Table of contents

This special issue is the outcome of a workshop held at Purdue University in April 2022. It comprises thematic syntheses of five overarching dimensions of the Global-to-Local-to-Global (GLG) challenge to ensuring the long-term sustainability of land and water resources. These thematic dimensions include: climate change, ecosystems and biodiversity, governance, water resources and cyberinfrastructure. In addition, there are eight applications of GLG analysis to specific land and water sustainability challenges, ranging from environmental stress in the Amazon River Basin to groundwater depletion in the United States. Based on these papers, we conclude that, without fine-scale, local analysis, interventions focusing on land and water sustainability will likely be misguided. But formulating such policies without the broader, national/global context is also problematic – both from the point of view of the global drivers of local sustainability stresses, as well as to capture unanticipated spillovers. In addition, because local and global systems are connected to – and mediated by – meso-scale processes, accounting for key meso-scale phenomena, such as labor market functioning, is critical for characterizing GLG interactions. We also conclude that there is great scope for increasing the complexity of GLG analysis in future work. However, this carries significant risks. Increased complexity can outstrip data and modeling capabilities, slow down research, make results more difficult to understand and interpret, and complicate effective communication with decision-makers and other users of the analyses. We believe that research guidance regarding appropriate complexity is a high priority in the emerging field of Global-Local-Global analysis of sustainability.

Carbon dioxide removal (CDR) at the scale needed to meet key climate goals will require the development of a massive industry. The development of regulatory architecture and effective incentive structures must proceed in parallel if this industry is to function in a way that is technically rigorous, environmentally conscious, and socially responsible. Most of the current capital flow, overall technological development in CDR, and third-party monitoring and verification are occurring in the private sector. We argue here that this will need to change in order for robust, responsible carbon removal to be brought to scale. In the short term, a focus on removing flawed incentive structures will be a critical ingredient in the transition to a stable, large-scale marketplace for durable carbon removal.

Mining causes intense socio-environmental impacts and threatens Indigenous peoples in Brazil, exposing them to violence, contagious diseases, mercury contamination, and loss of livelihoods. Recent collaborative efforts by society achieved positive advances against mining in Indigenous Lands (ILs). Notably, the National Mining Agency (ANM) has revoked thousands of mining requests that encroached upon ILs for decades, marking a historic but underpublicized milestone. However, in recent months, the National Congress has approved a series of counter-attacks against Indigenous rights. Despite these advancements, it is imperative for society to sustain pressure in combating illegal mining in ILs and the ongoing attacks by ruralist and mining groups, who have a long history of undermining Indigenous rights.

Extreme weather events are rising at a pace which exceeds expectations based on thermodynamic arguments only, changing the way we perceive our climate system and climate change issues. Every year, heatwaves, floods and wildfires, bring death and devastation worldwide, increasing the evidence about the role of anthropogenic climate change in the increase of extremes. In this viewpoint article, we summarize some of the most recent extremes and put them in the context of the most recent research on atmospheric and climate sciences, especially focusing on changes in thermodynamics and dynamics of the atmosphere. While some changes in extremes are to be expected and are clearly attributable to rising greenhouse gas emissions, other seem counterintuitive, highlighting the need for further research in the field. In this context, research on changes in atmospheric dynamics plays a crucial role in explaining some of these extremes and more needs to be done to improve our understanding of the physical mechanisms involved.

Mitigation of climate change requires comprehensive policy arrangements. This article applies a systematic analysis framework comprising 'vertical policy hierarchy—horizontal policy path—policy instruments' with Germany, France, and the Netherlands as study cases, and first-hand policy and data from government websites collected, clustered, and matched. The study conducts a comparative analysis of the three countries' systems, pathways, instruments, and their effectiveness in climate change mitigation. The findings indicate that, firstly, all three countries have relatively well-developed policy systems (laws, regulations, strategies, plans, and policy instruments) based on the six vertical policy hierarchy defined by government governance structure. Secondly, the three countries exhibit commonalities and disparities in seven sectors: energy, transport, buildings, industry, agriculture, forest, and waste. The commonalities stem from EU laws and directives, while disparities arise from resource endowments and emission structures. Thirdly, regarding policy instruments, the commonalities among the three countries are reflected in the dominance of Financial/Fiscal Mechanisms as the primary approach, the leadership position of Governance Mechanisms, the comprehensive coverage of Regulatory Reform, and the massive expenditure in the Direct investment. Individually, (1) the German Regulatory Reform primarily addresses energy resource transformation; France focuses on controlling the transport sector emissions; while the Netherlands commits to renewable energy generation. (2) Germany leads in terms of Commercialization Mechanisms. (3) Financial/Fiscal Mechanisms encompass all sectors, while Germany examplifies the transportation sector digitization, France's provision of ecological housing loans, and the Netherlands' support for sustainable agriculture. (4) France distinguishes itself with a forward-thinking approach towards Governance Mechanism including climate financial risks, ESG (Environmental, Social, and Governance) standards. Fourthly, the significant policy instruments analysis demonstrates that the climate governance of three countries incorporates not only direct or indirect efforts in emission reduction, but also considerations of institutional requirements, fairness, economic effectiveness, synergies, and transformative potential in policy considerations.

Anthropogenic dust (AD), as a crucial component of particulate matter, is defined as dust emitted through modifying or disturbing soil particles directly or indirectly associated with human activities in urban areas, croplands, pasturelands and dry lakes. The sources, characteristics, and impacts of AD remain poorly studied, in contrast to the large body of research on natural dust (ND). This review summarizes scientific findings published since the 1990s regarding the emissions, physical-chemical characteristics, and spatio-temporal distributions of AD from the micro to the global scale. AD accounts for 5%–60% of the global dust loading, with notable spread in existing estimates. Compared with ND, AD has more complex and variable compositions and physical-chemical properties. Influenced by human disturbances, AD exhibits small particle sizes, easily accessible critical friction velocity, and large emissions. Further research should improve the observations and simulations to investigate the complex interactions among AD, climate change, and human health.

Urbanization-induced atmospheric moisture changes, embodied as urban moisture island (UMI) and urban dry island (UDI) effects, are not as thoroughly understood as the urban heat island (UHI) effects, despite their significant influence on human comfort and well-being. This paper offers the first systematic review and quantitative meta-analysis of global urban–rural humidity contrasts, aiming to advance our comprehension of the mechanisms, intensity, patterns, and implications of urban humidity changes. The meta-analysis compiles observational data from 34 studies across 33 cities. It reveals that mid-latitude cities predominantly exhibit moderate UMI and UDI effects, and cities with low mean annual precipitation and distinct dry/wet seasons, however, exhibit extreme UMI and UDI effects. The diurnal cycle analysis presents more pronounced UMI effects at night, largely due to increased evapotranspiration and delayed dewfall linked with UHI. On a seasonal scale, UDI effects dominate in spring, while UMI effects peak in winter for mid-latitude cities and in summer for low-latitude cities. In addition, city characteristics such as topography, morphology, and size significantly shape urban–rural humidity contrasts. Coastal cities are subject to sea-breeze circulation, importing moisture from sea to land, whereas mountainous cities can accumulate humidity and precipitation due to geographical barriers and vertical airflow. High-density urban areas generally experience heightened UMI effects due to restricted airflow and ventilation. Larger cities with higher populations contribute to increased UMI effects, particularly in winter, due to stronger anthropogenic moisture sources. This paper also discusses multi-dimensional humidity impacts and strategies for humidity-sensitive urban planning in the context of climate change. It identifies critical gaps in current research, paving the way for future exploration into urban humidity changes.

The proliferation of case studies of shrinking cities in recent years has stimulated intense debate on the impacts of urban shrinkage. However, assessing the impacts of urban shrinkage from a comprehensive perspective could be more present. Also, there is a lake of analytical review of historical studies about the impacts of urban shrinkage. The built environment has different characteristics under different urban development patterns involving infrastructure, services, and social, economic, and structural factors, which provides a best practice for exploring the impacts of urban shrinkage. This study synthesizes the literature surrounding urban shrinkage and built environment changes, identifying that urban shrinkage notably affects the different components of the built environment and gives rise to four related environmental and sustainability impacts involving urban landscapes and structures, ecological sustainability, socioeconomic vitality, and residents' perceptions. Furthermore, there are interactions between the environmental and sustainability impacts, involving trade-offs and synergies between residents' perceptions, ecological sustainability, and socioeconomic vitality. The study also summarized the mainstream methods for assessing the impacts of urban shrinkage and explored the effects of urban shrinkage management strategies on improving the built environment. Finally, a framework for future direction is presented for the final to integrate the theories of urban shrinkage, people and land relationship, and sustainable urban development to guide further exploration in the field. In summary, this study implies that restoring and upgrading the built environment can pave the way for a common goal for long-term sustainable development. The value of this study is to provide relevant researchers with the knowledge to understand the developing frontiers of urban shrinkage impacts on built environments.

Soil, as the largest terrestrial carbon pool, has garnered significant attention concerning its response to global warming. However, accurately estimating the stocks and dynamics of soil organic carbon (SOC) remains challenging due to the complex and unclear influence mechanisms associated with biogeochemical processes in above- and belowground ecosystems, as well as technical limitations. Therefore, it is imperative to facilitate the integration of models and knowledge and promote dialogue between empiricists and modelers. This review provides a concise SOC turnover framework to understand the impact of climate change on SOC dynamics. It covers various factors such as warming, precipitation changes, elevated carbon dioxide, and nitrogen deposition. The review presents impact mechanisms from the perspective of organismal traits (plants, fauna, and microbes), their interactions, and abiotic regulation. Although valuable insights have been gained regarding SOC inputs, decomposition, and stabilization under climate change, there are still knowledge gaps that need to be addressed. In the future, it is essential to conduct systematic and refined research in this field. This includes standardizing the organismal traits most relevant to SOC, studying the standardization of SOC fractions and their resistance to decomposition, and focusing on the interactions and biochemical pathways of biological communities. Through further investigation of biotic and abiotic interactions, a clearer understanding can be attained regarding the physical protection, chemical stability, and biological driving mechanisms of SOC under climate change. This can be achieved by integrating multidisciplinary knowledge, utilizing novel technologies and methodologies, increasing in-situ experiments, and conducting long-term monitoring across multi-scales. By integrating reliable data and elucidating clear mechanisms, the accuracy of models can be enhanced, providing a scientific foundation for mitigating climate change.

