Table of contents

As current action remains insufficient to meet the goals of the Paris agreement let alone to stabilize the climate, there is increasing hope that solutions related to demand, services and social aspects of climate change mitigation can close the gap. However, given these topics are not investigated by a single epistemic community, the literature base underpinning the associated research continues to be undefined. Here, we aim to delineate a plausible body of literature capturing a comprehensive spectrum of demand, services and social aspects of climate change mitigation. As method we use a novel double-stacked expert—machine learning research architecture and expert evaluation to develop a typology and map key messages relevant for climate change mitigation within this body of literature. First, relying on the official key words provided to the Intergovernmental Panel on Climate Change by governments (across 17 queries), and on specific investigations of domain experts (27 queries), we identify 121 165 non-unique and 99 065 unique academic publications covering issues relevant for demand-side mitigation. Second, we identify a literature typology with four key clusters: policy, housing, mobility, and food/consumption. Third, we systematically extract key content-based insights finding that the housing literature emphasizes social and collective action, whereas the food/consumption literatures highlight behavioral change, but insights also demonstrate the dynamic relationship between behavioral change and social norms. All clusters point to the possibility of improved public health as a result of demand-side solutions. The centrality of the policy cluster suggests that political actions are what bring the different specific approaches together. Fourth, by mapping the underlying epistemic communities we find that researchers are already highly interconnected, glued together by common interests in sustainability and energy demand. We conclude by outlining avenues for interdisciplinary collaboration, synthetic analysis, community building, and by suggesting next steps for evaluating this body of literature.

To address concerns about the negative impacts of food supply chains in forest regions, a growing number of companies have adopted policies to influence their suppliers' behaviors. With a focus on forest-risk food supply chains, we provide a systematic review of the conservation and livelihood outcomes of the mechanisms that companies use to implement their forest-focused supply chain policies (FSPs)—certifications, codes of conduct, and market exclusion mechanisms. More than half of the 37 cases that rigorously measure the outcomes of FSP implementation mechanisms find additional conservation and livelihood benefits resulting from the policies. Positive livelihood outcomes are more common than conservation additionality and most often pertain to improvements in farm income through increases in crop yields on coffee and cocoa farms that have adopted certifications or codes of conduct. However, in some cases certifications lead to a reduction in net household income as farmers increasingly specialize in the certified commodity and spend more on food purchases. Among the five cases that examine conservation and livelihoods simultaneously, there is no evidence of tradeoffs or synergies—most often an improvement in one type of outcome is associated with no change in the other. Interactions with public conservation and agricultural policies influence the conservation gains achieved by all mechanisms, while the marketing attributes of cooperatives and buying companies play a large role in determining the livelihood outcomes associated with certification. Compliance with the forest requirements of FSP implementation mechanisms is high, but challenges to geospatial monitoring and land use related selection biases limit the overall benefits of these policies. Given the highly variable methods and limited evidence base, additional rigorous research across a greater variety of contexts is urgently needed to better understand if and when FSPs can be successful in achieving synergies between conservation and livelihoods.

Population growth, urbanization and economic development drive the use of resources. Securing access to essential services such as energy, water, and food, while achieving sustainable development, require that policy and planning processes follow an integrated approach. The 'Climate-, Land-, Energy- and Water-systems' (CLEWs) framework assists the exploration of interactions between (and within) CLEW systems via quantitative means. The approach was first introduced by the International Atomic Energy Agency to conduct an integrated systems analysis of a biofuel chain. The framework assists the exploration of interactions between (and within) CLEW systems via quantitative means. Its multi-institutional application to the case of Mauritius in 2012 initiated the deployment of the framework. A vast number of completed and ongoing applications of CLEWs span different spatial and temporal scales, discussing two or more resource interactions under different political contexts. Also, the studies vary in purpose. This shapes the methods that support CLEWs-type analyses. In this paper, we detail the main steps of the CLEWs framework in perspective to its application over the years. We summarise and compare key applications, both published in the scientific literature, as working papers and reports by international organizations. We discuss differences in terms of geographic scope, purpose, interactions represented, analytical approach and stakeholder involvement. In addition, we review other assessments, which contributed to the advancement of the CLEWs framework. The paper delivers recommendations for the future development of the framework, as well as keys to success in this type of evaluations.

Adaptation is a central component of climate policy, helping manage and reduce risks. Sometimes, however, adaptation to climate change may consume energy, threatening efforts to reduce greenhouse gas emissions. Examples are numerous, and include the use of air conditioning or water desalination. Nevertheless, no clear view exists on how energy demand globally can be impacted by climate change. In this paper we systematically map existing evidence on how and to what extent adaptation responses to climate change may impact energy demand. The literature is large, fast-growing and spans several disciplines, but we identify several research gaps. First, the literature focuses almost exclusively on heating and cooling demand, while overlooking other potential sectors. It also focuses heavily on a few world regions, while local specific climate and socio-economic conditions may highly influence the impacts, and focuses largely on average demand, while often disregarding peak energy demand. Finally, and most importantly, only a handful of papers—most of them with a specific geographical scope—consider that different adaptation possibilities may lead to different impacts on energy demand, which is an important prerequisite if the impact of adaptation on energy demand is to be lowered and maladaptation to be avoided. The reviewed papers study for the most part similar options, and most adaptation possibilities are conversely studied by just one or two papers.

To fully address the multi-faceted challenges of urban heat, it is paramount that humans are placed at the center of the agenda. This is manifested in a recent shift in urban heat studies that aim to achieve a 'human-centric' approach, i.e. focusing on personalized characteristics of comfort, well-being, performance, and health, as opposed to the one-size-fits-all solutions and guidelines. The proposed article is focused on systematically reviewing personalized urban heat studies and detailing the objectives posed, methodologies utilized, and limitations yet to be addressed. We further summarize current knowledge and challenges in addressing the impact of personal heat exposure on human life by discussing the literature linked with urban heat studies at the human, building, and city scales. Lastly, this systematic review reveals the need for future evaluations focused on accuracy and standardization of human-centric data collection and analytics, and more importantly, addressing critical geographic and socio-economic knowledge gaps identified in the field.

This paper undertakes a systematic review of the literature to understand current trends in the food–energy–water (FEW) nexus for development-oriented policy support. The paper follows three steps: (a) a bibliometric analysis of FEW nexus research, (b) a content analysis of FEW nexus research, and (c) development of a framework that fills existing gaps in FEW nexus research. The review found that FEW nexus approaches have gained ground in academia as a resource management tool and policy guide; however, the process does not have a robust conceptualization. The current FEW nexus approaches focus on national, regional, and international scales of analysis to understand the three sectors' interactions. Further, these approaches underline the nexus processes, which have been researched in detail, including synergies and tradeoffs. However, research on the FEW nexus has not adequately explored the social factors that form part of the nexus, especially at the local household scale. Factors such as the gender dynamics of resource ownership, work roles at different scales, household incomes, and culture are essential components that are yet to be explored in FEW nexus research. Most of the existing frameworks on the FEW nexus overemphasize models and the quantitative measurement of processes while paying limited attention to social aspects. Still, these social aspects are crucial, especially on the household scale; therefore, to overcome these gaps, this paper proposes a FEW nexus framework at the local household scale that includes socio-economic determinants.

Small island developing states (SIDS) are often at the forefront of climate change impacts, including those related to health, but information on mental health and wellbeing is typically underreported. To help address this research lacuna, this paper reviews research about mental health and wellbeing under climate change in SIDS. Due to major differences in the literature's methodologies, results, and analyses, the method is an overview and qualitative evidence synthesis of peer-reviewed publications. The findings show that mental health and wellbeing in the context of climate change have yet to feature prominently and systematically in research covering SIDS. It seems likely that major adverse mental health and wellbeing impacts linked to climate change impacts will affect SIDS peoples. Similar outcomes might also emerge when discussing climate change related situations, scenarios, and responses, irrespective of what has actually happened thus far due to climate change. In the context of inadequate health systems and stigmatisation of mental health diagnoses and treatments, as tends to occur globally, climate change narratives might present an opening for conversations about addressing mental health and wellbeing issues for SIDS.

Permafrost peatlands are found in high-latitude regions and store globally-important amounts of soil organic carbon. These regions are warming at over twice the global average rate, causing permafrost thaw, and exposing previously inert carbon to decomposition and emission to the atmosphere as greenhouse gases. However, it is unclear how peatland hydrological behaviour, vegetation structure and carbon balance, and the linkages between them, will respond to permafrost thaw in a warming climate. Here we show that permafrost peatlands follow divergent ecohydrological trajectories in response to recent climate change within the same rapidly warming region (northern Sweden). Whether a site becomes wetter or drier depends on local factors and the autogenic response of individual peatlands. We find that bryophyte-dominated vegetation demonstrates resistance, and in some cases resilience, to climatic and hydrological shifts. Drying at four sites is clearly associated with reduced carbon sequestration, while no clear relationship at wetting sites is observed. We highlight the complex dynamics of permafrost peatlands and warn against an overly-simple approach when considering their ecohydrological trajectories and role as C sinks under a warming climate.

The long-term trend in the annual mean lifetime maximum intensity (LMI) of rapidly intensifying tropical cyclones (RI-TCs) over the western North Pacific (WNP) is investigated in this study. During 1970–2019, a notable upward trend is observed in the average RI-TC LMI, which is primarily linked to a significant increase in the mean intensification rate prior to LMI. This intensification rate increase is caused by an increase in the mean magnitude of RI cases. By contrast there is no significant change in the RI ratio, which is calculated as the proportion of 24 h RI records to all 24 h records before a RI-TC reaches its LMI. Furthermore, there is a significantly greater RI magnitude west of 155° E, where the vast majority of RI cases occur on average. Over this region, there are significant increases in sea surface temperatures, TC heat potential, 700–500 hPa relative humidity and 200 hPa divergence during 1970–2019. Only a small region of significantly reduced 850–200 hPa vertical wind shear is observed to the northeast of the Philippines from 1970–2019. These results imply that both thermodynamic and dynamic variables play an important role in modulating RI magnitude over the WNP.

As the largest renewable energy source, hydropower is essential to the sustainability of the global energy market. However, a considerable amount of water can be lost in the form of evaporation from the associated multipurpose reservoirs, and hence enlarge the blue water footprint (BWF) of hydropower in a warming climate. To facilitate the sustainable management of both water and energy resources under the impact of climate change in the contiguous United States (CONUS), the BWF values of 143 major multipurpose reservoirs were evaluated during the historical period (1985–2014) and two future periods (2020–2049 and 2070–2099). The historical reservoir evaporation loss was calculated using the Landsat-based reservoir surface area and a new evaporation rate algorithm that considers the heat storage effect. Future projections of runoff availability, hydropower generation, and reservoir evaporation were estimated based on the downscaled climate model ensemble from phase 5 of the Coupled Model Intercomparison Project. It was found that the BWF for the CONUS is highly spatially heterogeneous, with an average value of 26.2 m³ MWh⁻¹ in the historical period. In the future, the BWF values are projected to increase under both Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. This is especially noticeable under RCP 8.5, which has an average BWF value of 30.2 m³ MWh⁻¹ for 2070–2099 (increasing by 15.3% from 26.2 m³ MWh⁻¹). The uncertainty ranges increase even more, from 3.4 m³ MWh⁻¹ during 2020–2049 to 5.7 m³ MWh⁻¹ during 2070–2099. These findings can benefit water and energy resources management in identifying suitable environmental, economic, operational, and investment strategies for multipurpose reservoirs in a changing environment.

Although slower translation speed can induce a larger amount of local rainfall for an individual tropical cyclone (TC), whether change in total TC precipitation (TCP) affecting China is related to TC translation speed in the satellite era remains unclear. Based on multiple TC best-track datasets and a reanalysis dataset, we find a significant increasing trend in total TCP over two regions of southern China during 1980–2018. This upward trend can be attributed to the enhancing atmospheric water vapor content and moisture transport over southern China, however, TC intensity, frequency, and translation speed have no contributions. Given the potential linkage between the increasing atmospheric water vapor content over southern China and the western Pacific warming under global warming, our results suggest a likely role of anthropogenic global warming in the increasing TCP over southern China during the past 4 decades.

Monitoring changes in freshwater availability is critical for human society and sustainable economic development. To identify regions experiencing secular change in their water resources, many studies compute linear trends in the total water storage (TWS) anomaly derived from the Gravity Recovery and Climate Experiment (GRACE) mission data. Such analyses suggest that several major water systems are under stress (Rodell et al 2009 Nature460 999–1002; Long et al 2013 Geophys. Res. Lett.40 3395–401; Richey et al 2015 Water Resour. Res.51 5217–38; Voss et al 2013 Water Resour. Res.49 904–14; Famiglietti 2014 Nat. Clim. Change.4 945–8; Rodell et al 2018 Nature 557 651–9). TWS varies in space and time due to low frequency natural variability, anthropogenic intervention, and climate-change (Hamlington et al 2017 Sci. Rep.7 995; Nerem et al 2018 Proc. Natl Acad. Sci.). Therefore, linear trends from a short time series can only be interpreted in a meaningful way after accounting for natural spatiotemporal variability in TWS (Paolo et al 2015 Science348 327–31; Edward 2012 Geophys. Res. Lett. 39 L01702). In this study, we first show that GRACE TWS trends from a short time series cannot determine conclusively if an observed change is unprecedented or severe. To address this limitation, we develop a novel metric, trend to variability ratio (TVR), that assesses the severity of TWS trends observed by GRACE from 2003 to 2015 relative to the multi-decadal climate-driven variability. We demonstrate that the TVR combined with the trend provides a more informative and complete assessment of water storage change. We show that similar trends imply markedly different severity of TWS change, depending on location. Currently more than 3.2 billion people are living in regions facing severe water storage depletion w.r.t. past decades. Furthermore, nearly 36% of hydrological catchments losing water in the last decade have suffered from unprecedented loss. Inferences from this study can better inform water resource management.

Livestock grazing covers half of Australia and vast areas of global terrestrial ecosystems. The sustainability of the beef cattle industries are being scrutinised amid ongoing environmental concerns. In response, industry discourse has identified public trust as critical to avoiding reactive environmental regulation. However, public perceptions of the cattle industry's sustainability performance and trust are largely unknown and speculative. We present the first model of public attitudes toward the Australian cattle industry (n = 2913). Our results reveal that societal perceptions of the industry's environmental performance strongly predict trust in the industry. However, trust only weakly predicts a perceived right for societal oversight and has only an indirect relationship on need for environmental regulation. Environmental values influence perceptions of industry performance and the perceived right for societal oversight. We conclude that effective industry governance must be values literate and recognise that strong environmental performance is critical for public trust. Public trust is high but does not translate to support for a relaxed regulatory environment.

