The Antarctic Circumpolar Current (ACC) is the world's strongest ocean current and plays a disproportionate role in the climate system due to its role as a conduit for major ocean basins. This current system is linked to the ocean's vertical overturning circulation, and is thus pivotal to the uptake of heat and CO₂ in the ocean. The strength of the ACC has varied substantially across warm and cold climates in Earth's past, but the exact dynamical drivers of this change remain elusive. This is in part because ocean models have historically been unable to adequately resolve the small-scale processes that control current strength. Here, we assess a global ocean model simulation which resolves such processes to diagnose the impact of changing thermal, haline and wind conditions on the strength of the ACC. Our results show that, by 2050, the strength of the ACC declines by ∼20% for a high-emissions scenario. This decline is driven by meltwater from ice shelves around Antarctica, which is exported to lower latitudes via the Antarctic Intermediate Water. This process weakens the zonal density stratification historically supported by surface temperature gradients, resulting in a slowdown of sub-surface zonal currents. Such a decline in transport, if realised, would have major implications on the global ocean circulation.

While controls over the Earth's climate system have undergone rigorous hypothesis-testing since the 1800s, questions over the scientific consensus of the role of human activities in modern climate change continue to arise in public settings. We update previous efforts to quantify the scientific consensus on climate change by searching the recent literature for papers sceptical of anthropogenic-caused global warming. From a dataset of 88125 climate-related papers published since 2012, when this question was last addressed comprehensively, we examine a randomized subset of 3000 such publications. We also use a second sample-weighted approach that was specifically biased with keywords to help identify any sceptical peer-reviewed papers in the whole dataset. We identify four sceptical papers out of the sub-set of 3000, as evidenced by abstracts that were rated as implicitly or explicitly sceptical of human-caused global warming. In our sample utilizing pre-identified sceptical keywords we found 28 papers that were implicitly or explicitly sceptical. We conclude with high statistical confidence that the scientific consensus on human-caused contemporary climate change—expressed as a proportion of the total publications—exceeds 99% in the peer reviewed scientific literature.

We analyze the evolution of the scientific consensus on anthropogenic global warming (AGW) in the peer-reviewed scientific literature, examining 11 944 climate abstracts from 1991–2011 matching the topics 'global climate change' or 'global warming'. We find that 66.4% of abstracts expressed no position on AGW, 32.6% endorsed AGW, 0.7% rejected AGW and 0.3% were uncertain about the cause of global warming. Among abstracts expressing a position on AGW, 97.1% endorsed the consensus position that humans are causing global warming. In a second phase of this study, we invited authors to rate their own papers. Compared to abstract ratings, a smaller percentage of self-rated papers expressed no position on AGW (35.5%). Among self-rated papers expressing a position on AGW, 97.2% endorsed the consensus. For both abstract ratings and authors' self-ratings, the percentage of endorsements among papers expressing a position on AGW marginally increased over time. Our analysis indicates that the number of papers rejecting the consensus on AGW is a vanishingly small proportion of the published research.

In light of uncertainties regarding climate sensitivity and future anthropogenic greenhouse gas emissions, we explore the plausibility of global warming over the next millennium which is significantly higher than what is usually expected. Although efforts to decarbonize the global economy have significantly shifted global anthropogenic emissions away from the most extreme emission scenarios, intermediate emission scenarios are still plausible. Significant warming in these scenarios cannot be ruled out as uncertainties in equilibrium climate sensitivity (ECS) remain very large. Until now, long-term climate change projections and their uncertainties for such scenarios have not been investigated using Earth system models (ESMs) that account for all major carbon cycle feedbacks. Using the fast ESM CLIMBER-X with interactive CO₂ and CH₄ (the latter typically not included in most models), we performed simulations for the next millennium under extended SSP1-2.6, SSP4-3.4 and SSP2-4.5 scenarios. These scenarios are usually associated with peak global warming levels of 1.5 ^∘C, 2 ^∘C and 3 ^∘C, respectively, for an ECS of ∼3 ^∘C, considered the best estimate in the latest Intergovernmental Panel on Climate Change (IPCC) report. As ECS values lower or higher than this estimate cannot be ruled out, we emulate a wide range of ECS from 2 ^∘C to 5 ^∘C, defined as the 'very likely' range by the IPCC. Our results show that achieving the Paris Agreement goal of a 2 ^∘C temperature increase is only feasible for low emission scenarios and if ECS is lower than 3.5 ^∘C. With an ECS of 5 ^∘C, peak warming in all considered scenarios more than doubles compared to an ECS of 3 ^∘C. Approximately 50% of this additional warming is attributed to positive climate–carbon cycle feedbacks with comparable contributions from CO₂ and CH₄. The interplay between potentially high ECS and carbon cycle feedbacks could drastically enhance future warming, demonstrating the importance of properly accounting for all major climate feedbacks and associated uncertainties in projecting future climate change.