Global disaster databases are prone to missing data. Neglect or inappropriate handling of missing data can bias statistical analyses. Consequently, this risks the reliability of study results and the wider evidence base underlying climate and disaster policies. In this paper, a comprehensive systematic literature review was conducted to determine how missing data have been acknowledged and handled in disaster research. We sought empirical, quantitative studies that utilised the Emergency Events Database (EM-DAT) as a primary or secondary data source to capture an extensive sample of the disaster literature. Data on the acknowledgement and handling of missing data were extracted from all eligible studies. Descriptive statistics and univariate correlation analysis were used to identify trends in the consideration of missing data given specific study characteristics. Of the 433 eligible studies, 44.6% acknowledged missing data, albeit briefly, and 33.5% attempted to handle missing data. Studies having a higher page count were significantly (p < 0.01) less prone to acknowledge or handle missing data, whereas the research field of the publication journal distinguished between papers that simply acknowledged missing data, with those that both acknowledged and handled missing data (p < 0.100). A variety of methods to handle missing data (n = 24) were identified. However, these were commonly ad-hoc with little statistical basis. The broad method used to handle missing data: imputation, augmentation or deletion was significantly (p < 0.001) correlated with the geographical scope of the study. This systematic review reveals large failings of the disaster literature to adequately acknowledge and handle missing data. Given these findings, more insight is required to guide a standard practice of handling missing data in disaster research.

PM_2.5 components may promote the development of breast cancer and increase the risk of mortality. This study aims to investigate the associations between long-term exposure to PM_2.5 components and multiple causes of mortality among women with breast cancer living in Inner Mongolia, China. We constructed an Inner Mongolia cohort of 33 952 breast cancer patients from 2012 to 2021 using data from the Inner Mongolia Regional Health Information Platform. We assessed each patient's exposure to PM_2.5 components using the Tracking Air Pollution in China database. Cox regression models were used to estimate adjusted hazard ratios and 95% confidence intervals (95% CIs). A total of 3295 deaths were identified. For each interquartile increase in concentration in the 5 years before diagnosis, the all-cause mortality increased significantly by 5% (HR: 1.05, 95%CI: 1.00–1.10) for black carbon and by 4% (HR: 1.04, 95%CI: 1.00–1.09) for sulfate (SO₄²⁻), and decreased by 7% (HR: 0.93, 95%CI: 0.88–0.98) for nitrate (NO₃⁻). An association between organic matter and an increased all-cause mortality was also observed. Similar results were found for associations with risk of death from breast cancer-specific causes, cardio-cerebrovascular disease (CCVD) causes, and respiratory causes. Stronger associations were observed in older age groups and in Han Chinese patients. Our results showed that long-term exposure to black carbon, organic matter, and SO₄²⁻ were more responsible for the increased risk of death from all causes, breast cancer-specific causes, CCVD causes, and respiratory causes. This suggests that more effective measures to control coal combustion emissions in Inner Mongolia are urgently needed. The elderly and Han Chinese populations may be at high risk.

Central Asia is the world's largest azonal arid region, with strong seasonal precipitation patterns. Vegetation in this region is relatively sparse and extremely sensitive to climatic changes. However, long-term trends in vegetation in Central Asia are still unclear or even controversially recognized, hindering the assessment of climate change's impact on regional sustainability. Here, we present the longest time series of vegetation index in Central Asia and investigated its response to precipitation seasonality from 1982 to 2022 by integrating normalized difference vegetation index data from the Global Inventory Monitoring and Modeling Studies and the Moderate Resolution Imaging Spectroradiometer. The results indicate a greening trend during 1982–2000 and a browning trend during 2000–2008. In contrast to previous studies, we detected a rapid greening trend during 2008–2022, largely resulted from a continuous warm-wet trend in Central Asia. In addition, strong spatial variation in vegetation is uncovered within the region, suggesting spatial differences in vegetation responding to contrasting precipitation seasonality. Under CMIP6 climate scenarios, spring wetting and summer drying are projected to prompt Central Asian vegetation change to a simultaneous greening south and browning north.

The thawing of permafrost in the Arctic has led to an increase in coastal land loss, flooding, and ground subsidence, seriously threatening civil infrastructure and coastal communities. However, a lack of tools for synthetic hazard assessment of the Arctic coast has hindered effective response measures. We developed a holistic framework, the Arctic Coastal Hazard Index (ACHI), to assess the vulnerability of Arctic coasts to permafrost thawing, coastal erosion, and coastal flooding. We quantified the coastal permafrost thaw potential (PTP) through regional assessment of thaw subsidence using ground settlement index. The calculations of the ground settlement index involve utilizing projections of permafrost conditions, including future regional mean annual ground temperature, active layer thickness, and talik thickness. The predicted thaw subsidence was validated through a comparison with observed long-term subsidence data. The ACHI incorporates the PTP into seven physical and ecological variables for coastal hazard assessment: shoreline type, habitat, relief, wind exposure, wave exposure, surge potential, and sea-level rise. The coastal hazard assessment was conducted for each 1 km² coastline of North Slope Borough, Alaska in the 2060s under the Representative Concentration Pathway 4.5 and 8.5 forcing scenarios. The areas that are prone to coastal hazards were identified by mapping the distribution pattern of the ACHI. The calculated coastal hazards potential was subjected to validation by comparing it with the observed and historical long-term coastal erosion mean rates. This framework for Arctic coastal assessment may assist policy and decision-making for adaptation, mitigation strategies, and civil infrastructure planning.

Tropical deforestation has local and regional effects on climate, but the sign and magnitude of these effects are still poorly constrained. Here we used satellite observations to evaluate the local land surface temperature and precipitation response to tropical deforestation in historical simulations from 24 CMIP6 models. We found tropical forest loss leads to an observed local dry season warming and reduced wet and dry season precipitation across the range of scales (0.25°-2°) analysed. At the largest scale analysed (2°), we observed a warming of 0.018 ± 0.001 °C per percentage point of forest loss (°C %⁻¹), broadly captured in the multi-model mean response of 0.017 ± 0.005 °C %⁻¹. The multi-model mean correctly simulates reduced precipitation due to forest loss in the dry season but simulates increased precipitation due to forest loss in the wet season, opposite to the observed response. We found that the simulated dry season surface temperature and precipitation changes due to forest loss depend on the simulated surface albedo change, with less warming and less drying in models with greater increases in surface albedo due to forest loss. Increased recognition of the local and regional climate benefits of tropical forests is needed to support sustainable land use policy.

Heatwaves are weather hazards that can influence societal and natural systems. Recently, heatwaves have increased in frequency, duration, and intensity, and this trend is projected to continue as a consequence of climate change. The study of heatwaves is hampered by the lack of a common definition, which limits comparability between studies. This applies in particular to the considered time scale for utilised metrics. Here, we study which durations of heatwaves are most impact-relevant for various types of impacts. For this purpose, we analyse societal metrics related to health (heat-related hospitalisations, mortality) and public attention (Google trends, news articles) in Germany. Country-averaged temperatures are calculated for the period of 2010–2019 and the warmest periods of all time scales between 1 and 90 days are selected. Then, we assess and compare the societal response during those periods to identify the heatwave durations with the most pronounced impacts. Note that these durations are based on average temperatures across the given time frame while individual days may be less warm. The results differ slightly between the considered societal metrics but indicate overall that heatwaves induce the strongest societal response at durations between 2 weeks and 2 months for Germany. Finally, we show that heatwave duration affects the societal response independent of, and additionally to, heatwave temperatures. This finding highlights the relevance of making informed choices on the considered time scale in heatwave analyses. The approach we introduce here can be extended to other societal indices, countries, and hazard types to reveal more meaningful definitions of climate extremes to guide future research on these events.

Global warming accelerates the rate of inter-regional hydrological cycles, leading to a significant increase in the frequency and intensity of hydrological wet extremes. The Tibetan Plateau (TP) has been experiencing a rapid warming and wetting trend for decades. This trend is especially strong for the upper Brahmaputra basin (UBB) in the southern TP. The UBB is the largest river on the TP, and these changes are likely to impact the water security of local and downstream inhabitants. This study explores the spatial-temporal variability of wet extremes in the UBB from 1981–2019 using a water- and energy-budget distributed hydrological model (WEB-DHM) to simulate river discharge. The simulated results were validated against observed discharge from the Ministry of Water Resources at a mid-stream location and our observations downstream. The major findings are as follows: (1) the WEB-DHM model adequately describes land-atmosphere interactions (slight underestimation of −0.26 K in simulated annual mean land surface temperature) and can accurately reproduce daily and monthly discharge (Nash-Sutcliffe efficiency is 0.662 and 0.796 respectively for Nuxia station); (2) although extreme discharge generally occurs in July and is concentrated in the southeastern TP, extreme wet events in the UBB are becoming increasingly frequent (after 1998, the number of extreme days per year increased by 13% compared to before) and intense (maximum daily discharge increased with a significant trend of 444 (m³s⁻¹) yr⁻¹), and are occurring across a wider region; (3) Precipitation is more likely to affect the intensity and spatial distribution of wet extremes, while the air temperature is more correlated with the frequency. Our wet extreme analysis in the UBB provides valuable insight into strategies to manage regional water resources and prevent hydrological disasters.

Global warming necessitates continual insights into changing atmospheric temperatures to enhance climate change monitoring and prediction. The thickness of an atmospheric layer serves as an effective proxy for the average temperature of that layer, playing a pivotal role in weather forecasting, understanding atmospheric dynamics, and detecting shifts in extreme weather conditions. This study investigates the global trends in thickness of the layer between 1000 hPa and 500 hPa, from 1940 to the present and evaluates the impact of tropical and extra-tropical climate modes on these trends. Our findings reveal a consistent, statistically significant positive trend in atmospheric layer thickness. However, the magnitude of this trend varies both regionally and seasonally. The most substantial absolute changes are observed in the high latitudes during their respective winter seasons; however, when considering global changes relative to each location's unique historical variability, the most pronounced increase occurs in the tropics, specifically over central Africa, with a standard deviation increase of up to 0.03 σ yr⁻¹. Based on the relative changes, the thickness over the Southern Hemisphere's high-latitude landmasses is increasing at a faster pace during its winter compared to the Northern Hemisphere during its winter. Furthermore, our analysis of the impact of dominant tropical and extra-tropical climate modes revealed a strong correlation (R ∼ 0.9) between sea surface temperature changes in the Pacific warm pool region and the global average thickness. This relationship accounts for about 76% to 78% variance of the inter-annual variability in thickness. Consequently, we identify the increase in sea surface temperature in the Indo-Pacific warm pool as a significant controller of the rate and magnitude of atmospheric layer thickness changes globally. This underscores the crucial role of oceanic-atmospheric interactions in driving global climate variations and extremes.