Economic inequality and climate change are pressing issues that have climbed high up the political agenda, yet action to mitigate both remains slow. As income is a key determinant of ecological impacts, the Global North—and wealthier classes elsewhere—are the primary drivers of global carbon emissions, while the least well off have contributed the least yet are set to be hit hardest by climate impacts. These inequalities are clearly unjust, but the interrelations between economic inequality and ecological impacts are complex, leaving open the question of whether reducing the former would mitigate the latter, in the absence of reductions in total economic output. Here, we contribute to these debates by estimating the carbon-footprint implications of reducing income (and hence expenditure) inequalities within 32 countries of the Global North to the levels people consider to be fair; levels that are substantially smaller than currently exist. We find that realising these levels of economic inequality brings comparable reductions in carbon-footprint inequalities. However, in isolation, implementing fair inequalities has a negligible impact upon total emissions. In contrast, recomposing consumption—by reducing inequalities in household expenditure and the overall levels, then reallocating the reductions to public services—reduces carbon footprint by up to 30% in individual countries and 16% overall and, crucially, still allows the consumption of those at the bottom to rise. Such reductions could be significant on a global level, and they would be additional to the full range of conventional technological and demand-side measures to reduce carbon emissions.

Widespread mismatches between proxy-based and modelling studies of the Last Glacial Maximum (LGM) has limited better understanding about interglacial-glacial climate change. In this study, we incorporate non-breaking surface waves (NBW) induced mixing into an ocean model to assess the potential role of waves in changing a simulation of LGM upper oceans. Our results show a substantial 40 m subsurface warming introduced by surface waves in LGM summer, with larger magnitudes relative to the present-day ocean. At the ocean surface, according to the comparison between the proxy data and our simulations, the incorporation of the surface wave process into models can potentially decrease the model-data discrepancy for the LGM ocean. Therefore, our findings suggest that the inclusion of NBW is helpful in simulating glacial oceans.

Mitigation of greenhouse gas emissions through transitions to biomass-based renewable energy may result in higher land needs, affecting ecosystem services and livelihoods. Charcoal is a biomass-based renewable energy that provides energy for hundreds of millions of households worldwide and generates income for 40 million people. However, it currently causes up to 7% of the global deforestation rate. In the absence of affordable alternative fuels, it is necessary to identify conditions that foster sustainable charcoal production. In this study, we (a) develop a stylized model that simulates feedbacks between forest biomass and charcoal production, and (b) use the model to examine the effects of interventions that foster sustainable charcoal systems through transitions to communal management or private systems, increases in carbonization efficiency and charcoal demand reductions. Our model simulations suggest that at low demand, a transition is unnecessary. At intermediate to high demands, interventions that increase carbonization efficiency and/or reduce demand should be combined with transitions to communal management (at intermediate forest biomass levels) or private systems (at low forest biomass levels) to ensure long-term sustainability of charcoal systems and avoid collapse within 100 years. These results highlight multiple pathways for sustainable charcoal production systems tailored to meet supply and demand. All pathways are feasible across tropical biomes and could foster the simultaneous continuation of forests and charcoal production in the near future.

Near-term climate forcers (NTCFs), including aerosols and chemically reactive gases such as tropospheric ozone and methane, offer a potential way to mitigate climate change and improve air quality—so called 'win-win' mitigation policies. Prior studies support improved air quality under NTCF mitigation, but with conflicting climate impacts that range from a significant reduction in the rate of global warming to only a modest impact. Here, we use state-of-the-art chemistry-climate model simulations conducted as part of the Aerosol and Chemistry Model Intercomparison Project (AerChemMIP) to quantify the 21st-century impact of NTCF reductions, using a realistic future emission scenario with a consistent air quality policy. Non-methane NTCF (NMNTCF; aerosols and ozone precursors) mitigation improves air quality, but leads to significant increases in global mean precipitation of 1.3% by mid-century and 1.4% by end-of-the-century, and corresponding surface warming of 0.23 and 0.21 K. NTCF (all-NTCF; including methane) mitigation further improves air quality, with larger reductions of up to 45% for ozone pollution, while offsetting half of the wetting by mid-century (0.7% increase) and all the wetting by end-of-the-century (non-significant 0.1% increase) and leading to surface cooling of −0.15 K by mid-century and −0.50 K by end-of-the-century. This suggests that methane mitigation offsets warming induced from reductions in NMNTCFs, while also leading to net improvements in air quality.

High nighttime urban air temperatures increase health risks and economic vulnerability of people globally. While recent studies have highlighted nighttime heat mitigation effects of urban vegetation, the magnitude and variability of vegetation-derived urban nighttime cooling differs greatly among cities. We hypothesize that urban vegetation-derived nighttime air cooling is driven by vegetation density whose effect is regulated by aridity through increasing transpiration. We test this hypothesis by deploying microclimate sensors across eight United States cities and investigating relationships of nighttime air temperature and urban vegetation throughout a summer season. Urban vegetation decreased nighttime air temperature in all cities. Vegetation cooling magnitudes increased as a function of aridity, resulting in the lowest cooling magnitude of 1.4 °C in the most humid city, Miami, FL, and 5.6 °C in the most arid city, Las Vegas, NV. Consistent with the differences among cities, the cooling effect increased during heat waves in all cities. For cities that experience a summer monsoon, Phoenix and Tucson, AZ, the cooling magnitude was larger during the more arid pre-monsoon season than during the more humid monsoon period. Our results place the large differences among previous measurements of vegetation nighttime urban cooling into a coherent physiological framework dependent on plant transpiration. This work informs urban heat risk planning by providing a framework for using urban vegetation as an environmental justice tool and can help identify where and when urban vegetation has the largest effect on mitigating nighttime temperatures.

Groundwater forms the basis of water supplies across much of Africa and its development is rising as demand for secure water increases. Recharge rates are a key component for assessing groundwater development potential, but have not been mapped across Africa, other than from global models. Here we quantify long-term average (LTA) distributed groundwater recharge rates across Africa for the period 1970–2019 from 134 ground-based estimates and upscaled statistically. Natural diffuse and local focussed recharge, where this mechanism is widespread, are included but discrete leakage from large rivers, lakes or from irrigation are excluded. We find that measurable LTA recharge is found in most environments with average decadal recharge depths in arid and semi-arid areas of 60 mm (30–140 mm) and 200 mm (90–430 mm) respectively. A linear mixed model shows that at the scale of the African continent only LTA rainfall is related to LTA recharge—the inclusion of other climate and terrestrial factors do not improve the model. Kriging methods indicate spatial dependency to 900 km suggesting that factors other than LTA rainfall are important at local scales. We estimate that average decadal recharge in Africa is 15 000 km³ (4900–45 000 km³), approximately 2% of estimated groundwater storage across the continent, but is characterised by stark variability between high-storage/low-recharge sedimentary aquifers in North Africa, and low-storage/high-recharge weathered crystalline-rock aquifers across much of tropical Africa. African water security is greatly enhanced by this distribution, as many countries with low recharge possess substantial groundwater storage, whereas countries with low storage experience high, regular recharge. The dataset provides a first, ground-based approximation of the renewability of groundwater storage in Africa and can be used to refine and validate global and continental hydrological models while also providing a baseline against future change.

There was a dramatic increase in aerosol loading in China after the 1960s due to rapid industrialization, followed by a gradual reduction due to improvements in air quality since the early 2000s. They are deemed to be the main cause of 'dimming' and 'brightening' in China, respectively. China, therefore, provides an ideal testbed to investigate the multi-decadal evolution of clouds under a background of large variations in the amount of atmospheric aerosols. We used a unique combination of long-term in-situ observational records combined with a radiative transfer model to investigate the changes in clouds and aerosols over the last 60 years (1958–2018) over China. We found during the dimming period, the clouds over China shrunk in area steadily, gradually thinned in terms of optical depth, and thereby blocked less and less solar radiation. This situation reversed during the brightening period. The clouds over China showed a quick expansion in extent and thickening in terms of optical depth, and accordingly the amounts of solar radiation blocked by clouds recovered rapidly. It is observationally demonstrated that high levels of pollution and the associated amount of aerosols cause a suppression of cloud growth and a reduction of optical depth.

Recent Chinese air pollution actions have significantly lowered the levels of fine particulate matter (PM_2.5) in North China via controlling emissions of sulfur dioxide (SO₂) and nitrogen oxides (NO_x) together with primary aerosols, while the emissions of another precursor, ammonia (NH₃), have not yet been regulated. This raises a question that how effective the NH₃ emission controls can be on the mitigation of PM_2.5 pollution along with the reduction of SO₂ and NO_x emissions. Here we use a regional air quality model to investigate this issue focusing on the PM_2.5 pollution in North China for January and July 2015. We find that the efficiency of the PM_2.5 reduction is highly sensitive to the NH₃ emission and its reduction intensity. Reductions in the population-weighted PM_2.5 concentration (PWC) in the Beijing–Tianjin–Hebei region are only 1.4–3.8 μg m⁻³ (1.1%–2.9% of PM_2.5) with 20%–40% NH₃ emission reductions, but could reach 8.1–26.7 μg m⁻³ (6.2%–21%) with 60%–100% NH₃ emission reductions in January 2015. Besides, the 2015–2017 emission changes (mainly reduction in SO₂ emissions) could lower the PM_2.5 control efficiency driven by the NH₃ reduction by up to 30% for high NH₃ emission conditions, while lead to no change or increase in the efficiency when NH₃ emissions become low. NO_x emission reductions may enhance the wintertime PM_2.5 pollution due to the weakened titration effect and can be offset by simultaneously controlling NH₃ emissions. Our results emphasize the need to jointly consider NH₃ with SO₂ and NO_x emission controls when designing PM_2.5 pollution mitigation strategies.

The 168 year trends of summer (July–September) sea ice area (SIA) variations in six Arctic regions during 1850–2017 are analyzed. SIA has been significantly decreasing in most Arctic regions since 1850. The rate of retreat for the period of 1948–2017 accelerated multi-fold. For the nearly four decades since 1979, most Arctic regions are experiencing the highest reduction rate. Besides the increasing surface air temperature, the key drivers to the accelerated summer Arctic sea ice decline are found to be the combined global warming and the regional Arctic warming exerted simultaneously by the Arctic Oscillation, North Atlantic Oscillation, Atlantic Multidecadal Oscillation and Pacific Decadal Oscillation during the last several decades. The dynamical and thermodynamical warming, driven by the internal variability of the teleconnection patterns, occurred in the last several decades, in particular on the multidecadal timescales. This leads to Arctic amplification that accelerates the positive ice/ocean albedo feedback loop, resulting in accelerating summer sea ice decline.

More and more studies have evaluated the associations between ambient temperature and coronavirus disease 2019 (COVID-19). However, most of these studies were rushed to completion, rendering the quality of their findings questionable. We systematically evaluated 70 relevant peer-reviewed studies published on or before 21 September 2020 that had been implemented from community to global level. Approximately 35 of these reports indicated that temperature was significantly and negatively associated with COVID-19 spread, whereas 12 reports demonstrated a significantly positive association. The remaining studies found no association or merely a piecewise association. Correlation and regression analyses were the most commonly utilized statistical models. The main shortcomings of these studies included uncertainties in COVID-19 infection rate, problems with data processing for temperature, inappropriate controlling for confounding parameters, weaknesses in evaluation of effect modification, inadequate statistical models, short research periods, and the choices of research areal units. It is our viewpoint that most studies of the identified 70 publications have had significant flaws that have prevented them from providing a robust scientific basis for the association between temperature and COVID-19.

Understanding the relationships between species richness patterns and environment constitutes a key issue in biogeography and conservation strategies. To our knowledge, this is the first integrative study that incorporates soil and human-influence data into species richness modelling. Our aims were to (a) estimate the richness patterns of four conifers groups (all conifers species, endemics, threatened, and endemic-threatened species) in south-west China, (b) assess the relative importance of environmental predictors (energy, water, climate, topography, and soil) and the human-influence on the conifers richness patterns and (c) identify hotspot ecoregions, nature reserves, or important plant areas as priority conservation areas. Generalized linear models and hierarchical partitioning were used by correlating 8962 distributional records of 97 conifer species with different environmental drivers. Results indicated that central Sichuan, northern Sichuan, northern Yunnan, and the southern areas of the Hengduan mountains were identified as distinct centres of conifers richness in China. Topographic heterogeneity and soil fertility were the strongest drivers of conifer richness patterns, while climate, energy, water, and human drivers were contributed to a lower degree. The identified conifers' important areas were mostly located outside of the existing nature reserves but inside the ecoregions. Our findings emphasize that incorporating soil data into spatial modelling provides great insights for the conservation of conifers species. We recommend conservationists to use soil variables and other environmental data to generate a comprehensive understanding of the key drivers underlying the patterns of conifer diversity and distribution.

Recent studies have found that terrestrial dryness indices like the Palmer Drought Severity Index (PDSI), Standardized Precipitation Evapotranspiration Index (SPEI), and Aridity Index calculated from future climate model projections are mostly negative, implying a drying land surface with warming. Yet, the same models' future runoff and bulk soil moisture projections instead show regional signals of varying sign, and their vegetation projections show widespread greening, suggesting that the dryness indices could overstate climate change's direct impacts. Most modeling studies have attributed this gap to the indices' omission of CO₂-driven stomatal closure. However, here we show that the index-impact gap is still wide even in future-like model experiments that switch off CO₂ effects on plants. In these simulations, mean PDSI, Aridity Index, and SPEI still decline broadly with strong warming, while mean runoff, bulk soil moisture, and vegetation still respond more equivocally. This implies that CO₂-plant effects are not the dominant or sole reason for the simulated index-impact gap. We discuss several alternative mechanisms that may explain it.