Current anthropogenic climate change is the result of greenhouse gas accumulation in the atmosphere, which records the aggregation of billions of individual decisions. Here we consider a broad range of individual lifestyle choices and calculate their potential to reduce greenhouse gas emissions in developed countries, based on 148 scenarios from 39 sources. We recommend four widely applicable high-impact (i.e. low emissions) actions with the potential to contribute to systemic change and substantially reduce annual personal emissions: having one fewer child (an average for developed countries of 58.6 tonnes CO₂-equivalent (tCO₂e) emission reductions per year), living car-free (2.4 tCO₂e saved per year), avoiding airplane travel (1.6 tCO₂e saved per roundtrip transatlantic flight) and eating a plant-based diet (0.8 tCO₂e saved per year). These actions have much greater potential to reduce emissions than commonly promoted strategies like comprehensive recycling (four times less effective than a plant-based diet) or changing household lightbulbs (eight times less). Though adolescents poised to establish lifelong patterns are an important target group for promoting high-impact actions, we find that ten high school science textbooks from Canada largely fail to mention these actions (they account for 4% of their recommended actions), instead focusing on incremental changes with much smaller potential emissions reductions. Government resources on climate change from the EU, USA, Canada, and Australia also focus recommendations on lower-impact actions. We conclude that there are opportunities to improve existing educational and communication structures to promote the most effective emission-reduction strategies and close this mitigation gap.

Much of the world's data are stored, managed, and distributed by data centers. Data centers require a tremendous amount of energy to operate, accounting for around 1.8% of electricity use in the United States. Large amounts of water are also required to operate data centers, both directly for liquid cooling and indirectly to produce electricity. For the first time, we calculate spatially-detailed carbon and water footprints of data centers operating within the United States, which is home to around one-quarter of all data center servers globally. Our bottom-up approach reveals one-fifth of data center servers direct water footprint comes from moderately to highly water stressed watersheds, while nearly half of servers are fully or partially powered by power plants located within water stressed regions. Approximately 0.5% of total US greenhouse gas emissions are attributed to data centers. We investigate tradeoffs and synergies between data center's water and energy utilization by strategically locating data centers in areas of the country that will minimize one or more environmental footprints. Our study quantifies the environmental implications behind our data creation and storage and shows a path to decrease the environmental footprint of our increasing digital footprint.