Water use for various sectors (e.g. irrigation, livestock, domestic, energy and manufacturing) is increasing due to a growing global population and economic development. Additionally, increases in frequency and severity of droughts, heatwaves and compound drought-heatwave events, also lead to responses in sectoral water use and a reduction in water availability, intensifying water scarcity. However, limited knowledge exists on the responses in sectoral water use during these hydroclimatic extremes. In this study we quantify the impacts of droughts, heatwaves and compound events on water use of irrigation, livestock, domestic, energy and manufacturing sectors at global, country and local scales. To achieve this, datasets of reported and downscaled sectoral water use (i.e. withdrawal and consumption) were evaluated during these hydroclimatic extremes and compared to normal (non-extreme) periods for 1990–2019. Our analysis shows that these hydroclimatic extremes affect water use patterns differently per sector and region. Reported data show that domestic and irrigation water use increases during heatwaves in Eastern Europe and central continental United States, while water use decreases for thermoelectric sector, particularly in Europe while it increases in north and Eastern Asia. Additionally, global water use response patterns reveal that irrigation and domestic sectors are mostly prioritized over livestock, thermoelectric and manufacturing. Reported local-scale data reveal that for most sectors and regions/locations, stronger water use responses are found for heatwaves and compound events compared to impacts during hydrological droughts. Our outcomes provide improved understanding of sectoral water use behaviour under hydroclimatic extremes. Nonetheless, given the future threats to water availability and the limited accessible information of water use, there is an urgency to collect more monitored-driven data of sectoral water use for improved assessments of water scarcity under these extremes. Consequently, this research reveals the necessity of more realistic water use models to better represent the sectoral responses to hydroclimatic extremes.

Amassing the available solar energy over the Sahara desert, through the installation of a large-scale solar farm, would satisfy the world's current electricity needs. However, such land use changes may affect the global carbon cycle, possibly offsetting mitigation efforts. Here a fully coupled Earth System model EC-Earth was used to investigate the impact of a Saharan solar farm on the terrestrial carbon cycle, simulated with prescribed reduced surface albedo approximating the albedo effect of photovoltaic solar panels over the Sahara desert. The resulting changes to the carbon cycle were an enhancement of the carbon sink across Northern Africa, particularly around the Sahel but a simultaneous weakening of the carbon sink in the Amazon basin. This is observed through spatial pattern changes to the values of net biome production (NBP), more evident during Northern Hemisphere summer season. NBP changes are contributed by competing responses in the net primary production and heterotrophic respiration rates. These changes to carbon exchange correspond to a wetter and warmer climate occurring in Northern Africa and a drier and warmer climate in the Amazon, with stronger driving effects of precipitation. Due to these coupled responses and complex teleconnections, thorough investigation of remote impacts of solar farms are needed to avoid unintended consequences on the terrestrial carbon cycle.

High-resolution air quality data products have the potential to help quantify inequitable environmental exposures over space and across time by enabling the identification of hotspots, or areas that consistently experience elevated pollution levels relative to their surroundings. However, when different high-resolution data products identify different hotspots, the spatial sparsity of 'gold-standard' regulatory observations leaves researchers, regulators, and concerned citizens without a means to differentiate signal from noise. This study compares NO₂ hotspots detected within the city of Chicago, IL, USA using three distinct high-resolution (1.3 km) air quality products: (1) an interpolated product from Microsoft Research's Project Eclipse—a dense network of over 100 low-cost sensors; (2) a two-way coupled WRF-CMAQ simulation; and (3) a down-sampled product using TropOMI satellite instrument observations. We use the Getis-Ord G_i^* statistic to identify hotspots of NO₂ and stratify results into high-, medium-, and low-agreement hotspots, including one consensus hotspot detected in all three datasets. Interrogating medium- and low-agreement hotspots offers insights into dataset discrepancies, such as sensor placement and model physics considerations, data retrieval caveats, and the potential for missing emission inventories. When treated as complements rather than substitutes, our work demonstrates that novel air quality products can enable researchers to address discrepancies in data products and can help regulators evaluate confidence in policy-relevant insights.

The heatwave in 2022 in South Asia disrupted the lives of millions of people and posed challenges to human health, energy, water, and food security. However, mega heatwaves' causes, impacts, and occurrence (like in 2022) remain largely unrecognized. Here, we analyzed the 2022 heatwave, its mechanisms, and future likelihood using observational datasets and climate model simulations. In the last few years, the frequency and duration of heatwaves have significantly increased in South Asia. South Asia faced five continuous heatwave spells that lasted about 35 d during late February and April 2022, affecting a large part of the region. The year 2022 heatwave was unprecedented that caused a deficit in soil moisture and crop yield. Moreover, our results show that the excessive radiative heating of arid and semi-arid regions resulted in a high geopotential height and low pressure in South Asia during the 2022 mega heatwave. The climate model simulations show that such mega heatwaves are projected to become more frequent under the warmer world, and their time of emergence could be as early as the 2030s under the highest emission scenario. Earlier occurrences of mega heatwaves in the future will pose considerable adaptation challenges for food and water security in the region.

Solar photovoltaic (PV) electricity is considered to be an important source of electricity generation in the quest for net-zero carbon emissions. However, the growth of solar electricity is creating both increased material demands and increased greenhouse gas (GHG) emissions from silicon and PV manufacturing (also referred to as embodied GHG emissions of solar electricity). Here we analyze the silicon and solar PV supply chain for the United States (U.S.) market and find that the embodied GHG emissions of solar PV panel materials (such as silicon), manufacture, logistics, and installation in the U.S. given the current supply chain are 36 g CO₂e kWh⁻¹ of solar electricity generated. Eighty-five percent of the embodied GHG emissions are from PV panel production processes in China and other Asia–Pacific countries. Moving the silicon and PV manufacturing to the U.S. would reduce the embodied GHG emissions of solar electricity by 16% from its current level, primarily because of the lower GHG emission intensity of the U.S. electrical grid and the lower GHG emissions for aluminum electrolysis in North America. Future scenario analysis shows that by 2030, with the U.S. PV domestic supply chain and its decarbonized grid electricity and aluminum production, as well as improving PV conversion efficiency, the embodied GHG emissions of solar electricity in the U.S. will be reduced to 21 g CO₂e kWh⁻¹.

Scenarios for deep decarbonization involve biomass for biofuels, biopower, and bioproducts, and they often include negative emissions via carbon capture and storage or utilization. However, critical questions remain about the feasibility of rapid growth to high levels of biomass utilization, given biomass and land availability as well as historical growth rates of the biofuel industry. We address these questions through a unique coordinated analysis and comparison of carbon pricing effects on biomass utilization growth in the United States using a multisectoral integrated assessment model, the Global Change Analysis Model (GCAM), and a biomass-to-biofuels system dynamics model, the Bioenergy Scenario Model (BSM). We harmonized and varied key factors—such as carbon prices, vehicle electrification, and arable land availability—in the two models. We varied the rate of biorefinery construction, the fungibility of feedstock types across conversion processes, and policy incentives in BSM. The rate of growth in biomass deployment under a carbon price in both models is within the range of current literature. However, the reallocation of land to biomass feedstocks would need to overcome bottlenecks to achieve growth consistent with deep decarbonization scenarios. Investments as a result of near-term policy incentives can develop technology and expand capacity—reducing costs, enabling flexibility in feedstock use, and improving stability—but if biomass demand is high, these investments might not overcome land reallocation bottlenecks. Biomass utilization for deep decarbonization relies on extraordinary growth in biomass availability and industrial capacity. In this paper, we quantify and describe the potential challenges of this rapid change.

The Himalaya Plateau including Nepal is 'greening up' that has important implications to ecosystem services such as water supply, carbon sequestration, and local livelihoods. Understanding the combined causes behind greening is critical for effective policy makings in forest management and climate change adaptation towards achieving sustainable development goals. This national scale study comprehensively examined the natural and anthropogenic drivers of the long-term trend of vegetation dynamics across Nepal by correlation analysis and multiple linear regression analysis. We integrated multiple sources of data including global satellite-based leaf area index (LAI), climate data, landcover data, and forest land management information. Our study reveals a remarkable annual mean LAI increase of 22% (0.009 m² m⁻² yr⁻¹) (p < 0.05) from 1982 to 2020, with an acceleration in the rate of increase to 0.016 m² m⁻² yr⁻¹ (p < 0.05) after 2004. The community forestry (CF) program, forest area changes, and soil moisture availability accounted for 40%, 12%, and 10% of LAI temporal variability, respectively. Our analysis found soil moisture and forest area changes to be the primary drivers of the greening trend before 2004, while CF and forest expansion were the dominant factors thereafter. Additionally, interannual vegetation dynamics were significantly influenced by winter precipitation, incoming solar radiation, and pre-monsoon soil moisture. The projections based on four Earth System Models from Coupled Model Intercomparison Project Phase 6 suggest that Nepal's greening trend is expected to continue at a rate of 0.009 m² m⁻² yr⁻¹ (p < 0.05) throughout the 21st century. We conclude that forest management program (CF) amid climate change that alters water and energy conditions have enhanced land greening, posing both opportunities and risks to ecosystem services in Nepal. This study provides much needed national-level information for developing forest management policies and designing Nature-based Solutions to respond to climate change and increasing demands for ecosystem services in Nepal.