Smart grids use digital information technology to simultaneously increase energy efficiency while integrating renewables into the electric grid, making it a critical component of achieving a low-carbon energy system. Prior research on the social acceptance of smart grids has relied on either single time point assessment (i.e. prior to a smart grid rollout) or experimental and lab settings. These approaches miss key aspects of social acceptance because they fail to capture change over time through the interaction between stakeholders, technology, and utilities. In contrast, we compare two waves of survey data on the social acceptance of smart grid technologies, the first (n = 609) prior to a local rollout of a smart grid program in upstate New York and the second (n = 533) two years after the same rollout. Our results demonstrate that in contrast to the hopes of smart energy advocates, the social acceptance of four dimensions of smart grids either remain steady or decline over time. Further analyses reveal that the factors that shape acceptance also change over time. This study demonstrates that the social acceptance of smart grids may actually decrease over time even with the robust engagement of consumers, not only challenging optimistic views of smart grid technology but also challenging broader theoretical arguments in the literature on the social acceptance of energy technologies.

Previous studies demonstrate that recent global warming hiatuses are associated with an ongoing cooling in the eastern Pacific. However, the possible driver for this cooling remains vigorously debated. Present theories can be generally categorized into three different frameworks, the most prevailing theory considering the increased heat uptake in ocean interior as a direct trigger in cooling the eastern equatorial Pacific, the next regarding the prolonged solar minimum as a potential driver in producing weak radiative forcing over the Pacific, while another suggesting that changes in atmospheric water vapour and aerosols play an unnegligible role in absorbing and reflecting solar radiation. Most recently, some studies argue that the ongoing cooling in the eastern Pacific is induced by a strengthening of the easterly trade winds. Nevertheless, observational records coming from the monitoring buoys deployed along the equator by NOAA since 1992 indicate that an intensification of the trade winds is only confined to the central tropical Pacific (around 170° E–170° W) during hiatus decades, elsewhere along the equatorial Pacific the trade winds exhibit a stable condition even a slight weakening in the eastern equatorial Pacific, rendering it as a trigger of this cooling in the eastern Pacific unlikely. Here we use a model and long-term observational data to demonstrate that a persistent cooling in the eastern Pacific is directly linked to an eastward displacement of the Southeast Pacific Subtropical Anticyclone (SPSA). Interactions between the Andes and an eastward shift of the SPSA generate greater pressure gradients in the eastern flank, in turn, stronger alongshore winds and more intense upwelling, ultimately contributing to hiatus decades.

Mandates, like the renewable fuel standard (RFS), for biofuels from corn and cellulosic feedstocks, impact the environment in multiple ways by affecting land use, nitrogen (N)-leakage, and greenhouse gas (GHG) emissions. We analyze the differing trade-offs these different types of biofuels offer among these multi-dimensional environmental effects and convert them to a monetized value of environmental damages (or benefits) that can be compared with the economic costs of extending these mandates over the 2016–2030 period. The discounted values of cumulative net benefits (or costs) are then compared to those with a counterfactual level of biofuels that would have been produced in the absence of the RFS over this period. We find that maintaining the corn ethanol mandate at 56 billion l till 2030 will lead to a discounted cumulative value of an economic cost of $199 billion over the 2016–2030 period compared to the counterfactual scenario; this includes $109 billion of economic costs and $85 billion of net monetized environmental damages. The additional implementation of a cellulosic biofuel mandate for 60 billion l by 2030 will increase this economic cost by $69 billion which will be partly offset by the net discounted monetized value of environmental benefits of $20 billion, resulting in a net cost of $49 billion over the 2016–2030 period. We explore the sensitivity of these net (economic and environmental) costs to alternative values of the social costs of carbon and nitrogen and other technological and market parameters. We find that, unlike corn ethanol, cellulosic biofuels can result in positive net benefits if the monetary benefits of GHG mitigation are valued high and those of N-damages are not very high.

Uncertainty in model initial states produces uncertainty in climate simulations because of unforced variability internal to the climate system. Climate scientists use initial-condition ensembles to separate the forced signal of climate change from the unforced internal variability. Our analysis of an 11-member initial-condition ensemble from the Community Earth System Model Version 2 that spans the period 1850–2014 shows that a similar ensemble approach is needed to robustly assess trends in the terrestrial carbon cycle. Uncertainty in model initialization gives rise to internal variability that masks trends in carbon fluxes, and also creates spurious unforced trends, during the period 1960–2014 across North America, meaning that a single model realization can diverge from the observational record or from other models simply because of random behavior. The forced response is, however, evident in the ensemble mean and emerges from the noise of unforced variability at decadal timescales. Our results suggest that trends in the observational record must be interpreted with caution because of multiple possible histories that would have been observed if the sequence of internal variability had unfolded differently. Furthermore, internal variability produces irreducible uncertainty in the carbon cycle, leading to ambiguity in the magnitude and sign of carbon cycle trends, especially at small spatial scales and short timescales. The small spread in initial land carbon pools at 1850 suggests that internal climate variability arising from atmospheric and oceanic initialization, not the biogeochemical initialization, is the predominant cause of carbon cycle variability among ensemble members. Initial-condition ensembles with other Earth system models are needed to develop a multi-model understanding of internal variability in the terrestrial carbon cycle.

Changes in snow and vegetation cover associated with global warming can modify surface albedo (the reflected amount of radiative energy from the sun), therefore modulating the rise of surface temperature that is primarily caused by anthropogenic greenhouse-gases emission. This introduces a series of potential feedbacks to regional warming with positive (negative) feedbacks enhancing (reducing) temperature increase by augmenting (decreasing) the absorption of short-wave radiation. So far our knowledge on the importance and magnitude of these feedbacks has been hampered by the limited availability of relatively long records of continuous satellite observations. Here we exploit a 31 year (1982–2012) high-frequency observational record of land data to quantify the strength of the surface-albedo feedback on land warming modulated by snow and vegetation during the recent historical period. To distinguish snow and vegetation contributions to this feedback, we examine temporal composites of satellite data in three different Northern Hemisphere domains. The analysis reveals and quantifies markedly different signatures of the surface-albedo feedback. A large positive surface-albedo feedback of +0.87 (CI 95%: 0.68, 1.05) ${\text{W}}\,{{\text{(}}{{\text{m}}^{\text{2}}}\, \cdot \,{\text{K)}}^{ - 1}}$ absorbed solar radiation per degree of temperature increase is estimated in the domain where snow dominates. On the other hand the surface-albedo feedback becomes predominantly negative where vegetation dominates: it is largely negative (−0.91 (−0.81, −1.03) ${\text{W}}\,{{\text{(}}{{\text{m}}^{\text{2}}}\, \cdot \,{\text{K)}}^{ - 1}}$) in the domain with vegetation dominating, while it is moderately negative (−0.57 (−0.40, −0.72) ${\text{W}}\,{{\text{(}}{{\text{m}}^{\text{2}}}\, \cdot \,{\text{K)}}^{ - 1}}$) where both vegetation and snow are significantly present. Snow cover reduction consistently provides a positive feedback on warming. In contrast, vegetation expansion can produce either positive or negative feedbacks in different regions and seasons, depending on whether the underlying surface being replaced has higher (e.g. snow) or lower (e.g. dark soils) albedo than vegetation. This work provides fundamental knowledge to model and predict how the surface-albedo feedback will evolve and affect the rate of regional temperature rise in the future.

Using state-of-the-art models from the Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5/6), future changes of sudden stratospheric warming (SSW) events under a moderate emission scenario (RCP45/SSP245) and a strong emissions scenario (RCP85/SSP585) are evaluated with respect to the historical simulations. Changes in four characteristics of SSWs are examined in 54 models: the SSW frequency, the seasonal distribution, stratosphere–troposphere coupling, and the persistency of the distorted or displaced polar vortex. The composite results show that none of these four aspects will change robustly. An insignificant (though positive) change in the SSW frequency from historical simulations to RCP45/SSP245 and then to RCP85/SSP585 is consistently projected by CMIP5 and CMIP6 multimodel ensembles in most wintertime months (December–March). This increase in the SSW frequency is most pronounced in mid- (late-) winter in CMIP6 (CMIP5). No shift in the seasonality of SSWs is simulated especially in the CMIP6 future scenarios. Both the reanalysis and CMIP5/6 historical simulations exhibit strong stratosphere–troposphere coupling during SSWs, and the coupling strength is nearly unchanged in the future scenario simulations. The near surface responds immediately after the onset of SSWs in both historical and future scenarios experiments, denoted by the deep downward propagation of zonal-mean easterly anomalies from the stratosphere to the troposphere. On average, the composite circumpolar easterly winds persist for 8 d in the reanalysis and CMIP5/6 historical experiments, which are projected to remain unchanged in both the moderate and strong emissions scenarios, implying the lifecycle of SSWs will not change.

This study investigates the impacts of African wildfire aerosols (primary organic carbon, black carbon and sulfate) on the Northern Hemispheric in January. We found that wildfire aerosols emitted from equatorial Africa result in two mid-to-high latitudes atmospheric Rossby wave trains. One is from subtropical Atlantic propagating northeastward across Europe to Siberia, and the other one propagates eastward from Middle East across Asia to Pacific Northwest. The maximum positive geopotential height anomaly locates in Europe, concurrent with a greater-than-2 K land surface warming. These Rossby wave trains are excited by the atmospheric heating that caused by the wildfire aerosols in equatorial Africa and propagate into extratropics with the help of the westerly jet. Based on the diabatic heat budget analysis, the Rossby wave source is primarily from the solar absorption of black carbon of African wildfire. The present study emphasizes that wildfire aerosols, especial the absorbing aerosols, would have profound climate effects on remote regions and thus need more attentions.

Global changes, e.g. global warming, elevated nitrogen deposition, and shifts of precipitation regime, exert a major influence on forests via affecting plant water use efficiency (WUE) and plant nitrogen (N) availability. Large-scale ecological sampling can help us to better understand variation across regions and provide opportunities to investigate the potential impacts of multiple aspects of global change on forest ecosystem responses. Here, we determine the geographical patterns of key isotopic measures of ecosystem function—plant WUE (calculated from foliar δ¹³C values) and plant N availability (assessed by foliar δ¹⁵N values)—across China's forests covering ∼21 latitude (∼22–43°N) and ∼28 longitude (∼93–121°E) degree, and investigate how a suite of soil, plant, and atmospheric factors regulate them. We found that plant WUE increased but N availability decreased with latitude, while plant WUE and N availability did not vary with longitudinal gradient. Different factors regulate the large-scale patterns in WUE and N availability. The mean annual temperature, atmospheric N deposition, and soil water content exhibit considerable effects on plant WUE over both the north-to-south and east-to-west transects, while the mean annual precipitation, soil potassium content, foliar N, and precipitation seasonality considerably affect the latitudinal patterns of plant N availability. In addition, the east-to-west spatial pattern in plant N availability is associated with the variation in solar radiation. Our results suggest that key forest ecological functions respond to an array of environmental factors, and imply that changes in many different environmental attributes need to be considered in order to successfully assess plant WUE and N availability responses to global changes this century.

Earth system models (ESMs) are widely used in scientific research to understand the responses of various components of Earth systems to natural and anthropogenic forcings. ESMs embody terrestrial ecosystems on the basis of the leaf area index (LAI) to formulate various interactions between the land surface and atmosphere. Here, we evaluated the LAI seasonality of deciduous forests simulated by 14 ESMs participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 to understand the efficacy of recent ESMs in describing leaf dynamics in the northern extratropics from 1982 to 2014. We examined three indicators of LAI seasonality (annual mean, amplitude, and phase) and three phenological dates (start (SOS), end (EOS), and length of growing season (LOS)) of the models in comparison to the third-generation LAI of Global Inventory Modeling and Mapping Studies (GIMMS LAI_3g) and the Climate Research Unit gridded time series dataset. CMIP6 models tend to simulate larger annual means (1.7 m² m⁻²), weaker amplitudes (0.9 m² m⁻²), and delayed phases (226 DOY) compared to the GIMMS LAI_3g (1.2 m² m⁻², 1.2 m² m⁻², and 212 DOY, respectively), yet are similar to the CMIP5 models (2.2 m² m⁻², 1.0 m² m⁻², and 225 DOY). The later phase is attributed to a systematic positive bias in EOS of the CMIP5 and CMIP6 models (later by 22 and 18 d, respectively) compared to the GIMMS LAI_3g (261 DOY). Further tests on phenological responses to seasonal temperature revealed that the majority of CMIP5 and CMIP6 ESMs inaccurately describe the sensitivities of SOS and EOS to seasonal temperature and the recent changes in mean SOS and EOS distributions (2005–2014 minus 1982–1991). This study suggests that phenology schemes of deciduous forests, especially for autumn leaf senescence, should be revisited to achieve an accurate representation of terrestrial ecosystems and their interactions.

A recovery of near-surface wind speed (SWS) in the last decade has been reported over China; nevertheless, the contributions of large-scale ocean-atmosphere circulations (LOACs) to the SWS changes are rarely investigated. In this study, the turning point (TP) of the terrestrial stilling was validated over eastern China for 1979–2017. Furthermore, a forward stepwise regression algorithm was used to assess the contribution of LOACs to SWS changes. The results revealed that the TP of the SWS reversal occurred in approximately 2011 during the study period. Mean annual and seasonal SWSs exhibited decreases before the TP, with the largest decrease in spring (–0.134 ± 0.014 m s⁻¹ decade⁻¹), while SWSs increased after the TP, most strongly in autumn (0.377 ± 0.053 m s⁻¹ decade⁻¹). The SWS decrease before the TP and increase after the TP were caused by the decreasing and increasing frequencies of strong windy days (>75th percentile of SWS), respectively. The effects of LOACs on the long-term changes of SWS were pronounced. The contributions of LOACs to the decreasing and increasing trends of SWSs were >60.0%, with the exception of autumn. The projected SWSs exhibited increases in the near-term (2021–2040) for the low-emission scenarios (e.g. Shared Socioeconomic Pathway 245). For the mid-term and long-term projections, the SWSs still displayed a downward trend, which was mainly attributed to the reduction of strong windy days. Consequently, the present SWS recovery in the recent decade may be only expected to last for a short amount of time before winds start decreasing again.

Exceptional events occur when air pollution in a specific location exceeds the National Ambient Air Quality Standards (NAAQS) due to an event that cannot be reasonably attributed to human activities, such as a wildland fire. Ground-level ozone (O₃) and particulate matter (PM) are Environmental Protection Agency (EPA) criteria pollutants regulated under the NAAQS. Smoke from wildland fires can increase PM and O₃ concentrations downwind of fire and impact air quality, visibility, and health. Our analysis shows that the frequency of exceptional event reporting for PM with aerodynamic diameters smaller than 2.5 μm or 10 μm (PM_2.5 and PM₁₀) had increased since 2007 when the air quality standards became more stringent. We also show that wildland fires and windblown dust drive many exceptional events in several EPA regions. We note the importance of growth in the number of exceptional event days due to wildfire smoke in the future due to climate change and point to possible changes to the NAAQS and implementations.

