase) to climate warming accounted for 62%, while areas that showed resilience (defined as a yield increase higher than 1% for a 1 °C increase) to climate warming accounted for 27% of the planting area. An increase in nitrogen fertilizer application is expected to reduce the risk of yield reduction in regions with low STN. Our findings highlight soil resilience to climate warming and underline the practice of fertilizer management to mitigate yield loss due to climate warming.

Flooding caused by high streamflow events poses great risk around the world and is projected to increase under climate change. This paper assesses how climate change will alter high streamflow events by changing both the prevalence of different driving mechanisms (i.e. 'flood generating processes') and the magnitude of differently generated floods. We present an analysis of simulated changes in high streamflow events in selected basins in the hydroclimatically diverse Pacific Northwestern United States, classifying the events according to their mechanism. We then compare how the different classes of events respond to changes in climate at the annual scale. In a warmer future, high flow events will be caused less frequently by snowmelt and more frequently by precipitation events. Also, precipitation-driven high flow events are more sensitive to increases in precipitation than are snowmelt-driven high flow events, so the combination of the increase in both frequency and magnitude of precipitation-driven high flow events leads to higher flood likelihood than under each change alone. Our comparison of the results from two emissions pathways shows that a reduction in global emissions will limit the increase in magnitude and prevalence of these precipitation-driven events.

Biomass-burning emissions (BBE) profoundly affect climate and air quality. BBE have been estimated using various methods, including satellite-based fire radiative power (FRP). However, BBE estimates show very large variability and the accuracy of emissions estimation is poorly understood due to the lack of good reference data. We evaluated fire emissions estimated using FRP from the Advanced Baseline Imager (ABI) on GOES-R (Geostationary Operational Environmental Satellites-R) by comparing with the Sentinel 5 Precursor TROPOspheric Monitoring Instrument (TROPOMI) Carbon Monoxide (CO) over 41 wildfires across the United States during July 2018—October 2019. All the ABI FRP-based CO and TROPOMI CO emissions were significantly correlated and showed a very good agreement with a coefficient of determination of 0.94 and an accuracy of 13–18%. We further reported a CO emission coefficient of 29.92 ± 2.39 g MJ⁻¹ based on ABI FRP and TROPOMI CO, which can be used to directly estimate BBE from FRP observed from satellites. Based on the CO emission coefficient and ABI FRP, we finally estimated a monthly mean CO of 596 Gg across the Conterminous United States for June—September 2018.

In this study, 14 years of climate, stream flow, land management, nitrate-nitrogen (NO₃^–N) load and concentration data were analyzed to identify potential drivers for NO₃^–N losses at two tile-drained catchments under cropland use in northeastern Germany. Mean (±standard deviation) annual NO₃^–N concentrations were 9.7 ± 2.9 (drainage plot) and 6.8 ± 2.4 mg l⁻¹ (ditch catchment), while mean annual NO₃^–N loads amounted to 22 ± 16 and 20 ± 16 kg ha⁻¹, respectively. Significant positive relationships between annual discharge and annual NO₃^–N losses underlined the importance of hydrologic conditions on NO₃^–N export mechanisms. No direct relationships were found between N soil surface surpluses and NO₃^–N losses. Any possible impact of N soil surface surpluses on NO₃^–N export rates was overridden by the hydro-meteorological conditions in the catchment. Positive correlations between the climatic water balance and NO₃^–N losses suggest that agricultural catchments with similar characteristics as ours may face—without countermeasures—increased N losses in the future as regional climate projections predict wetter winters in the coming decades. Our analysis has further shown that effects of land management strategies aiming at reducing N losses into surface waters might only become visible with a delay of years or even decades.

Atmospheric black carbon (BC) is the most important aerosol contributor to global warming. However, there is a lack of understanding about the climate impact of BC aerosols because of systematic discrepancies between model and observation estimates of light absorption enhancements (Eabs) in atmospheric processes after emissions, and such discrepancies are transferred directly into large uncertainties of aerosol radiative forcing assessments. In this study, we quantify Eabs of atmospheric BC aerosols with diverse particle morphology distributions using a multi-dimensional aerosol model. We show that current widely used Mie method may overestimate BC Eabs by ∼50% because variations in particle morphology are not considered. Although absorption calculation can be improved by including complex particle morphology and heterogeneity in composition, we find that neglect of the diverse particle morphology distributions in modeling may lead to 15% ∼ 30% relative deviations on Eabs estimations of BC aerosol ensembles. The results thus imply that particle morphology distribution should be included in models to accurately represent the radiative effects of BC aerosols.

Coastal areas have been affected by hazards such as floods and storms due to the impact of climate change. As coastal systems continue to become more socially and environmentally complex, the damage these hazards cause is expected to increase and intensify. To reduce such negative impacts, vulnerable coastal areas and their associated risks must be identified and assessed. In this study, we assessed the flooding risk to coastal areas of South Korea using multiple machine learning algorithms. We predicted coastal areas with high flooding risks, as this aspect has not been adequately addressed in previous studies. We forecasted hazards under different representative concentration pathway climate change scenarios and regional climate models while considering ratios of sea level rise. Based on the results, a risk probability map was developed using a probability ranging from 0 to 1, where higher values of probability indicate areas at higher risk of compound events such as high tides and heavy rainfall. The accuracy of the average receiver operating characteristic curves was 0.946 using a k-Nearest Neighbor algorithm. The predicted risk probability in 10 year increments from the 2030s to the 2080s showed that the risk probability for southern coastal areas is higher than those of the eastern and western coastal areas. From this study, we determined that a probabilistic approach to analyzing the future risk of coastal flooding would be effective to support decision-making for integrated coastal zone management.

Studying precipitation diurnal variation characteristics is crucial to better understand their formation mechanism. Moreover, it is fundamental in assessing regional climate variability and to validate the effectiveness of cloud and precipitation parameterization schemes in weather and climate models. Based on observational data from 2008 to 2017, this manuscript objective is to assess the aerosol effects on summer precipitation on an hourly-scale diurnal variation basis in the Beijing metropolitan area. The results put in evidence that the precipitation frequency and duration on polluted days were 25.0% and 14.8% lower with respect to clean days. No significant differences were observed in total daily accumulated rainfall between clear and polluted days. While heavy precipitation mean intensity on polluted days increased by 13.5%, which is potentially linked with aerosol microphysical effects. Note that precipitation occurs earlier on polluted days, with peak time of 1 ∼ 2-hour in advance compared with clean days over the urban areas of Beijing, which may be primarily ascribed to the influence of advanced turning local circulation of mountain-valley breezes and urban heat island circulation due to aerosol-radiation effects.

Particulate matter (PM) emissions from vegetation and peat fires in Equatorial Asia cause poor regional air quality. Burning is greatest during drought years, resulting in strong inter-annual variability in emissions. We make the first consistent estimate of the emissions, air quality and public health impacts of Equatorial Asian fires during 2004–2015. The largest dry season (August—October) emissions occurred in 2015, with PM emissions estimated as 9.4 Tg, more than triple the average dry season emission (2.7 Tg). Fires in Sumatra and Kalimantan caused 94% of PM emissions from fires in Equatorial Asia. Peat combustion in Indonesian peatlands contributed 45% of PM emissions, with a greater contribution of 68% in 2015. We used the WRF-chem model to simulate dry season PM for the 6 biggest fire years during this period (2004, 2006, 2009, 2012, 2014, 2015). The model reproduces PM concentrations from a measurement network across Malaysia and Indonesia, suggesting our PM emissions are realistic. We estimate long-term exposure to PM resulted in 44 040 excess deaths in 2015, with more than 15 000 excess deaths annually in 2004, 2006, and 2009. Exposure to PM from dry season fires resulted in an estimated 131 700 excess deaths during 2004–2015. Our work highlights that Indonesian vegetation and peat fires frequently cause adverse impacts to public health across the region.

Spring phenology is a sensitive indicator of climate change and has substantial impacts on the carbon cycle. The global N cycle has been greatly disturbed by anthropogenic activities resulting in altered atmospheric N deposition worldwide. Research has been focused on the changes in the spring phenology and its covariations with climatic factors. However, the influences of N deposition on spring phenology have not been well documented to date. Herein, we investigated the effects of N deposition on the start of growing season (SOS) in continental United States (CONUS) during the years 1986–2015 using the normalized difference vegetation index (NDVI) datasets derived from both the third generation NDVI dataset and the Moderate Resolution Imaging Spectroradiometer (MODIS). We observed that N deposition could only explain approximately 5% of temporal variation in SOS in CONUS. However, the sensitivities of SOS in response to unit change in both temperature (S_T) and precipitation (S_P) showed clear decreasing spatial patterns with increasing N deposition. The S_T generally decreased from −6 d/°C in low N deposition regions (<2 kg ha⁻¹) to −4 d/°C in areas with N deposition >4 kg ha⁻¹. Furthermore, the positive S_P also showed a continuously decreasing pattern with the increase in N deposition, but the negative S_P was gradually weakened when N deposition was >1.0 kg ha⁻¹. The results have important implications as it reveals the role of N deposition on spring plant phenology, and strongly suggest the consideration of N deposition effects when analyzing or predicting spring phenology in response to future climate change.

Cold season temperatures in Europe have increased rapidly by about 1.2^°C in the late 1980s, followed by relatively modest and regionally flat temperature trends thereafter. The abrupt change affected the entire European continent and coincided regionally with abrupt hydroclimatic changes such as a widespread reduction in snow days in Switzerland. However, the drivers and causes of the event are not well understood. Using a dynamical adjustment method based on statistical learning, we find that the continental-scale late 1980s abrupt winter warming and regional decreases in snow days can be attributed to cold conditions in the mid-1980s followed by a few exceptionally warm seasons. Both are caused by random atmospheric circulation variability superimposed upon a long-term and relatively homogenous warming trend, and do not require an external cause or change of the underlying dynamics of the system. This explanation is consistent with simulations from a 21-member regional climate model ensemble, in which four members display comparable abrupt temperature increases regionally driven by circulation and a long-term externally forced response. Overall, our analysis provides an observation-based interpretation of abrupt temperature change at the continental scale, associated hydroclimatic changes regionally, and its drivers. Furthermore, our method might contribute to improved mechanistic understanding of different observed climate phenomena in many regions of the world that experience high variability.

Tropical forests harbour the highest biodiversity on the planet and are essential to human livelihoods and the global economy. However continued loss and degradation of forested landscapes, coupled with a rapidly rising global population, is placing incredible pressure on forests globally. The United Nations has developed the Reducing Emissions from Deforestation and forest Degradation (REDD +) programme in response to the challenges facing tropical forests and in recognition of the role they can play in climate mitigation. REDD + requires consistent and reliable monitoring of forests, however, national-level methodologies for measuring degradation are often bespoke and, because of an inability to track degradation effectively, the majority of countries combine reporting for deforestation and forest degradation into a single value. Here, we extend a recent analysis that enabled the detection of selective logging at the scale of a logging concession to a regional-scale estimation of selective logging activities. We utilized logging records from across Brazil to train a supervised classification algorithm for detecting logged pixels in Landsat imagery then predicted the extent of logging over a 20 year period throughout Rondônia, Brazil. Approximately one-quarter of the forested lands in Rondônia were cleared between 2000 and 2019. We estimate that 11.0% of the forest area present in 2000 had been selectively logged by 2019, comprising >11 500 km² of forest. In general, rates of selective logging were twice as high in the first decade relative to the last decade of the period. Our approach is a considerable advance in developing an operationalized selective logging monitoring system capable of detecting subtle forest disturbances over large spatial scales.

A growing body of research indicates overall greenness offers potential psychological benefits. However, few studies have explored green space structures and their potential association with mental disorders. The aim of this study was to determine the existence of such an association. From two million randomly sampled people in Taiwan's National Health Insurance Research Database, we selected 3823 patients that received a first-time diagnosis of schizophrenia from 2005–2016. Moreover, we used a geographic information system and a landscape index to quantify three characteristics of green space structures including area and edge, shape, and proximity. Additionally, we collected the normalized difference vegetation index and enhanced vegetation index data to reconfirm the association between overall greenness and schizophrenia incidence. We used the indices to determine individuals' exposure according to their residential township. Spearman's correlation analysis was conducted to select variables by considering their collinearity. Cox proportional-hazards models were applied to assess the relationship between green space exposure and schizophrenia incidence following adjustment for potential confounders, such as air pollution (NO₂, SO₂, ozone, and PM₁₀), temperature, precipitation, and socioeconomic status, which are risk factors. We found a negative association between most green space structures indices and schizophrenia incidence. Our findings suggest that for green spaces, a larger mean patch area and edge density, higher complex (higher perimeter–area ratio), and greater proximity (higher contiguity index, aggregation index, and contagion index), may reduce the risk of schizophrenia. A sensitivity test and subgroup analysis revealed similar results.

