Table of contents

Vegetation composition shifts, and in particular, shrub expansion across the Arctic tundra are some of the most important and widely observed responses of high-latitude ecosystems to rapid climate warming. These changes in vegetation potentially alter ecosystem carbon balances by affecting a complex set of soil–plant–atmosphere interactions. In this review, we synthesize the literature on (a) observed shrub expansion, (b) key climatic and environmental controls and mechanisms that affect shrub expansion, (c) impacts of shrub expansion on ecosystem carbon balance, and (d) research gaps and future directions to improve process representations in land models. A broad range of evidence, including in-situ observations, warming experiments, and remotely sensed vegetation indices have shown increases in growth and abundance of woody plants, particularly tall deciduous shrubs, and advancing shrublines across the circumpolar Arctic. This recent shrub expansion is affected by several interacting factors including climate warming, accelerated nutrient cycling, changing disturbance regimes, and local variation in topography and hydrology. Under warmer conditions, tall deciduous shrubs can be more competitive than other plant functional types in tundra ecosystems because of their taller maximum canopy heights and often dense canopy structure. Competitive abilities of tall deciduous shrubs vs herbaceous plants are also controlled by variation in traits that affect carbon and nutrient investments and retention strategies in leaves, stems, and roots. Overall, shrub expansion may affect tundra carbon balances by enhancing ecosystem carbon uptake and altering ecosystem respiration, and through complex feedback mechanisms that affect snowpack dynamics, permafrost degradation, surface energy balance, and litter inputs. Observed and projected tall deciduous shrub expansion and the subsequent effects on surface energy and carbon balances may alter feedbacks to the climate system. Land models, including those integrated in Earth System Models, need to account for differences in plant traits that control competitive interactions to accurately predict decadal- to centennial-scale tundra vegetation and carbon dynamics.

In the context of strong evidence on mounting climate-related risks and impacts across the globe, the need for 'transformational change' in climate risk management and adaptation responses has been brought forward as an important element to achieve the Paris ambitions. In the past decade, the concept has experienced increasing popularity in policy debates and academic discussions but has seen heterogeneous applications and little practical insight. The paper aims to identify relevant perspectives on transformative approaches and transformational change in the context of climate risk management and adaptation to propose an actionable definition for practical application. Using a systematic search and review approach, we review different perspectives across policy and scientific publications, focusing on work published in the past decade and identify common features of what transformational change in the context of climate risk management and adaptation may involve. We show that different perspectives on transformational change in the context of climate risk management and adaptation persist, but certain areas of convergence are discernible. This includes understanding transformational change as part of a spectrum that begins with incremental change; involves climate risk management and adaptation measures focusing on deep-rooted, system-level change and tends to aim at enabling more just and sustainable futures; often oriented towards the long-term, in anticipation of future climate-related developments. In addition, we identify an 'operationalisation gap' in terms of translating transformational change ambitions into concrete transformative measures that can be replicated in practice.

Irrigation is critical to sustain agricultural productivity in dry or semi-dry environments, and center pivots, due to their versatility and ruggedness, are the most widely used irrigation systems. To effectively use center pivot irrigation systems, producers require tools to support their decision-making on when and how much water to irrigate. However, currently producers make these decisions primarily based on experience and/or limited information of weather. Ineffective use of irrigation systems can lead to overuse of water resources, compromise crop productivity, and directly reduce producers' economic return as well as bring negative impacts on environmental sustainability. In this paper, we surveyed existing precision irrigation research and tools from peer-reviewed literature, land-grant university extension and industry products, and U.S. patents. We focused on four challenge areas related to precision irrigation decision-support systems: (a) data availability and scalability, (b) quantification of plant water stress, (c) model uncertainties and constraints, and (d) producers' participation and motivation. We then identified opportunities to address the above four challenge areas: (a) increase the use of high spatial-temporal-resolution satellite fusion products and inexpensive sensor networks to scale up the adoption of precision irrigation decision-support systems; (b) use mechanistic quantification of 'plant water stress' as triggers to improve irrigation decision, by explicitly considering the interaction between soil water supply, atmospheric water demand, and plant physiological regulation; (c) constrain the process-based and statistical/machine learning models at each individual field using data-model fusion methods for scalable solutions; and (d) develop easy-to-use tools with flexibility, and increase governments' financial incentives and support. We conclude this review by laying out our vision for precision irrigation decision-support systems for center pivots that can achieve scalable, economical, reliable, and easy-to-use irrigation management for producers.

Long-lived capital-stocks (LLCS) such as infrastructure and buildings have significant and long-lasting implications for greenhouse gas emissions. They contribute to carbon lock-in and may hinder a rapid decarbonization of energy systems. Here we provide a systematic map of the literature on carbon lock-in induced by LLCS. Based on a structured search of the Web of Science and Scopus, we identified 226 publications from 38 095 search results using a supervised machine learning approach. We show biases toward power generation and toward developed countries. We also identify 11 indicators used to quantify carbon lock-in. Quantifications of committed emissions (cumulative emissions that would occur over the remaining operational lifetime of an asset) or stranded assets (premature retirement/retrofitting or under-utilization of assets along a given pathway) are the most commonly used metrics, whereas institutional indicators are scarcely represented. The synthesis of quantifications shows that (i) global committed emissions have slightly increased over time, (ii) coal power plants are a major source of committed emissions and are exposed to risk of becoming stranded, (iii) delayed mitigation action increases stranded assets and (iv) sectoral distribution and amount of stranded assets differ between countries. A thematic analysis of policy implications highlights the need to assure stability and legitimacy of climate policies and to enable coordination between stakeholders. Carbon pricing is one of the most cited policy instrument, but the literature emphasizes that it should not be the only instrument used and should instead be complemented with other policy instruments, such as technical regulations and financial support for low carbon capital deployment. Further research is warranted on urban-scale, in developing countries and outside the electricity generation sector, notably on buildings, where stranded assets could be high.

We undertake a systematic review of peer-reviewed literature to arrive at recommendations for shaping communications about uncertainty in scientific climate-related findings. Climate communications often report on scientific findings that contain different sources of uncertainty. Potential users of these communications are members of the general public, as well as decision makers and climate advisors from government, business and non-governmental institutions worldwide. Many of these users may lack formal training in climate science or related disciplines. We systematically review the English-language peer-reviewed empirical literature from cognitive and behavioral sciences and related fields, which examines how users perceive communications about uncertainty in scientific climate-related findings. We aim to summarize how users' responses to communications about uncertainty in scientific climate-related findings are associated with characteristics of the decision context, including climate change consequences and types of uncertainty as well as user characteristics, such as climate change beliefs, environmental worldviews, political ideology, numerical skills, and others. We also aimed to identify what general recommendations for communications about uncertainty in scientific climate-related findings can be delineated. We find that studies of communications about uncertainty in scientific climate-related findings substantially varied in how they operationalized uncertainty, as well as how they measured responses. Studies mostly focused on uncertainty stemming from conflicting information, such as diverging model estimates or experts, or from expressions of imprecision such as ranges. Among other things, users' understanding was improved when climate communications about uncertainty in scientific climate-related findings were presented with explanations about why climate information was uncertain, and when ranges were presented with lower and upper numerical bounds. Users' understanding also improved if they expressed stronger beliefs about climate change, or had better numerical skills. Based on these findings, we provide emerging recommendations on how to best present communications about uncertainty in scientific climate-related findings; and we identify research gaps.

Integrated assessment models (IAMs) have emerged as key tools for building and assessing long term climate mitigation scenarios. Due to their central role in the recent IPCC assessments, and international climate policy analyses more generally, and the high uncertainties related to future projections, IAMs have been critically assessed by scholars from different fields receiving various critiques ranging from adequacy of their methods to how their results are used and communicated. Although IAMs are conceptually diverse and evolved in very different directions, they tend to be criticised under the umbrella of 'IAMs'. Here we first briefly summarise the IAM landscape and how models differ from each other. We then proceed to discuss six prominent critiques emerging from the recent literature, reflect and respond to them in the light of IAM diversity and ongoing work and suggest ways forward. The six critiques relate to (a) representation of heterogeneous actors in the models, (b) modelling of technology diffusion and dynamics, (c) representation of capital markets, (d) energy-economy feedbacks, (e) policy scenarios, and (f) interpretation and use of model results.

Infrastructure-based heat reduction strategies can help cities adapt to high temperatures, but simulations of their cooling potential yield widely varying predictions. We systematically review 146 studies from 1987 to 2017 that conduct physically based numerical modelling of urban air temperature reduction resulting from green-blue infrastructure and reflective materials. Studies are grouped into two modelling scales: neighbourhood scale, building-resolving (i.e. microscale); and city scale, neighbourhood-resolving (i.e. mesoscale). Street tree cooling has primarily been assessed at the microscale, whereas mesoscale modelling has favoured reflective roof treatments, which are attributed to model physics limitations at each scale. We develop 25 criteria to assess contextualization and reliability of each study based on metadata reporting and methodological quality, respectively. Studies have shortcomings with respect to neighbourhood characterization, reporting areal coverages of heat mitigation implementations, evaluation of base case simulations, and evaluation of modelled physical processes relevant to heat reduction. To aid comparison among studies, we introduce two metrics: the albedo cooling effectiveness (ACE), and the vegetation cooling effectiveness (VCE). A sub-sample of 47 higher quality studies suggests that high reflectivity coatings or materials offer ≈0.2 °C–0.6 °C cooling per 0.10 neighbourhood albedo increase, and that trees yield ≈0.3 °C cooling per 0.10 canopy cover increase, for afternoon clear-sky summer conditions. VCE of low vegetation and green roofs varies more strongly between studies. Both ACE and VCE exhibit a striking dependence on model choice and model scale, particularly for albedo and roof-level implementations, suggesting that much of the variation of cooling magnitudes between studies may be attributed to model physics representation. We conclude that evaluation of the base case simulation is not a sufficient prerequisite for accurate simulation of heat mitigation strategy cooling. We identify a three-phase framework for assessment of the suitability of a numerical model for a heat mitigation experiment, which emphasizes assessment of urban canopy layer mixing and of the physical processes associated with the heat reduction implementation. Based on our findings, we include recommendations for optimal design and communication of urban heat mitigation simulation studies.

Resilience is the central concept for understanding how an ecosystem responds to a strong perturbation, and is related to other concepts used to analyze system properties in the face of change such as resistance, recovery, sustainability, vulnerability, stability, adaptive capacity, regime shift, and tipping point. It is extremely challenging to formulate resilience thinking into practice. The current state-of-art approaches of assessing ecosystem resilience may be useful for policy makers and ecosystem resource managers to minimize climatological or natural disaster related impacts. Here, we review the methods of assessing resilience and classify and limit them to three cases: (a) forest resilience based mainly on remote sensing and tree-ring data; (b) soil microbial community resilience based on laboratory and field studies; and (c) hydrological resilience of terrestrial biomes based on the Budyko framework and climate data.

Forests are major components of the global carbon (C) cycle and thereby strongly influence atmospheric carbon dioxide (CO₂) and climate. However, efforts to incorporate forests into climate models and CO₂ accounting frameworks have been constrained by a lack of accessible, global-scale synthesis on how C cycling varies across forest types and stand ages. Here, we draw from the Global Forest Carbon Database, ForC, to provide a macroscopic overview of C cycling in the world's forests, giving special attention to stand age-related variation. Specifically, we use 11 923 ForC records for 34 C cycle variables from 865 geographic locations to characterize ensemble C budgets for four broad forest types—tropical broadleaf evergreen, temperate broadleaf, temperate conifer, and boreal. We calculate means and standard deviations for both mature and regrowth (age < 100 years) forests and quantify trends with stand age in regrowth forests for all variables with sufficient data. C cycling rates generally decreased from tropical to temperate to boreal in both mature and regrowth forests, whereas C stocks showed less directional variation. Mature forest net ecosystem production did not differ significantly among biomes. The majority of flux variables, together with most live biomass pools, increased significantly with the logarithm of stand age. As climate change accelerates, understanding and managing the carbon dynamics of forests is critical to forecasting, mitigation, and adaptation. This comprehensive and synthetic global overview of C stocks and fluxes across biomes and stand ages contributes to these efforts.

Access to energy is a precondition for a decent standard of living. Some household decisions on energy consumption are however motivated to maintain or improve status, resulting in social zero-sum games, with environmentally harmful outcomes. Here, we review evidence relating status to energy consumption, elucidating consequential opportunities for climate change mitigation. To achieve this, we comprehensively collate and analyse existing published work that links status to household consumption decisions and behaviour across all end-use sectors, screening 2662 papers found with systematic search queries, identifying and fully reviewing 53 papers that comply with our criteria. We develop a systematic map of the literature and review quantitative and qualitative analysis relating energy end-use to status consumption. We identify 23 distinct (albeit some of them closely related) theories, with the literature most frequently referring to Veblen's theory of conspicuous consumption. We also detail estimations of status-related energy consumption and identify ten studies that quantitatively relate status to energy saving behaviour or decisions, and four studies that relate status to increased emissions. Status can explain up to 20% change in consumption levels or the willingness-to-pay for carbon reducing consumption. Surprisingly, we find that major status-related consumption decisions, such as for housing and big cars, are hardly captured by the literature that relates status consumption to energy use and greenhouse gas emissions. This is a considerable gap in the literature, omitting major sources of status related decisions with high carbon footprint. We conclude that framing energy saving behaviour as high status is a promising strategy for emission reduction. Progressive taxation of status items, such as floor space and vehicle size, can effectively internalize the positional externalities and signal social undesirability, but also reduce emissions.

Economic development, resource scarcity and climate change pose enormous challenges to the food–energy–water (FEW) nexus, calling for integrative resources governance to improve the synergy between subsystems. However, it is unclear about the synergy evolution of the FEW nexus in temporal and spatial scales. This paper uses the network analysis to explore the FEW nexus in China's Yangtze River Economic Belt. First, the comprehensive index system containing subsystems, order parameters and eigenvectors are determined in causal paths. Second, the synergetic network among order parameters is developed, and the centrality analysis is then conducted to identify the influencing factors. Third, the Bayesian network among eigenvectors is constructed to analyze the sensitivity of the dominant influencing factors. The results show that: (a) Energy subsystem has the highest centralities and dominates the FEW nexus. (b) From the perspective of time variability, the network centralization reaches the highest in 2007, but reaches the lowest in 2013, showing a downward trend, so we should adhere to the national strategy of synergetic development to realize the resource sustainability. (c) From the perspective of spatial sensitivity, upper reach (UR) is sensitive to food-related factors while lower reach (LR) is sensitive to energy-related factors. Therefore, the development of agriculture in upper UR should focus on protection, and the development of industry in LR should focus on remediation. The significance of the research is to construct a network analysis framework for better understanding the spatio-temporal variability of the FEW nexus in Yangtze River Economic Belt.

Economic growth is principally powered by energy fuels. While the potential energy transition pathways in developed countries are clear, they have not been well explored for developing countries. Here, we study the average annual growth rate of energy consumption in 12 aggregated regions during 2001–2017 and the driving factors behind that growth. The countries with high energy consumption growth rates were concentrated in Asia and North Africa and four of the top five regions were in Asia, while the energy consumption in developed countries was stable or even declined in that period. Therefore, based on a comprehensive consideration of factors such as population and economic development, to quantify the role of renewable energy, we analyze the long time series of energy consumption for China, India, Indonesia, Myanmar and Bangladesh since the 1970s. Despite economic development and population growth accelerating energy consumption substantially upward, energy intensity made energy consumption decrease. Coal and oil dominated the energy transition pathway in China and India, while biomass and natural gas dominated in Indonesia, Myanmar and Bangladesh. The amount of CO₂ emissions in different countries was closely related to the amount and type of the energy they used. Our research results emphasize the importance of improving energy efficiency and adjusting energy structure to reduce energy consumption and achieve sustainable development.

Land use changes are known to account for over 20% of human greenhouse gas emissions and tree cover losses can significantly influence land-climate dynamics. Land-climate feedbacks have been identified and evaluated for a long time. However, in addition to the direct effect of climate change on forest biomes, recent sparse evidence has shown that land use changes may increase as a result of weather shocks. In Western and Central Africa, agriculture is the main source of income and employment for rural populations. Economies rely on agricultural production, which is largely rainfed, and therefore dependent predominantly upon seasonal rainfall. In this article, I explore the impact of seasonal rainfall quality on deforestation, by combining high-resolution remotely-sensed annual tree cover loss, land cover, human activity and daily rainfall data. I show that in poor regions that are mainly reliant on rainfed agriculture, a bad rainy season leads to large deforestation shocks. These shocks notably depend on the proportion of agricultural land and on the remoteness of the areas in question, as remoteness determines the ability to import food and the existence of alternative income sources. In areas with significant forest cover, a short rainfall season leads to a 15% increase in deforestation. In unconnected areas with small proportions of crop area, the increase in deforestation reaches 20%. Findings suggest that a refined understanding of the land use changes caused by rainfall shocks might be used to improve the design and effectiveness of development, adaptation and conservation policies.

Tropical cyclone precipitation (TCP) has increasing impacts on many coastal regions under global warming. Causes of TCP variation have been principally explored in the troposphere. This study identifies the significant modulation of the stratosphere Quasi-Biennial oscillation (QBO) on the winter TCP in the coastal regions of the western North Pacific (WNP). In the westerly QBO winter, the zonal wind vertical shear anomalies in the stratosphere strengthen (weaken) convective activities around the East China Sea (the Philippines) and cause middle-level easterly (westerly) anomalies of the middle (low) latitudes in the troposphere, leading to more (less) TC activities around the East China Sea (the Philippines). Consequently, a TCP dipole pattern can be observed. The TCP increases in East China, Korean peninsula, Japan and Russian Far East, but decreases in Indo–China Peninsula, South China and the Philippines. These results not only improve the knowledge of QBO-TCP relationship but also provide a potential indicator for the seasonal prediction of the TCP in the coastal regions of the WNP due to the high predictability of the QBO.

The size of precipitation systems is an important parameter of precipitation process and dynamics. This study uses the latest Integrated Multi-satellitE Retrievals for Global Precipitation Measurement data during 2015–2019 to investigate the global distribution of precipitation system size, its spatial and temporal pattern, as well as its relationships with precipitation amount, frequency, intensity, and duration. Our results show that large precipitation systems (>10⁶ km²) occur more frequently over ocean. Most land areas are dominated by medium-size precipitation systems (10⁴–10⁶ km²), except that some relatively smaller precipitation systems (<10⁴ km²) are dominant over the eastern Pacific, some parts of southern Atlantic the northern Africa, and central Asia. The most apparent seasonal contrast in precipitation system size occur over midlatitude oceans, the southeast United States, and the Amazon Basin. The diurnal contrast of precipitation system size is weaker over the oceans where the latitude is greater than 30°, and stronger over land and tropical oceans. The precipitation system size is highly positively spatial-correlated with precipitation amount, frequency, intensity, and duration. The strongest temporal associations of precipitation system size with precipitation amount, frequency, intensity, and duration on monthly scale occur over the tropics, with correlation coefficients greater than 0.8. This study indicates evident regional differences, which can provide new information to deepen the understanding of local synoptic systems in regional studies.