New Zealand is one of many higher latitude countries where extreme heat is perceived to be a less consequential impact of climate change, by virtue of its relatively cool climate. Consequently, metrics to quantify the impacts of extreme heat in New Zealand have not kept pace with wider improvements in heatwave definitions. This study evaluates different methods to quantify extreme heat in New Zealand, with a view to improve the knowledge base underpinning future climate change risk assessments. Specifically, this analysis (1) reveals which of New Zealand's purportedly hottest years in the satellite era are robust to different definitions of extreme heat; (2) introduces a new method of quantifying extreme heat which is applicable across different regions, and serves equally well whether an analysis is contextualised relative to the past (attribution) or for the future (projections); (3) detects previously unidentified heatwaves over recent decades; (4) identifies locally significant increases in extreme heat and the potential lengthening of summer months after only 0.5 °C of global warming; and (5) discusses further research priorities to better understand the impacts of extreme heat in New Zealand over the coming decades.

Global climate is changing as a result of anthropogenic warming, leading to higher daily excursions of temperature in cities. Such elevated temperatures have great implications on human thermal comfort and heat stress, which should be closely monitored. Current methods for heat exposure assessments (surveys, microclimate measurements, and laboratory experiments), however, present several limitations: measurements are scattered in time and space and data gathered on outdoor thermal stress and comfort often does not include physiological and behavioral parameters. To address these shortcomings, Project Coolbit aims to introduce a human-centric approach to thermal comfort assessments. In this study, we propose and evaluate the use of wrist-mounted wearable devices to monitor environmental and physiological responses that span a wide range of spatial and temporal distributions. We introduce an integrated wearable weather station that records (a) microclimate parameters (such as air temperature and humidity), (b) physiological parameters (heart rate, skin temperature and humidity), and (c) subjective feedback. The feasibility of this methodology to assess thermal comfort and heat stress is then evaluated using two sets of experiments: controlled-environment physiological data collection, and outdoor environmental data collection. We find that using the data obtained through the wrist-mounted wearables, core temperature can be predicted non-invasively with 95 percent of target attainment within ±0.27 °C. Additionally, a direct connection between the air temperature at the wrist (T_a,w) and the perceived activity level (PAV) of individuals was drawn. We observe that with increased T_a,w, the desire for physical activity is significantly reduced, reaching 'Transition only' PAV level at 36 °C. These assessments reveal that the wearable methodology provides a comprehensive and accurate representation of human heat exposure, which can be extended in real-time to cover a large spatial distribution in a given city and quantify the impact of heat exposure on human life.

Due to advances in unconventional extraction techniques, the rate of fossil fuel production in the United States (US) is higher than ever before. The disposal of waste gas via intentional combustion (flaring) from unconventional oil and gas (UOG) development has also been on the rise, and may expose nearby residents to toxic air pollutants, light pollution and noise. However, little data exists on the extent of flaring in the US or the number of people living near UOG flaring activity. Utilizing nightly sattelite observations of flaring from the Visible Infrared Imaging Radiometer Suite Nightfire product, 2010 Census data and a dataset of remotely sensed building footprints, we applied a dasymetric mapping approach to estimate the number of nightly flare events across all oil shale plays in the contiguous US between March 2012 and February 2020 and characterize the populations residing within 3 km, 5 km and 10 km of UOG flares in terms of age, race and ethnicity. We found that three basins accounted for over 83% of all UOG flaring activity in the contiguous US over the 8 year study period. We estimated that over half a million people in these basins reside within 5 km of a flare, and 39% of them lived near more than 100 nightly flares. Black, indigenous, and people of color were disproportionately exposed to flaring.

The Amazon Basin, the largest watershed on Earth, experienced a significant increase in wet-season precipitation and high-season river discharge from the early 1990s to early 2010s. Some studies have linked the increased Amazon Basin hydrologic cycle to decadal trends of increased Pacific trade winds, eastern Pacific sea surface temperature (SST) cooling, and associated strengthening of the Pacific Walker circulation. However, it has been difficult to disentangle the role of Pacific decadal variability from the impacts of greenhouse gases and other external climate drivers over the same period. Here, we separate the contributions of external forcings from those of Pacific decadal variability by comparing two large ensembles of climate model experiments with identical radiative forcing agents but imposing different tropical Pacific wind stress. One ensemble constrains tropical Pacific wind stress to its long-term climatology, suppressing tropical Pacific decadal variability; the other ensemble imposes the observed tropical Pacific wind stress anomalies, simulating realistic tropical Pacific decadal variability. Comparing the Amazon Basin hydroclimate response in the two ensembles allows us to distinguish the contributions of external forcings common to both simulations from those related to Pacific trade wind variability. For the 1992–2012 trend, the experiments with observed tropical Pacific wind stress anomalies simulate strengthening of the Walker circulation between the Pacific and South America and sharpening of the Pacific–Atlantic interbasin SST contrast, driving increased Amazon Basin wet-season precipitation and high-season discharge. In contrast, these circulation and hydrologic intensification trends are absent in the simulations with climatological tropical Pacific wind stress. This work underscores the importance of Pacific decadal variability in driving hydrological cycle changes and modulating the hydroclimate impacts of global warming over the Amazon Basin.

The evacuation of the population from flood-affected regions is a non-structural measure to mitigate flood hazards. Shelters used for this purpose usually accommodate a large number of flood evacuees for a temporary period. Floods during a pandemic result in a compound hazard. Evacuations under such situations are difficult to plan as social distancing is nearly impossible in the highly crowded shelters. This results in a multi-objective problem with conflicting objectives of maximizing the number of evacuees from flood-prone regions and minimizing the number of infections at the end of the shelter's stay. To the best of our knowledge, such a problem is yet to be explored in literature. Here we develop a simulation-optimization framework, where multiple objectives are handled with a max–min approach. The simulation model consists of an extended Susceptible—Exposed—Infectious—Recovered—Susceptible model. We apply the proposed model to the flood-prone Jagatsinghpur district in the state of Odisha, India. We find that the proposed approach can provide an estimate of people required to be evacuated from individual flood-prone villages to reduce flood hazards during the pandemic. At the same time, this does not result in an uncontrolled number of new infections. The proposed approach can generalize to different regions and can provide a framework to stakeholders to manage conflicting objectives in disaster management planning and to handle compound hazards.

Climate change is projected to increase the aridity of semi-arid ecosystems, including Mongolian grasslands (MG), which provide ecosystem services that support food supply and pastoralist lifestyle. Here, we conducted a grid-scale (0.5° × 0.5°) probabilistic risk assessment of MG under climate change for 40 years (1976–2015) based on probability theory. We evaluated changes of risk (impacts) and vulnerability of MG to drought between the recent two decades R20 = 1996–2015 and the previous two decades P20 = 1976–1995. The risk is quantified as the product of the probability of hazardous drought and ecosystem vulnerability. The probability of hazardous drought is defined from the Standardized Precipitation–Evapotranspiration Index. Vulnerability is defined as the expected differences of key ecosystem variables between years with and without hazardous conditions. The ecosystem variables are productivity (peak aboveground biomass, net primary productivity, and leaf area index) and root-zone plant-available soil moisture, simulated with a process-based vegetation model Organizing Carbon and Hydrology in Dynamic Ecosystems-Grassland Management validated with field observations of biomass and soil moisture. Results reveal that MG experienced more frequent hazardous droughts with rapid warming and slight drying during R20 aggravated by ever-increasing grazing intensity (34% compared to P20), which resulted in a reduction in soil water availability and grassland productivity, particularly in northeastern areas (20%–65%). The risk of drought to productivity increased by 10% between P20 and R20 over extended areas, particularly in northcentral and northeast Mongolia. The increase in the risk to MG was mainly caused by climate change-induced increase in the probability of hazardous drought and, to a lesser extent, by the increasing vulnerability. Recent droughts modify the risk to grasslands, particularly in northcentral and northeast Mongolia, suggesting that these regions need strategic management for both adaptation and ecosystem conservation to cope with climate change impacts.

An increase in the frequency of extremely hot and dry events has been experienced over the past few decades in South America, and particularly in Brazil. Regional climate change projections indicate a future aggravation of this trend. However, a comprehensive characterization of drought and heatwave compound events, as well as of the main land–atmosphere mechanisms involved, is still lacking for most of South America. This study aims to fill this gap, assessing for the first time the historical evolution of compound summer drought and heatwave events for the heavily populated region of Southeast Brazil and for the period of 1980–2018. The main goal is to undertake a detailed analysis of the surface and synoptic conditions, as well as of the land–atmosphere coupling processes that led to the occurrence of individual and compound dry and hot extremes. Our results confirm that the São Paulo, Rio de Janeiro and Minas Gerais states have recorded pronounced and statistically significant increases in the number of compound summer drought and heatwave episodes. In particular, the last decade was characterized by two austral summer seasons (2013/14 and 2014/15) with outstanding concurrent drought and heatwave conditions stemmed by severe precipitation deficits and a higher-than-average occurrence of blocking patterns. As result of these land and atmosphere conditions, a high coupling (water-limited) regime was imposed, promoting the re-amplification of hot spells that resulted in mega heatwave episodes. Our findings reveal a substantial contribution of persistent dry conditions to heatwave episodes, highlighting the vulnerability of the region to climate change.

Climate change has contributed to recent declines in mountain snowpack and earlier runoff, which in turn have intensified hydrological droughts in western North America. Climate model projections suggest that continued and severe snowpack reductions are expected over the 21st century, with profound consequences for ecosystems and human welfare. Yet the current understanding of trends and variability in mountain snowpack is limited by the relatively short and strongly temperature forced observational record. Motivated by the urgent need to better understand snowpack dynamics in a long-term, spatially coherent framework, here we examine snow-growth relationships in western North American tree-ring chronologies. We present an extensive network of snow-sensitive proxy data to support high space/time resolution paleosnow reconstruction, quantify and interpret the type and spatial density of snow related signals in tree-ring records, and examine the potential for regional bias in the tree-ring based reconstruction of different snow drought types (dry versus warm). Our results indicate three distinct snow-growth relationships in tree-ring chronologies: moisture-limited snow proxies that include a spring temperature signal, moisture-limited snow proxies lacking a spring temperature signal, and energy-limited snow proxies. Each proxy type is based on distinct physiological tree-growth mechanisms related to topographic and climatic site conditions, and provides unique information on mountain snowpack dynamics that can be capitalized upon within a statistical reconstruction framework. This work provides a platform and foundational background required for the accelerated production of high-quality annually resolved snowpack reconstructions from regional to high ($\lt$12 km) spatial scales in western North America and, by extension, will support an improved understanding of the vulnerability of snowmelt-derived water resources to natural variability and future climate warming.

Permafrost degradation induced by climate warming is widely observed in the Northern Hemisphere. However, changes in permafrost sensitivity to climate warming (PSCW) in the future remains unclear. This study examined the changes in permafrost distribution in the Northern Hemisphere under global warming of 1.5 °C and 2 °C, and then characterized the spatial and temporal characteristics of PSCW. Global warming of 1.5 °C and 2 °C would result in 17.8 ± 5.3% and 28.3 ± 7.2% degradation of permafrost area under the climate scenario of Representative Concentration Pathway (RCP) 4.5, respectively, and 18.7 ± 4.6% and 28.1 ± 7.2% under the RCP 8.5, respectively. Permafrost tends to be more sensitive to climate change under the RCP 8.5 than RCP 4.5. PSCW shows small temporal variations in the 21st century under both RCPs, indicating a relatively stable sensitivity to warming on a hemisphere scale. However, PSCW varies greatly among regions, with high values at low latitudes and low values towards high latitudes. Air temperature is a major cause for the spatial heterogeneity of PSCW, explaining 66% of its variations. Permafrost under a warmer climate scenario tends to be more sensitive to the warming. Reducing snow depth and rising air temperature collectively enhances the permafrost sensitivity. Increasing in soil water content, by contrast, reduces the effect of warming. Permafrost in the south of the Northern Hemisphere is most vulnerable to climate warming. Our study highlights that permafrost in the region will respond differently under different warming scenarios across space (e.g. north vs south) and time (e.g. summer vs winter) in this century.

Increasing growing season temperatures and the seasonal redistribution of precipitation due to climate change have recently been recorded across the globe. Simultaneously, increases of severe droughts and windstorm frequency have also been documented. However, the impacts of climate change on tree growth performance and fitness might largely differ among coexisting species. Consequently, ongoing temperature increases could lead to extensive changes in tree species compositions in many forest biomes including temperate mountain forests. In this study we used an extensive dataset of 2824 cored trees of three species from two sites, and parameterized a purely climate driven process-based model (Vaganov–Shaskin) to simulate the growth dynamics and climatic limitations of coexisting Picea abies, Fagus sylvatica and Abies alba in two of the oldest mountain forest reserves in Central Europe (the Boubín and Žofín Primeval Forests). We assumed that the species composition reflects climatic growth limitations, and considered between-site differences in mean temperature due to elevation as a model of future climate change effects on mountain forests. Our results show a complexity of site- and species-specific responses of Central European forests to climate change. Over the last 70 years, the proportion of F. sylvatica in Central European natural forests has increased at the expense of conifers. During the investigated period, we observed an increase in the growth rates of the studied species mainly at the higher elevation site, while for the lower elevation site there was increasing intensity of moisture limitation. Despite being the most moisture-limited species, P. abies showed the highest simulated growth rates. In contrast, A. alba was the least moisture limited of all considered species. Given its recent proportion in the forest species composition and intermediate drought resistance, we anticipate the future expansion of F. sylvatica in Central European mountain forests.