As a long-term mechanism for eco-compensation, the 'Hematopoiesis-Compensation Policy' (HCP) such as industrial transformation can effectively improve the operability and efficiency of compensation policy. However, compared with the 'Transfusion-compensation Policy' (TCP) such as the cash subsidy, can HCP quickly achieve the goal of eco-compensation? Does HCP require more total investment in compensation funds? This paper takes the Shennongjia National Park System Pilot Zone (SNJNP) as the research area and takes the eco-compensation policy to encourage farmers to return farmland to forest as the research object. We set three different compensation modes of TCP and HCP, and study the compensation scenarios in the next 20 years. The results show that HCP can achieve the purpose of compensation faster than TCP. Although the annual payment of HCP is relatively higher at the beginning, both the annual expenditure and the cumulative expenditure will decrease significantly. Therefore, later annual expenditure and cumulative total expenditure of HCP will be lower than TCP.

As electric vehicles and their associated charging infrastructure continue to evolve, there is potential to simultaneous alleviate range and recharge concerns with the development of extreme fast chargers (XFC) that can fully charge batteries in PEVs in the span of a few minutes. Recent announcements from EVSE providers and vehicle manufacturers suggest that XFC charging stations, which can recharge a BEV at roughly 20 to 25 miles per minute of charging, and XFC-capable BEVs, could be commercially available within the next 5 years. Our study investigates the potential emission impacts of widespread use of extreme fast charging (350 kW) for electric vehicles in 2030. We conduct a novel vehicle charging simulation model by combining empirical charging behavior data across several data sources. These charging demands are then added as exogenous load to the Grid Optimized Operation Dispatch (GOOD) model, which simulates the operation of generators across the Untied States. We find that XFC can increase both greenhouse gas emissions and local air pollutants, though the results are sensitive to local contexts and grid composition.

Tropical cyclones (TCs) have devastating impacts and are responsible for significant damage. Consequently, for TC-induced direct economic loss (DEL) attribution all factors associated with risk (i.e. hazard, exposure and vulnerability) must be examined. This research quantifies the relationship between TC-induced DELs and maximum wind speed, asset value and Gross Domestic Product (GDP) per capita using a regression model with TC records from 2000 to 2015 for China's mainland area. The coefficient of the maximum wind speed term indicates that a doubling of the maximum wind speed increases DELs by 225% [97%, 435%] when the other two variables are held constant. The coefficient of the asset value term indicates that a doubling of asset value exposed to TCs increases DELs by 79% [58%, 103%]; thus, if hazard and vulnerability are assumed to be constant in the future, then a dramatic escalation in TC-induced DELs will occur given the increase in asset value, suggesting that TC-prone areas with rapid urbanization and wealth accumulation will inevitably be subject to higher risk. Reducing the asset value exposure via land-use planning, for example, is important for decreasing TC risk. The coefficient of GDP per capita term indicates that a doubling in GDP per capita could decrease DELs by 54% [39%, 66%]. Because accumulated assets constantly increase people's demand for improved security, stakeholders must invest in risk identification, early warning systems, emergency management and other effective prevention measures with increasing income to reduce vulnerability. This research aims to quantitatively connect TC risk (expected DELs, specifically) to physical and socioeconomic drivers and emphasizes how human dimensions could contribute to TC risk. Moreover, the model can be used to estimate TC risk under climate change and future socioeconomic development in the context of China.

In the present study, the impacts of eastern Pacific (EP) and central Pacific El-Niño on the winter North Pacific storm track (WNPST) are investigated, and the possible reasons for the different responses of the WNPST to the two types of El-Niño are revealed. It is found that only EP El-Niño episodes have a distinct influence on the strength and movement of the WNPST. During EP El-Niño episodes, the WNPST is significantly enhanced and extended equatorward. The patterns of atmospheric baroclinicity anomalies are consistent during the two types of El-Niño. The enhancement and equatorward extension of the WNPST during EP El-Niño episodes can be attributed to anomalous baroclinic energy conversion. In addition, EP El-Niño episodes can also intensify the strength of the WNPST by warming the lower-tropospheric air upstream of the WNPST, which generates more synoptic-scale disturbances entering the WNPST.

The problem of reducing the impacts of rising anthropogenic greenhouse gas on warming temperatures has led to the proposal of using stratospheric aerosols to reflect some of the incoming solar radiation back to space. The deliberate injection of sulfur into the stratosphere to form stratospheric sulfate aerosols, emulating volcanoes, will result in sulfate deposition to the surface. We consider here an extreme sulfate geoengineering scenario necessary to maintain temperatures at 2020 levels while greenhouse gas emissions continue to grow unabated. We show that the amount of stratospheric sulfate needed could be globally balanced by the predicted decrease in tropospheric anthropogenic SO₂ emissions, but the spatial distribution would move from industrialized regions to pristine areas. We show how these changes would affect ecosystems differently depending on present day observations of soil pH, which we use to infer the potential for acid-induced aluminum toxicity across the planet.

International efforts to avoid dangerous climate change aim for large and rapid reductions of fossil fuel CO₂ emissions worldwide, including nearly complete decarbonization of the electric power sector. However, achieving such rapid reductions may depend on early retirement of coal- and natural gas-fired power plants. Here, we analyze future fossil fuel electricity demand in 171 energy-emissions scenarios from Integrated Assessment Models (IAMs), evaluating the implicit retirements and/or reduced operation of generating infrastructure. Although IAMs calculate retirements endogenously, the structure and methods of each model differ; we use a standard approach to infer retirements in outputs from all six major IAMs and—unlike the IAMs themselves—we begin with the age distribution and region-specific operating capacities of the existing power fleet. We find that coal-fired power plants in scenarios consistent with international climate targets (i.e. keeping global warming well-below 2 °C or 1.5 °C) retire one to three decades earlier than historically has been the case. If plants are built to meet projected fossil electricity demand and instead allowed to operate at the level and over the lifetimes they have historically, the roughly 200 Gt CO₂ of additional emissions this century would be incompatible with keeping global warming well-below 2 °C. Thus, ambitious climate mitigation scenarios entail drastic, and perhaps un-appreciated, changes in the operating and/or retirement schedules of power infrastructure.

Beavers (Castor sp.) are ecosystem engineers that cause significant changes to their physical environment and alter the availability of resources to other species. We studied flood dynamics created by American beaver (C. canadensis K.) in a southern boreal landscape in Finland in 1970–2018. We present for the first time, to our knowledge, a temporally continuous long-term study of beaver-induced flood disturbances starting from the appearance of beaver in the area. During the 49 years, the emergence of new sites flooded by beaver and repeated floods (61% of the sites) formed a dynamic mosaic characterized by clustered patterns of beaver sites. As beaver dispersal proceeded, connectivity of beaver sites increased significantly. The mean flood duration was approximately three years, which highlights the importance of datasets with high-temporal resolution in detecting beaver-induced disturbances. An individual site was often part of the active flood mosaic over several decades, although the duration and the number of repeated floods at different sites varied considerably. Variation of flood-inundated and post-flood phases at individual sites resulted in a cumulative number of unique patches that contribute to environmental heterogeneity in space and time. A disturbance mosaic consisting of patches differing by successional age and flood history is likely to support species richness and abundance of different taxa and facilitate whole species communities. Beavers are thus a suitable means to be used in restoration of riparian habitat due to their strong and dynamic influence on abiotic environment and its biotic consequences.

Terrestrial evapotranspiration (ET) is thermodynamically expected to increase with increasing atmospheric temperature; however, the actual constraints on the intensification of ET remain uncertain due to a lack of direct observations. Based on the FLUXNET2015 Dataset, we found that relative humidity (RH) is a more important driver of ET than temperature. While actual ET decrease at reduced RH, potential ET increases, consistently with the complementary relationship (CR) framework stating that the fraction of energy not used for actual ET is dissipated as increased sensible heat flux that in turn increases potential ET. In this study, we proposed an improved CR formulation requiring no parameter calibration and assessed its reliability in estimating ET both at site-level with the FLUXNET2015 Dataset and at basin-level. Using the ERA-Interim meteorological dataset for 1979–2017 to calculate ET, we found that the global terrestrial ET showed an increasing trend until 1998, while the trend started to decline afterwards. Such decline was largely associated with a reduced RH, inducing water stress conditions that triggered stomatal closure to conserve water. For the first time, this study quantified the global-scale implications of changes in RH on terrestrial ET, indicating that the temperature-driven acceleration of the terrestrial water cycle will be likely constrained by terrestrial vegetation feedbacks.

Land use is affecting 70% of global ecosystems and their functioning. Forest management is a regionally dominant land use and affects forest ecosystems by changing both structure and functioning, but its impact on primary productivity is not well known. Here we investigated the effect of forest management on primary productivity by comparing managed secondary forests with relatively pristine unmanaged primary forests in Sweden. As proxy for primary productivity we used the satellite-based vegetation index NIRv which has been shown to be closely and linearly related to primary productivity. We produced a digital map of 390 primary forests across Sweden, and extracted NIRv over these and surrounding secondary forests forming spatially proximate pairs. By comparing the primary and secondary forests NIRv in the pairs we found that secondary forests on average show higher NIRv, but the highest values were found in primary forests. The difference in NIRv between pairs is related to their difference in mean stand age, and at equal stand age the NIRv of primary forests is higher than in their paired secondary forests. Overall, management leads to increased NIRv through regeneration of forests stands that reduce their mean age. However, primary forests show higher NIRv when controlling for age, despite being found on higher altitudes and on steeper slopes with lower soil moisture, which suggests that forest management other than regeneration is not increasing primary productivity of Swedish forests.

Satellite measurements and model simulations indicate the existence of the Asian tropopause aerosol layer (ATAL)—an enhanced aerosol layer in the upper troposphere and lower stratosphere (UTLS) associated with the Asian summer monsoon (ASM)—although it has rarely been evidenced by snapshots of balloon-borne in situ measurements. To better understand how the ATAL evolves, a portable optical particle counter (POPS) onboard a stratospheric balloon was released over the Tibetan Plateau (TP) during the ASM period of 2019. The POPS detected the ATAL in the UTLS during the ascending, descending periods, as well as during its quasi-horizontal floating periods. The aerosol number density in the ATAL showed obvious vertical variability. The peak aerosol number density in the ATAL was 180 cm⁻³ around the tropopause during the ascending and descending period and the maximum aerosol number density was 290 cm⁻³ around the tropopause during the floating period. And the aerosol concentration observed over the TP in the 2019 summer was approximately five times larger than that in the 2018 summer. Lagrangian simulations reveal that the minority of the observed aerosol particles were directly elevated in a region of uplift south of the Himalayas, and the majority of the particles were transported from the UTLS region situated approximately between the isentropic surfaces of 370 and 460 K. Up to 14% of the observed aerosol particles were directly influenced by the volcanic plumes from the eruption of the volcano Raikoke in June 2019.

The wildland-urban interface (WUI) is the spatial manifestation of human communities coupled with vegetated ecosystems. Spatial delineation of the WUI is important for wildfire policy and management, but is typically defined according to spatial relationships between housing development and wildland vegetation without explicit consideration of fire risk. A fire risk-based definition of WUI can enable a better distribution of management investment so as to maximize social return. We present a novel methodological approach to delineate the WUI based on a fire risk assessment. The approach establishes a geographical framework to model fire risk via machine learning and generate multi-scale, variable-specific spatial thresholds for translating fire probabilities into mapped output. To determine whether fire-based WUI mapping better captures the spatial congruence of houses and wildfires than conventional methods, we compared national and subnational fire-based WUI maps for Chile to WUI maps generated only with housing and vegetation thresholds. The two mapping approaches exhibited broadly similar spatial patterns, the WUI definitions covering almost the same area and containing similar proportions of the housing units in the area under study (17.1% vs. 17.9%), but the fire-based WUI accounted for 13.8% more spatial congruence of fires and people (47.1% vs. 33.2% of ignitions). Substantial regional variability was found in fire risk drivers and the corresponding spatial mapping thresholds, suggesting there are benefits to developing different WUI maps for different scales of application. We conclude that a dynamic, multi-scale, fire-based WUI mapping approach should provide more targeted and effective support for decision making than conventional approaches.