While the advective flux from cool melt runoff can be a significant source of thermal energy to mountainous rivers, it has been a much less addressed process in river temperature modeling and thus our understanding is limited with respect to the spatiotemporal effect of melt on river temperatures at the watershed scale. In particular, the extent and magnitude of the melt cooling effect in the context of a warming climate are not yet well understood. To address this knowledge gap, we improved a coupled hydrology and stream temperature modeling system, distributed hydrology soil vegetation model and river basin model (DHSVM-RBM), to account for the thermal effect of cool snowmelt runoff on river temperatures. The model was applied to a snow-fed river basin in the Pacific Northwest to evaluate the responses of snow, hydrology, stream temperatures, and fish growth potential to future climates. Historical simulations suggest that snowmelt can notably reduce the basin-wide peak summer temperatures particularly at high-elevation tributaries, while the thermal impacts of melt water can persist through the summer along the mainstem. Ensemble climate projections suggested that a warming climate will decrease basin mean peak snow and summer streamflow by 92% and 60% by the end of the century. Due to the compounded influences of warmer temperatures, lower flows and diminished cooling from melt, river reaches in high elevation snow-dominated areas were projected to be most vulnerable to future climate change, showing the largest increases in summer peak temperatures. As a result, thermal habitat used by anadromous Pacific salmon was projected to exhibit substantially lower growth potential during summer in the future. These results have demonstrated the necessity of accounting for snowmelt influence on stream temperature modeling in mountainous watersheds.

Peninsular Indian agriculture and drinking water availability are critically reliant on seasonal winter rainfall occurring from October to December, associated with the northeastern monsoon (NEM). Over 2016–2018, moderate-to-exceptionally low NEM rainfall gave rise to severe drought conditions over much of southern India and exacerbated water scarcity. The magnitude and dynamics of this drought remain unexplored. Here, we quantify the severity of this event and explore causal mechanisms of drought conditions over South India. Our findings indicate that the 3-year cumulative rainfall totals of NEM rainfall during this event faced a deficit of more than 40%—the driest 3-year period in ∼150 years according to the observational record. We demonstrate that drought conditions linked to the NEM across South India are associated with cool phases in the equatorial Indian and Pacific Oceans. Future changes in these teleconnections will add to the challenges of drought prediction.

Phragmites marshes, which are found in every continent except in Antarctica, are being removed by resource managers in the US because it is considered an invasive species with little ecosystem service value. Here we present a comprehensive study on the ecosystem service value of an invasive Phragmites marsh vs a native Typha marsh for flood protection during tropical cyclones. Using a vegetation-resolving three-dimensional surge-wave model and observed vegetation and building data, we assessed the value of the Piermont Marsh in buffering Piermont Village, New York, USA from wave, flood, and structural damage during Superstorm Sandy in October 2012. Observed and simulated wind and water level data along the Hudson River were used as boundary conditions. Model results showed that the Marsh, with predominantly invasive Phragmites australis, dissipated more than half of the wave energy, but negligible flood, at the Village during Sandy. River-borne debris could not be transported across the Marsh to the Village. If Phragmites were replaced with the shorter, native cattail, Typha angustifolia, simulations of Sandy suggested that Piermont Marsh's wave and debris buffering capacity would be preserved. However, had Sandy occurred in non-growth season when Typha is much shorter and sparser, the Marsh would be unable to buffer the wave and debris. Simulated residential structure damage during Sandy (>$10 M) agreed well with reported losses. If the Marsh were absent, the total loss would have increased by 26%. Since damage is dependent on the storm characteristics, we estimated the protective value of the Phragmites marsh for a 1% annual chance flood and wave event to be more than $2 M. This confirms the significant value of Piermont Marsh in protecting Piermont Village from flood and wave damage. To develop a balanced restoration plan, marsh managers should consider biodiversity as well as the significant ecosystem service value of Phragmites-dominated marsh for flood protection.

The growing number of oxygen-deficient coastal zones around the world and their impacts on marine life has always been a controversial issue as their development is largely attributed to anthropogenic activities which can be mitigated by human actions. However, contrary to this prevailing understanding, we show here for the first time, using new coherent datasets from estuaries to coastal to offshore regions, that the world's largest hypoxic-anoxic zone along the west coast of India is formed through a natural process, i.e. upwelling of deoxygenated waters during the summer monsoon. We further demonstrate that the persistence and extent of this coastal oxygen deficiency depend on the degree of deoxygenation of source waters for the upwelling. Consequently, the anoxia is confined only to the central shelf between 11° and 18° N, which is equivalent to almost half of the western Indian shelf, where upwelling brings suboxic waters from the core oxygen minimum zone in the Arabian Sea.

This work provides a new methodology based on a statistical downscaling with a perfect prognosis approach to produce seasonal predictions of near-surface wind speeds at the local scale. Hybrid predictions combine a dynamical prediction of the four main Euro-Atlantic Teleconnections (EATC) and a multilinear statistical regression, which is fitted with observations and includes the EATC as predictors. Once generated, the skill of the hybrid predictions is assessed at 17 tall tower locations in Europe targeting the winter season. For comparative purposes, hybrid predictions have also been produced and assessed at a pan-European scale, using the ERA5 100 m wind speed as the observational reference. Overall, results indicate that hybrid predictions outperform the dynamical predictions of near-surface wind speeds, obtained from five prediction systems available through the Climate Data Store of the Copernicus Climate Change Service. The performance of a multi-system ensemble prediction has also been assessed. In all cases, the enhancement is particularly noted in northern Europe. By being more capable of anticipating local wind speed conditions in higher quality, hybrid predictions will boost the application of seasonal predictions outside the field of pure climate research.

Flood damage to croplands poses a significant threat to global food security. Effective disaster management to cope with future climate change, especially extreme precipitation, requires a robust framework to estimate such damage. For this study, we develop a model based on a convolutional neural network to estimate the area (in acres) of cropland damaged by flooding at the county level. Then we demonstrate the model's performance for the period 2008–2019 over corn and soybean fields in the midwestern United States, which suffer frequent damage from recurrent flooding. We fed the network with remote sensing images and weather fields and divide the growing season into two windows, the early season (May–June) and the late season (July–November) for better performance. The results show mean relative error within $\pm$25% and relative root mean square error within 35%–75% in majority of the counties for most years. Finally, we show that the model forced with meteorological variables alone can provide acceptable accuracy, which indicates it can be applied to forecasting crop damage area in the upcoming season or the studying of future climate impact on crop productivity. In principle, the model can also be applied to food security assessment at the global scale using available records.

Eurasian spring snow cover is widely considered as an important predictor of Asian summer monsoon rainfall, but its possible role in the formation of the north–south dipole structure of rainfall anomalies (NSDR)—a major mode of the eastern China summer rainfall variability—remains elusive. Here, we show that, there is a close connection between the western Eurasian spring snow cover (WESS) and NSDR during our research period 1967–2018, with less WESS tends to be accompanied by a wetter south-drier north pattern over eastern China, and vice versa. However, this relationship was not significant before the late 1990s, but has since become significant. Further analyses demonstrate that the shift in the WESS–NSDR relationship could be attributed to the modulation of summer North Atlantic Oscillation (SNAO). After the late 1990s, the WESS-related anomalous atmospheric circulations during summer are largely reinforced by the constructive superposition of those with same signs induced by SNAO, which in turn would intensify the impact of WESS and hence lead to a strong WESS–NSDR connection. In contrast, the influences of WESS are counteracted by those with opposite signs associated with SNAO before the late 1990s and thereby result in a weak snow–rainfall relationship. Our findings, along with the decline in Eurasian spring snow cover, provide a potential explanation for the recent 'South Flood–North Drought' pattern observed over eastern China.

Tropical vegetation influences local, regional, and global climates, largely through its relationship with the atmosphere, including seasonal patterns of photosynthesis and transpiration. Removal and replacement of natural vegetation can alter both of these processes. In the Amazon, land use/land cover change (LULCC; e.g. deforestation) started decades ago and is expected to continue, with potentially strong effects on climate. However, long-term data on tropical photosynthetic activity and transpiration are scarce, limiting our ability to estimate large-scale effects of LULCC. Here, we use remote sensing data to analyze the impact of LULCC on seasonal patterns of photosynthetic activity and transpiration in the southern Amazon. This region, naturally dominated by forest and Cerrado, has seen high rates of LULCC. Within each of these two ecosystems, we compare estimates of photosynthetic activity (from GOME-2 and GOSIF solar induced fluorescence, SIF) and transpiration (from the Global Land Evaporation Amsterdam Model, GLEAM) in paired sites with high and low rates of LULCC. In forest-dominated regions, deforestation has reduced photosynthetic activity and transpiration, particularly during the dry season, and replaced dry season greening with dry season browning. The SIF datasets disagree on wet season responses; SIF increases with deforestation according to GOME-2, but decreases according to GOSIF. In Cerrado-dominated areas, LULCC has increased photosynthetic activity during the wet season. In both ecosystems, LULCC has resulted in a higher seasonal or annual range of photosynthetic activity levels. The observed effects are often stronger in regions with more extensive LULCC. We found large differences between the two SIF products in both forest- and Cerrado-dominated pixels, with GOME-2 consistently providing higher maximum SIF values. These discrepancies merit further consideration. This analysis broadly characterizes the effects of LULCC on photosynthetic activity and transpiration in this region, and can be used to validate model representations of these effects.

Drought is one of the most extreme climatic events in South Asia (SA) and has affected 1.44 billion people in last 68 years. The agriculture in many areas of this region is highly dependent on rainfall, which increases the vulnerability to drought. To mitigate the impact of drought on agriculture and food security, this study aims to develop a state-of-the-art system for monitoring agricultural drought over SA at a high spatial resolution (0.25^∘) in near real-time. This study currently focuses on the rain-fed area, and the impact of irrigation is not incorporated. This open and interactive tool can assist in monitoring the near-present soil moisture conditions, as well as assessing the historical drought conditions for better management. The South Asia Drought Monitor (SADM) runs the mesoscale hydrologic model to simulate the soil moisture using observation-based meteorological forcing (at near real-time), morphological variables, and land cover data. The soil moisture index (SMI) has been calculated by estimating the percentile of the simulated soil moisture. The drought monitor displays the SMI in five classes based on severity: abnormally dry, moderate drought, severe drought, extreme drought and exceptional drought. The main functions of this open interactive system include the provisioning of up-to-date and historical drought maps, displaying long-term drought conditions and downloading soil moisture data. Comparison of the SMI with the standardized precipitation evapotranspiration index (SPEI) shows that the SMI and SPEI depict similar temporal distribution patterns. However, the SPEI (for 4, 6, 9 and 12 months) differs in the representation of the dry conditions in 1992, 2009, and 2015 and the wet condition in 1983, 1988, and 1990. We evaluated the implications of using different precipitation forcings in a hydrological simulation. A comparison of major drought characteristics such as areal extent, duration, and intensity, using different precipitation datasets show that uncertainty in precipitation forcings can significantly influence model output and drought characteristics. For example, the areal extent of one of the most severe droughts from 1986 to 1988 differs by 9% between ERA5 and CHIRPSv2.

Small rivers (width <30 m) are crucial components of Arctic terrestrial river networks. Yet to date, spatial resolution limitations of commonly used satellite imagery have inhibited quantification of their hydrography. By merging newly available Sentinel-2 multispectral satellite imagery with 2-m ArcticDEM digital elevation data, we created a continuous actively-flowing river network map of the Colville (Kuukpik) River Basin (∼36 000 km²) in Alaska, which includes small rivers as narrow as 10 m. We quantified the river hydrography (stream order and river width, length,surface area, velocity, slope, sinuosity, and catchment area) of the Colville river network in detail, revealing the dominant role of small rivers. Our results show that: (1) small rivers occupy >80% of total river length and surface area of the Colville river network and drain >90% of the catchment area; (2) including numerous small rivers increases the peak of hillslope-channel travel time distribution (TTD) by ∼4 times and shortens the mean hillslope-channel travel time by at least an order of magnitude compared to coarser-resolution river hydrography products; and (3) 87% of the Colville River Basin's carbon dioxide is emitted from small rivers. In sum, we show that small Arctic rivers greatly influence streamflow TTD and carbon cycle. These findings expand our understanding of Arctic river hydrography to a 10-m spatial resolution and raise prospects for tracking dynamic surface water processes with high-resolution satellite observations.

Understanding the variation in reference evapotranspiration (ET_o) is vital for hydrological cycles, drought monitoring, and water resource management. With 1507 meteorological stations and 130 radiation-measured stations, the annual and seasonal ET_o were calculated at each site from 1960 to 2016 in mainland China. The phenomenon of coefficient 'a' being less than 0.25 and coefficient 'b' being greater than 0.50 in the Angstrom–Prescott model occurred in almost the whole country, except for a small area of western and northeastern China. Moreover, the Xiao's method was more applicable to calculate the net longwave radiation (R_nl) and then improve the estimation accuracy of ET_o. The annual ET_o varied from 538.8 to 1559.8 mm and had a high-value center located in the plateau and desert of northwestern China and a low-value center located in Northeast China and near the Sichuan Basin. The spatial distribution of seasonal ET_o was roughly similar to that of annual ET_o, except for that in winter when ET_o was high in the south and low in the north. In mainland China, the annual ET_o decreased by 21.2 mm decade⁻¹ because of the declining sunshine duration before 1993 and increased by 21.1 mm decade⁻¹ due to the decreased relative humidity (RH) after 1993. Generally, the abrupt change of ET_o mainly occurred in the southern China rather than northern China (except for Qinghai Tibet Plateau). Basically, the dominant driving factors of annual and seasonal ET_o were RH and/or T_max after the abrupt change in most parts of China.

Understanding the variability of spatial extents of precipitation extremes favors an accurate assessment of the severity of disasters caused by extreme precipitation events. Using a restricted neighborhood method, we identify the spatial extents of global precipitation extremes over 1983–2018 and examine their spatiotemporal variability and associated changes. Results show that the mid-latitudes shows the largest spatial extent of precipitation extremes, and the spatial extents in non-tropical regions over the Northern Hemisphere show significant seasonal differences. In non-monsoon regions, the spatial extents of precipitation extremes in autumn and winter are larger than those in spring and summer, and the annual average spatial extents of precipitation extremes all exceed 500 km, which are larger than those in monsoon regions. All the five non-monsoon regions over the Northern Hemisphere and three monsoon regions in the western Pacific show statistically significant increases in the spatial extent of precipitation extremes in most seasons.

After decades of declining cropland area, the United States (US) experienced a reversal in land use/land cover change in recent years, with substantial grassland conversion to cropland in the US Midwest. Although previous studies estimated soil carbon (C) loss due to cropland expansion, other important environmental indicators, such as soil erosion and nutrient loss, remain largely unquantified. Here, we simulated the environmental impacts from the conversion of grassland to corn and soybeans for 12 US Midwestern states using the EPIC (Environmental Policy Integrated Climate) model. Between 2008 and 2016, over 2 Mha of grassland were converted to crop production in these states, with much less cropland concomitantly abandoned or retired from production. The net grassland-cropland conversion increased annual soil erosion by 7.9%, nitrogen (N) loss by 3.7%, and soil organic carbon loss by 5.6% relative to that of existing cropland, despite an associated increase in cropland area of only 2.5%. Notably, the above estimates represent the scenario of converting unmanaged grassland to tilled corn and soybeans, and impacts varied depending upon crop type and tillage regime. Corn and soybeans are dominant biofuel feedstocks, yet the grassland conversion and subsequent environmental impacts simulated in this study are likely not attributable solely to biofuel-driven land use change since other factors also contribute to corn and soybean prices and land use decisions. Nevertheless, our results suggest grassland conversion in the Upper Midwest has resulted in substantial degradation of soil quality, with implications for air and water quality as well. Additional conservation measures are likely necessary to counterbalance the impacts, particularly in areas with high rates of grassland conversion (e.g. the Dakotas, southern Iowa).

While large companies routinely announce greenhouse gas emissions targets, few have derived targets based on global climate goals. This changed in 2015 with the creation of the science based targets (SBTs) initiative, which provides guidelines for setting emission targets in line with the temperature goal of the Paris Agreement. SBTs have now been set by more than 500 companies. Methods for setting such targets are not presented in a comparable way in target-setting guidelines and concerns that certain methods may lead to overshoot of the temperature goal have not been investigated. Here, we systematically characterize and compare all seven broadly applicable target-setting methods and quantify the balance between collective corporate SBTs and global allowable emissions for individual methods and different method mixes. We use a simplified global production scenario composed of eight archetypical companies to evaluate target-setting methods across a range of company characteristics and global emission scenarios. The methods vary greatly with respect to emission allocation principles, required company variables and embedded global emission scenarios. Some methods treat companies largely the same, while others differentiate between company types based on geography, economic sector, projected growth rate or baseline emission intensity. The application of individual target-setting methods as well as different mixes of methods tend to result in an imbalance between time-integrated aggregated SBTs and global allowable emissions. The sign and size of this imbalance is in some cases sensitive to the shape of the global emission pathway and the distribution of variables between the company archetypes. We recommend that the SBT initiative (a) use our SBT method characterisation to present methods in a systematic way, (b) consider our emission imbalance analysis in its method recommendations, (c) disclose underlying reasons for its method recommendations, and (d) require transparency from companies on the calculation of established SBTs.

This work addresses the relationship between major dynamical forcings and variability in NO₂ column measurements. The dominating impact in Northern Southeast Asia is due to El Niño-Southern Oscillation (ENSO); in Indonesia, Northern Australia and South America is due to Indian Ocean Dipole (IOD); and in Southern China Land and Sea, Populated Northern China, Siberia, Northern and Arctic Eurasia, Central and Southern Africa, and Western US and Canada is due to North Atlantic Oscillation (NAO). That NO₂ pollution in Indonesia is modulated by IOD contradicts previous work claiming that the emissions in Indonesia are a function of El Niño impacting upon Aerosol Optical Depth and Fire Radiative Power. Simultaneous impacts of concurrent and lagged forcings are derived using multi-linear regression, demonstrating ENSO impacts future NO₂ variability, enhancing NO₂ emissions 7–88 weeks in the future, while IOD and NAO in some cases increase the emissions from or the duration of the future burning season as measured by NO₂. This impact will also extend to co-emitted aerosols and heat, which may impact the climate. In all cases, lagged forcings exhibit more impact than concurrent forcings, hinting at non-linearity coupling with soil moisture, water table, and other dynamical effects. The regression model formed demonstrates that dynamical forcings are responsible for over 45% of the NO₂ emissions variability in most non-urban areas and over 30% in urban China and sub-arctic regions. These results demonstrate the significance of climate forcing on short-lived air pollutants.

Despite improvements in the management of flood risk and the introduction of new regulations, losses from flooding remain high. An important driver is the continuation of new assets being built in flood prone locations. Over the last decade over 120 000 new homes in England and Wales have been built in flood prone areas. While the yearly rates of new homes in flood risk areas have increased only moderately on the national level, significant differences between and within regions as well as between different flood types exist. Using property level data on new homes built over the last decade and information on the socio-economic development of neighbourhoods, we analyse spatial clusters of disproportional increase in flood exposure from recently built homes and investigate how these patterns evolve under different future climate scenarios. We find that a disproportionately higher number of homes built in struggling or declining neighbourhoods between 2008 and 2018 is expected to end up in areas at a high risk of flooding over their lifetime as a result of climate change. Based on these findings, we discuss issues regarding future spending on flood defences, affordability of private level flood protection and insurance as well as the role of spatial planning for adaptation in the face of climate change.