Methane (CH₄) is a potent greenhouse gas whose contribution to anthropogenic radiative forcing of the climate system is second only to carbon dioxide (CO₂). CH₄ emission reduction has become critical to global climate mitigation policy, resulting most notably in the global methane pledge (GMP), pledging a 30% reduction of CH₄ emissions by 2030. Methane is, however, much shorter-lived in the atmosphere than CO₂, so emissions reductions may have different impacts on global warming over time. We quantify the difference over time in global annual mean surface temperature of the GMP versus the equivalent amount of CO₂ emission reduction. The avoidance of CH₄ emissions in the 2020s due to the GMP initially results in greater relative cooling than the avoidance of the equivalent amount of CO₂ emissions over the same period, but less relative cooling after ∼2060, when almost all CH₄ emitted during the 2020s has been removed from the atmosphere but much of the CO₂ emitted during the 2020s remains. However, if the GMP places the world on a lower CH₄ emissions trajectory after 2030, this results in a persistently and substantially greater reduction to global warming than the equivalent change in the CO₂ emissions trajectory, with a maximum difference of 0.22 ± 0.06 ^∘C in 2055 and relative cooling for well over a century. This equates to a large difference in avoided climate change damages if momentum in CH₄ emission reduction from the GMP can be sustained after the 2020s. While the greatest reduction in warming is obtained by reducing both CH₄ and CO₂ emissions, our results underscore the striking global societal benefits of sustained reduction in CH₄ emissions.

Managing water security and sustaining ecosystem functions under future warming poses substantial challenges for semi-arid regions. The Murray–Darling Basin (MDB) is particularly vulnerable given the considerable demand for water that underpins Australia's agricultural production and contribution to the national economy. Understanding future drought risk requires a robust assessment of natural variability in drought length, frequency, and magnitude. In the absence of long instrumental records, past drought characteristics can be inferred from paleo-records. We reconstruct over 800 years of Murray River streamflow using a suite of tree-ring chronologies from regions with strong climate teleconnections to the MDB. The reconstruction (1190–2000 CE) captures a broad spectrum of natural climate variability, not fully represented in instrumental records, contributing to an improved understanding of the occurrence rate of multi-year droughts. We found that the Millennium Drought, which occurred in the 2000s, was the most severe (joint duration, magnitude, and peak) during the 800-year reconstruction. The return period of this event is estimated to be ∼2500 years. However, droughts in the early-1200s were of a longer duration and similar magnitude to the Millennium Drought. We used climate models to assess how the occurrence probability of severe droughts might change in the future. Compared to the 800-year baseline, climate models project an increase in future drought severity. While the increase in drought occurrence is within the uncertainty range for most future projections, the driest forecast shows a significant increase in the likelihood of severe droughts compared to natural variability. Our results highlight the need for water management strategies not to rely solely on instrumental data as it may not fully represent current and future risks. Ensuring a resilient MDB under future warming will require a robust water security policy that captures a broader range of natural and anthropogenic variability than currently recognised.

Solar photovoltaic (PV) energy is fundamental for decarbonizing the global economy and supporting the renewable energy transitions that are needed to combat climate change. Potential solar power production at a given location is a function of climatic variables that will change over time and so climate change needs to be accounted for in PV potential estimation. The future potential of PV in response to climate change has not previously been assessed consistently and globally across alternative scenarios. We develop global gridded estimates of PV potential between 2020 and 2100 as a function of spatial, climatic, technological and infrastructural conditions. We find a global technical potential of 175 111 T W h yr⁻¹ in 2050, which changes by between ca. −19% (high-emission scenario) and +16% (low-emission scenario), with larger geographic variations within these scenarios. We perform a sensitivity analysis to identify key uncertainties and assess the scope for emerging PV technologies to offset negative climate impacts. We find that suboptimal orientation and temperature losses have the largest negative effects (reducing PV potential by up to ca. 50% and ca. 10% respectively), but that new technologies may be able to generate gains of more than 200% if successfully deployed worldwide. Solar power can make an important contribution to energy production over the coming decades and the demand for renewable energy could be met by PV deployment on between 0.5% and 1% of the global land area, provided its deployment accounts for the location-specific impacts of climate change.

Intensive agriculture has been linked to increased nitrogen loads and adverse effects on downstream aquatic ecosystems. Sustained large net nitrogen surpluses have been shown in several contexts to form legacies in soil or waters, which delay the effects of reduction measures. In this study, detailed land use and agricultural statistics were used to reconstruct the annual nitrogen surpluses in three agriculture-dominated watersheds of Denmark (600–2700 km²) with well-drained loamy soils. These surpluses and long-term hydrological records were used as inputs to the process model ELEMeNT to quantify the nitrogen stores and fluxes for 1920–2020. A multi-objective calibration using timeseries of river nitrate loads, as well as other non-conventional data sources, allowed to explore the potential of these different data to constrain the nitrogen cycling model. We found the flux-weighted nitrate concentrations in the root zone percolate below croplands, a dataset not commonly used in calibrating watershed models, to be critical in reducing parameter uncertainty. Groundwater nitrate legacies built up in all three studied watersheds during 1950–1990 corresponding to ∼2% of the surplus (or ∼1 kg N ha yr⁻¹) before they went down at a similar rate during 1990–2015. Over the same periods active soil nitrogen legacies first accumulated by approximately 10% of the surplus (∼5 kg N ha yr⁻¹), before undergoing a commensurate reduction. Both legacies appear to have been the drivers of hysteresis in the diffuse load at the catchments' outlet and hindrances to reaching water quality goals. Results indicate that the low cropland surpluses enforced during 2008–2015 had a larger impact on the diffuse river loads than the European Union's untargeted grass set-aside policy of 1993–2008. Collectively, the measures of 1990–2015 are estimated to have reset the diffuse load regimes of the watersheds back to the situation prevailing in the 1960s.

Recent decarbonization policies are expected to significantly impact high greenhouse gas (GHG) emitting industries, as they will be forced to find ways to operate with a lower environmental footprint. Due to the energy required for the kilns and the unavoidable chemical-derived emissions during manufacturing, in addition to its high global consumption levels, the cement industry is anticipated to be among the early industries affected. California State Bill (SB 596) is one of the first rigorous legislative measures that sets GHG emissions from cement production to net-zero by 2045. As such, a case study on California cement production is evaluated here. While several groups have developed cement technology roadmaps with GHG mitigation strategies, these roadmaps do not consider concomitant environmental impacts, such as those that can influence local populations, thus limiting potential implementation from a policy perspective. Here, we examine several GHG emissions mitigation strategies for cement production and show the greatest reduction from an individual measure is from implementing carbon capture storage for cement kiln flue gas (87%), use of alternative clinkers (78%), or use of alkali-activated materials (88%). Yet even if GHG emissions are reduced, use of high-polluting energy sources could increase risks to human health impacts. Further, the efficacy of these decarbonization measures is lowered if multiple measures are implemented simultaneously. Finally, we examine the potential to meet net-zero emissions, focusing on California production due to recent legislation, and find a pathway to 96% GHG emissions reduction. Notably, these reductions do not reach goals to hit zero emissions, suggesting direct air capture mechanisms will need to be implemented.

We propose an approach to develop a solar radiation model with spatial portability based on deep neural networks (DNNs). Weather station networks in South Korea between 33.5–37.9° N latitude were used to collect data for development and internal testing of the DNNs, respectively. Multiple sets of weather station data were selected for cross-validation of the DNNs by standard distance deviation (SDD) among training sites. The DNNs tended to have greater spatial portability when a threshold of spatial dispersion among training sites, e.g. 190 km of SDD, was met. The final formulation of the deep solar radiation (DSR) model was obtained from training sites associated with the threshold of SDD. The DSR model had RMSE values <4 MJ m⁻² d⁻¹ at external test sites in Japan that were within ±6° of the latitude boundary of the training sites. The relative difference between the outputs of crop yield simulations using observed versus estimated solar radiation inputs from the DSR model was about 4% at the test sites within the given boundary. These results indicate that the identification of the spatial dispersion threshold among training sites would aid the development of DNN models with reasonable spatial portability for estimation of solar radiation.

Pathogenic fungi are a leading cause of crop disease and primarily spread through microscopic, durable spores adapted differentially for both persistence and dispersal via soil, animals, water, and/or the atmosphere. Computational Earth system models and air pollution models have been used to simulate atmospheric spore transport for aerial-dispersal-adapted (airborne) rust diseases, but the importance of atmospheric spore transport for soil-dispersal-adapted (soilborne) diseases remains unknown. While a few existing simulation studies have focused on intracontinental dispersion, transoceanic and intercontinental atmospheric transport of soilborne spores entrained in agricultural dust aerosols is understudied and may contribute to disease spread. This study adapts the Community Atmosphere Model, the atmospheric component of the Community Earth System Model, to simulate the global transport of the plant pathogenic soilborne fungus Fusarium oxysporum (F. oxy). Our sensitivity study assesses the model's accuracy in long-distance aerosol transport and the impact of deposition rate on simulated long-distance spore transport in Summer 2020 during a major dust transport event from Northern Sub-Saharan Africa to the Caribbean and southeastern United States (U.S.). We find that decreasing wet and dry deposition rates by an order of magnitude improves representation of long-distance, trans-Atlantic dust transport. Simulations also suggest that a small number of spores can survive trans-Atlantic transport to be deposited in agricultural zones. This number is dependent on source spore parameterization, which we improved through a literature search to yield a global map of F. oxy spore distribution in source agricultural soils. Using this map and aerosol transport modeling, we show how potentially viable spore numbers in the atmosphere decrease with distance traveled and offer a novel danger index for modeled viable spore deposition in agricultural zones. Our work finds that intercontinental transport of viable spores to cropland is greatest between Eurasia, North Africa, and Sub-Saharan Africa, suggesting that future observational studies should concentrate on these regions.

Summertime atmospheric teleconnection patterns over Eurasia have a significant influence on regional weather and climate. Despite extensive studies on the subtropical patterns, the high-latitude counterpart has received relatively less attention. This study proposes physical mechanisms for the formation and maintenance of the dominant high-latitude teleconnection pattern. The formation of the pattern is associated with variability in synoptic-scale eddy activity due to the meridional gradient of sea surface temperature anomalies in the vicinity of the Gulf Stream, causing a meridional shift of the central axis of storm track at the exit of Atlantic jet. The resultant convergence of transient vorticity fluxes to the west of the British Isles induces low-frequency cyclonic circulation anomalies and continued propagation of Rossby waves downstream along northern Eurasia. Once these circulation anomalies are formed, the subsequent latent heat-related diabatic anomalies over the northern Eurasian landmass act as another source of Rossby waves to maintain the teleconnection pattern. Regional temperature and precipitation variability is closely linked to the wave pattern along a route through northern Eurasia, and even precipitation over the East Asian summer monsoon region is influenced by the teleconnection pattern.