We present a long-term assessment of precipitation trends in Southwestern Europe (1850–2018) using data from multiple sources, including observations, gridded datasets and global climate model experiments. Contrary to previous investigations based on shorter records, we demonstrate, using new long-term, quality controlled precipitation series, the lack of statistically significant long-term decreasing trends in precipitation for the region. Rather, significant trends were mostly found for shorter periods, highlighting the prevalence of interdecadal and interannual variability at these time-scales. Global climate model outputs from three CMIP experiments are evaluated for periods concurrent with observations. Both the CMIP3 and CMIP5 ensembles show precipitation decline, with only CMIP6 showing agreement with long term trends in observations. However, for both CMIP3 and CMIP5 large interannual and internal variability among ensemble members makes it difficult to identify a trend that is statistically different from observations. Across both observations and models, our results make it difficult to associate any declining trends in precipitation in Southwestern Europe to anthropogenic forcing at this stage.

Air pollutants seriously impact climate change and human health. In this study, the gridpoint statistical interpolation (GSI) three-dimensional variational data assimilation system was extended from ground data to vertical profile data, which reduced the simulation error of the model in the vertical layer. The coupled GSI-Lidar-WRF-Chem system was used to improve the accuracy of fine particulate matter (PM_2.5) simulation during a wintertime heavy pollution event in the North China Plain in late November 2017. In this experiment, two vehicle-mounted Lidar instruments were utilized to make synchronous observations around the 6th Ring Road of Beijing, and five ground-based Lidars were used for long-term network observations on the North China Plain. Data assimilation was then performed using the PM_2.5 vertical profile retrieved from the seven Lidars. Compared with the model results, the correlation of assimilation increased from 0.74–0.86, and the root-mean-square error decreased by 36.6%. Meanwhile, the transport flux and transport flux intensity of the PM_2.5 were analyzed, which revealed that the PM_2.5 around the 6th Ring Road of Beijing was mainly concentrated below 1.8 km, and there were obvious double layers of particles. Particulates in the southwest were mainly input, while those in the northeast were mainly output. Both the input and output heights were around 1 km, although the input intensity was higher than the output intensity. The GSI-Lidar-WRF-Chem system has great potential for air quality simulation and forecasting.

The variability in the southern stratospheric polar vortex (SSPV) and its downward coupling with the troposphere are known to play a crucial role in driving climate variability over Antarctica. In this study, SSPV weakening events and their impacts on the surface climate of Antarctica are examined using in-situ observation and reanalysis data. Combining criteria from several previous studies, we introduce a new detection method for SSPV weakening events. Based on the new criteria, the occurrence frequency of SSPV weakening events has exhibited a systematic increasing trend since the 2000 s. However, the weakened anomalies of individual SSPV events are not statistically different (95% confidence level) between the earlier (1979–1999) and later (2000–2017) periods examined in this study. The recent increase in the occurrence of SSPV weakening events is largely controlled by tropospheric mechanisms, i.e. the poleward heat flux carried by southern hemispheric planetary waves and associated vertical wave propagation. Among the various scales of planetary waves, the wavenumber 1 contributes most of the poleward eddy heat flux. We show that SSPV weakening events induce statistically significant cooling over the Antarctic Peninsula (AP) region and warming over the rest of Antarctica. Typically, surface air temperature anomalies with large negative values smaller than − 0.6 °C and positive values larger than + 0.8 °C are observed over the east coast of the tip of the AP and King Edward VII Land, respectively. The influence of an SSPV weakening event on the surface lasts for approximately three months with higher height anomalies off western Antarctica, providing favorable conditions for the atmosphere to transport cold air from the interior of Antarctica to the AP via the Weddell Sea. Distinct positive surface air temperature anomalies over the rest of Antarctica are associated with the northerly circulation anomaly from the eastern Weddell Sea to east Antarctica.

Drought is a recurring extreme climate event over most parts of the world featured by long duration and low predictability. The secular trend of drought is of particular interest for investigators in agriculture, climate change and sustainability domains. In this study, we applied the ensemble empirical mode decomposition (EEMD) method and analyzed the spatio-temporal characteristics of the secular trends of meteorological drought over global land surface during the period 1950–2015 using a self-calibrating Palmer Drought Severity Index (PDSI) product. We found that there were 25.98% PDSI samples had turning point, namely the shift of trend, in the corresponding secular trend series; the probability distribution of the turning points position (period) extracted by EEMD closely follows a normal distribution with mean value at Nov. 1981. We showed that there is large discrepancy in the secular trend types extracted by EEMD and Mann–Kendall test, and exemplified the risk of using a monotonic trend to capture the changes of the intrinsic secular trend of PDSI series. We suggested that there was an accelerated drying trend over global land surface as a whole, but large areas with wetting trend existed in the meantime, especially at the high latitudes in the northern hemisphere. Additionally, we found that the PDSI secular trend change rate exhibits a multidecadal variability of about 50 years or so and it implies a potential relationship with periodic variations of the oceanic and atmospheric current. We showed that the secular trend of PDSI series extracted by EEMD could provide more detailed spatio-temporal characteristics, featured by the shifts of trend and nonlinear property of the secular trend, of global drought than that of the non-parametric or linear regression methods. The secular trend of PDSI could present more insights about the transition and progress of wetting/drying trend over global land surface at multidecadal scale.

Trees in global forests are exposed to warming climate, the rate of which is different between day and night, and associated with soil drought. Previous studies commonly show that forest growth responds positively to daytime warming but negatively to night warming. However, it remains unclear whether such asymmetric responses of forest growth to day and night warming still exist in extremely dry soils. Here, based on the long-term records of the normalized difference vegetation index and ring-width index at 2294 forest sites across the Northern Hemisphere, we found that the rising daytime maximum temperature (T_max) reduces stem growth but the rising nighttime minimum temperature (T_min) lowers canopy greenness when the soil is drier than a threshold. We further discuss three mechanisms that could drive such negative impacts. For example, data from experimental studies showed that the shifted biomass allocation from wood to leaves is one important mechanism driving the reductions of wood growth under day warming. These findings indicate that climate warming could negatively affect tree growth in extremely dry soils, regardless of whether temperature rises during the daytime or at night. Thus, understanding the interactions of water and temperature on the sub-diurnal scale is critical for improving our ability to predict the forest dynamics under future climate change.

To reach the Paris climate targets, the mitigation capacity needs to be maximized across all components of the Earth system, especially land. Mitigation actions through land management, such as cover crops in agricultural soils, are often evaluated in terms of their carbon sequestration potential, while radiative forcing related to surface albedo changes is often ignored. The aim of this study was to assess the mitigation potential of cover crops, both as changes in biogenic greenhouse gas fluxes (CO₂ and N₂O) and albedo-driven radiative forcing at the top of the atmosphere (TOA). To achieve this, we have integrated a biogeochemistry model framework running on approximately 8000 locations across the European Union with detailed soil data, supplemented with time series of albedo measurements derived from satellite remote sensing. We found that carbon sequestration remained the dominant mitigation effect, with 1th and 3rd interquartile of 5.2–17.0 Mg CO₂e ha⁻¹ at 2050, and negligible changes in N₂O emissions over that time-horizon. Cover crops were generally brighter than bare soils, hence, the reflected shortwave radiation at TOA ranged between 0.08–0.22 Wm⁻² on average, broadly equivalent to a removal of 0.8–3.9 Mg CO₂e ha⁻¹. Through scenarios analysis, we further showed how the mitigation potential could be substantially increased by growing a high albedo chlorophyll-deficient cover crop. This radiative land management option has an additional benefit of providing its mitigation effect more rapidly than carbon sequestration, although additional studies might be warranted to evaluate local and non-local associated climatic effects, such as changes in patterns of surface temperature and precipitation.

Although buildings produce a third of greenhouse gas emissions, it has been suggested that they might be one of the most cost-effective climate change mitigation solutions. Among building materials, wood not only produces fewer emissions according to life-cycle assessment but can also store carbon. This study aims to estimate the carbon storage potential of new European buildings between 2020 and 2040. While studies on this issue exist, they mainly present rough estimations or are based on a small number of case studies. To ensure a reliable estimation, 50 different case buildings were selected and reviewed. The carbon storage per m² of each case building was calculated and three types of wooden buildings were identified based on their carbon storage capacity. Finally, four European construction scenarios were generated based on the percentage of buildings constructed from wood and the type of wooden buildings. The annual captured CO₂ varied between 1 and 55 Mt, which is equivalent to between 1% and 47% of CO₂ emissions from the cement industry in Europe. This study finds that the carbon storage capacity of buildings is not significantly influenced by the type of building, the type of wood or the size of the building but rather by the number and the volume of wooden elements used in the structural and non-structural components of the building. It is recommended that policymakers aiming for carbon-neutral construction focus on the number of wooden elements in buildings rather than more general indicators, such as the amount of wood construction, or even detailed indirect indicators, such as building type, wood type or building size. A practical scenario is proposed for use by European decision-makers, and the role of wood in green building certification is discussed.

Two extreme heatwaves hit Western Europe in the summer of 2019, with historical records broken by more than a degree in many locations, and significant societal impacts, including excess mortality of several thousand people. The extent to which human influence has played a role in the occurrence of these events has been of large interest to scientists, media and decision makers. However, the outstanding nature of these events poses challenges for physical and statistical modeling. Using an unprecedented number of climate model ensembles and statistical extreme value modeling, we demonstrate that these short and intense events would have had extremely small odds in the absence of human-induced climate change, and equivalently frequent events would have been 1.5 °C to 3 °C colder. For instance, in France and in The Netherlands, the July 3-day heatwave has a 50–150-year return period in the current climate and a return period of more than 1000 years without human forcing. The increase in the intensities is larger than the global warming by a factor 2 to 3. Finally, we note that the observed trends are much larger than those in current climate models.

The 2010 western Russian heatwave was characterized by historically high surface temperatures that led to devastating impacts on the environment, economy, and society. Recent studies have attributed a quasi-stationary upper level ridge, sensible heat advection, and land-atmosphere temperature coupling as the primary components for the development of the heatwave event. The results in this study reveal that rapid drought intensification occurred prior to the extreme atmospheric conditions associated with the heatwave. The flash drought event developed from a lack of rainfall coupled with enhanced evaporative demand and resulted in rapid desiccation of the land surface. The region that underwent rapid drought intensification acted to prime the land-atmosphere interactions necessary to supplement the excessive surface temperatures experienced during the heatwave event. This area also provided a source region for the advection of warm, dry air to promote heatwave development downwind of the flash drought location. As such, the hydrometeorological extremes associated with the precursor flash drought and heatwave resulted in cascading impacts that severely affected ecosystems, agriculture, and human health. Given the findings from this research, we conclude that flash drought impacts should be expanded beyond vegetative and agricultural applications and should be viewed as a possible precursor and direct forcing for heatwave events and associated impacts.

In this study, we compare the spatial patterns of simulated geocentric sea-level change to observations from satellite altimetry over the period 1993–2015 to assess whether a forced signal is detectable. This is challenging, as on these time scales internal variability plays an important role and may dominate the observed spatial patterns of regional sea-level change. Model simulations of regional sea-level change associated with sterodynamic sea level, atmospheric loading, glacier mass change, and ice-sheet surface mass balance changes are combined with observations of groundwater depletion, reservoir storage, and dynamic ice-sheet mass changes. The resulting total geocentric regional sea-level change is then compared to independent measurements from satellite altimeter observations. The detectability of the climate-forced signal is assessed by comparing the model ensemble mean of the 'historical' simulations with the characteristics of sea-level variability in pre-industrial control simulations. To further minimize the impact of internal variability, zonal averages were produced. We find that, in all ocean basins, zonally averaged simulated sea-level changes are consistent with observations within sampling uncertainties associated with simulated internal variability of the sterodynamic component. Furthermore, the simulated zonally averaged sea-level change cannot be explained by internal variability alone—thus we conclude that the observations include a forced contribution that is detectable at basin scales.