The management of agricultural soils affect the composition and scale of their greenhouse gas (GHG) emissions. There is conflicting evidence on the effect of zero-tillage on carbon storage and GHG emissions. Here we assess the effects of zero-tillage over a range of time frames (1–15 years) on carbon storage and GHG release and their controls in the UK Net global warming potential was 30% lower under zero-tillage systems, due to lower carbon dioxide fluxes, with the greatest impacts after longer periods of zero-tillage management. Simultaneously, in zero-tillage systems, soil carbon stocks and the proportion of sequestered recalcitrant carbon increased while the temperature sensitivity of soil respiration decreased with time, compared to conventionally soils. We conclude that zero-tillage could play a crucial role in both reducing GHG emissions and at the same time increase soil carbon sequestration, therefore contributing to mitigate against climate change. Our findings are particularly important in the context of designing new policies (for example the Environmental Land Management Schemes in the UK) that ensure the sustainability of agricultural production in a changing climate.

Permafrost collapse can rapidly change regional soil-thermal and hydrological conditions, potentially stimulating production of climate-warming gases. Here, we report on rate and extent of permafrost collapse on the extensive Tibetan Plateau, also known as the Asian Water Tower and the Third Pole. Combined data from in situ measurements, unmanned aerial vehicles (UAV), manned aerial photographs, and satellite images suggest that permafrost collapse was accelerating across the Eastern Tibetan Plateau. From 1969 to 2017, the area of collapsed permafrost has increased by approximately a factor of 40, with 70% of the collapsed area forming since 2004. These widespread perturbations to the Tibetan Plateau permafrost could trigger changes in local ecosystem state and amplify large-scale permafrost climate feedbacks.

Understanding spatial patterns of diversity in tropical forests is indispensable for their sustainable use and conservation. Recent studies have reported relationships between forest structure and α-diversity. While tree α-diversity is difficult to map via remote sensing, large-scale forest structure models are becoming more common, which would facilitate mapping the relationship between tree α-diversity and forest structure, contributing to our understanding of biogeographic patterns in the tropics. We developed a methodology to map tree α-diversity in tropical forest regions at 50 m spatial resolution using α-diversity estimates from forest inventories as response variables and forest structural metrics and environmental variables as predictors. To include forest structural metrics in our modelling, we first developed a method to map seven of these metrics integrating discrete light detection and ranging (LiDAR), multispectral, and synthetic aperture radar imagery (SAR). We evaluated this methodology in the Chocó region of Colombia, a tropical forest with high tree diversity and complex forest structure. The relative errors (REs) of the random forest models used to map the seven forest structural variables ranged from low (6%) to moderate (35%). The α-diversity maps had moderate RE; the maps of Simpson and Shannon diversity indices had the lowest RE (9% and 13%), followed by richness (17%), while Shannon and Simpson effective number of species indices had the highest RE, 27% and 47%, respectively. The highest concentrations of tree α-diversity are located along the Pacific Coast from the centre to the northwest of the Chocó Region and in non-flooded forest along the boundary between the Chocó region and the Andes. Our results reveal strong relationships between canopy structure and tree α-diversity, providing support for ecological theories that link structure to diversity via niche partitioning and environmental conditions. With modification, our methods could be applied to assess tree α-diversity of any tropical forest where tree α-diversity field observations coincident with LiDAR data.

Despite low per capita emissions, with over a billion population, India is pivotal for climate change mitigation globally, ranking as the third largest emitter of greenhouse gases. We linked a previously published multidimensional population projection with emission projections from an integrated assessment model to quantify the localised (i.e. state-level) health benefits from reduced ambient fine particulate matter in India under global climate change mitigation scenarios in line with the Paris Agreement targets and national scenarios for maximum feasible air quality control. We incorporated assumptions about future demographic, urbanisation and epidemiological trends and accounted for model feedbacks. Our results indicate that compared to a business-as-usual scenario, pursuit of aspirational climate change mitigation targets can avert up to 8.0 million premature deaths and add up to 0.7 years to life expectancy (LE) at birth due to cleaner air by 2050. Combining aggressive climate change mitigation efforts with maximum feasible air quality control can add 1.6 years to LE. Holding demographic change constant, we find that climate change mitigation and air quality control will contribute slightly more to increases in LE in urban areas than in rural areas and in states with lower socio-economic development.

Previous literature suggests that active commuting has substantial health benefits. Yet, in polluted regions, it can also cause additional health risks by increasing riders' pollution exposure and raising their inhalation rate. We examine the effect of perceived air pollution on stated commuting choices using an on-site survey experiment for 2285 non-automobile commuters in Zhengzhou, a heavily polluted city in central China. We integrate a sequential randomized controlled trial in a survey where individuals in the treatment group received tailored information on their commuting-related pollution exposure, based on our 2 week peak-hour pollution monitoring campaign across transportation modes in the city. We find that travelers in Zhengzhou have already adopted pollution prevention actions by favoring indoor commuting modes on polluted days. Individuals receiving personalized pollution exposure information by mode further decrease active commuting by 8.4 percentage points (95% CI: 5.1, 11.6), accompanied by a 14.7 percentage points (95% CI: 10.7, 18.3) increase in automobile commuting. Travellers make sub-optimal, overly risk averse choices by reducing active commuting even for trips where epidemiological research suggests the exercise benefits outweigh pollution exposure risks. This pollution avoidance tendency significantly attenuates the effect of policies encouraging active commuting. Our findings show the intricately intertwined relationships between the public health targets of promoting active lifestyles and reducing pollution exposure, and between individual pollution avoidance and societal pollution mitigation.

Tropical dry forests (TDFs) worldwide have an environment-sensitive phenological signal, which easily marks their response to the changing climatic conditions, especially precipitation and temperature. Using TDF phenological characteristics as a proxy, this study aims to evaluate their current continental response to climate change across the Americas. Here, we show that TDFs are resilient to water stress and droughts by increasing their rain use efficiency (RUE) in drier years and recovering to average RUE in the year following the drought. Additionally, we find that TDF productivity trends over the past 18 years are spatially clustered, with sites in the northern hemisphere experiencing increased productivity, while equatorial regions have no change, and the southern hemisphere exhibiting decreased productivity. The results indicate that the TDF will be resilient under future climatic conditions, particularly if there are increasing drought conditions.

Before the 2010, studies in climate change (CC) projections embracing scales below 3° were difficult to find. This has changed dramatically over the past ten years, with literature addressing high resolution grids for climate studies, allowing a better understanding and forecasting of CC at finer scales. However, downscaling methods remain poorly explored in urban planning. Research shows that the main difficulties relate to mismatches between data needs and data availability, terminology, constraints of information technology and maps that inform spatial planning decision-making processes. Based on dynamic downscaled maps for RCP 4.5 and RCP 8.5 at 10 km resolution published by Ecuador's Ministry of Environment and Water (MAAE), we develop a method for augmenting the resolution scale at 30 m. We use digital elevation models and Landsat 4/5/7/8 satellite imagery for land surface temperature (LST) and present a series of steps and equations before applying Stefan Bolzman's law. We present the necessary equations between the filling-in of LST outliers, and their projection onto air temperature at 2 m height, taking surface emissivity estimates based on (Alves et al 2017 J. Hyperspectral Remote Sens.7 91–100). We extrapolate the resulting air temperature in time with Fourier's series, and for the purpose of coherence among scales, we upscale air temperature maps at 30 m to those at 10 km resolution. The resulting CC projection maps are validated with the temporal series of air temperature (max, min, mean) from the meteorological station in the Ecuadorian city of Portoviejo (Student's t-test) for the period between 1981 and 2005, with Portoviejo city facing temperature increases of up to 2 °C under RCP 4.5 scenario in the period 2011–2040 vs 1981–2005. The final CC maps have an augmented resolution of 30 m, are compatible with those of MAAE, and offer a low-cost procedure for informing land-use and urban planners, as well as local development decision makers, of temperature anomalies due to climate change.

Globally, countries report forest information to the Food and Agriculture Organization (FAO) of the United Nations Global Forest Resources Assessments (FRA) at regular intervals. While the status and trends of national forest monitoring capacities have been previously assessed for the tropics, this has not been systematically done worldwide. In this paper, we assess the use and quality of forest monitoring data sources for national reporting to the FRA in 236 countries and territories. More specifically, we (a) analyze the use of remote sensing (RS) for forest area monitoring and the use of national forest inventory (NFI) for monitoring forest area, growing stock, biomass, carbon stock, and other attributes in FRA 2005–2020, (b) assess data quality in FRA 2020 using FAO tier-based indicators, and (c) zoom in to investigate changes in tropical forest monitoring capacities in FRA 2010–2020. Globally, the number of countries monitoring forest area using RS at good to very good capacities increased from 55 in FRA 2005 to 99 in FRA 2020. Likewise, the number of countries with good to very good NFI capacities increased from 48 in FRA 2005 to 102 in FRA 2020. This corresponds to ∼85% of the global forest area monitored with one or more nationally-produced up-to-date RS products or NFI in FRA 2020. For large proportions of global forests, the highest quality data was used in FRA 2020 for reporting on forest area (93%), growing stock (85%), biomass (76%), and carbon pools (61%). Overall, capacity improvements are more widespread in the tropics, which can be linked to continued international investments for forest monitoring especially in the context of reducing emissions from deforestation and forest degradation in tropical countries (REDD+). More than 50% of the tropical countries with targeted international support improved both RS and NFI capacities in the period 2010–2020 on top of those that already had persistent good to very good capabilities. There is also a link between improvements in national capacities and improved governance measured against worldwide governance indicators (WGI). Our findings—the first global study—suggest an ever-improving data basis for national reporting on forest resources in the context of climate and development commitments, e.g. the Paris Agreement and Sustainable Development Goals.

In the last decade, the transition away from coal and to fossil gas and biomass in the U.S. has had a major influence on greenhouse gas emissions, especially from electricity generation. However, the effect of this transition on the public health burden of air pollution is not well understood. We use three reduced complexity models (RCMs) and emissions inventory data to reconstruct the changes in health impacts due to PM_2.5 exposure from stationary fuel combustion sources in the U.S., from 2008 to 2017. In 2008, the health impacts of air pollution from stationary sources was largely driven by coal combustion. By 2017, the contribution of coal has dropped precipitously, and the health burden of stationary air pollution sources is shared among a mixture of source types and fuels—largely gas and biomass in buildings and industry, and the remaining coal-fired electricity generation. Nationwide, in 2017, health impacts of biomass and wood combustion are higher than combustion of coal and gas individually. Industrial boilers had the highest emissions and health impacts, followed by residential buildings, electricity, and then commercial buildings. All three RCMs indicate that biomass and wood are the leading sources of stationary source air pollution health impacts in 24 states, and that the total health impacts of gas surpass that of coal in 19 states and the District of Columbia. We develop a projection method using state-level energy consumption data for 2018 and show that these trends likely continued. The RCMs had high agreement for 2008 emissions, when sulfur dioxide emissions from coal-fired power plants were the predominant air pollution source. However there was substantial disagreement between the three RCMs on the 2017 health burden, likely due to pollutants less well-characterized by the RCMs having a higher proportionate share of total impacts.

The dramatic and sudden reduction in anthropogenic activity due to lockdown measures in the UK in response to the COVID-19 outbreak has resulted in a concerted effort to estimate local and regional changes in air quality, though changes in underlying emissions remain uncertain. Here we combine satellite observations of tropospheric NO₂ from TROPOspheric Monitoring Instrument and the Goddard Earth Observing System (GEOS)-Chem 3D chemical transport model to estimate that NO_x emissions declined nationwide by ∼20% during the lockdown (23 March to 31 May 2020). Regionally, these range from 22% to 23% in the western portion of the country to 29% in the southeast and Manchester, and >40% in London. We apply a uniform 20% lockdown period emission reduction to GEOS-Chem anthropogenic emissions over the UK to determine that decline in lockdown emissions led to a national decline in PM_2.5 of 1.1 μg m⁻³, ranging from 0.6 μg m⁻³ in Scotland to 2 μg m⁻³ in the southwest. The decline in emissions in cities (>40%) is greater than the national average and causes an increase in ozone of ∼2 ppbv in London and Manchester. The change in ozone and PM_2.5 concentrations due to emission reductions alone is about half the total change from 2019 to 2020. This emphasizes the need to account for emissions and other factors, in particular meteorology, in future air pollution abatement strategies and regulatory action.

The outbreak of African swine fever (ASF) in China has significantly reduced the country's pig production capability, whilst also having far-reaching impacts on livestock products supply in the wider food system. Previous studies have quantified the potential long-terms impacts on food prices, however, little information is available regarding the direct short-term impacts on food system changes (livestock products supply and consumption patterns) and water quality protection associated with the outbreak. Here, we used multiple sources of data in relation to consumption patterns and water quality to fill this knowledge gap. Our results indicate that the ASF outbreak has changed the short-term livestock products consumption pattern in China, with increasing reliance on importation of livestock products. A rapid change in pork self-sufficiency rate has also driven a rapid increase in the consumer price index of many cities. Banned swill feeding and reversed environmental regulations in the watercourse intense regions has unintended consequences, especially on water quality. Swill, which is no longer fed, was dumped into water waste streams and lowered the sewage treatment efficiency. The re-establishment of pig production back into watercourse intense regions has led to exceedance of local manure nutrient loading capacity of agricultural land. We suggest (a) a short-term intermediate policy to prohibit discharge of swill to sewage systems, to return their previous efficiency, (b) the development of new technologies for the safe recycling of swills, and (c) the design of a long-term intelligent spatial planning of pig production, slaughter and transportation within China to ensure continued protection of water quality vulnerable zones.

To feed future populations on ever-scarcer natural resources, policy initiatives aim to decrease resource footprints of food consumption. While adopting healthier diets has shown great potential to reduce footprints, current political initiatives primarily address strategies to reduce food waste, with the target of halving food waste at retail and consumption levels by 2030. Using Germany as a case study, we compare the resource-saving potential of this political target with three scenarios of nutritionally viable, plant-based dietary patterns and investigate interactions and trade-offs. By using the Food and Agriculture Biomass Input–Output model, we capture biomass, cropland, and blue water footprints of global supply chains. The results show that dietary changes are particularly effective in reducing biomass and cropland footprints, showing a decrease of up to 61% and 48%, respectively, whereas halving food waste decreases biomass and cropland footprints by 11% and 15%, respectively. For blue water savings, halving food waste is more effective: water use decreases by 14% compared to an increase of 6% for dietary change with the highest water consumption. Subsequently, a combination of the scenarios shows the highest total reduction potential. However, our findings reveal that despite reduced footprints, a dietary shift can lead to an increased amount of food waste due to the rising consumption of products associated with higher food waste shares. Therefore, policy strategies addressing both targets might be contradicting. We conclude that international and national policies can be most effective in achieving higher resource efficiency by exploiting the reduction potentials of all available strategies while simultaneously considering strategy interactions.

A growing body of research indicates that in-utero exposure to ambient fine particulate matter (PM_2.5) is a risk factor for low birth weight (LBW). However, research on India, where the high and rising level of ambient air pollution is a significant health concern, is limited. In this study, we analyze the association between ambient PM_2.5 and LBW in India. We match data on birth weight from the National Family and Health Survey (NFHS) conducted in India in 2015–16 with high-resolution spatial data on annual ambient PM_2.5 concentration to calculate in-utero exposure based on the residential location of each child. We estimate the association of in-utero exposure to ambient PM_2.5 with birth weight and LBW, after adjusting for child level, maternal, and household level characteristics that predict birth weight. In our sample (n = 149 416), in comparison to the reference category of in-utero PM_2.5 level less than 26.7 µg m⁻³, the adjusted OR of LBW increases non-linearly from 1.098 (95% CI: 0.954, 1.263) for children in the exposure band 39.3–44.7 µg m⁻³ (i.e., the fourth octile) to 1.241 (95% CI: 1.065, 1.447) for those in the exposure band 44.7–51.6 µg m⁻³ (i.e., the fifth octile) and 1.405 (95% CI: 1.126,1.753) for those with in-utero PM_2.5 level greater than 77.3 µg m⁻³ (i.e., the last octile). Our findings show that exposure to ambient PM_2.5 is strongly associated with LBW in India and suggest that policies that improve air quality may be necessary for achieving the World Health Assembly target of 30% reduction in LBW by 2025.

For countries dependent on rainfed agriculture, failure of the rainy season can lead to serious consequences on the broader economy. Maize, a common staple crop in these countries, often expresses significant interannual variability, given its high sensitivity to water stress. It is traditionally planted at rainy season onset to maximize the growing season and potential yield; however, this risks planting during a 'false onset' that can damage the crop or require replanting. Rainy season onset forecasts offer some promise in reducing this risk; however, the potential for increasing yield has not been explicitly quantified. This study quantifies the yield gap associated with suboptimal maize planting times using a process-based crop model over a 36 year historical period across Ethiopia. Onset-informed and forecast-informed approaches are compared with a baseline approach, and results indicate a strong potential for yield gains in drier regions as well as reductions in interannual variance countrywide. In contrast, regions with reliably sufficient precipitation illustrate only minimal gains. In general, integration of onset forecasts into agricultural decision-making warrants inclusion in agricultural extension efforts.

Combining multiple sources of information on atmospheric composition, wildland fire emissions, and fire area burned, we link decadal air quality trends in Western US urban centers with wildland fire activity during the months of August and September for the years 2000–2019. We find spatially consistent trends in extreme levels (upper quantile) of fine particulate matter (PM_2.5), organic carbon, and absorption aerosol optical depth centered on the US Pacific Northwest during the month of August. Emerging trends were also found across the Pacific Northwest, western Montana, and Wyoming in September. Furthermore, we identify potential wildfire emission 'hotspots' from trends in wildfire derived PM_2.5 emissions and burned area. The spatial correspondence between wildfire emissions hotspots and extreme air quality trends, as well as their concomitant spatial shift from August to September supports the hypothesis that wildfires are driving extreme air quality trends across the Western US. We derive further evidence of the influence of wildland fires on air quality in Western US urban centers from smoke induced PM_2.5 enhancements calculated through statistical modeling of the PM_2.5-meteorology relationship at 18 Western US cities. Our results highlight the significant risk of increased human exposure to wildfire smoke in August at these Western US population centers, while also pointing to the potential danger of emerging trends in Western US population growth, wildfire emissions, and extreme air quality in September.

Around the world, efforts to contain the COVID-19 pandemic have profoundly changed human activity, which may have improved air quality and reduced greenhouse gas emissions. We investigated the impact of the pandemic on energy demand and subsequent emissions from electricity and gas throughout 2020 in the UK. The daily pattern of electricity demand changed in both lockdowns, with weekday demand shifting to that of a typical pre-pandemic weekend. Energy demand in 2020 was modelled to reveal the impact of the weather and the pandemic. The first lockdown reduced demand by 15.6% for electricity and 12.0% for commercial gas, whereas the second lockdown produced reductions less than half. Domestic gas demand did not change during the first lockdown, but increased by 6.1% in the second, likely due to increased domestic heat demand. The changes in demand for gas resulted in little change to overall gas consumption emissions during the pandemic. For electricity, large emission reductions occurred during the two lockdowns: up to 22% for CO_2, 47% for NO_x, and 29% for PM_2.5. Yet, the largest CO₂ emission reduction for electricity in 2020 (25%) occurred before the pandemic, which happened during a warm and stormy spell with exceptional wind generation. These observations suggest that future similar changes in activity may result in little change for gas demand and emissions. For electricity, emission reductions through changes in energy demand are made possible by the generation mix. To enable further emission reductions in the future, the generation mix should continue to decarbonise. This will yield emission reductions in both times of lowered energy demand, but more importantly, during times of high renewable output.