Rising greenhouse gas concentrations and declining global aerosol emissions are causing energy to accumulate in Earth's climate system at an increasing rate. Incomplete understanding of increases in Earth's energy imbalance and ocean warming reduces the capability to accurately prepare for near term climate change and associated impacts. Here, satellite-based observations of Earth's energy budget and ocean surface temperature are combined with the ERA5 atmospheric reanalysis over 1985–2024 to improve physical understanding of changes in Earth's net energy imbalance and resulting ocean surface warming. A doubling of Earth's energy imbalance from 0.6±0.2 Wm⁻² in 2001–2014 to 1.2±0.2 Wm⁻² in 2015–2023 is primarily explained by increases in absorbed sunlight related to cloud-radiative effects over the oceans. Observed increases in absorbed sunlight are not fully captured by ERA5 and determined by widespread decreases in reflected sunlight by cloud over the global ocean. Strongly contributing to reduced reflection of sunlight are the Californian and Namibian stratocumulus cloud regimes, but also recent Antarctic sea ice decline in the Weddell Sea and Ross Sea. An observed increase in near-global ocean annual warming by 0.1 $^\circ\mathrm{C}\,\mathrm{yr}^{-1}$ for each 1 Wm⁻² increase in Earth's energy imbalance is identified over an interannual time-scale (2000–2023). This is understood in terms of a simple ocean mixed layer energy budget only when assuming no concurrent response in heat flux below the mixed layer. Based on this simple energy balance approach and observational evidence, the large observed near-global ocean surface warming of 0.27 ${\,}^\circ\mathrm{C}$ from 2022 to 2023 is found to be physically consistent with the large energy imbalance of 1.85±0.2 Wm⁻² from August 2022 to July 2023 but only if (1) a reduced depth of the mixed layer is experiencing the heating or (2) there is a reversal in the direction of heat flux beneath the mixed layer associated with the transition from La Niña to El Niño conditions. This new interpretation of the drivers of Earth's energy budget changes and their links to ocean warming can improve confidence in near term warming and climate projections.

Global greenhouse gas (GHG) emissions can be traced to five economic sectors: energy, industry, buildings, transport and AFOLU (agriculture, forestry and other land uses). In this topical review, we synthesise the literature to explain recent trends in global and regional emissions in each of these sectors. To contextualise our review, we present estimates of GHG emissions trends by sector from 1990 to 2018, describing the major sources of emissions growth, stability and decline across ten global regions. Overall, the literature and data emphasise that progress towards reducing GHG emissions has been limited. The prominent global pattern is a continuation of underlying drivers with few signs of emerging limits to demand, nor of a deep shift towards the delivery of low and zero carbon services across sectors. We observe a moderate decarbonisation of energy systems in Europe and North America, driven by fuel switching and the increasing penetration of renewables. By contrast, in rapidly industrialising regions, fossil-based energy systems have continuously expanded, only very recently slowing down in their growth. Strong demand for materials, floor area, energy services and travel have driven emissions growth in the industry, buildings and transport sectors, particularly in Eastern Asia, Southern Asia and South-East Asia. An expansion of agriculture into carbon-dense tropical forest areas has driven recent increases in AFOLU emissions in Latin America, South-East Asia and Africa. Identifying, understanding, and tackling the most persistent and climate-damaging trends across sectors is a fundamental concern for research and policy as humanity treads deeper into the Anthropocene.

The consensus that humans are causing recent global warming is shared by 90%–100% of publishing climate scientists according to six independent studies by co-authors of this paper. Those results are consistent with the 97% consensus reported by Cook et al (Environ. Res. Lett. 8 024024) based on 11 944 abstracts of research papers, of which 4014 took a position on the cause of recent global warming. A survey of authors of those papers (N = 2412 papers) also supported a 97% consensus. Tol (2016 Environ. Res. Lett. 11 048001) comes to a different conclusion using results from surveys of non-experts such as economic geologists and a self-selected group of those who reject the consensus. We demonstrate that this outcome is not unexpected because the level of consensus correlates with expertise in climate science. At one point, Tol also reduces the apparent consensus by assuming that abstracts that do not explicitly state the cause of global warming ('no position') represent non-endorsement, an approach that if applied elsewhere would reject consensus on well-established theories such as plate tectonics. We examine the available studies and conclude that the finding of 97% consensus in published climate research is robust and consistent with other surveys of climate scientists and peer-reviewed studies.