As human populations move into cities they are increasingly isolated from the natural world, with associated negative impacts on health and well-being. However, as cities renew themselves through urban redevelopment and climate-adaptation, opportunities arise to improve people's access to urban green areas that can be informed by modeling the network of urban open spaces. Recent research identified the need for multi-criteria indices of access to urban green spaces. Including open spaces such as empty lots, ground- and air-spaces surrounding buildings, and spaces associated with roads and other linear features can improve planning for urban greenspaces by identifying areas of opportunity for additional greening. Further, the gradient of interconnections among open spaces can be used to prioritize urban greening locations to build green networks. We modelled all open-space connections across 605 km² in Seoul, population 10.3 million, using Omniscape, a landscape connectivity model. We combined the resulting open-space connectivity map with distance-based indices for existing urban parks and street trees. Combining these criteria permits rank-prioritization of locations where new green spaces would most improve residents' access. We found 2910 of 3375 (86.2%) locations where urban green spaces already exist within 300 m for city residents. Of the remaining 465 locations, 276 are in areas with the lowest-open space connections. For urban street trees, 44.3% of the 2588 km of the city's major roads are already planted with street trees. Of the remainder, 210 km (8.1%) are located in the areas with the least connections to green spaces. Nine new urban parks would provide relief for the most highly-impacted areas, where the flow of open space is lowest and where no green spaces are available within 300 m. The integration of a spatial model typically used for conservation assessments with city planning provides useful additional context for building urban health.

The summer of 2018 was characterized by high temperatures and low precipitation values in the Netherlands. The drought negatively impacted different sectors, resulting in an estimated damage of 450 to 2080 million Euros. Strong regional differences were observed in the precipitation shortfall across the country, with highest deficits in the southern and eastern regions. This raised two questions: (i) have increasing global temperatures contributed to changes in meteorological and agricultural droughts as severe or worse as in 2018? And (ii) are trends in these types of droughts different for coastal and inland regions? In this paper we show that there is no trend in summer drought (Apr-Sep) near the coast. However, a trend in agricultural drought is observed for the inland region where water supply is mainly dependent on local precipitation. This trend is driven by strong trends in temperature and global radiation rather than a trend in precipitation, resulting in an overall trend in potential evapotranspiration. Climate model analyses confirm that this trend in agricultural drought can at least in part be attributed to global climate change.

The use of social media platforms (e.g. Twitter and Facebook) to raise public awareness towards wildlife conservation is an emerging discussion. However, little is currently known about the propagation pattern of wildlife-related information on social media. In this study, a quantitative model was developed based on 230 independent cetacean stranding incidents (2008–2018) across mainland China from a popular Chinese social media platform (Sina Weibo). This model enabled analysis of the post formation process, identification of the key factors influencing the popularity of the posts and wildlife-related incidents, and allowed investigation of public opinions. The results showed that central media users can increase the overall possibility of elevating incident popularity by ∼75 times, an attractive species or incident by ∼5 times, and a negative social ethics incident by ∼3 times. Traditional media users and celebrity influencers performed key roles in affecting the level of re-posting. Online audiences of highly popular posts predominantly encompassed both users from relatively developed regions and female users. It was observed that posts which became popular within ∼12 h retained their influence for ∼3 d. Post popularity was closely related to comment counts rather than forwarding in the first day of posting and the whole status retention time. Public opinion generally expressed a supportive attitude towards wildlife conservation, but lacked in-depth thinking, and individual responsibility was expressed through revival incidents. In order to raise public awareness towards biodiversity conservation, social media–based wildlife information dissemination should balance the content of attractive and non-charismatic species or incidents and include more positive emotions. Posts via traditional users (especially central media users) and opinion leaders (celebrities) can attract a highly educated audience and females, and thus evoke increased comment numbers during the first day of posting. This will help to popularise conservation knowledge and legislation with continuous efforts.

Climate prediction skill on the interannual timescale, which sits between that of seasonal and decadal, is investigated using large ensembles from the Met Office and CESM initialised coupled prediction systems. A key goal is to determine what can be skillfully predicted about the coming year when combining these two ensembles together. Annual surface temperature predictions show good skill at both global and regional scales, but skill diminishes when the trend associated with global warming is removed. Skill for the extended boreal summer (months 7–11) and winter (months 12–16) seasons are examined, focusing on circulation and rainfall predictions. Skill in predicting rainfall in tropical monsoon regions is found to be significant for the majority of regions examined. Skill increases for all regions when active ENSO seasons are forecast. There is some regional skill for predicting extratropical circulation, but predictive signals appear to be spuriously weak.

In this study, the causes of the increase in global mean tropical cyclone translation speed (TCTS) in the post-satellite era were investigated. Analysis reveals that the global-mean TCTS increased by 0.31 km h⁻¹ per decade over the last 36 years, but the steering flow controlling the local TCTS decreased by −0.24 km h⁻¹ per decade in the major tropical cyclone (TC) passage regions. These values correspond to a change of 5.9% and −5.6% during the analysis period for the mean TCTS and steering flow, respectively. The inconsistency between these two related variables (TCTS and steering flows) is caused by relative TC frequency changes according to basin and latitude. The TCTS is closely related to the latitude of the TC position, which shows a significant difference in mean TCTS between basins. That is, the increased global-mean TCTS is mainly attributed to the following: (1) an increase (4.5% per decade) in the relative proportion of the North Atlantic TCs in terms of global TC's position points (this region has the fastest mean TCTS among all basins); and (2) the poleward shift of TC activities. These two effects account for 76.8% and 25.8% of the observed global-mean TCTS trend, respectively, and thus overwhelm those of the slowing steering flow related to the weakening of large-scale tropical circulation, which leads to a global mean increase in TCTS. Given that TC activity in the North Atlantic is closely related to the Atlantic Multi-decadal Oscillation and a poleward shift of TC exposure is likely induced by global warming, the recent increase in the global-mean TCTS is a joint outcome of both natural variations and anthrophonic effects.

Nutrient loss from sloping farmland with rocky desertification in karst regions leads to low farmland productivity and non-point source pollution. The mechanisms of nutrient outputs through water flow in such contexts under different rainfall intensities and slope angles were studied by using artificial rainfall simulation. Research showed that surface water flow occurred when the rainfall intensity was between 30 mm · h⁻¹ and 50 mm · h⁻¹, and the nutrient (TN, TP, TK) output through water flow showed the same pattern. Nutrient output through water flow was dominated by nutrient loss from surface and subsurface water flows when the rainfall intensity was ≥ 50 mm · h⁻¹. Rainfall intensity was found to be a dominant driver in comparison to slope angle and for limestone soil of the karst region in Southwest China, but slope angle only had a significant effect on TP output through surface water flow. The largest proportion of nutrient output was associated with surface flow, a lower proportion was associated with subsurface flow, and the lowest proportion with underground flow. The nutrient output through underground water flow directly led to groundwater pollution, although it was not large. The results of this study provide a theoretical reference for the control of nutrient output through water flow and the management of nonpoint source pollution in karst regions.

Due to high present-day temperatures and reliance on rainfed agriculture, sub-Saharan Africa is highly vulnerable to climate change. We use a comprehensive set of global (CMIP5) and regional (CORDEX-Africa) climate projections and a new convection-permitting pan-Africa simulation (and its parameterized counterpart) to examine changes in rainfall and temperature and the impact on crop suitability of maize, cassava and soybean in sub-Saharan Africa by 2100 (RCP8.5). This is the first time an explicit-convection simulation has been used to examine crop suitability in Africa. Increasing temperatures and declining rainfall led to large parts of sub-Saharan Africa becoming unsuitable for multiple staple crops, which may necessitate a transition to more heat and drought resistant crops to ensure food and nutrition security. Soybean was resilient to temperature increases, however maize and cassava were not, leading to declines in crop suitability. Inclusion of sensitivity to extreme temperatures led to larger declines in maize suitability than when this was excluded. The results were explored in detail for Tanzania, Malawi, Zambia and South Africa. In each country the range of projections included wetting and drying, but the majority of models projected rainfall declines leading to declines in crop suitability, except in Tanzania. Explicit-convection was associated with more high temperature extremes, but had little systematic impact on average temperature and total rainfall, and the resulting suitability analysis. Global model uncertainty, rather than convection parameterizations, still makes up the largest part of the uncertainty in future climate. Explicit-convection may have more impact if suitability included a more comprehensive treatment of extremes. This work highlights the key uncertainty from global climate projections for crop suitability projections, and the need for improved information on sensitivities of African crops to extremes, in order to give better predictions and make better use of the new generation of explicit-convection models.

Droughts in tropical South America have an imminent and severe impact on the Amazon rainforest and affect the livelihoods of millions of people. Extremely dry conditions in Amazonia have been previously linked to sea surface temperature (SST) anomalies in the adjacent tropical oceans. Although the sources and impacts of such droughts have been widely studied, establishing reliable multi-year lead statistical forecasts of their occurrence is still an ongoing challenge. Here, we further investigate the relationship between SST and rainfall anomalies using a complex network approach. We identify four ocean regions which exhibit the strongest overall SST correlations with central Amazon rainfall, including two particularly prominent regions in the northern and southern tropical Atlantic. Based on the time-dependent correlation between SST anomalies in these two regions alone, we establish a new early-warning method for droughts in the central Amazon basin and demonstrate its robustness in hindcasting past major drought events with lead-times up to 18 months.

Soil carbon and nutrient availability play crucial roles in ecosystem sustainability, and they are controlled by the interaction of climatic, biotic, and soil physico-chemical variables. Although soil physico-chemical properties have been recognized as vital variables for predicting soil organic carbon (SOC) and nutrients, their relative influence across broad geographical scales has yet to be evaluated when simultaneously considering many other drivers. Using boosted regression tree and structural equation modelling analyses of observations from topsoil (0–10 cm) and subsoil (20–30 cm) at 628 sites across Australia, we investigated the effects and relative influence of climate (mean annual temperature and aridity index), plant productivity, soil biodiversity (bacterial and fungal richness), and soil physical (clay and silt) and chemical (pH and iron) properties on SOC content and nutrient availability (i.e. nitrogen, phosphorus, and potassium). Among these variables, we found that soil physico-chemical properties primarily predicted the continent-scale SOC storage and nutrient availability. In contrast, climate, plant productivity, and soil biodiversity played relatively small roles. The importance of physico-chemical properties was evident across soil depths and ecosystem types (i.e. tropical, temperate, arid, and cropland). Our findings point to the need to better understand the role of soil physico-chemical properties in soil carbon and nutrient cycling and including these variables in predictions of SOC and nutrient dynamics at the ecosystem to continental scale.

Electricity rate subsidies are an important policy mechanism to help low-income households afford necessary energy services, bringing substantial quality of life benefits to roughly 30% of California households. Resulting increases in consumption may unintentionally increase costly peak demand while also raising emissions of greenhouse gases and criteria air pollution from electricity generation, potentially motivating additional measures to shift consumption to hours with fewer unintended consequences. In a difference-in-differences study of interval data from over 30 000 northern California dwellings, we find that enrollment in the California Alternate Rates for Energy subsidy is associated with a ∼13% increase in electricity consumption, varying modestly across regions and seasons, increasing by an additional ∼3% during summer peak times. We find that peak demand costs associated with CARE are about $45 M per year but could fall by ∼1/3 with peak shifting to level demand.

In recent years, several drought indices have been developed and used to monitor local to regional scale droughts on various temporal scales. However, to our knowledge, these indices do not possess generalized criteria to define a threshold in which to declare a national-scale drought. We present a statistical methodology to identify national-scale meteorological drought years in India. We implement a Superposed Epoch Analysis and bootstrap analysis to estimate annual cereal crop production losses as a result of widespread meteorological drought events. For this purpose, the meteorological definition of drought based on the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI), in combination with the country's cropland area and cereal crops production, is used. The results demonstrate that a national-scale meteorological drought is defined if approximately 19% or more of India's cropland is affected by meteorological drought (SPI3 and SPEI3 equal to or less than −1.00) throughout the monsoon season (June–September). According to this analysis, depending on the indicator data used, a total of 18to 20 national-scale meteorological droughts were identified in India during 1964–2015, causing a 3.61% to 3.93% composite decrease in cereal crops production. The years which were commonly identified as national scale meteorological droughts over cropland by using different approaches are 1965, 1972, 1987, 2002, and 2009. A similar statistical approach can also be used to define drought thresholds at various spatial scales using the drought indices most applicable to the purpose and scale of study.

Cost-effective achievement of the Paris Agreement's long-term goals requires the unanimous phase-out of coal power generation by mid-century. However, continued investments in coal power plants will make this transition difficult. India is one of the major countries with significant under construction and planned increase in coal power capacity. To ascertain the likelihood and consequences of the continued expansion of coal power for India's future mitigation options, we use harmonised scenario results from national and global models along with projections from various government reports. Both these approaches estimate that coal capacity is expected to increase until 2030, along with rapid developments in wind and solar power. However, coal capacity stranding of the order of 133–237 GW needs to occur after 2030 if India were to pursue an ambitious climate policy in line with a well-below 2 °C target. Earlier policy strengthening starting after 2020 can reduce stranded assets (14–159 GW) but brings with it political economy and renewable expansion challenges. We conclude that a policy limiting coal plants to those under construction combined with higher solar targets could be politically feasible, prevent significant stranded capacity, and allow higher mitigation ambition in the future.

