Table of contents

This perspective examines the relationship between climate change, health outcomes, and behavioural responses across the life course. It identifies three primary channels through which climate change impacts behaviours which in turn affect health: increased morbidity driving healthcare demand and accessibility, reduced productivity and income affecting health care investments, and combined health and economic risks shaping migration patterns, dietary choices and human capital investment across the life course and generations. Climate-induced changes in behaviours exacerbate existing health-related and socio-economic vulnerabilities. While climate-related shocks elevate demand for healthcare services, disruptions in infrastructure hinder access, especially for the poorest, widening health inequities. Loss of income and disrupted employment further compound health and economic risks, pushing vulnerable communities towards informal care options and impoverishment tied to health expenditures. Increased health and economic risks are associated with migration affecting healthcare access and health outcomes. They also influence dietary choices, with health consequences. Finally, deteriorating prospects of leading a long, prosperous and healthy life may induce individuals to reduce their time horizon and assign lower values to long-term survival, impacting human capital investments across the life course and generations. Again, these impacts are prone to exhibit a social gradient with vulnerable individuals being more likely to give up on striving for a healthier life. Effective policies must integrate climate, health, and socioeconomic factors, considering long-term behavioural responses and their health and socio-economic implications. Adapting health financing mechanisms to account for climate risks and incentivise resilience-building behaviours within health and social care systems is essential for protecting health across the life course, and avoiding widening inequities.

Households that burn biomass in inefficient open fires—a practice that results in $1.6 trillion in global damages from health impacts and climate-altering emissions yearly—are often unable to access cleaner alternatives, like gas, which is widely available but unaffordable, or electricity, which is unattainable for many due to insufficient supply and reliability of electricity services. Governments are often reluctant to make gas affordable. We argue that condemnation of all fossil fuel subsidies is short-sighted and does not adequately consider subsidizing gas for cooking as a potential strategy to improve public health and reduce greenhouse gas emissions.

While the (re-)establishment of Blue Carbon Ecosystems (BCE) is seen as an important tool to mitigate climate change, the credibility of such nature-based solutions has been marred by recent revelations ranging from weak accounting to malpractice. In light of this, there is a clear need to develop monitoring, reporting and verification (MRV) systems towards the reliable, practical, and accurate accounting of additional and durable carbon dioxide removal (CDR). We propose the development of a Blue Carbon Ecosystem Digital Twin (BCE-DT) as a practical solution, integrating real-time data and models into What-If Scenarios of CDR aimed at the quantification of CDR additionality and durability. Critically, such a solution would be amenable to projects across a broad range in spatial scale and ecosytem type. In parallel, we propose the creation of an independent and not-for-profit Standards Development Organization (SDO) for the management of this Digital Twin and oversight of the certification process based on MRV. Considering the interwoven nature of the scientific and policy/legal needs we raise, an improved dialogue and collaboration between the scientific and policy communities is clearly needed. We argue that this BCE-DT, along with its oversight and implementation by a SDO, would fit this niche and support the fair and accurate implementation of MRV critically needed for BCE-based CDR to proceed.

Climate change is one of the biggest threats to global food security, with observed impacts already affecting agriculture. This study aims to systematize and analyze the observed biophysical impacts and their evolution in agriculture in Brazil. For this, we carry out a systematic literature review that includes 1844 articles in the first stage, and 53 articles with synthesized information retrieved. Temperature and precipitation are the most studied climate variables when considering observed climate impact on food production, with corn, soybeans, and sugarcane as the major crops assessed. We also identify regional patterns of both positive and negative trends due to climate change: 67% of assessed studies report negative impacts, 15% both negative and positive, 11% neutral relationships and only 7% reveal positive effects. The diversity in scope and methodological approaches across studies presents a challenge, as only a fraction sufficiently contextualizes baseline conditions, not allowing for a comprehensive understanding of impact attribution. Nonetheless, the literature spotlights productivity declines in cassava, cocoa, rice and wheat. As for corn, soybeans, and sugarcane studies reveal both positive and negative impacts, depending on baseline premises. The call for more transparent and comprehensive studies is urgent, especially to encompass a broader range of food crops, particularly in family farming systems and across diverse regional scales. Such studies are imperative for advancing evidence-based climate-resilient strategies in agriculture aiming to map and prevent negative impacts, while promoting positive outcomes in food production.

When digestates from anaerobic digestion of crop residues are added to soil, a considerable body of information indicates that soil organic carbon (SOC) levels are comparable to those when crop residues are left in the field. This occurs although the amount of digestate added to soil is diminished by digestion and implies that digestion increases the proportion of carbon inputs stabilized as SOC. Here we examine the likelihood and implications of these features being manifested for soil application of high lignin-fermentation byproduct (HLFB) from liquid biofuel production. We show that steady-state SOC levels are much less sensitive to crop residue removal with HLFB return than without it, and provide an example supporting the feasibility of foregoing process energy and coproduct revenue when HLFB is returned to the soil. Informed by this review and analysis, we expect with moderate confidence that long-term SOC levels for soils amended with HLFB from some liquid cellulosic biofuel processes will not be substantially lower than those occurring when crop residues are left in the field. We have high confidence that the economically optimum rate of fertilizer nitrogen (N) application and N₂O emissions will be lower at most sites for HLFB return to the soil than if crop residues were left in the field. We estimate that the per hectare N demand for processing crop residues to liquid biofuels is about a third of the per hectare demand for crop production, giving rise to an opportunity to use N twice and thereby realize cost savings and environmental benefits. These observations support but do not prove the hypothesis that a 'win-win' is possible wherein large amounts of liquid biofuel feedstock can be obtained from crop residues while improving the economics and sustainability of food and feed production. A research agenda aimed at exploring and testing this hypothesis is offered.

Lightning has profound social implications for public safety and usually causes casualties and significant damage to infrastructure. Due to the advancement of both ground-based and spaceborne detection technology, lightning has been monitored globally in recent decades as an indicator of severe weather and an essential variable of climate change. This article reviews recent progress in the study of lightning variations and their response to temperature and aerosols, based on both spaceborne and ground-based lightning data. The responses of lightning to temperature and aerosol show large spatial and temporal heterogeneity, with relation to the meteorological and environmental conditions. The latest research show that lightning exhibited significant increase in some high altitude or high latitude regions, such as the Tibetan Plateau and Arctic regions, where undergone fast warming during the recent decades and the ecosystems is fragile. Aerosol particles play an important role in modulating lightning variations under certain dynamical and thermodynamic conditions in some regions, even on a global scale. The projected lightning activity will generally increase in the future but may with very few exceptions. Continuous long-term lightning observations with consistent spatial and temporal detection efficiency remain crucial for tracking the response of lightning to climate change in the coming decades.

Climate change adaptation options need to be prioritized so that decision-makers make the appropriate choice among multiple options using decision analysis methods. Although different decision analysis methods are applied in different sectors, the status and challenges of applying the methods in various sectors have not been investigated to date because this is a rapidly developing research field. We systematically reviewed the decision analysis literature in climate change adaptation to investigate how decision analysis methods have been applied in each sector and to identify ongoing challenges. We found that most articles focused on the agriculture, water resources, coastal disaster, and river flooding subsectors, whereas no articles were found in the poverty, settlement, and wellbeing subsectors. The applications of decision analysis methods that can account for the deep uncertainty of adaptation (the deep uncertainty group) comprised about 15% of the total, and they were concentrated in the water resources and disaster-related subsectors. In the poverty, settlement, and wellbeing subsectors, it can be inferred that academic articles are scarce because it is challenging to study climate change projections due to the strong impact of socioeconomic conditions, and because the actors are often reported at the local or individual levels. Although the sectors where climate change impact projections have been developed may have led to a relatively large proportion of applications of the deep uncertainty group, the small number of applications suggests inadequate consideration of uncertainty in all sectors. In the future, it will be crucial for each sector to develop methods to evaluate deep uncertainty; these include using applications in the deep uncertainty group and combining multiple decision analysis methods.

Climate change affects human health negatively in a number of complex ways, and children are particularly vulnerable. Quantifying the negative impacts of climate change on health, and identifying locations where children are at greater risk, can aid evidence-based policy making. We combine high-resolution climatic data with a dataset on infant and child mortality, wasting, and stunting, from more than a hundred countries, to estimate the effects of both gradual and acute climate change, focusing on drought and heatwaves, to plausibly attribute changing child health outcomes to historical climate change. Our results suggest a non-linear relationship between temperature and children's health, adverse effects of increases in acute events, and a strong regional heterogeneity in these impacts. Our findings also highlight the importance of poverty reduction. Greater wealth is associated with better child health outcomes, and partially mitigates the negative impacts of climate change on child health. Finally, using updated warming scenarios, our projections show that there are substantial health co-benefits from achieving low emissions scenarios.

As carbon-free fuel, ammonia has been proposed as an alternative fuel to facilitate maritime decarbonization. Deployment of ammonia-powered ships is proposed as soon as 2024. However, NO_x, NH₃ and N₂O from ammonia combustion could impact air quality and climate. In this study, we assess whether and under what conditions switching to ammonia fuel might affect climate and air quality. We use a bottom–up approach combining ammonia engine experiment results and ship track data to estimate global tailpipe NO_x, NH₃ and N₂O emissions from ammonia-powered ships with two possible engine technologies (NH₃–H₂ (high NO_x, low NH₃ emissions) vs pure NH₃ (low NO_x, very high NH₃ emissions) combustion) under three emission regulation scenarios (with corresponding assumptions in emission control technologies), and simulate their air quality impacts using GEOS–Chem high performance global chemical transport model. We find that the tailpipe N₂O emissions from ammonia-powered ships have climate impacts equivalent to 5.8% of current shipping CO₂ emissions. Globally, switching to NH₃–H₂ engines avoids 16 900 mortalities from PM_2.5 and 16 200 mortalities from O₃ annually, while the unburnt NH₃ emissions (82.0 Tg NH₃ yr⁻¹) from pure NH₃ engines could lead to 668 100 additional mortalities from PM_2.5 annually under current legislation. Requiring NH₃ scrubbing within current emission control areas leads to smaller improvements in PM_2.5-related mortalities (22 100 avoided mortalities for NH₃–H₂ and 623 900 additional mortalities for pure NH₃ annually), while extending both Tier III NO_x standard and NH₃ scrubbing requirements globally leads to larger improvement in PM_2.5-related mortalities associated with a switch to ammonia-powered ships (66 500 avoided mortalities for NH₃–H₂ and 1200 additional mortalities for pure NH₃ annually). Our findings suggest that while switching to ammonia fuel would reduce tailpipe greenhouse gas emissions from shipping, stringent ammonia emission control is required to mitigate the potential adverse effects on air quality.