China, the world's largest greenhouse gas emitter in 2022, aims to achieve carbon neutrality by 2060. The power sector will play a major role in this decarbonization process due to its current reliance on coal. Prior studies have quantified air quality co-benefits from decarbonization or investigated pathways to eliminate greenhouse gas emissions from the power sector. However, few have jointly assessed the potential impacts of accelerating decarbonization on electric power systems and public health. Additionally, most analyses have treated air quality improvements as co-benefits of decarbonization, rather than a target during decarbonization. Here, we explore future energy technology pathways in China under accelerated decarbonization scenarios with a power system planning model that integrates carbon, pollutant, and health impacts. We integrate the health effects of power plant emissions into the power system decision-making process, quantifying the public health impacts of decarbonization under each scenario. We find that compared with a reference decarbonization pathway, a stricter cap (20% lower emissions than the reference pathway in each period) on carbon emissions would yield significant co-benefits to public health, leading to a 22% reduction in power sector health impacts. Although extra capital investment is required to achieve this low emission target, the value of climate and health benefits would exceed the additional costs, leading to $824 billion net benefits from 2021 to 2050. Another accelerated decarbonization pathway that achieves zero emissions five years earlier than the reference case would result in lower net benefits due to higher capital costs during earlier decarbonization periods. Treating air pollution impacts as a target in decarbonization can further mitigate both CO₂ emissions and negative health effects. Alternative low-cost solutions also show that small variations in system costs can result in significantly different future energy portfolios, suggesting that diverse decarbonization pathways are viable.

Drylands are the world's largest biome and dominate the trends and interannual variability of global carbon sinks. Although a 'greening' trend of global drylands has been widely reported, large uncertainties remain in attributing its drivers. It is increasingly emphasized that elevated CO₂ has greatly contributed to the vegetation greening over global drylands. Here we quantified the contributions of climate change, elevated CO₂, and land use and land cover change (LULCC) on leaf area index (LAI) over drylands, using a process-based land surface model Noah-MP to investigate the drivers of vegetation change. The state-of-the-art model shows better performance in simulating the interannual variability of satellite-observed LAI over global drylands compared with that of the multi-model ensemble mean LAI from the TRENDY results. The area that LAI changes dominated by climate change (44.03%) is three times greater than that by CO₂ (13.89%), and climate change also contributes most to the global drylands greening trend (55.07%). LULCC shows regional dominance over 13.35% of the global drylands, which is associated with afforestation, woody plant encroachment, and agricultural intensification. Our results imply that the vegetation greening area driven by elevated CO₂ is much limited relative to the overwhelming climatic driving, which should be considered in predictions of trends and interannual variations of global carbon sinks.

Extreme summer heat can have severe socioeconomic impacts and has occurred frequently in North China in recent years, most notably in June–July 2023, when North China experienced the most widespread, persistent, and high-intensity extreme heat on record. Here, typical weather patterns covering North China and its surrounding areas were classified into seven types based on the Cost733class package, and the weather pattern type 4 (T4), characterized by the strengthened ridge and anticyclone anomaly in northeastern China, was found as the most favorable for the occurrence of extreme summer heat in North China (NCSH). Diagnostic and wave activity flux analyses indicate that the Eurasian teleconnection (EAT) pattern from the atmosphere and the Victoria mode (VM) from the ocean are the top two dominant climate drivers of the T4 weather pattern. The empirical models constructed based on the EAT and the VM can effectively simulate the number of days of the T4 weather pattern and the NCSH, respectively. Our results suggest that, with the help of the seasonal forecast from climate models, the EAT and the VM can be used to predict the number of days of the T4 weather pattern and the NCSH for the coming summer, enabling us to protect human health and reduce its socioeconomic impacts through proactive measures in advance.

With the increasing cross-regional impact of climate change increasing in recent years, the Han River Basin, as a vital water resource supply and densely populated area in China, faces severe cross-regional flood threats and challenges. The systematic consideration of flood regulation throughout the entire upstream and downstream of the basin has become imminent. Our research aims to gain a deeper understanding of the ecosystem flood regulation service flows from upstream to downstream within subbasins and catchments scales of the Han River Basin. The results showed that the overflow path from upstream to downstream of the flood was basically consistent with the trend of the stream and the main tributaries of the Han River. It emerged that subbasins c, e, g, h and k were the key areas for the overall regulation of the upstream and downstream floods at subbasin scale. A total of 11 catchments overflow into their adjacent downstream catchments across the subbasins, a fact which is critical for catchment scale flood regulation. What's more, there is evident interaction not only between adjacent subbasin and catchment units but also substantial exchange of service flows between non-adjacent units. Notably, catchment c's flood regulation service flow was primarily contributed by its non-adjacent units. These findings not only contribute to filling the current knowledge gap in cross-watershed flood overflow and flood regulation service flows, but also provide support for the integrated response of upstream and downstream flood disaster risk management at the whole basin scale.

Bangladesh is highly vulnerable to flood hazards, and its flood risk is projected to increase with global warming. In addition to climate change, internal climate variation, such as the El Niño–Southern Oscillation (ENSO), influences flooding in many rivers worldwide. However, the impact of internal climate variability on flooding in Bangladesh remains unclear due to the limited observations. Here, we assess the impacts of ENSO and climate change on flood occurrence in Bangladesh using a large-ensemble climate simulation dataset and a global river model (CaMa-Flood). After separating 6000 years of simulation (100-member ensemble river simulations for 1950–2010) into El Niño, La Niña, and neutral years, we calculated the extent to which each ENSO stage increased flood occurrence probability relative to the neutral state using the fraction of attributable risk method. In addition, we estimated the impact of historical climate change on past flood occurrence through a comparison of simulations with and without historical global warming. Under the no-global-warming climate, La Niña increased the occurrence probability of a 10 year return period flood at Hardinge Bridge on the Ganges River by 38% compared to neutral years. The influence of La Niña or El Niño state on flood occurrence probability in the Brahmaputra River at Bahadurabad station is negligible. Historical global warming increased the occurrence of flooding in the Ganges River, the Brahmaputra River, and their confluence by 59%, 44%, and 55%, respectively. The impact of ENSO on flood occurrence probability decreased in the historical simulation, likely due to the conflation of ENSO and climate change signals, and no significant correlation between ENSO and flood occurrence was detected when only small-ensemble simulations were used. These findings suggest that the use of large-ensemble climate simulation datasets is essential for precise attribution of the impacts of internal climate variability on flooding in Bangladesh.

Assessments of the potential responses of animal species to climate change often rely on correlations between long-term average temperature or precipitation and species' occurrence or abundance. Such assessments do not account for the potential predictive capacity of either climate extremes and variability or the indirect effects of climate as mediated by plant phenology. By contrast, we projected responses of wildlife in desert grasslands of the southwestern United States to future climate means, extremes, and variability and changes in the timing and magnitude of primary productivity. We used historical climate data and remotely sensed phenology metrics to develop predictive models of climate-phenology relations and to project phenology given anticipated future climate. We used wildlife survey data to develop models of wildlife-climate and wildlife-phenology relations. Then, on the basis of the modeled relations between climate and phenology variables, and expectations of future climate change, we projected the occurrence or density of four species of management interest associated with these grasslands: Gambel's Quail (Callipepla gambelii), Scaled Quail (Callipepla squamat), Gunnison's prairie dog (Cynomys gunnisoni), and American pronghorn (Antilocapra americana). Our results illustrated that climate extremes and plant phenology may contribute more to projecting wildlife responses to climate change than climate means. Monthly climate extremes and phenology variables were influential predictors of population measures of all four species. For three species, models that included climate extremes as predictors outperformed models that did not include extremes. The most important predictors, and months in which the predictors were most relevant to wildlife occurrence or density, varied among species. Our results highlighted that spatial and temporal variability in climate, phenology, and population measures may limit the utility of climate averages-based bioclimatic niche models for informing wildlife management actions, and may suggest priorities for sustained data collection and continued analysis.

Biomass burning is a significant source of aerosol emissions in some regions and has a considerable impact on regional climate. Earth system model simulations indicate that increased biomass burning aerosol emissions contributed to statistically significant decreases in tropical precipitation over the 20th century. In this study, we use the Community Earth System Model version 1 Large Ensemble (CESM1-LENS) experiment to evaluate the mechanisms by which biomass burning aerosol contributed to decreased tropical precipitation, with a focus on South America and Southeast Asia. We analyze the all-but-one forcing simulations in which biomass burning aerosol emissions are held constant while other forcings (e.g., greenhouse gas concentrations) vary throughout the 20th century. This allows us to isolate the influence of biomass burning aerosol on processes that contribute to decreasing precipitation, including cloud microphysics, the radiative effects of absorbing aerosol particles, and alterations in regional circulation. We also show that the 20th century reduction in precipitation identified in the CESM1-LENS historical and biomass burning experiments is consistent across Coupled Model Intercomparison Project Phase 5 models with interactive aerosol schemes and the CESM2 single-forcing experiment. Our results demonstrate that higher concentrations of biomass burning aerosol increases the quantity of cloud condensation nuclei and cloud droplets, limiting cloud droplet size and precipitation formation. Additionally, absorbing aerosols (e.g., black carbon) contribute to a warmer cloud layer, which promotes cloud evaporation, increases atmospheric stability, and alters regional circulation patterns. Corresponding convectively coupled circulation responses, particularly over the tropical Andes, contribute to further reducing the flow of moisture and moisture convergence over tropical land. These results elucidate the processes that affect the water cycle in regions prone to biomass burning and inform our understanding of how future changes in aerosol emissions may impact tropical precipitation over the 21st century.