The estimation of urban growth in megacities is a critical and intricate task for researchers and decision-makers owing to the complexity of these urban systems. Currently, the majority of megacities are located in Asia which is one of the most disaster-prone regions in the world. The high concentrations of people, infrastructure and assets in megacities create high loss potentials for natural hazards; therefore, the forecasting of exposure metrics such as built-up area is crucial for disaster risk assessment. This study aims to identify and project the dynamics of built-up area at risk using a spatio-temporal approach considering seismic hazard in three Asian megacities, namely Jakarta, Metro Manila and Istanbul. First, Landsat Thematic Mapper images were processed to obtain the built-up areas of 1995 and 2016 for Metro Manila, and of 1995 and 2018 for Jakarta and Istanbul. The SLEUTH urban growth model, a cellular automaton (CA)-based spatial model that simulates urban growth using historical geospatial data, was then employed to predict the urban growth of these megacities by 2030. Finally, seismic hazard maps obtained for 10% and 2% probabilities of exceedance were overlaid with built-up area maps. For a seismic hazard of 10% probability of exceedance in 50 years, the total urban area subjected to Modified Mercalli intensities (MMI) VIII and IX has increased nearly 65% over 35 years in Metro Manila. For Jakarta and Istanbul, the total urban area at the MMI VIII level has increased nearly 79% and 54% over 35 years, respectively. For a seismic hazard of 2% probability of exceedance in 50 years, the total urban area subjected to MMI IX has increased nearly 75%, 65% and 49% over 35 years in Jakarta, Metro Manila and Istanbul, respectively. The results show that urban growth modelling can be utilized to assess the built-up area exposed to high risk as well as to plan urban growth considering natural hazards in megacities.

Forested ecosystems dominated by trees, wetlands, and lakes occupy more than 65% of Canada's land base. This treed area is dynamic, subject to temporary reductions in area and biomass due to wildfire and timber harvesting, and increases due to successional processes and growth. As such, the net aboveground biomass accumulated over time is a function of multiple, complex factors: standing forests grow and accrue biomass over time, whereas disturbed forests lose biomass, and subsequent regeneration processes result in biomass accrual once again. Knowledge of these processes behind biomass gain and loss is important for a range of considerations including habitat provision, economic opportunities, and exchange of carbon between forests and the atmosphere. Herein, we used a 33 year satellite-derived time series of aboveground biomass estimates for Canada's forested ecosystems to quantify biomass dynamics partitioned by the presence or absence of disturbance, and by disturbance type. Findings suggest that over the analysis period considered (1984–2016), undisturbed forests accounted for accrual of 3.90 Petagrams (Pg) of biomass. In contrast, while occupying ∼75% less area, disturbed forests accounted for a loss of 3.94 Pg biomass. Of this total biomass reduction, 45.4% can be attributed to wildfire, 43.8% to harvesting, 8.3% to non-stand replacing disturbances, and 2.5% to detectable roads and infrastructure development. Following disturbance, an additional 1.32 Pg of biomass were accrued during the analysis period, along with an additional 4.09 Pg in newly treed areas. Overall, Canada's forested ecosystems have realized a net increase in biomass of 5.38 Pg. Results of this analysis demonstrate the decoupling of area disturbed from the resulting biomass consequences by disturbance type, with large areas of wildfire accounting for a change in biomass that is similar to that of forest harvesting, which occurs over a much smaller area of mature and productive forest.

Biofuel production is a key strategy for reducing CO₂ emissions globally and is expected to increase substantially in the coming decades, particularly in tropical developing countries. The adoption of sustainable biofuel production technologies that do not place large demands on agricultural or forested lands, has the potential to make a substantial contribution to decreasing greenhouse gas emissions while reducing biodiversity losses and degradation of native ecosystems resulting from high demand for land. With their high productivity per unit area and ability to grow on non-arable lands, microalgal biofuel production systems could become a major sustainable alternative to biofuel production from food crops (first-generation biofuels). However, the potential impacts of microalgal biofuels on food production, biodiversity, and carbon storage, compared to other biofuel production alternatives, are largely unknown. In the present study, the most suitable areas for siting microalgae production farms to fulfill 30% of future transport energy demands were determined within four Neotropical countries with high population densities and high importance for agricultural expansion and biodiversity conservation globally (Colombia, Ecuador, Panama, and Venezuela). These results were contrasted with the best areas for siting oil palm and sugarcane crops to fulfill the same target in future transport energy demands. Microalgal production systems offer the most sustainable alternative for future biofuel production within the Neotropics. Meeting 30% of future transport energy demands with microalgal biofuels reduced land area requirements by at least 52% compared to oil palm and sugarcane. Furthermore, microalgal biofuel production reduced direct competition with agricultural lands, biodiverse areas, and carbon-rich systems within countries, with little overlap with the biodiverse and carbon-rich rainforests. This study can guide decision making towards the identification and adoption of more sustainable biofuel production alternatives in the Neotropics, helping in avoiding unnecessary environmental impacts from biofuel expansion in the region.

In the past, India has suffered severe socio-economic losses due to recurring floods and droughts during boreal summer (June–August). In this analysis, we estimate the chance of extreme summer rainfall, i.e. flood and drought over India for the present climate using the UNprecedented Simulated Extremes using ENsembles (UNSEEN) method. This is the first application of the method to the hindcasts from multiple coupled atmosphere-ocean models. We first test individual models against the observed rainfall record over India and select models that are statistically indistinguishable from observations. We then calculate the chances of floods, droughts and unprecedented rainfall using 1669 realizations of summer precipitation from the selected set of models. It is found that the chance of drought is larger than the chance of flood in the present climate. There is a clear El Niño (La Niña) signal in dry (wet) summers and the occurrence of more frequent and intense droughts than floods in both models and observations is partly due to El Niño Southern Oscillation phase asymmetry. The chances of record-breaking drought and flood are 1.6% and 2.6%, respectively. There is also an estimated chance that a 30% rainfall deficit could occur around once in two centuries, which is far beyond the record deficit over India.

Exposure to fine particulate matter (PM_2.5) is a leading contributor to the disease burden in India, largely due to widespread household solid fuel use. The transition from solid to clean fuels in households has the potential to substantially improve public health. India has implemented large initiatives to promote clean fuel access, but how these initiatives will reduce PM_2.5 exposure and the associated health benefits have not yet been established. We quantified the impacts of a transition of household energy from solid fuel use to liquefied petroleum gas (LPG) on public health in India from ambient and household PM_2.5 exposure. We estimate that the transition to LPG would reduce ambient PM_2.5 concentrations by 25%. Reduced exposure to total PM_2.5 results in a 29% reduction in the loss of healthy life, preventing 348 000 (95% uncertainty interval, UI: 284 000–373 000) premature mortalities every year. Achieving these benefits requires a complete transition to LPG. If access to LPG is restricted to within 15 km of urban centres, then the health benefits of the clean fuel transition are reduced by 50%. If half of original solid fuel users continue to use solid fuels in addition to LPG, then the health benefits of the clean fuel transition are reduced by 75%. As the exposure–outcome associations are non–linear, it is critical for air pollution studies to consider the disease burden attributed to total PM_2.5 exposure, and not only the portion attributed to either ambient or household PM_2.5 exposure. Our work shows that a transition to clean household energy can substantially improve public health in India, however, these large public health benefits are dependent on the complete transition to clean fuels for all.

There is a growing demand for subnational population projections for informing potential demographic influences on many aspects of society and the environment at the scale at which interactions occur and actions are taken. Existing US subnational population projections have not fully accounted for regional variations of demographic rates and therefore under-estimate the uncertainties in and heterogeneity of population trends. We present a first set of population projections for US states that span a wide but plausible range of population outcomes driven by changing state-level demographic rates consistent with the widely used SSP scenario framework. The projections are carried out for all 50 states integrated through bilateral gross migration flows. They update the original national-level SSP population projections based on recently available data and introduce more plausible assumptions on long-term international migration. We project a national population ranging from about 250–650 million by 2100, somewhat lower than the SSP projections due mainly to updated base year data. Utah and other states in the Rocky Mountain region see the largest increases in population in proportional terms, while the Northeast and Great Lakes regions see the slowest growth or most decline, along with individual states like Alaska, California, Louisiana, and Mississippi. Aging occurs in all states and scenarios, but is most prominent in the Northeast, Florida, and in some cases states in the West and the Great Lakes region. The relative contributions of fertility, mortality, and migration to population change varies substantially across states.

Anthropogenic climate change is leading to the intensification of extreme rainfall due to an increase in atmospheric water holding capacity at higher temperatures as governed by the Clausius-Clapeyron (C-C) relationship. However, the rainfall-temperature sensitivity (termed scaling) often deviates from the C-C relationship. This manuscript uses classifications prescribed by regional-scale atmospheric circulation patterns to investigate whether deviations from the C-C relationship in tropical Australia can be explained by differing weather types (WT). We show that the rainfall-temperature scaling differs depending on the WTs, with the difference increasing with rainfall magnitude. All monsoonal WTs have similar scaling, in excess of the C-C relationship, while trade winds (the driest WTs) result in the greatest scaling, up to twice that of the C-C relationship. Finally, we show the scaling for each WT also varies spatially, illustrating that both local factors and the WT will contribute to the behaviour of rainfall under warming.

Summer 2018 in the United Kingdom (UK) was its joint hottest on record and the associated impacts raise questions over societal resilience to extremes of this magnitude or greater occurring in upcoming years. Better information on the current likelihood of extreme and unprecedented events feeds into improved understanding of risk, relevant for policy and contingency planning. However, making robust estimates of likelihood is difficult given that extremes in the historical record are few by definition. We overcome this by using a large ensemble of initialised climate model simulations to assess the chance of exceeding summer 2018 temperatures in the current climate and find it to be ∼11% each year, although a weak circulation bias may mean this estimate is conservative. This likelihood has increased sharply over the last few decades. A one in 100-year event would bring summertime temperatures to the UK of approximately 1 °C above 2018, an anomaly similar to that of the notable hot summer of 1976. Subsetting the large ensemble enables characterisation of the dynamics around hot summers, and investigation of possible remote influences. Several circulation patterns bring warm temperatures to the UK, and it is likely that influences from different remote regions are active or dominant in different years. We present evidence suggesting roles for tropical precipitation anomalies via extratropical wave trains. Circulation anomalies associated with wet conditions in the Caribbean project particularly strongly onto the hot UK summer conditions, with a weaker signal from the tropical Pacific consistent with developing La Niña. We also highlight possible influence in some years from springtime high sea ice anomalies in the Sea of Okhotsk and low anomalies in the Barents/Kara seas. Building on this, we use new experiments that isolate the effects of opposing springtime sea ice anomalies in the two regions and find a causal relationship with the summertime circulation over the North Atlantic and northern Europe.

Using long-term Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat satellite observations, the inundation changes of Tonle Sap Lake between 1988 and 2018 were investigated. The results show that the inundation area was stable before 2000, followed by a significant shrinking trend between 2000 and 2018. Quantitative remote sensing retrievals for concentrations of the total suspended sediments (TSS) also demonstrate an evident increasing trend (7.92 mg l⁻¹yr⁻¹) since 2000. A strong correlation (R² = 0.67) was found between the annual mean inundation area and concurrent precipitation in a region located in the lower basin of the Mekong River (mostly outside the drainage basin of Tonle Sap Lake). A multiple general linear model (GLM) regression further pointed to the precipitation variation as a major contributor (76.1%) to the interannual fluctuation of the inundation area, while the dams constructed in China only contributed to 6.9%. The limited impacts of Chinese dams on the inundation area of the lake could be revealed through the limited fraction of water discharge from the Mekong River within China (∼17%). The analysis also found significant impacts of inundation changes on the recent lake turbidity increase in the dry seasons. We clearly revealed that the contribution of dam construction in China to the recent lake shrinkage was insignificant when compared with the impacts of the precipitation decrease. The results of this study provide important scientific evidence for settling water volume-related transboundary disputes regarding the control of the inundation area and water turbidity of Tonle Sap Lake.