Interactions between landfalling tropical cyclones (TCs) and monsoons in South China significantly influence precipitation duration, leading to severe disasters. Previous studies have primarily been individual cases, lacking systematic large-scale statistical analysis of the monsoon and landfalling tropical cyclone persistent precipitation (LTCPP) relationship. This study quantitatively investigated the relationship between monsoonal wind intensity before TCs landfall and post-landfall persistent precipitation induced by TCs in South China, employing the ERA5 reanalysis data and the best track data of 147 TCs from 1979 to 2018. The LTCPP was characterized by the frequency of persistent precipitation events during 0–72 h after TC landfall within a 500 km radius from the TC center. TCs were subdivided into weak and strong LTCPP groups based on the category-specific median of Frequency of 24 h Landfalling Tropical Cyclone Persistent Precipitation (FLTCPP24): 2705 h for TS, 6007 h for STS, and 6419 h for TY. A South China Tropical Cyclone Precipitation Monsoon Index (SCTCPM) was proposed to quantify monsoonal wind intensity derived from zonal winds at 850 hPa over two regions located in the Indian Ocean and Northwestern Pacific Ocean, within 5 d before TC landfall. The results reveal that SCTCPM < 9 m s⁻¹ yields a 72% probability of weak LTCPP occurrence, which increases to 77% when SCTCPM < 6 m s⁻¹. Conversely, SCTCPM > 18 m s⁻¹ corresponds to an 80% probability of strong LTCPP. SCTCPM is an effective indicator for monsoonal wind that impacts LTCPP. Enhanced monsoonal winds, quantified by higher SCTCPM, result in post-landfall changes in horizontal wind speed, moisture transport, convective activity and upward motion, ultimately increasing LTCPP. This study deepens our understanding of the monsoon-TC relationship, emphasizing the crucial role of monsoonal wind in LTCPP in South China and offering valuable insights for disaster preparedness and risk mitigation.

The Indian Coastal Current is the only channel for material exchange between the two largest marginal seas in the northern Indian Ocean: the Bay of Bengal and the Arabian Sea. However, its past history is poorly known, limiting accurate predictions of its future changes. Here, we present a new clay mineral record from south of India supported by interpretations of model simulations to trace its variability over the last 18 000 years. Decreased smectite/(illite + chlorite) ratios during the cold intervals suggest that a stronger northeasterly wind led to a mean southward flow of the Indian Coastal Current in the Bay of Bengal. In contrast, increased smectite/(illite + chlorite) ratios during the warm intervals suggest the opposite scenario. Combining the proxy record with model simulations, we infer that atmospheric circulation changes were the main driver of the changes. Moreover, a possible link is observed between a positive Indian Ocean Dipole (IOD) and weakened southward flow of the Indian Coastal Current in the Bay of Bengal during the Holocene. These findings imply that future warming scenarios, if associated with more intense positive IOD events as proposed, may lead to a reduction in fresh water transport from the Bay of Bengal to the Arabian Sea.

High ambient summertime temperatures are an increasing health concern with climate change. This is a particular concern for minoritized households in the United States, for which differential energy burden may compromise adaptive capacity to high temperatures. Our research question was: Do minoritized groups experience hotter summers than the area average, and do non-Hispanic white people experience cooler summers? Using a fine-scaled spatiotemporal air temperature model and U.S. census data, we examined local (within-county) differences in warm season cooling degree days (CDDs) by ethnoracial group as a proxy for local energy demand for space cooling across states of the northeast and mid-Atlantic U.S. in 2003–2019. Using state-specific regression models adjusted for year and county, we found that Black and Latino people consistently experienced more CDDs, non-Hispanic white people experienced fewer CDDs, and Asian populations showed mixed results. We also explored a concentration-based measure of residential segregation for each ethnoracial group as one possible pathway towards temperature disparities. We included the segregation measure as a smooth term in a regression model adjusted for county and year. The results were nonlinear, but higher concentrations of white people were associated with lower annual CDDs and higher concentrations of Latino people were associated with higher annual CDDs than the county average. Concentrations for Black and Asian people were nonmonotonic, sometimes with bowed associations. These findings suggest that present-day residential segregation, as modeled by spatially smoothed ethnoracial subgroup concentrations, may contribute to summertime air temperature disparities and influence adaptive capacity. We hope these findings can support place-based interventions, including targeting of energy insecurity relief programs.

Understanding how consumption patterns affect the environment and shape well-being hinges on the rationale that the data collected on what is consumed, who consumes it, and where it is consumed are indeed accurate. To identify these consumption patterns and recommend corresponding policies, researchers and policy makers often rely on national surveys. Studies have explored the accuracy of individual surveys and the level of agreement across surveys of the same type (e.g. household expenditures), but no studies have compared representative national surveys measuring consumption in different ways. This study compares household consumption measured as expenditures and as material consumption (i.e. physical units) to assess how well we currently measure what we consume. We use multiple rigorous, national surveys to estimate meat consumption, household energy use, and private automobile use in the United States, with consumption profiles parsed by affluence, race/ethnicity, and education. Our results indicate that commonly used surveys may not accurately track important aspects of household consumption. For meat consumption, which included 30 consumption profiles detailing the consumption patterns across different demographic characteristics and meat types (e.g. kilograms beef consumed/capita for Caucasians), there is considerable disagreement between data sources for 20 profiles. By contrast, national surveys accurately measure household energy and transport (disagreement for four profiles). Our findings indicate that national surveys more accurately measure consistently tracked, standardized consumables like electricity than irregularly tracked, variable goods such as food. These results cast doubt on studies that use national surveys to draw conclusions about the how the environmental impacts of food, and, potentially, other goods (e.g. manufactured goods) vary across demographic groups. Overcoming this challenge will necessitate new surveys, updating legacy databases, and harnessing breakthroughs in data science.

Coastal lowlands and river deltas worldwide are increasingly exposed to coastal, pluvial and fluvial flooding as well as relative sea-level rise (RSLR). However, information about both single and multiple flood-type hazards, their potential impact and the characteristics of areas, population and assets at risk is often still limited as high-quality data either does not exist or is not accessible. This often constitutes a main barrier for generating sound assessments, especially for scientific and public communities in the so-called Global South. We provide a standardised, integrative approach for the first-order assessment of these single and multiple flood-type hazards and show how this can be conducted for data-sparse, hardly accessible and inaccessible coastal lowlands such as the Ayeyarwady Delta in Myanmar by using only open accessible and freely available datasets of satellite imagery, global precipitation estimates, satellite-based river discharge measurements, elevation, land use, and population data. More than 70% of the delta, mainly used for agriculture, and about 40% of its present population are prone to flooding due to either monsoon precipitation and runoff, storm surge, and RSLR, or their combination, jeopardising food security and economic development in the region. The approach allows for the integration and combination of various datasets, combined in a highly flexible workflow that performs at low computational capacities, supporting the evaluation of flood-prone areas on regional and local scale for data-sparse coastal lowlands worldwide. It thereby allows to attribute different types of flood hazards, complements concepts of vulnerability and risk, and supports risk-informed decision making and development of effective multi-flooding adaptation strategies.

Land surface phenology (LSP) can reveal important connections between vegetation dynamics and climate but remains poorly understood in evergreen winter-rainfall shrublands globally. Field-based studies have indicated diverse plant functional strategies in shrublands, but further work is required to link LSP to vegetation functional composition in these regions. We analysed time-series of the normalised difference vegetation index (NDVI) in fynbos shrublands of South Africa using multi-spectral imagery from satellites and unmanned aerial vehicles (UAVs). We investigated the climate drivers of seasonal vegetative phenology and long-term NDVI trends at multiple spatial scales ranging from the landscape to individual species. At coarse spatial resolutions, NDVI time-series indicated rainfall-driven vegetation dynamics in fynbos, both at inter and intra-annual time scales. However, high-resolution time-series from UAVs exposed an underlying divergence in vegetative phenology and long-term NDVI trends between shallow and deep-rooted growth forms. Phenophases and NDVI trends of isolated, deep-rooted, overstory shrubs were decoupled from rainfall relative to dense overstory patches and shallow-rooted understory growth forms. Variations in growth form phenology were not detected at coarse spatial scales due to scaling and competitive effects based on the functional composition of the vegetation.

Fuel combustion for electricity generation emits a mix of health- and climate-relevant air emissions, with the potential for technology or fuel switching to impact multiple emissions together. While there has been extensive research on the co-benefits of climate policies on air quality improvements, few studies have quantified the effect of air pollution controls on carbon emissions. Here we evaluate three multi-pollutant emission reduction strategies, focused on sulfur dioxide (SO₂) controls in the electricity sector. Traditional 'add-on' pollution controls like flue gas desulfurization (FGD) reduce SO₂ emissions from coal combustion but increase emissions of nitrogen oxides (NO_X), volatile organic compounds (VOCs), fine particulate matter (PM_2.5), and carbon dioxide (CO₂) due to heat efficiency loss. Fuel switching from coal to natural gas and renewables potentially reduces all pollutants. We identified 135 electricity generation units (EGUs) without SO₂ controls in the contiguous US in 2017 and quantified the unit-level emission changes using pollution control efficiencies, emission rates, fuel heat input, and electricity load. A cost-benefit analysis is conducted, considering pollution control costs, fuel costs, capital and operation and maintenance (O&M) costs, the monetized health benefits from avoided multi-pollutant, and the social cost of carbon as the benefit for carbon reduction. We find that add-on SO₂ controls result in an average annual net benefit of $179.3 million (95% CI: $137.5-$221.0 million) per EGU, fuel switching from coal to natural gas, $432.7 million (95% CI: $366.4-$498.9 million) per EGU; and fuel switching from coal to renewable energy sources, $537.9 million (95% CI: $457.1-$618.9 million) per EGU. Our results highlight multi-pollutant emission reduction strategy as a cost-effective way to synergistically control air pollution and mitigate climate change.