The scientific literature on climate change adaptation has become too large to assess manually. Beyond standard scientometrics, questions about if and how the field is progressing thus remain largely unanswered. Here we provide a novel, inquisitive, computer-assisted evidence mapping methodology that combines expert interviews (n = 26) and structural topic modelling to evaluate open-ended research questions on progress in the field. We apply this to 62 191 adaptation-relevant scientific publications (1988–2020), selected through supervised machine learning from a comprehensive climate change query. Comparing the literature to key benchmarks of mature adaptation research, our findings align with trends in the adaptation literature observed by most experts: the field is maturing, growing rapidly, and diversifying, with social science and implementation topics arising next to the still-dominant natural sciences and impacts-focused research. Formally assessing the representativeness of IPCC citations, we find evidence of a delay effect for fast-growing areas of research like adaptation strategies and governance. Similarly, we show significant topic biases by geographic location: especially disaster and development-related topics are often studied in Southern countries by authors from the North, while Northern countries dominate governance topics. Moreover, there is a general paucity of research in some highly vulnerable countries. Experts lastly signal a need for meaningful stakeholder involvement. Expanding on the methods presented here would aid the comprehensive and transparent monitoring of adaptation research. For the evidence synthesis community, our methodology provides an example of how to move beyond the descriptive towards the inquisitive and formally evaluating research questions.

Aircraft at airport gates require power and air conditioning, provided by fossil fuel-combusting equipment, to maintain functionality and thermal comfort. We estimate the life-cycle greenhouse gas (GHG) emissions and economic implications from electrifying gate operations for 2354 commercial-traffic airports in the world. Here we show that complete electrification could yield GHG reductions of 63%–97% per gate operation relative to current practice, with greater reductions correlated with low-carbon electricity. Economic payback periods average just 1–2 years. Shifting to complete gate electrification could save a high-traffic airport an average of $5–6 million in annual climate economic damages relative to estimates of current practice. 10–12 million metric tons of annual GHG emissions are potentially saved if most airports in the world electrified gate operations, costing the 24 busiest global airports on average $25–30, U.S. airports $60–70, and non-U.S. airports $80–90 per metric ton of CO₂ mitigated, in some cases comparable to carbon-market prices. Environmental benefits depend primarily upon electricity sources and operational parameters such as aircraft fleet composition.

During El Niño to La Niña transitions in 1998 and 2010, satellite observations revealed a sharp increase in surface chlorophyll in the eastern equatorial Pacific (EEP), exceeding the interannual amplitude by threefold; however, the causes of such super phytoplankton blooms (SPBs) remain unclear. Here, observational data, climate model simulations, and coupled ocean-biogeochemical modeling experiments are adopted to show that Indian Ocean (IO) warming plays an active role in remotely triggering SPBs in the EEP. During the previous boreal winter in an El Niño year, IO warming generates anomalous easterlies over the western edge of the tropical Pacific, which excite upwelling Kelvin waves propagating into the EEP during the following boreal spring, remotely causing an uplift of the nutricline in the EEP. Seasonally, the mixed layer deepens and the upper ocean warms during the following late spring, and large amounts of nutrient-rich cold subsurface waters entrain into the mixed layer; interannually, the local grazing pressure is low after the peak of El Niño. These remote and local factors jointly promote SPBs in the EEP.

Year-to-year variability in CO₂ fluxes can yield insight into climate-carbon cycle relationships, a fundamental yet uncertain aspect of the terrestrial carbon cycle. In this study, we use global observations from NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite for years 2015–2019 and a geostatistical inverse model to evaluate 5 years of interannual variability (IAV) in CO₂ fluxes and its relationships with environmental drivers. OCO-2 launched in late 2014, and we specifically evaluate IAV during the time period when OCO-2 observations are available. We then compare inferences from OCO-2 with state-of-the-art process-based models (terrestrial biosphere model, TBMs). Results from OCO-2 suggest that the tropical grasslands biome (including grasslands, savanna, and agricultural lands within the tropics) makes contributions to global IAV during the 5 year study period that are comparable to tropical forests, a result that differs from a majority of TBMs. Furthermore, existing studies disagree on the environmental variables that drive IAV during this time period, and the analysis using OCO-2 suggests that both temperature and precipitation make comparable contributions. TBMs, by contrast, tend to estimate larger IAV during this time and usually estimate larger relative contributions from the extra-tropics. With that said, TBMs show little consensus on both the magnitude and the contributions of different regions to IAV. We further find that TBMs show a wide range of responses on the relationships of CO₂ fluxes with annual anomalies in temperature and precipitation, and these relationships across most of the TBMs have a larger magnitude than inferred from OCO-2. Overall, the findings of this study highlight large uncertainties in process-based estimates of IAV during recent years and provide an avenue for evaluating these processes against inferences from OCO-2.

Methane mitigation is essential for addressing climate change, but the value of rapidly implementing available mitigation measures is not well understood. In this paper, we analyze the climate benefits of fast action to reduce methane emissions as compared to slower and delayed mitigation timelines. We find that the scale up and deployment of greatly underutilized but available mitigation measures will have significant near-term temperature benefits beyond that from slow or delayed action. Overall, strategies exist to cut global methane emissions from human activities in half within the next ten years and half of these strategies currently incur no net cost. Pursuing all mitigation measures now could slow the global-mean rate of near-term decadal warming by around 30%, avoid a quarter of a degree centigrade of additional global-mean warming by midcentury, and set ourselves on a path to avoid more than half a degree centigrade by end of century. On the other hand, slow implementation of these measures may result in an additional tenth of a degree of global-mean warming by midcentury and 5% faster warming rate (relative to fast action), and waiting to pursue these measures until midcentury may result in an additional two tenths of a degree centigrade by midcentury and 15% faster warming rate (relative to fast action). Slow or delayed methane action is viewed by many as reasonable given that current and on-the-horizon climate policies heavily emphasize actions that benefit the climate in the long-term, such as decarbonization and reaching net-zero emissions, whereas methane emitted over the next couple of decades will play a limited role in long-term warming. However, given that fast methane action can considerably limit climate damages in the near-term, it is urgent to scale up efforts and take advantage of this achievable and affordable opportunity as we simultaneously reduce carbon dioxide emissions.

Plug-in electric vehicles (PEVs) are a promising option for greenhouse gas (GHG) mitigation in the transport sector - especially when the fast decrease in carbon emissions from electricity provision is considered. The rapid uptake of renewable electricity generation worldwide implies an unprecedented change that affects the carbon content of electricity for battery production as well as charging and thus the GHG mitigation potential of PEV. However, most studies assume fixed carbon content of the electricity in the environmental assessment of PEV and the fast change of the generation mix has not been studied on a global scale yet. Furthermore, the inclusion of up-stream emissions remains an open policy problem. Here, we apply a reduced life cycle assessment approach including the well-to-wheel emissions of PEV and taking into account future changes in the electricity mix. We compare future global energy scenarios and combine them with PEV diffusion scenarios. Our results show that the remaining carbon budget is best used with a very early PEV market diffusion; waiting for cleaner PEV battery production cannot compensate for the lost carbon budget in combustion vehicle usage.

Cyclones are a major cause of extreme weather in the extratropics. Projections of future climate change have focussed on extratropical cyclones identified close to the surface, but cyclones identified at multiple levels of the atmosphere ('deep' cyclones) make the largest contributions to total and extreme rainfall. Here we use ten CMIP5 models to assess projected changes in cyclone frequency and associated heavy rainfall between 1979–2005 and 2070–2099 under a high emissions scenario (RCP8.5), with a focus on changes in vertically organised ('deep') systems with cyclones present at both the surface and 500 hPa. We find a robust decrease in the number of deep cyclones by the end of the 21st century, together with an increase in the number of extreme rainfall events caused by deep cyclones. In contrast to deep cyclones, shallow cyclones identified only at the surface are found to produce less rain and are projected to increase in frequency in the future, particularly over land areas. Our findings demonstrate the benefits of considering vertically deep cyclones, as their connection to extreme rainfall has implications for risk assessment and climate adaptation strategies.

Over 70% of the 62 million hectares of cropland in the Midwestern United States is grown in corn-based rotations. These crop rotations are caught in a century-long simplification trend despite robust evidence demonstrating yield and soil benefits from diversified rotations. Our ability to explore and explain this trend will come in part from observing the biophysical and policy influences on farmers' crop choices at one key level of management: the field. Yet field-level crop rotation patterns remain largely unstudied at regional scales and will be essential for understanding how national agricultural policy manifests locally and interacts with biophysical phenomena to erode—or bolster—soil and environmental health, agricultural resilience, and farmers' livelihoods. We developed a novel indicator of crop rotational complexity and applied it to 1.5 million fields across the US Midwest. We used bootstrapped linear mixed models to regress field-level rotational complexity against biophysical (land capability, precipitation) and policy-driven (distance to the nearest biofuel plant and grain elevator) factors. After accounting for spatial autocorrelation, there were statistically clear negative relationships between rotational complexity and biophysical factors (land capability and precipitation during the growing season), indicating decreased rotation in prime growing areas. A positive relationship between rotational complexity and distance to the nearest biofuel plant suggests policy-based, as well as biophysical, constraints on regional rotations. This novel RCI is a promising tool for future fine-scale rotational analysis and demonstrates that the United States' most fertile soils are the most prone to degradation, with recent policy choices further exacerbating this trend.

Integrated assessment models (IAMs) form a prime tool in informing about climate mitigation strategies. Diagnostic indicators that allow comparison across these models can help describe and explain differences in model projections. This increases transparency and comparability. Earlier, the IAM community has developed an approach to diagnose models (Kriegler (2015 Technol. Forecast. Soc. Change90 45–61)). Here we build on this, by proposing a selected set of well-defined indicators as a community standard, to systematically and routinely assess IAM behaviour, similar to metrics used for other modeling communities such as climate models. These indicators are the relative abatement index, emission reduction type index, inertia timescale, fossil fuel reduction, transformation index and cost per abatement value. We apply the approach to 17 IAMs, assessing both older as well as their latest versions, as applied in the IPCC 6th Assessment Report. The study shows that the approach can be easily applied and used to indentify key differences between models and model versions. Moreover, we demonstrate that this comparison helps to link model behavior to model characteristics and assumptions. We show that together, the set of six indicators can provide useful indication of the main traits of the model and can roughly indicate the general model behavior. The results also show that there is often a considerable spread across the models. Interestingly, the diagnostic values often change for different model versions, but there does not seem to be a distinct trend.

Rapid warming and sea-ice loss in the Arctic Ocean are among the most profound climatic changes to have occurred in recent decades on Earth. Arctic Ocean biological production appears that it may be increasing as a result, but the consequences for nutrient concentrations are unknown. We have assembled a collection of historical field data showing that average concentrations of the macronutrients nitrate and phosphate have decreased by 79% and 29%, respectively, in surface waters of the western Arctic Ocean basin over the past three decades. The field observations and results from numerical ocean simulations suggest that this long-term trend toward more oligotrophic (nutrient-poor) conditions is driven primarily by the compound effects of sea-ice loss: a reduced resupply of nutrients from subsurface waters (due to fresh water addition and stronger upper-ocean stratification) coincident with increased biological consumption of nutrients (due to the greater availability of light needed for photosynthesis).

The CO₂ efflux from soil (soil respiration (SR)) is one of the largest fluxes in the global carbon (C) cycle and its response to climate change could strongly influence future atmospheric CO₂ concentrations. Still, a large divergence of global SR estimates and its autotrophic (AR) and heterotrophic (HR) components exists among process based terrestrial ecosystem models. Therefore, alternatively derived global benchmark values are warranted for constraining the various ecosystem model output. In this study, we developed models based on the global soil respiration database (version 5.0), using the random forest (RF) method to generate the global benchmark distribution of total SR and its components. Benchmark values were then compared with the output of ten different global terrestrial ecosystem models. Our observationally derived global mean annual benchmark rates were 85.5 ± 40.4 (SD) Pg C yr⁻¹ for SR, 50.3 ± 25.0 (SD) Pg C yr⁻¹ for HR and 35.2 Pg C yr⁻¹ for AR during 1982–2012, respectively. Evaluating against the observations, the RF models showed better performance in both of SR and HR simulations than all investigated terrestrial ecosystem models. Large divergences in simulating SR and its components were observed among the terrestrial ecosystem models. The estimated global SR and HR by the ecosystem models ranged from 61.4 to 91.7 Pg C yr⁻¹ and 39.8 to 61.7 Pg C yr⁻¹, respectively. The most discrepancy lays in the estimation of AR, the difference (12.0–42.3 Pg C yr⁻¹) of estimates among the ecosystem models was up to 3.5 times. The contribution of AR to SR highly varied among the ecosystem models ranging from 18% to 48%, which differed with the estimate by RF (41%). This study generated global SR and its components (HR and AR) fluxes, which are useful benchmarks to constrain the performance of terrestrial ecosystem models.

Financial inclusion is a key policy for achieving the UN Sustainable Development Goals worldwide. However, emerging evidence has challenged the universal effectiveness of this policy. Combining a cross-sectional socio-economic and ecological survey with regional macro-economic and climatic data, we undertook an integrated causal analysis of the impact of financial inclusion policy on the Inner Mongolian herder social-ecological system. Exposure to economic globalization and climate change threatened herder livelihoods via increased feed costs and reduced livestock sales prices. Financial inclusion loans were beneficial for herders with large grassland plot size who used their traditional ecological knowledge to adapt via seasonal herd mobility. However, most herders were sedentary, constrained by small plot size, and used financial inclusion loans to reserve livestock and maintain high stocking densities. This strategy exposed them to inflated feed costs, increased their debt, and led to widespread grassland degradation. The results illustrate the limitations of financial inclusion policy in achieving sustainable development when people are trapped in poverty, subject to novel social-ecological contexts, and their ability to adapt is compromised. Transformative adaptations based on community cooperation, traditional knowledge and institutions, complementary public policies, and technological innovation are crucial to support financial inclusion policy and enhance sustainable development.

Large-scale in situ observations are sorely lacking, leading to poor understanding of nationwide atmospheric turbulence over China. Nevertheless, high-resolution soundings have become available starting in 2011, providing a unique opportunity to investigate turbulence across China. Here, we calculated the mean turbulence dissipation rate () from radiosonde measurements across China for the period 2011–2018 using Thorpe analysis. The atmospheric layers that had stronger turbulence indicated by larger generally came with larger Thorpe length but with smaller Brunt–Väisälä frequency. Overall, the clear-air in the free atmosphere exhibited large spatial variability with a 'south-high north-low' pattern. Large clear-air values were observed in both the lower stratosphere (LS) and upper troposphere (UT), especially over the Tibetan Plateau (TP) and its neighboring regions with complex terrain likely due to large-amplitude mountain waves. Particularly, less frequent but more intense clear-air turbulence was observed in both lower troposphere (LT) and UT over the TP, while more frequent, less intense clear-air turbulence was found in northern China. The all-sky turbulence considering the moist-saturation effects was much stronger in the troposphere, notably in southern China where convective clouds and precipitation oftentimes dominated. In the vertical direction, the altitude of peak clear-air in the troposphere was found to decrease poleward, broadly consistent with the meridional gradient of tropopause height in the Northern Hemisphere. A double-peak mode stood out for the profiles of clear-air at midlatitudes to the north of 30° N in winter: one peak was at altitudes of 15–18 km, and another at altitudes of 5–8 km. The strong shear instabilities around the westerly jet stream could account for the vertical bimodal structures. The seasonality of was also pronounced, reaching maxima in summer and minima in winter. Our results may help understand and avoid clear-air turbulence, as related to aviation safety among other issues.

We examine the geographies of agricultural yields in the United States, home to some of the most productive agricultural systems on the planet. We model and map yield divergence from biophysical expectations and regional norms for five major crops—corn, soy, wheat, alfalfa, and hay—and assess how this divergence interacts with farm-level resources, farm(er) characteristics, and landscape context. Our results highlight the ways in which human activity has reinforced and intensified the yield geographies defined by sun, soil, and water alone. Yield gains brought by human activity are strongly associated with increased expenditure on inputs to production and receipts from federal programs, but not with net revenue gains for farmers. These yield gains vary across operator race, gender, farm size, and major US region. We also find that beyond a threshold, increased input expenditure is associated with marginally decreasing yields, raising important questions about the interactions between yields and farmer livelihoods. We conclude by discussing the importance of broadening the production-centric paradigm that has dominated agricultural innovation over the last century to include the well-being of the farmers and ecological systems on which agricultural production ultimately depends.

An understanding of mechanisms that underlie the steady increase in crop yields over recent decades is important for promotion of future sustainable yield gains and maintenance of future food security. In this study, we coupled observational maize yield and climate variables based on crop development data from 1981 to 2009 to construct an empirical model that can resolve the separate and combined effects of climate and agricultural practices related to crop timing on maize yield in Northeast China (NEC), the largest spring maize-producing region of China. Climate warming contributed to approximately 15.6% of the trend for increasing yield over the 29 year period. The beneficial effects of climate warming on yield were due to increases in accumulation of temperatures between 10 °C and 30 °C (growing degree days, GDD), which positively contributed to 29.7% of yield and offset the −14.1% yield reduction caused by a trend involving increasing accumulation of temperatures above 30 °C (or extreme degree days, EDD). Adaptive improvements in crop timing practices (e.g. shifts in planting date and selection of later-maturity cultivars) further optimized the impacts of GDD and EDD during the entire growing season by exploiting more GDD during the reproductive phase and fewer EDD during the vegetative phase, thereby contributing to a yield gain of 25.4% over the period from 1981 to 2009. Taken together, climate warming and crop timing practices contributed to 39.4% of the maize yield increase since 1981. Yield losses due to climate warming were detected at only one site located in the southern part of the NEC region, where yield losses must be offset by positive effects of crop timing changes. The trends in maize yields presented here may provide guidance for effective adaptation options for maize production under conditions of continued climate warming.

Concrete is the most produced manmade material globally. This widespread production results in significant anthropogenic environmental impacts, the awareness of which has spurred advances in material development to lower these burdens. However, proposed changes are often not assessed in the context of the data variability and uncertainty inherent in the environmental impact quantification methods employed. As such, the probability that any suggested strategy will result in a desired effect is not addressed. This work aims to quantitatively examine data variability, an inherent characteristic of elements in supply chains, and data uncertainty, a function of data quality for the system being modeled, in assessments of greenhouse gas (GHG) and air pollutant emissions from concrete production. Data variability is determined through ranges in requisite input values from the literature; data uncertainty is assessed through application of an established pedigree matrix method. Statistical analysis of the emissions from concrete production incorporating sources of variability and uncertainty are examined through Monte Carlo simulations. Concrete mixtures, representing a feasible structural concrete for use in California infrastructure and three alternative mixtures are assessed, as are three GHG emissions mitigation strategies, namely, a change in thermal energy fuel mix, a change in electricity grid, and use of carbon capture and storage. The distributions of emissions derived through statistical analyses are used to examine the probability of efficacy of these strategies, as well as potential co-benefits on air emissions. Results show each constituent change and each mitigation strategy considered would lead to a reduction in GHG emissions if only mean values are compared; however, the probability of these reductions varies. These findings suggest mitigation efforts may not be as definitive as current assessments suggest. Results indicate the importance of using statistical methods to target desirable mitigation efforts in the environmental impacts from concrete production.