Carbon pricing has been hailed as an essential component of any sensible climate policy. Internalize the externalities, the logic goes, and polluters will change their behavior. The theory is elegant, but has carbon pricing worked in practice? Despite a voluminous literature on the topic, there are surprisingly few works that conduct an ex-post analysis, examining how carbon pricing has actually performed. This paper provides a meta-review of ex-post quantitative evaluations of carbon pricing policies around the world since 1990. Four findings stand out. First, though carbon pricing has dominated many political discussions of climate change, only 37 studies assess the actual effects of the policy on emissions reductions, and the vast majority of these are focused on Europe. Second, the majority of studies suggest that the aggregate reductions from carbon pricing on emissions are limited—generally between 0% and 2% per year. However, there is considerable variation across sectors. Third, in general, carbon taxes perform better than emissions trading schemes (ETSs). Finally, studies of the EU-ETS, the oldest ETS, indicate limited average annual reductions—ranging from 0% to 1.5% per annum. For comparison, the IPCC states that emissions must fall by 45% below 2010 levels by 2030 in order to limit warming to 1.5 °C—the goal set by the Paris Agreement (Intergovernmental Panel on Climate Change 2018). Overall, the evidence indicates that carbon pricing has a limited impact on emissions.

Residential and commercial buildings account for 75% of electricity and 40% of the total energy consumption in the United States, costing over $400 billion annually. Electrification and energy efficiency retrofits offer a viable decarbonization pathway, especially since half of US homes were built before modern building codes. These older homes are often occupied by low-to-moderate-income (LMI) families. Equitable electrification provides a unique opportunity to considerably reduce emissions in communities where energy bill savings have the most impact on household finances. This study evaluates how the Inflation Reduction Act (IRA) impacts the adoption potential of air-source heat pumps (ASHPs), heat pump water heaters and clothes dryers, and electric cooking ranges across income groups in the United States. Using a database that statistically represents the US housing stock, we estimate the indicative adoption potential of these technologies under scenarios that represent Pre-IRA conditions and a reference case with IRA provisions. After IRA rebates were introduced, adoption potential for LMI households more than doubled for ASHPs, heat pump water heaters, and electric cooking ranges and more than tripled for heat pump clothes dryers relative to Pre-IRA adoption potential. Cooking retrofits had the lowest adoption potential, and homes that electrified space heating without weatherization had greater adoption potential than those that underwent basic or enhanced enclosure upgrades. Our results show that the introduction of IRA rebates and a gradually decarbonizing grid substantially improves adoption potential for LMI households and even surpasses the adoption potential of middle and upper-income households.

Compound climate hazards, such as co-occurring temperature and precipitation extremes, substantially impact people and ecosystems. Internal climate variability combines with the forced global warming response to determine both the magnitude and spatial distribution of these events, and their consequences can propagate from one country to another via many pathways. We examine how exposure to compound climate hazards in one country is transmitted internationally via agricultural trade networks by analyzing a large ensemble of climate model simulations and comprehensive trade data of four crops (i.e. wheat, maize, rice and soya). Combinations of variability-driven climate patterns and existing global agricultural trade give rise to a wide range of possible outcomes in the current climate. In the most extreme simulated year, 20% or more of the caloric supply in nearly one third of the world's countries are exposed to compound heat and precipitation hazards. Countries with low levels of diversification, both in the number of suppliers and the regional climates of those suppliers, are more likely to import higher fractions of calories (up to 93%) that are exposed to these compound hazards. Understanding how calories exposed to climate hazards are transmitted through agricultural trade networks in the current climate can contribute to improved anticipatory capacity for national governments, international trade policy, and agricultural-sector resilience. Our results highlight the need for concerted effort toward merging cutting-edge seasonal-to-decadal climate prediction with international trade analysis in support of a new era of anticipatory Anthropocene risk management.