International environmental initiatives, such as the Bonn Challenge and the UN Decade on Restoration, have prompted countries to put the management and restoration of forest landscapes at the center of their land use and climate policies. To support these goals, many governments are promoting forest landscape restoration and management through financial forestry incentives, a form of payment for ecosystem services. Since 1996, Guatemala has implemented a series of forestry incentives that promote active forest landscape restoration and management on private and communal lands. These programs have been widely hailed as a success with nearly 600 000 ha enrolled since 1998. However, there has been no systematic assessment of the effectiveness of these programs on preserving and restoring Guatemalan forests. This study evaluates the impacts of over 16 000 individual PES projects funded through two incentive programs using a synthetic control counterfactual. Overall, a program for smallholders resulted in lower rates of forest loss, while a program for industrial timber owners led to greater gains in forest cover. Across policies, we found dramatically higher forest cover increases from restoration projects (15% forest cover increase) compared to plantation and agroforestry projects (3%–6% increase in forest cover). Projects that protected natural forest also showed a 6% reduction in forest loss. We found forest cover increases to be under 10% of total enrolled area, although positive local spillovers suggest this is an underestimate. Restoration projects show the most promise at promoting forest landscape restoration, but these benefits need to be weighed against priorities like resilience and rural development, which may be better served by other projects.

Biomass waste-to-energy (WtE) offers a critical solution to carbon neutrality through improving the resource recycling and recovery. This study comprehensively assessed how WtE can be implemented in generating electricity for Cameroon with an estimation to the energy potential of anaerobic digestion of three organic waste streams including municipal solid waste, wastewater sludge, and livestock manure. We assessed the energy potential in terms of the theoretical, technical, and economic potentials. The findings highlighted a theoretical energy potential of 936.37 TWh y^r−1 in Cameroon. If only applied to a fraction of organic wastes, the technical potential could reach 48.64 TWh y^r−1. Furthermore, considering the economic costs of technology installation, 17.06 TWh y^r−1 could be generated, and this economic generation potential could supply to 38.9% of the country's current electricity demand. This study implies that WtE would significantly reduce fossil fuels consumption and greenhouse gases emissions from poorly disposed wastes, to enable decarbonization transition and improve human health in African countries.

The Agulhas retroflection (AR) region possesses the highest eddy kinetic energy (EKE) level in the Indian Ocean. However, mechanisms regulating EKE of the AR remain uncertain. Here, by analyzing an eddy-resolving coupled model simulation with improved EKE representation, we show that the upper-ocean EKE of the AR is mainly generated through barotropic instability in its upstream and leakage zones and is by nonlocal transport in its downstream zone. The interaction between mesoscale eddies and local winds plays a key role in EKE dissipation. The lack of eddy-wind interaction results in flawed EKE budget in the leakage zone in ocean-alone models, leading to severe biases in EKE distribution with overestimation and over-strong penetration into the South Atlantic. Our results highlight the essence of mesoscale air-sea interaction in the dynamics of the AR, with implications for understanding the inter-basin transport of the Agulhas leakage.

The Earth's global radiation budget depends critically on the relationship between outgoing longwave radiation (OLR) and surface temperature ($T_\mathrm{s}$). Using the fifth generation of European ReAnalysis dataset, we find that although OLR appears to be linearly dependent on $T_\mathrm{s}$ over a wide range, there are significant deviations from the linearity in the OLR–$T_\mathrm{s}$ relationship for regions warmer than 270 K $T_\mathrm{s}$, which covers 89% of the surface of Earth. While the AMIP runs of CMIP6 models largely capture the overall OLR–$T_\mathrm{s}$ relationship, considerable discrepancies are found in clear-sky OLR at given $T_\mathrm{s}$ ranges. In this study, we investigate physical mechanisms that control the clear-sky OLR–$T_\mathrm{s}$ relationship seen in reanalysis and CMIP6 models by using accurate radiative transfer calculations. Our study identifies three key mechanisms to explain both the linearity and departure from linearity of the clear-sky OLR–$T_\mathrm{s}$ relationship. The first is a surface contribution, controlled by the thermal emission of the surface and the infrared opacity of the atmosphere, accounting for 60% of the observed clear-sky OLR–$T_\mathrm{s}$ linear slope. The second is changes in atmospheric emission induced by a foreign pressure effect on water vapor and other greenhouse gases, which accounts for 30% of the linear slope in a clear-sky condition. The third is changes in atmospheric emission induced by variations in relative humidity (RH), particularly in the mid-troposphere (250 to 750 hPa), which determines the non-linearity in the clear-sky OLR–$T_\mathrm{s}$ relationship and adds to the remaining 10% of the slope. The inter-model spread in mid-tropospheric RH explains a large fraction of the differences in clear-sky OLR across CMIP6 models at given surface temperatures. Furthermore, the three key mechanisms outlined here apply to the OLR–$T_\mathrm{s}$ relationship in all-sky conditions: clouds disguise the surface contribution but increase the atmospheric contribution, retaining a similar linear slope to the clear-sky condition while amplifying the non-linear curvature.

Mountain environments are profoundly impacted by the deposition of mineral dust, yet the degree to which this material is far-traveled or intra-regional is typically unclear. This distinction is fundamental to model future changes in mountain geoecosystems resulting from climatic or anthropogenic forcing in dust source regions. We address this question with a network of 17 passive dust samplers installed in primarily mountain locations in Utah, Nevada, and Idaho between October, 2020 and October 2021. For each collector, the dust deposition rate was calculated, and the physical and chemical properties of the dust were constrained. Results were combined with backward trajectory modeling to identify the geologic characteristics of the area over which air passed most frequently in route to each collector (the 'hot spot'). Dust properties differ significantly between collectors, hot spots for many collectors are spatially discrete, and the dominant geologies in the hot spots corresponding to each collector vary considerably. These results support the hypothesis that the majority of the dust deposited in the areas we studied is sourced from arid lowlands in the surrounding region.

Green ecological communities have garnered significant interest due to their role in providing urban ecosystem services, and community greening plays a pivotal role in urban environmental enhancement. In the context of carbon neutrality-oriented goals, it is imperative to acknowledge the significance of various landscape designs in carbon sequestration within community greening initiatives. However, there is currently a lack of consideration for landscape designs that promote high carbon sequestration in community greening projects. Our research with literature research and experimental measurement data as data sources, established a database of carbon sequestration of 138 common vegetation species in Shanghai. Based on the vertical vegetation structure within landscape design, we propose seven modular planting structures that reflect the carbon sequestration potential of high-capacity plants within different community green spaces. Our findings reveal substantial variations in carbon sequestration among different tree species within arbor and shrub categories, whereas the differences in carbon sequestration among various herbaceous plants per unit area are comparatively smaller. Among the different combination patterns, the highest carbon sequestration is achieved by the vegetation configuration of the three-layer structure pattern, and the combination of arbors, shrubs and grasses can maximize the effective use of space. This study holds significant importance in optimizing the utilization of limited green spaces within communities and enhancing the carbon sequestration benefits of community landscapes. Ultimately, these efforts contribute significantly to Shanghai's journey toward carbon neutrality.

Vegetation carbon turnover time (τ) is a central ecosystem property to quantify the global vegetation carbon dynamics. However, our understanding of vegetation dynamics is hampered by the lack of long-term observations of the changes in vegetation biomass. Here we challenge the steady state assumption of τ by using annual changes in vegetation biomass that derived from remote-sensing observations. We evaluate the changes in magnitude, spatial patterns, and uncertainties in vegetation carbon turnover times from 1992 to 2016. We found the robustness in the steady state assumption for forest ecosystems at large spatial scales, contrasting with local larger differences at the grid cell level between τ under steady state and τ under non-steady state conditions. The observation that terrestrial ecosystems are not in a steady state locally is deemed crucial when studying vegetation dynamics and the potential response of biomass to disturbance and climatic changes.

The '21·7' Henan extreme rainfall event (HNER) caused severe damage and many fatalities. The daily precipitation during this event (from 1200 UTC on 19 July 2021–1200 UTC on 20 July 2021) was 552.5 mm and the maximum hourly precipitation was 201.9 mm (at 0900 UTC on 20 July 2021). Previous studies have suggested that an evaluation of the role of anthropogenic climate change in extreme rainfall events is crucial in disaster prevention and mitigation under the current global climate crisis. We examined the changes in the coverage and intensity of extreme rainfall during the '21·7' HNER event under anthropogenic climate change using a set of convective permitting simulations. Our results showed that the regional-average magnitude of the 48 h accumulated rainfall during the '21·7' HNER was increased by 5.7% (95% confidence interval: 4%–11%), which is in agreement with the Clausius–Clapeyron rate, while the area of extreme rainfall (⩾500 mm) increased by 29.9% (95% confidence interval: 21%–40%) as a result of anthropogenic climate change over the Henan region during the late 20th century. Anthropogenic climate change has led to a warm moist tongue over the target region, which has increased the column-integrated water vapor content and induced an anomalous cyclone–anticyclone pair. Anthropogenic warming has caused stronger southerly and southeasterly winds, leading to stronger convergence in the lower troposphere, stronger updrafts in the mid-troposphere and stronger divergent winds in the upper levels. These effects have all contributed to the increase in rainfall. These results enhance our understanding of the dynamic effects of anthropogenic warming on the '21·7' HNER and provide additional evidence that anthropogenic warming increased the magnitude of the '21·7' HNER in China.

Accelerated urbanization and frequent heatwave events pose significant threats to human health. Analyses of the differences in air and land surface temperature (LST) under extreme climates can aid in understanding human-nature ecosystem coupling and the required adaptations to climate change. In this study, we quantified differences in urban and rural temperatures in China under heatwave (CHW) and non-heatwave periods (NHW) conditions and the influence of meteorological factors on these differences. Based on impervious surface data, 2421 urban and rural stations were dynamically classified from 2008 to 2017. Heatwaves were identified using relative thresholds, and differences were explored using meteorological data and MODIS LST data. For LST, urban–rural temperature difference (U-R_Tempdiff) was highest during the day, whereas air temperature peaks occurred at night, under both NHW and CHW conditions. During CHWs, the daytime U-R_Tempdiff was greater for LST than for air temperature, reaching 4.24 ± 3.38 °C. At night, U-R_Tempdiff was slightly lower (1.04 ± 1.41 °C). The proportion of air U-R_Tempdiff contributed by rural air temperature was significantly higher during CHW nights than during NHW nights, whereas the proportion of land surface and air U-R_Tempdiff remained relatively stable during daytime. Spatially, the daytime temperature difference in the north decreased with latitude, whereas the difference in the south was lower. Under CHWs, urbanization had a stronger effect on LST than on air temperature, with a slightly smaller difference (0.01 °C yr⁻¹) during the day and a slightly larger difference (0.03 °C yr⁻¹) at night. The contribution of urbanization to LST was higher than that to air temperature, particularly during the day (16.34%). The effects of wind speed and precipitation on the average air urban–rural temperature difference was greater than those of LST under CHW, accounting for 16.13%, with the effects of wind speed being more significant. These results show that a comprehensive perspective is needed to understand the risks associated with a temperature rise risk under extreme climate conditions and to formulate effective mitigation measures that will they improve human thermal comfort under climate change.