Wildfires throughout western North America produce smoke plumes that can stretch across the agricultural regions of the American Midwest. Climate change may increase the number and size of these fires and subsequent smoke plumes. These smoke plumes change solar radiation, meteorological conditions, and surface pollutant concentrations during the crop growing season and consequently influence yields of both corn and soybeans. We use a twelve-year panel of county-level yields from all counties east of the 100th meridian combined with measures of exposure to smoke plumes of low and high-density during the growing season to show that low-density plumes enhance yields while high-density plumes decrease yields. These effects appear to be driven by different changes in solar radiation induced by each type of plume but we observe changes in surface pollutants and precipitation as well. Because there are more low-density plumes today, the net effect is a slight increase in yields on average. As climate change makes wildfires larger and more frequent, the overall impact of smoke on yields would be be substantially more negative.

With countries and economies around the globe increasingly relying on non-dispatchable variable renewable energy (VRE), the need for effective energy storage and international carriers of low-carbon energy has intensified. This study delves into hydrogen's prospective, multifaceted contribution to decarbonizing the electricity sector, with emphasis on its utilization as a scalable technology for long-duration energy storage and as an international energy carrier. Using Japan as a case study, based on its ambitious national hydrogen strategy and plans to import liquefied hydrogen as a low-carbon fuel source, we employ advanced models encompassing capacity expansion and hourly dispatch. We explore diverse policy scenarios to unravel the timing, quantity, and operational intricacies of hydrogen deployment within a power system. Our findings highlight the essential role of hydrogen in providing a reliable power supply by balancing mismatches in VRE generation and load over several weeks and months and reducing the costs of achieving a zero-emission power system. The study recommends prioritizing domestically produced hydrogen, leveraging renewables for cost reduction, and strategically employing imported hydrogen as a risk hedge against potential spikes in battery storage and renewable energy costs. Furthermore, the strategic incorporation of hydrogen mitigates system costs and enhances energy self-sufficiency, informing policy design and investment strategies aligned with the dynamic global energy landscape.

In recent years, use of solar-powered irrigation pumps (SIPs) has increased significantly in the agricultural plains (terai) of Nepal. Federal and local governments there have subsidized the pumps in an effort to expand irrigated agriculture using renewable energy. We use data from a cross-sectional survey of 656 farming households in the terai to examine how SIPs affect fossil fuel use and groundwater extraction. We find that most SIP users continued to use their fossil-fuel pumps, as very few completely replaced them with solar pumps. Farmers who received SIPs operated their irrigation pumps more hours than those who did not receive SIPs. Taken together, these findings suggest that groundwater use has increased, as SIP recipients 'stack' their pumps. We also find that solar pumps were more likely to be owned by richer households and those with better social networks than those who were poorer and had relative social disadvantage. As Nepal expands the use of solar pumps in agriculture, policy efforts may benefit from managing expectations about the carbon-mitigation potential of this technology, managing groundwater risks as SIP use expands, and making SIPs more inclusive.

The Northern Hemisphere mid-latitudes, with large human populations and terrestrial carbon sinks, have a high demand for and dependence on water resources. Despite the growing interest in vegetation responses to drought under climate change in this region, our understanding of changes in the relationship between vegetation growth and water availability (referred to as Rvw) remains limited. Here, we aim to explore the Rvw and its drivers in the Northern Hemisphere mid-latitudes between 1982 and 2015. We used the satellite-derived normalized difference vegetation index (NDVI) and the fine-resolution Palmer drought severity index (PDSI) as proxies for vegetation growth and water availability, respectively. The trend analysis results showed that changes in NDVI and PDSI were asynchronous over the past three decades. Moreover, we analyzed the spatiotemporal patterns of the correlation coefficient between NDVI and PDSI. The results indicated that the Rvw was getting closer in more areas over the period, but there were differences across ecosystems. Specifically, most croplands and grasslands were primarily constrained by water deficit, which was getting stronger; however, most forests were primarily constrained by water surplus, which was getting weaker. Furthermore, our random forest regression models indicated that the dominant driver of changes in the NDVI-PDSI correlation was atmospheric carbon dioxide (CO₂) in more than 45% of grid cells. In addition, the partial correlation analysis results demonstrated that elevated CO₂ concentrations not only boosted vegetation growth through the fertilizer effect but also indirectly enhanced water availability by improving water use efficiency. Overall, this study highlights the important role of atmospheric CO₂ in mediating the Rvw under climate change, implying a potential link between vegetation greening and drought risk.

The vast potential of hydropower remains crucial in addressing the escalating need for clean energy, particularly in Tropical Moist Forests (TMFs) regions. Yet, the widespread construction of reservoirs within TMFs has resulted in the inundation of forested areas, exacerbating the fragmentation of forest landscapes and contributing additional loss of carbon stored in these ecosystems. Despite this, the scale and degree of forest loss within reservoirs due to inundation from reservoir construction remain poorly quantified and mapped across tropical regions. Here, we leverage long-term TMFs data spanning from 1990 to 2021 to investigate forest loss within reservoirs across tropical regions. We reveal that forest losses within reservoirs total 3521 km², constituting a relatively small fraction of total forest loss. Nonetheless, the spatial distribution of forest loss within reservoirs varies significantly across the tropics, with patchy distributions observed in the American and African TMFs, and striped patterns evident in the Asian TMFs. Contrary to common assumptions, we show that small reservoirs exhibit higher levels of forest loss compared to large reservoirs, particularly pronounced in the African TMFs region. Furthermore, our projections indicate that the exclusive construction of small reservoirs contributes to approximately 27% of Africa's total forest area lost. We underscore the importance of policymakers carefully evaluating the trade-offs associated with the construction of large versus small reservoirs in TMF regions, to minimize the adverse impacts of hydropower development on forest ecosystems.

Climate warming has induced significant transitions from slowly-developing droughts to rapidly-developing flash droughts in China, causing broad impacts on ecosystems, hydrological regimes, and society. To date, most studies focused on temporal evolution of flash droughts, while neglected the spatial expansion which is essential for understanding their origins and spatial propagations, especially for mega flash droughts. Based on the long-term (1940–2022) dataset of the 5th generation of the European ReAnalysis, here we use a three-dimensional drought identification method to analyze the disparities and similarities in the spatiotemporal dynamics of flash and slow droughts at the subseasonal time scale over China. Although half of the flash and slow droughts are characterized by small areas (<5000 km²), short durations (30–45 d) and short propagation distances of drought centroids (<50 km), the probability of large-scale (>30 000 km²) flash droughts with long propagation distances (>100 km) is twice of slow droughts. Moreover, global and local spatial autocorrelation analyses reveal that South China (SC) and North China are hotspots for large-scale flash and slow droughts, respectively, and they both show significant increasing trends (0.11–0.12 events/decade) during 1940–2022. Without these large-scale droughts, there is no obvious difference in spatial distributions of the frequency of flash and slow droughts. Despite disparities, both large-scale flash and slow droughts show a preferential westward propagation, with 60%–67% of the movements consistent with the pathways of atmospheric water vapor flux anomaly. Our study urges the understanding and prevention of large-scale flash drought events, especially in SC.

Urbanisation is an important driver of changes in streamflow. These changes are not uniform across catchments due to the diverse nature of water sources, storage, and pathways in urban river systems. While land cover data are typically used in urban hydrology analyses, other characteristics of urban systems (such as water management practices) are poorly quantified which means that urbanisation impacts on streamflow are often difficult to detect and quantify. Here, we assess urban impacts on streamflow dynamics for 711 catchments across England and Wales. We use the CAMELS-GB dataset, which is a large-sample hydrology dataset containing hydro-meteorological timeseries and catchment attributes characterising climate, geology, water management practices and land cover. We quantify urban impacts on a wide range of streamflow dynamics (flow magnitudes, variability, frequency, and duration) using random forest models. We demonstrate that wastewater discharges from sewage treatment plants and urban land cover dominate urban hydrology signals across England and Wales. Wastewater discharges increase low flows and reduce flashiness in urban catchments. In contrast, urban land cover increases flashiness and frequency of medium and high flow events. We highlight the need to move beyond land cover metrics and include other features of urban river systems in hydrological analyses to quantify current and future drivers of urban streamflow.

The climate over Europe has been recorded to be hotter, drier, and more fire-prone over the last decade than ever before, leading to concerns about how climate change will alter fire weather in the future. A typical measure to estimate fire weather severity based on climate is the Canadian fire weather index (FWI). In this study, we used high-resolution, bias-corrected climate model output (∼9 km) from six CMIP6 climate models and four shared socio-economic pathway projections (SSPs) to calculate consistent and comparable daily FWI datasets for Europe from 1950 to 2080. Our study aims to identify regional and large-scale shifts in fire weather severity and its predictability over time to support adaptive planning. We show that irrespective of the future SSP, fire weather will become more severe, but the increase is much stronger under high greenhouse gas emissions. This leads to new areas being exposed to severe fire weather, such as central Europe and rapidly warming mountainous areas. Already fire-prone regions in southern Europe will experience more extreme conditions. We conclude that only the low-emission SSP1-2.6 pathway can prevent strong increases in fire weather beyond the 2050s. Fire surveillance and management will become more important, even in areas and in seasons where they have not been in the focus so far.

Many key feed commodities used in livestock and aquaculture production are highly traded in global agricultural markets. The dependence on these imported inputs may create vulnerabilities for importing countries when disturbances in global trade flows occur. Replacing feed imports with domestic food system byproducts—i.e. secondary products from crop, livestock and aquaculture processing—offers a solution to decrease trade dependency, increase food system resilience, and contribute to environmental sustainability. The potential impacts of such replacements on global food-trade patterns—and consequently on heightened self-sufficiency—remain largely unexplored. In this study, we assessed the material flows in the global feed trade at the country level and estimated the potential to replace imported feeds with more efficient use of domestic food system byproducts. We focus on three key feed groups in both livestock and aquaculture production: cereals, oilseed meals and fishmeal. We show that, at the global level, 19% of cereal, 16% of oilseed meals, and 27% of fishmeal feed imports can be replaced with domestic food system byproducts without affecting animal productivity. The high-input animal production countries in East and Southeast Asia, Western Europe, and North America show the highest potential. This study highlights the commodities and areas with the most potential to guide and inform decisions and investments to build more local and circular livestock and aquaculture production that would be more resilient to several kinds of shocks. Replacing feed imports with food system byproducts can increase food system resilience. Nevertheless, larger sustainability strategies, such as dietary change and reducing food loss and waste, should be implemented to ensure a transition towards more sustainable food systems.