Winter warming over the Barents–Kara Seas (BKS) has received extensive attention over the past two decades because it is closely associated with Arctic sea ice loss, winter Eurasian cooling, and extreme cold events over East Asia. However, the role of mid-latitude atmospheric circulation anomalies in resulting winter BKS warming is unclear. This study investigates the relationship between autumn (October–November) East Asian trough (EAT) and the BKS warming in the subsequent winter (December–February) for the period 1979–2022. The result shows that when the autumn EAT weakens, warming, increased moisture, and sea ice loss are observed in the BKS during winter. The weakened EAT promotes increased sea surface temperatures (SSTs) in the mid-latitude North Pacific through increasing solar radiation and reducing cold air activity and positive SST anomalies persist into winter. These continuous warm SSTs from autumn to winter trigger winter Rossby waves downstream, which favors the occurrence of a combination of a positive phase of the North Atlantic Oscillation and high pressure over the Ural region, further leading to BKS warming.

This paper presents a comprehensive survey of the process-based models currently available for blue-green infrastructure for the assessment of cooling potential, stormwater and pollution control, carbon sequestration, and water provision. The assessment of the modelling tools for blue-green elements (BGEs) documents that currently there is no process-based model for the simultaneous evaluation and optimisation of multiple ecosystem services of BGEs. To evaluate coupling options, this study conducted a meta-analysis on model interoperability by assessing the model scales, drivers, overlaps, gaps, and interfaces of these models for BGEs. Model meta-analysis points out the conceptual and constructual constraints preventing easy model coupling, and thus, an integrated assessment of ecosystem services. Constraints arise due to very different disciplinary approaches from different scientific communities involved in model development, differences in the simulation of transformation and transport processes at urban interfaces relevant for BGEs, and fundamental divergences in spatial and temporal scales and time steps of existing models for single ecosystem services. In particular, the lack of vegetation models tailored for BGEs hinders current model developments towards developing a process-based tool for multiple ecosystem services, which would be able to handle nonstationary climate conditions, including feedback assessments of drought and heatwave impacts on the functioning of BGEs.

India's ambitious climate goals include a significant role for wind energy, with plans for a nearly threefold expansion of the existing wind fleet within the next decade. At greater levels of wind deployment, the increased likelihood of extended periods of generation surplus and deficit presents a challenge for managing power supply. It is essential to characterise and predict how this energy source performs within India's monsoon climate to ensure the reliable operation of the electricity system. This study demonstrates, for the first time, how large-scale atmospheric variables are related to seasonal wind energy generation anomalies in India during boreal summer. Furthermore, an operational seasonal forecasting system is shown to skilfully predict the atmospheric predictor variables at a lead time of 1–4 months, indicating an ability to forecast summer wind energy generation at the country and regional level in India. The explanatory power of the chosen atmospheric predictor variables remains high under the near-term planned expansion of the Indian wind fleet. These findings demonstrate the potential utility of seasonal forecast information for electricity system management in India.

This paper presents a comprehensive survey of the process-based models currently available for blue-green infrastructure for the assessment of cooling potential, stormwater and pollution control, carbon sequestration, and water provision. The assessment of the modelling tools for blue-green elements (BGEs) documents that currently there is no process-based model for the simultaneous evaluation and optimisation of multiple ecosystem services of BGEs. To evaluate coupling options, this study conducted a meta-analysis on model interoperability by assessing the model scales, drivers, overlaps, gaps, and interfaces of these models for BGEs. Model meta-analysis points out the conceptual and constructual constraints preventing easy model coupling, and thus, an integrated assessment of ecosystem services. Constraints arise due to very different disciplinary approaches from different scientific communities involved in model development, differences in the simulation of transformation and transport processes at urban interfaces relevant for BGEs, and fundamental divergences in spatial and temporal scales and time steps of existing models for single ecosystem services. In particular, the lack of vegetation models tailored for BGEs hinders current model developments towards developing a process-based tool for multiple ecosystem services, which would be able to handle nonstationary climate conditions, including feedback assessments of drought and heatwave impacts on the functioning of BGEs.