The southwestern Korean Peninsula had experienced cumulative precipitation deficits from the early spring of 2022, causing a severe meteorological drought in March 2023. As a growing season was forthcoming, the sub-seasonal to seasonal outlook of this ongoing drought came into question. This study aims to investigate a key driver of the ongoing drought and the required precipitation for its termination, and examine the sub-seasonal and seasonal outlooks of the ongoing drought via probabilistic and climate model-based forecasts. Results show a comparable contribution of springtime and summertime precipitation deficits in 2022, indicating that six-month accumulated precipitation deficit of 2022 was a key driver of the ongoing drought. We find that at least 80, 150, and 210 mm (170, 310, and 440 mm) of accumulated precipitation are required for the recovery (full recovery) in March, April, and May 2023, respectively. These required cumulative precipitation are found from 25% and 20% of empirical and dynamic precipitation forecasts, respectively. This study highlights the importance of the collaborative effort of national and local governments and stakeholders on mitigating negative impacts of the ongoing drought.

Atmospheric ammonia (NH₃) plays a significant role in the nitrogen cycle, and can have impacts on air quality, ecological balance and climate change. While NH₃ associated with natural and agricultural processes has long been considered the primary source, the contribution of combustion-related NH₃, particularly from vehicular emissions, keeps on the rise. We found that high on-road NH₃ concentrations occurred in a metropolitan city based on mobile measurement, and inferred that urban vehicular NH₃ emission was likely underestimated in the past. NH₃ emission factors (EFs) were derived from ring roads and tunnels, showing levels 74% and 20% higher than the latest standard proposed by Euro VII, respectively. To quantify the underestimation, two methods based on car ownership and traffic flow were used to estimate the annual vehicular NH₃ emission in Shanghai as 2.59 and 1.76 Gg, respectively, substantially surpassing the predicted results by the Dynamic Projection model for Emissions in China. Given these discoveries, we recommend that it is urgent and imperative to establish relevant national standards and limits aiming at regulation on vehicular NH₃ emissions. And more representative EFs measurements should be adopted to improve the accuracy of inventory estimation.

Because many coastal developments have been continuously occurred in the Yellow and the East China Sea, it is necessary to analyze the effect of persistent topographic change. This study simulated the tidal change in response to stepwise tidal flat reclamation in East China and the Yellow Sea using the MOdelo HIDrodinâmico (MOHID) ocean model. Based on previous studies and historical coastal information maps, we conducted several numerical experiments with reliable coastal topography changes around two areas (Jiangsu Shoalwater and Gyeonggi Bay) from 1990 to 1994 when the most active development took place. The results show that, unlike other components (S2, O1, and K1), the simulated amplitude of the M2 constituent significantly increased with the disappearance of the tidal flat in the Yellow Sea. At the same time, it decreased in the East China Sea. These results are consistent with the quantile regression analysis using observational data. We also found an accumulating effect of tidal energy flux when the reclamation continued, which does not appear in the previous studies. These results indicate persistent man-made tidal flat reclamation in a specific area can cause more remarkable regional tidal changes through tidal energy redistribution and modification.

Rapid warming in Arctic tundra may lead to drier soils in summer and greater lightning ignition rates, likely culminating in enhanced wildfire risk. Increased wildfire frequency and intensity leads to greater conversion of permafrost carbon to greenhouse gas emissions. Here, we quantify the effect of recent tundra fires on the creation of methane (CH₄) emission hotspots, a fingerprint of the permafrost carbon feedback. We utilized high-resolution (∼25 m² pixels) and broad coverage (1780 km²) airborne imaging spectroscopy and maps of historical wildfire-burned areas to determine whether CH₄ hotspots were more likely in areas burned within the last 50 years in the Yukon–Kuskokwim Delta, Alaska, USA. Our observations provide a unique observational constraint on CH₄ dynamics, allowing us to map CH₄ hotspots in relation to individual burn events, burn scar perimeters, and proximity to water. We find that CH₄ hotspots are roughly 29% more likely on average in tundra that burned within the last 50 years compared to unburned areas and that this effect is nearly tripled along burn scar perimeters that are delineated by surface water features. Our results indicate that the changes following tundra fire favor the complex environmental conditions needed to generate CH₄ emission hotspots. We conclude that enhanced CH₄ emissions following tundra fire represent a positive feedback that will accelerate climate warming, tundra fire occurrence, and future permafrost carbon loss to the atmosphere.

The rare triple-dip 2020–2023 La Niña event has resulted in a series of extreme climate events across the globe. Here, we reveal the role of tropical Indo-Pacific oceanic interactions in driving the first triple-dip La Niña of the twenty-first century. Specifically, we found that the eastern Indian Ocean subsurface warming anomalies were associated with the re-intensification of the subsequent La Niña event. The subsurface warming anomaly signals were propagated eastward by equatorial and coastal subsurface Kelvin waves from the eastern Indian Ocean to the western Pacific Ocean through the Indo-Pacific oceanic pathway, which contributes to the accumulation of heat content and deepens the thermocline in the western tropical Pacific. The westward Indonesian Throughflow (ITF) transported more heat during multi-year La Niña events from the western Pacific Ocean to the eastern Indian Ocean than during single-year events, resulting in the injection of more warm water into the eastern Indian Ocean. The combination of subsurface Kelvin wave propagation and increased ITF volume transport in the Indo-Pacific region acted to prolong the heat content in the western Pacific during the decay phase of La Niña, ultimately leading to the rare triple-dip 2020–2023 La Niña event.

By 2050, 68% of the world's population and 90% of the UK's population are estimated to be living in urban areas. It is widely acknowledged that urban areas tend to be warmer than rural areas (the urban heat island (UHI) effect), and that increased summer temperatures increase morbidity and mortality. It is therefore important to know how the UHI intensity will change in the future. Recent work has used observed daily UHI-temperature relationships to suggest that the UHI intensity may decrease under warming temperatures. Here we analyse the ability of the regional UK Climate Projections, UKCP18-regional, to model the summer nighttime UHI intensity of ten UK cities. When compared to HadUK-Grid observational data, we find that the model accurately simulates both the mean magnitude of the UHI intensities and the daily relationship between urban and rural temperature. In particular, in 9 of the 10 cities, the model and observational data both show a decrease in UHI intensity with warmer temperature over the 1980–2020 period analysed. We then analyse the correlation between the projected future UHI intensities using UKCP18-regional and those inferred from the historical daily UHI-temperature relationships. We find that this relationship is not statistically significant and that the model-projected change in UHI intensity is greater than the change inferred from the historical relationship for all cities analysed. We conclude that using short-term variability to predict future UHI change, as proposed by some recent work, may not be appropriate. Our results motivate further research to understand processes impacting UHI changes on different timescales and in different regions.

Climate change and unabated greenhouse gas emissions are increasing the possibility that the world will turn to climate intervention to curb ever-increasing global temperatures. This paper uses game theory to elucidate the conditions that might make a state more or less likely to begin unilateral, as opposed to internationally coordinated, climate intervention (UCI). We solve this game for several specific scientific, economic, and climatological conditions that change the likelihood of a government starting its own climate intervention deployment program without the participation of the broader international community. Specifically, we demonstrate that the plausibility of UCI is linked to perceptions of three key elements: (1) the effectiveness of climate intervention strategies, (2) the sensitivity of specific governments to punishment by other states, and (3) satisfaction with climate and weather in the present. We conclude by discussing how this formal game theory model informs the design of future Earth system model simulations of UCI, international agreements related to climate intervention, and the development of solar climate intervention technologies.

Correlation analysis is the common method to evaluate the relationship between two variables; however, it may sometimes cause spurious correlations. Specifically, in the field of hydrometeorology, with the impacts of climate change and human activities, correlation analysis is difficult to identify the true relationship between variables, and thus, causality analysis should be adopted instead. This study analyzed the causal relationship between meteorological drought and hydrological drought in different climatic regions of China by using convergent cross mapping (CCM). We improved the identification of CCM convergence by using the coefficient of variation and applied it in the field of large-scale hydrometeorology. The results of correlation analysis were compared, and the applicability of causality analysis was explored. The results revealed that: In Southeast China, the correlation and causality between meteorological drought and hydrological drought were both large. In Northeast China and central Qinghai–Tibet Plateau, the correlation between meteorological drought and hydrological drought was small, but the causality was large. In view of the spurious correlation, introducing causality analysis can better explain the relationship between meteorological drought and hydrological drought, especially in areas with snowmelt runoff. Overall, CCM can provide valuable causal information from common time series in the field of large-scale hydrometeorology and has a wide range of application values. However, causality analysis cannot explain the positive or negative relationship between variables. Therefore, when analyzing the relationship between variables, the advantages of the two methods should be given full play.

An open question in the study of climate prediction is whether internal variability will continue to contribute to prediction skill in the coming decades, or whether predictable signals will be overwhelmed by rising temperatures driven by anthropogenic forcing. We design a neural network that is interpretable such that its predictions can be decomposed to examine the relative contributions of external forcing and internal variability to future regional sea surface temperature (SST) trend predictions in the near-term climate (2020–2050). We show that there is additional prediction skill to be garnered from internal variability in the Community Earth System Model version 2 Large Ensemble, even in a relatively high forcing future scenario. This predictability is especially apparent in the North Atlantic, North Pacific and Tropical Pacific Oceans as well as in the Southern Ocean. We further investigate how prediction skill covaries across the ocean and find three regions with distinct coherent prediction skill driven by internal variability. SST trend predictability is found to be associated with consistent patterns of decadal variability for the grid points within each region.