Tackling climate change and human development challenges will require major global investments in renewable energy systems, including possibly into large hydropower. Despite well-known impacts of hydropower dams, most renewable energy assessments neither account for externalities of hydropower nor evaluate possible strategic alternatives. Here we demonstrate how integrating energy systems modeling and strategic hydropower planning can resolve conflicts between renewable energy and dam impacts on rivers. We apply these tools to Myanmar, whose rivers are the last free-flowing rivers of Asia, and where business-as-usual (BAU) plans call for up to 40 GW of new hydropower. We present alternative energy futures that rely more on scalable wind and solar, and less on hydropower (6.7–10.3 GW) than the BAU. Reduced reliance on hydropower allows us to use river basin models to strategically design dam portfolios for minimized impact. Thus, our alternative futures result in greatly reduced impacts on rivers in terms of sediment trapping and habitat fragmentation, and result in lower system costs ($8.4 billion compared to $11.7 billion for the BAU). Our results highlight specific opportunities for Myanmar but also demonstrate global techno-ecological synergies between climate action, equitable human development and conservation of riparian ecosystems and livelihoods.

'No-till' (NT) agriculture, which eliminates nearly all physical disturbance of the soil surface on croplands, has been widely promoted as a means of soil organic carbon (SOC) sequestration with the potential to mitigate climate change. Here we provide the first global estimates of the SOC sequestration potential of NT adoption using a global land surface model (LSM). We use an LSM to simulate losses of SOC due to intensive tillage (IT) over the historical time period (1850–2014), followed by future simulations (2015–2100) assessing the SOC sequestration potential of adopting NT globally. Historical losses due to simulated IT practices ranged from 6.8 to 16.8 Gt C, or roughly 5%–13% of the 133 Gt C of global cumulative SOC losses attributable to agriculture reported elsewhere. Cumulative SOC sequestration in NT simulations over the entire 21st century was equivalent to approximately one year of current fossil fuel emissions and ranged between 6.6 and 14.4 Gt C (0.08–0.17 Gt C yr⁻¹). Modeled increases in SOC sequestration under NT were concentrated in cool, humid temperate regions, with minimal SOC gains in the tropics. These results indicate that the global potential for SOC sequestration from NT adoption may be more limited than reported in some studies and promoted by policymakers. Our incorporation of tillage practices into an LSM is a major step toward integration of soil tillage as a management practice into LSMs and associated Earth system models. Future work should focus on improving process-understanding of tillage practices and their integration into LSMs, as well as resolving modeled versus observed estimates of SOC sequestration from NT adoption, particularly in the tropics.

The deforestation rate in the Maritime Continent (MC) has been accelerating during the past several decades. Understanding the changes in local hydro-climatological cycles as deforestation takes place is essential because the MC is suffering from frequent and extreme droughts and fires, which often occur during the dry season and are more severe during El Niños. Therefore, this study explores how deforestation affects the hydrological cycle and precipitation in the MC during El Niños, focusing on the boreal autumn season and using the coupled atmosphere–land model simulations. It is found that the precipitation over the MC increases in the deforestation experiments, and the precipitation responses can be magnified during El Niño events. A strong subsidence anomaly associated with El Niño does not prevent enhanced convection associated with local deforestation. Instead, the subsidence reduces the cloud cover in the MC region during El Niño, which increases the incoming solar radiation and increases surface temperatures. Thus, a warmer environment induced by El Niño modulates the biogeophysical feedbacks associated with deforestation that also play a critical role in more substantial land surface warming. A warmer land surface induces a more unstable atmospheric environment associated with a tendency toward enhanced local convection and lateral moisture convergence. This study highlights how the different mean climate states may modulate the impact of local land-use changes on hydroclimatological cycles in the MC, and sheds light on the state of our knowledge of interactions between the local land surface and remote large-scale atmospheric circulations.

According to the characteristics of forced and unforced components to climate change, sophisticated statistical models were used to fit and separate multiple scale variations in the global mean surface temperature (GMST) series. These include a combined model of the multiple linear regression and autoregressive integrated moving average models to separate the contribution of both the anthropogenic forcing (including anthropogenic factors (GHGs, aerosol, land use, Ozone, etc) and the natural forcing (volcanic eruption and solar activities)) from internal variability in the GMST change series since the last part of the 19th century (which explains about 91.6% of the total variances). The multiple scale changes (inter-annual variation, inter-decadal variation, and multi-decadal variation) are then assessed for their periodic features in the remaining residuals of the combined model (internal variability explains the rest 8.4% of the total variances) using the ensemble empirical mode decomposition method. Finally, the individual contributions of the anthropogenic factors are attributed using a partial least squares regression model.

The impacts of climate change are affecting human societies today. In parallel, socio-economic development has increased the capacity of countries around the global to adapt to those impacts although substantial challenges remain. Ongoing climate change will continue to result in a pressure to adapt, while socio-economic development could make it easier to do so. Countries' effectiveness in fostering climate resilience will depend on the pace of both developments under different socio-economic and emission pathways. Here we assess trajectories of adaptation readiness in comparison with the continued emergence of hot days as a proxy for climate change hazards for different emission and socio-economic pathways over the 21st century. Putting the future evolution of both indices in relation to the observed dynamics over the recent past allows us to provide an assessment of the prospects of future climate resilience building beyond what has been experienced to date. We show that only an inclusive and sustainable stringent mitigation pathway allows for effective climate resilient development over the 21st century. Less inclusive or fossil-fuel driven development will not allow for improvements in resilience building beyond the recent past. Substantial differences emerge already in the 2020s. Our findings underscore the paramount importance of achieving the Paris Agreement goals to enable climate-resilient, sustainable development.

Enhanced warming of the Northern high latitudes has intensified thermokarst processes throughout the permafrost zone. Retrogressive thaw slumps (RTS), where thaw-driven erosion caused by ground ice melt creates terrain disturbances extending over tens of hectares, represent particularly dynamic thermokarst features. Biogeochemical transformation of the mobilized substrate may release CO₂ to the atmosphere and impact downstream ecosystems, yet its fate remains unclear. The Peel Plateau in northwestern Canada hosts some of the largest RTS features in the Arctic. Here, thick deposits of Pleistocene-aged glacial tills are overlain by a thinner layer of relatively organic-rich Holocene-aged permafrost that aggraded upward following deeper thaw and soil development during the early Holocene warm period. In this study, we characterize exposed soil layers and the mobilized material by analysing sediment properties and organic matter composition in active layer, Holocene and Pleistocene permafrost, recently thawed debris deposits and fresh deposits of slump outflow from four separate RTS features. We found that organic matter content, radiocarbon age and biomarker concentrations in debris and outflow deposits from all four sites were most similar to permafrost soils, with a lesser influence of the organic-rich active layer. Lipid biomarkers suggested a significant contribution of petrogenic carbon especially in Pleistocene permafrost. Active layer samples contained abundant intrinsically labile macromolecular components (polysaccharides, lignin markers, phenolic and N-containing compounds). All other samples were dominated by degraded organic constituents. Active layer soils, although heterogeneous, also had the highest median grain sizes, whereas debris and runoff deposits consisted of finer mineral grains and were generally more homogeneous, similar to permafrost. We thus infer that both organic matter degradation and hydrodynamic sorting during transport affect the mobilized material. Determining the relative magnitude of these two processes will be crucial to better assess the role of intensifying RTS activity in CO₂ release and ecosystem carbon fluxes.

The COVID-19 lockdowns drastically reduced human activity, emulating a controlled experiment on human–land–atmosphere coupling. Here, using a fusion of satellite and reanalysis products, we examine this coupling through changes in the surface energy budget during the lockdown (1 April to 15 May 2020) in the Indo-Gangetic Basin, one of the world's most populated and polluted regions. During the lockdown, the reduction (>10%) in columnar air pollution compared to a five year baseline, expected to increase incoming solar radiation, was counteracted by a ∼30% enhancement in cloud cover, causing little change in available energy at the surface. More importantly, the delay in winter crop harvesting during the lockdown increased surface vegetation cover, causing almost half the regional cooling via evapotranspiration. Since this cooling was higher for rural areas, the daytime surface urban heat island (SUHI) intensity increased (by 0.20–0.41 K) during a period of reduced human activity. Our study provides strong observational evidence of the influence of agricultural activity on rural climate in this region and its indirect impact on the SUHI intensity.

The marine economic activities has become a vital economic driving force for development of China's economy. However, the trajectory of greenhouse gas (i.e. GHG) emissions associated the fast growing marine economy and its role in emission mitigation remain unclear. Through compiling high-resolution and time-series environmental input–output tables for 2002, 2007, 2012 and 2017, this study quantify development of 13 key marine industries in driving national economic development and its supply chains, and assesses the direct and indirect contributions of marine industries to the national economy and GHGs emissions. Our results show that the total emissions of marine economy increased by 2.3 times from 2002 to 2017, and the share of that in national total emissions increased by 43.3%. The economic output of marine economy may lead to up to 1.8 times of the total economic output in the upstream industries, while the indirect emissions of major marine economy embodied in the upstream supply chains is on average 3.5 times of direct emissions from marine industries. Our findings highlight the necessity of considering total supply chain GHGs emissions associated with the fast growing marine economy to better achieve China's climate mitigation targets.

China's ongoing commitment to overseas infrastructure investment through the Belt and Road Initiative (BRI) has ignited concern over environmental impacts. The BRI's environmental impacts will be determined by China's decisions not only on what kinds of projects to fund, but also how those projects end up operating relative to projects without Chinese involvement. It is critical to understand current performance and establish a baseline understanding of the environmental impacts of China's overseas projects thus far. We examine the environmental performance of coal-fired power plants in Asia in terms of carbon dioxide emissions intensity. Using generating unit-level data and a regression-based analysis, we estimate the comparative emissions intensity of overseas coal plants owned, designed, or constructed, by Chinese and non-Chinese companies. We find that Chinese coal plants tend to have significantly lower emissions intensity than similar non-Chinese coal plants. Given that total emissions rather than relative emissions intensity primarily drive the global warming impact of a plant, we also estimate total annual emissions and committed lifetime emissions of the plants in our dataset. We find that while Chinese plants may have relatively lower emissions intensity, their total emissions will grow as a proportion of the coal plant emissions in Asia over time.

IPCC models project a likely increase in winter precipitation over northern Europe under a high-emission scenario. These projections, however, typically rely on relatively coarse ∼100 km resolution models that can misrepresent important processes driving precipitation, such as extratropical cyclone activity, and ocean eddies. Here, we show that a pioneering 50 km atmosphere–1/12° ocean global coupled model projects a substantially larger increase in winter precipitation over northwestern Europe by mid-century than lower-resolution configurations. For this increase, both the highest ocean and atmosphere model resolutions are essential: only the eddy-rich (1/12°) ocean projects a progressive northward shift of the Gulf Stream. This leads to a strong regional ocean surface warming that intensifies air–sea heat fluxes and baroclinicity. For this then to translate into a strengthening of North Atlantic extratropical cyclone activity, the 50 km atmosphere is essential, as it enables enhanced diabatic heating from water vapor condensation and an acceleration of the upper-level mean flow, which weaken vertical stability. Our results suggest that all recent IPCC climate projections using traditional ∼100 km resolution models could be underestimating the precipitation increase over Europe in winter and, consequently, the related potential risks.

The massive lockdown of global cities during the COVID-19 pandemic is substantially improving the atmospheric environment, which for the first time, urban mobility is virtually reduced to zero, and it is then possible to establish a baseline for air quality. By comparing these values with pre-COVID-19 data, it is possible to infer the likely effect of urban mobility and spatial configuration on the air quality. In the present study, a time-series prediction model is enhanced to estimate the nationwide NO₂ concentrations before and during the lockdown measures in the United States, and 54 cities are included in the study. The prediction generates a notable NO₂ difference between the observations if the lockdown is not considered, and the changes in urban mobility can explain the difference. It is found that the changes in urban mobility associated with various road textures have a significant impact on NO₂ dispersion in different types of climates.

Gross primary production (GPP) estimation usually involves a priori assumptions about biome-specific rules or climate controls, which hampers an objective analysis of driving mechanisms. Observation-based methods that are biome-invariant and globally uniform are thus highly desirable. To facilitate this, a reflectance index representing the ratio of chlorophyll to total pigments (R_chl) was proposed to consider the variation of energy conversion efficiency driven by different pigment contents in the canopy. Experiments based on simulated reflectance spectra showed that R_chl could explain over 83% of chlorophyll ratio dynamics. A model was then developed which approximates GPP as the product of R_chl, the normalized difference vegetation index, the near-infrared reflectance, and the photosynthetically active radiation. The model is simple, fast, with definite physical meaning and independent of climatic parameters such as temperature and humidity. Validated with over one hundred thousand field measurements, the model exhibited comparable accuracy to biome- and climate-based GPP models (r = 0.74 for both types of models), demonstrating satisfactory performance. It also achieved significantly better results compared with a regression model excluding R_chl, which emphasizes the important role of R_chl. By avoiding circular analyses in mechanism studies on GPP variations, this model may extend our previous understanding of global terrestrial carbon uptake.

Tropical Pacific upwelling-dependent ecosystems are the most productive and variable worldwide, mainly due to the influence of El Niño Southern Oscillation (ENSO). ENSO can be forecasted seasons ahead thanks to assorted climate precursors (local-Pacific processes, pantropical interactions). However, due to observational data scarcity, little is known about the importance of these precursors for marine ecosystem prediction. Previous studies based on Earth System Model simulations forced by observed climate have shown that multiyear predictability of tropical Pacific marine primary productivity is possible. With recently released global marine ecosystem simulations forced by historical climate, full examination of tropical Pacific ecosystem predictability is now feasible. By complementing historical fishing records with marine ecosystem model data, we show herein that equatorial Atlantic sea surface temperatures (SSTs) constitute a valuable predictability source for tropical Pacific fisheries, which can be forecasted over large-scale areas up to three years in advance. A detailed physical-biological mechanism is proposed whereby equatorial Atlantic SSTs influence upwelling of nutrient-rich waters in the tropical Pacific, leading to a bottom-up propagation of the climate-related signal across the marine food web. Our results represent historical and near-future climate conditions and provide a useful springboard for implementing a marine ecosystem prediction system in the tropical Pacific.

The global recognition of modern agricultural practices' impact on the environment has fuelled policy responses to ameliorate environmental degradation in agricultural landscapes. In the US and the EU, agri-environmental subsidies (AES) promote widespread adoption of sustainable practices by compensating farmers who voluntarily implement them on working farmland. Previous studies, however, have suggested limitations of their spatial targeting, with funds not allocated towards areas of the greatest environmental need. We analysed AES in the US and EU—specifically through the Environmental Quality Incentives Program (EQIP) and selected measures of the European Agricultural Fund for Rural Development (EAFRD)—to identify if AES are going where they are most needed to achieve environmental goals, using a set of environmental need indicators, socio-economic variables moderating allocation patterns, and contextual variables describing agricultural systems. Using linear mixed models and linear models we explored the associations among AES allocation and these predictors at different scales. We found that higher AES spending was associated with areas of low soil organic carbon and high greenhouse gas emissions both in the US and EU, and nitrogen surplus in the EU. More so than successes, however, clear mismatches of funding and environmental need emerged—AES allocation did not successfully target areas of highest water stress, biodiversity loss, soil erosion, and nutrient runoff. Socio-economic and agricultural context variables may explain some of these mismatches; we show that AES were allocated to areas with higher proportions of female producers in the EU but not in the US, where funds were directed towards areas with less tenant farmers. Moreover, we suggest that the potential for AES to remediate environmental issues may be curtailed by limited participation in intensive agricultural landscapes. These findings can help inform refinements to EQIP and EAFRD allocation mechanisms and identify opportunities for improving future targeting of AES spending.

There is much current debate about the way in which the earth's climate and temperature are responding to anthropogenic and natural forcing. In this paper we re-assess the current evidence at the globally averaged level by adopting a generic 'data-based mechanistic' modelling strategy that incorporates statistically efficient parameter estimation. This identifies a low order, differential equation model that explains how the global average surface temperature variation responds to the influences of total radiative forcing (TRF). The model response includes a novel, stochastic oscillatory component with a period of about 55 years (range 51.6–60 years) that appears to be associated with heat energy interchange between the atmosphere and the ocean. These 'quasi-cycle' oscillations, which account for the observed pauses in global temperature increase around 1880, 1940 and 2001, appear to be related to ocean dynamic responses, particularly the Atlantic multidecadal oscillation. The model explains 90% of the variance in the global average surface temperature anomaly and yields estimates of the equilibrium climate sensitivity (ECS) (2.29 ^∘C with 5%–95% range 2.11 ^∘C to 2.49 ^∘C) and the transient climate response (TCR) (1.56 ^∘C with 5%–95% range 1.43 ^∘C to 1.68 ^∘C), both of which are smaller than most previous estimates. When a high level of uncertainty in the TRF is taken into account, the ECS and TCR estimates are unchanged but the ranges are increased to 1.43 ^∘C to 3.14 ^∘C and 0.99 ^∘C to 2.16 ^∘C, respectively.

The implementation of circular economy (CE) thinking to reduce environmental impacts and resource use has led to the development of innovative recycling technologies and business models. The implications of these technologies and models, however, remain largely unclear. In many CE strategies, there is a high risk of rebound, meaning a situation in which activities aimed at environmental benefits are not realized because of external reasons. A similar risk relates to limited understanding about the behavioral changes required by extensive implementation of circular practices. Using life cycle assessment, we compare the global warming potential (GWP) of five ownership and end-of-life scenarios for creating and using a pair of jeans. The scenarios are as follows: (a) BASE, i.e. basic use with waste disposal; (b) REDUCE, i.e. extended use; (c) REUSE, i.e. re-selling; (d) RECYCLE, i.e. industrial processing into new raw materials; and (e) SHARE, i.e. a rental service. Our results show that the lowest global warming impacts are achieved in the REDUCE scenario, and the second lowest are achieved in the REUSE scenario. The RECYCLE scenario leads to relatively high overall emissions because the replaced emissions from cotton production are relatively low. The use of rental services is likely to increase customers' mobility, and if that happens in a large scale, then the SHARE scenario has the highest GWP. It was found that many new CE innovations come with a high rebound risk, and existing practices carry similar, yet smaller risks.