Concerns over climate change are motivated in large part because of their impact on human society. Assessing the effect of that uncertainty on specific potential impacts is demanding, since it requires a systematic survey over both climate and impacts models. We provide a comprehensive evaluation of uncertainty in projected crop yields for maize, spring and winter wheat, rice, and soybean, using a suite of nine crop models and up to 45 CMIP5 and 34 CMIP6 climate projections for three different forcing scenarios. To make this task computationally tractable, we use a new set of statistical crop model emulators. We find that climate and crop models contribute about equally to overall uncertainty. While the ranges of yield uncertainties under CMIP5 and CMIP6 projections are similar, median impact in aggregate total caloric production is typically more negative for the CMIP6 projections (+1% to −19%) than for CMIP5 (+5% to −13%). In the first half of the 21st century and for individual crops is the spread across crop models typically wider than that across climate models, but we find distinct differences between crops: globally, wheat and maize uncertainties are dominated by the crop models, but soybean and rice are more sensitive to the climate projections. Climate models with very similar global mean warming can lead to very different aggregate impacts so that climate model uncertainties remain a significant contributor to agricultural impacts uncertainty. These results show the utility of large-ensemble methods that allow comprehensively evaluating factors affecting crop yields or other impacts under climate change. The crop model ensemble used here is unbalanced and pulls the assumption that all projections are equally plausible into question. Better methods for consistent model testing, also at the level of individual processes, will have to be developed and applied by the crop modeling community.

A large proportion of local pollutants originating from the road transport sector is generated during the so-called cold-start phase of driving, that is, the first few minutes of driving after a car has stood inactive for several hours. Drawing on data from the German Mobility Panel (MOP), this paper analyzes the factors that affect the frequency of cold starts, approximated here by the number of car tours that a household takes over the course of a week. Based on fixed-effects panel estimations, we find a negative and statistically significant effect of fuel prices on the number of tours and, hence, cold starts. Using our estimates to explore the spatial implications arising from fuel price increases stipulated under Germany's Climate Programme 2030, we find substantial impacts on the number of avoided tours even for modest fuel price increases of 20 cents per liter, particularly in urban areas. This outcome lends support to using carbon pricing as a means to improve both global climate and local air quality, pointing to a co-benefit of climate policy.

The Tropical Atlantic is facing a massive proliferation of Sargassum since 2011, with severe environmental and socioeconomic impacts. As a contribution to this proliferation, an increase in nutrient inputs from the tropical rivers, in response to climate and land use changes or increasing urbanization, has been often suggested and widely reported in the scientific and public literature. Here we discuss whether changes in river nutrient inputs could contribute to Sargassum proliferation in the recent years or drive its seasonal cycle. Using long-term in situ and satellite measurements of discharge, dissolved and particulate nutrients of the three world largest rivers (Amazon, Orinoco, Congo), we do not find clear evidences that nutrient fluxes may have massively increased over the last 15 years. Moreover, focusing on year 2017, we estimate that along the year only 10% of the Sargassum biomass occurred in regions under river plume influence. While deforestation and pollution are a reality of great concern, our results corroborate recent findings that hydrological changes are not the first order drivers of Sargassum proliferation. Besides, satellite observations suggest that the major Atlantic river plumes suffered a decrease of phytoplankton biomass in the last two decades. Reconciling these observations requires a better understanding of the nutrient sources that sustain Sargassum and phytoplankton growth in the region.

Crops worldwide are simultaneously affected by weeds, which reduce yield, and by climate change, which can negatively or positively affect both crop and weed species. While the individual effects of environmental change and of weeds on crop yield have been assessed, the combined effects have not been broadly characterized. To explore the simultaneous impacts of weeds with changes in climate-related environmental conditions on future food production, we conducted a meta-analysis of 171 observations measuring the individual and combined effects of weeds and elevated CO₂, drought or warming on 23 crop species. The combined effect of weeds and environmental change tended to be additive. On average, weeds reduced crop yield by 28%, a value that was not significantly different from the simultaneous effect of weeds and environmental change (27%), due to increased variability when acting together. The negative effect of weeds on crop yield was mitigated by elevated CO₂ and warming, but added to the negative effect of drought. The impact of weeds with environmental change was also dependent on the photosynthetic pathway of the weed/crop pair and on crop identity. Native and non-native weeds had similarly negative effects on yield, with or without environmental change. Weed impact with environmental change was also independent of whether the crop was infested with a single or multiple weed species. Since weed impacts remain negative under environmental change, our results highlight the need to evaluate the efficacy of different weed management practices under climate change. Understanding that the effects of environmental change and weeds are, on average, additive brings us closer to developing useful forecasts of future crop performance.

This study aims to understand the role of near-surface temperatures in predicting US climatic extremes using the North American Multi-Model Ensemble (NMME) system. Here, the forecasting skill was measured by anomaly correlation coefficient (ACC) between the observed and forecasted precipitation (PREC)/2-meter air temperature (T2m) anomalies over the contiguous United States (CONUS) during 1982–2012. The strength of the T2m–PREC coupling was measured by ACC between observed PREC and T2m or forecasted PREC and T2m over the CONUS. We also assessed the NMME forecasting skill for the summers of 2004 (spatial anomaly correlation between PREC and T2m: 0.05), 2011 (−0.65), and 2012 (−0.60) when the T2m–PREC coupling was weaker or stronger than the 1982–2012 climatology (ACC: −0.34). We found that most of the NMME models show the bias of stronger T2m–PREC coupling than the observed coupling over 1982–2012, indicating that they failed to reproduce the interannual variability of T2m–PREC coupling. Some NMME models with skillful prediction for T2m show the skillful prediction of the precipitation anomalies and US droughts in 2011 and 2012 via strong T2m–PREC coupling despite the fact that the forecasting skill is year-dependent and model-dependent. Most of the NMME models show the limited seasonal forecasting skill of the PREC surplus from active Atlantic tropical cyclones in the summer of 2004 and thus fail to reproduce weak T2m–PREC coupling. Lastly, we explored how the role of sea surface temperatures in predicting T2m and PREC. The findings of this study suggest a need for the selective use of the current NMME seasonal forecasts for US droughts and pluvials.

Annual greenhouse gas (GHG) emissions from residential energy use in the United States peaked in 2005 at 1.26 Gt CO_2-eq yr⁻¹, and have since decreased at an average annual rate of 2% yr⁻¹ to 0.96 Gt CO_2-eq yr⁻¹ in 2019. In this article we decompose changes in US residential energy supply and GHG emissions over the period 1990–2015 into relevant drivers for four end-use categories. The chosen drivers encompass changing demographics, housing characteristics, energy end-use intensities, and generation efficiency and GHG intensity of electricity. Reductions in household size, growth in heated floor area per house, and increased access to space cooling are the main drivers of increases in energy and GHG emissions after population growth. Growing shares of newer homes, and reductions in intensity of energy use per capita, household, or floor area have produced moderate primary energy and GHG emission reductions, but improved generation efficiency and decarbonization of electricity supply have brought about far bigger primary energy and GHG emission reductions. Continued decline of residential emissions from electrification of residential energy and decarbonization of electricity supply can be expected, but not fast enough to limit climate change to 1.5 °C warming. US residential final energy demand will therefore need to decline in absolute terms to meet such a target. However, without changes in the age distribution, type mix, or average size of housing, improvements in energy efficiency are unlikely to outweigh growth in the number of households from population growth and further household size reductions.

Despite the increasing Siberian river discharge, the sensitivity of streamflow to climate forcing/permafrost thawing is poorly quantified. Based on the Budyko framework and superposition principles, we detected and attributed the changes in streamflow regimes for the three great Siberian rivers (Ob, Yenisei, and Lena) during 1936–2019. Over the past 84 years, streamflow of Ob, Yenisei and Lena has increased by ∼7.7%, 7.4% and 22.0%, respectively. Intensified precipitation induced by a warming climate is a major contributor to increased annual streamflow. However, winter streamflow appears to be particularly sensitive to temperature. Whilst rising temperature can reduce streamflow via evapotranspiration, it can enhance groundwater discharge to rivers due to permafrost thawing. Currently, every 1 °C rise in temperature likely leads to 6.1%–10.5% increase in groundwater discharge, depending on the permafrost condition. For permafrost-developed basins, the contribution to increased streamflow from thawing permafrost will continue to increase in the context of global warming.

Land regions are warming rapidly. While in a warming world at extra-tropical latitudes vegetation adapted to higher temperatures may move in from lower latitudes this is not possible in the tropics. Thus, the limits of plant functioning will determine the nature and composition of future vegetation. The most temperature sensitive component of photosynthesis is photosystem II. Here we report the thermal safety margin (difference between photosystem II thermotolerance (T₅₀) and maximum leaf temperature) during the beginning of the dry season for four tree species co-occurring across the forest-savanna transition zone in Brazil, a region which has warmed particularly rapidly over the recent decades. The species selected are evergreen in forests but deciduous in savannas. We find that thermotolerance declines with growth temperature >40 °C for individuals in the savannas. Current maximum leaf temperatures exceed T₅₀ in some species and will exceed T₅₀ in a 2.5 °C warmer world in most species evaluated. Despite plasticity in leaf thermal traits to increase leaf cooling in hotter environments, the results show this is not sufficient to maintain a safe thermal safety margin in hotter savannas. Overall, the results suggest that tropical forests may become increasingly deciduous and savanna-like in the future.

Indonesia has been the largest supplier of palm oil since 2007, and now supplies around 56% of the global market. While the existing literature has paid serious attention to the diverse impacts of oil palm plantation on socioeconomic factors and the environment, less is known about the joint role of biophysical and socioeconomic factors in shaping the temporal and spatial dynamics of oil palm expansion. This research investigates how the benefits and costs of converting other land use/ land cover (LULC) types to oil palm plantation affects these expansion patterns. We employ a spatial panel modeling approach to assess the contributions of biophysical and socioeconomic driving factors. Our modeling focuses on Sumatra and Kalimantan, two islands which have accounted for more than 90% of oil palm expansion in Indonesia since 1990, with Sumatra holding the majority of the country's plantations, and Kalimantan having the highest growth rate since 2000. The results show that the expansion in Kalimantan, which has been strongly stimulated by the export value of palm oil products, has occurred in areas with better biophysical suitability and infrastructure accessibility, following the 'pecking order' sequence, whereby more productive areas are already occupied by existing agriculture and plantations, and avoiding areas with high environmental values or socioeconomic costs. As demand for palm oil continues to grow, and land resources become more limited, the expansion in Kalimantan will tend towards the dynamics observed in Sumatra, with plantation expanding into remote and fertile areas with high conversion costs or legal barriers. Bare ground seems to have served as a clearing-up tactic to meet the procedural requirements of oil palm plantation for sustainable development. This research facilitates the improved projection of potential areas liable to future expansion, and the development of strategies to manage the leading drivers of LULC in Indonesia.

Steppes on the Mongolian Plateau, mainly within the Republic of Mongolia and the Inner Mongolia Autonomous Region (IMAR) of China, have been subjected to widespread degradation as a result of climate change and human utilization. Field experiments and long-term observations suggest that the productivity of degraded grassland ecosystems might show greater instability, i.e. stronger interannual variation in vegetation activities, when driven by climate change. However, it remains unknown whether this hypothesized destabilization of steppe vegetation activity has occurred in the past three decades and how this destabilization has fed back to livestock production on the plateau. Herein, we define temporal instability of vegetation activity using three indicators, the start and end of the growing season as indicated by the normalized difference vegetation index (NDVI) and the mean growing-season NDVI, and examine their trends between 1983 and 2015. Our results show a significant destabilization of vegetation activity over a large proportion of the total steppe area. Compared with the IMAR, vegetation destabilization has occurred to a significantly higher extent in Mongolia. Climate warming, drying and interannual climate variability accounted for approximately 60%–80% of the vegetation destabilization. The destabilization of steppe productivity was significantly associated with the interannual variability of livestock production in Mongolia, while the interannual variability of steppe productivity and livestock production were decoupled in the IMAR. Our findings highlight the need to improve livestock production systems and conserve degraded grasslands for sustainable development in view of the destabilization of steppe productivity on the Mongolian Plateau.

Societies worldwide make large investments in the sustainability of integrated human-freshwater systems, but uncertainty about water supplies under climate change poses a major challenge. Investments in infrastructure, water regulation, or payments for ecosystem services may boost water availability, but may also yield poor returns on investment if directed to locations where water supply unexpectedly fluctuates due to shifting climate. How should investments in water sustainability be allocated across space and among different types of projects? Given the high costs of investments in water sustainability, decision-makers are typically risk-intolerant, and considerable uncertainty about future climate conditions can lead to decision paralysis. Here, we use mathematical optimization models to find Pareto-optimal satisfaction of human and environmental water needs across a large drought-prone river basin for a range of downscaled climate projections. We show how water scarcity and future uncertainty vary independently by location, and that joint consideration of both factors can provide guidance on how to allocate water sustainability investments. Locations with high water scarcity and low uncertainty are good candidates for high-cost, high-reward investments; locations with high scarcity but also high uncertainty may benefit most from low regret investments that minimize the potential for stranded assets if water supply increases. Given uncertainty in climate projections in many regions worldwide, our analysis illustrates how explicit consideration of uncertainty may help to identify the most effective strategies for investments in the long-term sustainability of integrated human-freshwater systems.

Precipitation is changing as the climate warms, and downpours can become more intense due to the increased water holding capacity of the atmosphere. However, the exact nature of the precipitation response and its characteristics is still not well understood due to the complex nature of the physical processes underlying the formation of clouds and precipitation. In this study, present and future Norwegian climate is simulated at convection-permitting scales with a regional climate model. The future climate is a high emission scenario at the middle of the century. Hourly precipitation is separated into three categories (convective, stratiform, and orographically enhanced stratiform) using a physically-based algorithm. We investigate changes in the frequency, intensity and duration of precipitation events for each category, delivering a more nuanced insight into the precipitation response to a changing climate. Results show very strong seasonality, with significant intensification of autumn precipitation. An increase in convective precipitation frequency and intensity dominates the climate change signal regardless of season. While changes in winter and summer are well explained by thermodynamical theory, the precipitation response in autumn and spring deviates from the idealised thermodynamic response, partly owing to changes in cloud microphysics. These results show that changes in the precipitation distribution are affected in complex ways by the local climatology, terrain, seasonality and cloud processes. They illustrate the need for further and more detailed investigations about physical processes underlying projected precipitation changes and their seasonal and regional dependence.