Autumn typhoons, despite their lower occurrence rate, impose significant, and at times, greater economic impacts on Asia than summer typhoons. Accurately predicting the interannual variations in autumn typhoon frequency remains a persistent challenge. Our finding discovers a pattern of sea surface temperature (SST) anomaly in the western Pacific, termed the horseshoe-shaped mode (HWP), and finds a strong interannual correlation between the February–March SST anomalies in the western Pacific and September–October tropical cyclones (TCs) frequency in the western North Pacific. The February–March warming HWP triggers enhanced easterlies over the equatorial Pacific as a Kelvin wave response, strengthening the east-west SST gradient and, in turn, further intensifying the September–October HWP through positive feedback. The intensified September–October HWP could boost upwelling in the northwestern and southwestern Pacific and induce dynamic subsidence in the equatorial western Pacific, mirroring a localized reversal of Hadley-like circulation. This is accompanied by higher relative humidity, cloud cover, and longwave radiation over the western North Pacific, warming local SST and fostering TC formation. An SST-based statistical linear model could reproduce September–October TCs for both training and testing periods, demonstrating the reliability and stability of this linear model. Our results indicate that HWP could be an important indicator for improving TC prediction level.

Solar radiation is a key driver of ecosystem carbon and water fluxes. However, the impacts of radiation quantity and quality on the carbon-water coupling are not well distinguished. In this study, we used simultaneous flux and radiation measurements at two grassland sites in northeastern China to explore the joint impacts of photosynthetically active radiation (PAR) and diffuse fraction (K_d) on carbon and water fluxes and their coupling relationships. Under the low to medium PAR levels (<280 W m⁻²), gross primary productivity (GPP) and evapotranspiration (ET) increased continuously with K_d but the sensitivity of GPP (8.4%–8.8% per 0.1 increase in K_d) was significantly higher than that of ET (2.2%–5.0% per 0.1 increase in K_d) at both sites. Under the high PAR levels (>280 W m⁻²), the GPP continued to grow at the southern site but showed limited responses to K_d at the northern site, likely due to the temperature constraint in the latter. Meanwhile, the contribution of evaporation to ET increased under the high radiation conditions, resulting in a decreased ET due to the reduced direct radiation following K_d increment at both sites. Consequently, water use efficiency (WUE) increased with K_d for all radiation levels but showed low sensitivity to PAR changes due to the synchronized GPP and ET responses to PAR. This study unraveled the positive dependence of ecosystem WUE on the increased K_d though with varied sensitivities of GPP and ET under different PAR levels, highlighting the strong impacts of diffuse radiation on ecosystem fluxes over the regions with aerosol pollution and cloud variations.

As the remaining carbon budget for limiting warming to 1.5 °C rapidly diminishes, it is clear that, besides decarbonization, the world will need to remove 100–1000 GtCO₂ from the atmosphere by the end of the century. Yet, Africa, where many carbon removal schemes are planned, remains a 'blindspot' in existing studies. There is limited understanding of the trade-offs and synergies associated with carbon removal within Africa's energy-land-water system. To address this research gap, we model a stylized net-zero emissions (NZEs) in Africa by 2050, with focus on three land-based biological carbon removal approaches: afforestation/reforestation (AR), bioenergy with carbon capture and storage (BECCS), and biochar. We find that by 2050, the total gross carbon removal is projected to reach 1.2 GtCO₂ yr⁻¹ when all three carbon removal approaches are available, and 0.5 GtCO₂ yr⁻¹ when Africa relies solely on AR. Pursuing NZE with only AR or AR alongside biochar in Africa would be the most expensive mitigation option but they lead to the lowest residual fossil fuel and industry CO₂ emissions. An NZE by 2050 in Africa could reduce cropland by 30%–40% from 2020 to 2050, depending on the carbon dioxide removal deployment strategy adopted. Southern Africa would be particularly affected, facing significant challenges in balancing food security with climate goals. The highest increase in staple food prices will occur under AR only, while the availability of AR-BECCS-biochar produces the lowest rise in staple food prices. Our findings highlight the need for balanced and region-specific carbon dioxide removal strategies to ensure climate and other sustainability goals are met.

Recent studies have revealed the slowed Arctic sea-ice loss, but its climate effect on the ocean system remains unclear. By examining reanalysis datasets, we illustrate a paradoxical regime shift characterized by sustained ice loss and surface salinification in the Kara–Laptev Seas (KLS) during boreal summer since 2008. A notable phase transition from surface freshening during one period (1997–2008) with rapid sea-ice melting to salinification during another period (2009–2020) with gentle sea-ice melting has been identified in the KLS. Using a mixed-layer salinity budget, we characterized quantitatively the role of ice melting in driving salinification across different seas. We show that the salinification observed post-2008 mainly arises from the weakened summer sea-ice-ocean freshwater input, particularly the localized reduction in ice volume during June–August. This recent salinification in the KLS likely maintains a relatively stable state in the thinner seasonal sea-ice prevailing in the new Arctic.

The frequency of occurrence of hydrogeological disasters (HGDs), as well as the persistence of their impacts, are not evenly distributed. Hazardous areas, by definition, are more prone to extreme events, while in densely urbanized regions, the impacts of these events tend to be more severe. The objective of this study is to investigate statistical relationships between urban and natural environment features and HGD occurrences. Taking Italian provinces as a comprehensive case study, we assessed the coefficient of determination, the χ² test, and the p-value to determine the degree of statistical correlation between impact indicators and 57 hazard/risk/land management indicators, such as extension of at-risk areas or soil sealing. We discovered that HGDs persistence and frequency correlate best with an indicator describing the amount of soil sealing (i.e. urbanized soil) in medium-hazard areas. Building on that, a further dynamic analysis was carried out to investigate whether soil sealing trends changed significantly after the provinces were struck by HGDs. Our findings hold significant implications, challenging current policy norms. European directives and Italian national laws impose strict development restrictions in 'high-hazard' areas, but generally allow for urbanization in 'medium-hazard' areas, with only minor limitations. Moreover, a paradoxical positive urbanization trend is observed in the most sensitive areas, greater than in safer areas and generally unchanged after HGDs. This outcome highlights a critical gap in risk perception that reflects into territorial planning, decision-making processes, and existing policies.

Stratospheric aerosol geoengineering (SAG) has been proposed as one of the potential options to offset the impacts of anthropogenically induced climate change. Previous modelling studies have shown that the efficacy of the cooling via SAG increases with altitude of the aerosol layer. It has been also shown that the stratospheric heating associated with SAG could stabilize the tropical atmosphere and weaken the tropical hydrological cycle. Using a global climate model, we perform a systematic study by prescribing volcanic sulphate aerosols at three different altitudes (22 km, 18 km and 16 km) and assess the sensitivity of the global and tropical mean precipitation to the altitude. We find that even though the efficacy of cooling increases with altitude of the aerosol layer, the global and tropical mean precipitation changes are less sensitive to the height of the aerosol layer. This is because the magnitude of both the global and tropical mean precipitation reduction increases with aerosol altitude in response to increasing efficacy of aerosols, but this sensitivity related to the slow response is nearly offset by the sensitivity of fast precipitation adjustments to aerosol altitude. A perspective and analysis based on atmospheric energy budget is presented to explain the lack of sensitivity of the hydrological cycle to the altitude of the stratospheric sulphate aerosol layer.

The global spatial extent of croplands is a crucial input to global and regional agricultural monitoring and modeling systems. Although many new remotely-sensed products are now appearing due to recent advances in the spatial and temporal resolution of satellite sensors, there are still issues with these products that are related to the definition of cropland used and the accuracies of these maps, particularly when examined spatially. To address the needs of the agricultural monitoring community, here we have created a hybrid map of global cropland extent at a 500 m resolution by fusing two of the latest high resolution remotely-sensed cropland products: the European Space Agency's WorldCereal and the cropland layer from the University of Maryland. We aggregated the two products to a common resolution of 500 m to produce percentage cropland and compared them spatially, calculating two kinds of disagreement: density disagreement, where the two maps differ by more than 80%, and absence-presence of cropland disagreement, where one map indicates the presence of cropland while the other does not. Based on these disagreements, we selected continuous areas of disagreement, referred to in the paper as hotspots of disagreement, for manual correction by experts using the Geo-Wiki land cover application. The hybrid map was then validated using a stratified random sample based on the disagreement layer, where the sample was visually interpreted by a different set of experts using Geo-Wiki. The results show that the hybrid product improves upon the overall accuracy statistics in the areas where the underlying cropland layer from the University of Maryland was improved with the WorldCereal product, but more importantly, it represents an improved spatially explicit cropland mask for early warning and food security assessment purposes.

Despite high expectations about the role of carbon removal in meeting global climate targets, many of the proposed techniques remain nascent. This is especially so for techniques with potential for large-scale, permanent removal of CO₂, such as direct air carbon capture and storage (DACCS) and ocean alkalinity enhancement (OAE). In such a context, understanding public attitudes is crucial but challenging, since we do not have enough information about the sociotechnical configurations which might accompany such proposals over future timescales. Carbon removal at scale will not take place in a vacuum—it will co-evolve within political, social, economic, and legal structures which in turn will have a strong influence on public attitudes. This study used a nationally-representative survey (n = 1978) in the UK to test the impact of alternative sociotechnical systems on public attitudes to DACCS and OAE. Participants were randomly assigned to one of five scenario conditions, representing different forms of governance logic (top–down vs bottom–up) and market logic (planned vs liberal economy), plus one with minimal sociotechnical information. We find that the scenario condition significantly impacted perceptions of OAE, with participants preferring its implementation within a bottom–up, planned economy scenario, and rejecting scenarios which most closely resembled the status quo. There were no significant differences between scenarios for DACCS, suggesting that the technology may be more flexible across alternative sociotechnical arrangements. OAE arouses more negative emotions, particularly worry about impacts on ocean ecosystems, whereas DACCS arouses more hope. We found that climate worry is associated with stronger emotions—both positive and negative—toward both techniques, thus carbon dioxide removal (CDR) could be polarising for the most climate-worried, likely due to tensions between climate urgency and concerns about deterring emissions reductions. The most important criteria for future CDR deployment were deemed to be biodiversity, durability, and cost, with a strong discourse around the current cost-of-living crisis.