Wheat (Triticum aestivum) is the most widely grown food crop in the world threatened by future climate change. In this study, we simulated climate change impacts and adaptation strategies for wheat globally using new crop genetic traits (CGT), including increased heat tolerance, early vigor to increase early crop water use, late flowering to reverse an earlier anthesis in warmer conditions, and the combined traits with additional nitrogen (N) fertilizer applications, as an option to maximize genetic gains. These simulations were completed using three wheat crop models and five Global Climate Models (GCM) for RCP 8.5 at mid-century. Crop simulations were compared with country, US state, and US county grain yield and production. Wheat yield and production from high-yielding and low-yielding countries were mostly captured by the model ensemble mean. However, US state and county yields and production were often poorly reproduced, with large variability in the models, which is likely due to poor soil and crop management input data at this scale. Climate change is projected to decrease global wheat production by −1.9% by mid-century. However, the most negative impacts are projected to affect developing countries in tropical regions. The model ensemble mean suggests large negative yield impacts for African and Southern Asian countries where food security is already a problem. Yields are predicted to decline by −15% in African countries and −16% in Southern Asian countries by 2050. Introducing CGT as an adaptation to climate change improved wheat yield in many regions, but due to poor nutrient management, many developing countries only benefited from adaptation from CGT when combined with additional N fertilizer. As growing conditions and the impact from climate change on wheat vary across the globe, region-specific adaptation strategies need to be explored to increase the possible benefits of adaptations to climate change in the future.

Buildings energy consumption is one of the most important contributors to greenhouse gas (GHG) emissions worldwide, responsible for 23% of energy-related CO₂ emissions. Decarbonising the energy demand of buildings will require two types of strategies: first, an overall reduction in energy demand, which could, to some extent, be achieved at negative costs; and second through a reduction of the carbon content of energy via fuel switching and supply-side decarbonisation. This study assesses the contributions of each of these strategies for the decarbonisation of the buildings sector in line with a 1.5°C global warming. We show that in a 1.5°C scenario combining mitigation policies and a reduction of market failures in efficiency markets, 81% of the reductions in buildings emissions are achieved through the reduction of the carbon content of energy, while the remaining 19% are due to efficiency improvements which reduce energy demand by 31%. Without supply-side decarbonisation, efficiency improvements almost entirely suppress the doubling of emissions that would otherwise be expected, but fail to induce an absolute decline in emissions. Our modelling and scenarios show the impact of both climate change mitigation policies and of the alleviation of market failures pervading through energy efficiency markets. The results show that the reduction of the carbon content of energy through fuel switching and supply-side decarbonisation is of paramount importance for the decarbonisation of buildings.

Today, about 55% of the world population lives in cities and this is foreseen to increase to 68% by 2050. The urban activities of such a large number of people in relatively small spaces can make the air quality levels in cities harmful to human health. For this reason, the European Union (EU) has established a regulatory framework to control and improve air quality levels in cities (Directive 2008/50/EC) by defining a number of fixed stations and other requirements. The aim of this work is to evaluate the air quality reported by the official fixed stations via the installation of a complementary mobile network of air quality based on passive dosimetry of NO₂ measurement during the period 2017–2019. In this study, Valencia (Spain) is selected as a representative European city with seven fixed stations and a network of 424 passive dosimetry sensors distributed throughout the city. In addition, an index of impact of pollutant on population is developed to optimize the locations of air quality stations among neighbourhoods across the city based on the levels of pollution measured by mobile sensors and the population directly affected. The results obtained show that 43.7% of mobile sensors in Valencia exceeded the limit value established by the EU Directive as well as by the World Health Organization during the assessment period. This indicates that the air quality levels offered by the fixed stations are neither representative nor reliable for the air quality monitoring of the city. Thus, the fixed stations currently operating do not provide reliable information on the areas of the city where the majority of the population breathes air with the highest level of pollution. Specifically, the results show that 34.6% of citizens live in areas with an average annual value above the limit recommended for the protection of human health.

Smart home technologies refer to devices that provide some degree of digitally connected, automated, or enhanced services to household occupants. Smart homes have become prominent in recent technology and policy discussions about energy efficiency, climate change, and the sustainability of buildings. Nevertheless, do they truly promote sustainability goals? Based on an extensive original dataset involving expert interviews, supplemented with a review of the literature, this study elaborates on an array of social, technical, political, and environmental risks facing smart home innovation, with clear implications for research, policy, and technology development. Only with a more thoughtful and coordinated mix of policies in place will smart home adoption begin to fulfill some of the sustainability objectives their advocates continually promise.

Nutrients are recognized as one of the nine planetary boundaries, which could increase risks of unacceptable global environmental changes. In this study we developed a recent and complete high-resolution nutrient flows compilation that can be used for assessing global nutrient annual fluxes from land to sea. It consists of annual nitrogen (N) and phosphorus (P) fluxes with spatial resolution of five arc-minutes (about 10 × 10 km at the equator) centered on year 2005, assessing potential nutrient delivery to rivers, lakes and oceans. The dataset includes: nutrient inputs in agricultural areas (mineral and organic fertilization, nitrogen fixation), crop/fodder/grass harvest, nutrient inputs by domestic and industrial activities (i.e. wastewater treatment plants, industries, and phosphorus from detergents), nutrients from built-areas, nitrogen atmospheric deposition, N and P transported via erosion, and phosphorus release by weathering. The dataset was compared with other studies, was analyzed at different spatial scales showing the main environmental hotspots, and finally a qualitative uncertainty analysis was performed. The results showed that nitrogen surplus was the largest contributor to the potential losses on all continents, while for phosphorus the major contributors included the surplus, erosion and inputs from human wastewater. Hotspots were identified mainly in China and India. Rates exceeding 100 kg ha⁻¹ of N were observed locally in Europe, Egypt and North America coinciding with intensive agriculture practices. We also showed that N and P transported via erosion, domestic and industrial nutrient emissions, as well as manure resulted in the most uncertain fluxes.

Field studies in the global ocean have shown that plastic fragments make up the majority of plastic pollution in terms of abundance. It is not well understood how quickly plastics in the marine environmental fragment, however. Here, we study the fragmentation process in the oceanic environment by considering a model which captures continuous fragmentation of particles over time in a cascading fashion. With this cascading fragmentation model we simulate particle size distributions (PSDs), specifying the abundance or mass of particles for different size classes. The fragmentation model is coupled to an environmental box model, simulating the distributions of plastic particles in the ocean, coastal waters, and on the beach. We demonstrate the capabilities of the model by calibrating it to estimated plastic transport in the Mediterranean Sea, and compare the modelled PSDs to available observations in this region. Results are used to illustrate the effect of size-selective processes such as vertical mixing in the water column and resuspension of particles from the beach into coastal waters. The model quantifies the role of fragmentation on the marine plastic mass budget: while fragmentation is a major source of secondary plastic particles in terms of abundance, it seems to have a minor effect on the total mass of particles larger than 0.1 mm. Future comparison to observed PSD data allow us to understand size-selective plastic transport in the environment, and potentially inform us on plastic longevity.

This article examines whether individuals' preferences for government policy are affected by status quo bias. We designed a contingent valuation survey that asks respondents directly about their willingness to pay (WTP) for their state to be a part of a regional carbon mitigation policy. The survey has two randomized frames, which differ in whether or not their state is already part of the policy. We distributed the survey to a representative sample of Rhode Island residents (N = 844). We find that respondents who believe that Rhode Island would be joining the policy for the first time have a WTP to join of $170 (quite similar to previous research at a national scale), whereas those who believe Rhode Island is already part of the policy are willing to pay 2.5 times more, or $420, to stay in the program. Our results suggest that citizens greatly prefer existing carbon mitigation policies to new policies, which implies that carbon policy will be more successful if enacted through the legislature instead of popular vote.

Increased upright vegetation growth (i.e. trees and shrubs) in northern environments can profoundly impact ground surface thermal conditions through winter warming (e.g. enhanced snow trapping) and summer cooling (e.g. increased shading). The debate over these opposite effects emphasizes the need to better constrain net temperature impacts of upright vegetation on soils in northern environments. We generate a series of simulations with a widely-used permafrost model to partition the absolute warming and cooling impacts of upright vegetation on ground surface temperatures for a variety of shading scenarios, climates and surficial materials types (i.e. bedrock, mineral and organic soils). These scenarios simulate annual temperature differences between the air and ground surface caused by upright vegetation to provide likely ranges for the net effects induced by vegetation. These simulations showed that ground surface temperature warming in the winter mostly overwhelmed ground surface cooling in the thawing season even when simulations included extreme shading effects. Constraining the simulations to current best estimates of the possible summer cooling impact of vegetation yielded a dominant winter warming signal for most snow depths and climate types. Differences in the magnitude of air-surface temperature offsets between sites underlain by bedrock, mineral and organic soil highlights the importance of considering differences in unfrozen moisture content in areas where the ground freezes and thaws seasonally. The results of this study suggest that the net ground surface temperature impacts of increased snow trapping by vegetation will far exceed cooling caused by enhanced shading following increases in tall vegetation in most northern environments.

Plug-in hybrid electric vehicles (PHEVs) combine an electric motor with an internal combustion engine and can reduce greenhouse gas emissions from transport if mainly driven on electricity. The environmental benefit of PHEVs strongly depends on usage and charging behaviour. However, there is limited evidence on how much PHEVs actually drive on electricity and how much conventional fuel they use in real-world operation. Here, we provide the first systematic empirical analysis of real-world usage and fuel consumption (FC) of approximately 100 000 vehicles in China, Europe, and North America. We find that real-world mean CO₂ emissions of PHEVs are between 50 and 300 g CO₂ km⁻¹ depending on all-electric range, user group and country. For private vehicles, real-world CO₂ emissions are two to four times higher than test cycle values. The high CO₂ emissions and FC mainly result from low charging frequency, i.e. less than once per driving day. Our results demonstrate the importance of real-world vehicle emission measurements and indicate the need to adjust current PHEV policies, i.e. official emission values need to better reflect realistic electric driving shares and incentives need to put more emphasis on frequent charging.

Polluting biomass fuel use has adverse effects on human health, but there are limited studies exploring the association between biomass fuel use and undernutrition in adult and child population. The study aims to investigate the association between biomass fuel use and undernutrition status of adults and children under 5 years of age in low and middle income countries (LMICs). Data were from the Demographic and Health Surveys in 14 LMICs. The main exposure variable was type of fuel the household mainly used for cooking. Linear regression models and Modified Poisson regression models with robust error variance in consideration of complex survey design were used to estimate the association between type of fuel used for cooking and the outcomes of interest. Personal and household data were collected by questionnaire, and anthropometry data were collected by measurement with a standardised protocol. A total of 532 987 households were included in the analysis, and the majority of households (63.9%) used high polluting fuels. For women, use of high polluting fuels lead to a 0.66 kg m⁻² (95% CI: −0.74, −0.58) decrease in BMI and a 10% (95% CI: 7%, 13%) higher risk of underweight. For men, high polluting fuels lead to a 0.63 kg m⁻² (95% CI: −0.88, −0.38) decrease in BMI and a 11% (95% CI: 5%, 18%) higher risk of underweight. For children, high polluting fuels resulted in a 0.16 (95% CI: −0.20, −0.11), 0.17 (95% CI: −0.22, −0.11), and 0.09 (95% CI: −0.14, −0.04) unit decrease in weight-for-age, height-for-age, and weight-for-height z scores, respectively; high polluting fuel use can lead to a 10% (95% CI: 3%, 18%) higher risk of underweight and a 13% (95% CI: 7%, 19%) higher risk of stunting, respectively. Effective interventions should be adopted by policymakers to accelerate the transition of polluting fuels to cleaner energy in LMICs.

Soil erosion delivers enormous amounts of macro-nutrients including nitrogen (N) and phosphorus (P) from land to rivers, potentially sustaining water column bioavailable nutrient levels for decades. In this study, we represent erosional N and P fluxes in the Energy Exascale Earth System Model (E3SM) and apply the model to the continental United States. We estimate that during 1991–2019 soil erosion delivers 775 Gg yr⁻¹ (1 Gg = 10⁹ g) of particulate N (PN) and 328 Gg yr⁻¹ of particulate P (PP) on average to the drainage basins of the northern Gulf of Mexico, including the Mississippi/Atchafalaya River and other rivers draining to the Texas Gulf and the Eastern Gulf. Our model simulation shows that in these rivers PP is the dominant P constituent and over 55% of P exported by erosion comes from soil P pools that could become bioavailable within decades. More importantly, we find that during 1991–2019 erosional N and P fluxes increase at rates of about 15 Gg N yr⁻¹ and 6 Gg P yr⁻¹, respectively, due to increased extreme rains in the Mississippi/Atchafalaya river basin, and this intensification of erosional N and P fluxes drive the significant increase of riverine PN and PP yields to the northern Gulf of Mexico. With extreme rains projected to increase with warming, erosional nutrient fluxes in the region would likely continue to rise in the future, thus complicating the effort of reducing eutrophication in the inland and coastal waters.

Oil and gas production wells are a major anthropogenic source of the greenhouse gas methane (CH₄) in the United States. Oil and gas production rates from these wells fluctuate due to changes in demand, and is expected to decline over the coming decades to centuries due to the transition to renewable energy. The CH₄ emissions profile from wells that are 'shut-in' to accommodate changes in demand has not been previously measured, and thus it is unclear whether reduced demand will actually result in reduced CH₄ emissions from oil and gas production. Here we present the results of a measurement campaign of CH₄ emissions from shut-in and other non-producing oil wells in the Permian Basin, Texas, the largest oil production basin on Earth. All the wells we measured were conventionally drilled oil wells, and we did not measure CH₄ emissions from any shut-in unconventional wells. We found that, of 37 wells measured, two-thirds had an emission rate of less than 1 g CH₄ hr⁻¹, with the remaining seven wells ranging from 1.3 to 132.0 g CH₄ hr⁻¹. The average CH₄ emission rate from all wells was 6.2 g CH₄ hr⁻¹, lower than previous measurements of CH₄ emissions from active conventional wells in the Permian Basin (∼400 g CH₄ hr⁻¹) (Robertson et al (2020 Environ. Sci. Technol.54 13926–34)). Some shut-in wells could be a substantial source of CH₄ emissions if this category is not subject to leak detection and repair regulations. We also found five orphaned wells that were a source of produced water to the surface, sometimes in very large quantities (1000s of liters per minute), with evidence for emissions of CH₄, hydrogen sulfide, brine, and possibly other hazardous chemicals such as oil residue. Future work should further characterize the impacts of shut-in and orphaned wells on greenhouse gas emissions, water quality and human health.

Cocoa and oil palm are the major commodity crops produced in Ghana and livelihood options for hundreds of thousands of rural households. However, their production has negative environmental and socioeconomic impacts. Certification standards have been promoted as a market-led mechanism to ensure their sustainable production. Even though food security does not feature in the theory of change of most certification standards, there are interesting intersections. This paper assesses the food security outcomes of certification adoption among cocoa and oil palm smallholders in Ghana. We analyse 608 household surveys from two study sites using propensity score matching and multiple standardized metrics of food security such as the Food Consumption Score (FCS), the Household Food Insecurity Access Scale (HFIAS) and the Coping Strategies Index. Certified cocoa/oil palm farmers are more food secure than uncertified farmers and food crop farmers across most indicators and group comparisons. However, the differences are for most indicators not substantial or statistically significant (except the HFIAS). In fact, 65% and 68% of the certified cocoa and oil palm farmers are vulnerable to food insecurity in terms of the FCS. These results suggest that even though certification adoption can improve the livelihoods and yields of farmers, in reality it has marginal effect on food security. Certification standards would need to emphasize food security in their guidelines, theories of change and support packages to smallholders if they are to enhance food security and have a truly positive effect on the sustainability of cocoa and oil palm production.

Grassland ecosystems supporting wildlife and livestock populations have undergone significant transformation in the last millennium. Climate, herbivory, fire, and people are identified as important drivers of ecosystems dynamics; however, grassland resilience has been rarely explored in landscapes with mixed grazing histories. Here we analyse ecosystems states from a South African mountain valley grassland in the last 1250 years using palaeoenvironmental proxies. Our results suggest that a tallgrass phase maintained by climate, people and fire replaced a shortgrass phase driven by indigenous herbivores after ca. 690 cal BP. Furthermore, the tallgrass phase had unpalatable grasses and disturbed soil. We suggest these ecological changes were linked to climate change and arrival of pastoralists in the region. Therefore, our results indicate that human activities may undermine resilience of grasslands and that reversing some changes may be difficult.

Seawater quality degradation is caused by diverse, non-linearly interacting factors, knowledge of which is essential for understanding and predicting water quality trends. Currently, most water-quality research has been based on certain assumptions to employ linear approaches for solving simplified problems, such as numerical simulations or cumulative impact assessments. To improve the accuracy and ease of prediction, the random forest method has been increasingly employed as a good alternative to traditional prediction methods. In the present study, the random forest method was adopted to construct a model of the water quality response of Xincun Lagoon to anthropogenic nutrient inputs based on a limited amount of sample data, aiming to (a) identify the critical sources of nutrient inputs that affect the meeting of water quality objectives so as to minimize the socioeconomic impact on secondary stakeholders; and (b) predict the impact of a reduction of anthropogenic nutrient inputs on water quality improvement. It can be seen from the results that the intensity of stressors generated by different human activities presents an obvious non-linear superposition pattern, and the random forest method is one of the feasible solutions to this phenomenon; in addition, the impact on the lagoon ecosystem is not directly related to the intensity of the pressure source, for example, coastal aquaculture is more important than shallow sea cage aquaculture. Therefore, the method established in this paper can be used to identify the key pressure sources during the restoration of the lagoon environment, so as to achieve the unity of economy and effectiveness.

Although previous studies have proposed an association between maternal exposure to fine particulate matter (PM_2.5) and the risk of gestational diabetes mellitus (GDM), such evidence remains rare. Additionally, the effects of PM_2.5 on glycemic control in GDM patients are poorly known. In this study, we conducted a prospective birth cohort study in China, and aimed to investigate the association between maternal exposure to PM_2.5 and the risk of GDM, identify the susceptible exposure window, and quantify the exposure-response relationships between PM_2.5 and fasting glucose in GDM patients. A spatiotemporal land-use-regression model was used to estimate individual weekly PM_2.5 exposure during pregnancy. A distributed lag nonlinear model incorporated with a Cox proportional hazard model was used to estimate the association between maternal exposure to PM_2.5 and the risk of GDM. Among the 4174 pregnant women in our study, 1018 (24.4%) were diagnosed with GDM. Each 10 μg m⁻³ increment in PM_2.5 exposures during the 24th gestational week was significantly associated with a higher risk of GDM [hazard ratio (HR) = 1.03, 95% CI (confidence interval): 1.01, 1.06]. Compared to the lowest quartile (Q1) of PM_2.5 exposure, participants with the highest quartile (Q4) during the 21st–24th gestational weeks had a higher risk of GDM, and the strongest association was observed in the 22nd gestational week (HR = 1.15, 95%Cl: 1.02, 1.28). The mean PM_2.5 exposures during the 21st–24th weeks were positively associated with fasting plasma glucose in pregnant women with GDM. Each 10 μg m⁻³ increase in the mean PM_2.5 exposure was associated with a 0.07 mmol l⁻¹ (95% CI: 0.04, 0.11 mmol l⁻¹) increase in the fasting glucose level. Our findings suggest that maternal exposure to higher PM_2.5 during pregnancy may increase the risk of GDM, and result in poor glycemic control among pregnant women with GDM. The 21st–24th gestational week period might be the (most)? susceptible exposure window of PM_2.5.