A quarter of the deforested Amazon has regrown as secondary tropical forest and yet the climatic importance of these complex regenerating landscapes is only beginning to be recognised. Advances in satellite remote-sensing have transformed our ability to detect and map changes in forest cover, while detailed ground-based measurements from permanent monitoring plots and eddy-covariance flux towers are providing new insights into the role of secondary forests in the climate system. This review summarises how progress in data availability on Amazonian secondary forests has led to better understanding of their influence on global, regional and local climate through carbon and non-carbon climate benefits. We discuss the climate implications of secondary forest disturbance and the progress in representing forest regrowth in climate models. Much remains to be learned about how secondary forests function and interact with climate, how these processes change with forest age, and the resilience of secondary forest ecosystems faced with increasing anthropogenic disturbance. Secondary forests face numerous threats: half of secondary forests in the Brazilian legal Amazon were 11 years old or younger in 2023. On average, 1%–2% of Amazon secondary forests burn each year, threatening the permanence of sequestered carbon. The forests that burn are predominantly young (in 2023, 55% of burned secondary forests were <6 years old, <4% were over 30 years old). In the context of legally binding international climate treaties and a rapidly changing political backdrop, we discuss the opportunities and challenges of encouraging tropical forest restoration to mitigate anthropogenic climate change. Amazon secondary forests could make a valuable contribution to Brazil's Nationally Determined Contribution provided there are robust systems in place to ensure permanence. We consider how to improve communication between scientists and decision-makers and identify pressing areas of future research.

Extreme heat is an important public health concern, and heat stress exposure and related adaptive capacity are not equally distributed across social groups. We conducted a systematic review to answer the question: What is the effect of social disadvantage on exposure to subjective and objective heat stress and related adaptive capacity to prevent or reduce exposure to heat stress in the general population? We systematically searched for peer-reviewed journal articles that assessed differences in heat stress exposure and related adaptive capacity by social factors that were published between 2005 and 2024. One author screened all records and extracted data; a second author screened and extracted 10% for validation. Synthesis included the identification and description of specific social groups unequally exposed to heat stress and with lower adaptive capacity. We assessed European studies for the potential risk of bias in their assessment. We identified 123 relevant publications. Subjective heat stress appeared in 18.7% of articles, objective heat stress in 54.5%, and adaptive capacity in 54.5%. Nearly half came from North America (47.2%), 22.8% from Asia, and 17.1% from Europe. Publishing increased from zero articles in 2005 to 21 in 2023. Most studies considered socioeconomic status (SES) (78.8%), and many considered age (50.4%), race/ethnicity (42.3%), and sex/gender (30.1%). The identified studies show that lower-SES populations, young people, immigrants, unemployed people, those working in outdoor and manual occupations, and racial/ethnic minorities are generally more exposed to heat stress and have lower adaptive capacity. Most studies of objective heat stress use inadequate measures which are not representative of experienced temperatures. European studies generally have a low or moderate risk of bias in their assessments. Social inequalities in heat stress exposure and related adaptive capacity have been documented globally. In general, socially disadvantaged populations are more exposed to heat stress and have lower adaptive capacity. These social inequalities are context-dependent, dynamic, multi-dimensional, and intersectional. It is essential to consider social inequalities during heat-health action planning and when developing and implementing climate change adaptation policies and interventions.

Approaches to defining a heat wave vary globally. While they are mostly meteorology-centric, there is an increasing need to consider their health implications. Our methodology involved a review of biometeorological indices, followed by a systematic policy search of country-level heat wave definitions to explore the variability of heat protection mechanisms. We analyzed the regional coverage of heat wave definitions and warnings by examining the diversity of variables and threshold limits for 112 countries/territories. We identified the upper-most heat stress limits of biometeorological indices that trigger illness or death. The findings highlight that a large proportion of countries define heat waves based solely on maximum temperature, while only a few countries combine them with minimum temperature and/or humidity. We also find significant geographical variability in the incorporation of temperature limits with most countries in northern latitudes defining heat waves at lower thresholds. We highlight the need for policy reforms towards adjustment of heat warning thresholds to regionally appropriate levels considering rising extreme heat conditions. Given the predominance of maximum temperature-centric approaches, we argue that the focus of heat protection at the policy level must shift beyond projecting heat wave episodes and consider broader heat-health associations beyond mortality.