Cities spend millions of dollars on rodent mitigation to reduce public health risks. Despite these efforts, infestations remain high and distressing. Rodents thrive in the built environment in part due to reduced natural predators and their exploitation of garbage. Though sanitation and greenspace are important factors in rodent mitigation, more complex governance and actions are needed. Urban rodents are dynamic and commensal in nature, so understanding the influence of prolific urban features, like building attributes, warrants scrutiny and additionally intersects mitigation strategies with stakeholders at a localized level. Here, we model how residential structures' efficiency influences urban rodent populations. To do so, we created an agent-based model using characteristics of urban brown rats and their natural predator, red foxes, based on three distinct neighborhoods in Philadelphia, Pennsylvania. We varied whether retrofitting occurred and its duration as well as the percent of initial energy-efficient homes in each neighborhood. We found that initial housing conditions, retrofitting, and the duration of retrofitting all significantly reduced final rodent populations. However, retrofitting was most effective in reducing rodent populations in neighborhoods with extensive park access and low commercial activity. Additionally, across neighborhoods, single large efficiency initiatives showed greater potential for rodent reduction. Lastly, we show that the costs of large-scale retrofitting schemes are comparable to ten-year public health spending, demonstrating that retrofitting may have the potential to offset near-term costs. Our results showcase how system-view investments in integrated pest management can lead to sustained rodent pest mitigation and advance sustainable development goals, infrastructure innovation (Goal #9), reduced inequalities (Goal #10), and sustainable cities and communities (Goal #11).

Society is aiming to stabilise climate at key temperature thresholds, such as global warming at or below 1.5 °C or 2.0 °C above preindustrial levels. However, greenhouse gas emissions are failing to decline, and if they continue on their current trajectory it is likely that such thresholds will be crossed in the decades ahead. Because of this risk, there is an emerging focus on overshoot, where, for a temporary period, global warming is allowed to cross critical thresholds to reach a peak value before decreasing to the desired limit. A key question about overshoots is whether there are hysteresis effects—that is, whether global or regional climate has properties that differ between the phase of global warming increase and the phase of decreasing. Here, we analyse temperature and precipitation data from five Earth System Models (ESMs) forced by the SSP5-3.4-OS CMIP6 overshoot scenario. We look at the level of precipitation during two periods of near-identical global warming: one whilst temperatures are rising, and the other when they are falling. For global means, we find a statistically significant difference between precipitation values during the two periods. This is an example of hysteresis, as the reversion to an earlier global warming state results in a level of global rainfall which is different from that observed when warming was increasing. Spatial disaggregation of rainfall differences between the two near-identical warming levels shows the largest differences in the tropical region, which are statistically significant for four of the five ESMs. When considering much smaller regions, including parts of the tropics, there remains some evidence of hysteresis. However, the differences are no longer statistically significant against a background of substantial interannual rainfall variability. We discuss the implications of our findings for climate impacts assesments.

Over the past decade, 1000s of cities have pledged reductions in carbon dioxide emissions. However, tracking progress toward these pledges has largely relied exclusively on activity-based, self-reported emissions inventories, which often underestimate emissions due to incomplete accounting. Furthermore, the lack of a consistent framework that may be deployed broadly, across political boundaries, hampers understanding of changes in both city-scale emissions and the global summation of urban emissions mitigation actions, with insight being particularly limited for cities within the global south. Given the pressing need for rapid decarbonization, development of a consistent framework that tracks progress toward city-scale emissions reduction targets, while providing actionable information for policy makers, will be critical. Here, we combine satellite-based observations of atmospheric carbon dioxide and an atmospheric model to present an atmospherically-based framework for monitoring changes in urban emissions and related intensity metrics. Application of this framework to 77 cities captures ∼16% of global carbon dioxide emissions, similar in magnitude to the total direct emissions of the United States or Europe, and demonstrates the framework's ability to track changes in emissions via satellite-observation. COVID-19 lockdowns correspond to an average ∼21% reduction in emissions across urban systems over March–May of 2020 relative to non-lockdown years. Urban scaling analyses suggest that per capita energy savings drive decreases in emissions per capita as population density increases, while local affluence and economic development correspond to increasing emissions. Results highlight the potential for a global atmospherically-based monitoring framework to complement activity-based inventories and provide actionable information regarding interactions between city-scale emissions and local policy actions.

While issues of pollution, floods and drought in our rivers are widely studied, there is a hidden crisis with respect to the widespread global extraction of sand. Large volumes of sand are needed in the construction industry to make concrete. So far, calls for greater monitoring of sand mining activity have largely gone unmet. This is due to the fact mining is extensive, often hidden (e.g. underwater) and thus very difficult to properly assess. To meet this challenge, we use remote sensing methods to detect and monitor sand mining activities at the catchment scale, across the Ganges-Brahmaputra-Meghna River system (catchment size 1.72 million km²). Based on this analysis, here we show that mining activity is diverse and pervasive across the Ganges–Brahmaputra–Meghna Catchment system for our study period of 2016–2021, with rates of extraction increasing within some of the rivers. Results show the total estimate for sand extraction is ∼115 Mtyr⁻¹ ± 20 Mtyr⁻¹, which is of a similar order of magnitude to the natural bedload flux of the catchment. While there are some limitations to deriving estimates based solely on imagery, this work highlights both the widespread spatial extent and large magnitude of sand mining for one of the world's biggest catchments. Furthermore, given our estimated scale of sand extraction, it demonstrates the need to properly account for mining activities when considering delivery of sediment to deltas in terms of the management of these vulnerable systems in the face of rising sea-levels. Overall, this work stresses the urgent requirement for further similar studies of sand extraction in the world's large rivers, which is vital to underpin sustainable management plans for the global sand commons.

Income and its distribution profile are important determinants of residential energy demand and carry direct implications for human well-being and climate. We explore the sensitivity of residential energy systems to income growth and distribution across shared socioeconomic pathway-representative concentration pathways scenarios using a global, integrated, multisector dynamics model, Global Change Analysis Model, which tracks national/regional household energy services and fuel choice by income decile. Nation/region energy use patterns across deciles tend to converge over time with aggregate income growth, as higher-income consumers approach satiation levels in floorspace and energy services. However, in some regions, existing within-region inequalities in energy consumption persist over time due to slow income growth in lower income groups. Due to continued differences in fuel types, lower income groups will have higher exposure to household air pollution, despite lower contributions to greenhouse gas emissions. We also find that the share of income dedicated to energy is higher for lower deciles, with strong regional differences.

Surface albedo can affect the energy budget and subsequently cause localized warming or cooling of the climate. When we convert a substantial portion of lands to agriculture, land surface properties are consequently altered, including albedo. Through crop selection and management, one can increase crop albedo to obtain higher levels of localized cooling effects to mitigate global warming. Still, there is little understanding about how distinctive features of a cropping system may be responsible for elevated albedo and consequently for the cooling potential of cultivated lands. To address this pressing issue, we conducted seasonal measurements of surface reflectivity during five growing seasons on annual crops of corn-soybean–winter wheat (Zea mays L.- Glycine max L. Merrill—Triticum aestivum L.; CSW) rotations at three agronomic intensities, a monoculture of perennial switchgrass, and perennial polycultures of early successional and restored prairie grasslands. We found that crop-species, agronomic intensity, seasonality, and plant phenology had significant effects on albedo. The mean ± SD of albedo was highest in perennial crops of switchgrass (Panicum virgatum; 0.179 ± 0.04), intermediate in early successional crops (0.170 ± 0.04), and lowest in a reduced input corn systems with cover crops (0.154 ± 0.02). The strongest cooling potentials were found in soybean (−0.450 kg CO₂e m⁻² yr⁻¹) and switchgrass (−0.367 kg CO₂e m⁻² yr⁻¹), with up to −0.265 kg CO₂e m⁻² yr⁻¹ of localized climate cooling annually provided by different agroecosystems. We also demonstrated how diverse ecosystems, leaf canopy, and agronomic practices can affect surface reflectivity and provide another potential nature-based solution for reducing global warming at localized scales.

Anthropogenic climate change accelerates the decline of global biodiversity and disrupts ecosystem functioning, forcing terrestrial and aquatic species to change their ranges, phenology, physiology, and morphology. In our study, we have employed univariate and a newly-defined vector-algebra-derived multivariate estimate of the velocity of climate change (VoCC) derived from near-surface temperature and total precipitation to present the historical (1980–2005) and projected (2020–2097) shifts in the climate space over the Indian subcontinent. The multivariate VoCC was further used to derive climatic divergence (stress) and residence time of eight representative protected areas (PAs). VoCC is a versatile metric that approximates the 'required' migration speeds for the species. Our results from observations (CRU, ERA5) and model simulations (CMIP5, Regional Earth System Model) show that regions with relatively flatter terrain, such as Deserts, Semi-Arid, Deccan Peninsula and Gangetic Plains, displayed the highest historical velocities in the range of 2–15 km yr⁻¹, which are also projected to increase in the future period to range of 4–20 km yr⁻¹. The estimates of multivariate velocities were generally higher than the univariate velocities, leading to a better representation of shifts in real climate space. The high-resolution regional earth system model, ROM, performed better than the global circulations models in producing realistic VoCCs. The climatic stress (diverging vectors closer to 180 degrees) was higher for the Trans-Himalayas, Himalayas, Gangetic Plains, and parts of the Deccan Peninsula, and it is projected to increase in the near and mid future. The PAs with the shortest residence times were found to be Sundarbans (63 years) and Ranthambore (32 years), illustrating a severe challenge for conservationists under changing climate. Our results present the importance of employing multivariate velocities to simulate more realistic estimates of shifting climate and added benefits of measures of climatic divergence and stress on biodiversity.

This study examines the comparative atmospheric circulation and tropical sea surface temperature (SST) relationships during the developing and decaying stages of El Niño from a meridional structure standpoint. Results indicate a transition in the variability of the first two modes of the Hadley circulation (HC) during these stages, with the first mode exhibiting a larger explained variance in the decaying stage. The regime change in HC variability corresponds to underlying anomalous SST distributions, as confirmed by sensitive experiments. Quantitative assessment reveals the HC-SST response amplitudes are approximately two times stronger during the decaying stage compared to the developing stage. Employing the Kuo–Eliassen (KE) equation, diabatic heating anomalies during the decaying stage explain the difference in air-sea response intensity between the two stages. Diabatic heating variations are identified as the primary contributor to amplification or reduction of air-sea response intensity during the respective El Niño stages, providing insights into the different air-sea processes throughout the El Niño lifespan.