We investigate tropical cyclone (TC) activity and intensity within a 100 km radius of Bermuda between 1955 and 2019. The results show a more easterly genesis over time and significant increasing trends in TC intensity (maximum wind speed (Vmax)) with a decadal Vmax median value increase of 30 kts from 33 to 63 kts (r = 0.94, p = 0.02), together with significant increasing August, September, October sea surface temperature (SST) of 1.1 °C (0.17 °C per decade) r = 0.4 (p < 0.01) and increasing average ocean temperature between 0.5 °C and 0.7 °C (0.08 °C–0.1 °C per decade) r = 0.3(p < 0.01) in the depth range 0–300 m. The strongest correlation is found between TC intensity and ocean temperature averaged through the top 50 m ocean layer ($\overline {{T_{{\text{50}}\,{\text{m}}}}}$) r = 0.37 (p < 0.01). We show how TC potential intensity (PI) estimates are closer to actual intensity by using $\overline {{T_{50\,{\text{m}}}}}$ as opposed to SST using the Hydrostation S time-series. We modify the widely used SST PI index by using $\overline {{T_{{\text{50}}\,{\text{m}}}}}$ to provide a closer estimate of the observed minimum sea level pressure (MSLP), and associated Vmax than by using SST, creating a $\overline {{T_{{\text{50}}\,{\text{m}}}}} { }$ PI ($\overline {{T_{{\text{50}}\,{\text{m}}}}}$_PI) index. The average MSLP difference is reduced by 12 mb and proportional (r = 0.74, p < 0.01) to the SST/$\overline {{T_{50\,{\text{m}}}}}$ temperature difference. We also suggest the index could be used over a wider area of the subtropical/tropical Atlantic where there is a shallow mixed layer depth.

Nitrification is a major pathway of N₂O production in aerobic soils. Measurements and model simulations of nitrification and associated N₂O emission are challenging. Here we innovatively integrated data mining and machine learning to predict nitrification rate (${R_{{\text{nit}}}}$) and the fraction of nitrification as N₂O emissions (${f_{{{\text{N}}_{\text{2}}}{{\text{O}}_{{\text{Nit}}}}}}$). Using our global database on ${R_{{\text{nit}}}}$ and ${f_{{{\text{N}}_{\text{2}}}{{\text{O}}_{{\text{Nit}}}}}}$, we found that the machine-learning based stochastic gradient boosting (SGB) model outperformed three widely used process-based models in estimating ${R_{{\text{nit}}}}$ and N₂O emission from nitrification. We then applied the SGB technique for global prediction. The potential ${R_{{\text{nit}}}}$ was driven by long-term mean annual temperature, soil C/N ratio and soil pH, whereas ${f_{{{\text{N}}_{\text{2}}}{{\text{O}}_{{\text{Nit}}}}}}$ by mean annual precipitation, soil clay content, soil pH, soil total N. The global ${f_{{{\text{N}}_{\text{2}}}{{\text{O}}_{{\text{Nit}}}}}}$ varied by over 200 times (0.006%–1.2%), which challenges the common practice of using a constant value in process-based models. This study provides insights into advancing process-based models for projecting N dynamics and greenhouse gas emissions using a machine learning approach.

Fisheries are coupled human–natural systems locally, regionally, and globally. However, human–nature interactions within and between adjacent and distant systems (metacouplings) are rarely studied in fisheries despite their prevalence and policy relevance. We filled this knowledge gap by using network models to identify how the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has rewired couplings and reshaped resilience of Fishadelphia, a community-supported fishery program (CSF) in New Jersey and Pennsylvania, USA. As abstractions illustrating interactions among supply-chain actors, networks are helpful for characterizing flows and assessing resilience to disturbances such as those induced by the SARS-CoV-2 pandemic. Since Fall 2018, 18 seafood (finfish and shellfish) species totaling 6273 lbs have flowed from harvesters (n = 4), to processors (n = 2), to a distributor, to retailers (n = 2), and finally to customers (n = 183). The pandemic reduced the number of seafood harvesters and processors (−50%), seafood flow quantity (−25%), species diversity in the marketplace (−67%), and species per supplier (−50%) before stopping flows in mid-March 2020, when Fishadelphia closed for 3 months. Models of network optimality indicated that the pandemic fragmented metacouplings that previously allowed multiple seafood suppliers to provide diverse products to customers. However, demand-side resilience increased through dispersed, socially distanced, efficient seafood delivery that expanded the customer base and generally increased customer satisfaction. This resilience dichotomy—wherein the post-closure network was less resilient than the pre-closure network in supply-side species diversity, but more resilient in demand-side social distancing, delivery efficiency, and customer satisfaction—has implications for rewiring networks to sustain CSFs and other local food systems amid ecological and social disturbances.

Coal has historically been a primary energy source in the United States (U.S.). The byproducts of coal combustion, such as fine particulate matter (PM_2.5), have increasingly been associated with adverse birth outcomes. The goal of this study was to leverage the current progressive transition away from coal in the U.S. to assess whether coal PM_2.5 is associated with preterm birth (PTB) rates and whether this association differs by maternal Black/White race/ethnicity. Using a novel dispersion modeling approach, we estimated PM_2.5 pollution from coal-fired power plants nationwide at the county-level during the study period (2000–2018). We also obtained county-level PTB rates for non-Hispanic White and non-Hispanic Black mothers. We used a generalized additive mixed model to estimate the relationship between coal PM_2.5 and PTB rates, overall and stratified by maternal race. We included a natural spline to allow for non-linearity in the concentration–response curve. We observed a positive non-linear relationship between coal PM_2.5 and PTB rate, which plateaued at higher levels of pollution. We also observed differential associations by maternal race; the association was stronger for White women, especially at higher levels of coal PM_2.5 (>2.0 μg m⁻³). Our findings suggest that the transition away from coal may reduce PTB rates in the U.S.

This article analyzes the large-scale circulations producing daily precipitation extremes in the Southwestern Alps and their trends from 1958 to 2017. We consider a high-resolution precipitation data set of 1 × 1 km² and the weather patterns associated to the precipitation seasonal maxima at each grid point. The high-resolution allows us to analyze in details the atmospheric influences triggering seasonal maxima. Four influences are considered—the Atlantic influence, the Mediterranean influence, the northeast circulation and the Anticyclonic situation. We show that influences on maxima are very well organized in space but their organization depends on the season. Maxima are very mainly triggered by two types of influences in the region—the Atlantic influence and the Mediterranean influence. Trends in weather patterns producing maxima are also organized in space, with opposite trends for the Atlantic and the Mediterranean influences. The Mediterranean influence retreated very significantly over the period in winter and spring, while the Atlantic influence significantly extended further south. In autumn the Mediterranean influence strengthened where it was already dominant.

As more countries commit to emissions reductions by midcentury to curb anthropogenic climate change, decarbonization of the electricity sector becomes a first-order task in reaching this goal. Renewables, particularly wind and solar power, will be predominant components of this transition. How availability of the wind and solar resource will change in the future in response to regional climate changes is an important and underdiscussed topic of the decarbonization process. Here, we study changes in potential for wind power in China and India, evaluating prospectively until the year 2060. To do this, we study a downscaled, high-resolution multimodel ensemble of CMIP5 models under high and low emissions scenarios. While there is some intermodel variability, we find that spatial changes are generally consistent across models, with decreases of up to 965 (a 1% change) and 186 TWh (a 2% change) in annual electricity generation potential for China and India, respectively. Compensating for the declining resource are weakened seasonal and diurnal variabilities, allowing for easier large-scale wind power integration. We conclude that while the ensemble indicates available wind resource over China and India will decline slightly in the future, there remains enormous potential for significant wind power expansion, which must play a major role in carbon neutral aspirations.

The 2015 Paris Agreement led to a number of studies that assessed the impact of the 1.5 °C and 2.0 °C increases in global temperature over preindustrial levels. However, those assessments have not actively investigated the impact of these levels of warming on fire weather. In view of a recent series of high-profile wildfire events worldwide, we access fire weather sensitivity based on a set of multi-model large ensemble climate simulations for these low-emission scenarios. The results indicate that the half degree difference between these two thresholds may lead to a significantly increased hazard of wildfire in certain parts of the world, particularly the Amazon, African savanna and Mediterranean. Although further experiments focused on human land use are needed to depict future fire activity, considering that rising temperatures are the most influential factor in augmenting the danger of fire weather, limiting global warming to 1.5 °C would alleviate some risk in these parts of the world.

The climate mitigation potential of urban nature-based solutions (NBSs) is often perceived as insignificant and thus overlooked, as cities primarily pursue NBSs for local ecosystem services. Given the rising interest and capacities in cities for such projects, the potential of urban forests for climate mitigation needs to be better understood. We modelled the global potential and limits of urban reforestation worldwide, and find that 10.9 ± 2.8 Mha of land (17.6% of all city areas) are suitable for reforestation, which would offset 82.4 ± 25.7 MtCO₂e yr⁻¹ of carbon emissions. Among the cities analysed, 1189 are potentially able to offset >25% of their city carbon emissions through reforestation. Urban natural climate solutions should find a place on global and local agendas.

Recent wildfires in the western United States have led to substantial economic losses and social stresses. There is a great concern that the new climatic state may further increase the intensity, duration, and frequency of wildfires. To examine temporal and spatial features of historical wildfire trends and future changes, a common fire index, the Keetch–Byram Drought Index (KBDI), is calculated over the contiguous United States (CONUS) and Alaska. We introduce an efficient initialization method and calculate 36 years (1982–2017) of historical KBDI at 4 km using a high-quality observational dataset. KBDIs are also calculated at 12 km using regional climate models and extended into the mid- and late-21st century. Based on the observational data, annual mean (and 95th percentile) KBDI over forested regions in the southwestern and northwestern CONUS has risen since 1982 at a rate of 5.2 (4.0) and 2.9 (3.3) per year, respectively, indicating a persistent drying trend fostering fire activities; the number of days exceeding the top 5% historical KBDI has increased by 16 and 25 d in the 36 years. Multiple regional climate model simulations project increasing wildfire potential and longer fire seasons over broader areas based on the estimated KBDI for the mid- and late-21st century. By the end of the century, most of the CONUS would experience about 90–189 more days per year exceeding the historical local maximum KBDI; areas with high KBDI (>600), whose occurrence correlates with large burned area, are projected to broaden by nearly 60 times in the southern CONUS. While both temperature and precipitation contribute to future KBDI changes, warming is the main driver of more frequent, intense and wide-spread extreme wildfires indicated by high KBDIs in future projections.

Surface soil moisture (SSM) is a key factor for water and heat exchanges between land surface and the atmosphere. It is also important to water resources, agriculture, and ecosystems. In the backdrop of global warming, SSM variations and potential causes are not well-known at regional scales. Based on soil moisture (SM) data from GLDAS-Noah and 16 global climate models (GCMs) selected from 25 GCMs in CMIP5, we analyzed spatial distribution and temporal changes of SSM in China and quantified fractional contributions of four meteorological factors to the SSM variations. The selected models have the same direction of historic trends in SSM during 1981–2005 as those in the GLDAS SSM data which were also further used to calibrate the trends simulated by the 16 GCMs. Based on the calibration results for the 16 GCMs, future SSMs for nine regions were analyzed in mainland China under four Intergovernmental Panel on Climate Change emission scenarios. No significant changes were identified in SSM across most regions of mainland China under RCP2.6 scenario. However, there is a general wetting tendency in the arid regions and drying tendency across the humid regions under all the scenarios except RCP2.6. In general, the higher the global temperature raises, the more grids with significant increase or significant decrease in SSM. These findings contradicted prevailing view that wet regions get wetter and dry regions get drier. Attribution analysis indicates that precipitation acts as the major driver for SSM variations and contributes up to 43.4% of SSM variations across China. These results provide new insights into future SSM response to climate warming and a scientific basis to mitigation and adaptation works related to SSM in the future.

The recent dry and warm years in Europe are often assessed as extreme in terms of socio-economic and environmental losses. However, the impact of a drought is a function of its duration. This fact needs to be considered in the evaluation of a drought. In this study, we use a hydrological model to analyze the 2018 European drought, an event that significantly affected vegetation. We show that the severity of the soil moisture drought is high in Central Europe and Mediterranean, but it does not reach the levels observed in the first half of the 20th century. Nevertheless, we demonstrate that when the multi-year 2014–2018 period is considered, its soil moisture drought severity is exceptional in a 253 year period, especially for Central Europe. While single-year droughts can be sustained by ecosystems like forests, the repeated stress exposure of such multi-year droughts may have significant impacts on their functioning. This is already evident in some regions of Central Europe, e.g. in the Czech Republic, Germany, and Poland.

The COVID-19 pandemic has led to an unprecedented decline in global air transport and associated reduction in CO₂ emissions. The International Civil Aviation Organization (ICAO) reacted by weakening its own CO₂-offsetting rules. Here we investigate whether the pandemic can be an opportunity to bring the sector on a reliable low-carbon trajectory, with a starting point in the observed reduction in air transport demand. We model a COVID-19 recovery based on a feed-in quota for non-biogenic synthetic fuels that will decarbonize fuels by 2050, as well as a carbon price to account for negative externalities and as an incentive to increase fuel efficiency. Results suggest that until 2050, air transport demand will continue to grow, albeit slower than in ICAO's recovery scenarios, exceeding 2018 demand by 3.7–10.3 trillion RPK. Results show that synthetic fuels, produced by 14–20 EJ of photovoltaic energy, would make it possible to completely phase out fossil fuels and to avoid emissions of up to 26.5 Gt CO₂ over the period 2022–2050.

Many studies have linked temperature with respiratory deaths, but epidemiological evidence of temperature-attributable years of life lost (YLL) from respiratory diseases is limited. Daily respiratory YLL rates were calculated using mortality data from 364 locations of China during 2006–2017, and meteorological data were collected for the same period. First, the distributed lag non-linear model (DLNM) was applied to estimate specific temperature-respiratory YLL rate associations in each location. Then multivariable meta-analysis was conducted to pool the location-specific estimates. Finally, we calculated the average life loss per death (LLD) to quantify the respiratory mortality burden of non-optimal temperature. Subgroup analyses were conducted by gender, age, region and cause of death. Inversely J-shaped association was observed between non-optimal temperature and respiratory YLL rate in China. The minimum YLL-rate temperature was 26.9 °C nationwide. An average of 1.37 years (95% CI: 1.06–1.65) LLD was attributable to non-optimal temperatures with 2.06 years (95% CI: 1.57–2.60) for pneumonia, 2.03 years (95% CI: 1.76–2.31) for chronic lower respiratory infections (LRTI), 0.88 years (95% CI: 0.65–1.09) for chronic obstructive pulmonary disease (COPD), most of which was attributed to moderate cold (0.73 years, 95% CI: 0.65–0.80). LLD caused by non-optimal temperature was higher in males, the young, and north China. Exposure to non-optimal temperature increases respiratory YLL rate in China, most of which were attributed to moderate cold. People with respiratory diseases including pneumonia, chronic LRTI and COPD are vulnerable to non-optimal temperature exposure. The result of this study provides useful information to reduce temperature-related respiratory disease burden.