The increasing impact of global climate change on hydrogeological and hydrological systems presents substantial challenges to the sustainable management of groundwater quality (GWQ). Changes in precipitation regimes, temperature fluctuations, and the frequency of extreme hydro-climatic events driven by climate change accelerate the deterioration of GWQ, thereby threatening ecosystems and human health. In response to these challenges, recent research has increasingly focused on developing and refining analytical models (AM) and machine learning (ML) techniques to understand better and predict the impacts of climate change on GWQ. This systematic literature review critically examines the current state of knowledge on applying AM and ML models in the context of GWQ assessment under climate-induced stressors. By synthesizing findings from a comprehensive review of existing studies, this paper discusses the capabilities, limitations, and future directions of hybrid ML and traditional AM in GWQ prediction, vulnerability, and threshold estimation. The review reveals that while ML approaches significantly enhance predictive accuracy and model robustness, there remain substantial challenges in their application due to the complexity of climate-induced variables and the scarcity of high-resolution data. This paper aims to provide GWQ researchers, water resource managers, and policymakers with an advanced understanding of the interactions between climate change and GWQ and the innovative AM and ML modelling approaches available to address these challenges. By highlighting the potential and limitations of current models, this review offers insights into developing more effective and adaptive management strategies for safeguarding GWQ in an era of rapid climatic change.

Deep uncertainty about the costs and resource limits of carbon dioxide removal (CDR) options challenges the design of robust portfolios. To address this, we here introduce the CDR Sustainable Portfolios with Endogenous Cost (CDR-SPEC) model, a mixed-integer linear optimization model for cost-optimal and time-dependent CDR portfolios including endogenous treatment of technology cost dynamics. We explore future uncertainty in three key dimensions: realisable mitigation potentials, cost dynamics, and resource constraints. Our results demonstrate that afforestation and reforestation, and soil carbon sequestration appear as robust options, deployed regardless of the removals required. Direct air carbon capture and storage (DACCS) emerges as the most deployed technology in 2100 at median value (6.7 GtCO2/yr), but with the widest range of possible outcomes (interquartile range from 4 to 8.7 GtCO2/yr) depending largely on future renewable energy capacity and annual geological storage injection rates. Bioenergy with CCS (BECCS) deployment remains severely constrained by available land, as the median falls from 1.8 to 0.3 GtCO2/yr in land-constrained scenarios, but gains portfolio share when future energy availability is bounded. Our simulations also reveal that ocean alkalinisation could become a dominant solution in high removal scenarios. Evaluating the performance of portfolios beyond economic costs, we also provide a framework to explore trade-offs across different aspects relevant to planetary boundaries.

The subarctic North Pacific is a high-nutrient low-chlorophyll region in which decoupling of dissolved iron (dFe) and macronutrient is an essential control of primary production. In this study, we evaluated the influence of the decadal-scale climate changes on the net primary production (NPP) in the subarctic North Pacific by performing a hindcast experiment for 1979–2016 using an ice–ocean coupled model with a simple biogeochemical model with iron cycle. Simulation results show significant NPP decrease in the subtropical–subarctic gyre boundary (SGB) region since 1990s; the trend is −48 mgC/m2/day/37 years with a magnitude that is 14.3% of the climatological mean NPP. The NPP decrease in the SGB is prominent in spring, indicating weakening of the spring bloom. Diagnostic analysis of simulation data reveals that the NPP decrease in the SGB can be explained by the decrease in both dFe and light availability. Sensitivity experiments indicate that wind-driven circulation change mainly explains both the reduction in dFe and the light availability through the northward expansion of oligotrophic subtropical water (STW), but the thermohaline change in the Sea of Okhotsk also has a non-negligible effect on the dFe reduction. Results from our numerical model simulations suggest the importance of the lateral and vertical advection of dFe on the decadal-scale changes of NPP in the subarctic North Pacific.