Preterm birth is the largest contributor to neonatal mortality globally and it is also associated with several adverse health outcomes. Recent studies have found an association between maternal exposure to air pollution and an increased risk for preterm birth. As a constituent of air pollution, ozone is a highly reactive molecule with several negative health effects when present near earth's surface. This health impact assessment aims to estimate the proportion of preterm births—in current and future situations—attributable to maternal ozone exposure in 30 European countries (EU30). A literature search was performed using relevant keywords, followed by meta-analysis with STATA software in which five studies investigating exposure-response relationship of interest were included. The attributable proportion, and number of cases, was modelled with the software AirQ+ against current and future European ozone concentrations. According to our meta-analysis, the relative risk for giving birth preterm was calculated to 1.027 (95% CI 1.009–1.046) per 10 μg m⁻³ increase in ozone concentration. This rendered 7.1% (95% CI 2.5–11.7) of preterm births attributable to maternal ozone exposure to in EU30 during 2010, which is equal to approximately 27 900 cases. By 2050, the projected decrease in ozone precursor emissions rendered an estimated 30% decrease of ozone attributable preterm births. Not taking emission change into account, due to climate change the ozone-related preterm birth burden might slightly increase by 2050 in Central and Southern Europe, and decrease in Eastern and Northern Europe. In summation, these numbers make a substantial impact on public health.

The Arctic is warming twice as fast as the rest of the planet, leading to rapid changes in species composition and plant functional trait variation. Landscape-level maps of vegetation composition and trait distributions are required to expand spatially-limited plot studies, overcome sampling biases associated with the most accessible research areas, and create baselines from which to monitor environmental change. Unmanned aerial vehicles (UAVs) have emerged as a low-cost method to generate high-resolution imagery and bridge the gap between fine-scale field studies and lower resolution satellite analyses. Here we used field spectroscopy data (400–2500 nm) and UAV multispectral imagery to test spectral methods of species identification and plant water and chemistry retrieval near Longyearbyen, Svalbard. Using the field spectroscopy data and Random Forest analysis, we were able to distinguish eight common High Arctic plant tundra species with 74% accuracy. Using partial least squares regression (PLSR), we were able to predict corresponding water, nitrogen, phosphorus and C:N values (r² = 0.61–0.88, RMSEmean = 12%–64%). We developed analogous models using UAV imagery (five bands: Blue, Green, Red, Red Edge and Near-Infrared) and scaled up the results across a 450 m long nutrient gradient located underneath a seabird colony. At the UAV level, we were able to map three plant functional groups (mosses, graminoids and dwarf shrubs) at 72% accuracy and generate maps of plant chemistry. Our maps show a clear marine-derived fertility gradient, mediated by geomorphology. We used the UAV results to explore two methods of upscaling plant water content to the wider landscape using Sentinel-2A imagery. Our results are pertinent for high resolution, low-cost mapping of the Arctic.

In this investigation, we use a socio-environmental multi-proxy approach to empirically test hypotheses of recurrent resilience cycles and the role of climate forcing in shaping such cycles on the Iberian Peninsula during mid-Holocene times. Our approach combines time series reconstructions of societal and environmental variables from the southern Iberian Peninsula across a 3000 yr time interval (6000–3000 cal yr BP), covering major societal and climate reorganisation. Our approach is based on regional compilations of climate variables from diverse terrestrial archives and integrates new marine climate records from the Western Mediterranean. Archaeological variables include changes in material culture, settlement reconstructions and estimates of human activities. In particular, both detailed chronologies of human activities evolving from the Late Neolithic to the Bronze Age and mid- to Late Holocene climate change across the mid-Holocene are compared, aiming to assess potential human responses and coping processes associated with abrupt mid-Holocene climate changes.

Numerous extreme climate anomalies were recorded in the northern extratropics in January–March (JFM) 2020, significantly impacting human lives and ecosystems in the affected areas. Those anomalies were caused by an extreme positive Arctic Oscillation (AO) event, with the JFM 2020 AO index of 2.8 being the highest on the record. However, all well-established autumn precursors pointed towards the following wintertime AO phase being negative. Indeed, a negative AO phase was developing until late December when a sudden shift to the strong positive AO event occurred in the troposphere. The geopotential anomalies associated with positive AO spread into the lower stratosphere, and were steadily enhancing throughout JFM resulting in an extreme positive AO event. We show that the strong positive AO event was a result of the destructive interference of the anomalous planetary waves with climatological ones, which led to wave flattening and enhancement of the polar vortex.

This study examined the suitability of three different indicators as entry points for agricultural regulation for limiting excess nitrogen (N) fertilizer inputs in Germany: net soil surface balance, gross farm-gate balance, and fertilization planning. Data on about 6000 farms in Germany were grouped into types for comparative analysis. The design of the regulatory approaches and the reliability of constituent parameters were then examined, and proportions of affected farms and mean N reduction requirements were identified. This revealed that: (a) design and purpose of the regulatory approaches differ, but the data requirements are very similar; (b) the parameters involved differ in reliability and integrity; and (c) the limits for maximum N fertilizer input at farm level vary with approach and farm type.

Recent analyses indicate that global fruit and vegetable (F&V) production will need to increase by 50%–150% by 2050 in order to achieve sustainable and healthy diets for 10 billion people. Although global production of F&V has grown by 50% during the last two decades alone, simply scaling up current systems of F&V production, supply chains, and consumption will inevitably worsen environmental and socioeconomic tradeoffs. This article examines three examples of important F&V—avocados, leafy greens, and tomatoes—to assess the global challenge of meeting dietary recommendations at affordable prices to consumers while sustaining producer livelihoods and minimizing environmental damage. These three cases highlight key characteristics of F&V systems that make the challenge of sustainable expansion especially difficult: knowledge-, input-, and labor-intensive production, high rates of food loss and waste, and low affordability to consumers relative to less nutrient-dense food groups. Our analysis shows that only by investing in innovations that increase diversity, integrate technology, and improve equity will truly sustainable expansion of F&V systems be possible.

Buildings are responsible for a major share of global final energy consumption and carbon dioxide (CO₂) emissions. An analysis of the worldwide observed drivers of demand can highlight the policy actions most suited to drive the decarbonization of the building sector. To contribute to such an analysis, we carry out a mapping of the literature on determinants of energy demand and CO₂ emissions from buildings. The work includes a list and classification of relevant studies in an on-line geographical map, a description of trends and gaps, and a narrative review. We identify 4080 articles in the Scopus and the Web of Science databases, of which 712 are relevant after screening at the title and abstract level, and 376 are included for data extraction. The literature base mostly addresses electricity and water use, in North America and Europe (57% of the literature) and Asia (27%). Econometric modeling approaches using panel data to calculate demand elasticities, dominate. These findings highlight gaps in terms of the studied variables (only 5% focus on CO₂ emissions while a mere 1% have a lifecycle perspective), geographical scope (only 5% of the articles focus on Africa, 7% on Latin America and the Caribbean, and 5% on Oceania), and methodological approach (only 5% use qualitative methods). We confirm that worldwide, income, energy price and outdoor temperature are unequivocal drivers of buildings energy demand and CO₂ emissions, followed by other indicators of scale such as population or heated floor area. Our analysis makes it clear that decoupling from rising wealth levels has not been observed. This will continue to challenge reductions in energy use and CO₂ emissions from buildings in line with climate targets. Macroeconomic policies focusing on the impacts of income, energy price, population and growing floor area are needed in combination with technical policy to reduce the impact of outdoor climate.

Arctic warming has increased vegetation growth and soil respiration during recent decades. The rate of Arctic warming will likely amplify over the 21st century. Previous studies have revealed that the most severe Arctic warming occurred during the cold season (September to May). The cold-season warming has posited significant CO₂ emissions to the atmosphere via respiration, possibly offsetting warm-season (June to August) net CO₂ uptake. However, prevailing Earth system land models poorly represent cold-season CO₂ emissions, making estimates of Arctic tundra annual CO₂ budgets highly uncertain. Here, we demonstrate that an improved version of the energy exascale Earth system model (E3SM) land model (ELMv1-ECA) captures the large amount of cold-season CO₂ emissions over Alaskan Arctic tundra as reported by two independent, observationally-constrained datasets. We found that the recent seven-decades warming trend of cold-season soil temperature is three times that of the warm-season. The climate sensitivity of warm-season net CO₂ uptake, however, is threefold higher than for the cold-season net CO₂ loss, mainly due to stronger plant resilience than microbial resilience to hydroclimatic extremes. Consequently, the modeled warm-season net CO₂ uptake has a larger positive trend (0.74 ± 0.14 gC m⁻² yr⁻¹) than that of cold-season CO₂ emissions (0.64 ± 0.11 gC m⁻² yr⁻¹) from 1950 to 2017, supported by enhanced plant nutrient uptake and increased light- and water-use efficiency. With continued warming and elevated CO₂ concentrations under the representative concentration pathway (RCP) 8.5 scenario, the increasing rate of warm-season net CO₂ uptake is more than twice the rate of cold-season emissions (1.33 ± 0.32 gC m⁻² yr⁻¹ vs 0.50 ± 0.12 gC m⁻² yr⁻¹), making the modeled Alaskan Arctic tundra ecosystem a net CO₂ sink by 2100. However, other geomorphological and ecological disturbances (e.g. abrupt permafrost thaw, thermokarst development, landscape-scale hydrological changes, wildfire, and insects) that are not considered here might alter our conclusion.

With the merits of superior performance and easy implementation, the harmony search (HS), a famous population-based evolutionary method, has been widely adopted to resolve global optimization problems in practice. However, the standard HS method still suffers from the defects of premature convergence and local stagnation in the complex multireservoir operation problem. Thus, this study develops an enhanced harmony search (EHS) method to improve the HS's search ability and convergence rate, where adaptive parameter adjustment strategy is used to enhance the global search performance of the swarm, while the elite-learning evolutionary mode is used to improve the converge trajectory of the population. To verify its practicability, EHS is applied to solve numerical optimization and multireservoir operation problems. The results show that EHS can produce better results than several existing methods in different cases. For instance, the mean objective of EHS is improved by about 23.9%, 28.7% and 26.8% compared with particle swarm optimization, differential evolution and gravitational search algorithm in 1998–1999 typical runoff case. Hence, an effective optimizer is developed for sustainable ecological operation of cascade hydropower reservoirs in river ecosystem.

The adverse effects of air pollution during pregnancy have been studied intensively, but mainly utilizing linear and logistic models, which generally yield little information about how air pollution may change the distribution of birth weight in populations. We aimed to examine the effects of fine particulate matter (PM_2.5) on quantiles of birth weight, and if effects were heterogeneous in different populations and regions. We used a prospective cohort study of 196 283 singleton term live births from 16 counties across China during 2014–2018. PM_2.5 exposure for full gestation, each trimester and last gestational month were assessed by Inverse Distance Weighting interpolation. Linear and quantile regression were conducted to estimate associations between PM_2.5 exposure and mean birth weight, as well as birth weight distribution, with birthweight z-score as the main outcome. Stratified analyses and Cochran Q tests were conducted by maternal and geographical characteristics. Each 10 µg m⁻³ increase in average PM_2.5 over the entire pregnancy was associated with reduced birthweight z-score (−0.010, 95% CI: −0.015, −0.005) and birth weight (−3.21 g, 95% CI: −5.27, −1.15). In quantile regression, more pronounced effects were observed in lower and intermediate quantiles, with a decrease of 0.021 (95% CI: 0.033, 0.009) and 0.009 (95% CI: 0.015, 0.002) in the 5th and 50th quantiles of birthweight z-score, respectively. Additionally, we observed stronger associations among well-educated, migrant and primiparous mothers as well as in coastal areas. Maternal exposure to PM_2.5 was associated with reduction in birth weight, especially for those with very low birth weight. Well-educated, migrant and primiparous mothers, as well as births in coastal areas may be more sensitive to PM_2.5 in our study population. The results may be relevant to targeted public health interventions to reduce maternal exposure to air pollution.

Theoretical and empirical studies show increased diversity in crops, supply chains, and markets helps stabilize food systems. At the same time global commodity markets and industrial agriculture have driven homogenization of local and regional production systems, and consolidated power in fewer larger specialized farms and distributers. This is a global challenge, with no obvious global solutions. An important question therefore, is how individual countries can build their own resilience through maintaining or increasing diversity within their borders. Here we show, using farm level data from Germany, that spreading production risk by growing the same crops across different farms carries stabilizing benefits by allowing for increased spatiotemporal asynchrony within crops. We also find that increasing asynchrony between the year-to-year production of different crops has stabilizing effects on food supply. Importantly, the benefits of increasing crop diversity are lower in specialized landscapes growing the same crop on large patches. Our results illustrate clear benefits of diversified crops, producers, and agricultural landscapes to buffer supply side shocks, and for incorporation in subsidies and other regulatory measures aimed at stabilizing food systems.

In order to achieve worldwide food security, there is a focus on sustainable intensification of crop production. This requires sustainable irrigation water use for irrigated croplands, as irrigation withdrawals are already resulting in groundwater exploitation and unmet ecosystem water requirements. Our study aims to quantify attainable wheat, maize, rice and soybean production on currently irrigated cropland under sustainable water use. Attainable production accounts for increases in nutrient application, while limiting irrigation withdrawals to renewable water availability and without compromising river ecosystem water requirements. Attainable production was quantified using a newly developed two-way coupled hydrological model and crop model. This model framework could comprehensively simulate biophysical processes related to water availability and crop growth under water and nutrient limitations. Our results indicate worldwide crop nitrogen uptake should increase by 20%, to achieve production gap closure. However, worldwide irrigation withdrawals should decrease by more than a third in order to ensure sustainable water use. Under these constraints, a total (all crops) production decrease of 5% was estimated, compared to currently achievable production. Moreover, achievable irrigated crop production in the extensively irrigated croplands of northeastern China, Pakistan and northwestern India would be reduced by up to a third. On the other hand, increases in achievable irrigated crop production may be possible in regions such as southern America, eastern Europe and central Africa. However, in these regions currently only a small fraction of crops is irrigated. Our results imply that intensification on currently irrigated croplands is at odds with sustainable water management, and further locally-oriented research is needed to assess suitable water management options and solutions.

Excess atmospheric ammonia (NH₃) leads to deleterious effects on biodiversity, ecosystems, air quality and health, and it is therefore essential to monitor its budget and temporal evolution. Hyperspectral infrared satellite sounders provide daily NH₃ observations at global scale for over a decade. Here we use the version 3 of the Infrared Atmospheric Sounding Interferometer (IASI) NH₃ dataset to derive global, regional and national trends from 2008 to 2018. We find a worldwide increase of 12.8 ± 1.3 % over this 11-year period, driven by large increases in east Asia (5.80 ± 0.61% increase per year), western and central Africa (2.58 ± 0.23 % yr⁻¹), North America (2.40 ± 0.45 % yr⁻¹) and western and southern Europe (1.90 ± 0.43 % yr⁻¹). These are also seen in the Indo-Gangetic Plain, while the southwestern part of India exhibits decreasing trends. Reported national trends are analyzed in the light of changing anthropogenic and pyrogenic NH₃ emissions, meteorological conditions and the impact of sulfur and nitrogen oxides emissions, which alter the atmospheric lifetime of NH₃. We end with a short case study dedicated to the Netherlands and the 'Dutch Nitrogen crisis' of 2019.

Particulate air pollution causes a spectrum of adverse health effects affecting the respiratory, cardiovascular, neurological, and metabolic systems that are hypothesised to be driven by inflammation and oxidative stress. Millions of premature deaths each year are attributed to exposure to ambient particulate matter (PM). We quantified health and economic impacts from long-term exposure to ambient PM_2.5 in the population of Thailand for 2016. We collected data on ambient PM_2.5 concentrations from automatic monitoring stations across Thailand over 1996–2016. We used historic exposure to PM_2.5 to estimate the mortality in each province from lower respiratory infections (LRIs), stroke, chronic obstructive pulmonary disease, lung cancer, and ischaemic heart disease, and also assessed diabetes mortality, as well as incident cases of dementia and Parkinson's disease, in supplementary analyses. We applied risk estimates from the Global Exposure Mortality Model to calculate attributable mortality and quantify disability-adjusted life years (DALYs); we based economic costs on the value of a statistical life (VSL). We calculated 50 019 (95% confidence interval [CI]: 42 189–57 849) deaths and 508 918 (95% CI: 438 345–579 492) DALYs in 2016 attributed to long-term PM_2.5 exposure in Thailand. Population attributable fractions ranged from 20% (95% CI: 10% to 29%) for stroke to 48% (95% CI: 27% to 63%) for LRIs. Based on the VSL, we calculated a cost of US$ 60.9 billion (95% CI: US$ 51.3–70.4 billion), which represents nearly 15% of Thailand's gross domestic product in 2016. While progress has been made to reduce exposure to ambient PM_2.5 in Thailand, continued reductions based on stricter regulatory limits for PM_2.5 and other air pollutants would help prolong life, and delay, or prevent, onset of cardiorespiratory and other diseases.

This study applies an indicators framework to investigate climate drivers of tundra vegetation trends and variability over the 1982–2019 period. Previously known indicators relevant for tundra productivity (summer warmth index (SWI), coastal spring sea-ice (SI) area, coastal summer open-water (OW)) and three additional indicators (continentality, summer precipitation, and the Arctic Dipole (AD): second mode of sea level pressure variability) are analyzed with maximum annual Normalized Difference Vegetation Index (MaxNDVI) and the sum of summer bi-weekly (time-integrated) NDVI (TI-NDVI) from the Advanced Very High Resolution Radiometer time-series. Climatological mean, trends, and correlations between variables are presented. Changes in SI continue to drive variations in the other indicators. As spring SI has decreased, summer OW, summer warmth, MaxNDVI, and TI-NDVI have increased. However, the initial very strong upward trends in previous studies for MaxNDVI and TI-NDVI are weakening and becoming spatially and temporally more variable as the ice retreats from the coastal areas. TI-NDVI has declined over the last decade particularly over High Arctic regions and southwest Alaska. The continentality index (CI) (maximum minus minimum monthly temperatures) is decreasing across the tundra, more so over North America than Eurasia. The relationship has weakened between SI and SWI and TI-NDVI, as the maritime influence of OW has increased along with total precipitation. The winter AD is correlated in Eurasia with spring SI, summer OW, MaxNDVI, TI-NDVI, the CI and total summer precipitation. This winter connection to tundra emphasizes the role of SI in driving the summer indicators. The winter (DJF) AD drives SI variations which in turn shape summer OW, the atmospheric SWI and NDVI anomalies. The winter and spring indicators represent potential predictors of tundra vegetation productivity a season or two in advance of the growing season.

China is implementing a new power system reform, with one goal of renewable energy absorption such as hydropower. However, the forthcoming spot market challenges cascade hydropower generation in terms of the short-term hydro scheduling (STHS) problem. Specifically, STHS involves fulfilling bilateral market obligations and bidding for the day-ahead market with uncertainty. Coordination of these two tasks while managing market risks becomes a problem that must be urgently solved. Herein, we propose a method based on the information-gap decision theory (IGDT) to solve the cascade hydropower STHS problem, wherein the aforementioned tasks are coordinated simultaneously. The IGDT method was used to deal with the uncertainty of the day-ahead market price, and the robustness function was derived. A mixed-integer nonlinear programming model was used to describe the proposed problem, and a commercial solver was used to solve it. A four-reservoir cascade hydropower company was used as the research object. Through the robust dispatching results, the preset profit objectives of the power generation company were satisfied within the price information gap, and the day-ahead market bidding strategy and daily contract decomposition curve were obtained. The proposed model is found to be superior to the scenario-based probability method. Moreover, a comparative analysis of bilateral contract fulfillment showed that more profits can be obtained by coordinating contract fulfillment in the day-ahead market.