Taxes targeting fuel, road usage, or carbon emissions for environmental protection often face public opposition. Can widely accessible machine learning methods aid in predicting and understanding opposition to environmental taxes? This study uses the random forest algorithm to predict opposition to increased environmental taxes based on 41 theoretically relevant respondent characteristics. Drawing on nationally representative surveys, we predict individual tax opposition across 28 countries in 2010 and 2020 (N = 70 710). Personal values and environmental evaluations tend to be more influential than demographics in predicting tax opposition, with key variables differing between countries and over time. A lack of commitment to pro-environmental behavior is the most important predictor in emerging economies. Conversely, concerns about environmental issues and prioritization of jobs and prices are influential in high-income countries, gaining prominence over the previous decade. Policymakers can leverage these insights to tailor communication of environmental tax increases in different contexts, emphasizing, for instance, job creation.

Brazil's Atlantic Forest is a global restoration hotspot. Most of the remaining forest areas are degraded and separated by large cities, and agricultural lands essential for national food security. Brazil's restoration agenda is defined by multiple national and global restoration targets and policies, including Brazil's Native Vegetation Protection Law (No. 12,651/2012) also known as the Forest Code, which sets minimum levels of native vegetation to be maintained or restored in rural properties. In this study we simulate the impacts of alternative restoration policies addressing targets for Brazil, and explore their impacts on selected terrestrial species and agricultural development potential in the Atlantic Forest biome. Our results show several policy options could result in different restoration amounts and spatial distributions being implemented between 2020 and 2050, but trade-offs between agriculture, biodiversity and rural livelihoods differ. Compared to the baseline scenario (implementation of the Forest Code), a scenario which focuses restoration on small farms (not mandated to undergo restoration under the current legislation) could increase forest area by 6.7 Mha across the biome (139% more than with the Forest Code), while a scenario which maximizes biodiversity gains could lead to an additional 3.9 Mha by 2050 (81% more compared to the Forest Code). We find that our restoration scenarios still allow cropland expansion and an increase in cattle herd, while pasturelands decrease. There are relatively small agricultural production losses under the alternative restoration scenarios when compared to the baseline (up to 14.4%), meaning that cattle ranching intensification is critical to enable large-scale restoration to co-exist with agricultural production. Our scenarios suggest that ambitious restoration targets in the Atlantic Forest biome (up to 15.5 Mha, consistent with existing regional initiatives) could be feasible with necessary improvements in pasture yield and a focus on scaling up support and developing restoration policies for smallholder farmers.

While multi-model ensembles (MMEs) of seasonal climate models (SCMs) have been used for crop yield forecasting, there has not been a systematic attempt to select the most skillful SCMs to optimize the performance of a MME and improve in-season yield forecasts. Here, we propose a statistical model to forecast regional and national wheat yield variability from 1993–2016 over the main wheat production area in Argentina. Monthly mean temperature and precipitation from the four months (August–November) before harvest were used as features. The model was validated for end-of-season estimation in December using reanalysis data (ERA) from the European Centre for Medium-Range Weather Forecasts (ECMWF) as well as for in-season forecasts from June to November using a MME of three SCMs from 10 SCMs analyzed. A benchmark model for end-of-season yield estimation using ERA data achieved a R² of 0.33, a root-mean-square error (RMSE) of 9.8% and a receiver operating characteristic (ROC) score of 0.8 on national level. On regional level, the model demonstrated the best estimation accuracy in the northern sub-humid Pampas with a R² of 0.5, a RMSE of 12.6% and a ROC score of 0.9. Across all months of initialization, SCMs from the National Centers for Environmental Prediction, the National Center for Atmospheric Research and the Geophysical Fluid Dynamics Laboratory had the highest mean absolute error of forecasted features compared to ERA data. The most skillful in-season wheat yield forecasts were possible with a 3-member-MME, combining data from the SCMs of the ECMWF, the National Aeronautics and Space Administration and the French national meteorological service. This MME forecasted wheat yield on national level at the beginning of November, one month before harvest, with a R² of 0.32, a RMSE of 9.9% and a ROC score of 0.7. This approach can be applied to other crops and regions.

Drought, a potent natural climatic phenomenon, significantly challenges hydropower systems, bearing adverse consequences for economies, societies, and the environment. This study delves into the profound impact of drought on hydropower generation (HG) in the United States, revealing a robust correlation between hydrologic drought and hydroelectricity generation. Our analysis of the period from 2003 to 2020 for the Contiguous United States (CONUS) indicates that drought events led to a considerable decline in hydroelectricity generation, amounting to approximately 300 million MWh, and resulting in an estimated loss of $28 billion to the sector. Moreover, our findings highlight the adverse environmental effect of drought-induced HG reductions, which are often compensated by increased reliance on natural gas usage, which led to substantial emissions of carbon dioxide (CO₂), sulfur dioxide (SO₂), and nitrogen oxide (NO_X), totaling 161 700 kilotons, 1199 tons, and 181 977 tons, respectively. In addition to these findings, we assess the state-level vulnerability of hydropower to drought, identifying Washington and California as the most vulnerable states, while Nevada exhibits the least vulnerability. Overall, this study enhances understanding of the multifaceted effects of drought on hydropower, which can assist in informing policies and practices related to drought management and energy production.

Excessive nitrogen (N) pollution in the Chesapeake Bay is threatening ecological health. This study presents a multilayer N flow network model where each network layer represents a stage in the production step from raw agricultural commodities such as corn to final products such as packaged meat. We use this model to assess the impacts of alternative future agricultural production and land use changes on multiple pathways of N pollution within the Chesapeake Bay Watershed (CBW). We analyzed N loss via all pathways under multiple future scenarios, considering crop-specific projections based on empirical data and US Department of Agriculture projections. We found two model parameters, fertilizer nitrogen application rate (FNAR) and feed conversion ratio (FCR), to be particularly important for seeing measurable N loss reductions in the Bay. Our results indicate a large increase in N loss under the business-as-usual trajectory in geographic locations with intensive agricultural production. We found that numerous management scenarios including improvements in FNAR and FCR, N losses fall short of the 25% total maximum daily load targets. Our work suggests that achieving the CBW N loss reduction goals will necessitate large deviations from business as usual. Our model also highlights substantial regional variations in nitrogen loss across the U.S., with central regions like the Corn Belt and Central Valley of California experiencing the highest losses from crop-related stages, while eastern areas such as the Chesapeake Bay exhibit major losses from live animal production, underscoring the need for region-specific management strategies. Thus, implementation of effective N management strategies, combined with improved crop residue management, remains pivotal in mitigating N pollution in the Chesapeake Bay.

A carbon border adjustment mechanism (CBAM) is a policy that increases the cost of carbon-intensive imports from countries with no or weak national carbon regulation. Proponents advocate that it helps avoid industrial relocation and protects jobs in the importing country; its critics say that it impedes free trade and drives up prices. Despite European Union legislation to introduce a CBAM policy, we find that citizens across four European countries—Germany (n = 3500), Hungary (n = 2512), Switzerland (n = 2500), and the United Kingdom (n = 2500)—have not formed clear opinions about the policy yet. Results from survey experiments, conducted over the course of 1.5 years, show a strong dislike for price increases associated with a carbon border tax, while the prospect of job protection does little to increase CBAM support—not even among subgroups most affected by import competition. However, employment effects become relevant when we prompt survey respondents to assess the effects of the carbon border tax for their country as a whole instead of for themselves as individuals. Consistent with exploratory findings that right-leaning voters express a much stronger opposition to the CBAM policy, our results speak to growing evidence of the politically polarizing nature of costly, green policies when citizens' policy preferences are malleable.

Rapid warming in the Arctic threatens to destabilize mercury (Hg) deposits contained within soils in permafrost regions. Yet current estimates of the amount of Hg in permafrost vary by ∼4 times. Moreover, how Hg will be released to the environment as permafrost thaws remains poorly known, despite threats to water quality, human health, and the environment. Here we present new measurements of total mercury (THg) contents in discontinuous permafrost in the Yukon River Basin in Alaska. We collected riverbank and floodplain sediments from exposed banks and bars near the villages of Huslia and Beaver. Median THg contents were 49⁺¹³/₋₂₁ ng THg g sediment⁻¹ and 39⁺¹⁶/₋₁₈ ng THg g sediment⁻¹ for Huslia and Beaver, respectively (uncertainties as 15th and 85th percentiles). Corresponding THg:organic carbon ratios were 5.4^+2.0/_−2.4 Gg THg Pg C⁻¹ and 4.2 ^+2.4/_−2.9 Gg THg Pg C⁻¹. To constrain floodplain THg stocks, we combined measured THg contents with floodplain stratigraphy. Trends of THg increasing with smaller sediment size and calculated stocks in the upper 1 m and 3 m are similar to those suggested for this region by prior pan-Arctic studies. We combined THg stocks and river migration rates derived from remote sensing to estimate particulate THg erosional and depositional fluxes as river channels migrate across the floodplain. Results show similar fluxes within uncertainty into the river from erosion at both sites (95⁺¹²/₋₄₇ kg THg yr⁻¹ and 26⁺¹⁵⁴/₋₁₃ kg THg yr⁻¹ at Huslia and Beaver, respectively), but different fluxes out of the river via deposition in aggrading bars (60⁺⁴⁰/₋₂₉ kg THg yr⁻¹ and 10^+5.3/_−1.7 kg THg yr⁻¹). Thus, a significant amount of THg is liberated from permafrost during bank erosion, while a variable but generally lesser portion is subsequently redeposited by migrating rivers.

During January–May 2023, an extreme prolonged drought dominates Southwest China, which caused a severely damage of local water availability, power supply and productivity in Yunnan Province. It is noted that the initiation and maintenance of this extreme drought was concurrent with phase transition from La Niña into El Niño. We demonstrate that this severe drought event was partly attributed to the relay influences of La Niña and El Niño evolution. The anomalous enhanced cyclone over the western North Pacific (WNP) associated with mature La Niña triggered anomalous downwards motion and reduced moisture supply to Southwest China, contributing to drought initiation. As the La Niña decay, the anomalous WNP cyclone gradually weakened in late winter and early spring. Moreover, the eastwards shifting of anomalous WNP cyclone intensified by the El Niño developing and maintained anomalous northerlies in this region. The preceding winter La Niña favored the prolonged MJO activities over the tropical western Pacific in late spring, which re-intensified anomalous WNP cyclone and aggravated drought in Yunnan. The local extreme droughts are a footprint over Southeast Asia, showing a few months predictability as a possible response to the transition from the phase of La Niña to El Niño. This is supported by the similar extreme droughts in history during phase transition from La Niña into El Niño.