Producing more nutritious food with less resources, while preserving the natural ecosystems, is a key challenge of our society. In this paper we propose a macronutrient-based indicator of productivity, the nutrient land productivity (NLP), to measure the amount of calories, proteins, and fats produced per hectare of cropland. Over the period 1961−2016, we find that the global NLP has increased by 2.7–2.9% per year for calories and proteins, and between 2.1 and 4.6% for fats. However, such rates exhibit significant spatial patterns throughout the world depending on whether farmers adopted intensification (e.g. Eastern and South Asia, North America) or extensification (e.g. Sub-Saharan Africa) practices to boost nutrients production. Our outcomes, based on a production basket including 144 crops, show that cereals and pulses cultivations have been dominated by intensification practices coupled with a stable or decreasing harvested area. Conversely, for fruits and nuts cultivations extensification prevailed over intensification, while for oil crops most cultivations experienced a coupled action of the two practises. Finally, by coupling the NLP indicator with its nutrient water productivity (NWP) counterpart, we find that NWP has mainly changed following land patterns, with the exception of locations having undergone significant crop substitutions, namely from less toward more water demanding crops. Indeed, the transition toward perennial crops has increased the evapotranspiration demand over cultivated land by 14% on a global average.

Forest management and disturbances are among the main drivers of changes in forest dynamics in temperate ecosystems. To promote and maintain forest multifunctionality and species persistence in the landscape, it is critical that the interactions between these factors and forest biodiversity are disentangled. Still, the relationships between disturbances and forest management are poorly understood and may hinder an adequate planning of management and conservation actions in these forests. Here we address this issue via a coupled ecological-economic modeling system under different climate change scenarios. We employed data from a large-scale field-based research in southwestern Germany, in combination with a climate-sensitive forest growth model. Thereby, we quantified changes in multiple biodiversity indicators (including richness of birds, bats and flying insect orders) and tree microhabitats (TreMs) in the face of disturbance and management interventions. Our results show that windstorms may cause economic damage in managed forests, but at the same time improve biodiversity indicators in these areas. Salvage logging, however, may dampen these positive impacts for the majority of indicators considered. Moreover, management solutions targeting at wind risk mitigation may be detrimental to some taxa (e.g. forest birds) but still performed better than the business-as-usual management, in terms of the biodiversity indicators applied. We conclude that considering disturbance impacts on forest dynamics will be key to maintain the sustainability in the use of forest resources and support species persistence in temperate forest landscapes.

System-level integration and optimization of food-energy-water systems (FEWS) require coordination of multiple agencies and decision-makers and incorporating their interdependence. In general, such coordination might be hard to achieve. As a result, the literature on FEWS management either optimizes the operations for one sector (or one decision-maker), or models interdependence among the sectors without optimizing their operations. In this article, we develop a novel multi-agent management optimization approach that is able to incorporate stochasticity and uncertainty in the system's dynamics and interdependence of the water and energy resources for food production. The proposed method is the first attempt to utilize fundamentals of decision and game theories to optimize operations of multi-agent FEWS. We specifically focus on differentiating between (1) cooperative decision optimization of the operations, where all decision-makers cooperate to achieve the best outcome for the whole system, the social optimum, and (2) non-cooperative decision-making of the agents, the Nash equilibrium. Illustrating with a real-world case study of FEWS in Ventura County, California, we show the difference between the cooperative and non-cooperative decision making in terms of long-term expected cost of managing the system. We further show how the extra costs associated with utilizing the renewable sources of water and energy could be incentivised, so that the non-cooperative solution (the Nash equilibrium) would naturally converge to the best outcome for the whole system (the social optimum).

Plug-in electric vehicles (PEVs) offer a promising pathway to decarbonizing the personal transportation sector, but PEV sales remains low. Prior research has found that direct experience with PEVs increases consumers' stated purchase consideration, but these studies have used relatively long exposure times (days to months) with a PEV. To assess the effect of shorter exposure times (e.g. minutes) on stated purchase consideration, we conducted an experiment at the 2019 Washington D.C. Auto Show. Participants (n = 6518) were asked to rate their level of consideration to adopt a PEV before and after riding in one of four different PEVs for just 3–5 min. We find that the experience of riding in a PEV on average had a significant, positive effect on participants' consideration ratings. We also find that the vast majority of respondents were unable to correctly answer basic knowledge questions about refueling a PEV and federal subsidies available for purchasing a PEV. These results suggest that while consumer knowledge about PEVs remains low, short rides or drives in a PEV could be an effective, more scalable strategy for increasing PEV consideration across larger populations.

This study aims to investigate the relationship between nuclear energy consumption and economic growth in Switzerland over the period 1970–2018. We use data on capital, labour, and exports within a multivariate framework. Starting from the consideration that Switzerland has decided to phase out nuclear energy by 2034, we examine the effect of this structural economic-energy change in the country. To do so, two distinct estimation tools are performed. The first model, using a time-series approach, analyze the relationship between bivariate and multivariate causality. The second, using a Machine Learning methodology, test the results of the econometric modelling through an Artificial Neural Networks process. This last empirical procedure represents our original contribution with respect to the previous energy-GDP papers. The results, in the logarithmic propagation of neural networks, suggest a careful analysis of the process that will lead to the abandonment of nuclear energy in Switzerland to avoid adverse effects on economic growth.

Anthropogenic aerosol emissions are predicted to decline sharply throughout the 21st century, in line with climate change and air quality mitigation policies, causing a near-term warming of climate that will impact our trajectory towards 1.5 °C above pre-industrial temperatures. However, the persistent uncertainty in aerosol radiative forcing limits our understanding of how much the global mean temperature will respond to near-term reductions in anthropogenic aerosol emissions. We quantify the model and scenario uncertainty in global mean aerosol radiative forcing up to 2050 using statistical emulation of a perturbed parameter ensemble for emission reduction scenarios consistent with three Shared Socioeconomic Pathways. We then use a simple climate model to translate the uncertainty in aerosol radiative forcing into uncertainty in global mean temperature projections, accounting additionally for the potential correlation of aerosol radiative forcing and climate sensitivity. Near-term aerosol radiative forcing uncertainty alone causes an uncertainty window of around 5 years (2034–2039) on the projected year of exceeding a global temperature rise of 1.5 °C above pre-industrial temperatures for a middle of the road emissions scenario (SSP2-RCP4.5). A correlation between aerosol radiative forcing and climate sensitivity would increase the 1.5 °C exceedance window by many years. The results highlight the importance of quantifying aerosol radiative forcing and any relationship with climate sensitivity in climate models in order to reduce uncertainty in temperature projections.

Increased aridity and drought risks are significant global concerns. However, there are few comprehensive studies on the related risks with regard to the differences between relatively weak levels of warming, including the recent targets of the United Nations Framework Convention on Climate Change (UNFCCC) of 1.5 °C or 2 °C. The present study investigates the impacts of 1.5 °C and 2 °C warming on aridification and their non-linearity based on the relationship between available water and energy at the Earth's terrestrial surface. Large multi-model ensembles with a 4000-model-year in total are sourced from the Half a degree Additional warming, Prognosis, and Projected Impacts (HAPPI) project. Results demonstrate that 2 °C warming results in more frequent dry states in the Amazon Basin, western Europe, and southern Africa, and a limited warming to 1.5 °C will mitigate aridification and increase the frequency of extreme dry-year in these regions. In the Mediterranean region, a significant acceleration of aridification is found from the 1.5 °C to 2 °C warming projections, which indicates a need to limit the warming by 1.5 °C. A substantial portion of Asia is projected to become increasingly humid under both 1.5 °C and 2 °C warming scenarios. In some geographic regions, such as Australia, a strong nonlinear shift of aridification is found as 2 °C warming results in shift to wetter state contrast to significant increases in aridity and dry-year frequency at the weaker level of warming. The results suggest that the responses of regional precipitation to global warming cause the aridity changes, but their nonlinear behaviors along with different warming levels should be assessed carefully, in particular, to incorporate the additional 0.5 °C warming.

We examine how levels of multiple ecosystem services (ESs) change with succession in forests with different tree species composition. More specifically we ask how ecosystem age interacts with environmental conditions to regulate ES delivery. Using the nationwide Swedish forest inventory, comprising boreal and temperate regions, we investigated how levels of six provisioning, regulating, recreational, and/or cultural forest ESs changed with forest age (10–185 years) in stands of different tree species composition. We also tested whether the number of ESs delivered (i.e. multifunctionality) changed substantially with stand age, using different threshold levels for ES delivery. Accounting for environmental conditions and stand properties, we found that levels of single ESs changed with stand age. Tree biomass production usually peaked in young to medium aged stands. In contrast, production of berries and game, and services related to biodiversity, were typically highest in old stands (120–185 years). Consistent with this strong temporal tradeoff, multifunctionality at lower threshold levels increased with stand age in most monocultures and mixtures, with the highest multifunctionality being reached somewhere between 100 and 185 years, depending on tree species composition. This was not evident for the highest threshold ES level (the top-20%), however. Moreover, multifunctionality usually decreased with warmer climatic conditions, with the exception of spruce–pine–birch mixtures. Taken together, our results show that a reduced forest age, e.g. due to forestry targeting early harvest of stands, most likely would limit the delivery of several ESs valued by society and result in less multifunctional forests. To maintain the capacity of forests to deliver high levels of multiple ESs, the role of stand age and tree species composition should be considered in decisions on how to manage future forests.

To date, COVID-19 has claimed more than 100 000 American lives. Early inquiry suggests preexisting conditions are key risk factors contributing to COVID-19 mortality and air pollution exposure could exacerbate this relationship. Building on prior research linking deaths from respiratory viruses to air pollution exposures, we investigate how 2014 National Air Toxics Assessment hazardous air pollutants (HAPs) respiratory hazard quotient and respiratory hazard index are related to COVID-19 mortality. Our focus on HAPs builds upon the knowledge base linking poor air quality to COVID-19 mortality, since most (if not all) earlier studies only include criteria pollutants. Herein, we examine the relationship between HAP exposure and US-based COVID-19 mortality, while controlling for socioeconomic status, population health indicators, and exposure to PM2.5 and ozone. We fit county-level negative binomial mixed models, predicting COVID-19 mortality as a function of HAP respiratory toxicity levels and relevant covariates. We include models for combined exposure to HAPs, as well as for specific pollutants. We find that an increase in the respiratory hazard index is associated with a 9% increase in COVID-19 mortality. Although differing in magnitude, this association holds for individual HAPs acetaldehyde, and diesel PM. These findings help us to understand variation in US-based COVID-19 mortality rates, reinforce existing research linking air pollution to mortality, and emphasize the importance of regulatory efforts to limit air pollution exposure risk.

Human-induced soil erosion is a serious threat to global sustainability, endangering global food security, driving desertification and biodiversity loss, and degrading other vital ecosystem services. To help assess this threat, we amassed a global inventory of soil erosion rates consisting of 10 030 plot years of data from 255 sites under conventional agriculture and soil conservation management. We combined these with existing soil formation data to estimate soil sustainability expressed as a lifespan, here defined as the time taken for a topsoil of 30 cm to be eroded. We show that just under a third of conventionally managed soils in the dataset exhibit lifespans of <200 years, with 16% <100 years. Conservation measures substantially extend lifespan estimates, and in many cases promote soil thickening, with 39% of soils under conservation measures exhibiting lifespans exceeding 10 000 years. However, the efficacy of conservation measures is influenced by site- and region-specific variables such as climate, slope and soil texture. Finally, we show that short soil lifespans of <100 years are widespread globally, including some of the wealthiest nations. These findings highlight the pervasiveness, magnitude, and in some cases, the immediacy of the threat posed by soil erosion to near-term soil sustainability. Yet, this work also demonstrates that we have a toolbox of conservation methods that have potential to ameliorate this issue, and their implementation can help ensure that the world's soils continue to provide for us for generations to come.

While previous research often finds flood impacts outside of conventional flood risk zones such as FEMA's 100-year floodplain maps, we have less of a sense of the social and demographic composition of the areas outside of floodplains that experience these impacts, even though social inequalities in flood risk and impacts more broadly is well-documented in the United States. Using data on 100-year floodplains, flood impacts, socio-demographic characteristics, and residential parcels, this study focuses on race as a primary marker of socio-spatial inequality to examine flooding inside and outside of floodplains during Hurricane Harvey in Greater Houston. Descriptive findings show that a large majority of flooding occurred outside of 100-year floodplains. Regression models show that while there is limited evidence of racial inequalities in flood risk as conceptualized as location in 100-year floodplains, there are substantial racial inequalities in flood extent during Hurricane Harvey. Results further show that these overall racial inequalities in flood extent are primarily driven by impacts that occurred outside of 100-year floodplains. Conclusions center on how and why conventional delineations of flood risk can underestimate racial inequalities to natural hazards.