Brazilian savanna is a seasonally dry biome, highly diverse and distributed mainly on nutrient-limited soils. Interactions between water stress and nutrient availability are important evolutionary filters in these environments. Previous evidence indicated that reducing the nutritional limitation increases growth rate, optimizes water transport and decreases stomatal conductance in woody plants. However, the anatomical mechanisms that explain such responses are not well-understood. We studied the effects of long-term (20 years) nutrient addition (N, NP and P) on soil chemistry and hydraulic morphological and anatomical traits in six dominant woody savanna species. Nutrient addition and decrease in soil moisture, probably related to changes in grass cover, influenced the plant hydraulic traits at the anatomical level, namely increasing the xylem vessels' diameter. Consequently, the specific theoretical xylem conductivity (K_tx) increased in five species under NP and P addition. Additionally, the stomatal pore index (SPI) decreased with species-specific responses regarding the intrinsic water use efficiency (iWUE). Four species had higher vulnerability to cavitation (l_vul) under NP and P addition. Using community-weighted mean and structural equation modeling approaches, we observed that nutrient enrichment at the community level did not affect iWUE, while the vulnerability to cavitation (l_vul) strongly increased. The K_tx and SPI were positively and negatively affected by nutrient addition, respectively, but the effects were not as strong as expected due to contrasting species responses. These changes optimized water transport with a hydraulic safety cost and reduced water loss. In comparison with responses to N addition, the greater P-limitation in Cerrado vegetation explains the inter-specific convergence in the responses of P-fertilized individuals. We showed that long-term responses to increased nutrient availability in dystrophic soils include anatomical changes in savanna woody vegetation with relevant interactions with soil-plant–atmosphere water relations.

Developing spatially explicit permafrost datasets and climate assessments at scales relevant to northern communities is increasingly important as land users and decision makers incorporate changing permafrost conditions in community and adaptation planning. This need is particularly strong within the discontinuous permafrost zone of the Northwest Territories (NWT) Canada where permafrost peatlands are undergoing rapid thaw due to a warming climate. Current data products for predicting landscapes at risk of thaw are generally built at circumpolar scales and do not lend themselves well to fine-scale regional interpretations. Here, we present a new permafrost vulnerability dataset that assesses the degree of permafrost thaw within peatlands across a 750 km latitudinal gradient in the NWT. This updated dataset provides spatially explicit estimates of where peatland thermokarst potential exists, thus making it much more suitable for local, regional or community usage. Within southern peatland complexes, we show that permafrost thaw affects up to 70% of the peatland area and that thaw is strongly mediated by both latitude and elevation, with widespread thaw occuring particularly at low elevations. At the northern end of our latitudinal gradient, peatland permafrost remains climate-protected with relatively little thaw. Collectively these results demonstrate the importance of scale in permafrost analyses and mapping if research is to support northern communities and decision makers in a changing climate. This study offers a more scale-appropriate approach to support community adaptative planning under scenarios of continued warming and widespread permafrost thaw.

Evidence about the impact of air pollution on cognitive development of children has been growing but remains inconclusive. To investigate the association of air pollution exposure and the cognitive development of children in the UK Millennium Cohort Study. Longitudinal study of a nationally representative sample of 13 058–14 614 singleton births, 2000–2002, analysed at age 3, 5 and 7 years for associations between exposure from birth to selected air pollutants and cognitive scores for: School Readiness, Naming Vocabulary (age 3 and 5), Picture Similarity, Pattern Construction (age 5 and 7), Number Skills and Word Reading. Multivariable regression models took account of design stratum, clustering and sampling and attrition weights with adjustment for major risk factors, including age, gender, ethnicity, region, household income, parents' education, language, siblings and second-hand tobacco smoke. In fully adjusted models, no associations were observed between pollutant exposures and cognitive scores at age 3. At age 5, particulate matter (PM_2.5, PM₁₀), nitrogen dioxide (NO₂), sulphur dioxide (SO₂) and carbon monoxide (CO) were associated with lower scores for Naming Vocabulary but no other outcome except for SO₂ and Picture Similarity. At age 7, PM_2.5, PM₁₀ and NO₂ were associated with lower scores for Pattern Construction, SO₂ with lower Number Skills and SO₂ and ozone with poorer Word Reading scores, but PM_2.5, PM₁₀ and NO₂ were associated with higher Word Reading scores. Adverse effects of air pollutants represented a deficit of up to around four percentile points in Naming Vocabulary at age 5 for an interquartile range increase in pollutant concentration, which is smaller than the impact of various social determinants of cognitive development. In a study of multiple pollutants and outcomes, we found mixed evidence from this UK-wide cohort study for association between lifetime exposure to air pollutants and cognitive development to age 7 years.

Extreme heat and drought often reduce the yields of important food crops around the world, putting stress on regional and global food security. The probability of concurrently hot and dry conditions, which can have compounding impacts on crops, has already increased in many regions of the globe. The evolution of these trends in coming decades could have important impacts on global food security. However, regional variation and the influence of natural climate variability on these trends remains an important gap in understanding future climate risk to crops. In this study, we examine trends in concurrent hot-and-dry extremes over global maize and wheat croplands since 1950. We find that the mean extent of cropland in a joint hot-and-dry extreme increased by ∼2% over 1950–2009, and this trend has accelerated substantially since the mid-2000s, notably in the tropics. While joint hot-and-dry seasons affected at most 1%–2% of global cropland per year during the mid-20th century, they regularly exceeded this extent after about 1980, affecting up to 5% of global crop area. These results suggest that the global climate is transitioning from one in which concurrent heat and drought occur rarely to one in which they occur over an important fraction of croplands every year. While these long-term global trends are primarily attributable to anthropogenic climate change, we find they have been suppressed by decadal climate variability in some regions, especially ones with chronic food insecurity. Potential reversals in these tendencies of decadal variability would accelerate exposure of croplands to concurrent heat and drought in coming decades. We conclude by highlighting the need for research and adaptive interventions around multivariate hazards to global crops across timescales.

Across the globe, recent work examining the state of freshwater resources paints an increasingly dire picture of degraded water quality. However, much of this work either focuses on a small subset of large waterbodies or uses in situ water quality datasets that contain biases in when and where sampling occurred. Using these unrepresentative samples limits our understanding of landscape level changes in aquatic systems. In lakes, overall water clarity provides a strong proxy for water quality because it responds to surrounding atmospheric and terrestrial processes. Here, we use satellite remote sensing of over 14 000 lakes to show that lake water clarity in the U.S. has increased by an average of 0.52 cm yr⁻¹ since 1984. The largest increases occurred prior to 2000 in densely populated catchments and within smaller waterbodies. This is consistent with observed improvements in water quality in U.S. streams and lakes stemming from sweeping environmental reforms in the 1970s and 1980s that prioritized point-source pollution in largely urban areas. The comprehensive, long-term trends presented here emphasize the need for representative sampling of freshwater resources when examining macroscale trends and are consistent with the idea that extensive U.S. freshwater pollution abatement measures have been effective and enduring, at least for point-source pollution controls.

Accurate quantification of forest carbon stocks and fluxes over regions is needed to monitor forest resources as they respond to changes in climate, disturbance and management, and also to evaluate contributions of forest sector to the regional and global carbon balances. In previous work we introduced a national forest carbon monitoring system (NFCMS) that combines forest inventory data, satellite remote sensing of stand biomass and forest disturbances, and an ecosystem carbon cycle model to assess contemporary forest carbon dynamics at a 30 m resolution. In this study, we evaluate the NFCMS estimates of biomass and carbon fluxes with available data products for the Pacific Northwest (PNW) region, and then analyze the regional carbon balance over the period 1986–2010. The biomass estimates have good agreements with evaluation datasets (eMapR, NBCD2000, and Hagen2005) at regional and forest type levels, and at spatial scales of 1 km² and larger. Regionwide, PNW forests acted as a stable net sink for atmospheric CO₂ (18.5 Tg C yr^–1) within forestlands. However, harvesting activities removed significant amounts of carbon, equating to over 75% of annual net carbon sink, though only 25% of this (∼3.5 Tg C yr^–1) is emitted to the atmosphere within 50 years. Wildfires contributed modestly to carbon emissions in most years, however, the severe fires of 2002 and 2006 released 16.6 and 7.1 Tg C, respectively. The study demonstrates the potential of the NFCMS framework to serve as a candidate measuring, reporting and verification system, informed by field and remotely sensed inventories, and tracking the carbon balance of the forest sector across the United States.

Reducing greenhouse gas emissions has the 'co-benefit' of also reducing air pollution and associated impacts on human health. Here, we incorporate health co-benefits into estimates of the optimal climate policy for three different climate policy regimes. The first fully internalizes the climate externality at the global level via a uniform carbon price (the 'cooperative equilibrium'), thus minimizing total mitigation costs. The second connects to the concept of 'common but differentiated responsibilities' where nations coordinate their actions while accounting for different national capabilities considering socioeconomic conditions. The third assumes nations act only in their own self-interest. We find that air quality co-benefits motivate substantially reduced emissions under all three policy regimes, but that some form of global cooperation is required to prevent runaway temperature rise. However, co-benefits do warrant high levels of mitigation in certain regions even in the self-interested case, suggesting that air quality impacts may expand the range of possible policy outcomes whereby global temperatures do not increase unabated.

Active layer thickness (ALT) is a critical metric for monitoring permafrost. How soil moisture influences ALT depends on two competing hypotheses: (a) increased soil moisture increases the latent heat of fusion for thaw, resulting in shallower active layers, and (b) increased soil moisture increases soil thermal conductivity, resulting in deeper active layers. To investigate their relative influence on thaw depth, we analyzed the Field Measurements of Soil Moisture and Active Layer Thickness (SMALT) in Alaska and Canada dataset, consisting of thousands of measurements of thaw depth and soil moisture collected at dozens of sites across Alaska and Canada as part of NASA's Arctic Boreal Vulnerability Experiment (ABoVE). As bulk volumetric water content (VWC) integrated over the entire active layer increases, ALT decreases, supporting the latent heat hypothesis. However, as VWC in the top 12 cm of soil increases, ALT increases, supporting the thermal conductivity hypothesis. Regional temperature variations determine the baseline thaw depth while precipitation may influence the sensitivity of ALT to changes in VWC. Soil latent heat dominates over thermal conductivity in determining ALT, and the effect of bulk VWC on ALT appears consistent across sites.

The frequency and intensity of high-temperature events continue to increase, resulting in a surge of pathogenicity and mortality. People with low levels of risk perception and adaptability, such as the elderly, suffer more from high temperatures. Effective intervention measures may lead to reduced levels of high temperature-related risk. The purpose of this study was to compare changes in temperature exposure, risk perception and coping behaviors under different intervention methods. Herein we conducted three different interventions including education, subsidies for electricity and uses of spray-cooling appliances as well as collected data about temperature exposure, risk perception, and coping behaviors. Before and after the experiment, we evaluated the intervention effectiveness with a number of variables related to alerting human responses under high temperatures. We conducted nonparametric tests for paired samples and generalized linear mixed effect models. Compared with subsidy support and outdoor spray-cooling methods, education is more effective as it leads to lower levels of temperature exposure, higher levels of risk perception, and more behavioral responses. The subsidy support intervention is useful in increasing risk perception and promoting home cooling practices as well. In comparison, spray cooling barely contributes to the reduction of personal temperature exposure. The encouragement of risk-related education and continued government subsidy may prevent elderly individuals from experiencing high-temperature exposure.

Archaeologists and palaeoclimatologists have focused on the impact of climate on the prehistoric civilizations around the world; however, social resilience in the face of the climate change remains unclear, especially during the Neolithic and Bronze Age in the Central Plains of China (CPC). In this paper, we present palynological results from the Dahecun Core, Henan Province, China. Our pollen data indicate a warm and wet climate condition from 9200 to 4000 cal BP, which then switches to a cool and dry climatic condition during the Neolithic-Bronze Age transition (∼4000–3600 cal BP). We analyze ¹⁴C dates from archaeological sites to demonstrate four episodes of population increase and present vegetation dynamics, determined from available pollen data, to provide evidence for the synchronous shifts in vegetation and human population during the Neolithic. Our results indicate that the aridification in the early Bronze Age did not cause population collapse, highlighting the importance of social resilience to climate change. The pollen, radiocarbon dates and archaeobotanical records from the CPC provides new evidence that supports the claim that the development of agriculture and complex societies, under the stress of a dry climate, set the stage for the dramatic increase of human population around 3800–3400 cal BP.

Sustainable energy systems can only be achieved when reducing both carbon emissions and water use for energy generation. Although the water use for electricity generation has been well studied, integrated assessments of the water use by low-carbon heat systems are lacking. In this paper we present an analysis of the water use of scenarios for heat and electricity production for the year 2050 for the Netherlands and its capital, Amsterdam. The analysis shows that (i) the water withdrawal for heating can increase up to the same order of magnitude as the current water withdrawal of thermoelectric plants due to the use of aquifer thermal energy storage, (ii) the virtual water use for heating can become higher than the operational water consumption for heating, and (iii) the water use for electricity production becomes a relevant indicator for the virtual water use for heat generation because of the increase of power-to-heat applications.

Reliable load time series forecasting plays an important role in guaranteeing the safe and stable operation of modern power system. Due to the volatility and randomness of electricity demand, the conventional forecasting method may fail to effectively capture the dynamic change of load curves. To satisfy this practical necessity, the goal of this paper is set to develop a practical machine learning model based on feature selection and parameter optimization for short-term load prediction. In the proposed model, the ensemble empirical mode decomposition is used to divide the original loads into a sequence of relatively simple subcomponents; for each subcomponent, the support vector machine is chosen as the basic predictor where the real-valued cooperation search algorithm (CSA) is used to seek the best model hyperparameters, while the binary-valued CSA is set as the feature selection tool to determine the candidate input variables; finally, the aggregation of all the submodules' outputs forms the final forecasting result. The presented method is assessed by short-term load data from four provincial-grid dispatching centers in China. The experiments demonstrate that the proposed model can provide better results than several conventional models in short-term load prediction, while the emerging CSA method is an effective tool to determine the parameter combinations of machine learning method.

In Canada, the agricultural sector has long held a prominent economic, social and cultural position, from substantial evidence of extensive fishing and farming since the times of the first human settlements, to currently accounting for over 100 billion dollars of production and employing 2.3 million people. Steady growth in agricultural production in the country over several decades, supported by strong investment in public agricultural science, has allowed an increasing supply of a wide variety of food and agricultural goods to be available both within the country as well as allowing for substantial exports abroad and deep integration of the Canadian agricultural sector into global markets. Along with securing continued productivity growth in agricultural output for the future, policy makers and public sector agricultural scientists in Canada have become increasingly concerned with managing environmental externalities associated with agricultural production in order to achieve the objective of sustainable intensification of the sector. However, the process of identification of the best tools and practices to improve the sustainability of the agricultural sector in Canada has evolved over time due to shifting research priorities and dynamic changes in the problems facing the sector. In this paper we discuss applied and direct-to-farmer agricultural science research initiatives that are focused on identification and implementation of best environmental management practices at the farm level. We believe that involving farmers directly in scientific research and communication of scientific results provides for a deeper understanding of agro-environmental externalities. It also allows farmers to find greater adoption potential in their specific farm system, thus combining both environmental and economic sustainability. We trace the history of public agricultural science engagement with Canadian farmers to address economic and environmental problems in the sector. We then provide examples of successful public sector projects based in applied agricultural science research that foster effective farmer/scientist collaboration, leading to improved agriculture sustainability in Canada.

Under the medium- and long-term electric markets, cascaded hydropower stations face a series of practical challenges due to the uncertainty of inflow and market price. For long-term dispatch scheduling, the allocation of power generation in multimarkets is critical, including clean energy priority consumption market, inter provincial market and intra provincial market in order to maximize the operator's expected revenue and reduce the market operation risks. Based on the hydro-dominant electricity market structure and settlement rules, we propose a long-term optimal operation method for cascade hydropower stations considering the uncertainty of multiple variables. First, a seasonal autoregressive integrated moving average model is used to handle the time-varying and seasonal characteristics of inflow series by using a copula connect function to fit the joint distribution of the monthly inflow, the clearing price of the intra provincial market and the delivery volume of the inter provincial market. Then, uncertain chance-constrained programming is established. Finally, a developed particle swarm optimization algorithm embedded in a Monte Carlo simulation is solved for hydropower operation policies, and the maximum revenue, resource allocation and scheduling strategy are obtained under the corresponding risk tolerance. Taking the actual data of cascaded hydropower stations in Yunnan Province, China, as an example, a simulation analysis is carried out. The results show that the proposed method can reasonably describe the uncertainty and correlation between the variables, realizing the optimal allocation of resources among multimarkets, and provide references for the long-term optimal operation of cascade hydropower stations in a multimarket environment. The results also show that the decision strategies should be determined considering the decision-maker's risk preference.

Soil emissions of NO and N₂O from typical land uses across Lowland and Highland Scotland were simulated under climate change conditions, during a short-term laboratory study. All locations investigated were significant sources of N₂O (range: 157–277 µg N₂O–N m⁻² h⁻¹) and low-to-moderate sources of NO emissions (range: 0.4–30.5 µg NO–N m⁻² h⁻¹), with a general tendency to decrease with altitude and increase with fertiliser and atmospheric N inputs. Simulated climate warming and extreme events (drought, intensive rainfall) increased soil NO pulses and N₂O emissions from both natural and managed ecosystems in the following order: natural Highlands < natural Lowlands < grazed grasslands < natural moorland receiving high NH₃ deposition rates. Largest NO emission rates were observed from natural moorlands exposed to high NH₃ deposition rates. Although soil NO emissions were much smaller (6–660 times) than those of N₂O, their impact on air quality is likely to increase as combustion sources of NO_x are declining as a result of successful mitigation. This study provides evidence of high N emission rates from natural ecosystems and calls for urgent action to improve existing national and intergovernmental inventories for NO and N₂O, which at present do not fully account for emissions from natural soils receiving no direct anthropogenic N inputs.

Recent hydropower development in the Mekong River has triggered a lot of discussion about its impact on flood dynamics along the river, as well as in one of the world's most productive lake-floodplain systems—the Tonle Sap Lake. A recent article by Wang et al (2020 Environ. Res. Lett.15 0940a1) in this journal conclude that changes in precipitation have played a much larger role than the operation of hydropower dams, contradicting existing research. However, we argue that by using an annual mean discharge and inundation area Wang et al (2020 Environ. Res. Lett.15 0940a1) ignore the fundamentals of the system: the difference between dry season water level and peak water level, and thus the extent of the flooded area, which is the key function of the flood pulse. Further, by using annual mean discharge authors are not able to capture the actual operation of hydropower dams, and thus their impacts. Hydropower dams consume very little water through evaporation, but shift the flow regime from wet to dry season. We show here that when taking into account the characteristics of the system, and analysing changes from anthropogenic impacts on low and high flows separately, dams play a central role in recent changes in the flood characteristics of the Mekong.