To help meet its near-term NDC goals and long-term net-zero 2070 target, the Government of India has planned to establish a Carbon Credit Trading Scheme (CCTS), i.e. a domestic emission trading scheme (ETS). An ETS is an inherently cost-effective policy instrument for emission reduction, providing the greatest flexibility to reduce emissions from within and across sectors. An effective ETS requires design features that consider country-specific challenges and reflect its role within the larger policy package to achieve long-term emission reduction. Within the Indian context and in this study we therefore investigate—(i) what might be the role of the ETS in achieving India's long-term mitigation targets? (ii) How might the various sectors interact under an emissions cap? (iii) How might the ETS interact with existing energy and climate policies? We do this analysis by running four main scenarios using the integrated assessment model GCAM (v6.0), adapted to India-specific assumptions and expectations. These scenarios are—(i) NZ (net-zero), (ii) NZ + ETS, (iii) NZ + CC (command and control), and (iv) NZ + RPO (renewables purchase obligations) + ETS. The NZ scenario assumes India's near-term and long-term climate commitments of net zero by 2070. Scenarios with ETS (ii) and (iv) apply an emissions cap on four sectors—electricity, iron and steel, cement, and fertilizer. The scenario with CC applies a homogenous emission cap on each of the chosen sectors but does not allow cross-sectoral trading. The last scenario includes renewables purchase obligations (RPOs along with an ETS. We show that under a specific ETS emissions cap: (i) the electricity sector emerges as the largest source of cost-effective greenhouse gas (GHG) reduction options; (ii) ETS with trading across sectors is around 24% more cost-effective than ETS with trading only within sectors, (iii) RPOs can be complementary to an ETS although the impact of RPOs on GHG reductions in the electricity sector would need to be considered when setting the level of the ETS cap (or emissions intensity targets) or the RPO targets to avoid low carbon prices, and (iv) the direction and volume of financial transfers across sectors depends on allocation targets set by the government. Based on these results we provide design recommendations for India's ETS.

Coastal wetlands remarkably influence terrestrial carbon (C) stock by serving as natural reservoirs for 'blue carbon'. Anthropogenic nitrogen (N) enrichment shapes the dynamics of soil and plant communities, consequently affecting the C balance and ecosystem functions. The impacts of various levels of N enrichment on CO₂ sequestration in coastal wetlands, however, remain elusive. Here we conducted a long-term field study of N fertilization in a coastal wetland in the Yellow River Delta, China, to investigate N effects on soil properties, indicators of plant dynamics, and fluxes of ecosystem CO₂. The results indicated that moderate N enrichment (5 g N m⁻² y⁻¹) stimulated C fluxes with increases in gross primary productivity (+26.4%), ecosystem respiration (+23.3%), and net ecosystem exchange (NEE, +31.5%) relative to the control. High (10 g N m⁻² y⁻¹) and extreme (20 g N m⁻² y⁻¹) amounts of N enrichment, however, had relatively minor impacts on these CO₂ fluxes. Overall, we observed a decrease in soil electrical conductivity (−24.6%) and increases in soil organic C (+25.2%) and microbial biomass C (+369.3%) for N enrichment. N enrichment also altered the composition of plant species, with a higher proportion of a local dominant species (Phragmites australis), and affected root biomass distribution, with more biomass near the soil surface. Structural equation modeling explained 65.2% of the variance of NEE and supported the assumption that N enrichment could alter the dynamics of soil properties and plant conditions and accelerate ecosystem CO₂ sequestration. These findings have important implications for forecasting the C cycle with increasing N deposition in coastal wetlands, contributing to the projections of the global C budget.

Driven by the existential threats of climate change to planetary health, the United Nations Framework Convention on Climate Change (UNFCCC) established a mandate for National Adaptation Plans (NAPs) to facilitate adaptation planning in low- and middle-income countries. However, the extent to which NAPs consider health risks, particularly those affecting maternal and child health in the adaptation planning process, remains unexplored. Employing the READ approach for document analysis, this study assesses the thoroughness with which these risks were considered during the development and implementation of NAPs in selected Asia-Pacific countries: Cambodia, Nepal, Sri Lanka, and Timor-Leste. The findings reveal health is consistently identified as a high-priority sector vulnerable to climate change. Cambodia, Nepal, and Timor-Leste prioritized maternal and child health issues. Consequently, these countries have outlined a broader gender-based approach in their NAP development and implementation processes, addressing some of the maternal and child health threats posed by climate change. The findings underscore the need for enhanced efforts to prioritize reducing maternal and child health risks associated with climate change through effective interventions in national adaptation planning. This need could be met through evidence generation based on the maternal and child health impacts of climate change in under-represented countries. Additionally, the future development and updating of NAPs should involve a more comprehensive and diverse representation of women from various cultural and geographic backgrounds to prioritize the protection of maternal and child health in the climate change policy discourse.

In recent years, circumglobal heatwaves are becoming increasingly frequent, motivating concerns about the concurrent exposure of global breadbaskets to heat extremes during crop reproductive periods. Here we project the likelihood of concurrent exposure of global breadbaskets of staple crops to widespread reproductive heat extremes. We find that circumglobal reproductive extreme heat exposure would be an agriculturally relevant climate feature in the coming decades. By 2028‒2057 under the Shared Socioeconomic Pathway 5-8.5 (with approximately 2 °C warming above preindustrial levels), the probability of major breadbaskets of the world concurrently enduring at least 5 d of reproductive extreme heat over more than half of their croplands in a typical year is projected to rise from virtually unlikely to 0.43 for maize, 0.27 for wheat, 0.33 for rice and soybean. While as of 2050‒2079 (with approximately 3 °C warming above preindustrial levels), these probabilities would grow rapidly to 0.91, 0.83, 0.87, and 0.80, respectively. Should such dramatic increases in circumglobal reproductive extreme heat exposure occur, they could pose substantial stress on food production and agricultural adaptation, particularly when coinciding with agricultural droughts.

The Environmental Lapse Rate (ELR) depicts how the temperature near the surface varies with altitude and can be used for temperature downscaling coarse resolution data and for understanding boundary layer processes. We calculated the ELR using ERA5 reanalysis data, examined its temporal and larger-scale spatial variability, and found a prevalent seasonal ELR cycle over the Arctic. There are extensive positive ELR values resulting from pervasive inversions over most of the Arctic in winter; hence, we also explored the possible factors that lead to inversions in polar regions. Our results can serve as a reference for future research on the inversions in different morphological regions at different pressure levels. By improving the characterization of the ELR, we obtain a more explicit representation of the vertical temperature variation across the Arctic region and examine potential trends in ELR over time. Our results challenge the commonly assumed fixed ELR values that are typically used in the Arctic region in, for example, correcting ice-core temperature reconstructions or estimating higher-resolution runoff from land ice.

While China's clean air actions implemented since 2013 have been effective in mitigating PM_2.5 air pollution, the large emission reductions during the COVID-19 lockdown period in early 2020 did not similarly alleviate PM_2.5 pollution in North China, reflecting a distinct nonlinear chemical response of PM_2.5 formation to emission changes. Here we apply emission-concentration relationships for PM_2.5 diagnosed using the adjoint approach to quantitatively assess how chemical nonlinearity affects PM_2.5 over Beijing in February 2020 in response to two emission reduction scenarios: the COVID-19 lockdown and 2013–2017 emission controls. We find that, in the absence of chemical nonlinearity, the COVID-19 lockdown would decrease PM_2.5 in Beijing by 17.9 μg m^–3, and the 2013–2017 emission controls resulted in a larger decrease of 54.2 μg m^–3 because of greater reductions of SO₂ and primary aerosol emissions. Chemical nonlinearity offset the decrease for Beijing PM_2.5 by 3.4 μg m^–3 during the lockdown due to enhanced sensitivity of aerosol nitrate to NO_x emissions, but enhanced the efficiency of 2013–2017 emission controls by 11.9 μg m^–3 due to the weakened heterogeneous reaction of sulfate. Such nonlinear chemical effects are important to estimate and consider when designing or assessing air pollution control strategies.

The phenological cycles of terrestrial ecosystems have shifted with the changing climate, and the altered timings of biogeochemical fluxes may also exert feedback on the climate system. As regulators of land carbon balance, relative shifts in photosynthetic and respiratory phenology under climate change are of great importance. However, the relative seasonal dynamics of these individual processes and their sensitivity to climate factors as well as the implications for carbon cycling are not well understood. In this study, we examined the relationship in the seasonality of gross primary production (GPP) and ecosystem respiration (RE) as well as their temperature sensitivities and the implications for carbon uptake with around 1500 site-years' of data from FLUXNET 2015 and Boreal Ecosystem Productivity Simulator (BEPS) at 212 sites. The results showed that RE started earlier in the spring and ended later in the autumn than GPP over most biomes. Furthermore, the flux phenology metrics responded differently to temperature: GPP phenology was more sensitive to changes during the spring temperature than RE phenology, and less sensitive to autumn temperature than RE. We found large BEPS-observation discrepancies in seasonality metrics and their apparent temperature sensitivity. The site-based BEPS projections did not capture the observed seasonal metrics and temperature sensitivities in either GPP or RE seasonality metrics. Improved understanding of the asynchrony of GPP and RE as well as different sensitivity of environmental factors are of great significance for reliable future carbon balance projections.

Escalating impacts from climate change and urban heat are increasing the urgency for communities to equitably plan for heat resilience. Cities in the desert Southwest are among the hottest and fastest warming in the U.S., placing them on the front lines of heat planning. Urban heat resilience requires an integrated planning approach that coordinates strategies across the network of plans that shape the built environment and risk patterns. To date, few studies have assessed cities' progress on heat planning. This research is the first to combine two emerging plan evaluation approaches to examine how networks of plans shape urban heat resilience through case studies of Tempe and Tucson, Arizona. The first methodology, Plan Quality Evaluation for Heat Resilience, adapts existing plan quality assessment approaches to heat. We assess whether plans meet 56 criteria across seven principles of high-quality planning and the types of heat strategies included in the plans. The second methodology, the Plan Integration for Resilience Scorecard™ (PIRS™) for Heat, focuses on plan policies that could influence urban heat hazards. We categorize policies by policy tool and heat mitigation strategy and score them based on their heat impact. Scored policies are then mapped to evaluate their spatial distribution and the net effect of the plan network. The resulting PIRS™ for Heat scorecard is compared with heat vulnerability indicators to assess policy alignment with risks. We find that both cities are proactively planning for heat resilience using similar plan and strategy types, however, there are clear and consistent opportunities for improvement. Combining these complementary plan evaluation methods provides a more comprehensive understanding of how plans address heat and a generalizable approach that communities everywhere could use to identify opportunities for improved heat resilience planning.