Recently, the issue of air quality in South Korea reached an unprecedented level of social concern regarding public health, quality of life, and environmental policies, even as the level of particulate matter less than 10 μm (PM₁₀) showed a decreasing trend. Why have social concerns emerged in recent years, specifically after 2013–2014? This study aims to understand how people perceive air quality apart from the measured levels of airborne pollutants using internet search volume data from Google and NAVER. An empirical model that simulates the air quality perception index (AQPI) is developed by employing the decay theory of forgetting and is trained by PM₁₀, visibility, and internet search volume data. The results show that the memory decay exponent and the accumulation of past memory traces, which represent the weighted sum of past perceived air quality, play key roles in explaining the public's perception of air quality. A severe haze event with an extremely long duration that occurred in the year 2013–2014 increased public awareness of air quality, acting as a turning point. Before the turning point, AQPI is more influenced by sensory information (visibility) due to the low awareness level, but after the turning point it is more influenced by PM₁₀ and people slowly forget about air quality. The retrospective AQPI analysis under a low level of awareness confirms that perceived air quality is indeed worst in the year 2013–2014. Our results provide a better understanding of public perception of air quality, and will contribute to the creation of more effective regulatory policies. It should be noted, however, that the proposed model is primarily meant to diagnose historic public perception and that more sophisticated models are needed to reliably predict perception of air quality.

The forests of Central Africa contain some of Earth's few remaining intact forests. These forests are increasingly threatened by infrastructure development, agriculture, and unsustainable extraction of natural resources (e.g. minerals, bushmeat, and timber), all of which is leading to deforestation and forest degradation, particularly defaunation, and hence causing declines in biodiversity and a significant increase in carbon emissions. Given the pervasive nature of these threats, the global importance of Central African forests for biodiversity conservation, and the limited resources for conservation and sustainable management, there is a need to identify where the most important areas are to orientate conservation efforts. We developed a novel approach for identifying spatial priorities where conservation efforts can maximize biodiversity benefits within Central Africa's most intact forest areas. We found that the Democratic Republic of Congo has the largest amount of priority areas in the region, containing more than half, followed by Gabon, the Republic of Congo and Cameroon. We compared our approach to one that solely prioritizes forest intactness and one that aims to achieve only biodiversity representation objectives. We found that when priorities are only based on forest intactness (without considering biodiversity representation), there are significantly fewer biodiversity benefits and vice versa. We therefore recommend multi-objective planning that includes biodiversity representation and forest intactness to ensure that both objectives are maximized. These results can inform various types of conservation strategies needed within the region, including land-use planning, jurisdictional REDD + initiatives, and performance related carbon payments, protected area expansion, community forest management, and forest concession plans.

Rising crop production over the last half century has had far-reaching consequences for human welfare and the environment. With food demand projected to rise, one of the central challenges in minimizing agriculture's impacts on the climate and biodiversity is to increase crop production with higher yields rather than more cropland. However, quantifying progress is challenging. When analyzed at the most aggregated, global level, yields can be defined as the total crop output per unit area per year, but aggregate yields are driven by multiple factors, only some of which have a clear relationship to improved agricultural production. To date, there is no research that simultaneously determines how much of rising crop production has been met by rising aggregate yields versus cropland expansion, while also quantifying the unique contribution of each yield driver. Using LMDI decomposition analysis, we find that rising aggregate yields contributed far more than cropland expansion (89% compared to 11%). That is, growing global food demand has by and large been met by growing more crops on the same amount of land, rather than expanding cropland. Our second-stage decomposition showed that nearly two-thirds of aggregate yield improvements have come from pure yield, or the output of a given crop per unit of harvested cropland area in a given country per unit area per year. The remainder has come from less-discussed drivers of aggregate yields, including cropping intensity, changes in the geographic distribution of cropland, and crop composition. Further, we use attribution analysis to show the contributions to different decomposition factors from countries grouped by climate, income, and region, as well as from different crops. Such granular yet comprehensive breakdowns of crop production and aggregate yields offer more accurate forecasts and can help focus policies on the most promising levers to meet rising food demand sustainably.

The Fire Weather Index (FWI) is widely used to assess the meteorological fire danger in several ecosystems worldwide. One shortcoming of the FWI is that only surface weather conditions are considered, despite the important role often played by atmospheric instability in the development of very large wildfires. In this work, we focus on the Iberian Peninsula for the period spanning 2004–2018. We show that atmospheric instability, assessed by the Continuous Haines Index (CHI), can be used to improve estimates of the probability of exceedance of energy released by fires. To achieve this, we consider a Generalized Pareto (GP) model and we show that by stepwisely introducing the FWI and then the CHI as covariates of the GP parameters, the model is improved at each stage. A comprehensive comparison of results using the GP with the FWI as a covariate and the GP with both the FWI and CHI as covariates allowed us to then define a correction to the FWI, dependent on the CHI, that we coined enhanced FWI (FWIe). Besides ensuring a better performance of this improved FWI version, it is important to stress that the proposed FWIe incorporates efficiently the effect of atmospheric instability into an estimation of fire weather danger and can be easily incorporated into existing systems.

Fire is an integral part of Earth's system that links regional and global biogeochemical cycles, human activities, and ecosystems. Global estimates for biomass burning indicate that Africa is responsible for ~70% of global burned area and ~50% of fire-related carbon emissions. Previous studies have documented an overall decline in burned area in the African continent, but changes in fire patterns, such as the frequency and size of different fire categories, have not been assessed. In this study, long-term fire trends were investigated using the latest burned area data from the MODerate resolution Imaging Spectroradiometer (MODIS) and the Global Fire Emission Database (GFED4s) over Central Africa (10°E–40°E, 15°N–15°S). A 3D (latitude, longitude, time) connected-component labeling algorithm was applied to identify individual fires and their sizes. The results show a decline in burned area by 2.7–3.2 Mha yr⁻¹ (~1.3% yr⁻¹) for the period 2003–2017, particularly in northern Central Africa. This decline was attributed to significant decreases in both fire frequency and size, particularly for large fires (>100 ha) which contribute to ~90% of the total burned area. Burned area declined in tropical savannas and grasslands but increased at the edges of the Congolese rainforest. A random forest regression model was applied to quantify the influences of climatic conditions, fuel availability, and agricultural activity on burned area changes. Overall, suppressed fuel, increased dry season length, and decreased rainfall contributed to significant declines in burned area in savannas and grasslands. At the edges of the southern Congolese rainforest, suppressed rainfall and warmer temperature were responsible for the increased burned area.

Long-term changes in the East Asian summer monsoon (EASM) lifecycle since 1979 are analyzed based on observational datasets and historical simulations of the Coupled Model Intercomparison Project Phase 6 (CMIP6). According to the observation, the active and break phases of EASM have intensified resulting in a shorter but stronger rainy season, followed by a longer dry spell. This intensification in the active-phase precipitation is accompanied by increased lower tropospheric southwesterly wind and subsequent convergence of water vapor flux. These changes are accompanied by the widely reported westward extension of the North Pacific Subtropical High, which has been associated with the warming climate. CMIP6 models generally underestimated the observed intensification of the EASM lifecycle and the monsoon precipitation. However, some of the models did simulate the intensified EASM lifecycle similar to that observed. The result highlights the reasonable performance on EASM shown in some CMIP6 models and those simulations lend support to a dynamically-driven intensification of the EASM lifecycle in the warmer climate.

The surface warming response to carbon emissions is diagnosed using a suite of Earth system models, 9 CMIP6 and 7 CMIP5, following an annual 1% rise in atmospheric CO₂ over 140 years. This surface warming response defines a climate metric, the Transient Climate Response to cumulative carbon Emissions (TCRE), which is important in estimating how much carbon may be emitted to avoid dangerous climate. The processes controlling these intermodel differences in the TCRE are revealed by defining the TCRE in terms of a product of three dependences: the surface warming dependence on radiative forcing (including the effects of physical climate feedbacks and planetary heat uptake), the radiative forcing dependence on changes in atmospheric carbon and the airborne fraction. Intermodel differences in the TCRE are mainly controlled by the thermal response involving the surface warming dependence on radiative forcing, which arise through large differences in physical climate feedbacks that are only partly compensated by smaller differences in ocean heat uptake. The other contributions to the TCRE from the radiative forcing and carbon responses are of comparable importance to the contribution from the thermal response on timescales of 50 years and longer for our subset of CMIP5 models and 100 years and longer for our subset of CMIP6 models. Hence, providing tighter constraints on how much carbon may be emitted based on the TCRE requires providing tighter bounds for estimates of the physical climate feedbacks, particularly from clouds, as well as to a lesser extent for the other contributions from the rate of ocean heat uptake, and the terrestrial and ocean cycling of carbon.

The transportation sector is at the beginning of a transition represented by electrification, shared mobility, and automation, which could lead to either increases or decreases in total travel and energy use. Understanding the factors enabling deep decarbonization of the passenger vehicle sector is essential for planning the required infrastructure investments and technology adoption policies. We examine the requirements for meeting carbon reduction targets of 80% and higher for passenger vehicle transport in the United States (US) by midcentury under uncertainty. We model the changes needed in vehicle electrification, electricity carbon intensity, and travel demand. Since growth in fleet penetration of electric vehicles (EVs) is constrained by fleet stock turnover, we estimate the EV penetration rates needed to meet climate targets. We find for a base case level of passenger vehicle travel, midcentury deep decarbonization of US passenger transport is conditional on reducing the electricity generation carbon intensity to close to zero along with electrification of about 67% or 84% of vehicle travel to meet decarbonization targets of 80% or 90%, respectively. Higher electricity generation carbon intensity and degraded EV fuel economy due to automation would require higher levels of fleet electrification and/or further constrain the total vehicle travel allowable. Transportation deep decarbonization not only depends on electricity decarbonization, but also has a total travel budget, representing a maximum total vehicle travel threshold that still enables meeting a midcentury climate target. This makes encouraging ride sharing, reducing total vehicle travel, and increasing fuel economy in both human-driven and future automated vehicles increasingly important to deep decarbonization.

Fishery observers are prevalent actors in the global effort to reduce discards in fisheries, but there remains considerable uncertainty about how effective they are. We analyzed high-resolution logbook records of individual hauls (n = 127 415) across five-and-a-half-years (2012–2018) for all of Greenland's large-scale fisheries to determine if onboard fishery observers influence the mandatory reporting of discards. To do so, we used exact matching to compare reported discards for observed and unobserved hauls (each time a catch is recorded), thus controlling for systematic differences between monitored and unmonitored practices. After adjusting for variables that represent species caught, gear, vessel, owner, year, license, and location, we found that skippers systematically underreport discards when no observers are on board. Systematic underreporting was most pronounced in less valuable fisheries, in contrast to theoretical arguments in previous studies. The differences between reported discards from observed and unobserved fishing leads us to assume that onboard observers encourage more faithful logbook records. Thus, onboard observers play a vital role in improving information on the environmental impact of fishing and in turn, make a key contribution to sustainable fisheries management.

With increasing urbanisation, urban green spaces are expected to be crucial for urban resilience and sustainability, through the delivery of ecological, economic and social benefits. In practice, however, planning, management and evaluation of urban green spaces are rarely structured and evidence-based. This represents a missed opportunity to account for, track and foster the multiple benefits that green spaces are expected to deliver. To gain insight into this gap, this study assesses the availability and uptake of relevant evidence by city governments. Interviews, focus groups and quantitative surveys were applied in four medium-sized European cities: Coimbra (Portugal), Genk (Belgium), Leipzig (Germany), and Vilnius (Lithuania), covering the main governance and climatic gradients in Europe. Using straightforward data exploration and regression, we analyse which ecological, economic and social indicators are typically chosen by cities and why. Together with the city stakeholders, we derived a common set of benefit categories and key performance indicators which can be adapted to diverse local contexts. We conclude that cities tend to make pragmatic decisions when composing their indicator sets, but nevertheless cover multiple urban green space dimensions. Finally, we explore how indicator choice could be optimised towards a complementary and credible indicator set, taking into account a realistically feasible monitoring effort undertaken by the cities.