The semi-arid Sahel is a global hotspot for poverty and malnutrition. Rainfed agriculture is the main source of food and income, making the well-being of rural population highly sensitive to rainfall variability. Studies have reported an upward trend in annual precipitation in the Sahel since the drought of the 1970s and early '80s, yet farmers have questioned improvements in conditions for agriculture, suggesting that intraseasonal dynamics play a crucial role. Using high-resolution daily precipitation data spanning 1981–2017 and focusing on agriculturally-relevant areas of the Sahel, we re-examined the extent of rainfall increase and investigated whether the increases have been accompanied by changes in two aspects of intraseasonal variability that have relevance for agriculture: rainy season duration and occurrence of prolonged dry spells during vulnerable crop growth stages. We found that annual rainfall increased across 56% of the region, but remained largely the same elsewhere. Rainy season duration increased almost exclusively in areas with upward trends in annual precipitation (23% of them). Association between annual rain and dry spell occurrence was less clear: increasing and decreasing frequencies of false starts (dry spells after first rains) and post-floral dry spells (towards the end of the season) were found to almost equal extent both in areas with positive and those with no significant trend in annual precipitation. Overall, improvements in at least two of the three intraseasonal variables (and no declines in any) were found in 10% of the region, while over a half of the area experienced declines in at least one intraseasonal variable, or no improvement in any. We conclude that rainfall conditions for agriculture have improved overall only in scattered areas across the Sahel since the 1980s, and increased annual rainfall is only weakly, if at all, associated with changes in the agriculturally-relevant intraseasonal rainfall characteristics.

Convective weather such as thunderstorms and rain is one of the main causes of irregular flights including delays, cancelations, turnbacks and diversions. In China, summer (April–September) flights accounted for 94% of irregular flights due to convective weather in 2016–2019. The impact of summer convective weather conditions on irregular flights is however not well understood. In this research, we find that thunderstorms, as indicated by the lifted index (LI), are greatly related to these irregular flights over Southeast China. The global climate model ensemble indicates there will be robust increases in the occurrence of convective weather environments in response to further global warming. We also find that as the LI is decreasing over time, the likelihood of thunderstorm-related irregular flights is increasing. Such an increase indicates there will be a 17% increase in irregular flights by the end of the century.

Nitrogen (N) is a vital input to crop production, but its excess use is a cause of environmental and human health problems in many parts of the world. In the United States (US), as in other nations, reducing N pollution remains challenging. Developing effective N policies and programs requires understanding links between cropland N balances (i.e. N inputs minus N harvested in crops) and potential contributing factors. We present novel insights into these links using a national county-level assessment and propose a criteria-scoring method to inform US N policy and programs. First, we characterize cropland N balances across the US in 2011–2013 and identify counties (∼25%) where N input reductions are less likely to result in crop yield declines. Second, we identify agronomic, environmental, social, demographic, and economic factors correlated with N balance, as well as counties that are underperforming based on these characteristics. Finally, we employ criteria scoring and hot spot analysis to identify 20 spatial clusters of opportunity for improved cropland nitrogen management. These hot spots collectively account for ∼63% of total surplus N balance for croplands but only ∼24% of cropland area in the US. N flows for these hot spots indicate variable opportunities across the US landscape to improve cropland N balances by reducing N fertilizer use, better managing manure N, and/or increasing N use efficiency. These findings can guide future efforts to integrate N balance into regulatory and voluntary frameworks in US policy and programs.

Water quality has suffered as humans have increased nutrient inputs across the landscape. In many cases, management actions to reduce nutrient inputs have not been met with concomitant ecosystem responses. These missed expectations are partly due to the continued slow delivery of nutrient-enriched groundwater pre-dating input reductions resulting from management actions. Land use legacies as expressed through this time lag are important to quantify in order to adjust management expectations. We present a novel coupling of nitrogen source maps with groundwater transport times to create a high-resolution (120 m) fully distributed estimate of the timing and magnitude of groundwater nitrogen deliveries to surface water across Michigan's Lower Peninsula. This new view of the landscape has been designed around common management timelines for: elected officials looking to make a difference for re-election (<5 years), career managers hoping to see the fruits of their labor (5–30 years), and advocacy groups whose work can span generations (>30 years). One striking result is that after 100 years, in our study area, approximately 50% of the nitrogen that enters the groundwater system remains in transit. This means that actions taken now may not show the expected lower nitrogen loads to receiving ecosystems for decades to centuries. We show that differences in groundwater travel times create a heterogeneous patchwork over which managers can prioritize actions to best match their targeted response times. Across the highest nitrogen inputs in our study region, less than 10% had short enough groundwater legacies to match the management timeline of most government and agency work. Agricultural practices (manure and chemical fertilizer) are the main nitrogen contributors across the top three management classes; however, human contributions through septic tank effluent and lawn fertilizers contribute 5%–8% of nitrogen.

Allergic diseases are a major public health problem globally and are increasing. The impacts of climate change on aeroallergens such as pollen and fungal spores and allergic respiratory diseases such as allergic asthma and allergic rhinitis have been considered since the early years of climate change and human health research, and exploration of this topic has accelerated over the past decade or so. This review examines the impacts of climate change on aeroallergens, including interactions with air pollutants, and the resulting impacts on allergic respiratory diseases. It discusses mitigation and adaptation in this context. It does this with a focus on advances over the last 2 years (2019 and 2020) to highlight research at the frontier of this field. It also explores the growing recognition of the need for a more holistic and integrated approach to environmental monitoring and exposure and presents the concept of the aeroexposome as a frame through which these impacts of climate change and responses to them could be viewed moving forward. As the evidence of impacts of climate change on aeroallergen production and atmospheric concentration, seasonality, distribution, and allergenicity mounts, crucial research demonstrating the resulting impacts on health outcomes such as aeroallergen sensitisation prevalence, asthma emergency department visits, and asthma hospitalisations is now emerging. It is vital that the momentum of the last decade continue with research to fill the many gaps that remain in our knowledge of this complex topic—refining analytical techniques, broadening the geographical coverage (to include, for example, the Southern Hemisphere), and more explicitly exploring the impacts of climate change on indoor aeroallergens.

The biological mechanisms behind health effects of air pollution have not been well known. Inflammation plays an important role in occurrence and development of a wide range of diseases. In this study, we assessed the effects of short-term exposure to ambient air pollution on systemic inflammatory biomarkers among 12 508 participants who underwent routine physical examination annually at the Hebei General Hospital in Shijiazhuang, China. For each participant, white blood cell count (WBC), lymphocytes, neutrophils and eosinophils were measured for two or three times during September 2016 to December 2018. Daily concentrations of nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃) and particulate matter less than 2.5 µm in aerodynamic diameter (PM_2.5) were interpolated to each district, where the participants worked. The linear mixed-effects regression with a constrained distributed lag model was applied to examine the associations between air pollution and inflammatory biomarkers during lag 0–14 d. It was observed that WBC, neutrophils and eosinophils [percent change (%Δ) and 95% confidence interval (95%CI)] significantly decreased by −0.07 (−0.11, −0.04), −0.08 (−0.12, −0.03) and −0.15 (−0.25, −0.05) at lag 14 d, associated with per 10 µg m⁻³ increase in O₃. WBC, lymphocytes and eosinophils (%Δ and 95%CI) significantly elevated by 0.08 (0.04, 0.12), 0.16 (0.11, 0.21) and 0.22 (0.10, 0.35) at lag 0 d, associated with per 10 µg m⁻³ increase in PM_2.5. This study reveals short-term effects of air pollution on systemic inflammatory biomarkers in routine blood test, which is helpful for further study to explore the biological mechanisms.

Arctic tundra exhibits large landscape heterogeneity in microtopography, hydrology, and active layer depth. While many carbon flux measurements and experiments are done at or below the mesoscale (⩽1 km), modern ecosystem carbon modeling is often done at scales of 0.25°–1.0° latitude, creating a mismatch between processes, process input data, and verification data. Here we arrange the naturally complex terrain into mesoscale landscape types of varying microtopography and moisture status to evaluate how landscape types differ in terms of CO₂ and CH₄ balances and their combined warming potential, expressed as CO₂ equivalents (CO₂-eq). Using a continuous 4 year dataset of CO₂ and CH₄ fluxes obtained from three eddy covariance (EC) towers, we investigate the integrated dynamics of landscape type, vegetation community, moisture regime, and season on net CO₂ and CH₄ fluxes. EC towers were situated across a moisture gradient including a moist upland tundra, a heterogeneous polygon tundra, and an inundated drained lake basin. We show that seasonal shifts in carbon emissions buffer annual carbon budget differences caused by site variability. Of note, high growing season gross primary productivity leads to higher fall zero-curtain CO₂ emissions, reducing both variability in annual budgets and carbon sink strength of more productive sites. Alternatively, fall zero-curtain CH₄ emissions are equal across landscape types, indicating site variation has little effect on CH₄ emissions during the fall despite large differences during the growing season. We find that the polygon site has the largest mean warming potential (107 ± 8.63 g C–CO₂-eq m⁻² yr⁻¹) followed by the drained lake basin site (82.12 ± 9.85 g C–CO₂-eq m⁻² yr⁻¹) and the upland site (77.19 ± 21.8 g C–CO₂-eq m⁻² yr⁻¹), albeit differences were not significant. The highest temperature sensitivities are also at the polygon site with mixed results between CO₂ and CH₄ at the other sites. Results show a similar mean annual net warming effect across dominant landscape types but that these landscape types vary significantly in the amounts and timing of CO₂ and CH₄ fluxes.

The effectiveness of many stream restorations in improving water quality is unmeasured. In the Mid-Atlantic region of the United States, activity by European settlers resulted in upland erosion and deposition of sediments 1–3 m in thickness in stream valleys. Subsequently, streams incised those legacy sediments creating steep, exposed banks, infrequent floodplain inundation, and water tables disconnected from floodplains. Legacy sediment removal (LSR) and floodplain reconnection (FR) proposes water quality improvement by restoration to a hydrological state closer to pre-European. We investigated water quality at nine sites, six restored with LSR/FR and three comparison sites. Nitrogen baseflow concentrations and fluxes were elevated in urban and agricultural watersheds with little apparent effect due to restoration. Denitrification appeared to be constrained by carbon availability. Ion concentrations were elevated in all watersheds compared to a forested reference and represent a substantial ecological stressor for the post-restoration aquatic community. Storm event data from one site suggest small reductions in nutrient and sediment loads across the restored reach. High-frequency time series indicate that restoration effects are not observable at larger scales. The effects of restoration, particularly for denitrification, may not be observable for years and can be obscured by weather and climate-driven variability.

Thermokarst lakes accelerate deep permafrost thaw and the mobilization of previously frozen soil organic carbon. This leads to microbial decomposition and large releases of carbon dioxide (CO₂) and methane (CH₄) that enhance climate warming. However, the time scale of permafrost-carbon emissions following thaw is not well known but is important for understanding how abrupt permafrost thaw impacts climate feedback. We combined field measurements and radiocarbon dating of CH₄ ebullition with (a) an assessment of lake area changes delineated from high-resolution (1–2.5 m) optical imagery and (b) geophysical measurements of thaw bulbs (taliks) to determine the spatiotemporal dynamics of hotspot-seep CH₄ ebullition in interior Alaska thermokarst lakes. Hotspot seeps are characterized as point-sources of high ebullition that release ¹⁴C-depleted CH₄ from deep (up to tens of meters) within lake thaw bulbs year-round. Thermokarst lakes, initiated by a variety of factors, doubled in number and increased 37.5% in area from 1949 to 2009 as climate warmed. Approximately 80% of contemporary CH₄ hotspot seeps were associated with this recent thermokarst activity, occurring where 60 years of abrupt thaw took place as a result of new and expanded lake areas. Hotspot occurrence diminished with distance from thermokarst lake margins. We attribute older ¹⁴C ages of CH₄ released from hotspot seeps in older, expanding thermokarst lakes (¹⁴C_CH4 20 079 ± 1227 years BP, mean ± standard error (s.e.m.) years) to deeper taliks (thaw bulbs) compared to younger ¹⁴C_CH4 in new lakes (¹⁴C_CH4 8526 ± 741 years BP) with shallower taliks. We find that smaller, non-hotspot ebullition seeps have younger ¹⁴C ages (expanding lakes 7473 ± 1762 years; new lakes 4742 ± 803 years) and that their emissions span a larger historic range. These observations provide a first-order constraint on the magnitude and decadal-scale duration of CH₄-hotspot seep emissions following formation of thermokarst lakes as climate warms.

Large-scale airborne lidar data collections can be used to generate high-resolution forest aboveground biomass maps at the state level and beyond as demonstrated in early phases of NASA's Carbon Monitoring System program. While products like aboveground biomass maps derived from these leaf-off lidar datasets each can meet state- or substate-level measurement requirements individually, combining them over multiple jurisdictions does not guarantee the consistency required in forest carbon planning, trading and reporting schemes. In this study, we refine a multi-state level forest carbon monitoring framework that addresses these spatial inconsistencies caused by variability in data quality and modeling techniques. This work is built upon our long term efforts to link airborne lidar, National Agricultural Imagery Program imagery and USDA Forest Service Forest Inventory and Analysis plot measurements for high-resolution forest aboveground biomass mapping. Compared with machine learning algorithms (r² = 0.38, bias = −2.3, RMSE = 45.2 Mg ha⁻¹), the use of a linear model is not only able to maintain a good prediction accuracy of aboveground biomass density (r² = 0.32, bias = 4.0, RMSE = 49.4 Mg ha⁻¹) but largely mitigates problems related to variability in data quality. Our latest effort has led to the generation of a consistent 30 m pixel forest aboveground carbon map covering 11 states in the Regional Greenhouse Gas Initiative region of the USA. Such an approach can directly contribute to the formation of a cohesive forest carbon accounting system at national and even international levels, especially via future integrations with NASA's spaceborne lidar missions.