The rapid warming of the Tibetan Plateau (TP) in recent decades has led to severe consequences, including the melting of glaciers and snow cover, which further accelerates warming. Accurately projecting the magnitude of future warming is crucial for effective climate change adaptation. However, the projection of future temperature change is model dependent. In this study, we demonstrate a significant correlation between the historical inter-model warming trend and future temperature change, suggesting this relationship could be used to calibrate the best estimate of projections and reduce the uncertainty by observations. For a high emission scenario, the constraint helps to narrow down the uncertainty range of annual and summer temperature change on the western TP by up to 2 °C and 4 °C, respectively, in the end of this century. The most substantial calibrated increase of future change is in winter by up to 2 °C, followed by autumn with an increase by about 1 °C. Discrepancies of historical warming trend among different observation datasets expose the largest impact on the constrained best estimate compared with emergent relationship derived from different climate models and warming trend in different historical periods.

This study examines the performance of 52 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) in capturing the effects of the Indian summer monsoon on the evolution of El Niño–Southern Oscillation (ENSO). The ISM's impacts on ENSO show a substantial diversity among the models. While some models simulate the strength of the impacts comparable to observations, others represent much weaker influences. Results indicate that the diversity is highly related to inter-model spread in interannual variability of ISM rainfall (ISMR) among the models. Models with a larger ISMR variability simulate stronger ISM-induced anomalies in precipitation and atmospheric circulation over the western North Pacific during the monsoon season. As a result, these models exhibit larger wind anomalies induced by monsoon on the south flank of the anomalous circulation in the western Pacific, thereby influencing subsequent ENSO evolution more significantly by causing stronger air-sea coupling processes over the tropical Pacific.

Quantifying spatiotemporal characteristics of intraseasonal variability (ISV) of upper ocean (100 m) zonal currents (U) in the equatorial western Pacific remains difficult due to a lack of direct observations. Here we investigate the characteristics by analyzing five subsurface mooring data at 140°–142° E, 1.7° S–4.7° N, from January 2014 to April 2021. Our analysis revealed that U ISV has an amplitude exceeding 40 cm s⁻¹, comparable to their long-term mean, and pronounced seasonality, with a peaking period in boreal winter–spring (October–April) and a weakening period in summer–autumn (May–September). U ISV intensity in the south of the equator is 50% stronger than that in the north. Analysis of satellite/reanalysis data and model experiments suggests that atmospheric intraseasonal oscillation accounts for 80% of the U ISV seasonality through wind forcing, while the oceanic internal process contributes 20% through nonlinear baroclinic instability. The consistent variation in mixed layer depth indicates the potential roles of oceanic ISVs in seasonal phase locking of El Niño–Southern oscillation. Our results highlight the significance of direct observations for better understanding and estimating ISVs of ocean circulation.

Land-use changes affect hydrologic processes, but their impact on flooding remains obscure amid increasingly heavy precipitation. Instrumental records are short relative to land-use change history and inadequate for flood attribution studies. Here we integrate a high-resolution paleodischarge record spanning the past ∼200 years from the largest basin in the Southeastern United States with instrumental data and hydrological modeling. We find that the 100 yr flood magnitude for large regional rivers exhibits 50%–75% reductions in the mid-20th century. We attribute at least 50% of the reductions to a regional shift from widespread agricultural land to conservation and reforestation and the rest to streamflow regulations. A sensitivity test of the largest post-1950s flood in our study area using the WRF-Hydro model shows that the peak early-1900s agriculture activity in the region could have doubled the flood's magnitude. Our findings suggest that land-use change can profoundly impact flood severity at catchment to regional scales. Therefore, reforestation and soil conservation contribute to alleviating flood hazard in some regions, while aggressive agriculture expansion in other areas will amplify the hazard.

Fragmentation caused by artificial barriers is one of the main stressors of rivers worldwide. However, many barrier inventories only record large barriers, which underestimates barrier numbers, and hence fragmentation. Corrected barrier numbers can be obtained via river walkovers, but these are costly and time consuming. We assessed the performance of remote sensing as an alternative to river walkovers for barrier discovery by comparing the number and location of barriers detected in the field with those detected using Google Earth imagery. Only 56% of known barriers could be detected remotely, but machine learning models predicted the likelihood of remote detection with 62%–65% accuracy. Barriers located downstream were twice as likely to be detected remotely than those in the headwaters, the probability of detection diminishing by 3%–4% for every decrease in Strahler stream order and for every 10 km increase in distance from the river mouth. Barriers located in forested reaches were 35% less likely to be detected than those in open reaches. Observer skills also affected the ability to locate barriers remotely and detection rate varied by 11% between experienced and less experienced observers, suggesting that training might improve barrier detection. Our findings have implications for estimates of river fragmentation because they show that the most under-represented structures in barrier inventories, i.e. small barriers located in forested headwaters, are unlikely to be detected remotely. Although remote sensing cannot fully replace 'boots on the ground' field surveys for filling barrier data gaps, it can reduce the field work necessary to improve barrier inventories and help inform optimal strategies for barrier removal under data-poor scenarios.

Severe heatwaves have become increasingly frequent over the Indian subcontinent in recent decades. This study found that the increase in extreme heatwaves is related to a significant decadal change in surface temperatures over the Indian subcontinent, and revealed that the increase in convective activity in the Philippine Sea plays a crucial role in this decadal change in surface temperature. Specifically, the surface temperature over the Indian subcontinent in spring has increased significantly by approximately 0.64 °C in recent years (1998–2022: post-1998) compared to the past (1959–1997: pre-1998), leading to more intense and frequent heatwaves, particularly in March and April. The difference in atmospheric changes between these two periods shows that the enhancement of convective activity over the Philippine Sea drives an anomalous elongated anticyclonic circulation over the Indian subcontinent. This circulation pattern, marked by clearer skies and increased incident solar radiation, significantly contributes to the heat extremes in the Indian subcontinent. Additionally, stationary wave model experiments demonstrate that local diabatic heating over the Philippine Sea is significantly linked to robust spring Indian heatwaves through the Matsuno–Gill response.

Risks, such as climate change, disease outbreak, geopolitical tension, may exacerbate food insecurity by negatively impacting crop yield. Additional agricultural nitrogen input may partly offset yield losses, with a corresponding increase in nitrogen pollution. The problems of food insecurity and nitrogen pollution are urgent and global but have not been addressed in an integrated fashion. Current efforts to combat food insecurity occur primarily through the United Nations' World Food Program at the international level, and, at the local community level, through food banks. The international program to monitor and reduce global nitrogen pollution is in its early stage. Food provision and nitrogen pollution reduction from agriculture presents a dual challenge that requires an integrated solution. Here, we propose a cooperative food bank, where membership is a matter of choice and is not coerced. Membership requires participants to reduce nitrogen pollution in agriculture but creates a risk-buffering system, providing food compensation when participants are affected by risk factors. We delineate the structure of the cooperative food bank, its operation, from the short-term mobilization of resources to long-term capacity building. Lastly, we assess the feasibility of its implementation and highlight the potential major roadblocks to its implementation within the current socio-political context. The cooperative food bank showcases a novel solution that simultaneously tackles food insecurity and nitrogen pollution via governance. We hope this proposal will stimulate a research agenda and policy discussions focused on integrated approaches to effective governance regimes for linked socio-environmental problems.

The protein shift, or transition, entails a reduction in the production and consumption of animal-source foods, and an increase in plant-based foods and alternative proteins, at a global level. The shift is primarily motivated by the need to minimise the impact of the food system on social-ecological systems. We argue that rather than focusing singularly on transitioning a 'protein gap' in diets, redressing the 'justice gap' is a prerequisite for transformative change in food systems. In this context the justice gap is understood as the gap delineating those who have access to just food systems and those who do not. To substantiate our argument a justice lens is used to analyse the political–economic dimensions of such a transformation and to propose that the future of protein must engage with three core elements to be transformative—disruption, innovation and redistribution. Disruption entails challenging both the food trends that encourage the 'meatification' of diets, and the influence of 'Big Meat' in perpetuating these trends. Innovation emphasises that true novelty is found by designing justice into practices and processes, rather than by firing alternative protein silver bullets within existing food system paradigms. Redistribution stresses that food system redesign is predicated upon establishing fair shares for remaining protein budgets, using approaches anchored in contextual specificity and positionality. Through the application of a justice framework, we expose existing food system injustices related to production and consumption of protein, invite discussion on how such injustices can be addressed and reflect on implications for food system transformations. By reshaping the crux of the protein debate around the more salient concern of the justice gap, food system transformation can take shape.

Clouds produced by aircraft (known as contrails) contribute over half of the positive radiative forcing from aviation, but the size of this warming effect is highly uncertain. Their radiative effect is highly dependent on the microphysical properties and meteorological background state, varying strongly over the contrail lifecycle. In-situ observations have demonstrated an impact of aircraft and fuel type on contrail properties close to the aircraft, but there are few observational constraints at these longer timescales, despite these having a strong impact in high-resolution and global models. This work provides an observational quantification of these contrail controlling factors, matching air traffic data to satellite observations of contrails to isolate the role of the aircraft type in contrail properties and evolution. Investigating over 64 000 cases, a relationship between aircraft type and contrail formation is observed, with more efficient aircraft forming longer-lived satellite-detectable contrails more frequently, which could lead to a larger climate impact. This increase in contrail formation and lifetime is primarily driven by an increase in flight altitude. Business jets are also found to produce longer-lived satellite-detectable contrails despite their lower fuel flow, as they fly at higher altitudes. The increase in satellite-detected contrails behind more efficient aircraft suggests a trade-off between aircraft greenhouse gas emissions and the aviation climate impact through contrail production, due to differences in aircraft operation.