The Global Ecosystem Dynamics Investigation (GEDI) lidar began data acquisition from the International Space Station in March 2019 and is expected to make over 10 billion measurements of canopy structure and topography over two years. Previously, airborne lidar data with limited spatial coverage have been used to examine relationships between forest canopy structure and faunal diversity, most commonly bird species. GEDI's latitudinal coverage will permit these types of analyses at larger spatial extents, over the majority of the Earth's forests, and most importantly in areas where canopy structure is complex and/or poorly understood. In this regional study, we examined the impact that GEDI-derived Canopy Structure variables have on the performance of bird species distribution models (SDMs) in Sonoma County, California. We simulated GEDI waveforms for a two-year period and then interpolated derived Canopy Structure variables to three grid sizes of analysis. In addition to these variables, we also included Phenology, Climate, and other Auxiliary variables to predict the probability of occurrence of 25 common bird species. We used a weighted average ensemble of seven individual machine learning models to make predictions for each species and calculated variable importance. We found that Canopy Structure variables were, on average at our finest resolution of 250 m, the second most important group (32.5%) of predictor variables after Climate variables (35.3%). Canopy Structure variables were most important for predicting probability of occurrence of birds associated with Conifer forest habitat. Regarding spatial analysis scale, we found that finer-scale models more frequently performed better than coarser-scale models, and the importance of Canopy Structure variables was greater at finer spatial resolutions. Overall, GEDI Canopy Structure variables improved SDM performance for at least one spatial resolution for 19 of 25 species and thus show promise for improving models of bird species occurrence and mapping potential habitat.

This paper presents a prototype Carbon Monitoring System (CMS) developed to produce regionally unbiased annual estimates of aboveground biomass (AGB). Our CMS employed a bottom-up, two-step modeling strategy beginning with a spatially and temporally biased sample: project datasets collected and contributed by US Forest Service (USFS) and other forestry stakeholders in 29 different project areas in the northwestern USA. Plot-level AGB estimates collected in the project areas served as the response variable for predicting AGB primarily from lidar metrics of canopy height and density (R² = 0.8, RMSE = 115 Mg ha⁻¹, Bias = 2 Mg ha⁻¹). This landscape model was used to map AGB estimates at 30 m resolution where lidar data were available. A stratified random sample of AGB pixels from these landscape-level AGB maps then served as training data for predicting AGB regionally from Landsat image time series variables processed through LandTrendr. In addition, climate metrics calculated from downscaled 30 year climate normals were considered as predictors in both models, as were topographic metrics calculated from elevation data; these environmental predictors allowed AGB estimation over the full range of observations with the regional model (R² = 0.8, RMSE = 152 Mg ha⁻¹, Bias = 9 Mg ha⁻¹), including higher AGB values (>400 Mg ha⁻¹) where spectral predictors alone saturate. For both the landscape and regional models, the machine-learning algorithm Random Forests (RF) was consistently applied to select predictor variables and estimate AGB. We then calibrated the regional AGB maps using field plot data systematically collected without bias by the national Forest Inventory and Analysis (FIA) Program. We found both our project landscape and regional, annual AGB estimates to be unbiased with respect to FIA estimates (Biases of 1% and 0.7%, respectively) and conclude that they are well suited to inform forest management and planning decisions by our contributing stakeholders.

Social media abstract

Lidar-based biomass estimates can be upscaled with Landsat data to regionally unbiased annual maps.

Meeting the increasing global demand for agricultural products without depleting the limited resources of the planet is a major challenge that humanity is facing. Most studies on global food security do not make projections past the year 2050, just as climate change and increasing demand for food are expected to intensify. Moreover, past studies do not account for the water sustainability limits of irrigation expansion to presently rainfed areas. Here we perform an integrated assessment that considers a range of factors affecting future food production and demand throughout the 21st century. We evaluate the self-sufficiency of 165 countries under sustainability, middle-of-the-road, and business-as-usual scenarios considering changes in diet, population, agricultural intensification, and climate. We find that under both the middle-of-the-road and business-as-usual trajectories global food self-sufficiency is likely to decline despite increased food production through sustainable agricultural intensification since projected food demand exceeds potential production. Contrarily, under a sustainability scenario, we estimate that there will be enough food production to feed the global population. However, most countries in Africa and the Middle East will continue to be heavily reliant on imports throughout the 21st century under all scenarios. These results highlight future hotspots of crop production deficits, reliance on food imports, and vulnerability to food supply shocks.

This paper presents metal speciation calculations that are based on mathematical modelling of chemical reactions in natural waters. Metal concentrations (Hg, Cd, Pb, Ni, Cu, Al, Sr) were determined, and their speciation in water were calculated for 22 water areas in the Kola region. Meanwhile, the accumulation of metals in fish organs and tissues was studied (e.g. whitefish). The biogeochemical activity of metals determines the proportions of labile and non-labile speciation in water. In the distribution zones of non-ferrous industry effluents, metal aqua-ions prevail; during the distribution, the proportions change in accordance with the metal activity. The bioavailability of metal speciation is estimated depending on aqueous geochemical conditions and, accordingly, the speciation of metals (in situ), based on the original studies of the lakes of the Kola region in northern Russia. The connection among the metal contents in fish and water has been identified using multidimensional scaling and redundancy analysis techniques. Using the example of natural conditions in northern low-salinity freshwaters, it is demonstrated that labile Cd, Pb, Ni, Cu, Al, and Sr are the species most bioavailable and able to penetrate fish; meanwhile, the organic complexes of Hg, Pb, and Al have a greater affinity to accumulation in the gills. This study demonstrates the need to correct the approved water quality standards in Russia, taking into account the high bioavailability of metals in northern low-salinity waters.

In eastern Siberia, topography controls the abundance of the larch forest via both drought and flooding stresses. For the reconstruction of these topographical effects, we modified a dynamic vegetation model to represent soil water relocation owing to within-grid heterogeneity of elevation, over-wet-kill of trees, and air temperature differences within-grid. After calibration, the model reasonably reconstructed the geographical distributions of observation-based-estimates of fundamental properties of plant productivity and thermo-hydrology. Thus, the model appropriately responded to environmental gradients in eastern Siberia. The modified model also partially reconstructed the topography control on tree abundance and thermo-hydrology status in eastern Siberia, although its geographical distribution was not always good. In the modified model, soil water redistribution increased the risk of over-wet-kill in lower elevation classes, whereas it reduced the risk of over-wet-kill for larch trees in higher elevation classes. We demonstrated that without considering the latter effect, forest collapse due to over-wet stress would happen throughout eastern Siberia under a forecasted climatic condition during the 21st century, which will deliver a much moister environment throughout eastern Siberia. Therefore, modeling the over-wet-kill of trees without considering topographical heterogeneity would result in the overestimation of forest collapse caused by the over-wet-kill of trees under an expected climate trend in eastern Siberia.

Remote sensing analyses of boreal forest regions have found widespread decreasing or increasing trends in normalized difference vegetation index (NDVI). Initially, these trends were attributed to climate change induced shifts in primary productivity. It is emerging, however, that fire disturbance and subsequent succession also strongly impact the optical properties of boreal forests. Here we use NDVI time series data from Landsat (1999–2018) paired with surveys of 102 forest stands with known recent fire history to investigate the relationship between NDVI and forest structure during succession. We found that NDVI varies systematically with stand age as a result of successional changes in forest structure and composition and that the proportion of deciduous (broad-leaved) trees in the upper canopy is a better predictor of NDVI than leaf area index. Recent fire disturbance led to strong NDVI decreases and early post-fire recovery of herbaceous and deciduous vegetation to strong NDVI increases. The mid-succession transition from deciduous to evergreen (needle-leaved) stands led to weak NDVI decreases, while mid-to-late succession thinning of evergreen canopies led to weak NDVI increases. Thus, both increasing and decreasing NDVI stands occur naturally across the landscape, and do not necessarily reflect a large-scale shift in boreal forest productivity.

The use of clean fuel such as liquid petroleum gas (LPG) is globally recommended for household cooking to reduce exposure to household air pollution and its adverse health consequences. Adoption of LPG in resource-poor settings such as South Asia is low and driven by many factors. In Bangladesh, more than 90% of the rural population relies on biomass fuels for cooking. Identifying factors among households that self-adopt LPG, i.e. 'natural users' may provide insight into how LPG programs could be modified to improve the adoption of clean fuels. We aimed to assess factors that drive LPG adoption and use in a rural setting amongst natural users of LPG in Bangladesh. We conducted a household survey of natural users of LPG who were pregnant and were identified by a census listing of households in 63 villages of five unions of Tangail district. Of 337 existing pregnant natural users, we could complete interview of 299 women using a structured questionnaire which included socio-demographic, kitchen structure, cooking behaviours and potential factors related to LPG use. Nearly all natural users had multiple cookstoves, and 85% reported using LPG as an alternative fuel to their main cooking and fuel (traditional cooking with biomass fuels). Factors related to high use of LPG (defined as at least 50% of all cooking time in previous 24 h) included households in second wealth quintile, (adjusted Prevalence Ratio, aPR 3.03; 95% CI:1.15–8.00), middle wealth quintile, (aPR 2.72; 95% CI:1.01–7.30) and highest wealth quintile (aPR2.71;95% CI: 1.02–7.28. Health issues also influenced LPG use; if LPG was described as alleviating breathing problems (aPR 1.65; 95% CI: 1.08–2.52), there was more LPG use. Adoption of LPG stove as a backup option for emergency purpose cooking reduced greater use of LPG (aPR 0.59; 95% CI: 0.39–0.91). High use of LPG was associated with LPG cooking being reported as easy to use (aPR 4.13; 95% CI: 1.95–8.73). Women's perception that LPG alleviated breathing difficulties was associated with high-use of LPG cooking, as was household wealth and ease of use. Women who reported to use LPG only for emergency purposes were less likely to be high users. Clean fuel programs as well as being financially supportive could be modified to include a trial period so that the experience of LPG would further support clean cooking adoption.

Understanding the joint impact of anthropologic and climatic changes on ecosystem function and dynamics is among the frontiers in global environmental change studies. Carbon and water balances are especially crucial to the sustainable ecosystem and functional returns in sensitive regions such as the Mongolian Plateau. In this study, the significance of non-climatic component (NCC) on carbon and water use efficiency (CUE and WUE) is quantified among the ecosystem types on the Mongolian Plateau. We mapped the spatial gradients of carbon/water balance and delineated the hotspots of NCC-driven CUE and WUE for 2000–2013 using gross and net primary production (GPP and NPP) and evapotranspiration (ET) products derived from the MODIS databases. Significantly higher CUE and WUE values were found in Mongolia (MG) than in Inner Mongolia (IM) due to both climatic forcing (CF) and NCC. NCC was found to dominate the changes in CUE and WUE in the steppes on the plateau by over 16% and 22%, respectively, but with spatially uneven distributions. NCC-driven WUE values were much higher than those driven by CF. The hotspots for NCC-driven CUE did not overlap with those of WUE, with CUE hotspots concentrated in the east of MG and northeast of IM; WUE hotspots were found in the central and Khangai regions of MG and eastern regions of IM. The NCC-driven CUE area in MG was from population growth and the industrial shares in gross domestic product, while the NCC-driven WUE area was due to livestock growth in MG but driven by the growth of cultivated lands in IM. In sum, we conclude that NCC provoked substantial spatiotemporal changes on carbon and water use. CF and NCC effects on carbon and water balance varied in space, by ecosystem type, and between the two political entities.

We estimate CO₂ emissions from the Nile Delta region of Egypt, using over five years of column-averaged CO₂ dry air mole fraction (XCO₂) data from the NASA's OCO-2 satellite. The Nile Delta has significant anthropogenic emissions of CO₂ from urban areas and irrigated farming. It is surrounded by the Sahara desert and the Mediterranean Sea, minimizing the confounding influence of CO₂ sources in surrounding areas. We compiled the observed spatial and temporal variations of XCO₂ in the Nile Delta region (XCO_2,del), and found that values for XCO_2,del were on average 1.1 ppm higher than XCO_2,des (mean XCO₂ in desert area). We modelled the expected enhancements of XCO₂ over the Nile Delta based on two global CO₂ emission inventories, EDGAR and ODIAC. Modelled XCO₂ enhancements were much lower, indicating underestimation of CO₂ emissions in the Nile Delta region by mean factors of 4.5 and 3.4 for EDGAR and ODIAC, respectively. Furthermore, we captured a seasonal pattern of XCO₂ enhancement (ΔXCO₂), with significantly lower ΔXCO₂ during the summer agriculture season in comparison to other seasons. Additionally, we used solar-induced fluorescence (SIF) measurement from OCO-2 to understand how the CO₂ emissions are related to agricultural activities. Finally, we estimated an average emission of CO₂ from the Nile Delta from 2014–2019 of 470 Mt CO₂/year, about 1% of global anthropogenic emissions, which is significantly more than estimated hitherto.