Evaluating the reduction in pollution caused by a sudden change in emissions is complicated by the confounding effect of weather variations. We propose an approach based on machine learning to build counterfactual scenarios that address the effect of weather and apply it to the COVID-19 lockdown of Lombardy, Italy. We show that the lockdown reduced background concentrations of PM_2.5 by 3.84 µg m⁻³ (16%) and NO₂ by 10.85 µg m⁻³ (33%). Improvement in air quality saved at least 11% of the years of life lost and 19% of the premature deaths attributable to COVID-19 in the region during the same period. The analysis highlights the benefits of improving air quality and the need for an integrated policy response addressing the full diversity of emission sources.

The recently proposed Green Deals and 'building back better' plans have affirmed the importance to make green transitions inclusive. This is particularly related to the labour market, which may witness significant changes. Empirically, this issue has until now received limited attention. The links between poverty and climate change are explored mainly through the lenses of climate change adaptation, or via the effects of rising energy prices on the purchasing power of poor households. We aim to address this gap by using results from a simulation of the global energy transition required to meet the 2-degree target, and compare this to a 6-degree baseline scenario. The simulation with a multi-regional input–output model finds that, overall, this transition results in a small net job increase of 0.3% globally, with cross-country heterogeneity. We complement this macro-level analysis with cross-country household data to draw implications of the effects on poverty through labour market outcomes. The few job losses will be concentrated in specific industries, while new jobs will be created in industries that currently witness relatively high in-work poverty rates, such as construction. We show that high in-work poverty in the industries of interest, and especially in middle-income countries, is often associated with low skills and an insufficient reach of social protection mechanisms. We conclude that green transitions must ensure that the jobs created are indeed decent including fair wages, adequate working conditions, sufficient social protection measures, and accessible to the vulnerable and poorest households.

Concern over the ecological damage of excess nitrogen has brought increased attention to the role of research institutions and universities in contributing to this problem. Institutions often utilize the concept of the ecological 'footprint' to quantify and track nitrogen emissions resulting from their activities and guide plans and commitments to reduce emissions. Often, large-scale changes and commitments to reduce nitrogen footprints are not feasible at small institutions due to monetary and manpower constraints. We partnered with managers in the dining and facilities departments at the Marine Biological Laboratory (MBL), a small research institution in Woods Hole, Massachusetts, to develop five low-effort strategies to address nitrogen emissions at the institution using only resources currently available within those departments. Each proposed strategy achieved emissions reductions in their sector and in the overall nitrogen footprint of the MBL. If all modelled strategies are applied simultaneously, the MBL can achieve a 7.7% decrease in its nitrogen footprint. Managers at MBL considered strategies that required no monetary input most feasible. The intersection of carbon and nitrogen emissions also means the modelled strategies had the co-benefit of reducing the MBL's carbon footprint, strengthening the argument for applying these strategies. This paper may serve as a model for similar institutions looking to reduce the ecological impact of their activities.

Satellite-based burned area products are accurate for many regions. However, only limited assessments exist for Indonesia despite extensive burning and globally important carbon emissions. We evaluated the accuracy of four MODIS-derived (moderate resolution imaging spectroradiometer) burned area products (MCD45A1 collection 5.1, MCD64A1 (collection 5.1 and 6), FireCCI51), and their sensitivity to burned-area size and temporal window length used for detection. The products were compared to reference burned areas from SPOT 5 imagery using error matrices and linear regressions. The MCD45A1 product detected <1% of burned areas. The other products detected 38%–48% of burned area with accuracies increasing modestly (45%–57%) when smaller burns (<100 ha) were excluded, with MCD64A1 C6 performing best. Except for the MCD45 product, linear regressions showed generally good agreement in peatlands (R² ranging from 0.6 to 0.8) but detections were less accurate in non-peatlands (R² ranging from 0.2 to 0.5). Despite having higher spatial resolution, the FireCCI51 product (250 m) showed lower accuracy (OE = 0.55–0.88, CE = 0.33–0.50) than the 500 m MCD64A1 C6 product (OE = 0.43–0.79, CE = 0.36–0.51) but it was comparable to the C5.1 product (OE = 0.52–0.91, CE = 0.37–0.67). Dense clouds and smoke limited the accuracies of all burned area products, even when the temporal window for detection was lengthened. This study shows that emissions calculations based on burned area in peatlands remain highly uncertain. Given the globally significant amount of emissions from burning peatlands, specific attention is required to improve burned area mapping in these regions in order for global emissions models to accurately reflect when, where, and how much emissions are occurring.

Milldams and their legacies have significantly influenced fluvial processes and geomorphology. However, less is known about their effects on riparian zone hydrology, biogeochemistry, and water quality. Here, we discuss the potential effects of existing and breached milldams on riparian nitrogen (N) processing through multiple competing hypotheses and observations from complementary studies. Competing hypotheses characterize riparian zone processes that remove (sink) or release (source) N. Elevated groundwater levels and reducing soil conditions upstream of milldams suggest that riparian zones above dams could be hotspots for N removal via denitrification and plant N uptake. On the other hand, dam removals and subsequent drops in stream and riparian groundwater levels result in drained, oxic soils which could increase soil nitrification and decrease riparian plant uptake due to groundwater bypassing the root zone. Whether dam removals would result in a net increase or decrease of N in riparian groundwaters is unknown and needs to be investigated. While nitrification, denitrification, and plant N uptake have typically received the most attention in riparian studies, other N cycle processes such as dissimilatory nitrate reduction to ammonium (DNRA) need to be considered. We also propose a novel concept of riparian discontinuum, which highlights the hydrologic and biogeochemical discontinuities introduced in riparian zones by anthropogenic structures such as milldams. Understanding and quantifying how milldams and similar structures influence the net source or sink behavior of riparian zones is urgently needed for guiding watershed management practices and for informed decision making with regard to dam removals.

Increasing trends in base cations, pH, and salinity of freshwaters have been documented in US streams over 50 years. These patterns, collectively known as freshwater salinization syndrome (FSS), are driven by multiple processes, including applications of road salt and human-accelerated weathering of impervious surfaces, reductions in acid rain, and other anthropogenic legacies of change. FSS mobilizes chemical cocktails of distinct elemental mixtures via ion exchange, and other biogeochemical processes. We analyzed impacts of FSS on streamwater chemistry across five urban watersheds in the Baltimore-Washington, USA metropolitan region. Through combined grab-sampling and high-frequency monitoring by USGS sensors, regression relationships were developed among specific conductance and major ion and trace metal concentrations. These linear relationships were statistically significant in most of the urban streams (e.g. R² = 0.62 and 0.43 for Mn and Cu, respectively), and showed that specific conductance could be used as a proxy to predict concentrations of major ions and trace metals. Major ions and trace metals analyzed via linear regression and principal component analysis showed co-mobilization (i.e. correlations among combinations of specific conductance (SC), Mn, Cu, Sr²⁺, and all base cations during certain times of year and hydrologic conditions). Co-mobilization of metals and base cations was strongest during peak snow events but could continue over 24 h after SC peaked, suggesting ongoing cation exchange in soils and stream sediments. Mn and Cu concentrations predicted from SC as a proxy indicated acceptable goodness of fit for predicted vs. observed values (Nash–Sutcliffe efficiency > 0.28). Metals concentrations remained elevated for days after SC decreased following snowstorms, suggesting lag times and continued mobilization after road salt use. High-frequency sensor monitoring and proxies associated with FSS may help better predict contaminant pulses and contaminant exceedances in response to salinization and impacts on aquatic life, infrastructure, and drinking water.

Satellite-based inverse modeling has the potential to drive aerosol precursor emissions, but its efficacy for improving chemistry transport models (CTMs) remains elusive because of its likely inherent dependence on the error characteristics of a specific CTM used for the inversion. This issue is quantitively assessed here by using three CTMs. We show that SO₂ emissions from global GEOS-Chem adjoint model and OMI SO₂ data, when combined with spatial variation of bottom-up emissions, can largely improve WRF-Chem and WRF-CMAQ forecast of SO₂ and aerosol optical depth (in reference to moderate resolution imaging spectroradiometer data) in China. This suggests that the efficacy of satellite-based inversion of SO₂ emission appears to be high for CTMs that use similar or identical emission inventories. With the advent of geostationary air quality monitoring satellites in next 3 years, this study argues that an era of using top-down approach to rapidly update emission is emerging for regional air quality forecast, especially over Asia having highly varying emissions.

Wildfire activity in the western United States (US) has been increasing, a trend that has been correlated with changing patterns of temperature and precipitation associated with climate change. Health effects associated with exposure to wildfire smoke and fine particulate matter (PM_2.5) include short- and long-term premature mortality, hospital admissions, emergency department visits, and other respiratory and cardiovascular incidents. We estimate PM_2.5 exposure and health impacts for the entire continental US from current and future western US wildfire activity projected for a range of future climate scenarios through the 21st century. We use a simulation approach to estimate wildfire activity, area burned, fine particulate emissions, air quality concentrations, health effects, and economic valuation of health effects, using established and novel methodologies. We find that climatic factors increase wildfire pollutant emissions by an average of 0.40% per year over the 2006–2100 period under Representative Concentration Pathway (RCP) 4.5 (lower emissions scenarios) and 0.71% per year for RCP8.5. As a consequence, spatially weighted wildfire PM_2.5 concentrations more than double for some climate model projections by the end of the 21st century. PM_2.5 exposure changes, combined with population projections, result in a wildfire PM2.5-related premature mortality excess burden in the 2090 RCP8.5 scenario that is roughly 3.5 times larger than in the baseline period. The combined effect of increased wildfire activity, population growth, and increase in the valuation of avoided risk of premature mortality over time results in a large increase in total economic impact of wildfire-related PM_2.5 mortality and morbidity in the continental US, from roughly $7 billion per year in the baseline period to roughly $36 billion per year in 2090 for RCP4.5, and $43 billion per year in RCP8.5. The climate effect alone accounts for a roughly 60% increase in wildfire PM2.5-related premature mortality in the RCP8.5 scenario, relative to baseline conditions.

Pediatric asthma incidence has been associated with exposure to nitrogen dioxide (NO₂) in ambient air. NO₂ is predominantly emitted through fossil fuel use in land transportation, power generation and the burning of solid biofuels in households. We simulated NO₂ with a global atmospheric chemistry model, combined with a land use regression model, to estimate NO₂ exposure in all countries worldwide. The global asthma incidence among children and adolescents attributable to NO₂ was estimated by deriving an exposure-response function from a meta-analysis which included epidemiological studies from multiple countries, baseline incidence rates from the Global Burden of Disease and gridded population data. The sectoral contribution to pediatric asthma from NO₂ exposure (NO₂-related asthma incidence: NINC) was estimated for different source categories to provide guidance to mitigation policies. We estimate 3.52 (2.1–6.0) million NINC per year globally, being about 14% of the total asthma incidence cases among children and adolescents. We find that emissions from land transportation are the leading contributor to NINC globally (∼44%), followed by the domestic burning of solid fuels (∼10.3%) and power generation from fossil fuels (∼8.7%). Biogenic emissions which are not anthropogenically induced may contribute ∼14% to the total NINC. Our results show large regional differences in source contributions, as the domestic burning of solid fuels is a main contributor to NINC in India and Nepal (∼25%), while emissions from shipping are the leading source in Scandinavian countries (∼40%), for example. While only 5% of all children and adolescents live in areas where NO₂ exceeds the WHO annual guideline of 21.25 ppb (40 μg m⁻³) for NO₂, about 90% of the NINC is found in regions that meet the WHO guideline, related to the uneven distribution of children and adolescents in the population. This suggests the need for stricter policies to reduce NO₂ exposure, and revisiting the current WHO guideline to reduce the health risks of children and adolescents.

The recent Environmental Research Letters article by Caesar, Rahmstorf and Feulner (hereafter CRF) is essentially a Comment on our Nature paper (Chen and Tung 2018 Nature559 387–91), but without an accompanying rebuttal from us. In this unusual format for the exchange outside Nature, our rebuttal then becomes a Comment here at Environmental Research Letters. Our original proposal that the rate of global warming is enhanced by a weak Atlantic Meridional Overturning Circulation (AMOC) remains valid and is strengthened with this exchange. CRF used "established evidence" to argue against our finding, but such evidence is either misapplied (i.e. applying model results from preindustrial control runs with constant greenhouse gasses to the industrial era with increasing greenhouse gasses), or misinterpreted (i.e. climate model results for the industrial era specifically for the trends interpreted as for the AMOC cycles). While we used the observed energy budget to show that a strong (weak) AMOC transports more (less) heat to below 200 m, CRF replaces the actual budget with a simple energy-balance equation. They used an inappropriate equilibrium approximation to their simple equation to argue that global mean surface temperature (GMST) and AMOC should be in phase. We show here that the exact solution to that same equation actually supports our claim on the relationship between the rate of change of GMST and the AMOC state, which they misunderstood as we claiming a negative correlation between GMST and AMOC themselves. They claimed, incorrectly, that a positive correlation coefficient, no matter how small and even though none of them is statistically significant, is strong evidence that the two time series are in phase. The correlation coefficients that they found using observational data (0.01, 0.28 and 0.45), though positive, correspond to $89^\circ ,74^\circ ,63^\circ$ out of phase, far from being in-phase. Visually they were made to look somewhat in-phase with decadal smoothing and short-period detrending. Both model and observational evidence supports the conclusion of our original paper that the period of AMOC minimum is a period of rapid rate of surface warming.

In their comment on our paper (Caesar et al 2020 Environ. Res. Lett.15 024003), Chen and Tung (hereafter C&T) argue that our analysis, showing that over the last decades Atlantic meridional overturning circulation (AMOC) strength and global mean surface temperature (GMST) were positively correlated, is incorrect. Their claim is mainly based on two arguments, neither of which is justified: first, C&T claim that our analysis is based on 'established evidence' that was only true for preindustrial conditions—this is not the case. Using data from the modern period (1947–2012), we show that the established understanding (i.e. deep-water formation in the North Atlantic cools the deep ocean and warms the surface) is correct, but our analysis is not based on this fact. Secondly, C&T claim that our results are based on a statistical analysis of only one cycle of data which was furthermore incorrectly detrended. This, too, is not true. Our conclusion that a weaker AMOC delays the current surface warming rather than enhances it, is based on several independent lines of evidence. The data we show to support this covers more than one cycle and the detrending (which was performed to avoid spurious correlations due to a common trend) does not affect our conclusion: the correlation between AMOC strength and GMST is positive. We do not claim that this is strong evidence that the two time series are in phase, but rather that this means that the two time series are not anti-correlated.