Around one-fourth of the global population lacks access to clean fuels and technologies for cooking, most of them living in low- and middle-income countries. Reliance on rudimentary and inefficient biomass cookstoves results in high pollutant concentrations that adversely affect the health of those exposed to indoor air pollution, the environment, and the climate. In this study, we systematically reviewed the literature on aerosol and particle properties from biomass cookstoves of relevance to health, climate and the environment. We identified 187 articles reporting aerosol characterization (i.e., particulate mass or number concentrations, or particle size distributions). Of these, 82 presented detailed particle characterization (e.g., chemical composition). Articles were classified based on the reported particle properties and the study type and location, which allowed mapping research efforts to date, as well as identifying major knowledge gaps. Most studies on particle properties to date have primarily focused on carbonaceous fractionation determination, both under laboratory and field conditions. Findings from this systematic review highlight the need for further studies on particle properties from biomass cookstoves that use a multidimensional approach simultaneously combining several properties and different cookstove-fuel combinations. We also assessed the policy landscape, including the three main global policies concerning biomass cookstove emissions and evaluated whether those policies include the state of the knowledge on particle properties and their adverse effects on human health, climate and the environment. We finally identify key aspects that future policies should integrate as well as critical knowledge gaps that must be filled to advance the overall development of the field. Cookstove manufacturers, practitioners, policymakers, and the overall society will benefit from a solid knowledge base regarding particle properties from biomass cookstoves and their related adverse effects on human health, climate and the environment.

This study investigates the impact of sea spray parameterization on typhoon prediction in the Yellow and East China Seas (YECS) region. Using an air-sea-wave coupled model, we evaluate changes due to sea spray effects in the simulated intensity and structure of Typhoons Lingling (2019) and Maysak (2020). Enabling sea spray effect enhances surface turbulent heat fluxes considerably around the typhoon centers (74% increase for Lingling, 92% for Maysak), leading to a better representation of typhoon intensification phases. Analysis of thermodynamic processes reveals that sea spray-induced warming emerges before rapid intensification, with enhanced temperature and moisture profiles throughout the troposphere supporting stronger secondary circulation. As a result, key aspects of typhoon prediction exhibit significant improvements: root-mean-squared errors decreased by 63% in minimum central pressure and 60% for maximum wind speed in the case of Maysak. The results demonstrate that sea spray effects are strongly modulated by sub-surface ocean conditions, with a greater surface heat flux enhancement for Maysak that moved along warmer Kuroshio and Tsushima currents than for Lingling which passed over Yellow Sea Bottom Cold Water. Our findings demonstrate the significant potential to improve typhoon predictions in the YECS region by incorporating sea spray effects.

Carbon dioxide removal (CDR) is a crucial component of climate mitigation required to reach international climate targets. However, gaps exist in our understanding of the responses and feedbacks of the Earth system to the deployment of CDR. In this study, we compare two complementary approaches that enhance the terrestrial and marine carbon sinks with afforestation and reforestation (A/R) and ocean alkalinity enhancement (OAE), respectively, under the high emission scenario SSP5-8.5. Eight CMIP6 Earth system models are utilized, enabling a quantification of both inter-model and internal variability. By mid-century, simulated large-scale deployment of A/R and OAE individually reduces atmospheric CO2 concentrations by up to 20 ppm. For both methods, while carbon removal from the atmosphere is robust, it is difficult to detect the effects on global mean temperature, posing challenges for monitoring, reporting and verification of mitigation efforts. To quantify the carbon cycle feedbacks, we define the carbon cycle feedback ratio of A/R (OAE) as the ratio of changes in the marine (terrestrial) sink to changes in the terrestrial (marine) sink. We show that the carbon cycle feedback ratios of A/R and OAE have similar magnitudes, which is -16% and -13%, respectively. Moreover, although inter-model differences of the simulated amounts of carbon removal due to A/R are large, the corresponding carbon cycle feedback ratios of A/R are similar.