In August 2022, Death Valley, the driest place in North America, experienced record flooding from summertime rainfall associated with the North American monsoon (NAM). Given the socioeconomic cost of these type of events, there is a dire need to understand their drivers and future statistics. Existing theory predicts that increases in the intensity of precipitation is a robust response to anthropogenic warming. Paleoclimatic evidence suggests that northeast Pacific (NEP) sea surface temperature (SST) variability could further intensify summertime NAM rainfall over the desert southwest. Drawing on this paleoclimatic evidence, we use historical observations and reanalyzes to test the hypothesis that warm SSTs on the southern California margin are linked to more frequent extreme precipitation events in the NAM domain. We find that summers with above-average coastal SSTs are more favorable to moist convection in the northern edge of the NAM domain (southern California, Arizona, New Mexico, and the southern Great Basin). This is because warmer SSTs drive circulation changes that increase moisture flux into the desert southwest, driving more frequent precipitation extremes and increases in seasonal rainfall totals. These results, which are robust across observational products, establish a linkage between marine and terrestrial extremes, since summers with anomalously warm SSTs on the California margin have been linked to seasonal or multi-year NEP marine heatwaves. However, current generation earth system models (ESMs) struggle to reproduce the observed relationship between coastal SSTs and NAM precipitation. Across models, there is a strong negative relationship between the magnitude of an ESM's warm SST bias on the California margin and its skill at reproducing the correlation with desert southwest rainfall. Given persistent NEP SST biases in ESMs, our results suggest that efforts to improve representation of climatological SSTs are crucial for accurately predicting future changes in hydroclimate extremes in the desert southwest.

Using the Alfred Wegener Institute Climate Model (AWI-CM 1.1 LR), we explored how Arctic and extra-Arctic warming affect the response of Atlantic meridional overturning circulation (AMOC) to quadruple carbon dioxide (4 × CO₂) forcing. The results suggest that AMOC weakening is mainly affected by circulation adjustment caused by extra-Arctic warming, while Arctic warming has a limited local impact and a relatively small contribution to AMOC weakening. Due to the warming outside the Arctic, the increase in northward advective heat transport dominates the weakening of deep convection in Nordic Seas. While in the Labrador Sea, the decrease in advection heat transport is compensated by a more significant decrease in ocean heat loss to the atmosphere, leading to an enhancement of the upper ocean stratification. Besides, the weakening of deep convection associated with AMOC response under global warming is more pronounced in Nordic Seas than in Labrador Sea.

During protracted dry spells, there is considerable interest from water managers, media and the public in when and how drought termination (DT) will occur. Robust answers to these questions require better understanding of the hydroclimatic drivers of DT than currently available. Integrated vapour transport (IVT) has been found to drive DT in Western North America, but evidence elsewhere is lacking. To evaluate this association for the British–Irish Isles, event coincidence analysis is applied to 354 catchments in the UK and Ireland over the period 1900–2010 using ERA-20C reanalysis IVT data and 7589 DT events extracted from reconstructed river flow series. Linkages are identified for 53% of all DT events across all catchments. Associations are particularly strong for catchments in western and southern regions and in autumn and winter. In Western Scotland, 80% of autumn DTs are preceded by high IVT, whilst in Southern England more than two thirds of winter DTs follow high IVT episodes. High IVT and DT are most strongly associated in less permeable, wetter upland catchments of Western Britain, reflecting their maritime setting and orographic enhancement of prevailing south-westerly high IVT episodes. Although high IVT remains an important drought-terminating mechanism further east, it less frequently results in DT. Furthermore, the highest rates of DT occur with increasing IVT intensity, and the vast majority of the most abrupt DTs only occur following top decile IVT and under strongly positive North Atlantic Oscillation (NAO) conditions. Since IVT and NAO forecasts may be more skilful than those for rainfall which underpin current forecasting systems, incorporating these findings into such systems has potential to underpin enhanced forecasting of DTs. This could help to mitigate impacts of abrupt recoveries from drought including water quality issues and managing compound drought–flood hazards concurrently.

Carbon-emitting technologies often cost less than carbon-emission-free alternatives; this difference in cost is known as the Green Premium. Innovations that decrease the Green Premium contribute to achieving climate goals, but a conceptual framework to quantify that contribution has been lacking. Here, we devise a framework to translate reductions in the Green Premium into equivalent reductions in carbon emissions. We introduce a new integrated assessment model designed for teaching and communication, the Climate Optimized INvestment model, to facilitate transparent investigation of cost-saving innovation. We look at consequences of introducing a new technology with potential for learning and improvement for scenarios with three levels of stringency of carbon constraint: an Unlimited budget scenario in which carbon emissions abatement is determined only by balancing marginal costs; a Large budget scenario with a maximum budget for future cumulative emissions equivalent to 50 times the initial-year emissions; and a Small budget scenario with a maximum budget for future cumulative emissions equivalent to 15 times the initial-year emissions. At all of these stringency levels, we find the least-cost solutions involve investing in a learning subsidy to bring the cost of the new technology down the learning curve. Reducing the Green Premium can lead to enhanced carbon abatement, lower abatement costs even after reaching net-zero emissions, less climate damage, and increased net-present-value of consumption. We find both the value of Green Premium reductions and the value of carbon dioxide removal are greater under more stringent mitigation targets. Our study suggests a crucial role for both public and private sectors in promoting and developing innovations that can contribute to achieving zero emissions goals.

Lateral melting is an important process driving the sea ice decay, yet it is not well represented in many Coupled Model Intercomparison Project Phase 6 (CMIP6) models. This study explores the impact of lateral melting on Arctic sea ice simulation by implementing lateral melting and floe size parameterization schemes in the medium resolution version of the Beijing Climate Center Climate System Model. Results from a series of CMIP6 historical-type experiments indicate that inclusion of lateral melting results in a reduction in both the Arctic sea ice concentration and thickness, thus improving the sea ice extent and volume simulation. Lateral melting increases open waters, leading to an enhanced net sea surface heat flux into the ocean and further increased lateral and bottom melting. This positive feedback is intensified from 1982 to 2014, particularly when the floe size parameterization scheme is introduced. This accelerates the Arctic sea ice decline from 1982 to 2014 in the model, which is more consistent with observations. Further analysis indicates that the enhancement of this feedback is associated with accelerated lateral melting due to the increased (decreased) trend of the sea surface temperature (floe size) from 1982 to 2014. This study highlights that sea ice lateral melting is an important factor affecting the simulation of Arctic sea ice decline and needs to be better represented in current climate models.

Thawing of ice-rich permafrost soils in sloped terrain can lead to activation of retrogressive thaw slumps (RTSs) which make organic matter available for decomposition that has been frozen for centuries to millennia. Recent studies show that the area affected by RTSs increased in the last two decades across the pan-Arctic. Combining a model of soil carbon dynamics with remotely sensed spatial details of thaw slump area and a soil carbon database, we show that RTSs in Siberia turned a previous quasi-neutral ecosystem into a strong source of carbon dioxide of 367 ± 213 gC m-1 a-1. On a global scale, recent CO₂ emissions from Siberian thaw slumps of 0.42 ± 0.22 Tg carbon per year are negligible so far. However, depending on the future evolution of permafrost thaw and hence thaw slump-affected area, such hillslope processes can transition permafrost landscapes to become a major source of additional CO₂ release into the atmosphere.

Global cooling capacity is expected to triple by 2050, as rising temperatures and humidity levels intensify the heat stress that populations experience. Although air conditioning (AC) is a key adaptation tool for reducing exposure to extreme heat, we currently have a limited understanding of patterns of AC ownership. Developing high resolution estimates of AC ownership is critical for identifying communities vulnerable to extreme heat and for informing future electricity system investments as increases in cooling demand will exacerbate strain placed on aging power systems. In this study, we utilize a segmented linear regression model to identify AC ownership across Southern California by investigating the relationship between daily household electricity usage and a variety of humid heat metrics (HHMs) for ~160000 homes. We hypothesize that AC penetration rate estimates, i.e. the percentage of homes in a defined area that have AC, can be improved by considering indices that incorporate humidity as well as temperature. We run the model for each household with each unique heat metric for the years 2015 and 2016 and compare differences in AC ownership estimates at the census tract level. In total, 81% of the households were identified as having AC by at least one heat metric while 69% of the homes were determined to have AC with a consensus across all five of the heat metrics. Regression results also showed that the r² values for the dry bulb temperature (DBT) (0.39) regression were either comparable to or higher than the r² values for HHMs (0.15–0.40). Our results suggest that using a combination of heat metrics can increase confidence in AC penetration rate estimates, but using DBT alone produces similar estimates to other HHMs, which are often more difficult to access, individually. Future work should investigate these results in regions with high humidity.

The 2022 Indian heatwave impacted key geographies of wheat production in northwestern and central India. It coincided with this crop's harvest season in the region, and is expected to have considerably reduced national wheat production. Here we provide spatially disaggregated estimates of the likely impact on wheat yield using statistical relationships derived from historical climate and wheat yield data. Compared to a normal year (median of 1992–2021 climate), national wheat production in 2022 is expected to have fallen by 4.5%; some regions may have experienced wheat yield losses of up to 15%. Our analysis can also be analyzed as a proof of concept for using current and forecasted weather data to estimate real-time impact of short-term weather variability on end-of-season crop yields, even while the crop season is ongoing.

Reducing nutrient loss from agriculture to improve water quality requires a combination of management practices. However, it has been unclear what pattern of mitigation is likely to emerge from different policies, individually and combined, and the consequences for local and national land use and farm returns. We address this research gap by constructing an integrated multi-scale framework for evaluating alternative nitrogen loss management policies for corn production in the US. This approach combines site- and practice-specific agro-ecosystem processes with a grid-resolving economic model to identify locations that can be prioritized to increase the economic efficiency of the policies. We find that regional measures, albeit effective in reducing local nitrogen loss, can displace corn production to the area where nitrogen fertilizer productivity is low and nutrient loss rate is high, thereby offsetting the overall effectiveness of the nutrient management strategy. This spatial spillover effect can be suppressed by implementing the partial measures in tandem with nationwide policies. Wetland restoration combined with split fertilizer application, along with a nitrogen loss tax could reduce nitrate nitrogen loss to the Mississippi River by 30% while only increasing corn prices by less than 2%.